India COUNTRY ECONOMIC MEMORANDUM Becoming a High-Income Economy in a Generation © 2025 The World Bank 1818 H Street NW, Washington DC 20433 Telephone: 202-473-1000; Internet: www.worldbank.org Some rights reserved This work is a product of The World Bank. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent. The World Bank does not guarantee the accuracy, completeness, or currency of the data included in this work and does not assume responsibility for any errors, omissions, or discrepancies in the information, or liability with respect to the use of or failure to use the information, methods, processes, or conclusions set forth. 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India Country Economic Memorandum: Becoming a High-income Economy in a Generation. © World Bank.” Any queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, The World Bank, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; email: pubrights@worldbank.org. Cover photo: Gorodenkoff/Shutterstock Used with the permission of Gorodenkoff / Shutterstock. Further permission required for reuse. Cover design: alejandro espino/sonideas Contents Contents List of Abbreviations xi Acknowledgements xii V O LU ME I Analysis and Policy Proposals 1 E X E C U T I V E S U M MAR Y Becoming a High-Income Economy in a Generation 2 I. To Reach High-income Status within a Generation, India Needs to Boost all Growth Engines. 2 II. Growth Scenarios and Reform Proposals 8 C HA P T E R 1 Sustaining Rapid Growth to a High-Income Economy by 2047 13 I. India Achieved Rapid Growth and Poverty Reduction Over the Past Two Decades 14 II. Lessons from Successful Middle-Income Countries 24 III. Possible Growth Pathways to a High-Income Economy 28 References 30 C HA P T E R 2 Accelerating Productivity Growth and Boosting Trade 31 I. Sectoral and Firm-level Productivity 32 II. Learning from Past Successes in Manufacturing and Services 36 III. Key Factors Affecting Productivity Gains: Trade, Innovation, Land Availability, and Market Competition 43 IV. Trade and Global Value Chains 47 References 57 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation iii Contents C HA P T E R 3 Expanding Investments in Physical and Human Capital 59 I. Investment and the Financial Sector 60 II. Human Capital and Efficiency of Education and Health Expenditures 71 References 76 C HA P T E R 4 More and Better Jobs to Reap the Demographic Dividend 77 I. To Reap the Benefits of its Demographic Dividend, India Requires More Jobs 78 II. India Also Requires Better Quality Jobs 82 III. Female Labor Force Participation 85 References 90 C HA P T E R 5 Indian States: Growing Faster Together 91 I. Divergence of Incomes across States and Districts 92 II. Spatial Concentration of Economic Activity; Unlocking Growth in Lagging States 98 References 102 C HA P T E R 6 Program of Reforms to Achieve India’s High-Income Aspirations 104 I. Promoting Structural Transformation, Trade, and the Infusion of Modern Technology and Business Practices 105 II. Turbocharging and Sustaining Investment 108 III. Creating Nabling Conditions for More and Better Jobs 111 IV. Facilitating the States to Grow Together 112 References 114 V O LU ME I I Technical Annexes 115 Annexes to Chapter 1 116 Annexes to Chapter 2 117 Annexes to Chapter 3 120 Annexes to Chapter 4 132 Annexes to Chapter 6 138 iv India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Contents BOXES CHAPTER 1 Box 1.1: Major Reforms and Investments in India in Recent Years 19 CHAPTER 2 Box 2.1: Learning from the Success of Modern Market Services 39 Box 2.2: Learning from the Success of Technology Intensive Manufacturing 40 Box 2.3: India’s Participation in Global Value Chains 53 Box 2.4: India’s Success in Mobile Phone Exports 56 CHAPTER 3 Box 3.1: The Public Investment Multiplier in India 63 Box 3.2: FDI in India 66 Box 3.3: The Corporate Bond Market in India 72 CHAPTER 5 Box 5. 1: A Review of the Literature on Convergence among Indian States 94 Box 5.2: A Review of Studies on District-level Convergence in India 97 FIGURES EXECUTIVE SUMMARY Figure 1: India and Peers: Annual GDP growth rate, 3 Figure 2: All drivers of growth have contributed in successful MICs 3 Figure 3: TFP and investment drove growth in India, but the latter has declined and labor’s contribution was marginal 3 Figure 4: Labor productivity change decomposition (Average over periods, percentage points) 4 Figure 5A: Exports/ GDP against log GNI per capita 5 Figure 5B: Trade/ GDP against log GNI per capita 5 Figure 6A: The investment rate has declined since 2008, with slight pick-up in 2022 and 2023 5 Figure 6B: The slowdown occurred at a lower per-capita income vis-à-vis countries that transitioned to either UMIC or HIC 5 Figure 7: India has not fully leveraged its demographic dividend; labor force participation has remained low 7 CHAPTER 1 Figure 1.1: India’s share in world GDP (percent) 15 Figure 1.2: Contribution in GVA growth (percentage points, constant prices) 15 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation v Contents Figure 1.3: Real GDP growth in India vis-à-vis other emerging economies (average 2000-19, percent) 15 Figure 1.4: India vis-à-vis peers ((Real GDP growth, percent) 15 Figure 1.5: Real GDP growth (percent) 16 Figure 1.6: Contribution to India’s growth 17 Figure 1.7: Macroeconomic stability index 17 Figure 1.8: Monetary poverty rates in India 18 Figure B1.11: Households: Access to improved sanitation (percent) 20 Figure B1.12: Households: Access to clean cooking fuel (percent) 20 Figure B1.13: Households: Access to electricity (percent) 20 Figure B1.14: Progress under the PMJDY 21 Figure 1.9: Non-monetary poverty headcount rate by states 24 Figure 1.10: Income structure of rural households 25 Figure 1.11: Income structure of urban households 25 Figure 1.12: Contribution to growth: TFP, capital, labor in peers, high-growth countries, and other selected MICs (percentage points) 26 Figure 1.13: Contribution to India’s growth (percentage points) 27 Figure 1.14: Upper middle-income status achievable by early next decade (GNI per capita, Atlas Method USD) 28 Figure 1.15: India’s Economic Performance vis-a-vis UMICs and HICs. 29 CHAPTER 2 Figure 2.1: Labor productivity growth, India vis-a-vis peers 33 Figure 2.2: Labor productivity change decomposition, India, annual average contribution to per-capita value-added growth 33 Figure 2.3: Labor productivity change decomposition (percentage points) 33 Figure 2.4: Labor productivity change decomposition, within and between states and sectors (average in subperiods, percentage points) 34 Figure 2.5: Reasons cited by firms for not adopting more advanced technology 34 Figure 2.6: Average labour productivity growth, sectors (percent) 35 Figure 2.7: Agriculture TFP growth rate (decadal average) 35 Figure 2.8: Within-firm productivity (by size quartiles) 36 Figure 2.9: Relationship between TFP and market share 37 Figure 2.10: Productivity dispersion (ratio of the 90th to the 10th firm percentile) 37 Figure B2.11: Modern market services have grown at an unparalleled pace since 2005 39 Figure B2.12: FDI regulatory restrictions in services have been eased considerably since 1997 39 Figure B2.21: Electrical and transport equipment witnessed high labor productivity and value added growth 41 Figure B2.22: Value of mobile exports have been on a rise since 2018, due to various policy initiatives 41 Figure 2.11: Within-firm drivers of productivity 43 vi India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Contents Figure 2.12: Share of firms engaged in international trade (percent) 44 Figure 2.13: Share of firms engaged in R&D and technology transfer (percent) 44 Figure 2.14: Estimated adjustment costs in land 45 Figure 2.15: Perceptions of market organization, 2022   46 Figure 2.16: Perceptions of competition policy, 2022  46 Figure 2.17: The new business entry density gap   46 Figure 2.18: Enterprises owned directly and indirectly by the State, by type of sectors, 2019  47 Figure 2.19: Trade openness, (2011-20) 48 Figure 2.20: Growth in services and goods exports (volume) 48 Figure 2.21: India’s commercial services export (sector-wise share) 49 Figure 2.22: Composition of services exports (share, percent) 49 Figure 2.23: Services: The least and the most restricted sectors 49 Figure 2.24: Ad-valorem tariff equivalents of services trade policies index for selected services, India, and comparators (percent) 50 Figure 2.25: Export markets and product diversification 50 Figure 2.26: Superstar exporters (percent of total exporters) 51 Figure 2.27: India’s export potential, goods 51 Figure 2.28: Global Value Chains, products, and destination 52 Figure B2.31: GVC participation rate, (2018) 53 Figure 2.29: India’s import tariff profile (percent) 54 Figure B2.32: Average female labor intensity and skill intensity of India’s manufacturing workforce (2019) 54 Figure 2.30: Trade facilitation indicators 55 Figure 2.31: India’s gains from inter-regional and multilateral integration 55 Figure B2.41: India has become a net exporter of mobile phones 56 CHAPTER 3 Figure 3.1: Gross fixed capital formation, (nominal, percent of GDP) 61 Figure 3.2: Investment rate and per-capita income 61 Figure 3.3: Manufacturing share and per-capita income 61 Figure 3.4: Sectoral share in GFCF 62 Figure 3.5: Sector-wise contribution in GFCF growth 62 Figure 3.6: Expenditure (central government, percent of GDP) 62 Figure 3.7: Estimated impact of public investment on real GDP 62 Figure 3.8: Estimated impact of public investment on capital stock, employment, and wages 64 Figure 3.9: Share of ICT investment in total investment (percent) 64 Figure 3.10: Growth decomposition: ICT versus non-ICT investment (percentage points) 64 Figure 3.11: Net FDI inflows as a share of GDP 65 Figure 3.12: FDI equity restrictions, India, China, and Vietnam (2020) 65 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation vii Contents Figure B3.21: Framework for foreign investment regulation 66 Figure B3.22: OECD FDI regulatory restrictiveness by type 66 Figure 3.13: Reasons claimed for investment decrease (left) and increase (right) among MNE affiliates in India 67 Figure 3.14: Probability of firm-level investment based on innovation, digitization, trade behavior and green practices, 2022 68 Figure 3.15: Share of sectors in the total flow of resources to the financial sector (percent) 69 Figure 3.16: Gross NPA, banks 69 Figure 3.17: Credit growth, banks 69 Figure 3.18: Profitability indicators, banks (percent) 70 Figure 3.19: Profitability indicators, listed companies (2020=100) 70 Figure 3.20: Gross NPA and capital adequacy ratio, NBFCs (percent) 71 Figure 3.21: Per-capita human capital index 71 Figure B3.31: Bank credit and corporate bonds (INR trillion) 72 Figure B3.32: Corporate Bonds Turnover Ratio (Annual Trading) 73 Figure 3.22: Gross school enrollment (percent) 74 Figure 3.23: Efficiency in terms of education expenditure 75 Figure 3.24: Efficiency in terms of health expenditure 75 CHAPTER 4 Figure 4.1: Labor force participation in India is lower than most EMDEs (percent, 2023) 79 Figure 4.2: The growth rate of India’s working age population will slow over the next three decades 79 Figure 4.3: Composition of working-age population, India (percent of total population 15+) 79 Figure 4.4: Labor force participation rate, India and comparators (percent of female population, 15-64 years) 79 Figure 4.5: Employment elasticity of growth 80 Figure 4.6: Employment elasticity of growth 80 Figure 4.7: Employment and real GVA, sectors (Index: 2000=100, for 2000-23) 80 Figure 4.8: Labor productivity (level and change), employment change, sectoral employment share (percent, 2000-19) 81 Figure 4.9: Sectoral productivity and employment share, percent 81 Figure 4.10: Key employment indicators, India vis-à-vis EMEs (percent share) 82 Figure 4.11: Sectoral employment 83 Figure 4.12: Employment distribution in the informal and formal sectors (by firm size, percent) 83 Figure 4.13: Drivers of employment elasticity: results from factor analysis (factor loadings) 84 Figure 4.14: Selecting the sectors with high employment potential 85 Figure 4.15: Female Labor Force Participation among major emerging markets 86 Figure 4.16: Female labor force participation rate (all ages, rural and urban) 86 viii India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Contents Figure 4.17: Distribution of usual status workers by location and gender (percentage share of total employment) 87 Figure 4.18: Average female employment composition in India 87 Figure 4.19: Rural-urban wage gap and female labor force participation (July-June 2019) 88 CHAPTER 5 Figure 5.1: GDP per-capita (USD adjusted using the Atlas method) 92 Figure 5.2: Average real GDP per-capita and average real GDP growth (2001-20) 93 Figure 5.3: Distribution of real GSDP per-capita 93 Figure 5.4: Relative transition path of convergence groups and divergent states (1993-2019) 96 Figure 5.5: GSVA composition by sector (1981) 98 Figure 5.6: GSVA composition by sector (2020) 98 Figure 5.7: Share of manufacturing in GSVA 98 Figure 5.8: Concentration in top-5 states (percent) 99 Figure 5.9: Price dispersion across markets by commodity, coefficient of variation of prices 100 Figure 5.10: Contribution to fiscal consolidation (percentage point over the average episodes, 2010-18) 101 Figure 5.11: State-level fiscal multipliers for categories of development capital outlay 101 CHAPTER 6 Figure 6.1: A comprehensive competition policy framework 108 TECHNICAL ANNEXES Figure 3.11: Gross Domestic Savings 120 Figure 3.12: Components of Gross Domestic Savings 120 Figure 3.31: NBFC advances and borrowings (INR trillion) 122 Figure 3.32: Incremental growth (INR trillion) 122 Figure 3.33: Financing patterns of NBFCs based on Financial Health (2015=1) 123 Figure 3.51: Pre-2009: Total credit cycle length, 1980-2009 (years) 127 Figure 3.52: Post-2009: Total credit cycle length, 2009-2021 (years) 127 Figure 3.53: India: Turning points in the total credit cycle 128 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation ix Contents TABLES CHAPTER 1 Table 1.1: Countries that transitioned to UMIC in past three decades and achieved HIC status 26 Table 1.2: India and Successful UMICs and Trapped UMIC 27 CHAPTER 3 Table B3.21: Estimates of the central government’s fiscal multipliers 63 CHAPTER 5 Table 5.1: India’s convergence Groups 95 Table 5.2: Covariates of state-group membership 95 Table 5.3: Measures of the policy environment for private sector development 96 Table 5.4: District groups 97 TECHNICAL ANNEXES Table 2.11: Access to ICT Technology for Firms 117 Table 3: Credit composition and cycle responses 129 Table 3.61: Education Sector Multiple Output-Outcomes (percent of GDP) 130 Table 3.62: Multiple Output-Outcome Health Sector Efficiency (percent of GSDP) 131 Table 4.1: Detailing the Criteria of ISNA for Economic and Non-Economic Work 133 Table 4.21: The Average Treatment Effect on Treated (ATET) due to the deregulation 136 Table 4.22: Test for Parallel Trends for the alternative wage model of direct workers 137 Table 4.23: The Average Treatment Effect on Treated (ATET) due to the deregulation for the alternative wage model of direct workers 137 x India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation List of Abbreviations List of Abbreviations AQR Asset Quality Review MOSPI Ministry of Statistics and Programme Implementation ASEAN The Association of Southeast Asian Nations NARCL National Asset Reconstruction Company ASI Annual Survey of Industries Limited BOS Businesses of the State NEP National Education Policy CIRP Corporate Insolvency Resolution Process NIMZ National Investment and Manufacturing CPHS Consumer Pyramids Household Survey Zone(s) CPI Consumer Price Index NITI National Institution for Transforming India CSS Centrally Sponsored Scheme NLP National Logistics Policy DILRMP Digital India Land Records Modernization NSO National Statistics Office Programme NSS National Sample Survey DPI Digital Public Infrastructure ONORC One Nation One Ration Card (Scheme) EASE Enhanced Access and Service Excellence PDS Public Distribution System EBP Electronic Bidding Platform PLFS Periodic Labor Force Survey EPZ Export Processing Zone PLI Production Linked Incentive (Scheme) ESDM Electronics System Design and Manufacturing PPIRP Pre-Packaged Insolvency Resolution Process FDI Foreign Direct Investment RBI Reserve Bank of India FLFPR Female Labour Force Participation Rate SEBI Securities and Exchange Board of India GER Gross Enrollment Ratio STRI Services Trade Restrictiveness Index GST Goods and Services Tax TFP Total Factor Productivity HCI Human Capital Index WBES World Bank Enterprise Survey IDA Industrial Disputes Act WITS World Integrated Trade Solution Database MGNREGS Mahatma Gandhi National Rural Employment WPR Worker Population Ratio Guarantee Scheme India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation xi Acknowledgements Acknowledgements This Report is a product of collaborative efforts of various teams in the India office of the World Bank led by the Economic Policy (EP) Global Practice. The preparation of the Report was led by Emilia Skrok (Task-Team Leader), former Lead Economist for India, and Rangeet Ghosh (co-Task-Team Leader), Senior Economist, EP. The core team consisted of: Aurelien Kruse, Vincent De Paul Tsoun- gui Belinga, Naresh Kumar, Mohini Gupta, Rishabh Choudhary, Tanvir Malik, Ran Li, Nora Carina Dihel, Nandini Krishnan, Ruchita Manghnani, Paulina Eva Holda, Kanika Bhatnagar, Saloni Khurana, Abhishek Anand, Venkat Bhargava Sreedhara, Nayantara Sarma, Farah Zahir, Michel Ragnvald Mallberg, Mohan Nagarajan, Rajni Bajpai, Dhruv Sharma, and Kavya Singh. The Report was prepared under the oversight of Auguste Tano Kouame, Country Director of the World Bank, India, Mathew A. Verghis, South Asia Regional Director, Prosperity Practice Group, and Hoon Sahib Soh, South Asia Region Practice Manager, EP and Public Sector. The Report has benefitted from extensive comments from Martin Raiser, Vice President for the South Asia Region, World Bank, Marcin Piatkowski, Program Leader, ESADR, Bhavna Bhatia, Program Manager, SACIN, Johanna Maria Gradl, ET Consultant, SACIN, Sudip Mozumdar, External Affairs Advisor, World Bank India, and Shilpa Banerji, External Affairs Officer, ECRSA. The team benefitted from extensive comments from the peer reviewers; Franziska Lieselotte Ohnsorge, Chief Economist, South Asia Region, Gonzalo J. Varela, Senior Economist (ESAC1), and Ivailo V. Izvorski, Chief Economist (ECACE). In the initial stages of preparation, the team received valuable guidance from Manuela Francisco, Global Director (EMFDR), Junaid Kamal Ahmad, Vice President, Operations (MIGA), and Zoubida Kherous Allaoua, former South Asia Regional Director, World Bank. In addition to the core team, the Report has been enriched by contributions from; Franz Ulrich Ruch, Nancy Puranbhai Devpura, Pravakar Sahoo, Rishabh Sinha, Sinem Kilic Celic, and Steven Michael Pennings (Chapter 1); Andrew Goodland, Arun Sharma, Dennis Sanchez-Navarro, Francis R. Ratsimbazafy, Graciela M. Murciego, Geethanjali Nataraj, M. Denisse Pierola, Manivannan Pathy, Pavel Chakraborty, and Xavier Cirera (Chapter 2); Divyanshu Jain, Emil Verner, Pablo Cesar Benitez, Karsten Müller, Laurent Gonnet, Sangeeta Goyal, Shabnam Sinha, Supriyo De, Swati Ghosh, and Tushar Arora (Chapter 3); Arup Mitra, Dino Leonardo Merotto, and Naveen Thomas (Chapter 4); Debajit Jha, Mayank Jain, and Sabyasachi Kar (Chapter 5); Anindo Kumar Chatterjee, Kamalika Das, and Benedicte Leroy de la Brière (Chapter 6). The analysis has also benefitted from comments from participants in ‘Technical Sessions’ including from: V. Anantha Nageswaran (Chief Economic Adviser, Ministry of Finance); Rana Hasan (Asian Development Bank); Deepak Mishra and Radhicka Kapoor (Indian Council for Research on International Economic Relations, New Delhi); Sunil Mani (Centre for Development Studies, Trivandrum); Ritu Mathur (NITI Aayog); E. Somanathan (Indian Statistical Institute, New Delhi); Pinaki Chakraborty (former Director, NIPFP); R. Vyasan (Prime Minister’s Office); and Antony Cyriac and Syed Zubair Noqvi (Ministry of Finance). The team has also benefitted from outreach exercises with various stakeholders and policymakers, including with the Ministry of Finance, the Office of the Executive Director of India to the World Bank, and the NITI Aayog. Valuable guidance on topics and stakeholder consultations were received from time to time from Rajat Kumar Mishra, former Additional Secretary, and Hanish Chhabra, former Director, Multilateral Insti- tutions Division, Department of Economic Affairs, Ministry of Finance. The team is also grateful to William Shaw, Sonia Chauhan, and Sheela Bajaj, for their assistance in editing the document, and to Alejandro Espinosa of www.sonideas.com and www.grafox.in for designing the Report. Savita Dhingra, Mamata Baruah, Rima Sukhija and Shweta Iyer, Program Assistants provided excellent administrative support. xii India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation © 2023 PradeepGaurs/Shutterstock VOLUME I Analysis and Policy Proposals India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 1 Executive Summary Becoming a High-Income Economy in a Generation EXECUTIVE SUMMARY Becoming a High-Income Economy in a Generation O ver the past decades, India has developed at a scale and pace that few would have thought possible. From 2000 to today, in real terms, the economy has grown nearly four-fold, and GDP per capita has almost tripled. Because India grew faster than the rest of the world, its share in the global economy has doubled from 1.6 percent in 2000 to 3.4 percent in 2023 and India has become the world’s fifth largest economy. This remarkable development story also includes a steep decline in extreme poverty, and massive expansion of service delivery and essential infrastructure. Building on these achievements, India has set the ambitious goal of becoming a high-income country by 2047. However, reaching the ambitious target of becoming a high-income economy by 2047 will not be possible in a busi- ness-as-usual scenario. In recent years, the government has introduced a host of structural reforms to transform India into a global manufacturing hub, to boost infrastructure, improve human capital, and leverage digitization, while at the same time bolstering macroeconomic stability. However, for India to become a high-income economy by 20471, its GNI per capita2 would have to increase by nearly 8 times over the current levels; growth would have to accelerate further and to remain high over the next two decades, a feat that few countries have achieved. To meet this target, given the less conducive external environment, India would need to not only maintain ongoing initiatives but in fact expand and intensify reforms. This report outlines what it would take to realize the vision of High-Income India. I. To Reach High-income Status within a Generation, India Needs to Boost all Growth Engines. India has made a fast ascent on the lower rungs of the income ladder: climbing further is possible but it will not be easy. India became a Low Middle-Income Country3 (LMIC) in 2007-08 and is currently on track to become an Upper Middle-Income Country (UMIC) by 2032. Over the two decades prior to the pandemic, India grew at an annual average rate of 6.7 percent, faster than all other large economies,4 except China (Figure 1). However, to fulfill the country’s aspirations of reaching High Income Country (HIC) Status by 2047, the economy will have to sustain two more decades of very high growth. Historical experience suggests this is not impossible but difficult. Only a handful of countries have managed to make the transition from middle to high income in less than two decades5. A larger group of countries —including Brazil, Malaysia, Mexico, South Africa and Türkiye—have spent more than 20 1 By World Bank standards this implies reaching gross national income per capita (Atlas GNI per capita) of around US$20,000 by 2047. The projections for 2024-47 are based on the LTGM tool. The cut off for income classification is assumed to grow at an annual rate of 1.5 percent from 2022 (the average growth observed over the past two decades). 2 India’s Atlas GNI per capita was US$2,540 in 2023. 3 The World Bank defines UMIC as nominal Gross National Income (GNI) per capita between USD 4,516 and USD 14,005 (2023) as per the Atlas method. 4 China, Vietnam, Philippines, and Bangladesh were selected as India’s peers due to their similar level of development in 2000, large population (over 50 million), and sustained high rate of growth (average growth rate of at least 6 percent in the last 20 years). The report draws comparison to Asian high-growth economies identified by the Growth Commission and the group of MICs transitioning to higher income status (based on the World Bank classification). Asian peers include China (1961-2005), Indonesia (1966-1997), Republic of Korea (1960- 2001), Singapore (1967-2002), and Thailand (1960-97). Throughout the Report, based on the context and data availability, India is compared to other emerging market economies like Argentina, Brazil, Indonesia, Malaysia, Mexico, and Poland. 5 Countries such as Chile, Czechia, Poland, and Romania belong to this group. 2 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Executive Summary Becoming a High-Income Economy in a Generation years in the UMIC group (“trapped-UMICs”) without “graduat- FIGURE 1: India and Peers: Annual GDP growth rate, ing” to high-income, a phenomenon commonly referred to as (real, percent) the “middle income trap” (Figure 2). 20 India will need to chart its own path towards high income. 15 Countries that “graduated” can be roughly organized into three groups: Resource-rich countries (like Saudi Arabia) that were 10 able to leverage substantial natural wealth, European countries (like Poland) that benefited from the European Union conver- 5 gence bandwagon, and East-Asian countries (like Korea) that 0 integrated into the global economy and developed manu- facturing exports. Given its different circumstances, India will -5 need to chart its own path. However, the key ingredients of -10 growth—capital investment, labor force growth and total factor productivity (TFP)— remain unchanged and India has room to -15 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 improve further in all these dimensions. While TFP and capital accumulation drove growth in India over the past two decades, Bangladesh China India the contribution of the latter has fallen in the years prior to the Philippines Viet Nam pandemic and the contribution of labor has been limited (Figure 3). Reviving investment and creating more and better jobs, while Source: World Development Indicators (WDI) creating the conditions for continued TFP growth will be critical for India to accelerate and sustain its growth going forward. FIGURE 2: All drivers of growth have contributed in FIGURE 3: TFP and investment drove growth in India, successful MICs but the latter has declined and labor’s contribution was marginal 8.0 10 7.0 8 6.0 5.0 6 4.0 3.0 4 2.0 1.0 2 0.0 0 -1.0 Asian peers CEM peers Successful UMICs Trapped UMICs 2000-2004 2005-2009 2010-2014 2015-2019 (growth commission) TFP Capital composition Capital stock Capital Labor TFP Real GDP Labor quality Employment Source: World Bank (WB) Staff Calculations. Penn World Tables (PWT) 10.01. “Success Source: India KLEMS. UMICs” includes countries that successfully transitioned from UMIC to HIC, “Trapped UMICs” includes those that did not make this transition. Note: Asian peers include China (1961-2005), Indonesia (1966-1997), Republic of Korea (1960-2001), Singapore (1967-2002), and Thailand (1960-97). CEM peers include Bangladesh, Philippines, and Viet Nam with growth over 2010-19. Successful UMICs include Chile (1993-2012), Czechia (1994-2006), Poland (1996-2009), Romania (2005-19), Slovakia (1996- 2007). Trapped UMICs include Brazil (1989-2021), Malaysia (1992-21), Mexico (1990-2021), Türkiye (1997-2021), and South Africa (1988-2021). Figure 3 shows the composition of growth in India over five-year periods since 2000. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 3 Executive Summary Becoming a High-Income Economy in a Generation India can boost productivity by promoting greater structural transformation and leveraging global integration Structural transformation has been a key driver of produc- FIGURE 4: Labor productivity change decomposition tivity growth in successful developing countries, but (Average over periods, percentage points) relatively less so in India. In successful East Asian and East India European economies, productivity growth was underpinned by the movement of labor from low to high productivity 1987-1993 sectors (i.e. from agriculture to manufacturing, typically for export) and from low to high productivity regions (i.e. from 1993-1999 rural hinterlands to urban growth poles). In India, however, this transition has been relatively slow; agriculture still Period 1999-2005 accounted for over 45 percent of total employment in 2023- 24, while traditional market services and construction (low 2005-2011 productivity) together accounted for nearly 30 percent. In contrast, the share of manufacturing in total employ- ment was around 11 percent and modern market services 2011-2017 accounted for only 7 percent. Over 2000-19, three-quar- 0 2 4 6 8 ters of labor productivity growth came from within-sector Annual labor productivity growth, in percent productivity improvements, particularly in services, rather Across state Within state, across sector Within state, within sector than reallocations of resources across occupations and firms (Figure 4). Productivity gains have also been predominantly achieved through resources reallocation to large firms while small and medium firms have not managed to become more productive. During 1995-2018, within-firm productivity has remained constant for small and medium sized firms across the manufacturing and services sectors. However, the largest firms —75th percentile in size and above— experienced productivity increases, especially in the services sector. The ratio of the productivity of the most productive firms (90th percentile) to the least productive (10th percentile) more than doubled in services, with very large increases in utilities, trade, financial services, and administrative support services. In contrast, the dispersion remained stable in manufacturing. Openness tends to drive productivity growth, but India’s economy is less open to trade today than it was in the previous decade. India’s export performance has been strong in services, particularly in high-skill intensive sectors like IT and business process outsourcing (BPO). It is one of the most diversified goods exporters and recent reforms have facilitated a remarkable turnaround in sectors such as mobile phone exports. However, the share of goods and services exports and imports in GDP (46 percent in 2023) remains below the peak of 56 percent in 2012. Relatively high import tariffs, especially on intermediate and capital goods, and significant non-tariff barriers contribute to high trade costs which inhibit greater openness in terms of imports and exports as well as fuller participation in global value chains (GVCs). In contrast, several of the economies that transitioned from either LMIC to UMIC or UMIC to HIC demonstrated greater openness during their transition years (Figure 5A and Figure 5B). Firm-level TFP growth is typically associated with trade and innovation, including technology adoption. In India, the share of firms involved in international trade has been declining in the years prior to the pandemic across the manufacturing and services sectors. As a result, Indian firms exhibit modest levels and relatively low adoption of cutting-edge Industry 4.0 technologies, even though sectors such as financial services and pharmaceuticals use more advanced technology compared to apparel, leather and footwear. More than government regulations or weak infrastructure, it appears that the main constraint to technology adoption is the lack of skilled labor and weak access to finance. 4 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Executive Summary Becoming a High-Income Economy in a Generation FIGURE 5A: Exports/ GDP against log GNI per capita FIGURE 5B: Trade/ GDP against log GNI per capita 120.0 180.0 160.0 100.0 140.0 80.0 120.0 Trade (% of GDP) Exports (% of GDP) 100.0 60.0 80.0 40.0 60.0 40.0 20.0 20.0 0.0 0.0 2.4 2.9 3.4 3.9 4.4 2.4 2.9 3.4 3.9 4.4 Log GNI per capita (US$, Atlast Method) Log GNI per capita (US$, Atlast Method) Chile China Hongkong India South Korea Malaysia Poland Thailand Source: WDI, World Bank Staff Calculations Note: The periods covered in the charts refer to the 20 years before the transition of the selected economies from either LMIC-UMIC or UMIC-HIC. Data for India reflects the period 2003-2023. Private investment needs to accelerate sustainably. Investment is a critical ingredient to sustainable growth, but India has not maintained the investment acceleration of the early 2000s. Countries that successfully transitioned to high income followed a similar trajectory whereby growth accelerations coincided with investment accelerations, and the share of investment in GDP fell back after crossing a high level of per capita GDP (Figure 6B). In contrast, in India, investment declined as a share of GDP at a lower level of per-capita income. Private investment rose sharply after the liberalization reforms in the 1990s, but it has fallen as a share of GDP, particularly since the global financial crisis (Figure 6A). FIGURE 6A: The investment rate has declined since FIGURE 6B: The slowdown occurred at a lower 2008, with slight pick-up in 2022 and 2023 per-capita income vis-à-vis countries that transitioned (Investment percent of GDP) to either UMIC or HIC (Investment nominal, USD, share of GDP) 40.0 50 35.8 35.0 45 30.0 27.5 40 Investment (% of GDP) 25.0 35 20.0 30 15.0 25 10.0 20 5.0 15 0.0 10 2 2.5 3 3.5 4 4.5 5 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 log Per capita GNI (Atlas method, US$) Public Private Total Korea Singapore India China Thailand Source: Ministry of Statistics and Programme Implementation (MOSPI), WDI (2022), WB staff calculations. Note: The break-up of investment (gross fixed capital formation), in Figure 6A, into public and private components, is not yet available for FY23/24. The period considered in Figure 6B is 1972-2019. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 5 Executive Summary Becoming a High-Income Economy in a Generation Availability of credit is no longer a major constraint on investment. In the period following the Global Financial Crisis (GFC), corporate overleverage translated into non-performing assets (NPAs) on the balance sheets of banks, especially public sector banks. The resulting “Twin Balance Sheet” (TBS) problem (Economic Survey 2016-17) caused bank credit to industry to decelerate signifi- cantly, and the credit composition of banks to shift towards personal loans and loans to non-bank financial companies (NBFCs). However, NPAs fell precipitously in the aftermath of the pandemic and bank profitability improved thanks to various measures to expedite the resolution of NPAs. Corporate balance sheets improved in tandem and the pre-pandemic stress in the NBFC sector was largely contained due to timely steps taken by the Government and the Reserve Bank of India (RBI). In short, access to credit to support private investment appears to be less of a barrier than before. India has emerged as a major destination for foreign direct investment (FDI), but net FDI remains modest as a share of GDP. FDI over GDP averaged 1.6 percent over the past two decades, compared to nearly 5 percent for Vietnam, 3.3 percent for Malaysia, 3.1 percent for China, and 2.8 percent for Organization for Economic Cooperation and Development (OECD) members. India’s FDI has declined further to 0.3 percent of GDP in 2024. According to the OECD’s “FDI Regulatory Restrictiveness Index”, regulatory restrictions were halved over 2003-20, particularly in services subsectors (broadcasting, media, retail, communications, and tele- communications). However, India still imposes equity restrictions in several sectors, especially in legal and accounting services, real estate, and business services. Compared to financial or tax incentives, regulatory changes to reduce entry barriers are relatively more important for attracting FDI. Distortions in land markets undermine the productivity of land-intensive activities, especially in the manufacturing sector. The market for buying and selling land is constrained by the lack of conclusive land titles and land fragmentation. Most agricultural land is inherited, and states impose restrictions on leasing of land. Some states have made significant progress in digitizing land records, but the quality of the records and land-related services have substantial room for improvement. Although it has become easier for large firms to access land since the mid-2000s, small firms have found it increasingly difficult. Labor reforms are critical, to give firms greater flexibility to manage human resources while protecting workers. Over time, states have generally relaxed labor regulations and have sought to make compliance less onerous. The evidence on recent labor reforms in Rajasthan and Andhra Pradesh suggests a mixed record in terms of improving firm productivity. However, simpler labor laws are important as excessive rigidity has led large firms to circumvent laws and regulations, including the Industrial Disputes Act (IDA), by increasingly relying on contract workers hired via staffing agencies.6 Implementation of the four labor codes – the Code on Wages, 2019, the Industrial Relations Code, 2020, the Code on Social Security, 2020 and the Occupational Safety, Health and Working Conditions Code, 2020 – that subsumed 29 labor laws, can make labor regulations simple and flexible and potentially strengthen worker protection, including for workers in the unorganized sector. India can do more to leverage the “demographic dividend”, but time is running out Low labor force participation means India is not fully capitalizing its demographic dividend. In all rapidly growing peer countries, growth has been both productivity and employment intensive. By contrast, in India, the employment rate has dropped system- atically and significantly in the pre-pandemic period. Over 2000-19, the working-age population increased by 37.4 percent, but employment increased by only 15.7 percent; during this period, the labor force participation rate fell from 58 to 49 percent. Despite a significant increase since 2022, India’s overall labor force participation remains low by middle-income countries standards (Figure 7). Over the next three decades, the growth of the working-age population will decelerate, and the dependency ratio is projected to increase from 45 percent in 2032 to 49 percent in 2050. This implies that there is a limited window to maximize demographic gains by boosting labor force participation and job creation. 6 The contract workers are employees of the staffing companies, and as a result these companies are responsible for abiding by the IDA. 6 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Executive Summary Becoming a High-Income Economy in a Generation FIGURE 7: India has not fully leveraged its demographic dividend; labor force participation has remained low (percent, 2023) 75 Viet Nam 70 Indonesia Malaysia Philippines Labor Force Participation rate, 15+ 65 Brazil China Russia Chile 60 Bangladesh South Africa Colombia 55 India PLFS (Current Weekly Status, 2023-24) India PLFS (Usual Status, 2023+24) 50 India Sri Lanka 45 40 60 65 70 75 80 85 Share of working age population in total population, 15+ Source: WDI and WB staff calculations. More jobs are required but also better jobs. New jobs have been mostly created in informal and low productivity occupations. Growth has been driven by modern market services, which require relatively few – mostly high-skilled – labor inputs. Instead, job creation has occurred mostly in construction and traditional market services where employment tends to be informal and have limited scope for productivity growth. Agriculture remains the primary employer, accounting for over 45 percent of total employ- ment (PLFS, 2023-24). The bulk of manufacturing employment (nearly 70 percent) is in micro and small firms that are informal enterprises and hire less than 10 workers. As a result, informal sector enterprises account for 73.2 percent of employment (PLFS, 2023-24) against 32.7 percent on average in emerging market economies (EMEs). Moreover, wage jobs often lack some of the features typically associated with “good” jobs. In 2023-24, 53.4 percent of regular wage and salaried workers in the formal sector lacked social security benefits, 47.3 percent were not eligible for paid leave, and 58 percent had no formal, legally enforceable contract, resulting in job insecurity and inadequate social protection. More than 70 percent of working Indians are in “vulnerable employment” whereas the median is 50 percent among peer EMEs.7 Despite significant improvements, women’s participation in paid employment remains limited. Female labor force partic- ipation increased substantially to 35.6 percent8 in 2023-24 (compared to 21.6 percent in 2018-19). The rise in women’s partic- ipation mainly reflects a sharp rise in rural women’s participation and a modest increase among urban women, it can also be partly attributed to changes in survey methods that classify women’s unpaid work in household enterprises more accurately in the latest rounds of the Periodic Labor Force Survey. As of 2024, 33.8 percent of Indian women aged 15 years and above9 participated in the workforce, compared to 50-60 percent in most emerging markets.10 In addition, nearly 37 percent of women in the workforce were unpaid workers in household enterprises, and around 64 percent were engaged in agricultural activities (primarily farm jobs). In urban areas, only 20 percent of women participated in the workforce. Regulatory restrictions, limited access to finance and digital technologies, alongside domestic duties, social norms and safety concerns deter women from fully participating in paid employment. 7 The EMEs include Bangladesh, Brazil, Indonesia, Malaysia, Mexico, Philippines, South Africa, and Viet Nam. 8 According to the current weekly status, closer in definition to the ILO’s methodology. According to Usual Status Methodology, which captures a broader status of employment and reflects the seasonal nature of employment in India, the female labor force participation rate reached 41.7 percent in 2023-24. 9 Workforce participation ration by Current Weekly Status; closer in definition to the ILO’s methodology. According to Usual Status Methodology, which captures a broader status of employment and reflects the seasonal nature of employment in India, the female work force participation rate reached 40.3 percent in 2023-24. 10 The estimates use international standards developed by the International Labor Organization: https://ilostat.ilo.org/resources/concepts-and-definitions/forms-of-work/ ; Employment under ILO standards is defined as work for pay or profit. It does not include the “augmented FLFP rate” that accounts for activity code 93 of the PLFS- engaged in domestic duties and also engaged in free collection of goods (vegetable, firewood, cattle feed, etc.), tailoring, etc. for household use. The Economic Survey 2022-23 uses the augmented FLFPR definition according to which the FLFPR was 39.6 percent in 2019. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 7 Executive Summary Becoming a High-Income Economy in a Generation India has improved its human capital, but the pace needs to accelerate. India’s “human capital index”11 has improved since the early 1980s, thanks to increased enrollment in primary and tertiary school education. To catch up with countries such as Indonesia and China in educational attainment, India will need to further increase school enrolment and improve the quality of learning. Progress in secondary school enrollment has been weaker than in primary and tertiary education. The share of adults without upper-secondary education is 71 percent, compared to 36 percent on average across G20 countries. Recognizing this, the Government of India is implementing key initiatives, such as the Skill India Mission12 and the National Education Policy (NEP, 2020). There is also significant scope to enhance the efficiency of education and health expenditures to bolster human capital outcomes even without increasing budgetary allocations. Indeed, there is substantial heterogeneity in the efficiency of public spending on education and health across states, which implies efficiency gains can be achieved by the relatively weaker performers. II. Growth Scenarios and Reform Proposals To reach high income by 2047, India’s growth rate needs to average 7.8 percent, in real terms, over the coming decades. Under “business as usual” (scenario 2 below), India will experience tangible welfare gains but still fall short of its ambitions. If the reform pace was to slow down significantly over the coming decades (scenario 1), India would fall short by a wide margin. Only an “accelerated reforms” package (such as illustrated by scenario 3), would put India on track to become High-Income by 2047: • In Scenario 1, which models a slowdown in reforms relative to “business as usual”, (i) investment-to-GDP peaks at 35 percent in 2035 and then moderates, (ii) the share of ICT capital in total investment remains broadly at current levels, (iii) the FLFPR does not improve and (iv) TFP growth peaks at 2.5 percent by the beginning of the next decade. Under this scenario, growth remains below 6 percent, on average, until 2047. • In Scenario 2, a baseline or “business as usual” scenario in which the momentum of reforms remaining strong: (i) invest- ment reaches 37 percent of GDP by 2035 driven by an equal contribution of ICT and physical capital accumulation, and then declines, (ii) the FLFPR increases to 45 percent by 2045, and (iii) TFP growth is assumed to peak at 2.7 percent by the beginning of the next decade prior to moderating thereafter. In scenario 2, growth averages 6.6 percent per year which is still insufficient to achieve high income status by 2047. • In Scenario 3, which models “accelerated reforms” (i) investment reaches 40 percent of GDP by 2035, led by an equal contribution of ICT and physical capital, and moderates thereafter, (ii) The FLFPR increases to 55 percent by 2050, and (iii) TFP growth is assumed to be 40-50 basis points higher than in scenario 2. Growth averages 7.8 percent, allowing India to reach high income status by 2047. Scenario 3 is challenging but possible, as some countries have managed to raise investment and TFP, improve human capital, and boost FLFP at the implied speed and scale. Korea increased investment-to-GDP from 32 percent to above 40 percent during 1978-96 during its transition to HIC status. In China, the investment-to-GDP rose from 26 percent in 1995 to 45 percent in 2014. Thailand (1956-80), Taiwan, China (1957-81), Botswana (1965-89) sustained average TFP growth at around or above 3 percent. Over 1991-2022, Israel increased the female labor force participation rate (FLFPR) by nearly 20 percentage points, and Chile by 14 percentage points. What would it take for India to transition from “business as usual” to “accelerated reforms”? The CEM offers the following suggestions: 11 The index is based on the average years of schooling derived from Barro and Lee (2013) and an assumed rate of return to education, based on the estimation of a Mincer equation in Psacharopoulos (1994). For details, please refer to chapter 3 and https://www.rug.nl/ggdc/docs/human_capital_in_pwt_90.pdf 12 According to the Human Development Report 2020, the government has made remarkable progress with the launch of the Skill India Mission 2014 and has successfully increased the skilled labor force to 21.2 percent in 2020. 8 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Executive Summary Becoming a High-Income Economy in a Generation A. Promoting structural transformation, trade, and technology infusion to improve productivity India has been successful in developing modern market services and segments of technology-intensive manufacturing. The policies that underpinned this success can now be replicated in other sectors. India can promote both industry and services by applying more widely the lessons learned from its successes in advanced manufacturing and modern market services. The strong productivity increases in these subsectors benefited from high quality infrastructure, efforts to address land constraints, the availability of specific skills, and a supportive regulatory environment, including the liberalization of trade and capital inflows. The successes of these subsectors suggest the following policy package will be needed for India to sustain a high growth rate over the next decades: rekindling investment and trade, facilitating the infusion of cutting-edge technologies throughout the economy, and continuing structural transformation – supporting the creation of more and better jobs into the more productive parts of the economy to absorb low-skilled workers. More specifically, key policy actions include: a) Easing access to land – currently a key constraint to operations and growth, especially for SMEs. Digitizing land records would help, and the quality and accuracy of records can be improved by leveraging ongoing initiatives such as the Digital India Land Records Modernization Programme (DILRMP) and implementing the Local Government Directory (LGD) codes across all tiers of government. Place-based policies, for example, the creation of National Investment and Manufacturing Zones (NIMZ) can improve the supply of land for industrial use; however, they work best when complemented by local enabling factors such as good quality public services and infrastructure. b) Boosting agricultural productivity to support structural transformation. Strengthening Farmers Producers Organizations (FPO) can improve market access through the aggregation of supply and demand, enhanced access to public services and markets, and by including small farmers in the agricultural value chain. Facilitating land leasing and pooling can also help overcome the constraints on scale and productivity from land fragmentation. Investing in new technologies, such as improved seeds, and modernizing farm and agri-food businesses would yield efficiency gains. Further, incentivizing farmers to diversify to high-value crops particularly in rain-fed areas with high poverty levels, easing restrictions on marketing and exports, and improving access to credit for small farmers would lead to productivity gains. c) Facilitating labor mobility to bolster labor productivity. Reforms required include steps to strengthen the “One Nation One Ration Card” (ONORC) scheme, as food insecurity is a significant challenge to migration, especially to urban areas. This can be achieved by raising awareness of the program, focusing on targeted beneficiaries (including shop owners), and closing gaps in Aadhar seeding at fair price shops. d) Expanding and improving physical and digital infrastructure. Ongoing initiatives to strengthen physical and digital infra- structure could be leveraged further by facilitating infrastructure financing, reducing the rural-urban divide in digital access, and incentivizing firms, especially MSMEs, to adopt advanced digital technologies to boost the impact of ICT investments on growth. Updating the classification of infrastructure to inform the Harmonized List of Infrastructure13, so that the sectors identified in the list are more clearly defined and the list includes key digital and environmental infrastructure services, will strengthen infrastructure planning and monitoring. It will also facilitate the creation of a consolidated database of public and private infrastructure projects by standardizing the measurement of progress and financial flows into sectors defined in the Harmonized List across various existing infrastructure databases in the country. e) Boosting learning outcomes and improving the efficiency of public spending on human capital development. A multi- pronged strategy, including measures to reverse learning losses from the pandemic, can be implemented, by (i) measuring 13 Since 2012, India has a harmonized master list of infrastructure that is reviewed from time to time. The sectors included in this list enjoy specific incentives. In 2022, the list was revised to include data centers and energy storage systems. Following the announcement in the Union Budget 2023-24, the Government of India created a review committee to develop an updated classification of infrastructure. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 9 Executive Summary Becoming a High-Income Economy in a Generation and tracking learning; (ii) increasing instructional time for remediation; (iii) preparing teachers to teach according to the students’ capabilities; and (iv) providing psychological and social support to students. In addition, the large dispersion of outcomes across Indian states indicates there is significant scope to improve the efficiency of expenditure in education and health in underperforming states. f) Bolstering trade and the innovation ecosystem to allow resources to flow to more competitive parts of the economy. India could consider: i) Ramping-up participation in GVCs by adopting a “whole of the supply chain” approach and greater collaboration with its trading partners. This approach would require sustained investments in infrastructure, especially in transport, energy, digital and green sectors; negotiating deeper trade agreements that cover not only tariffs but also non-tariff barriers, services, investment, intellectual property rights and e-commerce; and creating GVC ‘hubs’ in areas of strengths such as IT, pharmaceuticals, and automotive. ii) Reducing tariffs and adopting reforms to promote trade facilitation, services trade, and FDI. Simulations of regional integration indicate that gains are higher for comprehensive regional integration that includes measures that help in trade facilitation, promote trade in services and FDI liberalization. To enhance trade facilitation, India could further simplify and streamline customs procedures, increase transparency and predictability in regulations and policies, and reduce red tape. iii) Facilitating the infusion of technology across domestic firms to realize productivity gains, lay the foundation for greater innovation and promote “creative destruction” in the economy. Trade facilitation, greater GVC integration, R&D focused tertiary education, and market competition create conditions for firms to climb up the “Capabilities Esca- lator” (Cirera & Maloney, 2017) and transition from basic toward more sophisticated production capabilities. In India, a burgeoning startup sector coexists with many unproductive small firms that account for most of employment in the manufacturing sector. Policies to promote infusion, and ultimately innovation, could include: (i) working with sector organizations on technology adoption roadmaps and skill training needs, and improving the provision of business services and technology extension services, (ii) undertaking regulatory impact assessments to identify whether regula- tions enable the adoption of technologies for different firm sizes, (iii) providing financial support (for example, subsidies to first adopters, tax incentives to technologies with large positive externalities like green technologies) to incentivize firms to adopt new technologies and (iv) incentivizing industry-academia collaboration to incentivize R&D activity. iv) Strengthening market competition and reducing barriers to entry in sectors with high levels of market concen- tration. Strong market institutions and effective implementation of the competitive neutrality principles14 are keys to ensuring a level playing field for private enterprises. India has reformed its competition policies through the passing of the Competition (Amendment) Act, 2023 that strengthened the mandate of the Competition Commission of India (CCI). Effective implementation of the amended Bill would help ensure more contestable and open markets that would enhance incentives for entrepreneurship. India’s digital economy would benefit from new analytical frameworks, enforcement tools and regulations to promote market competition. B. Turbocharging and sustaining investment Achieving a sustained increase in private investment is key. Areas of policy attention include: a) Further financial sector reforms to allow more efficient credit allocation and minimize risks: i) Further strengthening systems to detect financial risks by establishing a centralized systemic risk database and developing advanced risk assessment tools to improve financial sector supervision. The current macro-financial 14 Competitive neutrality is a principle according to which all enterprises, public or private, domestic, or foreign, should face the same set of rules, and where government’s involvement in the marketplace, in fact or in law, does not confer an undue competitive advantage on any actual or potential market participant. See OECD, Roundtable on Competition Neutrality, Issues paper by the Secretariat, 2015, p. 4.   10 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Executive Summary Becoming a High-Income Economy in a Generation situation, characterized by low NPA ratios and sound balance sheets, provides an opportunity to expand countercy- clical capital buffers to preserve banks’ ability to lend in future episodes of financial stress. ii) Strengthening resolution mechanisms by: enhancing the performance incentives of the National Company Law Tribunal (NCLT) and Debt Recovery Tribunals (DRTs); expediting resolution of small-scale bankruptcies given the increased exposure of banks and financial institutions to retail and personal loans and ensuring that the Pre-Packaged Insolvency Resolution Process (PPIRP) is better utilized; and notifying all the provisions of the personal bankruptcy framework under the IBC. iii) Deepening the corporate bond market through tailor-made instruments and guarantees to help expand market access of lower rated corporates; regulatory measures targeted at institutional investors; targeted improvements in market infrastructure; and enhancements in risk-management frameworks. iv) Providing adequate financing for micro, small and medium enterprises (MSMEs) by strengthening NBFCs’ ability to lend to them by removing the existing interest cap on NBFCs to be eligible for guarantees provided by the Credit Guarantee Fund Trust for Micro and Small Enterprises (CGTMSE) and introducing risk-sharing mechanisms for bank lending to NBFCs. In the context of tightening regulatory supervision of NBFCs, access to finance for the sector can be improved by introducing a permanent liquidity arrangement in the form of periodic liquidity facilities through development finance institutions, Targeted Long-Term Repo Operations (TLTROs) and partial credit guarantee schemes. b) Removing barriers to FDI by rationalizing minimum investment requirements in certain sectors; assisting companies to meet the mandatory local sourcing requirements; minimizing inconsistencies between national and state-level policies; and strengthening capacity of the states to formulate and implement investment policies. c) Targeting greater public investments in sectors that crowd-in private investments, especially toward agriculture and allied activities, urban development, and transport. Digitalization at all levels of government has made it easier to target expenditures. Reforms to enhance fiscal space for the states to undertake more productive expenditures are critical to expand public investments. C. Creating enabling conditions for more and better jobs15 To ensure that more and “better” (more productive and less vulnerable) jobs are created, differentiated policies could target high job creating sectors and high productivity sectors, respectively. Priority interventions could focus on the following: a) Targeting employment generation through stronger growth in labor-intensive sectors and scaling up MSMEs. i) Agro-processing manufacturing should be prioritized to leverage strong backward linkages with agriculture and a high potential to absorb less-educated workers. To enable formal firms in this sector, the implementation of factor market reforms (labor and especially land) can be prioritized, followed by the development of a seamless supply chain infrastructure encompassing processing, storage and distribution activities and improvement in basic infrastructure, especially electricity and water supply. ii) Traditional market services and intermediate manufacturing have a high capacity to absorb less-educated work- ers and strong backward linkages with downstream manufacturing units. In order to enhance the capabilities of workers in these sectors, public-private partnerships could be expanded for designing and implementing training programs for the workforce. Policies targeting firms in traditional market services (hospitality, commercial trade and communications) can focus on improving road and power infrastructure, access to finance, and gradually dismantling the remaining barriers to the entry of new firms. For the intermediate manufacturing sector, policies targeting labor regulations and land availability along with improvement in quality of logistics infrastructure will be critical. 15 Better jobs include regular wage jobs with social security benefits and a written job contract. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 11 Executive Summary Becoming a High-Income Economy in a Generation b) In the higher productivity sectors, policy can focus on improving the relevance of tertiary education, boosting the capabilities of firms and workers to adopt technology, and nurturing an innovation ecosystem. The most important policy intervention for increasing the employability of graduates and post-graduates consists in improving skills and re-aligning tertiary education programs to make them more market relevant. Research funding targeted at priority sectors, such as pharmaceuticals and renewable energy, are additional critical priorities. To improve export shares and strengthen integration in GVCs, India could review inverted duty structures in a few sub-sectors and relatively high import tariffs. Boosting women’s participation in paid activities requires better access to child and elderly care, skilling, formal credit and infrastruc- ture.16 Policies to improve women’s participation in market activities include: (i) access to affordable child and elderly care; (ii) facilitating education and market-relevant skills training; (iii) improving access to finance by promoting women’s financial inclusion; (iv) reforming laws to make them gender neutral (and reduce firms’ costs of hiring women); (v) strengthening infrastructure – including affordable housing and hostels – to provide women with safe work and mobility environments; (vi) facilitating access to technology and digital infrastructure, and promoting digital literacy to enable home-based and flexible remote work; (vii) addressing social barriers and regres- sive gender norms; and (viii) generating demand for work through job referrals and targeted recruitment of women in new industries. Facilitating states to grow together. D.  In the absence of large-scale inter-state migration, inclusive growth will require greater income convergence across states. States are not converging in terms of per-capita incomes. A differentiated policy approach rather than a “one size fits all” is required. Less developed states should focus on strengthening the fundamentals of growth, while relatively more developed states could prioritize the next generation of reforms: Less developed states can focus more on increasing the share of manufacturing in output. High-income states have higher shares of manufacturing in GVA. For less developed states, which are characterized by relatively higher shares of the population employed in agriculture, and significant constraints on state capacity and infrastructure, transitioning quickly into modern services- led growth will be difficult. Therefore, creating manufacturing jobs is a priority for these states. They should “focus on fundamentals”, by improving basic infrastructure and state capacity, and consider targeted policies to compensate for geographic disadvantages. Leading states can focus on modern marketable services and attracting more private investment. To sustain their growth, leading states can focus on “next generation reforms” including policies that facilitate access to international markets, improve trade facilitation, reduce the regulatory burden, and support easier exit of firms. Within-state heterogeneity calls for dedicated focus on lagging districts. NITI Aayog’s “Aspirational Districts” program provides a good example: an early appraisal of the program found that program districts outperformed non-program districts across several indicators of health and nutrition, as well as financial inclusion (UNDP, 2020). Another approach is to focus on the top-performing districts in the less developed district groups and design incentives to attract more investments in the districts that are already performing relatively well. This can, in turn, spur broader development through agglomeration effects. Other “spatially connec- tive” interventions by the central government, including incentivizing internal migration, and reducing the digital divide, would complement these initiatives and reduce spatial disparities. 16 The recommendations have been prepared based on recent World Bank publications: “Promoting Female Labor Force Participation,” 2020; South Asia Regional Report, 2022 and Job Flagship Concept Note, 2022 (unpublished). 12 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation © 2021 PradeepGaurs/Shutterstock CHAPTER 1 Sustaining Rapid Growth to a High-Income Economy by 2047 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 13 Chapter 1 Sustaining Rapid Growth to a High-Income Economy by 2047 O ver the past two decades, India experienced a remarkable period of sustained rapid growth, which was made possible by major reforms to liberalize and deregulate the economy in the 1990s and 2000s. In real terms, the economy grew four times in size and GDP per capita nearly tripled. As a result, India emerged as the fifth largest economy in the world. The extreme poverty rate nearly halved between 2011 and 2021, lifting 106 million people out of poverty, particularly in rural areas. Growth slowed in the years up to the COVID-19 pandemic and dipped precipitously in FY20/21. Although it recovered quickly thereafter, average growth has remained below 2000s levels. India’s past achievements provide the foundation for its future ambitions, but the recent relative slowdown also indicates challenges. India aspires to become a high-income economy by 2047. This would require raising the average annual real rate of growth to 7.8 percent over 2024-47. Toward that objective, in recent years, the government has expanded investments in physical and digital infrastructure, streamlined regulations in factor markets, enhanced human capital development, and strengthened macroeconomic stability. But more needs to be done: the scope and pace of reforms need to broaden and accelerate to boost productivity growth; increase the rate and efficiency of capital investments, particularly ICT investment, and facilitate the infusion of new ideas and technology throughout the economy to lay the foundations of a stronger, innovation-led and more productive economy. Transitioning to a high-income economy in the next two decades is a significant challenge, but India shares many of the characteristics of middle-income econo- mies that have successfully made the transition, and it can continue to learn from and emulate their successful growth experience while charting its own course. I. India Achieved Rapid Growth and Poverty Reduction Over the Past Two Decades India has transitioned from low to middle-income country status and emerged as one of the fastest-growing large econo- mies in the world. The reforms of the 1990s that liberalized and deregulated the economy, followed by a second wave of reforms during the early 2000s allowed India to take advantage of the favorable global economic environment with two decades of sustained rapid growth. Over 2000-19, India grew at an annual average growth rate of 6.7 percent and transitioned from low- to lower middle-income country (LMIC) status in 2008. The share of the Indian economy in the global total doubled between 2000 and 2023 (Figure 1.1). India is now the fifth largest economy, in nominal terms, from tenth position a decade ago. The structure of the economy transformed: the share of agriculture in the real GVA more than halved while the GVA shares of manufacturing and services increased by almost 2 and 10 percentage points, respectively. India has been among the fastest growing major emerging economies in recent years (Figure 1.3). Real GDP grew at an annual average rate of 6.7 percent over 2000-19, one percentage point faster than the average for emerging markets and developing economies (EMDEs) and at a more rapid pace than most large and fast-growing developing countries1. Over that period, on average, India grew faster than Viet Nam (6.5 percent), Bangladesh (5.9 percent), the Philippines (5.3 percent), Malaysia (5.2 percent), and Indonesia (5.0 percent), but below China (9.1 percent) (Figure 1.4). India’s growth performance was only slightly below that of the 13 high-growth economies in the post-1950 period, identified in the 2008 Growth Commission Report. India’s growth has been driven by the services sector, particularly skill-intensive services, despite being relatively abundant in low-skilled labor. Over the period 2000-19, on average, services contributed to over half of total growth (around 3.8 percentage points), while agriculture’s contribution was modest, and that of industry declined from an annual average of 2.3 percentage points 1 China, Viet Nam, Philippines, and Bangladesh were selected as India’s peers due to their similar level of development in 2000, large population (over 50 million), and sustained high rate of growth (average growth rate of at least 6 percent in the last 20 years). The report draws comparison to Asian high-growth economies identified by the Growth Commission and the group of MICs transitioning to higher income status (based on the World Bank classification). Asian peers include China (1961-2005), Indonesia (1966-1997), Republic of Korea (1960- 2001), Singapore (1967-2002), and Thailand (1960-97). Throughout the report, based on the context and data availability, India is compared to other emerging market economies like Argentina, Brazil, Indonesia, Malaysia, Mexico, and Poland. 14 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 1 Sustaining Rapid Growth to a High-Income Economy by 2047 FIGURE 1.1: India’s share in world GDP (percent) FIGURE 1.2: Contribution in GVA growth (percentage points, constant prices) 3.5 12 3.3 10 3.1 8 2.9 India: 10th 7th 6 largest largest 2.7 economy 4 2.5 2 2.3 0 5th largest 2.1 -2 1.9 -4 1.7 -6 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 1.5 Agriculture Industry 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 Services Real GVA growth Source: WDI, 2024 and Ministry of Statistics and Programme Implementation (MOSPI). World Bank (WB) staff calculations. Figure 1.1 shows the size of the economy in nominal terms. FIGURE 1.3: Real GDP growth in India vis-à-vis other FIGURE 1.4: India vis-à-vis peers ((Real GDP growth, emerging economies (average 2000-19, percent) percent) 10 20 9.1 9 15 8 7 6.7 10 6.5 6 5.9 5 5.3 5.2 5 5 0 4 3 -5 2.4 2 2 -10 1 -15 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 0 ina ia am esh es a a zil o Bangladesh China India ysi esi xic Ind pin Bra Ch tN lad la on Me ilip Ma Vie Philippines Viet Nam Ind ng Ph Ba Source: WDI, WB staff calculations. For India, the year 2000 refers to the fiscal year 1999-2000. during 2000-10 to 1.9 percentage points over 2011-19 (Figure 1.2). India’s services-led growth contrasts with the path followed by the East Asian “Tigers” whose growth was initially powered by relatively low-skilled labor-intensive manufacturing (prior to their diversification to more sophisticated manufacturing). In the wake of a balance-of-payments crisis in 1991, India adopted wide-ranging reforms of industrial and trade policies. Prior to 1991, India’s economic policy was largely based on central planning, industrial licensing, and import tariffs and controls. Starting in 1991, industrial licensing was abandoned, and the private sector was allowed to operate freely in most sectors under India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 15 Chapter 1 Sustaining Rapid Growth to a High-Income Economy by 2047 the “New Industrial Policy”. Trade policy was overhauled with the removal of import licensing restrictions on intermediate inputs and capital goods and a gradual reduction in import tariffs. Capital controls were gradually reduced, and the rupee was made fully convertible on the trade account. These reforms laid the foundation for India’s deeper integration in the global economy, as well as innovation and private sector-led growth over the following decades. Over the following decades, import controls and limits on FDI were liberalized further. The real GDP growth rate accelerated, and trade increased as a share of GDP from 23 percent in 1995 to nearly 44 percent in 2019. The impact of the post-1991 reform was significant, but liberalization was not uniform across sectors. The public sector continued to maintain a significant presence in many sectors, private industry continued to be constrained by inspections and clearances relating to labor regulations, and the reforms largely bypassed the agriculture sector. Several significant reforms were also implemented in the FIGURE 1.5: Real GDP growth (percent) early 2000s. During these years, investments in both urban 12.0 and rural roads—through the Golden Quadrilateral project Global Financial Crisis Demonetization COVID-19 10.0 and the Pradhan Mantri Gram Sadak Yojana (PMGSY)—contrib- uted to ease infrastructure constraints. Significant changes in 8.0 regulations in sectors such as power and telecommunica- 6.0 tions ushered in greater competition, prepared the ground 4.0 for subsequent reforms in these sectors, and helped India to 2.0 consolidate its position as an IT hub. The focus shifted to the disinvestment of several state-owned enterprises to reduce 0.0 the footprint of the public sector in the economy. The enact- -2.0 ment of the Fiscal Responsibility and Budget Management -4.0 Act, 2003 helped to institutionalize fiscal discipline as a corner- GST stone of macroeconomic policy. The reforms of the early 2000s -6.0 ushered in historically high growth from 2004 to 2008 (annual -8.0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 average of nearly 8 percent). However, growth plummeted to 3.1 percent in 2009 because of the Global Financial Crisis (GFC), Source: MOSPI. The dotted lines indicate the year of major events. The year 2000 followed by a strong recovery in 2010 and 2011, thereafter with refers to the fiscal year 1999-2000. growth averaging 6.8 percent during 2012 to 2017 (Figure 1.5). Growth slowed to 3.9 percent in 2020, just before the onset of COVID-19, reflecting a slowdown in private investment. Weak investment was primarily due to the ‘Twin Balance Sheet’ (TBS) problem— which constrained bank credit to industry over 2012-19, and the stress in the non-banking financial companies (NBFC) sector constrained credit further (see Chapter 3). The slowdown was exacerbated by several cyclical factors —weak global growth in the aftermath of the GFC— and the impact of one-off policies, including demonetization (Mankar and Shekhar, 2017; Singh, 2019) and the introduction of the Goods and Services Tax (GST) (Behera and Wahi, 2018). Demonetization of 86 percent of the currency in circulation in November 2016 caused a momentary dip in growth and impacted poorer and informal sector households disproportionately2. Over the two decades prior to the pandemic, growth was driven primarily by capital accumulation and productivity growth. Additions to the capital stock contributed more than 3 percentage points to growth annually in each of the five-year periods from 2000-19, or roughly half of the total increase in GDP, even though this contribution moderated over the second half of the last decade (2015-19). The contribution of total factor productivity (TFP) increased substantially from 2005 onwards, reaching almost 3 percentage points over 2015-19 (Figure 1.6). The regulatory changes of the early 2000s, coupled with substantial investments in infrastructure, helped raise the investment rate from 27.5 percent of GDP in 2000 to 35.8 percent in 2008.3 By contrast, the contribution of employment was marginal after 2004, as the rapid increase in the working-age population was offset by a decline in labor force participation (see Chapter 4). 2 Chodorow-Reich, et. al. (2020) estimate that aggregate employment and output (based on nightlights data) contracted by at least 2 percentage points in 2016Q4 relative to their coun- terfactual paths, but these effects are seen to dissipate over the following months. 3 Private investment increased significantly from the early 1990s up to the GFC but has weakened in the following period up until the pandemic. 16 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 1 Sustaining Rapid Growth to a High-Income Economy by 2047 FIGURE 1.6: Contribution to India’s growth FIGURE 1.7: Macroeconomic stability index (percentage points) 10 25% POL UK 8 20% ITA IND USA 6 15% BRA MEX FRA 2020 ESP 4 10% AUS CAN DEU JPN 2 CHN 5% KOR RUS IDN 0 2000-2004 2005-2009 2010-2014 2015-2019 0% -10% 0% 10% 20% 30% TFP Capital composition Capital stock 2022 Labor quality Employment Source: India KLEMS and WB staff calculations. Source: WDI and WB staff calculations. A movement to the left of the 45-degree line Note: Figure 1.6 uses the national accounts series with 2011-12 base. indicates improvement in macroeconomic stability.= India’s economy rebounded quickly from the COVID-19 pandemic shock. Real GDP contracted by 23.1 percent year-on-year in April-June 2020 as the government implemented severe restrictions on mobility to control the spread of the pandemic.4 With the gradual easing of mobility restrictions, the rapid deployment of vaccines, and the implementation of well-targeted fiscal and monetary policy responses, the economy rebounded between 2020 and 2022. Domestic demand recovered at a strong pace; investment was bolstered by a surge in government capital spending and improving bank and corporate balance sheets, and private spending was buoyed by the release of pent-up demand by households. Consequently, despite the Omicron wave in Q4 FY22, real output expanded by 9.7 percent and surpassed its pre-COVID level by the second half of the fiscal year (October 2021 – March 2022). Real GDP expanded 7.0 percent in 2023 despite challenging global conditions caused by slowing growth in major trade partners (such as the US, UK, and China), the Russian invasion of Ukraine, and commodity price shocks. Macroeconomic stability improved during the post-pandemic period. Simple parameters—captured in an index that is a sum of inflation, the general government gross fiscal deficit as a share of GDP, and the current account deficit as a share of GDP— suggest that India became more stable in the aftermath of the pandemic despite the advent of two large external shocks—the rise in commodity prices following the Russian invasion of Ukraine and the sharp global tightening of monetary policy to control inflation in 2022. That year, India enjoyed greater macroeconomic stability than Brazil, China, Indonesia, and the USA, although almost all countries, including India, ran elevated fiscal deficits, and experienced sustained inflation and higher current account deficit (Figure 1.7). However, India’s fiscal stimulus to counter the pandemic was contained and targeted. The steady buildup of foreign exchange reserves in recent years provides comfortable buffers. The cautious approach to capital account liberalization and the low share of external debt in public debt help shield India from the risks of a ‘sudden stop’ in capital flows. 4 India’s lockdown was rated among the most restrictive in the world. The stringency index, Oxford COVID-19 Government Response Tracker (https://covidtracker.bsg.ox.ac.uk/). Contact-in- tensive activities, including the retail, transport, and hospitality sectors, that account around 18 percent of GDP— was higher than in most G-20 countries, although lower than in some peers— Vietnam (18 percent), the Philippines (23 percent) and Indonesia (21 percent). India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 17 Chapter 1 Sustaining Rapid Growth to a High-Income Economy by 2047 Structural reforms undertaken in recent years are expected to enhance the resilience of growth. The TBS problem has eased considerably since 2020. The stress in the NBFC segment has also been contained thanks to timely interventions by the RBI and the government (see Chapter 3). The government has introduced several initiatives to transform India into a global manufacturing hub, including the ‘Make in India’ initiative, “Invest India”, the “Startup India” program, and Production- and Employment-Linked Incentive schemes. Infrastructure received a significant boost via the PM Gatishakti Scheme and the National Logistics Policy. In parallel, labor regulations have been streamlined. A push towards digitization—utilizing the Jan Dhan, Aadhar, Mobile (JAM) trin- ity—is rapidly expanding digital services and promoting financial inclusion. The government has also undertaken several steps to bolster macroeconomic stability—by adopting an inflation-targeting framework, enhancing budget transparency, and simpli- fying the tax structure. Significant measures have been undertaken to improve regulation and risk-management in the financial sector, including the introduction of a new scale-based regulation for NBFCs5. Measures are being undertaken to boost human capital by strengthening elementary and tertiary education, as well as skilling programs, and improving healthcare access for poor households. Box 1.1 highlights the major initiatives across key areas. The monetary poverty rate in India declined significantly FIGURE 1.8: Monetary poverty rates in India over the past decade. In the absence of official data until 80.0% very recently, estimates of poverty rates in India (as per inter- $2.15 and $3.65 poverty rates - National, Rural, and Urban national poverty lines) were based on data from non-official surveys. Estimates based on Consumer Pyramids House- 63.0% 60.0% hold Survey (CPHS) data, adjusted to improve the national and state level representativeness and ensure comparabil- ity with the National Sample Survey (NSS) 6, suggested the 48.2% 48.3% extreme poverty rate (PPP $2.15) declined from 22.9 percent 40.0% 44.8% 44.0% 44.1% in 2011 to 12.9 percent in 2021 driven by a decline in rural areas (Figure 1.8) 7. The corresponding fall in the lower-mid- dle-income-country poverty line (PPP $3.65) is 19 percentage 22.9% 20.0% 13.2% 12.9% points. The COVID-19 pandemic is estimated to have increased 16.3% LMIC poverty by 4 percentage points in 2020 to 48 percent, 14.2% 15.5% although in 2021, LMIC poverty declined to 44 percent, same 0.0% as the pre-pandemic level. With the release of new official 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 surveys for 2022-23 and 2023-24, poverty estimates will shift National $2.15 Rural $2.15 Urban $2.15 from uniform recall period to the mixed modified recall period, National $3.65 Rural $3.65 Urban $3.65 leading to a revision of the series. Source: WB Staff calculations using the survey-to-survey multiple imputation meth- odology of Roy and Van der Weide (2022), please refer to footnote 6. 5 The RBI introduced guidelines whereby regulatory obligations of NBFCs depend on their scale of operations, asset size and risk exposure. 6 Poverty rates are estimated using survey-to-survey multiple imputation methodology as per Roy and Van der Weide (2022) Approach 1 applied on uniform recall period series from consumer expenditure survey (2011-12) and 2017-21 data from Consumer Pyramids Household Surveys (CPHS), Periodic Labor Force Surveys (PLFS), and National Family Health Survey (NFHS-5). The break in the series indicates that the two time periods are not perfectly comparable. 7 The World Bank defines the extreme poverty line at $2.15 a day, the lower-middle-income country (LMIC) or moderate poverty line at $3.65 a day, and the upper-middle-income country (HMIC) poverty line at $6.85 a day in 2017 PPP. 18 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 1 Sustaining Rapid Growth to a High-Income Economy by 2047 Box 1.1: Major Reforms and Investments in India in Recent Years A. Manufacturing and Infrastructure The government has renewed its focus on manufacturing. The National Manufacturing Policy (NMP) 2011, aimed to improve the share of manufacturing in GDP to 25 percent by 2022 (the actual share in real GVA was 18.5 percent in 2022). The government’s flagship initiative ‘Make in India’ (MII) was launched in 2014 to attract investors, both domestic and foreign, to make India a global manufacturing hub by making it easier to conduct business. These initiatives have provided a supply side push by expanding both hard and soft infrastructure facilities and easing the regulatory norms. The government has launched complementary initiatives to promote manufacturing, including “Invest India” and “Startup India”. The latter, launched in 2016, facilitates start-up businesses by providing credit guarantees, time-bound income-tax exemptions, regulatory reforms, intellectual property protection and access to international markets. The Production-Linked Incentive (PLI) scheme was launched in 2021 with an outlay of over USD 26 billion for 5 years to strengthen international competitiveness of India’s manufacturing by providing incentives to companies that meet global quality standards and facilitate access to advanced technologies and innovations. The scheme focuses on 14 key manufacturing sectors and provides incentives ranging from 4 to 6 percent on incremental sales of goods produced in India (Ministry of Electronics and Information Technology8). The scheme is also designed to strengthen domestic supply chains by encouraging companies to source raw materials locally and by promoting collaboration among different players in the supply chain.9 Infrastructure has received a significant boost in the Union Budgets over the last 7 years.10 The share of capital expenditure in total expenditure reached a 15-year high in 2023. Furthermore, states are being conditionally supported for capital investment projects by the “Scheme for Special Assistance to States for Capital Expenditure” which provides a 50-year interest-free loan to the states. In December 2019, the government introduced the National Infrastructure Pipeline (NIP) 2020-25 to develop world class infrastructure in various sectors, comprising of projects of over INR 111 trillion covering both greenfield and brownfield invest- ment, in collaboration with the private sector. At present, the NIP is implementing nearly 9,000 projects. To enhance private sector participation in infrastructure, the government has implemented initiatives such as the revival of the Public-Private Partnership (PPP) and National Monetization Pipeline (NMP). The estimated potential for monetization under the NMP is INR 6.0 trillion through the Central Government’s core assets, spread over 2020-25. The PM Gatishakti and National Logistics Policy (NLP) was launched in October 2021 and September 2022, respectively, to coordinate efforts to better plan and implement infrastructure projects. The aim of the PM Gati shakti National Master Plan is to expedite infrastructure development through the integration of the seven key drivers of growth—mass transport, roads, railways, ports, waterways, airports, and logistic infrastructure.11 The government is implementing 11 industrial corridor projects under the National Industrial Corridor Programme (NICP) that includes the Delhi-Mumbai, Amritsar-Kolkata, Bengaluru-Mumbai, and the Odisha corridors, among others. Other projects such as the “Ude Desh Ka Aam Nagrik” (UDAN) scheme aims to carry forward private and public sector aviation infrastructure into lesser connected regions of the country. The scheme has operationalized 87 unserved and underserved airports so far. Additionally, to expand road, waterways, and coastline infrastructure, the “Bharatmala” pariyojna complements the “Sagarmala” scheme by improving connectivity through dedicated freight and industrial corridors. 8 https://www.meity.gov.in/esdm/pli 9 https://www.ibef.org/industry/electronics-system-design-manufacturing-esdm. The PLI scheme for pharmaceuticals manufacturing was launched in July 2020, with a goal to create a level playing field for the domestic medical device manufacturers by providing a financial outlay of INR 3,420 crore over 2021-28 (https://www.pib.gov.in/PressReleasePage.aspx- ?PRID=1710134). The scheme is expected to create over 2.5 lakh direct jobs in the pharmaceutical sector. The PLI scheme for textiles manufacturing was launched in March 2021, with an allocation of INR 10,683 crore (approx. USD 1.4 billion) over a period of five years. The scheme for the auto sector aims to address the cost disadvantages faced by the industry during manufacturing of advanced automotive technology products in India. The incentive framework is expected to encourage new investments by the industry towards developing an homegrown led global supply chain for these products. The scheme is anticipated to result in a fresh investment of more than ₹42,500 crores over the next five years, leading to a significant increase in production, worth over ₹2.3 lakh crore, and create over 7.5 lakh jobs. Additionally, this move is expected to boost India’s global automotive trade (https://www. pib.gov.in/PressReleasePage.aspx?PRID=1755062). 10 For example, in 2019, the government allocated a record amount of INR 134,572 crore (USD 16.9 billion) for transportation, and an additional allocation of INR 60 crore (USD 7.52 million) for disaster-resilient infrastructure. 11 Under this initiative, 85 major projects, with an estimated cost of INR 5.4 trillion have been identified by various Ministries. The States have identified 205 projects with an estimated cost of INR 5,496 Crores, under “Scheme for Special Assistance to States for Capital Investment for 2022-23” (Lok Sabha Unstarred Q. No. 1140, July 26, 2023). India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 19 Chapter 1 Sustaining Rapid Growth to a High-Income Economy by 2047 Finally, the Asset Monetization Plan was launched in August 2021 to unlock capital for new infrastructure projects, with the proceeds to be utilized in new projects. India has benefited substantially from initiatives, such as investment in trade-related soft and hard infrastructure connecting port gateways to rural areas. Innovative digital solutions have been used to ensure end-to-end tracking of supply chains. These interventions have led to an improvement of the country’s logistics performance. India’s ranking in the World Bank Logistics Performance Index (LPI) improved to 38th in 2023 from 44th in 2018, but it remains lower than the rank of 35 achieved in 2016. The government has also expanded basic infrastructure facilities. Initiatives such as the Pradhan Mantri Awas Yojna (PMAY)— urban and gramin (rural)— provides affordable ‘pucca’ housing to eligible poor households. Water supply and conservation, reform of property taxes and urban local bodies, and the provision of other urban amenities are being implemented through the Atal Mission for Rejuvenation and Urban Transformation (AMRUT), and piped water supply through the Jal Jeevan Mission. Urban and municipal infrastructure is receiving significant policy attention, through schemes such as the Smart Cities Mission, 2015. Schemes such as the Swachh Bharat Mission (2014) have expanded access to improved sanitation facilities, especially for rural households (Figure B1.11). Initiatives such as the Pradhan Mantri Ujwala Yojana (PMUY) has led to a sharp increase in rural households’ access to clean cooking fuel (such as, LPG) (Figure B1.12). The government’s emphasis on rural electrification through the Deen Dayal Upadhyaya Gram Jyoti Yojana (DDUGJY), 2014 has led to an almost universal electrification of rural villages by 2019 (Figure B1.13). FIGURE B1.11: Households: Access to FIGURE B1.12: Households: Access to FIGURE B1.13: Households: Access to improved sanitation (percent) clean cooking fuel (percent) electricity (percent) 100 100 100 90 90 90 80 80 80 70 70 70 60 60 60 50 50 50 40 40 40 30 30 30 20 20 20 10 10 10 0 0 0 2005 2015 2019 2005 2015 2019 2005 2015 2019 Urban Rural Total Source: National Family Health Survey (NFHS) and WB staff calculations. India has undertaken significant steps to boost digitalization. Powered by the Jan-Dhan-Aadhar-Mobile (JAM) trinity, India has prepared the basis for widespread adoption of a variety of digital technologies to facilitate financial inclusion and public service delivery. To complement these efforts, the government has initiated the Pradhan Mantri Gramin Digital Saksharta Abhiyan (PMGDISHA), to enhance digital literacy of citizens with a focus on rural areas. During the period 2014-23, internet penetration and the number of smart phone users have increased by 238 and 430 percent, respectively (source: MEITY). The annual number of digital transactions per capita has increased from 2.4 in 2014 to 22.4 in 2019, facilitated by increasing access to relatively inex- pensive mobile data. Indiastack, the national portfolio of digital public services that includes the Unified Payments Interface (UPI), Digilocker, Aadhar, and the Government e-marketplace, is attracting global attention for the variety of public service solutions provided through a single platform. B. Monetary Policy and the Financial Sector The monetary policy framework was reformed to focus on price stability. The Monetary Policy Framework Agreement was agreed between the RBI and the Government of India in February 2015 which accorded RBI the mandate the focus on ensuring price stability while taking into account the objective of promotion of growth. The RBI adopted flexible inflation 20 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 1 Sustaining Rapid Growth to a High-Income Economy by 2047 targeting with the CPI (combined) as the nominal anchor. FIGURE B1.14: Progress under the PMJDY The target for inflation was set at 4 percent with a band of 600 5000 (+/-) 2 percent. With the adoption of inflation targeting, the 4500 Monetary Policy Committee (MPC) was set-up to pursue the 500 4000 inflation target. 400 3500 3000 Millions Wide-ranging reforms have been introduced to bolster the 300 2500 stability of the financial sector. The Insolvency and Bank- 2000 ruptcy Code (IBC) Act was introduced in 2016 to strengthen 200 1500 the management of financial stress arising from the TBS 1000 problem. Further, to address the stock of non-performing 100 500 assets (NPAs), the National Asset Reconstruction Company 0 0 Limited (NARCL) was established in 2022. The government Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 also undertook various steps to improve risk management Number of Total Bene ciaries Deposits per account (right scale, INR) systems in banks to stem the flow of new NPAs. Several measures have been undertaken to strengthen financial Source: Government of India and WB staff calculations. sector regulation and supervision (see Chapter 3). Also, to streamline the regulatory provisions of the securities and commodity derivatives markets, the Forwards Markets Commission (FMC) was merged with the Securities and Exchange Board of India (SEBI) in September 2015. The Micro Units Development Refinance Agency (MUDRA), was set-up in 2015 under the Pradhan Mantri Mudra Yojana (PMMY), to provide concessional finance to micro finance institutions. India has witnessed a massive push to financial inclusion. In order to facilitate access to credit, the Pradhan Mantri Jan Dhan Yojana (PMJDY) was launched in August 2014. The scheme led to the opening of zero balance bank account for unbanked households. Over the past few years, in the number of beneficiaries has increased to over 50 crores (more than 500 million) with the average deposit per account increasing by five-fold during 2014-24 (Figure B 1.14). C. Fiscal Policy The government has introduced major steps to usher in budget transparency. This includes reporting of extra-budgetary resources in the budget documents. Additionally, with a shift away from the concept of central planning, and the abolition of the Planning Commission, the GOI has discontinued the classification of plan and non-plan expenditures since 2018 and has put more focus on the current and capital expenditure classifications for effective allocation of resources. A major reform was carried out with the merger of the Railway Budget with the Union Budget in 2017 to provide more financial space to the exchequer. This move eased the Railways’ burden of the mandatory provision of dividends to the central government. Important tax reforms have been introduced in recent years. These reforms were initiated by the government to broaden the tax base and reduce the tax rates. In 2018, the income tax rate for individuals with income between INR 2.5- 5 lakhs was reduced from 10 to 5 percent. In 2021, the government proposed a new optional personal income tax system with different tax rates. Building on these efforts, the Union Budget FY25/26 announced further tax relief, making income up to INR 12 lakhs tax free. Another watershed tax reform was the introduction of the Goods and Services Tax (GST) in 2017 that replaced various taxes, including excise duty, service tax, VAT, etc., and sought to eliminate the cascading effect of taxation on the transaction of products and services. Even though the rate structure is still evolving, India has uniformly rationalized its tax rates with a shift of rate structure into several slabs. The Union Budget FY25/26 budget introduced GST reforms to streamline tax slabs, clarify Input Tax Credit (ITC) rules, and strengthen compliance. Key changes include restricted ITC claims, improved tax governance, and stricter track-and-trace mechanisms to enhance transparency and minimize litigation. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 21 Chapter 1 Sustaining Rapid Growth to a High-Income Economy by 2047 D. Human Capital The Skill India Mission, 2015 is helping to re-skill and up-skill the youth through various vocational training and certi- fication programs. The government is implementing schemes like the Jan Shikshan Sansthan (JSS) Scheme, the Pradhan Mantri Kaushal Vikas Yojana (PMKVY), the National Apprenticeship Promotion Scheme (NAPS), and the Craftsman Training Scheme (CTS) to offer training to the youth. The Ministry of Rural Development is implementing the Rural Self Employment Training Institutes (RSETIs) scheme for poor youth aged between 18-45 years, under which financial support is extended for the training of unemployed youth with the potential to take up self-employment. Other initiatives for skill development include the development of the India International Skill Centres (IISCs), implementation of the Advanced Vocational Train- ing Scheme (AVTS), the Skill Acquisition and Knowledge Awareness for Livelihood Promotion (SANKALP) and the Pradhan Mantri YUVA Yojana. Labor reforms are being implemented to enhance the flexibility of the labor markets. The 29 central labour laws were combined and rationalized into four codes. These include the Industrial Relations Code 2020, the Code on Wages 2019, the Occupational Safety, Health & Working Conditions Code 2020, and Code on Social Security 2020. This reform was introduced to streamline labor regulations in the country. Concurrently, it accommodates the welfare needs and the minimum wage requirements of the workers from the unorganized sector. Some states, starting with Rajasthan in 2014, have amended the most restrictive provisions of the Industrial Disputes Act (IDA) (see Chapter 4). India has witnessed progressive changes in its health policy with wide ranging reforms undertaken to ensure health equity. The Government aligned its vision with the goals of the National Health Policy (NHP), 2017 and set up two components. The first component, the Ayushman Bharat-Health and Wellness Centres (AB-HWCs) has been providing Comprehensive Primary Health Care (CPHC) though tie-ups with referral hospitals. The other component, the Pradhan Mantri Jan Arogya Yojana (PMJAY) has been instrumental in providing a financial protection—a yearly cover of INR 5 lakh per family— to the bottom 40 percent of the population, including in the most underserved regions, for treatment in public as well as private hospitals. Measures such as the Swachh Bharat Mission (SBM), the Free Drugs and Diagnostics Service Initiative (FDDSI), the Pradhan Mantri Bhartiya Janau- shadhi Pariyojana (PMBJP), along with the other components of the Ayushman Bharat – Digital Mission (ABDM) and Pradhan Mantri Ayushman Bharat - Health Infrastructure Mission (PM-ABHIM) are strengthening India’s health system. Pharmaceutical manufacturing and exports are being encouraged through the PLI as well as the “Strengthening of Pharmaceuticals” schemes. The country has become one of the global leaders in vaccine production, accounting for about 60 percent of DPT, BCG and Measles vaccines. India’s National Education Policy (NEP), 2020 offers a framework to transform not only higher education, but also elementary education by 2040. The NEP, 2020 prioritizes continual learning, seeking to provide a quality education that creates learning opportunities for all and ensures productive employment opportunities. It aims to raise the Gross Enrolment Ratios (GER) to 100 percent at the preschool to secondary level by 2030 and the GER in higher education, including vocational education, to 50 percent by 2035. In addition, the scheme seeks to strengthen the infrastructure for open and distance learning, promote online education and widen the use of technology. Samagra Shiksha, launched in 2018, a complementary programme for pre-school to class XII to foster an inclusive and equitable quality education, has been extended to 2026. The reform of the Universities Grants Commission (UGC) and the All-India Council for Technical Education (AICTE) are among the other measures that the NEP seeks to implement. In October 2022, the Ministry of Education launched the National Curriculum Framework for Foundational Stage (NCF-FS) to provide education for children between three and eight years. 22 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 1 Sustaining Rapid Growth to a High-Income Economy by 2047 E. External Sector The government has undertaken various efforts to boost exports, including from the state and district levels. India’s Foreign Trade Policy, 2023 aims to increase India’s exports to USD 2 trillion by 2030 and is based on four pillars: (i) shifting from an incen- tive-based to a remission-based trade regime12, (ii) export promotion through the collaboration of exporters, states, districts, and Indian Missions, (iii) facilitating the ease of doing business through the reduction in transaction cost and the use of digital initiatives, and (iv) focusing on emerging areas—e-Commerce, developing districts as export hubs and streamlining the special chemicals, organisms, materials, equipment and technology (SCOMET) policy. The government has introduced several schemes to boost exports during the last ten years including the Service Exports from India Scheme (SEIS, 2015), the Advance Authorisa- tion Scheme (2015), Trade Infrastructure for Export scheme (TIES, 2018), Rebate of State and Central Taxes and Levies (RoSCTL, 2019), Rebate of Duties & Taxes on Export Products (RoDTEP scheme, 2021), and the Market Access Initiative (MAI scheme, 2021). In 2019, the Department of Commerce launched the “Districts as Export Hubs” (DEH) initiative to create institutional mechanisms for facilitating the exports of identified products/services from the districts. At the sub-national level, the government initiated the “One District One Product” (ODOP) across different states/Union Territories. The ODOP initiative is now operationally merged with the ‘Districts as Export Hub’ initiative to boost exports from the districts. India’s exports have become more diversified in terms of products and destinations. In the last decade, India made significant efforts to enhance trade opportunities, diversify the product basket and the destination of exports through various Free Trade Agreements (FTAs) (see Chapter 2). In last five years, India showed a renewed focus on signing new bilateral FTAs with strategic interests. India has so far concluded 13 FTAs (starting with Sri Lanka in 2010) and 6 Preferential Trade Agreements. Some studies suggest that India’s exports to the FTA countries have declined compared to non-FTA countries, and India has not adequately utilized the trade preference opportunities (NITI Aayog, 2017). However, Krishna (2021), shows that trade shares of India’s FTA partners stayed nearly constant in the past decade, and trade deficits with FTA partners (as a share of the overall deficit) did not increase over time, thus challenging the view that India’s trade agreements widened the trade deficits and contributed to the stagnation of its manufacturing sector. Moreover, imports from the FTA partner countries fulfilled some of the crucial import requirements of the manufacturing sector. The decline in multidimensional poverty has been rapid, although some components such as nutrition have been slower to improve. Multidimensional13 or non-monetary poverty declined by 27 percentage points from 2006 to 2016. Over that period the incidence of multi-dimensional poverty fell by 7.1 percent on average each year, while per-capita national income grew by 5.2 percent. Thus, the growth elasticity of the incidence of multi-dimensional poverty was 1.4. In this sense, growth in India has been “multidimensionally inclusive” (Alkire and Seth, 2021). Recent data suggests higher elasticities, as multidimensional poverty further decreased during a period of declining GDP per-capita growth. During 2016-21, multidimensional poverty decreased by 11 percentage points, thus lifting approximately 140 million people out of non-monetary poverty. During the same period, while annualized growth in GDP per-capita was only 3.3 percent, the incidence of multidimensional poverty declined by 11 percent on average each year.14 This improvement was in part due to a reduction in the dependency ratio (the ratio of non-working to work- ing family members), as well as improvements in educational attainment and outcomes related to service delivery. The greatest reductions in multidimensional poverty occurred in the poorest states (Figure 1.9). 12 In a remission-based regime, the taxes and duties incurred in the process of exports are refunded to the exporter. 13 The multidimensional poverty index (MPI) is an implementation of the adjusted headcount ratio proposed by Alkire and Foster, which identifies poor people on multiple counts of deprivation, and then measures poverty by incorporating both the incidence of multiple deprivations as well as the average breadth of deprivations among the poor. 14 Owing to the pandemic, the National Family Health Survey was staggered over 2019-21. The CAGR for multi-dimensional poverty is over a five-year period 2016-21 and likewise for GDP per capita with the simple average of 2020 and 2021. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 23 Chapter 1 Sustaining Rapid Growth to a High-Income Economy by 2047 FIGURE 1.9: Non-monetary poverty headcount rate by states Multidimensional poverty MPI headcount ratio: 2019 vs 2015 2015 2019 WB UT UP TR TG TN RK RJ PB PY OR NL MZ ML MN MH MP States KL KA JH JK HP HR GJ GA DL CG CH BR AS AR AP AN 0 5 10 15 20 25 30 35 40 45 50 55 Headcount poverty rate (in %) Source: WB staff calculations using the National Family Health Surveys (NFHS) and UNDP-OPHI definition. Growth has translated into poverty reduction in India, but not through the expansion of jobs as typically seen in other econ- omies. This is because growth has not created a commensurate number of jobs (see Chapter 4). Rather, welfare improvements have been supported through improved endowments (livestock, housing, health, and education), higher non-labor earnings and transfers, as well as demographic changes leading to reduced dependency. Rural poverty reduction, which has been faster than urban poverty reduction, has been accompanied by a larger share of public transfers in total household incomes over time (Figure 1.10 and Figure 1.11). This is especially true during the COVID-19 pandemic in 2021. While the coverage of government transfers is broader in rural areas, urban households also seem to have benefited from them. II. Lessons from Successful Middle-Income Countries India aspires to become a high-income country by 2047 and it shares many characteristics with countries that have success- fully made that transition. The Growth Report by the Commission on Growth and Development (2008) identified 13 high-growth economies15, among approximately 200 economies, which were able to sustain annual per-capita growth of 7 percent (in real terms) for 30 years or more in the post WWII period, from a lower-income level. Thus “growth miracles” are rare but possible. Despite the diversity of these successful countries (for instance in size and natural resource endowments), the similarities of their growth 15 Botswana; Brazil; China; Hong Kong, SAR, China; Indonesia; Japan; the Republic of Korea; Malaysia; Malta; Oman; Singapore; Taiwan province of China; and Thailand. 24 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 1 Sustaining Rapid Growth to a High-Income Economy by 2047 FIGURE 1.10: Income structure of rural households FIGURE 1.11: Income structure of urban households Income structure: Rural Income Structure: Urban 100% 100% 80% 80% 60% 60% Share (%) Share 40% 40% 20% 20% 0% 0% 2015-16 2017-18 2019-20 2021-22 2015-16 2017-18 2019-20 2021-22 2015-16 2017-18 2019-20 2021-22 2015-16 2017-18 2019-20 2021-22 2015-16 2017-18 2019-20 2021-22 2015-16 2017-18 2019-20 2021-22 2015-16 2017-18 2019-20 2021-22 2015-16 2017-18 2019-20 2021-22 2015-16 2017-18 2019-20 2021-22 2015-16 2017-18 2019-20 2021-22 1 2 3 4 5 1 2 3 4 5 Quintile and Fiscal Year Quintile and Fiscal Year Government Transfers Business Income Wage Income Other Income Source: WB staff calculations. paths provide an invaluable reference for other countries aiming to make similar transitions. The Commission concluded that successful economies: (i) fully exploited the opportunities in the world economy;16 (ii) maintained macroeconomic stability; (iii) maintained high savings and investment rates; (iv) relied on the market to allocate resources efficiently; and (v) benefited from a credible, committed, and proactive government. The Indian experience— especially through the reforms starting in the 1990s, their continuation in the early 2000s and renewed dynamism in recent years— conforms to many of these characteristics, which contributed to its fast growth over the last two decades and facilitated its transition from Low-income to Low Middle-Income Country status. However, to make further progress toward high-income status by 2047 requires India to sustain high growth over the coming decades. Among the 13 economies, studied in the Growth Commission Report, that sustained high growth over more than 30 years, only six managed to sustain high growth all the way into high-income levels (Hong Kong SAR, China; Japan; Korea; Malta; Singapore; and Taiwan, province of China), while several lost momentums and remained “trapped” in the upper middle-income country (UMIC) category. UMICs that successfully transitioned to HICs (so-called “successful UMICs”) spent an average of 15 years at middle-income levels, growing by an average of around 4.5 percent annually17. Countries such as Chile, Czechia, Poland, and Romania belong to this group. By contrast, Brazil, Malaysia, Mexico, South Africa and Türkiye, have spent more than 20 years in the UMIC group (“trapped-UMICs”). India became a LMIC economy in 2008 and continues to grow at a rapid pace. However, to reach the HIC status by 2047, India would need to grow above its historical average of 6.7 percent for the next two decades (see Section III). India can draw lessons from both “successful UMICs” and “trapped UMICs” to successfully make the leap from LMIC to HIC status by 2047. 16 These economies imported ideas, technology, and know-how from the rest of the world. They also exploited global demand, which provided a deep, elastic market for their goods (Commission on Growth and Development, 2008). 17 China’s real GDP growth was highest during this period at 9 percent. In per capita terms, India’s growth averaged 5 percent, while China had 8.4 percent. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 25 Chapter 1 Sustaining Rapid Growth to a High-Income Economy by 2047 TABLE 1.1: Countries that transitioned to UMIC in past three decades and achieved HIC status No. of years in UMIC Year of transition to Year of transition Average per capita Average real UMIC to HIC real GDP growth GDP growth rate, rate, percent percent Chile 19 1993 2012 3.8 5.0 Romania 14 2005 2019 4.4 3.6 Poland 13 1996 2009 4.5 4.4 Czechia 12 1994 2006 3.5 3.4 Slovak Republic 11 1996 2007 5.2 5.2 Countries that transitioned to UMIC in past three decades but trapped for more than 20 years South Africa 33 1988 - 0.6 2.3 Brazil 32 1989 - 0.9 2.2 Mexico 31 1990 - 1.1 2.6 Malaysia 29 1992 - 3.5 5.5 Türkiye 24 1997 - 3.1 4.6 Source: WBG, PWT 10.01, WDI. Note: 1. Average growth for trapped countries is calculated till 2019 to avoid the pandemic impact. For transitioned countries, average is over the specific time period it stayed UMIC. 2. The reference start period for income classification is 1987 and end period is 2021. 3. The transition status is determined by the country achieving any upper rank income category for the first time in the sample period (1987-2019). For instance, if HIC is attained in 2010 but later revoked in 2015 and attained back in 2016, it is considered that the HIC status was achieved in 2010. While accumulation of factors of production (capital and FIGURE 1.12: Contribution to growth: TFP, capital, labor labor) is important for both low- and middle-income econo- in peers, high-growth countries, and other selected mies, the transition to HIC also requires major contributions MICs (percentage points) from total factor productivity (TFP). Factor accumulation, 8.0 predominantly capital but also labor, accounted for the bulk of growth in the fast-growing Asian economies (Figure 1.12). 7.0 The same is true for India’s peers – China, Viet Nam, Philippines, 6.0 and Bangladesh – although the contribution of TFP to growth 5.0 has been more important for India’s peers. The successful 4.0 UMICs primarily relied on capital accumulation and TFP to 3.0 drive growth all the way to HIC status. No country would have 2.0 made it across the HIC threshold without igniting some TFP 1.0 growth. 0.0 Trapped UMICs show the opposite trend– broadly, a negative -1.0 Asian peers CEM peers Successful UMICs Trapped UMICs contribution of productivity to growth. For LICs and UMICs, (growth commission) productivity growth is largely driven by the reallocation of Capital Labor TFP Real GDP growth (average) resources between sectors (structural transformation), while in the transition to higher income, within-sector (between firms) Source: WB staff calculations. PWT 10.01. Note: Asian peers include China (1961-2005), Indonesia (1966-1997), Republic of and within-firm productivity becomes key. Two transitions are Korea (1960-2001), Singapore (1967-2002), Thailand (1960-1997). The CEM peers critical for middle-income countries aiming to achieve high-in- include Bangladesh, Philippines, Viet Nam with growth over 2010-19. Successful UMICs include Chile (1993-2012), Czechia (1994-2006), Poland (1996-2009), Romania come status. The first transition is from investment-driven (2005-19), Slovak Republic (1996-2007). Trapped UMICs include Brazil (1989-2021), Malaysia (1992-2021), Mexico (1990-2021), Türkiye (1997-2021), and South Africa growth to the infusion of global technologies. This involves (1988-2021). importing advanced technologies, knowledge, and business 26 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 1 Sustaining Rapid Growth to a High-Income Economy by 2047 practices from more developed economies and diffusing these FIGURE 1.13: Contribution to India’s growth throughout the domestic economy. The second transition is (percentage points) from infusion to innovation, where countries build on imported technologies to create new products, processes, and services 10 (World Development Report [WDR], 2024, World Bank). 8 India’s sustained and rapid growth over the last two decades was driven by high TFP growth and capital deepening 6 (Figure 1.13). Capital accumulation and TFP contributed the most to overall growth during 2000-19. Moreover, labor 4 productivity growth in India has also been high compared to peers except China, driven by within-sector productivity, 2 specifically in services. 0 2000-2004 2005-2009 2010-2014 2015-2019 Several other characteristics of successful and trapped TFP Capital composition Employment UMICs provide lessons for India. India compares favorably in Capital stock Labor quality terms of growth volatility—having experienced a lower vola- tility compared to both successful” and trapped UMICs—the Source: India KLEMS. level of public debt remains comparatively higher (Table 1.2). TABLE 1.2: India and Successful UMICs and Trapped UMIC India Successful UMICs Trapped UMICs(b) which transitioned to HIC status(a) Macroeconomic stability1 Real GDP growth volatility (standard deviation of real GDP growth) 1.5 2.4 2.8 Gross government debt, as percent of GDP 68.9 36.5 48.9 External debt (percent of GDP) 20.1 45.1 40.8 Foreign exchange cover of imports (months) 8.0 5.9 7.6 Human capital1 Labor force participation rate (%) 51.5 56.6 60.6 Labor force participation rate, Tertiary educated (%) 62.3 82.0 79.8 Labor force participation rate, basic education (%) 20.8 73.1 68.4 Structural Transformation Agriculture and allied activity, % of GVA 16.8 3.0 4.4 Innovation R&D expenditure (percent of GDP) 0.39 0.50 0.73 Trade openness (exports and imports of goods and services, % of GDP) 46.3 76.5 54.6 Patent applications per million people 13.0 59.0 31.3 Source: Haver analytics, WDI database and WB staff calculations. Note: Basic education comprises primary education and/or lower secondary education. 1 The Growth Report: Strategies for Sustained Growth and Inclusive Development, 2008. (a) Median for following countries depending on their period of transition from UMIC to HIC have been calculated: (i) Chile (median, 1993-2012) (ii) Czechia (median, 1994-2006),(iii) Poland (median, 1996-2009),(iv) Romania (median, 2005-2019),(v) Slovakia (median, 1996-2007). (b) Includes 5 UMICs, which remained in the category for 15 years: Brazil, Malaysia, Mexico, South Africa and Türkiye. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 27 Chapter 1 Sustaining Rapid Growth to a High-Income Economy by 2047 However, the share of external debt in GDP is much lower, and India’s large foreign exchange reserves provide a comfortable cover for imports. Both the successful and trapped UMICs seemed to have benefitted more significantly than India from labor partic- ipation and inter-sectoral allocation. In contrast to its peers and these UMICs, India has not leveraged a favorable demographic transition: labor force participation is low and structural transformation limited18. While labor productivity has improved signifi- cantly in agriculture relatively few workers have exited from the sector. The successful UMICs differ from the trapped UMICs with respect to their innovation ecosystem and trade openness. Trade and GVC integration have been key drivers of the transition to high income, supported by new sources of comparative advantage in scale economies and agglomeration. III. Possible Growth Pathways to a High-Income Economy Unlocking India’s vast untapped economic potential will FIGURE 1.14: Upper middle-income status achievable by require: (i) boosting productivity by facilitating structural early next decade (GNI per capita, Atlas Method USD) transformation, and the infusion of modern technologies 22,000 throughout the economy; (ii) reviving investment, by creat- 20,000 ing the conditions for higher domestic private investment 18,000 and FDI; (iv) facilitating the creation of more and better jobs; and (v) promoting economic convergence across states. 16,000 14,250 These priorities are incorporated into the World Bank’s Long 14,000 Term Growth Model (LTGM) to simulate the impact of reforms 12,000 on India’s potential growth up to 2047. 10,000 8,000 11,420 To allow India to reach UMIC status by the early 2030s and 4,420 HIC status by 2047, growth needs to accelerate to 7.8 percent 6,000 5,320 on average over the next two decades (Figure 1.14). This will 4,000 require closing the investment, productivity, and employ- 2,000 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 ment gaps vis-a-vis middle- and high-income countries (Figure 1.15). Simple calculations provide a measure of the gap Scenario 1 Scenario 2 Scenario 3 India needs to close relative to UMICs and HICs. In 2021, India’s GDP per person employed (in constant 2017 PPP dollars) and Source: WB staff calculations. gross-capital formation per worker (in constant 2015 USD) Note: The vertical dashed lines indicate the year in which country achieves upper-middle income status under different scenarios. The projections for 2024- were one-fourth and one-tenth of the averages of UMICs and 47 are based on the LTGM tool. The cut off for income classification is assumed to grow at an annual rate of 1.5 percent (the average growth observed over the past HICs, respectively. The employment to population ratio in two decades). The numbers in Figure 1.17 represent the implied GNI per capital in these group of countries is 1.3 times higher than that of India. USD, as per the World Bank’s Atlas method, that pertain to UMIC and HIC status under the baseline and scenario 1. The World Bank determines the size of economies and income classifications using GNI per capita. To make cross-national comparisons, To get to high income by 2047 India needs to “jump” from estimates from local currency are converted to current U.S. dollars using the Atlas method, which stabilizes exchange rates with a three-year moving average and the current baseline scenario (scenario 2) to the more ambi- price adjustments. tious scenario (scenario 3). The scenarios are as follows (see Table 1.3 Annex 1.1 for the methodology used in the LTGM): • Scenario 1 is a slower reform scenario. It would materialize if India’s reform momentum slowed significantly over the next decades. In this scenario, the investment-to-GDP ratio—in constant prices— remains around 35 percent of GDP up to 2035 and declines thereafter and the share of ICT capital in total investment broadly stays at current levels with ICT investment contributing to 10 percent of the annual change in investment. The female labor force participation rate (FLFPR) does not improve. TFP growth peaks at 2.5 percent by the beginning of the next decade prior to moderating thereafter. 18 See Chapter 2 and Chapter 4 for an analysis of these issues. 28 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 1 Sustaining Rapid Growth to a High-Income Economy by 2047 FIGURE 1.15: India’s Economic Performance vis-a-vis UMICs and HICs. Employment to population Productivity (GDP per person Investment per worker ratio, 15+ (percent) employed, constant 2017 PPP USD) (constant 2015 USD) 70 120,000 25,000 60 100,000 20,000 50 80,000 40 15,000 60,000 30 10,000 40,000 20 5,000 10 20,000 0 0 0 Employment rate GDP per person employed Investment per worker India Upper middle income High income Source: WDI, WB staff calculations. Note: A weighted average of the employment rate and GDP per worker for both UMICs and HICs are presented. However, to calculate the investment per worker, the total gross capital formation for each income group is divided by the corresponding total number of workers. The total number of workers is estimated using the employment rate and population statistics obtained from the WDI. • Scenario 2 is a baseline assumption whereby the reform momentum remains strong. The investment to GDP ratio—in constant prices— rises gradually to 37 percent by 2035, but declines thereafter, driven by an equal contribution (50 percent) of both physical and ICT capital. The FLFPR increases to 45 percent by 2045. TFP growth is assumed to peak at 2.7 percent by the beginning of the next decade prior to moderating thereafter. • Scenario 3 is an accelerated reforms scenario. The investment to GDP ratio increases to 40 percent by 2035, led by an equal contribution of both physical and ICT capital, and moderates thereafter. The FLFPR increases to 55 percent by 2050. Consequently, the TFP is assumed to be 40-50 basis points higher than in scenario 2. The scenarios involve a decline in the incremental capital-output ratio (ICOR) over the decade, signaling efficiency gains, and an increasing share of industry in GDP. The ICOR declines in all scenarios up to the next decade, as ongoing initiatives to strengthen physical and digital infrastructure will lead to increased efficiency of capital. The decline in ICOR is greater in scenario 3 of “accelerated reforms”. The sectoral composition of the economy is expected to change as well with the share of agriculture declining by around 8 percentage points by 2047 in scenario 3, with the share gained by industry, and the share of services remaining constant. Under scenario 3, India can achieve UMIC status by early 2030s and HIC status by 2047. Under scenario 2, growth averages 6.6 percent per year during 2024-47 and achieving HIC status by 2047 remains elusive. In scenario 3, annual average growth is 7.8 percent over 2024-47. Thus, by raising the FLFPR to above 50 percent by 2047, augmenting the TFP growth rate compared to scenario 2, and maintaining an investment to GDP ratio of nearly 38 percent on average over the coming decade, average growth could be raised by more than 1 percentage point to achieve HIC status by 2047. The rest of the Report analyses these issues in greater detail and provides policy options to achieve them (see Chapter 6). India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 29 Chapter 1 Sustaining Rapid Growth to a High-Income Economy by 2047 References Alkire, Sabina, and Suman Seth. 2021. “Multidimensional Poverty and Inclusive Growth in India: An Analysis Using Growth Elasticities and Semi-Elasticities”. Available at SSRN: https://ssrn.com/abstract=3862857 Behera, Harendra, and Garima Wahi. 2018. “Mint Street Memo No. 13.” August 17. https://m.rbi.org.in/scripts/MSM_Mintstreet- memos13.aspx. Chodorow-Reich, Gabriel, Gita Gopinath, Prachi Mishra, and Abhinav Naraynan. 2020. “Cash and the Economy: Evidence from India’s Demonetization.” Quarterly Journal of Economics, 135 (1): 57-103. Deaton, Angus, and Valerie Kozel. 2005. “Data and Dogma: The Great Indian Poverty Debate” World Bank Research Observer, 20(2): 177-199. Krishna, Pravin. 2021. “India’s Free Trade Agreements” Indian Public Policy Review, 2(2): 1-12. Mankar, Ritika, and Sumit Shekhar. 2017. “Demonetisation and the Delusion of GDP growth.” Economic and Political Weekly, May 6: 17-20. Saraswat, V.K., Priya, P., and Ghosh, G., 2017, “A Note on Free Trade Agreements and their Costs,” NITI Aayog, New Delhi. Sinha Roy, Sutirtha, and Roy Van Der Weide. 2022. “Poverty in India Has Declined over the Last Decade But Not As Much As Previ- ously Thought.” World Bank Policy Research Working Paper 9994. Singh, Pratap. 2019. Demonetisation 2016 and Its Impact on Indian Economy and Taxation. Bangalore: The Institute for Social and Economic Change. http://www.isec.ac.in/WP%20450%20-%20Pratap%20Singh_2%20-%20Final.pdf. The Growth Report: Strategies for Sustained Growth and Inclusive Development. 2008. Commission on Growth and Development, Washington DC: The International Bank for Reconstruction and Development. The World Bank. 2024. World Development Report. Washington DC: International Bank for Reconstruction and Development. Internal unpublished. 30 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation © 2014 Rawpixel.com/Shutterstock CHAPTER 2 Accelerating Productivity Growth and Boosting Trade India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 31 Chapter 2 Accelerating Productivity Growth and Boosting Trade L abor productivity nearly tripled from 2000 to 2019 in India, growing more rapidly than in peer countries, except China. This overall improvement has been driven primarily by productivity growth in the services sector, whereas intersectoral reallocation of resources contributed moderately. At the firm level, productivity increases have been driven by the large firms in manufacturing and services. To accelerate productivity growth, India could apply lessons learned from its own successful experience in promoting modern market services and specific advanced manufacturing segments, by expanding the relevant infrastructure, addressing land constraints, ensuring the availability of skilled labor, and reducing barriers to trade and FDI. More broadly, participating more in international trade, enhancing market competition, and promoting enterprise R&D and the wider adoption of advanced technologies will be critical to reap further productivity gains. In India, firms that participate in international trade have higher levels of productivity. International trade has been a key driver of India’s successes in modern market services. India has also achieved significant diversification in merchandise exports, with notable successes such as mobile phones. However, the share of exports of goods and services in GDP has fallen below the peak reached in the pre-pandemic period. The share of firms engaging in international trade has declined since 1995, particularly in manufacturing. India’s participation in GVCs is low compared to many peer countries. Tariff and non-tariff barriers, including relatively high trade and logistics costs, weigh on India’s participation in international trade. India would gain significantly from liberalizing tariffs, non-trade, and FDI barriers and improving trade facilitation. I. Sectoral and Firm-level Productivity Labor productivity has grown rapidly since the turn of the century, driven primarily by productivity growth in the services sector. This chapter shows a decomposition of labor productivity growth into “within” and “between” sector components (Padil- la-Pérez and Villarreal, 2017). Within-sector productivity growth typically reflects improvements in technology and human capital, investments in physical capital, and the reallocation of resources from relatively less toward relatively more productive firms within each sector (Dieppe, 2021). Between-sector productivity growth is driven by the reallocation of labor and capital resources from relatively less toward relatively more productive sectors. Between 2000 and 2019, India’s labor productivity nearly tripled, growing more rapidly than in peer countries, except China (Figure 2.1). Three quarters of that growth was driven by within-sector improvements, particularly in services (Figure 2.2 and Figure 2.3). Dynamic reallocation—movement of workers to fast-growing industries— was largely absent.19 In comparison, labor productivity growth has been more broad-based in peer countries, with greater contributions from the manufacturing sector, and intersectoral reallocations of labor The contribution of inter-state labor mobility to labor productivity growth was limited (Figure 2.4). This contrasts with the experience of other countries such as Türkiye or Mexico, where relocation across states played an important role in structural transformation (see Chapter 4 and Chapter 6). Firms have relatively limited access to Industry 4.0 technologies. Technology adoption is positively correlated with labor produc- tivity. An enterprise survey20 carried out in two states in India finds that most firms have access to Industry 3.0 (ICT) technologies, with more than 97 percent using the internet. However, the share of firms using Industry 4.0 technologies is limited compared to firms in Korea and Brazil: approximately 9 percent use cloud computing, 5 percent use additive manufacturing technologies 19 Dynamic reallocation represents the joint effect of changes in employment shares and sectoral productivity. It is positive if workers are moving to sectors experiencing positive produc- tivity growth. 20 The Firm-level Adoption of Technology (FAT) survey was carried out through face-to-face contacts in 2020, based on a random sample of 1,519 manufacturing and services firms, 766 from Tamil Nadu and 753 from Uttar Pradesh. Large firms employ more than 100 workers, while small firms employ between 5 and 19 workers. The survey asked questions on 300 technologies associated with nearly 50 business functions, across eight sectors—food processing, wearing apparel, leather and footwear, motor vehicles, pharmaceuticals, retail and wholesale, financial services, and land transportation. Two indices were constructed: (i) the extensive margin, and (ii) the intensive margin. The extensive margin identified whether the firm was adopting a technology to perform a given task based on a yes or no question for the adoption of a given technology to perform a specific task. The intensive margin was based on the most used technology to perform this task (Cirera, et. al., 2021). 32 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 2 Accelerating Productivity Growth and Boosting Trade FIGURE 2.1: Labor productivity growth, India vis-a-vis FIGURE 2.2: Labor productivity change decomposition, peers India, annual average contribution to per-capita value- added growth (percentage points, average over subperiods) 350 8 6.6 6.5 300 6.3 6 250 4 200 2.7 150 2 100 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 0 2000-2004 2005-2009 2010-2014 2015-2019 India China Bangladesh Philippines Dynamic reallocation Static reallocation Viet Nam Within e ect Labor productivity growth Source: PWT 10.0 and WB staff calculations. Source: INDIA KLEMS. FIGURE 2.3: Labor productivity change decomposition (percentage points) India and Peers India Bangladesh Philippines Viet Nam China India -2 0 2 4 6 8 -2 0 2 4 6 8 Within, Agriculture Within, Industry Within, Services Inter-sectoral, Agriculture Inter-sectoral, Industry Inter-sectoral, Services Source: CEM 2.0 tool, WB staff calculations. such as 3D printing, and 2.4 percent use robots. There is also significant variation across sectors; firms in financial services and pharmaceuticals use advanced technologies to a greater extent firms in apparels, leather and footwear. There is also a significant technology gap between large and smaller firms.21 Exporting and foreign companies use more advanced technologies than their local and domestic market-focused counterparts. 21 On average, large firms have 15.5 mobile phones and 29.2 computers, whereas small firms have 2.2 mobile phones and 2.5 computers. A similar pattern is also observed in the use of telephones and smartphones (see Table 2.11, Annex 2.1). India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 33 Chapter 2 Accelerating Productivity Growth and Boosting Trade FIGURE 2.4: Labor productivity change decomposition, FIGURE 2.5: Reasons cited by firms for not adopting within and between states and sectors (average in more advanced technology subperiods, percentage points) India Lack of demand and uncertainty 1987-1993 Lack of capabilities 1993-1999 Lack of nance Period 1999-2005 Government regulations 2005-2011 Poor infrastructure 2011-2017 Other 0 2 4 6 8 0% 25% 50% 75% 100% Annual labor productivity growth, in percent Small Medium Large Across state Within state, across sector Within state, within sector Source: Jobs Flagship Report Concept Note, World Bank, 2020. Source: FAT survey, World Bank (2021). Infrastructure primarily refers to electricity and internet access. Limited technology adoption is primarily attributable to high uncertainty, lack of capabilities, and limited access to finance (Figure 2.5). These reasons do not vary widely by firm size, although large firms cite lack of capabilities—firms’ managerial capabil- ities and workers’ skills— more often and small firms cite uncertainty and lack of finance more often. Firms with higher technology indexes are more likely to have managers with a post-graduate degree and are more likely to export. Many firms, especially at lower levels of technological sophistication, seem to be overestimating their technology sophistication and therefore underesti- mate the need for technology upgrading. The technology indexes are positively correlated with the use of government programs and subsidies. However, even though 80 percent of firms are aware of government programs and subsidies, only 30 percent take advantage of them. In other words, India has significant scope to promote the infusion of modern technologies throughout the economy, which is a precondition to transition from a middle-income to a high-income trajectory (WDR, 2024). Infusion, the process of welcoming and adapting imported technologies has been critical for MICs to improve their productivity, and become competitive in global markets. In turn, this process lays a strong foundation for domestic innovation, which is essential for sustain- ing growth over the longer run. Trends in labor productivity differed across sectors with services catching up post-GFC. Within services, labor productivity growth was strongest in trade and telecommunications (driven by capital deepening and ICT adoption). Traditional market services, such as retail, repair, hotel and restaurant, transport, storage, and communication, have also experienced impressive improvements, possibly due to the transformation of supply chains, digitalization, improvements in managerial and operational processes, and more recently to the rise of e-commerce. Within manufacturing, capital-intensive industries like electrical, and optical equipment, and transport equipment also witnessed high labor productivity growth, particularly over the last few years (Figure 2.6). 34 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 2 Accelerating Productivity Growth and Boosting Trade FIGURE 2.6: Average labour productivity growth, sectors (percent) 14% Wood 12% Other Manf. Transport Equip. 10% Average labor productivity growth Post & Telecomm 8% Trade 6% Transport Equip. Agriculture 4% Business Service 2% Business Service 0% -2% Other Manf. Wood -4% 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% Real GVA share Services,2010-19 Manufacturing,2010-19 Agriculture,2010-19 Construction,2000-09 Services,2000-09 Manufacturing,2000-09 Agriculture,2000-09 Construction,2010-19 Source: INDIA KLEMS 2020 and WB staff calculations. In agriculture, labor productivity has improved.22 Improving FIGURE 2.7: Agriculture TFP growth rate (decadal agricultural productivity further is vital for structural trans- average) formation, food security, poverty reduction, and environ- 4.0 mental sustainability. Agricultural TFP growth has improved 3.5 significantly over the past ten years, averaging 3.7 percent 3.0 between 2010-19 and placing India among the top perform- 2.5 ers compared to peers (Figure 2.7). India’s performance in agricultural labor productivity is noteworthy, with the decadal 2.0 average growth rate increasing more than threefold over 1.5 2010-19. The labor productivity growth rate over 2000-19 is 1.0 in line with peers, with a particularly strong performance over 0.5 the past decade (close to 6 percent on average) driven more 0.0 by high growth of value added than the shedding of labor.23 -0.5 Initiatives like land-leasing and pooling, reducing transactions -1.0 India Indonesia Brazil Türkiye China Mexico Philippines Thailand Malaysia Bangladesh costs for labor and inputs coupled with technology adoption can enhance productivity, especially of intermediate firms. Increasing investment in agricultural infrastructure, particu- 2000-09 2010-19 larly in irrigation,24 can also boost productivity and enhance Source: United States Department of Agriculture [USDA] (2022). resilience to climate shocks. 22 Agriculture is a cornerstone of the economy, employing nearly half of the workforce and accounting for 16 percent of GVA. In rural areas, agriculture plays an even more crucial role, providing employment to over 60 percent of the workforce, and approximately 75 percent of female workers. Non-perennial crop production employs more than 80 percent of agricultural workers. 23 India is diversifying from resource intensive to high value crops, with a move away from staple crops to cash crops, and particularly horticulture, and livestock products. High value crops contribute to approximately 45 percent of the value added in the crop sector. 24 TFP (weighted average of firm-level productivity) is decomposed into two main components: (i) the “within” component that is the unweighted average productivity; and (ii) the “between/ covariance” component that is the covariance between productivity and output and represents the contribution to TFP resulting from the reallocation of market share and resources across firms of different productivity levels. The larger this covariance, the higher the share of output that goes to more productive firms and the higher the TFP. If the latter component is positive and increasing, then more output is produced by the more efficient firms. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 35 Chapter 2 Accelerating Productivity Growth and Boosting Trade FIGURE 2.8: Within-firm productivity (by size quartiles) Manufacturing Services 200 200 150 150 TFP TFP 100 100 50 50 0 0 1995 2000 2005 2010 2015 2020 1995 2000 2005 2010 2015 2020 Q1 Q2 Q3 Q4 Source: Chakraborty, Khurana, and Manghnani (2023). TFP gains in manufacturing and services have been driven by resources flows toward large firms.25 Within-firm productivity—that is primarily determined by inherent firm capabilities— has remained constant for small and medium sized firms since the mid-1990s across the manufacturing and services sectors (Figure 2.8). The largest firms26— 75th percentile in size, and above— experienced productivity increases, especially in the services sector (Chakraborty, Khurana and Manghnani, 2023). More productive firms generated higher sales as well (Figure 2.9). The correlation between market share and TFP was positive and gradually increasing for manufacturing throughout the past two decades. The correlation for services was also positive and increasing until the GFC and declined thereafter. There is significant potential to boost within-firm productivity across firm sizes. Since productivity increases were mainly limited to large firms, the dispersion of TFP across firms was high and increased during 1995-2013. This was true particularly for services. The ratio of the productivity of some of the most productive firms (90th percentile) to some of the least productive firms (10th percen- tile) more than doubled in services up to 2013, with very large increases in the productivity dispersion of firms in wholesale and retail trade, and financial services. In contrast, the dispersion of firm productivity in manufacturing was modest (Figure 2.10). The median firm remained largely unchanged in terms of capabilities and productivity. This indicates that there is significant potential to strengthen the capabilities of firms through addressing market failures that constraint adoption of technologies, innovation, and participation in international markets. II. Learning from Past Successes in Manufacturing and Services The relatively stronger performance of services compared to manufacturing in India holds important lessons. The share of manufacturing in value added is much smaller than the share of services and is declining for low-skill-intensive (and labor intensive) manufacturing (KLEMS, 2022). Meanwhile, services’ share in value added has been rising, including for low-skill-in- tensive services such as trade (KLEMS, 2022). Several explanatory factors have been put forward. First, labor law rigidities affect low-skilled manufacturing sectors more than medium-high-skills and technology-intensive sectors, because wages 25 TFP (weighted average of firm-level productivity) is decomposed into two main components: (i) the “within” component that is the unweighted average productivity; and (ii) the “between/ covariance” component that is the covariance between productivity and output and represents the contribution to TFP resulting from the reallocation of market share and resources across firms of different productivity levels. The larger this covariance, the higher the share of output that goes to more productive firms and the higher the TFP. If the latter component is positive and increasing, then more output is produced by the more efficient firms. 26 The analysis uses data from the PROWESS database that covers larger listed firms in formal sector which accounts for more than 70 percent of economic activity, and 75 percent (95 percent) of corporate (excise duty) taxes collections. 36 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 2 Accelerating Productivity Growth and Boosting Trade FIGURE 2.9: Relationship between TFP and market share Manufacturing Services 40 40 35 35 Coe cient of Year # Share of Sales Coe cient of Year # Share of Sales 30 30 25 25 25 25 15 15 10 10 5 5 0 0 -5 -5 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Source: Chakraborty, Khurana, and Manghnani (2023). Figure 2.9 shows the yearly coefficients obtained from a fixed effects panel regression, where productivity is regressed on the interactions between the share of sales and year fixed effects. The analysis utilizes a firm-level panel dataset extracted from Prowess IQ. FIGURE 2.10: Productivity dispersion (ratio of the 90th to the 10th firm percentile) Manufacturing Services 40 40 P (90) / P (10) TFPs P (90) / P (10) TFPs 30 30 20 20 10 10 0 0 1995 2000 2005 2010 2015 2020 1995 2000 2005 2010 2015 2020 Year Year Source: Chakraborty, Khurana, and Manghnani (2023). Notes: Figure 2.10 represents the mean productivity dispersion ratios – ratio of productivity ratios for 90th percentile firms to 10th percentile firms for the manufacturing and services sector, respectively. Productivity is computed as per the methodology proposed by Levinsohn and Petrin (2003), in which energy costs serve as a proxy variable for intermediate inputs in manufacturing firms, and communication and advertising expenditures are utilized as the proxy variable for intermediate inputs in service firms. constitute a significant part of production costs for the latter relative to the former. In addition, demand for low-skilled manu- facturing products is highly elastic to price. Second, modern market services have benefited from better regulatory institu- tions governing trade in services, compared to the institutions governing trade in goods, for example customs authorities (Goswami, Mattoo, and Sáez, 2012). Third, the infrastructure for service delivery (for example, telecommunications networks) has improved dramatically due to significant investments, while manufacturing has been constrained by the comparatively slow expansion of infrastructure for goods delivery (roads, rail, and ports). Fourth, industrial and commercial electricity India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 37 Chapter 2 Accelerating Productivity Growth and Boosting Trade prices27 are relatively high in India compared to middle- and even high-income countries (Gokarn, Tyagi and Tongia, 2022). Fifth, low-skilled manufacturing has been hampered by the limited availability and high cost of land. Sixth, location matters: capital-intensive manufacturing sub-sectors have performed relatively well in the last two decades as they rely less on labor and are situated in states with better infrastructure and governance quality. Modern market services and advanced manufacturing benefited from good quality infrastructure, less binding constraints on land, availability of skills, and incentives for trade and capital inflows. The growth of IT-enabled and other business services was underpinned by the availability of skilled workers (engineers and English-speaking graduates), the expansion of digital infrastructure (aided by privatization) and fit-for-purpose regulatory frameworks in the telecommunication sector, in addition to various proactive policies to incentivize private investment (Box 2.1). Similarly, the transport and electrical equipment (TEE) industry benefited from improved access to quality infrastructure, such as roads, supportive logistics, the liberalization of backbone services that support manufacturing, and a conducive regulatory framework. Advanced manufacturing also has been supported by industrial policies that have de facto lifted local land-use restrictions (Box 2.2). However, these positive conditions for expansion in modern market services and the TEE industry were concentrated in only a few states (see Chapter 5). Segments of advanced manufacturing benefitted from spatial policies, but the success of such policies depends on complementary enabling factors. Cross-country evidence on the impact of spatial development programs, such as SEZs, is mixed and impact depends crucially on complementary policies and country-specific factors.28 Factors that encompass design aspects (such as the incentives package and the organizational framework) as well as other characteristics (including age, size, targeted sectors, location, and technological content) matter a lot to the success of SEZs. The literature is ambiguous on the effects of fiscal incentives. Instead, national ‘contextual’ factors are found to be more important: (a) the macroeconomic policy framework, exchange rate policies, market size, trade policies, political stability; and (b) regional economic and export infra- structure, availability of labor, labor laws and regional governance (Asian Development Bank, 2015; Competitive Industries and Innovation Program, 2017). Therefore, spatial policies are likely to be more impactful when they are integrated into a broader comprehensive growth strategy. 27 The cost of electricity in India is among the most expensive in the world and higher than USA, Indonesia, South Africa, and China, when measured in terms of purchasing power parity (PPP). Commercial and industrial users pay high prices (industrial users especially so), because of social welfare redistribution norms and attendant cross-subsidies. Users in these two categories pay high prices in both relative and absolute terms (using market exchange rate comparisons). 28 In a recent study for India, Galle et. al. (2022) found that the SEZs set up between 2005 and 2013 fostered structural change in the municipalities where they were located, by increasing the number of new non-farm jobs and reducing the number of marginally employed workers (defined as workers with 183 workdays or less per year). In terms of firm size, while within the SEZ two-thirds of employment was created by large firms, in areas around SEZ the employment increase was driven by small and informal enterprises. However, the effect on employment opportunities for women and local public good provision was limited. The positive spillovers were observed only in close proximity to the SEZ (up to 50 kilometers) and the impact was constrained by the absorptive capacity of the areas where the SEZs were situated. This capacity was influenced by factors such as education attainment levels and, general economic and institutional conditions of these areas. 38 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 2 Accelerating Productivity Growth and Boosting Trade Box 2.1: Learning from the Success of Modern Market Services Modern market services – IT and IT enabled services, and other business services – have grown by almost 13 percent annually, in real terms, since 2000—supported by policy and external factors. Services and manufacturing are becoming increasingly interlinked and are often provided together (servicification of manufacturing29). However, in India, the growth of modern market services has been faster compared to other services as well as manufacturing. These services have been largely exported (serving manufacturers outside India). The share of ‘servitized’ manufacturing firms increased from approximately 31 percent in 1997 to close to 60 percent in 2013 (Nayyar et. al., 2021). The growth in services occurred in the backdrop of rapid global growth in technology, transportability, and tradability that changed the nature, productivity, and tradability of services (Figure B2.11) and benefited from an interplay of the following factors (Goswami et. al., 2012): FIGURE B2.11: Modern market services have grown at FIGURE B2.12: FDI regulatory restrictions in services an unparalleled pace since 2005 have been eased considerably since 1997 (Real GVA, Index 1990=100) (Size of bubbles: real value-added growth (CAGR), 1998-2019) 2000 Gradual easing of 0.70 1800 FDI restrictions in select services 0.60 Business Services comprising 1600 SEZ Act, 2005 of IT and IT enabled services, 1400 0.50 and other commercial services IT Act, 2000 1200 National Telecom 0.40 1000 Policy, 1994 Trade 800 0.30 Financial 1991 services 600 liberalization reforms and 0.20 400 STPI Act Telecommunication 200 0.10 Transport 0 0.00 99 01 03 05 07 09 11 13 15 17 19 Hospitality 89 91 93 95 97 19 20 20 20 20 20 20 20 20 20 20 19 19 19 19 19 Traditional market services Non-market services 0.10 Modern market services Total Value Added, ex services 0.00 0.20 0.40 0.60 0.80 1.00 Source: KLEMS and WB staff calculations. Source: OECD, KLEMS and WB staff calculations. Note: FDI restrictiveness index is high for business services due to 100 percent restriction in legal, accounting, and real estate services. The restrictions are low for engineering (IT and IT enabled) services. • Skills: A large pool of trained engineers and graduate-level English speaking workers as well as network effects of a well-es- tablished diaspora of technical and professional workers in the US aided the expansion of demand and supply sides for the modern market services. • Digital Infrastructure: Gradual liberalization in the telecommunications sector helped to put in place the required ICT infrastructure and incentivized higher internet penetration. Goswami, Gupta, & Mattoo (2012) find that internet penetra- tion had a significantly large positive impact on India’s exports of commercial services compared to other economies after controlling for income levels. Underdeveloped physical infrastructure was not a constraint, as the sector is less dependent on such infrastructure compared to manufacturing and traditional market services such as tourism. 29 Services being increasingly supplied together with physical products. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 39 Chapter 2 Accelerating Productivity Growth and Boosting Trade Box 2.1: Learning from the Success of Modern Market Services (continuation) • Institutional quality: Regulatory quality improved by the late 1990s with substantial easing in product market regulations and delicensing reforms. This was particularly important for the financial sector, where deregulation was initiated in 1992. • Labor regulation: The rules governing workers in the services sector, under the Shops and Establishment Act, are relatively flexible. Some states have also relaxed provisions of this Act for IT and IT-enabled services30. • Land: Modern market services tend to be less land intensive compared to manufacturing and traditional market services. Therefore, land availability and cost has been less of a concern. • Supportive policies: India gradually liberalized FDI since the mid-1990s (Figure B2.12), which boosted growth in business services and telecommunications. Other critical policies designed to promote exports and investment were implemented post-1990, including the Software Technology Parks (STP) Act of 1991 (that helped create establishments with utilities and statutory approvals), IT Act of 2000 (that provided legal framework for electronic governance)31, and the New National Tele- com Policy of 1999 (that restructured regulatory institution, introduced policy on spectrum management, and addressed standardization). Box 2.2: Learning from the Success of Technology Intensive Manufacturing The technology-intensive transport and electrical equipment industry experienced high growth in productivity and value added post-1991. The TEE manufacturing industry – comprising electronics, auto and auto parts – has performed better than other manufacturing sub-sectors in the post-1991 period, with the highest value-added and labor productivity growth (Figure B2.21). Exports of TEE increased by an impressive 15 percent annually between 2000 and 2020, although the global export share of India’s TEE industry remains only approximately 1 percent indicating potential for further growth. The success of transport and equipment manufacturing in India stems from improvement in 3Cs— competitiveness, capabilities, and connectedness (Hall- ward-Driemeier and Nayyar, 2017). • Competitiveness of an industry is built through access to quality infrastructure, liberalization of key supporting backbone services, reduction of trade barriers and regulatory frameworks, and the strengthening of the firm’s ecosystem. Competi- tiveness improved due to the following policy interventions: • Post-1991, government policies reduced the cost of doing business for the automobile industry. The Auto Policy (2002) provided incentives to attract investment, lowered tariffs and duties, and promoted modernization and indige- nous design and development within the country to make the industry globally competitive. The National Automotive Testing and R&D Infrastructure Project (NATRIP, 2002) was launched to boost R&D infrastructure by establishing world- class testing, homologation,32 and certification facilities, as well as nine R&D centers. 30 Refer to http://niranjanraoassociates.com/images/publications/IMPORTANT-LABOUR-LAWS-FOR-IT-ENABLED-SERVICES-AT-A-GLANCE.PDF and https://vakilsearch.com/blog/ indian-labour-laws-and-its-impact-on-it-ites-industry/ 31 This incorporates legal recognition of digital signatures, electronic records etc., and other aspects applying the use of ICT to deliver government services and exchange of information. 32 Homologation is the process of certifying the vehicles for roadworthiness according to specified criteria laid down by the government for all vehicles made or imported in the country. 40 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 2 Accelerating Productivity Growth and Boosting Trade Box 2.2: Learning from the Success of Technology Intensive Manufacturing (continuation) FIGURE B2.21: Electrical and transport equipment FIGURE B2.22: Value of mobile exports have been on a witnessed high labor productivity and value added rise since 2018, due to various policy initiatives growth (X axis: Value addition compound annual growth (USD million) (CAGR) in percent, Y axis: Labor productivity compound annual growth (CAGR) in percent) 14 1200 Phased National Production Linked Wood Manufacturing Electronics Incentives, Electronics 12 Program Policy Manufacturing 1000 Clusters 2.0, Food Textiles COVID 10 Minerals Electrical and transport machinery 800 8 LP growth, CAGR Chemicals 6 600 4 Rubber 400 Paper 2 Metals 200 0 Machinery -2 0 0 2 4 6 8 10 12 Jan-16 May-16 Sep-16 Jan-17 May-17 Sep-17 Jan-18 May-18 Sep-18 Jan-19 May-19 Sep-19 Jan-20 May-20 Sep-20 Jan-21 May-21 Sep-21 Jan-22 May-22 Sep-22 GVA growth, CAGR Source: KLEMS and WB staff calculations. Source: UN Comtrade. • The electronics sector, particularly the manufacturing of mobile phones, registered a surge in exports post-2016 underpinned by policies strengthening the competitiveness of the industry (Figure B2.22). The Phased Manufactur- ing Policy, 2016-17 aimed to promote domestic manufacturing using a graded duty structure, specifically a rise in basic customs duty (BCD). This was followed by policies incentivizing firms to shift production lines to India. The National Policy on Electronics, 2019 aimed to make India a global hub for Electronics System Design and Manufacturing (ESDM) by providing financial incentives, such as tax benefits across the ESDM value chain and exemption of export duty on capital equipment. The policy also incorporated the Electronic Manufacturing Clusters 2.0 program, 2020 which aimed to provide world-class infrastructure and logistics to the industry. Moreover, the Production Linked Incentive (PLI) Scheme, 2020 offered sales-based incentives to large-scale manufacturers of mobile phones and other electronic components. Reflecting these initiatives, the value of mobile exports increased by 20 times between 2016 and 2023, notwithstanding a momentary lull in the aftermath of the pandemic. • The upgrading of roads along key industrial centers and the availability of quality logistics in a few regions led to a concentration of the TEE industry in a few hubs. The TEE industry was able to overcome infrastructure-related challenges. Firstly, expansion of road networks has helped improve connectivity between towns and cities. For instance, projects such as the Golden Quadrilateral, which upgraded the highway network for major industrial centers, led to a substantial increase in manufacturing activity in the districts along the highway network. Secondly, states such as Punjab, Haryana, Tamil Nadu, and Maharashtra have provided a strong ecosystem for the TEE industry. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 41 Chapter 2 Accelerating Productivity Growth and Boosting Trade Box 2.2: Learning from the Success of Technology Intensive Manufacturing (continuation) • Agglomeration of auto sector firms has been positively associated with a better business climate, lower operating costs, and higher capacity utilization. Over 80 percent of India’s auto firms are situated in clusters compared to only 55 percent of firms in other manufacturing industries. These clusters include export processing zones (EPZs) and industrial parks, which provide special incentives— such as tax breaks and exemption from certain aspects of labor laws, as well as facilitating access to land— to attract investors (Saraf, 2016). • Capabilities of workers are important to sustain competitiveness. India continues to possess a comparative advantage in this area because of the following factors: • India has a large pool of trained engineers and a network of good institutions, such as the Indian Institutes of Technology (IITs) and the Indian Institutes of Management (IIMs). • The economy has been gaining global recognition as an automobile manufacturer, given the availability of low cost and high-quality engineering skills (Rani & Unni, 2004). • Large auto firms have achieved economies of scale and therefore, have been able to start skill development centers and have increased spending on R&D and innovation (Saraf, 2016). • Connectedness of firms is important for improving the access to foreign markets. Increasing connectedness involves reducing trade restrictions for both goods and services, low non-tariff barriers and an improved logistics performance. India liberalized the TEE industry gradually through the 1990s, and the process has continued in the twenty-first century: • Trade liberalization in 1991 led to increased competition and higher productivity (Kambhampati et. al., 1997). • Delicensing of vehicles and auto components in the post-1991 period opened the industry to foreign players, encour- aging the establishment of joint ventures with domestic players (Ranawat & Tiwari, 2009). The development of the automobile industry also helped boost growth in other manufacturing industries such as electrical equipment (Rani & Unni, 2004). The TEE industry has performed well but there is still scope for improvement. India’s electronics industry could further improve its competitiveness through (i) resolving the problem of inverted duty structure in the electronics industry (Francis, 2018); (ii) attracting more FDI; and (iii) aligning industrial and trade policies (for example, while the Make in India policy envis- ages a more open economy, the Phased Manufacturing Scheme requires a rise in basic customs duty (Gupta, et. al., 2021). Addressing these issues will help in strengthening competitiveness while connectedness will be bolstered by the potential integration of firms in GVCs. Further, continuous, and further upskilling of the workforce is required to develop complex parts and components in tandem with the new technological developments in the electronics industry. Upskilling would also support the expansion of the small-scale auto industry (automobile firms with less than 250 employees). Finally, continued efforts to improve domestic supply chain and logistics and expanded access to reasonably priced and reliable power supply will complement these efforts. 42 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 2 Accelerating Productivity Growth and Boosting Trade III. Key Factors Affecting Productivity Gains: Trade, Innovation, Land Availability, and Market Competition Trade, innovation, and IT adoption are associated with higher within-firm productivity. For manufacturing firms, imports of intermediate goods and R&D are the most significant factor associated with higher within-firm productivity (Figure 2.11). For services firms, trade plays a significant role, followed by R&D and IT adoption. The evidence on outsourcing is less clear. FIGURE 2.11: Within-firm drivers of productivity Manufacturing Services Exporter Exporter Importer - Capital goods Importer - Capital goods Importer - Interm. goods Importer - Interm. goods R&D R&D Technology Transfers Technology Transfers IT IT Outsourcing Outsourcing 0 .05 .1 .15 .2 0 .1 .2 .3 Coe cients Coe cients Source: Chakraborty, Khurana, and Manghnani (2023). Notes: Coefficient estimates are derived using a fixed-effects regression model, which examines the impact of firm-level attributes—exporting, importing, R&D, technology transfer, IT, and outsourcing—on within-firm productivity, while controlling for the firm’s age, its square, industry-year fixed effects, and clustering standard errors at the firm level. The vertical red line is positioned at zero. If the confidence interval of a coefficient crosses the red line, it indicates that the coefficient is statistically insignificant. The limited participation of Indian firms in trade and R&D activities (relative to firms in comparator countries) explains the slow growth of within-firm productivity, especially in small and medium sized firms. During 1995–2018, with significant entry of new firms in both manufacturing and services, the number of firms that trade increased. However, the share of firms engaging in international trade has declined since 1995, across the manufacturing and services sectors (Figure 2.12). These patterns are reflected in the slowing growth in imports of capital goods over the past decade and slowing GVC participation of firms prior to the pandemic (see section IV). The share of firms engaging in R&D adoption and technology transfers remains low (Figure 2.13). Despite major reforms, more can be done to improve access to land for firms. The repeal of the Urban Land Ceiling and Regu- lation Act (ULCRA) between 1999 and 2003, significantly improved the allocation of land and buildings in the areas where it was strictly enforced (Duranton, et. al., 2015a and Duranton, et. al., 2016).33 Access to land is governed by the Right to Fair Compensation and Transparency in Land Acquisition, Rehabilitation and Resettlement (LARR) Act, 2013, replacing a legislation from 1894. The Government of India introduced an amendment bill in 2015, proposing key changes in the statute (see Annex 6.1).34 A few states followed with the requisite legislative changes, but several state policies still restrict urban land supply. For example, the transfer of farmland for non-agricultural use is prohibited in some states and restricted in others. The absence of titles in slum areas makes the land non-marketable. Other restrictive regulatory provisions, which vary across cities and states, include the enforcement of 33 In 1999, the Repeal Act provided rights to state governments to repeal ULCRA. Several states and UTs repealed ULCRA by 2003 including Delhi, Gujarat, Haryana, Karnataka, Madhya Pradesh, Odisha, Punjab, Rajasthan, and Uttar Pradesh, while Andhra Pradesh, Assam, Bihar, Maharashtra, and West Bengal kept ULCRA effective until 2008. 34 The amendment sought to do away with the prior consent clause and compulsory social impact assessment for defence, rural infrastructure, affordable housing, industrial corridors set-up by the government, and development of infrastructure including PPP where the government owns the land. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 43 Chapter 2 Accelerating Productivity Growth and Boosting Trade FIGURE 2.12: Share of firms engaged in international trade (percent) Manufacturing Services 80 40 60 30 Percentage Percentage 40 20 20 10 0 0 1995 2000 2005 2010 2015 2020 1995 2000 2005 2010 2015 2020 Years Years Exporter Importer-Capital Goods Importer-All goods and services Importer-Intermediate goods FIGURE 2.13: Share of firms engaged in R&D and technology transfer (percent) Manufacturing Services 50 50 40 40 30 30 Percentage Percentage 20 20 10 10 0 0 1995 2000 2005 2010 2015 2020 1995 2000 2005 2010 2015 2020 Years Years Technology Transfer R&D Adopter Source: Chakraborty, Khurana, and Manghnani (2023). Figure 2.18 illustrates the percentages of firms engaged in exporting, importing, undertaking R&D expenditure, and foreign technology transfer. The calculations are based on the data from Prowess IQ. low building height and low floor space indices in urban areas (relative to other Asian cities), urban land ceilings in certain cities, and rent control measures favoring tenants over owners, which in practice transfer ownership rights to tenants but not the actual title and the ability to sell the property. Some states have made significant progress in the registration and digitization of land records, but significant improvements can be made to the quality of records. The acquisition of land through direct transactions between landowners and businesses is constrained by the limited availability of reliable land records. States have unevenly implemented the Digital India Land Records Modernization Program (DILRMP), which aims to develop a modern, comprehensive, and transparent land records management system through a single window system. The DILRMP targets the automation of processes following registration, improvements in the quality of the land-titling system, and computerization of all land records. However, land titles remain presumptive, and the quality of land records has significant potential for improvements. The National Council for Applied Economic Research (NCAER) Land Records and Services Index, 2021 shows that some states have made significant progress in digitizing land 44 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 2 Accelerating Productivity Growth and Boosting Trade records and registration— with West Bengal, Tamil Nadu, FIGURE 2.14: Estimated adjustment costs in land Madhya Pradesh, Odisha, and Andhra Pradesh— among the 0.5 top performers.35 However, the index also indicates that the 0.4 quality of land records and services can be improved further even in states with high digitization scores. 0.3 0.2 These constraints impair the productivity of land-intensive 0.1 activities, particularly for small firms. Large-scale textile 0 manufacturing, for example, requires large plots of contiguous -0.1 land; however, in India, land is fragmented into small parcels of an average size almost 100 times smaller than the average U.S. -0.2 parcel (McDonald, et. al., 2013). Private textile establishments, -0.3 which account for 86 percent of textile manufacturing firms, -0.4 therefore, face substantial land aggregation costs. Similarly, -0.5 constraints on access to land impair productivity in agriculture 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 (see Box 2.1). The market for buying and selling of land is almost Small rms Large rms non-existent in India, as most agricultural land is inherited (Foster and Rosenzweig, 2017). States impose varying restric- Source: Chaurey, Manghnani, Perego, and Sharma (2022). tions on land-leasing, and barriers to engaging in rental-market transactions contribute to about one quarter of the differences in agricultural productivity across states (Bolhuis, 2021). States with more rental-market activity feature less misallocation and reallo- cate land more efficiently over time.36 Duranton, et. al. (2015a) estimate that one standard deviation decrease in the misallocation of land and buildings is associated with a 20-25 percent increase in output per worker in manufacturing. Small manufacturing firms are particularly affected; modelling factor misallocation as adjustment costs, Chaurey, et. al (2022) show that it has become easier for large firms to access land, but small firms have found it increasingly difficult (as captured by increases in adjustment costs – Figure 2.14). Market competition is another area where improvements can be made. Greater competition creates incentives for firms to be more efficient, innovate and be more productive (Acemoglu et. al. [2007]; Aghion and Griffith [2008]; Bloom, Draca and Von Reenen [2016]; Bassanini and Ersnt [2002]; Kitzmuller and Licetti [2012]; Buccirossi et al. [2013]; Voigt [2009]). Well-functioning markets provide firms with equal opportunities to thrive based on their competitiveness. Firms that survive market competition push the production frontier and help the economy transition to higher levels of income.  Market competition in India is perceived to be relatively strong, but there is room for improvement. In the Bertelsmann Transformation Index,37 India’s score is lower than that of South Africa, the Republic of Korea, or Singapore38 (Figure 2.15). India’s competition policy (policies that prevent anti-competitiveness practices) is perceived to be better than that of Indonesia, Thailand, the Philippines, and Viet Nam, but worse than that of successful MICs and countries such as Brazil and South Africa39 (Figure 2.16). Perceptions of business risks discourage firms from entering and competing in markets. Indeed, few businesses are created in India relative to the size of the economy, indicating weak competitive pressure. During 2010-20, the average business entry density rate was only 7 percent, lower than in many peers (Figure 2.17).   35 D B Gupta, Shashanka Bhide, Deepak Sanan, Charu Jain, Falak Naz, Somnath Sen, Prerna Prabhakar, Aswani Munnangi, Chandni Mishra, Disha Saxena, Arundhati Sharma, Vijay Singh Bangari, Apoorva, and Rupal Taneja, “Land Records and Services Index (N-LRSI) 2021”, NCAER, March 2021. The index scores are reported in Figure 7.1 and Figure 7.2 in Chapter 7 of the Report. 36 Studies adopt different approaches to measuring misallocation. For example, Duranton, et. al. (2015a) uses the negative of the covariance terms between output/sales and TFP, while Chaurey, et. al. (2022) use adjustment costs as a measure. 37 The Index is based on the perceptions of country experts who evaluate the extent to which 17 criteria (some of which are an aggregation of up to 6 subindexes) have been met. For further details, see the BTI methodology available at https://bti-project.org/en/methodology. 38 The BTI index on perception of market-based competition is based on experts’ assessment of the following: to what level have the fundamentals of market-based competition developed (including the low importance of administered pricing, currency convertibility, no significant entry and exit barriers in product and factor markets, freedom to launch and withdraw investments, and no discrimination based on ownership (state/private, foreign/local) and size. The scores range from 1 to 10, with higher values suggesting better competition-enabling environment.  39 The BTI index on perception of competition policy is based on experts’ assessment of the following: to what extent do safeguards exist to prevent the development of economic monop- olies and cartels, and to what extent are they enforced (including the existence of antitrust or competition laws and enforcement)? The scores range from 1 to 10. Higher values suggest stronger policy in place.  India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 45 Chapter 2 Accelerating Productivity Growth and Boosting Trade FIGURE 2.15: Perceptions of market organization, 2022   FIGURE 2.16: Perceptions of competition policy, 2022  Hic avg. for market-based competition: 8.1 Hic avg. competition: 7.8 10 10 8 8 6 6 4 4 2 2 0 0 India China Russian Federation Brazil South Africa Indonesia Korea Rep. Singapre Thailand Bangladesh Philippines Viet Nam Chile Czechia Polamd Romania India China Russian Federation Brazil South Africa Indonesia Korea Rep. Singapre Thailand Bangladesh Philippines Viet Nam Chile Czechia Polamd Romania BRICS Asian peers CEM peers Successful BRICS Asian peers CEM peers Successful UMICS UMICS Source: Bertelsmann Transformation Index. Note: Higher value implies better competition-enabling environment and a stronger policy in place. Some segments of the economy are characterized by FIGURE 2.17: The new business entry density gap   significant market concentration and a relatively large state presence, which is likely to discourage market entry. 12 Some subsectors of industry such as petroleum, computer 10 and communications equipment, and cement have medium 8 to high levels of market concentration (Saraswathy, 2021). In addition, the presence in competitive sectors of firms that 6 are owned directly or indirectly by the government is higher 4 than in many other countries (Figure 2.18)40 and likely to deter 2 private entry (World Bank, 2023). In such instances, strong 0 market institutions and effective implementation of the India China Russian Federation Brazil South Africa Indonesia Korea Rep. Singapre Thailand Bangladesh Philippines Viet Nam Chile Czechia Poland Romania competitive neutrality principles41 are critical to ensuring a level playing field and preventing potential market distortions.  Despite progress in strengthening the competition law and institutional framework, further efforts are needed BRICS Asian peers CEM peers Successful UMICS to address competition challenges in digital markets. The Observerd Predicted Competition (Amendment) Act, 2023 significantly strength- ened the mandate of the Competition Commission of India Source: Enterprise Surveys and WDI, 2010-20. (CCI), reinforced its anticartel policy, streamlined the frame- Note: New business entry density is defined as the number of newly registered formal private limited-liability firms per 1,000 working-age people (aged 15-64). The work to limit anticompetitive effects of mergers and acquisi- dots show the predicted number of entries obtained from a linear regression with tions, and introduced key tools to foster efficient adjudication (the log of ) the average GDP per capita 2006-18 adjusted for the 2011 purchasing power parity as the explanatory variable. of cases, notably through commitments and settlements. 40 Figure 2.18 is based on the World Bank’s Business of the State (BOS) database, which is a database of firms both directly or indirectly owned by either the central or the state government at or above 10 percent level of participation that are engaged in market production and are legally separated entities. 41 Competitive neutrality (CN) is a principle according to which all enterprises, public or private, domestic, or foreign, should face the same set of rules, and where government’s contact, ownership, or involvement in the marketplace, in fact or in law, does not confer an undue competitive advantage on any actual or potential market participant. See OECD, Roundtable on Competition Neutrality, Issues paper by the Secretariat, 2015, p. 4.   46 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 2 Accelerating Productivity Growth and Boosting Trade FIGURE 2.18: Enterprises owned directly and indirectly by the State, by type of sectors, 2019  (share of total enterprises owned directly and indirectly by the government)  120% 4% 1% 6% 3% 100% 11% 2% 9% 10% 7% 19% 5% 20% 80% 9% 24% 22% 26% 33% Competitive 60% Partially Contestable Natural Monopoly 40% 75% 74% 67% 65% Missing 61% 48% 20% 0% Viet Nam India** Indonesia Türkiye Philippines Bangladesh Source: World Bank Global Businesses of the State (BOS) database. Note: The BOS data for India is still under validation and may be subject to change. Economic activities are classified based on their intrinsic characteristics and associated market failures (Dall’Olio et. al. 2022b). The taxonomy is country-agnostic and classifies more than 560 economic activities into 3 groups: Competitive: Activities in which incumbents and entrants have access to similar information and production technologies, and where the provision of good and services that are private (that is, rival and excludable) and production activities do not generate significant externalities. Partially contestable:  Activities that exhibit significant fixed costs for entry may reduce the number of competitors in the market (that is, oligopolistic markets). This category also includes activity sectors characterized by public goods, externalities, and asymmetries of information. Natural monopoly: Activities that exhibit economies of scale, or sub-additivity cost structures. Under this market technology, the most cost-efficient provision is reached when provided by a single market player. However, traditional antitrust tools need to be adapted in the case of digital markets. Digital platforms require competition author- ities to tailor their analysis to multisided markets and consider the impact of the control of data in their assessments. Competition authorities, including in India, are adapting their analytical frameworks, and complementing their enforcement tools with ex ante regulation. The new competition law added merger notification thresholds geared to address problematic digital mergers. A Committee on Digital Competition Law (CDCL) has been constituted in 2023 to assess the requirement for a separate competition law in digital markets. IV. Trade and Global Value Chains During 2011-20, India lagged behind its peers in terms of exports as a share of GDP. The share of goods and services exports in GDP increased from 11.5 percent in 2000 to 21.8 percent in 2024, with India’s share in global merchandise exports rising from 0.6 percent to 1.8 percent during the same period.42 Prior to the pandemic, the ratio of goods trade to GDP remained below the ratios registered by countries at similar levels of development (Figure 2.19, Panel A). India’s trade in modern market services (Figure 2.19, Panel B) is a notable exception: export performance has been stronger in services than in goods (Figure 2.20), particularly in areas, such as IT and business process outsourcing (BPO). Services trade has been supported by liberalization of FDI inflows, particularly in computer and IT, banking, insurance, business, R&D, and telecommunications sectors. Telecom services, including computer and information services, and other business services account for over two-thirds of services exports (Figure 2.21). Approximately 75 percent of India’s services export is highly skills-intensive (Figure 2.22). 42 During the same period, the share of China increased from 3.9 percent to 14.7 percent, and that of Viet Nam increased from 0.2 percent to 1.6 percent. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 47 Chapter 2 Accelerating Productivity Growth and Boosting Trade FIGURE 2.19: Trade openness, (2011-20) Panel A: Adjusted trade openness (log of trade in goods to GDP) 1.0 Viet Nam 11/15 Viet Nam 16/20 Thailand 11/15 Cambodia 16/20 Thailand 16/20 0.5 Cambodia 11/15 0.0 -0.5 -1.0 China 16/20 -1.5 India 16/20 Bangladesh 11/15 China 11/15 Bangladesh 16/20 Indonesia 11/15 -2.0 India 11/15 Indonesia 16/20 -2.5 6.0 7.0 8.0 9.0 10.0 11.0 12.0 Level of Development Source: World Integrated Trade Solution (WITS), 2022. Note: Log of openness to trade (goods and services) plotted against log of GDP per-capita (level of development), controlling for the effect of population size. The sample contains all countries with available data in WITS for 2011–20 (the graph shows period averages for 2011-15 and 2016-20). “India 11/15” refers to the average trade to GDP ratio for India over 2011-15, while “India 16/20” refers to the same over 2016-20. Panel B: Trade openness in services, (2018) 25 Traditional Services Modern Services 20 Traditional Services Exports, % of GDP Modern Services Exports, % of GDP 20 15 15 10 10 IND PHL MYS 5 5 IND IDN MYS KOR KOR CHN BRA JPN USA PHL MEX IDN 0 CHN BRA JPN USA 0 MEX 6 8 10 12 6 8 10 12 Level of Development Level of Development Source: Services Competitiveness in India (2018). FIGURE 2.20: Growth in services and goods exports (volume) Services exports (2000=100) Goods exports (2000=100) 1400 1400 1200 1200 1000 1000 800 800 600 600 400 400 200 200 0 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 India China Bangladesh Philippines Viet Nam Source: UNCTAD. 48 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 2 Accelerating Productivity Growth and Boosting Trade FIGURE 2.21: India’s commercial services export FIGURE 2.22: Composition of services exports (share, (sector-wise share) percent) 100% 100 90% 23% 90 27% 80% 80 70% 70 9% 10% 60% 60 14% 14% 50% 50 40% 40 30% 30 20% 47% 44% 20 10% 10 0% 2016 2019 0 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Communication Intellectual Property Construction Transport Travel Other business services Insurance Financial Services High-skilled services exports Low-skilled services exports Source: IMF Balance of Payments Statistics, 2023. Note: The high-skilled services include financial services, pension and insurance services, services pertaining to intellectual property charges, telecom, computer, and information services and, other business services. Low-skilled services are all the other services. FIGURE 2.23: Services: The least and the most restricted sectors 1 1 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 Engineering Computer Road freight Sound Accounting Legal Architecture Rail freight services services transport recording services services services transport Least restrictive Most restrictive STRI score World average Source: OECD (2022). STRI database. Note: Selections was made based on how far the sectors’ score were from the world average score, as a percentage difference i.e. (STRIcountry, sector - STRIworld average, sector)/STRIworld average, sector The relatively better performance of services exports reflects the impact of reforms introduced to reduce restrictions in key sectors. Sectors such as computer services, engineering services, and road freight transport possess the lowest score relative to average Service Trade Restrictiveness Index (STRI)43 scores across all countries (Figure 2.23). These services benefit from relatively low restrictions on foreign entry, movement of people and barriers related to regulatory transparency, that are comparable to the most liberal countries. By contrast, accounting services, legal services, architecture services and rail freight transport have the highest score in India relative to the STRI world average. Across countries, ad valorem equivalents (AVEs) of services barriers, quantified using machine learning techniques and a gravity model estimation,44 are high relative to tariff rates for goods, at 43 The STRI is compiled by the OECD and captures information on trade restrictions across 19 major services sectors. 44 The AVEs were calculated using a gravity model to convert the Services Policy Index (SPI) generated by Hoekman and Shepherd (2021) though machine learning techniques. The model can be considered similar to the standard regression framework, with independent variables (inputs) and a dependent variable (output), with the relationship between the two summarized in a set of coefficients estimated by solving an optimization problem. In this case, the input is the set of policy measures from I-TIP (the World Bank/WTO services policy data), and the output is the SPI. Conversion to AVE terms is standard for non-tariff measures in the literature. However, it involves the simplifying assumption that policies can accurately be summarized through their impacts on trade costs in percentage of value terms. Services policies also have effects on the fixed costs of foreign market entry, which implies that the true restrictiveness of the measures captured in the SPI may be higher than that suggested in Figure 2.23. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 49 Chapter 2 Accelerating Productivity Growth and Boosting Trade FIGURE 2.24: Ad-valorem tariff equivalents of services trade policies index for selected services, India, and comparators (percent) 60 50 40 30 20 10 0 Bangladesh China India Indonesia Malaysia Philippines Singapore Thailand Viet Nam Distribution Transport Source: Estimates using data from Hoekman and Shepherd, 2021. Note: The ad valorem tariff equivalent is the equivalent ad valorem tariff that would restrict trade to the same degree as the bundle of regulatory measures captured by the STRI. Figure 2.24 combines estimates for horizontal measures with sector-specific ones, which are numerous in the database and therefore cannot be presented individually. more than 10 percent in all countries, including in India (Figure 2.24). AVEs are relatively high for sectors where India’s services exports are below peers, such as in distribution (for which the AVE is higher than all peers except China and Indonesia). The main areas relevant for India are visa processing restrictions, policy restrictions related to land access, and some restrictions related to data transfer. India is one of the most diversified goods exporters in terms of both products and markets with “superstar” exporters account- ing for a significant share of exports. India’s top export destinations are the United States, the United Arab Emirates, China, Bangla- desh, Hong Kong SAR China, China, Singapore, the United Kingdom, the Netherlands, and Germany. The largest contribution to merchandise export growth stems from moderate complexity products, such as chemicals and agriculture.45 India has diversified into several new products46 (with predominantly medium and high-technology sophistication) but volumes remain low. India has one of the lowest market concentration indices and exports the largest number of products in absolute terms relative to peer countries (Figure 2.25). At the same time diversification is relatively limited at the firm-level. On average, Indian firms that export, FIGURE 2.25: Export markets and product diversification HH market concentration index Number of exported HS6 products 0.14 5000 0.12 4500 0.1 4000 0.08 3500 0.06 3000 0.04 0.02 2500 0 2000 2011 2012 2013 2014 2015 2016 2017 2018 2019 2011 2012 2013 2014 2015 2016 2017 2018 2019 China India Indonesia Malaysia Phiippines Thailand Viet Nam Source: Manghnani, Winkler, et. al. (2023), WITS. 45 https://atlas.cid.harvard.edu/countries/104/growth-dynamics 46 For example, gas turbines or parts for use of electric generators. 50 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 2 Accelerating Productivity Growth and Boosting Trade FIGURE 2.26: Superstar exporters (percent of total exporters) Panel A: Share of top 1 percent exporters Panel B: Superstars, (percent of export value) 0.61 Bangaldesh 0.24 0.60 India 0.54 0.59 South Asia 0.36 0.58 East Asia & Paci c 0.32 0.57 0.56 Middle East & North Africa 0.36 0.55 Latin America & Caribbean 0.42 0.54 Europe & Central Asia 0.44 0.53 Sub-Saharan Africa 0.41 0.52 2016 2017 2018 2019 2020 2021 Source: World Bank Exporter Dynamics Database, 2022 and India Customs data, Panjiva, 2022. Note: Data pertains to calendar the year. sell fewer products abroad and export lower volumes as compared to firms in countries like Mexico and Türkiye.47 Also, India’s export sector is dominated by a small number of large firms (“superstars”) accounting for approximately 60 percent of total export value (Figure 2.26). In terms of export potential, it is expected that products from sectors such as jewelry, machinery, chemicals, motor vehicles and pharma will continue to be strong performers, benefiting from increasing demand and the growth of the engineering and capital goods sectors (Figure 2.27). FIGURE 2.27: India’s export potential, goods Jewellery & precious metal articles Machinery, electricity Chemicals Motor vehicles & parts Pharmaceutical components Apparel Ferrous metals Plastics & rubber Rice Metal products Metals (except ferrous & precious) Fish & shell sh Electronic equipment Cotton (fabric) Synthetic textile fabric Miscellanous manufactured products Optical products, watches & medical instruments Home textiles Food products n.e.s. (processed or preserved) Footwear 10 bn 20 bn 30 bn 40 bn 50 bn 60 bn Source: Export Potential Map, International Trade Center. Note: The export potential indicator identifies products in which the exporting country has already proven to be internationally competitive, with good prospects of export success in specific target market(s) (intensive product margin). 47 Manghnani, Winkler, et. al. (2023) based on the World Bank Exporter Dynamics Database. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 51 Chapter 2 Accelerating Productivity Growth and Boosting Trade FIGURE 2.28: Global Value Chains, products, and destination Panel A: Exporters with GVC linkages have stronger export performance and are more diversified 18 16 14 12 10 8 6 4 2 0 Non-GVC Firms GVC Firms Non-GVC Firms GVC Firms Non-GVC Firms GVC Firms Average Firm Export Growth Average Number of HS6 Products per Firm Average Number of Destinations per Firm Panel B: Indian exporters with GVC linkages Panel C: Entering in GVCs gives a boost to have lower exit rates from export markets firms’ exports and diversification 0.45 Gains from integrating GVCs 0.4 30 25.77 Increase due to rm integration 0.35 25 0.3 0.25 20 in GVCs (%) 0.2 15 0.15 8.92 10 8.70 0.1 0.05 5 0 Non-GVC Firms GVC Firms Non-GVC Firms GVC Firms 0 Firm export value Number of HS6 Number of Firm Entry Rate Firm Exit Rate products per rm destinations per rm Source: UNCTAD-Eora GVC database. India Customs data, Panjiva 2022. India’s participation in GVC remains relatively low. Integrating into GVCs boosts firms’ exports and promotes product diver- sification. Indian exporters with GVC linkages have stronger export performance and are more diversified in terms of products and destinations (Figure 2.28, Panel A and Panel C). They have lower exit rates from export markets (Figure 2.28, Panel B). India’s participation in GVCs, as measured by GVC participation rate (that includes forward and backward participation in GVCs), declined post-GFC but increased in the post-pandemic years, driven by rising backward linkages; however, it remains lower than that of many peers (Economic Survey, 2023-24). Infrastructure constraints, insufficient access to technology and skills, and barriers to trade (see Box 2.3) are key constraints. Tariff and non-tariff barriers inhibit India’s participation in global merchandise trade. These barriers include a protective tariff regime, and non-tariff barriers — inefficiencies in border processes, red tape and high trade and logistics costs. Despite reductions of applied tariffs, import tariffs remain higher in India than in comparator countries (Figure 2.29, Panel A). Tariffs on raw materials and consumer goods have decreased but import duties on intermediate inputs and capital goods increased prior to the pandemic (Figure 2.29, Panel B). Policies that will help exporters include tariff/value-added tax reductions and duty drawbacks on imported materials. India performs better than peers on the time taken for export-related documentary compliance, but the time taken for border compliance remains elevated and higher than in China or Thailand. The implementation rate of the WTO Trade Facilitation Agreement is high, but lower than in China, Indonesia and Thailand (Figure 2.30). 52 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 2 Accelerating Productivity Growth and Boosting Trade Box 2.3: India’s Participation in Global Value Chains Since the 1990s, rapidly declining trade barriers and technological advancements have led to the surge in fragmentation of production processes, commonly known as Global Value Chains. Even though GVC trade stagnated following the global financial crisis, GVC exports accounted for nearly 70 percent of global exports in 2017. The GVCs offer opportunities for dynamic growth by fostering productivity, promoting technological advancement, and enhancing market accessibility. The GVC trade differs from trade in final goods and services, presenting more opportunities for productivity growth. By participating in GVCs, firms can specialize in the most productive segments of the value chain, benefit from improved technol- ogy and cost-effective inputs, and gain access to global markets. This specialization, in turn, leads to increased productivity and, consequently, higher output per worker. Despite these benefits, India remains a relatively small player in the GVC landscape. In 2018, India’s GVC participation rate was above 40 percent, lower than China, Malaysia, Indonesia, Mexico, and other peer economies (Figure B2.31) and accounted for only 1.7 percent of the world’s GVC exports. FIGURE B2.31: GVC participation rate, (2018) 70% 60% Average GVC Participation rate for emerging economies 50% GVC participation 40% 30% 20% 10% 0% Malaysia South Africa Philippines Türkiye Thailand Indonesia Viet Nam China Mexico India Argentina Bangladesh Source: UNCTAD Eora database, WB staff estimates. While GVC firms make up a small share of manufacturing firms in India, they contribute to nearly 20 percent of the value added in the manufacturing sector, exhibiting higher productivity than non-GVC firms. Shifting from a domestic-only to a GVC firm is associated with an average increase in value added by approximately 250 percent. GVC firms in India’s manufacturing sector also tend to employ more women (Figure B2.32). Boosting GVC integration will require identifying key constraints that hamper participation. India faces several challenges that inhibit GVC integration: • FDI Regulations: Despite significant liberalization, India’s score on the OECD’s FDI Restrictiveness Index indicates room for improvement. Inconsistencies between national and state-level policies, coupled with weak capacity at the state-level to formulate and implement investment policies, are some of the factors that limit the ability to attract efficiency-seeking FDI (see Chapter 3). India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 53 Chapter 2 Accelerating Productivity Growth and Boosting Trade Box 2.3: India’s Participation in Global Value Chains (continuation) • Skill Gap: Although India’s education system is of high FIGURE B2.32: Average female labor intensity and skill quality, the supply of skilled labor is limited, reflecting a intensity of India’s manufacturing workforce (2019) large share of low-skill labor in agriculture. Only 4.7 percent of the workforce has received formal training, significantly 30% lower than other large Asian economies. 25% Female Labor Share (Average) • Trade Barriers: Despite the importance of low tariffs for 20% firms to access imported inputs and capital goods, India’s 15% manufacturing tariffs are among the highest in the world. Additionally, technical barriers to trade reflecting higher 10% technical standards pose an additional challenge. 5% • Infrastructure Deficit: While India’s rail and port quality 0% Domestic Exporter Importer GVC Firms Total fare well, road quality lags many peers. Logistics perfor- Firms Only Firms Only Firms mance reveals several bottlenecks, particularly the quality Source: Manghnani, Winkler, et. al., (2023). of trade and transport infrastructure. Note: Importer-only firm= a firm that imports inputs but does not export. Export- er-only firm=a firm that exports but does not import inputs. GVC firm= a firm that exports and imports inputs. Domestic firms do not export or import. FIGURE 2.29: India’s import tariff profile (percent) Panel A: Trade-weighted average applied import tariff Panel B: Trade-weighted average applied import tariff India by stage of processing, (Percent) 12 12 10 10 8 8 6 6 4 4 2 2 0 0 China India Viet Nam Thailand 2016 2020 2021 2016 Intermediate goods Raw materials Capital goods Consumer goods Source: World Bank– United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP) trade costs database. Note: TFA = Trade Facilitation Agreement; WTO = World Trade Organization. After a pause of almost ten years, India recently signed new bilateral trade agreements to reduce trade barriers, eliminate tariffs and gain preferential access to global markets. It recently entered into bilateral free trade agreements (FTAs) with the UAE and Australia. In addition, deliberations with other countries and regions (UK, Russia, Israel, and the Southern African Customs Union) are ongoing. The new FTAs incorporate lessons from successes and failures of previous ones. While India has benefited substantially from agreements such as the Agreement on South Asian Free Trade Area (SAFTA), the India-ASEAN Comprehensive Economic Cooperation Agreement (CECA) and the India-Korea Comprehensive Economic Partnership Agreement (CEPA), further gains are possible. For example, research on the ASEAN-India FTA finds that trade has increased as a result, but not India’s exports. The study identified rising non-tariff measures as one important impediment to realizing India’s export potential to ASEAN (Khati and Kim, 2022). 54 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 2 Accelerating Productivity Growth and Boosting Trade FIGURE 2.30: Trade facilitation indicators Panel A. Documentary/ border compliance (hours) Panel B. WTO TFA implementation 120 120 100 100 80 80 60 60 40 40 20 20 0 0 India Viet Nam Indonesia Thailand China India Viet Nam Indonesia Thailand China Time to export: Border Time to export: Documentary TFA Implementation rate compliance (hours) compliance (hours) Trade facilitation index Source: World Bank– United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP) trade costs database. Note: TFA = Trade Facilitation Agreement; WTO = World Trade Organization. Regional integration scenarios suggest high potential gains from comprehensive integration scenarios that include trade facilitation, services, and FDI reform. The results of an economy-wide general equilibrium model that explicitly incorporates trade in services suggest India could reap significant benefits from participating in various regional and multilateral integration initiatives. Depending on the level of ambition of the agreements, the estimated increase in GDP is up to 13.6 percent compared to a baseline scenario that involves only bilateral tariff reductions (Figure 2.31). 48 The highest gains are obtained from comprehensive integration scenarios that combine liberalization of tariffs, non-trade barriers, trade facilitation, and FDI barriers. The analysis shows that a regional (South and Southeast Asia) comprehensive integration agenda could boost India’s GDP by 8.4 percent (Scenario 4). If the trade and investment reforms are extended in a multilateral integration involving countries within South and Southeast Asia, India’s GDP could rise by 13.6 percent (Scenario 6). India’s recent focus on bilateral FTAs with Australia, UAE, US, the UK, and the European Union is a step in the right direction as the FTAs aim to secure greater market access as well as high quality imports. FIGURE 2.31: India’s gains from inter-regional and multilateral integration 70 58.8 57.4 60 50 40 36.2 33.8 37.2 34.7 30 22.1 24.8 19.2 16.9 20 13.6 10 7 8.4 7.4 4.6 3.8 3.2 0.2 0.4 0.6 1.1 0 Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6 Scenario 7 GDP Exports Imports Source: World Bank. Note: The results reflect the medium- to long-term effects of policy changes on GDP and exports expressed in constant 2021 terms relative to the baseline once the suggested reforms take effect. 48 See Annex 2.4 for details on the GTAP-FDI model and the simulation scenarios. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 55 Chapter 2 Accelerating Productivity Growth and Boosting Trade Box 2.4: India’s Success in Mobile Phone Exports India’s mobile phone exports has witnessed a remarkable turnaround in recent years. Between 2011 and 2017, exports collapsed by more than 95 percent from USD 3.2 billion to USD 138 million. By 2023 exports increased by more than 50 times to USD 11 billion, while imports declined as well by more than 85 percent from USD 8 billion in 2015 to USD1.4 billion in 2023. As a result, India has turned net exporter of mobile phones since 2020. The government initiated several policy measures in the recent years -- the phased manufacturing plan, the National Electronics Policy 2019, and the PLI scheme for mobiles – which have played a role in the resurgence of the sector (Figure B2.41). India has jumped from being the 17th to 6th largest mobile exporter between 2018 and 2022. Rising mobile exports have helped India recover some of the lost ground in the global market. India’s share in the global exports market have risen from 0.1 percent in 2017 to 2.4 percent in 2022. Vietnam, on the other hand, managed to increase its market share from a meager 0.2 percent in 2009 to 13.2 percent in 2023 to become the second largest global player after China (52 percent). FIGURE B2.41: India has become a net exporter of mobile phones (12 months rolling average) 1,800 1,600 1,400 Phased Import duties National PLI scheme for 1,200 manufacturing on mobile increase Electronics mobiles announced program for to 20% Policy 2019 USD mll 1,000 mobile launches 800 600 400 200 0 Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 Exports Imports Source: UNCOMTRADE, WITS. 56 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 2 Accelerating Productivity Growth and Boosting Trade References Acemoglu, Daron and Ozdaglar, Asuman. 2007. Competition and Efficiency in Congested Markets. Mathematics of Operations Research, Volume 32, No.1. Available at http://www.jstor.org/stable/25151769. Aghion, Philippe; Griffith, Rachel. 2008. 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India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 57 Chapter 2 Accelerating Productivity Growth and Boosting Trade Gallé, J., D. Overbeck, N. Riedel, and T. Seidel. 2022. Place-Based Policies and Structural Change: Evidence from India’s Special Economic Zones. STEG Working Paper available at https://steg.cepr.org/sites/default/files/2022-11/WP040%20GalleOver- beckRiedelSeidel%20PlaceBasedPoliciesAndStructuralChange.pdf Gokarn, K., Tyagi, N., & Tongia, R., 2022. A Granular Comparison of International electricity Prices and Implications for India. New Delhi: Centre for Social and Economic Progress. https://csep.org/wp-content/uploads/2022/06/AGranular-Comparison-of-In- ternational-Electricity.pdf Goswami, A.G., Mattoo, A. and Sáez, S. eds., 2012. Exporting Services: A Developing Country Perspective. World Bank Publications. Goswami, A. G., P. Gupta, and A. Mattoo. 2012. A Cross Country Analysis of Service Export: Lessons from India. 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The ‘Twin Balance Sheet’ problem has now been addressed and stress in the NBFC sector has been contained with timely interventions and regulatory reforms introduced by the RBI and the government. Thus, there is a sound basis for a sustained credit expansion. The government has also prioritized public capital spending to crowd in private investment. India has attracted foreign investment to complement domestic invest- ments; but while it is a top global FDI destination in absolute amounts, net FDI is modest as a share of GDP. To sustainably boost private investment, India can continue to reduce regulatory barriers to FDI, deepen the corporate bond market to attract long-term financing for critical investments such as infrastructure, strengthen the resilience of the financial system and the incentives for a more efficient allocation of credit, and expand financing for NBFCs as well as small firms. Human capital also needs to improve. India has made steady gains in primary and tertiary school enrolment. Progress in secondary school enrolment has been slower. The COVID-19 pandemic resulted in a significant loss of learning opportunities and exacerbated some of the existing weaknesses in education outcomes. Given budget constraints and rigidities at the state level, it will be critical to increase the efficiency of education and health sector spending. Significant heterogeneities among the states regarding the efficiency of such expenditures imply there is significant scope to replicate successful experiences in relatively lagging states. I. Investment and the Financial Sector Investment A.  India’s investment rate has rebounded since 2021, but it remains below the levels required to achieve 2047 targets. From 2000 to 2008, gross fixed capital formation, in nominal terms, increased by 8.3 percentage points of GDP, with private investment contributing 7 percentage points to the increase. In the wake of the GFC, investment to GDP fell from 35.8 percent in 2008 to 28.5 percent in 2020, driven by a 5.5 percentage points decline in private investment.49 Over this period, Viet Nam, South Africa, and Thailand also experienced a decline—of 4.7, 3.6 and 2.9 percentage points respectively (Figure 3.1)— while investment increased in China, the Philippines, and Indo- nesia. In 2024, India’s investment rate had recovered to 30.8 percent, reflecting government policies to boost infrastructure and logistics. India’s current investment rate is higher than that of most peers—except China—but the decline prior to the pandemic occurred at an early stage of development compared to other transition economies. India’s investment rate is currently higher than in Thailand, Indonesia, Malaysia, and Brazil. However, put in “development time” perspective, other emerging MICs (such as China and Thailand) and HICs (such as Korea, and Singapore) sustained higher investment rates when they were at India’s current levels of income. India’s East Asian peers experienced a sustained rise in their investment to GDP ratios in the 25 years prior to their transition to upper middle/high income status (Figure 3.2) and a steadily rising share of manufacturing in GDP (Figure 3.3). In India, policy uncertainty and financial sector weaknesses depressed private corporate investment following the GFC. Even though India’s growth recovered strongly from the GFC in 2009, the subsequent years (2010-13) were characterized by substan- tial policy and regulatory uncertainty, related to tax policy announcements and delayed approvals for projects (as government decisions came under increased scrutiny following controversies related to the auction of coal and telecom spectrum). Addition- ally, supply bottlenecks in the mining and power sectors constrained investment (Anand and Tulin, 2014). The pick-up in private corporate investment between 2017 and 2019, triggered by new measures to expedite project implementation, was cut short by intensifying financial sector weaknesses. Increasing corporate indebtedness coupled with rising NPAs in the balance sheets of public sector banks 2016 onwards, led to the emergence of the TBS problem (see subsection B). As a result, the contribution of private non-financial corporations in fixed capital formation, on average, halved over the four years prior to the advent of COVID- 19 (2017-20) compared to the four-year period prior (2013-16). 49 In real terms, investment growth slowed from an average of 10.1 percent over 2001-2011 to 6.6 percent over 2012-20. 60 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 3 Expanding Investments in Physical and Human Capital FIGURE 3.1: Gross fixed capital formation, (nominal, percent of GDP) India India and peers 40.0 50 35.8 35.0 40 30.0 27.5 25.0 30 20.0 15.0 20 10.0 5.0 10 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 0.0 Brazil China India 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 Indonesia Malaysia Philippines Public Private Total Viet Nam Thailand South Africa Source: MOSPI and WDI (2022). FIGURE 3.2: Investment rate and per-capita income FIGURE 3.3: Manufacturing share and per-capita income 50 50 45 45 40 40 Manufacturing (% of GDP) Investment (% of GDP) 35 35 30 30 25 25 20 20 15 15 10 10 2 2.5 3 3.5 4 4.5 5 2 2.5 3 3.5 4 4.5 5 log per-capita GNI (Atlas method) log per-capita GNI (Atlas method) Korea Singapore India China Thailand Malaysia Source: WDI (2022), WB staff calculations. Note: The period is 1972-2019: Countries are selected based on data availability. The contribution of the household sector in investment remained weak prior to the pandemic.50 The share of households in investment increased between 2008 and 2012 as the economy grew rapidly and higher social welfare spending boosted dispos- able incomes and household savings. However, it fell from 2012 onwards as growth slowed, household savings declined,51 and fiscal consolidation resumed (Sahoo and Bishnoi, 2022) (Figure 3.4 and Annex 3.1). Thus, the contribution of the household sector to investment growth remained mostly weak in recent years, except during 2017-19, and turned negative in 2020 (Figure 3.5).52 50 Households are the most important source for financing of investment, accounting for nearly 60 percent of gross capital formation and 40 percent of gross fixed capital formation during 2012-2020. 51 Household savings declined by approximately 7 percentage points of GDP between 2012 and 2016. 52 Household investment consists mainly of the purchase and renovation of dwellings. Consumer durables (which include passenger cars) are not considered as part of household investment. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 61 Chapter 3 Expanding Investments in Physical and Human Capital FIGURE 3.4: Sectoral share in GFCF FIGURE 3.5: Sector-wise contribution in GFCF growth (percent of current prices) (percentage points for GFCF in constant prices) 100 20 90 15 80 70 10 60 5 50 40 0 30 -5 20 -10 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 10 0 Household Sector General Government 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Private Financial Corporations Public Financial Corporations Private Non-Financial Corporations Public Non-Financial Corporations Public sector Household sector Private corporate sector GFCF Source: MOSPI and World Bank staff calculations. Public capital expenditure has remained modest in India. Over the past two decades, the share of central government capital expenditure has remained around 2.2 percent of GDP. On average, current (or revenue) expenditure accounted for more than 80 percent of total central government spending over 2000-20 (Figure 3.6). Compared to current expenditure, capital expenditure has a greater impact on growth. The current expenditure multiplier—defined as the estimated unit increase in GDP for each unit increase in the current expenditure— is estimated to be less than one. By contrast, GDP is estimated to rise by 2.8 to 4.1 rupees for every one rupee increase in the central government capital expenditure (Figure 3.7) (Box 3.1). Public investment also crowds in private investment and is associated with higher formal manufacturing employment and real wages, although with a lag of a couple of years. (Figure 3.8). Similarly, the analysis in Chapter 5 shows that states could raise output by increasing investment in urban development, agriculture and allied services, and transport. FIGURE 3.6: Expenditure (central government, percent FIGURE 3.7: Estimated impact of public investment of GDP) on real GDP 20.0 1.4 1.2 1.0 15.0 0.8 0.6 0.6 0.6 0.4 10.0 0.4 0.3 0.2 5.0 0.0 -0.2 -0.4 0.0 -0.6 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 1 2 3 4 5 6 7 8 9 10 Interest payments Subsidies Capital outlay Quarters Other current expenditure Loans and advances 90% con dence interval 62 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 3 Expanding Investments in Physical and Human Capital Box 3.1: The Public Investment Multiplier in India The public investment multiplier in India is much larger than for current expenditures (Table B3.21). Comparing the multipliers of current and capital expenditures is important as they can have varying effects on the economy, and policymakers can use this comparison to allocate resources efficiently. Jain and Kumar (2013) find that capital outlay (for both central and state government) is more growth-inducing with a multiplier effect which continues up to four years, while the impact of current expenditures dissipate immediately after the first year.53 They also find that the multiplier effect for all expenditure categories by the central government is lower than that of the state governments. Bose and Bhanumurthy (2015) find a large capital expenditure multiplier of 2.5 as compared to a current spending multiplier of 1.54 Goyal and Sharma (2018) estimate that the central government capex multiplier is 2.4 to 6.5 times the size of the current expenditure multiplier.55 RBI (2019) finds that the current expenditure multipliers are less than unity while the capital expenditure multiplier is 3.25 for the central government and 2 for the state governments. Mishra (2019) also finds the multiplier effect of states’ capital outlay on income to be greater than that of current expenditure (in both the short and long run).56 TABLE B3.21: Estimates of the central government’s fiscal multipliers WBG Staff estimates Bose, 2014* Goyal, 2018** RBI, 2019*** Capital expenditure 2.8 to 4.1# 4.8 0.74 to 4.02 3.25 Current (or revenue) expenditure 0.95-0.96 0.4 to 1.69 0.45 Note: # Range is based on different model specifications, *Cumulative multipliers for over a period of 7 years, ** Cumulative multipliers for over a period of 2 years, ***Peak multipliers. The contribution of ICT capital in investment is rising, with positive impacts on growth. ICT adoption can boost innovation and improve firm productivity by increasing the efficiency of physical capital. Using firm level ASI data, Sharma and Singh (2012) find some evidence that manufacturing plants with higher GVA tend to have higher levels of ICT capital stock, controlling for other inputs. Estimating the aggregate effects of ICT adoption on growth is difficult given the paucity of data. Using the methodology outlined in Erumban and Das (2020) with minor modifications, and assuming that ICT capital has two components— software, and hardware— it is estimated that the share of ICT investment in total investment averaged 14.5 percent over the past decade (Figure 3.9) (see Annex 3.2 for the methodology). Its contribution to growth increased to 0.35 percentage points in 2017 (Figure 3.10). Software investment was the main driver of the increase, accounting for approximately 70 percent of total ICT investment. India is among the top-10 global FDI destinations, but the share of net FDI in GDP has remained subdued despite substantial liberalization. In 2022, India was among the top-5 recipients of international greenfield projects in US dollar terms.57 In recent years, it has also emerged as one of the top-5 destinations for FDI in sectors such as renewable energy. However, as a share of GDP, India’s performance is modest; FDI to GDP averaged 1.7 percent over during 2011-20, up only marginally from 1.5 percent during 2000-10 (Figure 3.11). The volume of greenfield FDI flows in services (in US dollars) increased from 2008-12 to 2013-18, but greenfield FDI in the manufacturing sector declined. According to the OECD FDI Regulatory Restrictiveness Index, regulatory restrictions were halved over 2003-20, with the greatest easing occurring in the services subsectors (broadcasting, media, retail, communications, and telecommunications). Improvements notwithstanding, restrictions on FDI still remain relatively high in India in a cross-country perspective, although lower than China, Indonesia, Malaysia, the Philippines, and Thailand. Most restrictions are 53 The size of capital outlay multiplier at state level is 2.13, significantly higher than 0.39 estimated for the central government. 54 They use a structural macroeconomic model based on annual data from 1991 to 2012. 55 They use a Structural Vector Auto Regression (VAR) approach with quarterly data from 1998-Q1 to 2014-Q2. 56 Analysis is based on the state-level panel data from 2002 to 2014. 57 UNCTAD, “World Investment Report 2023: Investment and Sustainable Energy”, p. 8. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 63 Chapter 3 Expanding Investments in Physical and Human Capital FIGURE 3.8: Estimated impact of public investment on capital stock, employment, and wages A. Impact of a rupee increase B. Impact of a 1 percent shock C. Impact of a 1 percent shock in development capital outlay to development capital to development capital outlay on states’ capital stock outlay on development on real wages per-capita Real investment, INR Employment, percent Real wages per-capita, percent 2.0 5.0 6.0 1.5 4.0 5.0 3.0 4.0 1.0 3.0 2.0 0.5 2.0 1.0 0.0 1.0 0.0 0.0 -0.5 -1.0 -1.0 -1.0 -2.0 -2.0 t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8 t+9 t+10 t+1 t+2 t+3 t+4 t+5 t+6 t+1 t+2 t+3 t+4 t+5 t+6 Years Years Years 90% con dence intervals Source: WB staff calculations. Note: State’s capital stock is computed using data from the Annual Survey of Industries (ASI). The horizontal axis denotes “years”. Based on a panel local projection model including state taxes, GDP deflator, state output gap, state revenue expenditure, a dummy for the GFC, and a lag of the variable of interest. The findings are robust to GMM estimation. The estimates indicate that development capital outlays lead to a statistically significant increase in the private sector capital stock in states, and hence net private investment, but with a significant lag (Panel A). Such outlays are also associated with an increase in wages: a 1.0 percentage point increase leading to a 1.8 percent increase in real wages per worker after four years, with the impact insignificant in the initial years (Panel C). Formal sector manufacturing employment increases by 1 percent in the first year on higher capital outlay, and the impact is particularly strong after five years as private investment increases (Panel B). FIGURE 3.9: Share of ICT investment in total investment FIGURE 3.10: Growth decomposition: ICT versus (percent) non-ICT investment (percentage points) 19 9 8 17 7 15 6 13 5 11 4 3 9 2 7 1 5 0 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2012 2013 2014 2015 2016 2017 2018 2019 2020 Other Non-ICT ICT Source: LTGM, World Bank. Note: Other includes contribution of TFP, LFPR, working-age population ratio, and population growth. in the form of equity restrictions (see Box 3.2). In recent years, FDI equity caps have been liberalized in several sectors: in 2021, the FDI cap in the insurance sector was increased from 49 to 74 percent through the automatic route for insurance companies58 and to 100 percent for insurance intermediaries; the Union Budget 2025-26 announced that the FDI limit for the insurance sector will be raised from 74 to 100 percent with the enhanced limit available for companies which invest the entire premium in India. However, equity restrictions persist in several sectors, including legal and accounting services, real estate, and business services (Figure 3.12). 58 FDI in India is regulated through two routes –automatic route and approval route. The automatic route involves more liberalized regulation in that the foreign investor, or the Indian company does not require RBI or Government approval. 64 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 3 Expanding Investments in Physical and Human Capital FIGURE 3.11: Net FDI inflows as a share of GDP 12 10 8 6 4 2 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 Brazil China India Türkiye Viet Nam Philippines Source: Manghnani, Winkler, et. al. (2023), WDI. FIGURE 3.12: FDI equity restrictions, India, China, and Vietnam (2020) Real estate investment Engineering Architectural Accounting & audit Legal Business services Other nance Insurance Banking Financial services Mobile telecoms Fixed telecoms Communications Media Hotels & restaurants Air Maritime Surface Transport Retail Wholesale Distribution Construction Electricity Viet Nam India China Transport equipment Electric, Electronics and other instruments Metals, machinery and other minerals Oil ref. & Chemicals Food and other Manufacturing Fisheries Forestry Agriculture 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Source: OECD, 2020. Note: The FDI Index gauges the restrictiveness of FDI rules according to four main types of restrictions: (i) limitations in foreign equity limitations; (ii) screening or approval mechanisms; (ii) restrictions on the employment of foreigners as key personnel; and (iv) operational restrictions (for example, restrictions on branching and capital repatriation or land ownership). India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 65 Chapter 3 Expanding Investments in Physical and Human Capital Box 3.2: FDI in India FDI in India is regulated by codified foreign exchange laws, sector specific laws and government policies, and international agree- ments (Figure B3.21). FIGURE B3.21: Framework for foreign investment regulation Central • Foreign Exchange Management Act, 1999, as amended (FEMA). • Foreign Exchange Management (Non-debt Instruments) Rules, 2019 (FEM[NDI] Rules). • FDI Policy Legal Framework States • State Laws • International Agreements and Committments (WTO TRIMS, BITs, TIPs, other IIAs). • Department of Economic A airs • Department for the Promotion of Industry and Internal Trade (DPIIT) • Directorate of Enforcement (ED) Key Insitutions • RBI • Line Ministries. Invest India Investment • Mandated to lead national investment promotion as single point of contact for foreign investors Promotion • Autonomous, following private-sector mindset and a consulting-style operational model. India maintains a list of sectors in which FDI is allowed with conditions. According to the OECD index of FDI regulatory restrictive- ness, India’s FDI restrictiveness in 2020 was lower compared to peers such as China, Malaysia, Indonesia, and the Philippines, but higher than Singapore, Korea, Viet Nam, and the OECD average. The most prominent FDI restrictions in India exist in the form of limitations to “FDI equity”, in line with many other countries but in contrast to Mexico and Brazil (Figure B3.22). In some cases, 100 percent FDI is permitted in a sector, but sectoral laws and regulations impose conditions that make it commercially infeasible to invest in the sector. FIGURE B3.22: OECD FDI regulatory restrictiveness by type 0.4 0.4 0.35 0.35 0.3 0.3 0.25 0.25 Scale: 0 to 1 0.2 0.2 0.15 0.15 0.1 0.1 0.05 0.05 0 0 am a d sia ina ia o zil ye esi xic an Ind Bra rki lay tN Ch ail on Me Tü K m Ma Vie Th Ca apan g ia CD re My avge So iet N ar Au orea n-O Br lia Da i e Ne I os Ze a Ch d Ma ina Th ysia Ind and ilip sia es Ind EC une vg w ndi n uth a Sin od OE apo V m pin La a ala Ph one str ail la an mb J Equity restriction Other restrictions Key foreign personnel Screening and approval No Source: OECD 2020. Note: Higher FDI index values correspond to higher restrictiveness. 66 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 3 Expanding Investments in Physical and Human Capital FIGURE 3.13: Reasons claimed for investment decrease (left) and increase (right) among MNE affiliates in India Shifting closer to Operations shift to consumers (nearshoring) lower-cost countries Operations shift to nal Operations shift to nal consumers (reshoring) consumers (reshoring) Total investment is Parent seeks country changing diversi cation Changing Out-Insourcing Changing Out-Insourcing strategies strategies Operations shifts to more Operations shifts to more promising markets promising markets Operations shift to Total investment lower-cost countries is changing Local regulatory Shifting closer to consumers environment has changed (nearshoring) Parent seeks country Local regulatory diversi cation environment has changed 0 10 20 30 40 50 60 70 80 0 20 40 60 80 Source: Manghnani, Winkler, et. al. (2023); Note: calculated from MNE Pulse Survey, World Bank. Country diversification, the local regulatory environment (entry barriers), and nearshoring are key determinants of investments by multinational enterprises (MNEs) in India (Figure 3.13), according to the World Bank’s MNE Pulse Survey. FDI entry restrictions have been gradually relaxed, but affiliates of MNEs in India still identify entry barriers reductions as most critical for expanding their investments. In other MICs, financial and tax incentives are more important (Manghnani, Winkler, et. al., 2023). At the firm-level, a higher probability of investment is associated with trade and the adoption of innovation, digitalization, and green practices. Firms that introduce product or process innovations have a higher probability to invest (Figure 3.14A). Firms with better digitalization profiles, and those engaged in import and export activities are more likely to make fixed asset purchases (Figure 3.14B and Figure 3.14C). The adoption of green practices is positively associated with investment as monitoring emissions and adopting measures to reduce carbon emissions require changes in the production technology and service delivery, and therefore require investments (Figure 3.14D). The green or RECP (resource efficient and cleaner production) practices can lower operational costs of production through more efficient use of energy and other inputs. Also, specific management practices—particularly, machinery and equipment upgrading and energy management— are associated with higher investments. Financial sector B.  From 2015 to 2020, non-banks were the main source of finance to the commercial sector (compared to banks), but this reversed after pandemic. The share of banks in the flow of financial resources to the commercial sector declined from 51 percent on average over 2012-16 to 42 percent over 2017-20. The flipside was an increase in the shares of (i) NBFCs (including housing finance companies [HFCs]), (ii) capital raised from financial markets, and (iii) foreign resources59 (Figure 3.15). However, the share of the banking sector has increased again in the wake of the pandemic reflecting their improving financial positions and with banks serving as intermediaries for a variety of COVID-related credit support. 59 Includes FDI, external commercial borrowings and short-term trade credits from abroad. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 67 Chapter 3 Expanding Investments in Physical and Human Capital FIGURE 3.14: Probability of firm-level investment based on innovation, digitization, trade behavior and green practices, 2022 A. Innovation B. Digitization Technology licensed from foreign companies Internationally recognized Have own website (y/n) quality certi cation Introduced a new Proportion of sales product/service through internet Introduced a process innovation Introduced a product or Formal element of lean process innovation manufacturing/operations Spent on R&D -0.5 0 0.5 .1 0 .02 .04 .06 .08 .1 Coe cient in regressions on: Coe cient in regressions on: buying xed assets buying xed assets C. Trade Behavior D. Green Practices Export-only monitor own energy consumption adopted heating and cooling improvements made machinery and equipment upgrades Import-only adopted energy management adopted waste minimization, recycling and waste management adopted air pollution control measures Both adopted water management made upgrades of vehicles Either adopted improvements to lighting systems adopted other pollution control measures -0.5 0 0.5 .1 0 .02 .04 .06 .08 .1 Coe cient in regressions on: buying xed assets Coe cient in regressions on: buying xed assets Source: Karalashvili, Manghnani and Muzi (2023). Note: Each bar represents the marginal effect of the variable from a separate logit regression using the WB Enterprises Survey, 2022. NBFCs have gained prominence in the financial system. NBFCs complement banks by extending innovative financial services to small-scale industries and the retail sectors that tend to be underserved by commercial banks. The size of the sector, as a share of GDP, increased from 18 percent in 2002 to over 60 percent in 2020 (RBI, 2022). NBFCs have been progressively subjected to more stringent regulations with the inclusion of HFCs under the RBI’s regulatory purview in 2019 and the introduction of a “scale-based” regulatory framework in 2022. Following the credit boom of the mid-2000s and the GFC, the TBS problem constrained credit. The credit boom in the mid-2000s, driven by private investment financed by credit from public sector banks (PSBs), was characterized by weak appraisal processes. As a result, many corporate projects became unviable as the GFC unfolded. Corporate balance sheets came under stress as earnings prospects turned uncertain, and the debt of the top 10 stressed corporates increased by approximately threefold between 2010 and 2016 (Economic Survey 2016-17, Volume I, Chapter 4). This stress was eventually reflected as NPAs in bank balance sheets 68 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 3 Expanding Investments in Physical and Human Capital FIGURE 3.15: Share of sectors in the total flow of resources to the financial sector (percent) 100 90 80 70 60 50 40 30 20 10 0 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 (10) Adjuted non-food bank credit Housing Finance Companies Public & rights issues by non- nancial entities Other domestic sources Gross private placements by non- nancial entities Foreign sources NBFCs Source: RBI. (especially PSBs) and led to the TBS problem (Economic Survey 2016-17, Felman and Subramanian, 2019). The magnitude of the problem was explicitly recognized only following the Asset Quality Review (AQR) of banks in 2016 (RBI, 2020) (Figure 3.16). With recognition, the NPA ratio increased, and the focus of regulatory policy shifted from recognition to resolution, with the Insolvency and Bankruptcy Code (IBC) 2016 introduced to address the issue. The TBS problem disrupted the flow of bank credit to industry and led to a change in credit composition. Even though explicit recognition of bank NPAs occurred only in 2016, the GFC-induced stress played out from 2012 onward. Growth in bank credit to industry started to decline significantly, and the composition shifted towards personal and NBFC loans. With the recognition of NPAs in 2016, bank credit to industry dried up further as debt-saddled corporates scaled back on investments and banks became increasingly averse to extending new loans. Over this period, the exposure of banks to the retail sector and NBFCs increased from 23.7 percent of gross non-food credit in 2012 to 28 percent in 2018 and 35.6 percent in 2020. Bank credit to NBFCs increased sharply following the AQR, as banks became increasingly averse to lending to corporates (Figure 3.17). Steady fund flows from banks and FIGURE 3.16: Gross NPA, banks FIGURE 3.17: Credit growth, banks (percent of gross advances) (nominal percent, year-on-year) 16 90.0 14 80.0 70.0 IL&FS default COVID-19 12 RBI AQR 60.0 10 50.0 8 40.0 6 30.0 4 20.0 2 10.0 0 0.0 -10.0 00 02 04 06 08 10 12 14 16 18 20 22 24 20 20 20 20 20 20 20 20 20 20 20 20 20 De -04 De -06 De -08 De -10 De -12 De -14 De -16 De -18 De -20 De -22 4 Au -04 Au -06 Au -08 Au -10 Au -12 Au -14 Au -16 Au -18 Au -20 Au -22 Ap 5 Ap -07 Ap 9 Ap 1 Ap -13 Ap 5 Ap 7 Ap -19 Ap -21 Ap 3 r-2 g-0 g-0 g-1 g-1 g-1 g-2 r r r r r r r r r r c c c c c c c c c c g g g g Ap Scheduled Commercial banks Public Sector Banks Total non-food Industry Personal Loans NBFCs Source: RBI. The NBFCs and personal loans are included in bank credit to the services sector. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 69 Chapter 3 Expanding Investments in Physical and Human Capital FIGURE 3.18: Profitability indicators, banks (percent) FIGURE 3.19: Profitability indicators, listed companies (2020=100) 20 1.5 250.0 15 1 200.0 10 5 0.5 150.0 0 -5 0 100.0 -10 -0.5 50.0 -15 -20 -1 0.0 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Q1 2020 Q2 2020 Q3 2020 Q4 2020 Q1 2021 Q2 2021 Q3 2021 Q4 2021 Q1 2022 Q2 2022 Q3 2022 Q4 2022 Q1 2023 Q2 2023 Q3 2023 Q4 2023 Q1 2024 Q2 2024 Q3 2024 Q4 2024 Q1 2025 Q2 2025 -50.0 Return on Equity (RoE) Return on Assets (RoA) (RHS) Net Pro ts Sales EBITDA Source: RBI. mutual funds to NBFCs continued up to 2018 when the default of a systemically important NBFC (the Infrastructure Leasing & Financial Services [IL&FS]) subjected the NBFC segment to considerable stress. Bank and corporate balance sheets have improved since the pandemic, as reflected in the sustained decline of NPAs and improved corporate profitability. Bank NPAs have declined since 2018 with the partial resolution and write-offs of stressed assets coupled with minimal fresh slippages over the pandemic years (given that the exposure of industry to bank credit was already low at the start of the pandemic). Bank profitability—measured by the Return on Assets (RoA) and the Return on Equity (RoE)— increased in 2021 and 2022 on the back of improved profit after tax (PAT) ratios of PSBs (Figure 3.18). Banks have also bolstered their capital base. Profitability of corporates have improved on the back of increasing sales and improved earnings (Figure 3.19). The effect of NBFC stress on the banking system was largely contained. Despite the increased exposure of the banking system to NBFCs, spillovers to the banking sector were limited as only the healthier NBFCs were able to access long-term sources of fund- ing (see Annex 3.3). Also, following the IL&FS crisis, cancellations of NBFC licenses increased by more than 800 percent in 2019. NPAs of NBFCs increased sharply in 2020 and 2021 but they have been declining in recent years, and the sector remains well capitalized. Gross NPAs of NBFCs and HFCs declined steadily between 2012 and 2019 but increased sharply in 2020 and 2021. Gross NPA levels were lower in HFCs, reflecting lower default rates in mortgage loans. The rise in the gross NPA ratio was accompanied by a rise in the capital adequacy ratio (CAR) in 2020 and 2021 (Figure 3.20), owing to the regulatory measures introduced by the RBI and the government that ensured the flow of credit, especially to the highly rated NBFCs, and revived investor confidence in the sector (Annex 3.4 outlines the key measures). The continued decline in banking sector NPAs and improving corporate balance sheets have reduced macroeconomic risks. Financial systems are prone to cycles—peaks and troughs in credit—with attendant implications for the real sector. Cross country econometric evidence on credit cycles shows that a peak in the credit cycle that is accompanied by an increase in NPAs are associated with undesirable macroeconomic outcomes (see Annex 3.5). The recovery in investment and GDP growth to reach their pre-peak highs after such a “bad peak” can typically take a significant amount of time. Thus, the sustained decline in banking sector NPAs since 2018 implies that the probability of bad macroeconomic outcomes has declined significantly. However, addressing remaining regulatory gaps in the financial sector would support a sustained recovery in credit and private investment. With the easing of the TBS problem, bank credit has increased in the aftermath of the pandemic. However, the recovery has been largely driven by personal loans and services. Cross-country analysis also indicates that the length of credit cycle—defined as the time period between one peak 70 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 3 Expanding Investments in Physical and Human Capital in the level of credit to another— has shortened in India over FIGURE 3.20: Gross NPA and capital adequacy ratio, the past decade (see Annex 3.5).60 Structural reforms that stem NBFCs (percent) the emergence of future NPAs and minimize incentives for 30 9 the “evergreening” of bad loans can facilitate a more efficient allocation of credit and create the conditions for a sustained 8 25 expansion of credit in the economy (see Chapter 6). 7 20 6 A deep corporate bond market is critical for infrastruc- 5 ture investments. The Government of India has laid out 15 4 an ambitious target of more than USD 1.4 trillion of infra- 10 3 structure investments under the National Infrastructure Pipeline. Because the banking sector’s appetite to lend for 2 5 long-term capital investments is constrained by asset-liabil- 1 ity mismatches61, a well-developed corporate bond market 0 0 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 is a critical complement. A market-based source of finance, Gross NPA (RHS) Capital adequacy ratio such as the CBM, is more effective in dissipating risk across a wider category of investors and thereby contributing to Source: RBI. overall financial stability. Box 3.3 outlines the status of the CBM in India. II. Human Capital and Efficiency of Education and Health Expenditures Human Capital A.  India’s per-capita human capital index has improved since FIGURE 3.21: Per-capita human capital index the early 1980s (Figure 3.21),62 thanks to increases of the years 3.0 enrolled in primary and tertiary education. However, progress in secondary school enrollment has been weaker (Figure 3.22): 2.5 the share of adults without upper secondary education is 71 2.0 percent in India, compared to 36 percent, on average, across G20 countries. To address this issue, the Government of India 1.5 has implemented initiatives, such as the National Education Policy (NEP) in 2020 and the Skill India Mission.63 The NEP 1.0 focuses on lifelong learning, seeking to provide quality educa- 0.5 tion and learning opportunities for all and aims to raise the 1950 1954 1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010 2014 2018 Gross Enrolment Ratio (GER) to 100 percent from preschool India China Indonesia to secondary levels, and to 50 percent in higher education by 2035. The NEP’s impact is noticeable as the GER has increased Source: PWT 10.01. steadily over the last two years. However, an even more rapid 60 The credit cycle has also shortened in other countries, but it has been more pronounced in India. The shortening of the credit cycle implies that an increase in credit can ebb within a relatively short period of time. 61 Long term deposits – over the maturity of five years are just about 15 percent of the total deposits with the Indian banks, and deposits with maturity of less than two years are nearly two thirds of the total term deposits. Thus, the banking sector liabilities are predominantly short term relative to the maturity of general loans extended for long-term capital investments (Data on maturity profile of deposits is from: Report of the Committee on the Development of Housing Finance Securitisation Market, RBI, 2019). 62 The index is based on the average years of schooling derived from Barro and Lee (2013) and an assumed rate of return to education, based on the estimation of a Mincer equation in Psacharopoulos (1994). For details, please refer to https://www.rug.nl/ggdc/docs/human_capital_in_pwt_90.pdf 63 According to the Human Development Report 2020, the government has made remarkable progress with the launch of the Skill India Mission 2014 and has successfully increased the skilled labour force to 21.2 percent in 2020. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 71 Chapter 3 Expanding Investments in Physical and Human Capital Box 3.3: The Corporate Bond Market in India Realizing its importance, regulators have been taking many steps to develop the CBM in India. Regulators have been strengthen- ing market infrastructure and have undertaken regulatory reforms, including: (i) the introduction of an electronic bidding platform (EBP) to bring efficiency and transparency in price discovery for primary issuances; (ii) making it mandatory for the large borrowers to raise a share (approximately, 50 percent) of their incremental borrowings through market instruments; (iii) permitting banks to provide partial credit enhancement to incentivize a larger investor base; (iv) introducing request for quote (RFQ) platforms to improve price discovery; and (v) liberalizing the investment limits of institutional investors. These steps have been instrumental in improving the mobilization of resources through the CBM. The outstanding stock of corporate bonds increased four-fold from USD 127 billion (INR 10.5 trillion) in 2012 to USD 487 billion (INR 40.2 trillion) in 2022. Annual issuances during this period increased from USD 46 billion (INR 3.8 trillion) to close to USD 72 billion (INR 6.0 trillion). Relative to bank credit, there has been a much faster growth in the amount of resources mobilized through the CBM. While the outstanding credit mobilized through the banking system is higher than that mobilized through the CBM, the share of the CBM has increased rapidly. During the last ten years, the stock of corporate bonds has increased four-fold (Figure B3.31). Compared to the equity market, funds mobilized through the debt market was almost 2.5 times in 2022. FIGURE B3.31: Bank credit and corporate bonds (INR trillion) 70 61.7 60 56.2 57.1 52.2 50 44.8 47.5 40.7 42.7 40.2 40 38.5 33.8 35.1 29.6 30.7 32.5 30 25 24 27.4 17.5 20.2 20 14.7 10.5 12.9 10 8.5 0 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Credit outstanding (Industry+Services) Outstanding corporate bonds Source: RBI, SEBI, CEIC, Bank of Baroda Research. Note: Data for credit up to July 2022, and for corporate bonds up to June 2022. Despite a substantial increase, the market remains small and poorly diversified. There is scope to undertake further measures to make the CBM more vibrant. Some of the major characteristics are outlined below: • The market is relatively small compared to peer countries. This is so both as a share of GDP as well as a percentage of bank deposits. The outstanding amount in CBs in local currency represented approximately 16 percent of GDP compared to 56 percent in Malaysia, 36 percent in China, 24 percent in Thailand, and 85 percent in South Korea. India’s CBM as a share of corporate bank loan market is approximately 33 percent, compared to 97 percent in Malaysia; 63 percent in China, 40 percent in Singapore, and 79 percent in South Korea. • Issuances are highly concentrated by sectors and ratings. Issuances are dominated by financial sector entities (~50 percent) and public sector companies (utilities), as well as AAA- and AA credits. While many EMs have similar characteristics, in some peer countries CBMs have been more accessible to lower rated issuers and from non-financial sectors. In fact, the ability of issuers in rated categories below AAA or AA- to access bond markets indicates the appetite of investors to diversify their investments and invest in lower rated bonds. It is also partly a reflection of the ability (or challenge) of issuers in the lower 72 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 3 Expanding Investments in Physical and Human Capital Box 3.3: The Corporate Bond Market in India (continuation) rated categories to tap the CBM. For example, 57 percent of the corporate debt issuers in the USA in early 2019 were entities below investment grade. There are successful examples in EMs as well. In Thailand, the share of AAA and AA rated CBs is just 20 percent, with approximately 80 percent of the issuances from A and BBB categories. • Issuances are concentrated in relatively short tenors. Approximately 65 percent of the issues in India are up to 5-year maturity, and 90 percent are up to 10-year maturity. In comparison, the average tenor of CBs in Malaysia is 9.3 years, reflect- ing the sizable portion of its market represented by long-dated project-finance infrastructure bonds. The Philippines also managed to develop the market for medium to long-term (LT) tenors. In some of the advanced economies like Japan, the average maturity in 2019 was approximately 10 years. The average original maturity of investment grade (IG) CBs issues globally has been increasing in the recent years and currently stands at approximately 12 years. • The investor base is mostly confined to institutional FIGURE B3.32: Corporate Bonds Turnover Ratio (Annual investors. The investor base is dominated by large insti- Trading) tutional investors. Individual investors hold only approx- 100 imately 3 percent of the total market share. While this 90 87 is broadly in line with other EMEs, some peer countries 80 such as Thailand have been successful in attracting other 70 types of investors to the market, such as high net-worth 60 60 individuals. 50 48 40 38 • The market is relatively liquid compared to peers. In 29 30 general, the lack of liquidity of the CBs is mentioned as 21 20 a characteristic of the CB in India. However, the overall 10 liquidity of India’s market compares favorably with its peers 0 although lower rated bonds are illiquid and liquidity levels Indonesia India Korea China Thailand Malaysia are constrained during periods of volatility and market Source: WB staff calculations. stress, which is not unusual in most EMEs (Figure B3.32). Note: Data for Korea is for 2018. increase in human capital accumulation will be necessary for India to catch up with countries such as Indonesia and China. In addition to increasing enrolment, this will require recovering the losses due to school closures during the COVID-19 pandemic and improving the quality of learning. The Annual Status of Education Report (ASER) 2024 highlights steady progress in rural education post-pandemic, with high enrollment rates and improvements in foundational learning. Primary education participation remains strong (98.1 percent), and Class III students have shown gains in reading and arithmetic—23.4 percent can now read a Class II-level text (up from 16.3 percent in 2022), and 25.6 percent can solve basic subtraction problems. However, learning gaps persist, as over 50 percent of Class V students still struggle with lower grade reading skills. Dropout rates among older students (15-16 years) have stabilized at 7.9 percent, indicating some improvement in retention. However, digital learning remains underutilized, as most students use smartphones primarily for social media rather than education. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 73 Chapter 3 Expanding Investments in Physical and Human Capital FIGURE 3.22: Gross school enrollment (percent) Primary Secondary Tertiary 140 140 140 120 130 120 120 100 100 110 80 80 100 60 60 90 40 40 80 20 70 20 0 60 0 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017 2020 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017 2020 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017 2020 Bangladesh China Indonesia India Philippines Viet Nam Source: WDI, 2024. Despite these gains, many middle and secondary students still struggle with grade-level skills. COVID-19 school closures exacerbated existing weaknesses in the education system, with the Education Quality Score64 dropping from 0.64 in 2017 to 0.59 in 2021. A 2021 survey across five Indian states found that 82-92 percent of children lost at least one language or mathematical ability65. Another survey across 15 states revealed significant rural-urban disparities in learning access, largely due to unequal access to technology.66 Bridging these gaps through targeted interventions in digital learning, and infrastructure improvements is essential to minimize future losses in productivity and earnings. Efficiency of Education and Health Expenditures B.  Given the significant share of committed expenditures in state budgets, increasing the efficiency of expenditure will be important to improve human development outcomes. This is because the efficiency of public expenditure varies significantly across states that can be grouped in four broad categories in a metric of high/low income and high/low spending efficiency. The analysis of expenditure efficiency in education and health is carried out at the state-level using the variable returns to scale (VRS) output-oriented single-stage Data Envelopment Analysis (DEA) Model.67 It estimates the most efficient possible output given a set of inputs (expenditures)68 for 2019 and 2020.69 The VRS method captures both technical and scale efficiencies, which is important 64 The score is based on harmonized test scores from major international student achievement testing programs and the number of years of school that a child can expect to obtain by age 18 given the enrolment rates across grades in respective countries. 65 Azim Premji Foundation, February 2021. 66 SCHOOL survey, August 2021. 67 The DEA measures efficiency of Decision-Making Units (DMUs) using linear programming to envelop observed input-output vectors as tightly as possible and allows multiple inputs-out- puts without assumptions on the data distribution. The DEA is a relative measure of efficiency and is subject to caveats such as high sensitivity to sample selection and measurement error. The methodology focuses on inputs and outputs that can be quantified, and thus may overlook factors, such as quality, that are difficult to measure. Also, many policy targets are impacted by private spending. Thus, large differences across states in private health or education spending could bias efficiency scores. 68 DEA models are subdivided into input-oriented models which minimize inputs while satisfying at least the given output levels and output-oriented models which maximizes output without requiring more observed input values. DEA models can also be subdivided in terms of returns to scale: (1) constant returns to scale (CRS) where all DMUs are operating at their optimal scale (Charnes, Cooper, and Rhodes, 1978); (2) VRS efficiency measurement model allowing the breakdown of efficiency into technical and scale efficiencies (Banker, Charnes, and Cooper, 1984). There is also a Malmquist DEA framework. The Malmquist index (MI) evaluates the efficiency change over time. In the non-parametric framework, it is measured as the product of catch-up (or recovery) and frontier-shift (or innovation) terms, both coming from the DEA technologies. 69 Owing to data availability issues, in some cases earlier data are used. 74 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 3 Expanding Investments in Physical and Human Capital FIGURE 3.23: Efficiency in terms of education FIGURE 3.24: Efficiency in terms of health expenditure expenditure (percent of GDP) (percent of GDP) Low Income High Income Low Income High Income High Efficiency BH, MG, MN, AP, GJ, HP, HR, KL, High Efficiency MN, MZ, NG KL, MH, SK, GO, MZ, NG PB, SK, TN, TS, UK TN, PB, KA, TS, GJ, HP, HR Relatively Low AS, CG, JH, JK, KA, MH Efficiency MP, OD, RJ, TR, Relatively Low AS, BH, CG, JH, JK, AP, UK UP Efficiency MG, MP, OD, RJ, TR, UP Source: WB staff estimates. Not all states/union territories have reported data. Note: AP=Andhra Pradesh, AS=Assam, BH=Bihar, CH=Chandigarh, CG=Chhattisgarh, GO=Goa, GJ=Gujarat, HR=Haryana, HP=Himachal Pradesh, JK=Jammu & Kashmir, JH=Jharkhand, KA=Karnataka, KL=Kerala, MP=Madhya Pradesh, MH=Maharashtra, MN=Manipur, MG=Meghalaya, MZ=Mizoram, NG=Nagaland, OD=Odisha, PB=Punjab, RJ=Rajasthan, SK=Sikkim, TN=Tamil Nadu, TS=Telangana, TR=Tripura, UP=Uttar Pradesh, UK=Uttarakhand. given differences in size and population across states.70 The analysis uses expenditure data reported in state budgets and the RBI’s study of state finances, and links them to various output and outcome indicators published in the RBI database on states, the Annual Status of Education Report (ASER) and data published by the NITI Aayog. The efficiency analysis indicates significant heterogeneity in spending efficiency across states. Using a combination of quanti- tative and qualitative indicators (such as the Gross enrolment Ratio [GER], percentage of schools with computers, teacher-student ratio, ASER reading and math scores) to measure outcomes, most high-income states show high efficiency in terms of education expenditure as percentage of GSDP (Figure 3.23 and Table 3.61 in Annex 3.6). The efficient high-income states include Gujarat, Andhra Pradesh, Kerala, Haryana, Himachal Pradesh, Sikkim, Tamil Nadu, Punjab, Uttarakhand and Telangana. A few northeastern states including Manipur, Mizoram, and Nagaland, as well as Bihar also demonstrate high efficiency. Despite this, several of the latter states are lagging in indicators such as the GER and high school passing rates, indicating the need for social and economic interventions for greater focus in these areas. The relatively less efficient low-income states are estimated to be Assam, Chhattis- garh, Tripura, Odisha, Jharkhand, Rajasthan, Madhya Pradesh, Uttar Pradesh and Jammu & Kashmir. To evaluate the efficiency of health expenditures, assessment is conducted using multiple health-related indicators (such as infant survival rate, tuberculosis recovery rate and immunization coverage) to measure outcomes. Most high-income states such as Maharashtra, Haryana, Himachal Pradesh, Kerala, Sikkim, Goa, Tamil Nadu, Punjab, Karnataka, Gujarat and Telangana demonstrate high levels of efficiency. However some low-income northeastern states,71 also show high efficiency in terms of health expenditure as percentage of GSDP (Figure 3.24 and Table 3.62, Annex 3.6). Heterogeneity in spending efficiency indicates a differentiated approach in improving education and health related outcomes. For example, to achieve better outcomes, a high-income but relatively low-efficiency state might prioritize revamping the archi- tecture of health and education management systems rather than increasing budgetary allocations. By contrast, a state that belongs to the low income-high efficiency category, may want to prioritize increased financing. The northeastern states show large efficiency gaps between high and low performers. This indicates the possibility of transferring good practices and governance improvements within these states. 70 The analysis uses a Stata program based on Ji and Lee (2010), that reports CRS results simultaneously. The results are robust to change of technique in ordinal terms, but the efficiency scores naturally vary in cardinal terms once scale effects are accounted for. CRS results in only one most efficient DMU while VRS allows for more than one efficient DMU based on vari- ations in scale. 71 The analysis uses the inverse of the mortality rate since lower mortality are better outcomes. Using survival rates (1000-moratlity/1000) yield similar ordinal results but alters the cardinal scale of the efficiency measure. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 75 Chapter 3 Expanding Investments in Physical and Human Capital References Alcott, B, and P Rose. 2017. Learning in India’s Primary Schools: How do Disparities Widen Across the Grades? International Journal of Educational Development 42-51. Anand, R. & Tulin, V., 2014. Disentangling India’s Investment Slowdown. IMF Working Paper No. 2014/047.https://www.imf.org/en/ Publications/WP/Issues/2016/12/31/Disentangling-Indias-Investment-Slowdown-41436 Annual Status of Education Report (ASER). March 2021. Karnataka Rural. ASER Centre. 2022. Annual Status of Education Report (ASER) 2022. http://asercentre.org/Keywords/p/412.html Azim Premji Foundation. February 2021. “Loss of Learning during the Pandemic”. Field Studies in Education. Banker, R. D., A. Charnes, and W. W. Cooper. 1984. Some Models for Estimating Technical and Scale Inefficiencies in Data Envelop- ment Analysis. Management Science 30: 1078–1092. Bhargava, V., Ganesh, G., Ghosh, R. & Kulkarni, N., 2023. Stress in the Indian non-banking Financial Companies Segment and Its Aftermath: What are the Risks?. World Bank, forthcoming Bose S., Bhanumurthy N. R., 2015. Fiscal multipliers for India. Margin: The Journal of Applied Economic Research, 9(4), 379–401. Charnes, A., W. W. Cooper, and E. Rhodes. 1978. Measuring the Efficiency of Decision-Making Units. European Journal of Operational Research 2: 429–444. Colechhia, A. & Schreyer, P., 2002. ICT Investment and Economic Growth in the 1990s: Is the United States a Unique Case? 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World Bank (2021). Shifting Gears: Digitization and Services-Led Development. South Asia Economic Focus, Fall 2021. Washington, DC: World Bank. https:// openknowledge.worldbank.org/handle/10986/36317 License: CC BY 3.0 IGO. UNESCO. 2021. No teacher, no class: state of the education report for India. New Delhi. 76 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation © 2020 stockpexel/Shutterstock CHAPTER 4 More and Better Jobs to Reap the Demographic Dividend India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 77 Chapter 4 More and Better Jobs to Reap the Demographic Dividend I ndia’s sizeable and growing working-age population can be leveraged to achieve its high-income aspirations. However, too many Indians are currently unable (or in some cases unwilling) to contribute fully via the labor market. Employment growth has not been commensurate with the overall growth of the labor force, and labor force participa- tion is lower in India than in peer countries. Raising overall labor force participation would require addressing specific constraints to participation by women and youth. While India’s female labor force participation rate has increased substantially in recent years, it remains relatively low at 35.1 percent (of women 15 years and above) in 2023 according to standard ILO methodology.72 In addition, most women in the workforce are engaged in unpaid work in household enterprises and farm jobs. Notwithstanding notable government initiatives, barriers such as child/elderly care responsi- bilities, insufficient access to formal credit for women-led MSMEs, safety concerns, and high reservation wages relative to available jobs are constraining further progress. Job quantity constraints are magnified by quality concerns. Overall, employment remains concentrated in low-productivity and low-wage sectors, such as agriculture, traditional market services (including retail trade, hospitality and accommo- dation) and construction; the shares of jobs in medium and high productivity sectors increased only modestly over 2000- 23. India’s labor market is also characterized by a low share of regular wage workers as well as high informality in fast growing sectors. A significant proportion of wage workers lack enforceable contracts and social security benefits, even if some states, including Gujarat, Haryana, and Karnataka, have seen a reduction in the share of informal employment over the past decade. Policies aimed at addressing restrictive labor market regulations, promoting firm growth and skill development, and removing infrastructure constraints will be critical to generating more and better jobs. These measures will help create higher-quality employment by fostering the reallocation of employment to more productive sectors. I. To Reap the Benefits of its Demographic Dividend, India Requires More Jobs India has a window of opportunity to reap the benefits of its potential demographic dividend. The working-age population has expanded at 2 percent over the past two decades, and more than three in four Indians are now of working-age (Figure 4.2). However, the labor force has grown slower than the working-age population (even when accounting for recent improvements in the labor force participation rate (LFPR), based on the official PLFS, 2023-24) (Figure 4.1). Between 2000-23, nearly half of the population aged 15 years and above remained out of the workforce (with two-thirds of those not working in rural areas), mainly on account of low labor force participation among Indian women (Figure 4.4) and youth. India needs more and better-quality jobs. Employment generation was not commensurate with GDP growth. Over the two decades prior to the pandemic, the employment elasticity of growth in India was below that of most large, middle-income countries (Figure 4.5). Total employment growth accelerated in 2021-23 (higher than the long-term average), but this was mostly thanks to a sharp increase in farm and construction jobs, while more productive sectors grew at a slower pace. Employment remains concentrated in farm and other low-productivity jobs. As expected in a rapidly developing economy73, agricultural employment contracted in 2000-19, while employment grew in services and industry. However, the share of agricul- ture in overall employment remained high in 2019 (at over 40 percent) and it has increased since, to around 46 percent in 2023. Employment that exited agriculture shifted mostly to low-productivity, low-wage sectors like construction and traditional market services (such as trade and hospitality), which accounted for over 30 percent of total employment in 2023. These two sectors generated more than 30 million new jobs each from 2000 to 2019 (employment in construction increased threefold), or three quarters of the total number of jobs created (Figure 4.8). Meanwhile, the share of manufacturing remained stagnant; it still stood at 11 percent of total employment in 2023. 72 According to the latest Periodic Labor Force Survey, India’s female labor force participation rate increased to 35.6 percent (current weekly status) in 2023-24 (July-June). 73 As productivity gains in agriculture facilitate a reallocation of labor to higher-productivity sectors 78 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 4 More and Better Jobs to Reap the Demographic Dividend FIGURE 4.1: Labor force participation in India is lower FIGURE 4.2: The growth rate of India’s working age than most EMDEs (percent, 2023) population will slow over the next three decades 75 Viet Nam 90 2.5 70 80 Indonesia 70 2.0 China 65 Malaysia Labor force participation 60 Brazil 1.5 Philippines Colombia 50 60 South Africa Bangladesh 40 India 1.0 55 30 Pakistan India PLFS 20 0.5 50 10 45 0 0.0 02 05 08 11 14 17 20 23 26 29 32 35 38 41 44 47 50 Years 40 Share of working-age population in total population 40 50 60 70 80 90 Share of working age population in total population Growth rate of working-age population (RHS) Source: WDI and WB staff calculations. Source: WDI, UN Population Division and WB staff calculations. Note: Working age population is defined as the share of the total population aged Note: Working-age population refers to population of age 15 years and above. 15 years and above. FIGURE 4.3: Composition of working-age population, FIGURE 4.4: Labor force participation rate, India and India (percent of total population 15+) comparators (percent of female population, 15-64 years) 100 90.00 90 80.00 29.7 30.0 28.1 32.1 30.6 80 35.7 70.00 70 60.00 13.5 14.4 14.0 60 16.2 16.0 50.00 % of WAP 18.2 50 40.00 40 30.00 44.3 43.0 44.5 37.5 39.3 30 32.0 20.00 20 10.00 10 14.2 14.1 14.2 12.5 12.7 13.4 0.00 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 1994 2000 2005 2012 2019 2023 Urban not in labor force/ unemployed Urban employed IND IDN VNM LMC Rural not in labor force/ unemployed Rural employed UMC BGD CHN Source: PLFS and EUS, WB Staff Calculations. Source: WDI. Note: ILO estimates were used for comparability. The slow transition away from agriculture jobs reflects relatively slow employment growth in non-farm sectors over 2000-23. Employment growth in non-farm sectors (industry and services) was relatively strong over 2000-10; however, it slowed in 2010-19 despite rapid output growth in these sectors. The employment elasticity of growth declined in more than half of the non-farm sectors post-2010, particularly in labor-intensive manufacturing sectors such as food processing and textiles (Figure 4.6). In the post COVID period non-farm employment growth accelerated compared to its long-term average, particularly in some subsectors of manufactur- ing (such as food processing and textiles), traditional market services (trade and hospitality), as well as in business services (Figure 4.7). India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 79 Chapter 4 More and Better Jobs to Reap the Demographic Dividend FIGURE 4.5: Employment elasticity of growth India and comparators India 1.20 0.80 0.70 1.00 0.60 0.80 0.50 0.40 0.60 0.30 0.40 0.20 0.10 0.20 0.00 -0.10 5 /05 /10 /15 /19 00 4/9 0.00 /20 04 09 14 18 99 20 20 20 20 99 am to1 zil bia ica sia es esh a ia esi 19 Ind to to to to pin Bra lay Afr tN lom lad /91 on to /01 /06 /11 /16 ilip Ma Vie uth Ind ng Co /96 90 00 05 10 15 Ph Ba So 19 20 20 20 20 95 19 Elasticity, 1999-2009 Elasticity, 2010-19 Aggregate Aggregate, excl. agriculture sector Source: WDI, ILO, KLEMS, and WB staff calculations. FIGURE 4.6: Employment elasticity of growth FIGURE 4.7: Employment and real GVA, sectors (Index: 2000=100, for 2000-23) 1.00 650 0.80 0.60 0.40 550 Business 0.20 Construction services 0.00 450 -0.20 Financial Education -0.40 services services Employment 350 Machinery, -0.60 nec -0.80 -1.00 250 Textiles g Oth uf ure fac y um t se n nin ke dern Con ing ion anu uring ry Ele es nu icit ucl es Tra oods ufac g ole rke tio No rket s ing Trade ark vices el 150 an rin ult vic & n rvic Mi tur r fu Ma ctr etr ma truc r iat er M actu ma factu ser ric t n-m er ea s Ag et an Food gM dit M 50 processing al ne sin chi ces eg M o Ma pro -50 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ,p ed ro- erm Ag Co Real gross value add Hundreds Int 1999-2000 to 2009-10 2010-11 to 2018-19 Source: KLEMS and WB staff calculations. Employment has grown rapidly in high-skill services and capital-intensive manufacturing sectors, but from a very low base, which means they still employ a relatively small share of the workforce. The employment elasticity of growth in modern market services (such as financial, business and real estate services) was among the highest (compared to other sectors) in 2000-23 (Figure 4.9) and their share in total employment rose from around 1 percent in 2000 to almost 5 percent in 2023. 80 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 4 More and Better Jobs to Reap the Demographic Dividend FIGURE 4.8: Labor productivity (level and change), employment change, sectoral employment share (percent, 2000-19) Mining -0.6 0.4 Electricity 0.8 0.4 Modern market services 14.3 4.0 Non-labor-intensive 4.0 manufacturing 8.3 Non-market services 20.2 12.2 Traditional market services 31.5 18.6 Labor-intensive 7.1 manufacturing 0.0 Construction 38.2 11.9 2018-19 2000-01 Agriculture -46.7 41.3 0 200 400 600 800 10001200140016001800 -50 0 50 0 20 40 60 Labor productivity, INR per employed Employment change 2000-2019, million Share in total employment, 2000 and 2018, % Source: Skrok (2022). FIGURE 4.9: Sectoral productivity and employment share, percent 100 90 80 70 60 50 40 30 20 10 0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Agriculture,Hunting,Forestry and Fishing Very-low productivity Low productivity Medium productivity High productivity Source: Bhatnagar and Gupta (2023). Note: Labor productivity levels by sectors, excluding agriculture and allied activities, have been aggregated as – very-low productivity (minimum to first quartile), low productivity (first to second quartiles), medium productivity (second to third quartile) and high productivity (above the fourth quartile). Facilitating the reallocation of labor from relatively less to relatively more productive sectors will require addressing skills- gaps and mismatches. Skills mismatches, which measure mismatches that occur when the skills and educational attainment of employees do not match the skills needed for the job, are estimated to be higher in India (60.5 percent) than in other G20 countries, followed by Korea (53.2 percent), Indonesia (51 percent), Türkiye (50.8 percent) and South Africa (50.5 percent) (ILO and OECD, 2019). Nearly every second worker in India is employed in jobs requiring higher levels of education. Although India has achieved substantial increases in enrolment rates in the past two decades, disparities in learning outcomes and educational attainment across social groups and geographic locations amplify skills gaps and employment mismatches.74 74 For example, in rural India, on average half of students in Grade 5 can read a Grade 2 level text (ASER, 2019). Alcott and Rose (2017) find that students belonging to the poorest households are less likely to be able to perform simple mathematical operations like subtraction compared to students of the wealthiest households. The learning gaps widen during primary school, with the performance of disadvantaged girls deteriorating by Grade 5. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 81 Chapter 4 More and Better Jobs to Reap the Demographic Dividend FIGURE 4.10: Key employment indicators, India vis-à-vis EMEs (percent share) 2000 2023 90 100 80 90 70 80 60 70 50 60 50 40 40 30 30 20 20 10 10 0 0 Share of agriculture Wage & Salaried Vulnerable Share of agriculture Wage & Salaried Vulnerable employment workers/total Employment/total employment workers/total Employment/total employed workers employment employed workers employment India Source: WDI, WB staff calculations. Note: Boxes show the interquartile range within the comparison group and the whiskers represent the mean −/+ twice the standard deviation. Vulnerable employment is the ILO modeled estimate, which covers the own-account workers and contributing family members. The EMEs include Bangladesh, Brazil, Indonesia, Malaysia, Mexico, Philippines, South Africa, and Viet Nam. II. India Also Requires Better Quality Jobs Regular salaried jobs remain scarce and job insecurity is an issue even for salaried workers. The share of workers in the informal sector remains high. Even in the formal sector, many workers lack contracts and/or social security benefits and remain vulnerable to arbitrary dismissals.75 Relative to most fast growing emerging markets, India still has a relatively large share of the workforce engaged in agriculture, and a relatively low share of regular wage workers in the workforce (Figure 4.10).76 The share of regular salaried workers increased from 15.3 percent in 2009 to 23.5 percent in 2018, and it declined to 21.7 percent in 2023; the average for EMEs is 62.8 percent. Moreover, having a regular wage does not always come with employment benefits, such as social security or even employment security in the form of enforceable contracts with employers. Nearly 60 percent of regular wage/ salaried workers in 2023 did not have enforceable contracts with their employers, while over 50 percent were not eligible for any social security benefits. Non-farm employment tends to be in small and informal enterprises (Figure 4.12). In 2023, informal enterprises (that employ less than 10 workers) accounted for 73.2 percent of total employment.77 Sectors like construction, traditional market services, and agro-processing manufacturing are dominated by own-account workers as well as micro and small enterprises. Informal enterprises also make up 70 percent of total manufacturing employment (Figure 4.11). These firms have lower productivity and wages than formal firms, with labor productivity in firms employing less than 5 workers just one-eighth of that in large firms (100+ workers). Informal sector workers in manufacturing are also likely earn significantly less than those in the formal sector.78 Non-market and modern market services and machinery manufacturing, which have below average shares of informal enterprises, account for a relatively small share of total employment. 75 The formal sector comprises all non-agricultural public and private establishments employing with ten or more workers, while the informal sector refers mainly to unincorporated enterprises with less than ten workers (NCEUS, 2008). The informal sector primarily represents proprietary and partnership enterprises that are traditionally unincorporated and even quasi corporates. Within the formal sector, the quality of employment can be assessed by the share of informally employed – persons with no paid leave or no written contract with the employer guaranteeing job security and a legal redressal mechanism or no social security benefits. 76 This is the ILO modeled estimate, which covers the own-account workers and contributing family members. 77 The National Commission for Enterprises defined the unorganized sector as unincorporated private enterprises owned by individuals or households engaged in the sale and production of goods and services operated on a proprietary or partnership basis and with less than ten total workers. 78 The methodology used for these regressions is explained in Mitra, op. cit. 82 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 4 More and Better Jobs to Reap the Demographic Dividend FIGURE 4.11: Sectoral employment 100 20 90 18 80 16 70 14 60 12 50 10 40 8 30 6 20 4 10 2 0 0 Mining Electricity Intermediate Modern market Non-market Agro-processing Construction Other Traditional market and machinery services services manufacturing manufacturing services Informal sector employment, share 2018/19 share of total employment;RHS 2018/19 Source: PLFS, WB Staff Calculations. FIGURE 4.12: Employment distribution in the informal and formal sectors (by firm size, percent) Informal Formal 50 50 46.63 46.51 40 40 34.22 30 30 20 20 18.85 12.19 8.90 7.83 9.34 10 5.93 10 2.25 3.76 1.12 0.07 0.30 0.09 0.00 0.28 1.11 0 0 1 2-5 6-9 10-19 20-49 50-99 100-199 200-499 500+ 1 2-5 6-9 10-19 20-49 50-99 100-199 200-499 500+ Source: Chakraborty, Khurana and Manghnani (2023b). The high share of informality can be attributed to efforts to avoid complex regulatory compliance and the low optimum size of firms (Bussolo and Sharma, 2022).79 The Factories Law requires all manufacturing factories with more than 10 employees to register: as a result, some firms chose to remain small/informal to avoid tax enforcement or to escape registration costs. Recent research finds that entry barriers and distortions (for example tax and registration requirements avoidance) play a role in keeping firm sizes small (Fattal Jaef, 2022). In other cases, informality stems from the fact that firms are inherently small (for example, a market stall) and would remain so even if attaining a larger size was not associated with higher costs. Thus, policies to remove distortions (for example, reducing the costs of registration) would significantly reduce informality. Other policies focusing on the internal constraints to firms’ growth would be necessary, including (i) subsidizing training to improve the skills of informal sector workers; (ii) improving access to credit markets and land for small firms (see Chapter 2); and (iii) expanding their access to technology (for example, by increasing the reliability of and access to digital infrastructure and services). Restrictions on firing appear to have depressed hiring of permanent workers in large firms. Firms seem to have adjusted to restrictions on firing in the Industrial Disputes Act (IDA) by hiring more flexible contractual workers (Chaurey, 2015). In 2021, India’s Supreme Court issued a decision clarifying that firms need not absorb contract workers into the permanent workforce upon the expiry of their contract (Bertrand, et. al., 2021). Between 1999 and 2018, the average number of contract-based workers in formal manu- facturing firms increased in the largest formal firms (over the 90th percentile in size) (Chakraborty, Khurana and Manghnani, 2023). 79 The alternative definition, used in the Bussolo (2022), is that a firm is informal if it is not registered with the relevant authorities. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 83 Chapter 4 More and Better Jobs to Reap the Demographic Dividend To simplify restrictive labor regulations, new labor codes were introduced in 2020. The Government of India introduced four labor codes - the Code on Wages, 2019, the Industrial Relations Code, 2020, the Code on Social Security, 2020 and the Occupational Safety, Health and Working Conditions Code, 2020 - that subsumed 29 labor laws, including the Payments of Wages Act, 1936, The Factories Act, 1948, The Trade Unions Act, 1926, The Employees Compensation Act, 1923 and the IDA. The aim was to make labor regulations simpler and more flexible and to strengthen worker protection, including for workers in the unorganized sector. However, the evidence on the impact of these reforms is inconclusive. The reform of the IDA gives greater flexibility to firms in hiring and firing workers. However, impact evaluations in Andhra Pradesh, have failed to detect significant impacts on labor productivity or the structure of employment (Annex 4.3). Moreover, wages of directly employed regular workers as well those of casual workers grew at a slower rate following the reforms (possibly because they resulted in lower worker bargaining power). Chaudhary and Sharma (2022) found that the IDA reforms did not lead to a discernable impact on output or employment in Rajasthan. Educational attainment increases the probability of being in regular-wage, formal sector employment, with higher earnings. Education significantly impacts the occupational options and wage outcomes of workers, even after controlling for socioeconomic factors such as caste, religion, gender, age, and household size (Mitra, 2023).80 Earnings rise with the level of education (Mitra, 2023) (Figure 4.13). Relatively less educated workers are more likely to find only low-productivity jobs in the informal sector or —equally vulnerable— casual employment in the formal sector.81 FIGURE 4.13: Drivers of employment elasticity: results from factor analysis (factor loadings) Aggregate employment growth Formal sector Share of workers, secondary school Formal employment growth Share of workers, 1.0 high-secondary school 1.0 Share of workers, GFCF, share of GSVA 0.8 GFCF, share of GSVA 0.8 secondary school Urbanization 0.6 Share of workers, 0.6 0.4 graduate-level Urbanization rate Share of workers, rate 0.2 0.4 high-secondary 0.0 0.2 school Social -0.2 Share of 0.0 -0.4 Social Share of workers, expenditure, -0.6 regular-wage expenditure, -0.2 share of GSVA -0.8 workers -0.4 graduate-level share of GSVA -1.0 -0.6 Post o ces Post o ces Share of per one lakh Share of VA industry per one lakh regular-wage population population workers Hospital beds per Rail density Hospital beds per Share of VA 1000 population 1000 population industry Teachers Schools Tele density per per 1000 per 1000 1000 population Teachers Schools per Tele density population population per 1000 1000 population per 1000 population population Factor 1 (31% explained) Factor 2 (25% explained) Factor 3 (11% explained) Source: Mitra (2023). 80 A multinomial logistic function is used to calculate the marginal probability of workers with given characteristics being employed in the informal versus the formal sector, and as regular employees, self-employed, or casual workers. 81 Casual jobs are defined in the PLFS as: public works other than MGNREGS public works, MGNREGS public works, did not work owing to sickness though there was work in household enterprise, did not work owing to other reasons though there was work in household enterprise, did not work owing to sickness but had regular salaried/wage employment and, did not work owing to other reasons but had regular salaried/wage employment. 84 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 4 More and Better Jobs to Reap the Demographic Dividend Policy interventions can prioritize sectors with the greatest FIGURE 4.14: Selecting the sectors with high potential to create more and better jobs (Figure 4.14). employment potential (Size of the bubble indicates the employment • Agro-processing manufacturing is an important industry share, 2019, X-axis: Employment elasticity of growth in 2010-19; Y-axis: Sectoral efficiency) with a substantial informal sector and a high capacity to absorb less-educated workers. The employment elastic- 6.0 Electricity ity has been negative reflecting job-losses over the last Modern market decade coupled with low sectoral efficiency (defined as 5.0 services the ratio of labor productivity in the sector to total labor 4.0 Intermediate goods productivity). The sector has a relatively high employment Agro-processing Manufacturing Machinery share and offers easier entry for firms with respect to capi- 3.0 Manufacturing Manufacturing tal investment and could benefit from targeted policies to 2.0 Other Non-market services support firm growth, productivity and scalability. Manufacturing 1.0 Traditional market Construction • Traditional market services and intermediate manu- 0.0 services facturing industries had low employment elasticity and -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 median level of sectoral efficiency in the post-GFC period. Share of Primary-Middle school-educated and literate workers These industries also possess a high capacity to absorb less-educated workers and have strong backward link- More than 50% 40-49% 30-39% Less than 30% ages with downstream manufacturing units. Policy inter- ventions to encourage skilling, boost productivity and Source: PLFS, WB Staff Calculations increase formalization could result in stronger employ- Note: Sectoral efficiency is the ratio of labor productivity in the sector to labor ment growth. productivity of the entire economy. • Modern market services and machinery manufacturing have high employment elasticity, and the highest sectoral efficiency compared to other sectors in the economy. Machinery manufacturing employs a larger share of less-educated workers than modern market services, where only 9% had basic education in 2019. However, productivity gains from labor shifts and technology adoption will slow over time. To sustain growth in these sub-sectors, India will be required to focus on innovation. These industries could also benefit from skill development, and knowledge, supporting economic growth. III. Female Labor Force Participation India’s FLFPR is low compared to that of most EMEs.82 According to standard ILO methodology, only 35.1 percent of women participated in the labor force in India in 2023. By contrast, around 50-60 percent of working-age women participate in the labor force in EMDEs (except in the Middle East and North Africa and South Asia) (Figure 4.15). Despite a significant improvement in the years since the pandemic, which is partially attributable to more accurate classification of women’s participation as unpaid helpers in household enterprises, women’s participation in paid and market economic activities remains very low. Prior to the pandemic, of the 70 percent of working-age women (aged 15-64 years) not in the labor force in 2019, roughly 14 percent were in education, and more than 80 percent were engaged in household responsibilities and the uncompensated collection of goods (for example, collection of firewood or fodder, Figure 4.15). Overall recent FLFPR gains were in rural areas. The recent increase in FLFPR largely reflects an increase in rural FLFPR; in urban areas FLFP gains were modest (Figure 4.16). Urban women are significantly less likely to participate in the labor force than rural women, across all age groups and levels of education (except for women with college education or above). Urban women’s labor force participation is also likely to be lower than that of rural women when the size of the household is high. 82 See Annex 4.1 on the challenges in the measurement of FLFPR. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 85 Chapter 4 More and Better Jobs to Reap the Demographic Dividend FIGURE 4.15: Female Labor Force Participation among major emerging markets 80 70 60 50 40 30 20 10 0 ica la ina d rea a a zil ia A eria d Arg d a ia U exico Ba tus esh WS ud e ia LO t an t n yp ssi esi tin y an nte lan Ira rab go Bra rki ia I Afr ta Ch Ko Eg ia C kis lad Ru Nig ail on en An Po me Tu iA lS M Ind Pa uth Th uth Ind ng Ind sua ug So So Sa Ind Ind Source: WB staff calculations, WDI, PLFS, 2023. Note: India Augmented shows the augmented participation rate to include unpaid household domestic activities (for both time periods and for both men and women), India CWS refers to Current Weekly Status and India Usual Status refers to principal and subsidiary status from the PLFS. Women’s participation in paid employment remains low. FIGURE 4.16: Female labor force participation rate (all Despite a significant reduction in fertility (replacement rates ages, rural and urban) have fallen from 4 in the 1990s to around 2 in 2019) (Figure 45 4.18), and improvements in primary school enrollment rates (Figure 4.19) female participation in non-household enter- 40 prises and regular wage/ salaried work remains low.83 Almost 35 40 percent of women in the workforce were employed as 30 unpaid workers in household enterprises in 2023 (nearly 42.3 percent of rural women and 13.8 percent of urban women 25 are engaged in the workforce as helpers in household enter- 20 prises); only around 16 percent were employed in regular 15 wage jobs. These statistics primarily reflect the fact that rural women are mostly employed in agriculture. Urban women 10 are substantially more likely to be employed as regular wage 5 workers. In 2023, some 50 percent of urban women were in 0 formal employment (Figure 4.17). In urban areas, the services 2018 2019 2020 2021 2022 2023 2024 sector employs a higher share of women than men for profes- FLFP FLFP Rural FLFP Urban sional and technical level positions (PLFS unit level data 2018– 19 and 2019–20, Roy, et. al., 2022). Source: WB staff calculations, PLFS, 2023-24, Current Weekly Status. Women’s employment is concentrated in agriculture, low-productivity traditional market and non-market services (Figure 4.18). Within manufacturing, women’s participation is largely concentrated in food processing and textile, while men are more likely to be employed in the manufacturing of machinery and intermediate goods. Within services, women are predominantly present in education and household related services, while men participate more in retail and logistics activities. 83 These factors have been positively associated with greater entry of women in paid work but not in India possibly due to a combination of factors, including the lack of appropriate jobs and a relatively high reservation wage among educated groups. 86 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 4 More and Better Jobs to Reap the Demographic Dividend FIGURE 4.17: Distribution of usual status workers by location and gender (percentage share of total employment) 70 60 50 40 30 20 10 0 Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban Males Males Females Females Males Males Females Females Males Males Females Females Self-employed Regular Salaried Casual 1999 –00 2007-08 2009 –10 2018-19 Source: PLFS unit level data 1999-2019; WB staff calculations. FIGURE 4.18: Average female employment composition in India Sectoral share of employment (Percent) Share of employment by status (Percent) 100 100 90 90 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 0 Male Female Male Female Male Female Male Female Male Female Male Female Rural Urban Total Total Rural Urban Agriculture Construction Mining Traditional market services Casual Unpaid worker in household enterprise Manufacturing Other services Utilities Regular wage Self-employed (own-account/ employer) Source: National Sample Survey Office (NSSO), WB staff calculations (2023-24). There is a significant wage gap between men and women in all sectors, except for modern market services. Irrespective of the status of employment—regular wage/ salaried workers or self-employed—earnings of women are on average lower than those of their male counterparts. The wage gap is wider in sectors with large female presence (Figure 4.19). In rural areas, women engaged in agriculture, nonmarket services, and agro-processing manufacturing (which account for over 85 percent of total rural female employment) earn roughly 65 percent of the earnings of their male counterparts. Similar trends prevail for women in urban areas engaged in non-market services and agro-processing manufacturing (which contribute to almost 60 percent of total urban female employment). By contrast, urban women engaged in modern market services are likely to earn similar or even higher wages on average than their male counterparts. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 87 Chapter 4 More and Better Jobs to Reap the Demographic Dividend FIGURE 4.19: Rural-urban wage gap and female labor force participation (July-June 2019) Rural Urban 1.2 1.2 Modern market services Other manufacturing Proportion of Female Wage to Male Wage Proportion of Female Wage to Male Wage Traditional market services 1 1 Traditional market services Agro-processing manufacturing Construction Electricity .8 .8 Intermediate and machinery manufacturing Agriculture Mining Mining Non-market services .6 .6 Construction Agriculture Non-market services .4 Modern market services .4 Electricity Intermediate and machinery manufacturing Other manufacturing Agro-processing manufacturing .2 .2 0 0 0 .2 .4 0 .2 .4 Proportion of female workers Proportion of female workers Agriculture/Mining Manufacturing/Construction Services Source: PLFS, 2018-19. Note: Daily wages are calculated for casual and salaried workers. The Y axis is the wage gap between female and male wages. The X axis is the proportion of female workers out of total workforce. The government has undertaken important initiatives to encourage women’s participation in the labor force. Three types of interventions are noteworthy: i) Public employment and reservations: Public sector employment has been one of the largest sources of formal or regu- lar salaried jobs for women: 22.6 percent of female non-agricultural workers are employed by the government, with 27 percent in government employment in rural areas (Initiative for What Works to Advance Women and Girls in the Economy [IWWAGE], 2021). Public workfare programs have traditionally offered a unique opportunity for women to earn cash incomes despite social norms which constrain their ability to work outside the home (Narayanan and Das, 2014). Some state governments have introduced affirmative action at Panchayati Raj institutions and have reservation schemes for women in government service. In addition, the Ministry for Home Affairs has mandated a 33 percent quota for women in the national and state police forces (constables). ii) Self-help groups and microfinance: Engaging in off-farm economic activity as members of self-help groups has proven benefits. Research (Lakshmi and Vadivalagan, 2011, Alam and Nizamuddin, 2012) shows that women in self-help groups are often better off than those who aren’t in terms of employment opportunities, income, decision-making, access to finance, awareness, and social status. iii) Legal policies: Several provisions have been incorporated in various labor laws to enable women with children to work— care centers, time-off for feeding children, enhancement of paid maternity leave from 12 weeks to 26 weeks, and mandatory provision of a nursery facility for children aged six months to six years in establishments with 50 or more employees. Other legal provisions are aimed at improving safety, for example providing women workers in night shifts adequate safety measures (Lok Sabha Unstarred Question No. 4674—July 22, 2019). In addition, the Equal Remu- neration Act, 1976, mandates the payment of equal remuneration to men and women workers for the same work or work of similar nature. 88 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 4 More and Better Jobs to Reap the Demographic Dividend However, several issues still constrain fuller participation of women into the labor force. A review of the literature on drivers of FLFP in India points to several barriers: i) Women’s disproportionate burden of childcare, care work and home production limit their labor supply (Verick, 2016). ii) Women have achieved important gains in education, but limited increases in employment and participation. This suggests there are bottlenecks in school-to-work transitions as well as skills gaps and mismatches that prevent women from joining the labor force (Kanjilal-Bhaduri and Pastore, 2018). iii) Access to formal credit affects the ability of women to start a business or thrive as self-employed (Zaware, et. al., 2019). Nearly 23 percent of India’s women do not have a bank or savings accounts for their own use. Consequently, women entrepreneurs also face barriers in accessing finance, especially to establish and meet the needs of their enterprises (World Bank, 2022). iv) Concerns over personal safety and social norms prevent women from working outside the household. This is also driven by infrastructural constraints, such as poor roads and public transportation that reduce women’s mobility. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 89 Chapter 4 More and Better Jobs to Reap the Demographic Dividend References Alam, P. and Nizamuddin, S., 2012. Role of Micro Finance & Self Help Groups in Women Empowerment: A Case Study of District Mewat. International Journal of Entrepreneurship & Business Environment Perspectives, 1(2). Alcott, B. & Rose, P., 2017. Learning in India’s Primary Schools: How do Disparities Widen Across the Grades?. International Journal of Educational Development, pp. 42-51. Annual Status of Education Report (ASER), March 2021. Karnatak rural, s.l.: s.n. Azim Premji Foundation, February 2021. Loss of Learning during the Pandemic, s.l.: s.n. Bertrand, M., Hsieh, C.T. and Tsivanidis, N., 2021. Contract labor and firm growth in india (No. w29151). National Bureau of Economic Research. Besley, T. & Burgess, R., 2004. Can labor regulation hinder economic performance? Evidence from India, s.l.: The Quarterly Journal of Economics 119, no. 1: pp. 91-134. Bhatnagar, K. and M. Gupta. 2023. From Job-loss Growth to Job-Rich Growth in India. Forthcoming Bussolo, M., A. Kotia, and S. Sharma. Workers at Risk: Panel Data Evidence on the COVID-19 Labor Market Crisis in India. Chapter 7 in M. Bussolo and S. Sharma eds. Hidden Potential: Rethinking Informality in South Asia. World Bank. Chakraborty, P., S. Khurana, and R. Manghnani. 2023. Employment Patterns in Indian Manufacturing Enterprises. forthcoming Chaudhary, S., and S. Sharma. 2022. The Impact of Lifting Firing Restrictions on Firms: Evidence from a State-Level Labor Law Amendment. World Bank Policy Research Working Paper 10039. Chaurey, R., 2015. Labor regulations and contract labor use: Evidence from Indian firms. Journal of Development Economics, pp. 224-232. Cirera, X. & Maloney, W. F., 2017. The Innovation Paradox. s.l.:The World Bank. Fattal Jaef, R. N. 2022. Formal Sector Distortions, Entry Barriers, and the Informal Economy: A Quantitative Exploration. Chapter 2 in in M. Bussolo and S. Sharma eds. Hidden Potential: Rethinking Informality in South Asia. World Bank. IDFC Institute, 2019. Infrastructure Priorities for Job Creation in India, s.l.: s.n. International Labour Organization and OECD, 2023. Global Skills Gaps Measurement and Monitoring: Towards a Collaborative Framework, Technical Paper prepared for the 1st meeting of the Employment Working Group under Indian Presidency available at https://www.ilo.org/wcmsp5/groups/public/---dgreports/---ddg_p/documents/publication/wcms_867533.pdf . Kanjilal-Bhaduri, S., and F. Pastore. 2018. Returns to Education and Female Participation Nexus: Evidence from India. Indian Journal of Labour Economics, 61(3) Kannan, K.P., and G. Raveendran. 2019. From Jobless to Job-loss Growth: Gainers and Losers during 2012–18. Economic and Political Weekly, Issue 9, pp. 38–44. Lakshmi, R. and Vadivalagan, G., 2011. Impact of self help groups on empowerment of women: a study in dharmapuri district, tamilnadu. Journal of Management and Science, 1(2), pp.107-118. Mehrotra, S., 2020. Industrial Strategy with Employment Policy for Job Creation. In: Reviving Jobs: An Agenda for Growth. s.l.:Pen- guin Books. Mitra, Arup. 2023. Barriers to Employment: Impact of Macro, Individual and Enterprise-level Variables accessed at https://link. springer.com/book/10.1007/978-981-99-4570-2 Narayanan, S. and Das, U., 2014. Women participation and rationing in the employment guarantee scheme. Economic and Political Weekly, pp.46-53. Roy, D., S. Saroj, and M. Pradhan. 2022. Nature of Employment and Outcomes for Urban Labor: Evidence from the Latest Labor Force Surveys in India. Indian Economic Review, 57, pp. 157-221. Thomas, N. J., A. Anand, and R. Ghosh. 2023. Employment Protection Regulations and Behaviour of Manufacturing Establishments: Findings of a Natural Experiment from two Southern Indian States. forthcoming Verick, S.S., 2016. Manufacturing and jobs: is India different?. The Indian Journal of Labour Economics, 59(1), pp.57-84. The World Bank, Fall 2020. Beaten or Broken - Informality and COVID-19, s.l.: South Asia Economic Focus. World Bank Group, 2021. South Asia Economic Focus Fall 2021, s.l.: s.n. Zaware, N., S. Shinde, A. Pawar, and S. Aptet. 2019. Review and Assessment of Financial Constraints of Women Entrepreneurs in Maharashtra. Available at SSRN: https://ssrn.com/abstract=3819184 or http://dx.doi.org/10.2139/ssrn.3819184 90 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation © 2021 PradeepGaurs/Shutterstock CHAPTER 5 Indian States: Growing Faster Together India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 91 Chapter 5 Indian States: Growing Faster Together T here are significant disparities in income levels among Indian states, and the gap has widened over the past three decades. While some convergence has taken place—states have been converging into four “convergence groups” within which per capita incomes gaps have narrowed—these groups have diverged from one another. However, some degree of convergence is desirable in India as a large share of the population is spatially concentrated in relatively less developed states. Promoting greater convergence – or at the very least fast growth in low-income states would require: (i) Emulating and adapting strategies that worked for relatively developed states. States belonging to the higher income convergence groups tend to have larger shares of manufacturing, trade, and FDI, and higher levels of education. They have policy environments that are relatively more supportive of private sector development, including on access to land and finan- cial depth; (ii) Promoting labor mobility across India (from lower to higher productivity regions); and (iii) encouraging greater subnational spending in areas that are key to long-term growth prospects by increasing the fiscal space of states. I. Divergence of Incomes across States and Districts A 2047 high-income scenario will require relatively developed states to continue to grow fast while less developed states catch up. In 2023, relatively developed states (states with higher-than-average per capita income levels) such as Maharashtra, Gujarat, Karnataka, Tamil Nadu, and Delhi accounted for 26 percent of India’s population but for over 44 percent of its GDP. Meanwhile, less developed states (with lower-than-average growth and per-capita income) such as Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan accounted for 38 percent of India’s population but only 19 percent of its GDP. Going forward, whether India grows at business-as-usual or accelerated rates will depend largely on the rate of growth its less developed states achieve. In contrast to a “business-as-usual” scenario (which assumes states continue to grow at their historical averages), an alternative scenario in which the states that currently have per capita income levels below the national average start “catching up” (growing at an average real rate of 9 percent per annum), would allow India to reach the UMIC threshold two years earlier and to reach the HIC threshold by 2047 (Figure 5.1) FIGURE 5.1: GDP per-capita (USD adjusted using the Atlas method) 25000 20000 15000 10000 5000 0 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 Baseline_BAU Upper-middle income threshold Catching Up High income threshold Source: WB staff calculations. Note: Under the baseline scenario, the nominal GSDP in all states is assumed to be growing at the 20-year CAGR between 2000-21 through the projection period. Under the “Catch- ing Up” scenario, nominal GSDP is assumed to be growing at a faster rate of 14 percent, through the projection period, for all states with below-average levels of per-capita income. 92 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 5 Indian States: Growing Faster Together FIGURE 5.2: Average real GDP per-capita and average real GDP growth (2001-20) 13 1: Catching up SK 2: Moving ahead Average per capita GDP growth, percent (2000-2020) 12 11 10 TR UK 9 GJ MZ TN HR 8 AP AN KL NG KA BH MP WB HP 7 OD MH DL RJ CG PU 6 AS CH GO UP JK JH 5 PB MN MG 4 0 20000 40000 60000 80000 100000 120000 140000 Real per capita GDP, INR (2001) Source: NSO and WB staff calculations. Note: The blue lines indicate all-Indian averages for average per-capita GDP growth and real per capita GDP in 2001. Mineral-dependent states marked in red (states with mining share of GVA higher than 10 percent). AN=Andaman & Nicobar, AP=Andhra Pradesh, AS=Assam, BH=Bihar, CH=Chan- digarh, CG=Chhattisgarh, DL=Delhi, GO=Goa, GJ=Gujarat, HR=Haryana, HP=Himachal Pradesh, JK=Jammu & Kashmir, JH=Jharkhand, KA=Karnataka, KL=Kerala, MP=Madhya Pradesh, MH=Maharashtra, MN=Manipur, MG=Meghalaya, MZ=Mizoram, NG=Nagaland, OD=Odisha, PU=Puducherry, PB=Punjab, RJ=Rajasthan, SK=Sikkim, TN=Tamil Nadu, TS=Telangana, TR=Tripura, UP=Uttar Pradesh, UK=Uttarakhand, WB=West Bengal. Regional divergence often precedes convergence in the FIGURE 5.3: Distribution of real GSDP per-capita early stages of a country’s development. Significant regional 0.08 differences prevailed in the US, UK, and Japan in the initial stages of their development, which declined subsequently 0.07 (World Development Report, 2009). But convergence is partic- 0.06 ularly important and critical in India because the gaps are very large (real per capita income ranged from INR 32,174 in Bihar 0.05 to INR 295,114 in Goa in 2023) and development challenges 0.04 (relatively larger and poorer populations with higher services and support needs) are spatially concentrated in less devel- 0.03 oped states (Figure 5.2),84 which account for over 64 percent of the population, and over 75 percent of India’s poor.85 0.02 0.01 In the post-1991 period, Indian states have converged within groups of states, but these groups have diverged 0 0 0.5 1 1.5 2 2.5 3 from one another. Box 5.1 describes this phenomenon of Initial Distribution Final Distribution ‘club’ or ‘group’ convergence which became particularly salient in the decades that followed economic liberalization in 1991. Source: Calculations based on per capita GSDP data, NSO, Government of India (Kar, Jha and Jain, 2022). Note: Figure 5.3 shows the distribution dynamics of relative per capita income for Over time, the distributions of state income have diverged a 22-year transition period. Observations during 1994-97 have been used to esti- mate the distribution of initial state per capita incomes taken for all states (initial and become polarized. The distribution of state per capita distribution). Similarly, observations during 2016-20 have been used to estimate income levels became more spread out in the 2010s compared the distribution of final state per capita incomes taken for all states (so-called final distribution). to the 1990s (Figure 5.3)86. The shapes of the two distributions 84 States with per-capita income levels below the national average. 85 Based on 2011-12 poverty line headcount. 86 The data on state incomes is the GSDP data from the National Statistics Office (NSO), splicing data corresponding to four different base years (1993-94, 1999-00, 2004-05 and 2011-12). India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 93 Chapter 5 Indian States: Growing Faster Together Box 5. 1: A Review of the Literature on Convergence among Indian States Some early studies find evidence of regional convergence: Cashin and Sahay (1996) for 1961-91 in 20 states, Dholakia (1994) for 1960-89 in 20 major states and Patel (2003) for 1980-99 in 14 states. However, several studies document divergence across states: Ghosh, Marjit and Neogi (1998) for the period 1961-95, Rao, Shand and Kalirajan (1999) for 1965-95 among 14 major states, and Sachs, Bajpai and Ramiah (2002), Ahluwalia (2000), Ahluwalia (2002), and Clark and Wolcott (2003) for data from 1960. Cashin and Sahay (1996) find the speed of convergence to be relatively slow. Ghosh, Marjit and Neogi (1998) argue that divergence is driven by increasing returns, so that labor and capital tend to flow to higher-income states. Overview of the results from the literature in Li, Rama, and Zhao (2018) and Tiwari, Bhattacharjee and Chakrabarti (2020) strongly support the conclusion of divergence at the state level. Some studies distinguish between absolute and conditional convergence. Ghate and Wright (2008) find no evidence of either for 15 major states over 1960-2003. Nagaraj, Varoudakis and Véganzonès (1998) find no evidence of absolute convergence in a panel of 14 states over 1970-94. However, after controlling for the share of agriculture in output, infrastructure, and political and institu- tional factors, they find evidence of conditional convergence. Aiyar (2001) confirms conditional convergence using infrastructure, private investment, and non-measured institutional factors as the conditioning variables. The evidence is much starker for the period after the reforms of the 1990s, indicating incomes have diverged in the three decades since liberalization. Ahluwalia (2000 and 2002) show that inequality in real per capita income increased from 1992 to 1999 among 14 major states. Sachs, Bajpai and Ramiah (2002) find no evidence of absolute or conditional convergence in post-reform India. Shetty (2003) confirms divergence including all states and union territories. Nagaraj, Varoudakis and Véganzonès (1998) and Rao, Shand and Kalirajan (1999) find that the coefficient of variation of per capita regional output increased in the 1990s, while Bhat- tacharya and Sakthivel (2004) find that inequality in per capita regional income increased in the post-reform period compared to the 1980s. Ghosh (2008) shows evidence of divergence in the post-reform period as well, while Singh, et al. (2003) find no clear evidence of convergence or divergence over the same period. Finding no evidence of absolute convergence, several studies have instead analyzed group or “club” convergence across Indian states. The idea is that even if states may not be converging to any particular level of per-capita income, they can form “convergence groups” at different levels of per capita income. Gunji and Nikaido (2004) find that GSDP diverged across the 14 major states over 1970-2000, with no evidence of group convergence. In contrast, Ghosh (2008) concludes that there existed two distinct groups – one group of four states (out of 15) were converging to each other, while the others diverged. Analysis by Chaudhuri and Marimoutou (2007) for 16 states find the existence of three convergence groups – one low-income, one high-income, and one transitional – during 1965-2002. The low and high-income groups exhibited divergence from each other while the transitional group moved towards the high-income group after reforms. Kar, Jha and Kateja (2011) and Bandyopadhyay (2006) identify the presence of two convergence groups and polarization of the distribution. Ghosh, Ghoshray and Malki (2013) report three convergence groups among 15 major states during 1968-2008. While the groups were diverging from each other since early to mid-1980s, the trend of divergence increased after reforms. 94 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 5 Indian States: Growing Faster Together TABLE 5.1: India’s convergence Groups Groups Absolute Convergence Group 4 Group 3 Group 2 Group 1 Divergent Group (Single group) 1 2 3 4 5 6 7 Group size 31 7 8 9 5 2 Members All the states in the Andaman & Andhra Chhattisgarh, Assam, Bihar, Haryana, Uttar sample Nicobar Island, Pradesh, Jammu & Kash- Jharkhand, Pradesh Delhi, Chan- Himachal mir, Madhya Manipur, digarh, Goa, Pradesh, Karna- Pradesh, Meghalaya Gujarat, Sikkim, taka, Kerala, Nagaland, Uttarakhand Maharashtra, Odisha, Punjab, Puducherry, Rajasthan, West Tamil Nadu, Bengal Tripura t-stat -203.36 -0.40 7.15 - 0.73 2.00 -101.77 Source: Kar, Jha and Jain, 2023. Note: t-statistic larger than -1.65 indicates convergence. TABLE 5.2: Covariates of state-group membership Employment Share of Ease of Mean Share of Share of Capital Gross fixed Actual share of formal doing years of agriculture industry expenditure capital current manufacturing workforce business schooling in total GVA in total to GSDP formation expenditure GVA ratio to GSDP to GSDP ratio ratio Coefficient 0.95 0.002 0.04 1.18 -0.18 0.01 0.5 -0.00 0.8 Standard (0.50) (0.06) (0.02) (0.67) (0.09) (0.01) (0.29) (0.29) (0.17) Error Z Statistics 1.91* 0.03 1.45 1.75* -1.92* 0.89 1.73* -0.02 0.46 Source: Kar, Jha and Jain, 2022. Note: Pseudo R^2 is 0.56. A starred z-statistic shows that the variable is a statistically significant predictor of belonging to a higher income convergence group. A positive coefficient indicates that a higher value of the dependent variable is associated with a higher likelihood of belonging to a higher income convergence group while a negative coefficient indicates that a higher value of the dependent variable is associated with a lower likelihood of belonging to a higher income convergence group. are similar, but the highest points in the more recent distribution (indicating the greatest frequency of states at that income level) have diverged from the initial distribution. This shows that the dispersion of states’ per-capita incomes increased on both sides of the national average.87 From the early 1990s to the late 2010s, India’s states have converged into four “convergence groups” within which per capita incomes have become closer to one another. (Table 5.1). A Phillips-Sul (log-t) test is used to determine the existence of such groups; a t-statistic greater than -1.65 indicates convergence in incomes across the states in the group. Although this test presents no evidence of absolute convergence across the entire sample of states (Column 2, Table 5.1), Columns 3 to 7 indicate the existence of four convergence groups and a divergent group of two states (Uttar Pradesh, and Haryana). Moreover, a similar statistical analysis rejects the possibility that any of these groups could be merged. Within group mean per capita incomes, as a share of the average per capita income of all states (the relative transition paths), have diverged steadily from 1994 to 2020. This shows the relative 87 The modes of the initial distribution occur at income levels 0.7, 1.0 and 2.0 times the average, while the modes of the final distribution are at 0.5, 1.0 and 2.0 times the average, with some evidence of a fourth mode at approximately 2.7 times the average. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 95 Chapter 5 Indian States: Growing Faster Together income of states belonging to richer convergence groups has FIGURE 5.4: Relative transition path of convergence increased at a faster pace than the relative income of states groups and divergent states (1993-2019) belonging to poorer convergence groups. (Figure 5.4). 2.0 1.8 Members of convergence groups have similar character- istics. Key economic characteristics of member states are 1.6 significantly correlated with those of group members, includ- 1.4 ing: (i) employment share of manufacturing; (ii) mean years 1.2 of schooling; (iii) share of agriculture in GVA; and (iv) public 1.0 capital expenditure as a ratio of GSDP (Table 5.2 includes all variables used in the estimations).88 Simply put, a state with 0.8 a higher employment share of manufacturing, higher mean 0.6 years of schooling, a lower share of agriculture in GVA and a 0.4 higher level of public capital spending as a share of GDP is 0.2 more likely to belong to a higher income convergence group. 0.0 19 4 19 5 19 6 19 7 19 8 20 9 20 0 20 1 20 2 20 3 20 4 20 5 20 6 20 7 20 8 20 9 20 0 20 1 20 2 20 3 20 4 20 5 20 6 20 7 20 8 20 9 20 9 9 9 9 9 9 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 States belonging to higher-income groups perform better on 19 Group 1 Group 2 Group 3 Group 4 many indices measuring the policy environment for private sector development. States belonging to the two high-income Source: Kar, Jha, and Jain (2022). convergence groups outperform, on average, the states in the low-income convergence groups across a range of proxy measures for the policy environment such as innovation (as measured by enablers and outcomes in the innovation index) export preparedness, agglomeration, and enrolment in secondary education. These states also perform better on land related measures (land availability, the number of SEZs that ease land access, and lower share of land stalled projects as percentage of total stalled projects in that state), and financial depth. However, there is no clear relation with “labor management” performance (states in group 4 have a higher number of days lost due to strikes than states in Group 3) (Table 5.3). The group-convergence framework can be applied to districts as well. Some studies of district incomes find that they have converged, while others find the opposite (Box 5.2). Applying the same statistical test to data on nightlight luminosity (as a proxy of growth), the existence of absolute convergence at both state and district levels, can be rejected. 89 TABLE 5.3: Measures of the policy environment for private sector development Innovation Export Urbanization School NCAER- NCAER- NCAER-SIPI NCAER-SIPI Road Index Preparedness rate Education SIPI Land SIPI Labor Financial Secondary Quality Index Quality availability Index Depth Education Index Index Group 4 18.7 39.5 55.8 56.4 30.3 50.5 71.1 62.8 37.5 Group 3 15.2 47.7 39.2 62.5 27.6 58.5 60.6 52.5 55.9 Group 2 12.5 35.0 26.7 43.6 21.6 46.9 33.6 17.8 54.4 Group 1 14.3 32.0 20.4 42.6 4.7 46.1 9.5 10.7 34.8 Source: NITI Aayog, ASER, NCAER and WB staff calculations. SIPI refers to the State Investment Potential Index of NCAER. Note: Green indicates better relative performance on each indicator and yellow indicates relatively worse performance. Road quality is measured by the average speed between the two largest cities in the state based on Moszoro and Soto (2022). 88 An ordered probit model is used, where the dependent variable is the number of the club. The two divergent states have been added to their nearest clubs. Haryana has been added to club 2 (the second-highest income club) while Uttar Pradesh has been added to club 1 (lowest income club). 89 The district-level nightlight luminosity data are from Chanda and Kabiraj (2020). This is the radiance-calibrated version of the DMSP-OLS data, available for eight distinct years between 1996 and 2010. The data are described in a background paper for this study: Kar, Sabyasachi, Debajit Jha, and Mayank Jain. 2022. “Second Interim Report on Convergence Study.” World Bank mimeo. 96 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 5 Indian States: Growing Faster Together Box 5.2: A Review of Studies on District-level Convergence in India Studies examining convergence with respect to more disaggregated spatial units (district-level or below) are mixed. This issue is less studied than state-level convergence owing to insufficient data. Das, et. al. (2015) and Misra, et. al. (2020) find evidence of unconditional divergence across districts. Their analysis, however, is limited to 388 districts in 12 states and their results cannot strictly be termed “unconditional” since they cluster states based on certain initial conditions such as production structures. Chakravarty and Dehejia (2017) also work with a sample of the net state domestic product (NSDP) and nightlight luminosity of only 12 states. They argue that using a “crisper” dataset composed of the largest states has two main advantages. First, it takes care of the skewness and outlier effects that smaller states can introduce since standard ways of testing for convergence (OLS regres- sions) accord the same weight to all geographical units. Second, focusing on the largest states is more useful since most of India’s population and economic activity is concentrated in a few large states. Using this dataset and evidence from OLS regressions, they focus on within-state growth paths of districts and find unconditional divergence. Other studies find evidence of convergence across districts. Li, et. al. (2018) use household consumption expenditure data and find evidence of strong convergence at the district level and below. Using nightlights data and a 2x2 classification scheme, Tewari & Godfrey (2016) find that districts with lower-than-median levels of initial development grew relatively faster than their counterparts with higher-than-median levels of initial development, indicating unconditional convergence. Chanda & Kabiraj (2020) also use nightlights and OLS regressions to arrive at the same result. Tiwari, et al. (2020) apply both parametric techniques, such as OLS regressions and non-parametric ones, such as kernel density plots on the district domestic product (DDP) data to find absolute convergence. Districts have also been converging into groups that are diverging from each other. The algorithm developed by Phillips and Sul (2007) can be used to identify clusters in the panel within which the log-t test supports absolute convergence. This leads to the identification of six groups in the district data (Table 5.4).90 In most cases, there is some geographical contiguity between districts belonging to the top 10 percent of each district group. This likely reflects agglomeration effects. TABLE 5.4: District groups Log(t) Absolute Convergence Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 (Single group) Coeff -0.9045 0.118 0.396 0.027 1.078 0.046 0.414 T-stat -147.1540 10.303 -1.476 5.586 0.691 5.860 2.419 90 The algorithm also identifies a third group within which states do not converge, so that each state can be considered a group unto itself. Divergent states/UTs in this group include Delhi, Bihar, Manipur, and Lakshadweep. Districts within each club display a strong tendency to converge towards each other, as seen by the high, positive values of the t-statistic. Group 2 is an exception in this regard, with its t-statistic of -1.476 being close to the critical value of -1.65. The use of nightlight luminosity data results in somewhat different results compared to the GSDP data. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 97 Chapter 5 Indian States: Growing Faster Together II. Spatial Concentration of Economic Activity; Unlocking Growth in Lagging States There is considerable variation across states in terms of the sectoral composition of GVA. The share of agriculture declined significantly across states between 1981 and 2020. In 1981, agriculture accounted for more than 30 percent of GSVA in all states but declined to less than 30 percent in nearly every state in 2020. However, the relative gains of industry and services have varied from one state to the next. In 2020, industry accounted for over 30 percent of GSVA in only one-third of the states (Figure 5.5 and Figure 5.6)—mostly the coastal states or states with access to ample hydroelectricity91 (Figure 5.7). FIGURE 5.5: GSVA composition by sector (1981) FIGURE 5.6: GSVA composition by sector (2020) 100 100.0 90 90.0 80 80.0 70 70.0 60 60.0 50 50.0 40 40.0 30 30.0 20 20.0 10 10.0 0 0.0 GO MH TN AS WB GJ OD KA HP AP HR KL ML RJ UP MP PB MN TR BR GO GJ UK HP CG JH OD TN AS HR UP RJ MH PB KL MPAP WBKA TE TR JK BR Industry Services Industry Services Source: MOSPI, WB staff calculations Source: MOSPI, WB staff calculations. Note: Uttarakhand, Chhattisgarh, Jharkhand, and Telangana were created by bifur- cating existing states. FIGURE 5.7: Share of manufacturing in GSVA 50 45 40 35 30 25 20 15 10 5 0 GO GJ UK HP TN HR OD CG MH PB KA WB UP AS RJ AP ML KL MP JK BH TR MN NL MZ Manufacturing share 2000 Manufacturing share 2020 Source: MOSPI, WB staff calculations. 91 States with the highest manufacturing share in GSVA in 2020 are Goa, Gujarat, Uttarakhand, Himachal Pradesh, Tamil Nadu, Haryana and Odisha. 98 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 5 Indian States: Growing Faster Together Manufacturing, exports, and FDI are highly concentrated. FIGURE 5.8: Concentration in top-5 states (percent) Five states—Maharashtra, Gujarat, Tamil Nadu, Karnataka, and the National Capital Territory of Delhi—account for over half of India’s manufacturing and modern market services Manufacturing GVA value-added, over half of total merchandise exports and over three-fourths of total FDI (Figure 5.8). Ramaswamy (2021) finds Modern market services GVA that this spatial concentration of manufacturing has increased over the last two decades, mainly due to reinforcement and positive feedback effects from agglomeration. Modern market Foreign Direct Investment services, such as finance, business, and professional services, are also highly concentrated, which is not surprising as many Merchandise Exports of the modern market services cater to demand from manu- facturing businesses. Thus Maharashtra, Karnataka, and 0 10 20 30 40 50 60 70 80 90 100 Tamil Nadu have a high share of modern market services. Top-5 States Others Exports and FDI are also highly concentrated in a few major states. Tamil Nadu, Maharashtra, Telangana, Andhra Pradesh, Source: MOSPI, WB staff calculations. Karnataka, and Gujarat account for 75 percent of all Special Economic Zones (SEZs) in the country92 and more than 80 percent of FDI inflows. Exporting firms, particularly firms that participate in GVCs, are mainly located in Maharashtra, Delhi, Tamil Nadu, Gujarat, and Karnataka. These states also belong to the relatively higher income convergence groups; not only do they have a relatively higher share of manufacturing, but many of the manufac- turing firms in these states are exporters and are more likely to have benefitted from productivity gains through greenfield FDI. Investments in infrastructure can help mitigate geographic determinism and diffuse agglomeration effects, but only with comple- mentary policies. Geography plays a significant role in determining the distribution and concentration of economic activity. Dasgupta and Grover (2022) find that during 1989-2009, organized manufacturing activity was concentrated in a few districts that were within 200 kilometers of the nearest port and that interior regions (districts more than 400 kilometers away from the nearest port) lagged in all aspects of manufacturing activity. Infrastructure investments and other interventions that shorten “economic distance” can reduce the concentration in economic activity. Dasgupta and Grover (2022) find that large infrastructure investments that reduce the internal cost of trade (such as highways) can help spread manufacturing activity, particularly to districts along the infrastructure networks. Central level policies and investments that help to remove barriers to the movement of goods and services (such as GST reform) and expand physical and digital infrastructure are also helpful. For instance, commodity markets became much more integrated after the introduction of the GST, as captured in the decline in the coefficient of variation of prices across markets (Figure 5.9). However, reducing internal trade costs exposes local producers in lagging regions to greater competition. For the net effect to be positive complementary conditions (such as high-quality local government services and the strength of local comparative advantage in export sectors), are key determinants. Labor mobility is one of the surest ways to address spatial differences in incomes and opportunities and to boost overall productivity. However, the reallocation of labor across states is impeded by the poor portability of social welfare programs and the reliance on informal insurance provided by social networks. There were over 450 million internal migrants in India according to the 2011 census (37 percent of the population), but less than 20 percent of these migrants had moved to another district and even fewer had moved to another state (Global Knowledge Partnership on Migration and Development, KNOMAD policy brief 2021). A cross-country study of internal migration finds that India had the lowest rate of internal migration in a sample of 96 coun- tries (Bell, et. al., 2015), and long-distance, inter-state migration in India is lower than in other large developing countries such as Brazil and China (Kone, et. al., 2018). Munshi & Rosenzweig (2016) suggest that the low rate of long-distance economic migration is explained by a high degree of dependence on informal rural insurance networks, which have a limited sphere of influence, and the absence of formal insurance. For most households, access to private credit was low and publicly provided social safety nets were, until recently, not portable across locations. Households could only access food subsidies through the public distribution system (PDS) in one place, and these benefits could not be transferred easily from one state to another. The recently introduced One 92 Source: Ministry of Commerce and Industry. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 99 Chapter 5 Indian States: Growing Faster Together FIGURE 5.9: Price dispersion across markets by commodity, coefficient of variation of prices 1.2 1.0 0.8 0.6 0.4 0.2 0.0 ple ar a jra san t ad l Ch r Co cken Oil gs un ee Oil ida ize sur Mo k Mu ng Oil ion o gi e r To ji Va t o ti t nja tte ga cui ea l Att Ric Su t at spa Mi Ra Arh ma Eg Gro Gh Bre Ba o Ma Ma Ma Ap On ut ut rd Wh Su Be Bu Bis Bri Po i na st a con dn 2019 2010 Source: WB Staff calculations, CEIC, Directorate of Economics and Statistics, Department of Agriculture and Farmers Welfare. Note: The coefficient of variation (the ratio of the standard deviation of prices to the mean) measures the dispersion of prices across markets. Nation, One Ration Card (ONORC) program aims to address this issue by enhancing the portability of these entitlements. Besides the weakness of formal insurance systems, which is common across low- and middle-income countries, the existence of strong informal insurance networks also restricts migration to relatively shorter distances and explains the preference for temporary, seasonal migration over more permanent migration. Unleashing conditional convergence across state would require proactive fiscal policies and higher investment, but “one size fits all” subnational fiscal rules do not provide the required flexibility. Over the past two decades, state governments have compromised on capital and development-related current spending to meet the numerical targets embedded in fiscal rules. Subnational fiscal rules as enacted by most states in 2007, target a fiscal deficit of 3 percent of GSDP. On aggregate, subnational deficits and debt declined, as a share of GDP, after the adoption of fiscal rules. But fiscal consolidation post-2010 was achieved by reducing productive expenditures93. In “non-special” category states,94 the fiscal consolidations were relatively small on average, and primarily driven by increased revenues. Nevertheless, these states reduced capital and development related revenue expenditures, on average, during these episodes (Figure 5.10A). The fiscal consolidations in “special” category states were larger and were driven more by expenditure reductions than by increases in revenue (Figure 5.10B). The reliance on reduction of capital expenditures to achieve fiscal consolidation indicates that there are significant budget rigidities95. Capital spending by states, particularly on urban development, transport, and agriculture and allied activities, can boost output. A one rupee increase in capital outlay for development purposes increases the states’ real GDP by 0.4 rupee after two years. The cumulative multiplier, however, fades over time96 (Figure 5.11A). The largest multiplier impacts on output stem from investments in urban development (one rupee increase leads to a 2.5 rupee increase in output in the first year and the cumulative multiplier remaining large and statistically significant over the next four years (Figure 5.11B); transport (which increases state output by 0.9 rupee after two years (Figure 5.11C); and agriculture and allied activities ( which raises state output by 1.2 rupee in the first year (Figure 5.11D). States account for more than 50 percent of total general government capital spending and states’ capital spending has increased to 2.8 percent of GDP in 2024 in response to incentives by the central government. However, capital outlay on urban development, transport and agriculture was less than 1 percent of GDP in 2023. 93 Defined as capital expenditure and development related revenue (or current) expenditure. 94 Ten states-- Jammu & Kashmir, Uttarakhand, Himachal Pradesh, Assam, Sikkim, Manipur, Meghalaya, Mizoram, Tripura and Nagaland-- are categorized as belonging to the ‘special cate- gory’ since difficult mountainous terrain makes economic activity in general and mobilization of revenue difficult. These states possess different fiscal arrangements with the central government, compared to the other “non special category” states. 95 These include an expanding public sector wage bill, pension commitments and a higher debt servicing burden due to the increase in public debt as a share of GDP. These pressures tend to “crowd out” spending on health and education and capital outlays for infrastructure development. 96 The estimates are based on the panel local projection model, following Jordà, (2005). 100 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 5 Indian States: Growing Faster Together FIGURE 5.10: Contribution to fiscal consolidation (percentage point over the average episodes, 2010-18) A: Relatively Small Consolidations, “Non-special” Category States B: Large Consolidations, “Special” Category States 0.3 1.5 1 0.2 0.26 0.5 1.10 0.1 0 0 0.04 -0.5 -1.27 -0.14 -1 -0.1 -1.5 -0.53 -0.2 -0.04 -3.34 -0.41 -2 -0.44 -0.3 -2.5 -3 -0.4 -3.5 -0.5 -4 Primary de cit Revenue Receipts Expenditures Primary de cit Revenue Receipts Expenditures Capital expenditure Dev. revenue expenditure Non-dev. Revenue expenditure Source: WB staff calculations. Note: On average, large consolidation periods saw the primary deficit improve by 0.41 percentage points. Of this, 0.26 ppts was contributed by revenue increase, and 0.15 ppts by reduction in expenditure. Capex and development-revenue expenditure was cut by 0.14 and 0.04 ppts, respectively, while non-development revenue expenditure increased by 0.04 ppts (-0.14-0.04+0.04=-0.15). FIGURE 5.11: State-level fiscal multipliers for categories of development capital outlay A. Impact of a rupee increase in development B. Impact of a rupee increase in urban capital outlay Real GDP, INR development expenditure Real GDP, INR 4.5 4.5 4.0 4.0 3.5 3.5 3.0 3.0 2.5 2.5 2.0 2.0 1.5 1.5 1.0 1.0 0.5 0.5 0.0 0.0 -0.5 1 2 3 4 -0.5 1 2 3 4 Years Years 90% con dence intervals C. Impact of a rupee increase in capital D. Impact of a rupee increase in capital outlay for outlay for transport Real GDP (INR) agriculture and allied activities Real GDP (INR) 1.4 2.0 1.2 1.0 1.5 0.8 0.6 1.0 0.4 0.2 0.5 0.0 -0.2 0.0 -0.4 -0.6 1 2 3 4 -0.5 1 2 3 4 Years Years 90% con dence intervals Source: WB staff calculations. Note: Results are based on a panel local projection model including state taxes, GDP deflator, state output gap, state revenue expenditure, a dummy for 2009 great recession, and a lag in the variable of interest. The reported variable is an equivalent of cumulative multiplier as in (Ramey and Zubairy, 2018). India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 101 Chapter 5 Indian States: Growing Faster Together References Ahluwalia, Montek S. 2000. Economic Performance of States in Post-Reforms Period. Economic and Political Weekly, Vol. 35, No. 19, pp. 1637-1648. Ahluwalia, Montek S. 2002. State level performance under economic reforms in India. 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World Development Report: Reshaping Economic Geography accessed at https://elibrary.worldbank.org/doi/ abs/10.1596/978-0-8213-7607-2 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 103 © 2018 Sk Hasan Ali/Shutterstock CHAPTER 6 Program of Reforms to Achieve India’s High-Income Aspirations 104 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 6 Program of Reforms to Achieve India’s High-Income Aspirations I ndia can achieve its national aspiration to become a high-income economy by 2047 if annual real GDP growth rises to an average of 7.8 percent over the next two decades. This will require harnessing untapped domestic sources of growth and expanding international trade and GVC participation by building on ongoing reforms and introducing new reforms in critical areas. The proposed program of reforms focuses on: (i) promoting structural transformation, trade and the infusion of modern technology and business practices throughout the economy by easing key constraints in the factor (land and labor) markets, improving agricultural productivity, strengthening physical and digital infrastruc- ture further, increasing participation in GVCs, undertaking targeted measures to facilitate technology adoption by firms, and accelerating human capital development by improving the efficiency of health and education spending; (ii) reviving and sustaining the investment rate by expanding financial sector reforms, lowering or removing barriers to FDI, expanding financing for infrastructure, and targeting greater public investment in sectors with the greatest potential to crowd in private investments; (iii) facilitating job creation by prioritizing interventions in labor inten- sive sectors and by undertaking specific policies to increase the participation of women in the labor force; and (iv) addressing growth constraints faced by relatively less developed states in India by focusing on fundamental precon- ditions and promoting manufacturing sector development in these states. Subnational growth can be encouraged by strengthening the availability quality of associated public services that complement initiatives like the provision of infrastructure to attract private investments. Additionally, enhancing the fiscal space of states will allow them to spend more in areas that are key to their long-term growth prospects. I. Promoting Structural Transformation, Trade, and the Infusion of Modern Technology and Business Practices To achieve its high-income aspirations, India needs to accelerate the structural transformation of its economy. India can promote faster growth in industry and services, by applying the lessons learned from its successes in advanced manufacturing and modern market services (see chapter 2). These subsectors achieved strong productivity increases; key factors included high quality infrastructure, efforts to address land constraints, the availability of specialized skills, and a supportive regulatory environment (the liberalization of trade and capital inflows). i) Easing constraints on key inputs, especially land for manufacturing activity. The government could expand recent reforms to simplify land transactions (Annex 6.1): • Implementing the Land Title Act would pave the way for conclusive land titles and a unified legal framework to facilitate state guaranteed land ownership, reduce litigation, enhance transparency in land transactions, and reduce transactions cost and time for procuring land. • Deploying place-based policies and programs where key underlying conditions exist, with greater focus on SMEs. The experience of India’s technology-intensive manufacturing industries (Box 2.3) indicates that the provision of land and infrastructure through SEZs and industrial parks was an important success factor. A few states, for example Karnataka, have introduced Industrial Areas (IAs) programs which have functioned as a de facto reform of local land use restrictions. However, larger firms appear to have benefitted more from these place-based interventions than smaller firms (Chaurey, et. al., 2022). The success of spatial policies is also dependent on the overall macroeconomic policy framework as well as factors such as the quality of local infrastructure and governance (see Chapter 2). The quality of public support services matters for firm performance both inside and outside these designated zones. These public services include governance, trade, connectivity, and infrastructure support and fiscal incentives. • Improving the digitization and quality of land records. Almost all states have made progress on land digitization and associated services. However, gaps remain with respect to user accessibility and record quality. In turn these gaps point to underlying weaknesses: delayed updating of land transactions; limited coverage of rented properties, vertical spaces, and rural areas; lack of interlinkages among various databases such as birth and death registers and building plan approvals; India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 105 Chapter 6 Program of Reforms to Achieve India’s High-Income Aspirations and slow court cases (NCAER, 2021). To promote faster digitization and better quality of land records, a standardized land IT system could be introduced by leveraging ongoing initiatives such as the Digital India Land Records Modernization Programme (DILRMP) and implementing the Local Government Directory (LGD) codes across all tiers of government.   ii) Improving agricultural productivity. Policy measure that would boost agricultural productivity and expedite structural transformation include: • Expanding and strengthening farmer organizations, and facilitating land leasing and pooling, to mitigate challenges associated with land fragmentation. Expanding and strengthening Farmers Producers Organizations (FPOs) would facilitate the integration of small farmers into the agricultural value chain by improving their access to public services and markets through the aggregation of supply and demand. This would benefit small farmers by strengthening their bargaining power for better prices, and facilitating their access to credit, technology, and knowledge. To strengthen tenant farmers’ land leasing rights, the committee on Doubling Farmers’ Income recommended enacting the model Agricultural Land Leasing Act (prepared by NITI Aayog). A few states, such as Madhya Pradesh, Maharashtra, and Uttara- khand, have recently introduced land leasing legislation; more states should follow suit. • Investing in new technologies and modernizing infrastructure. Priorities include expanding access to improved seeds, mechanization, and public investment in agricultural R&D—including in the modernization of farming and agri-food businesses to promote the adoption of digital technologies such as artificial intelligence, smart sensors and precision farming. Addressing challenges related to storage, transportation, and market access through investment in infrastructure modernization and management can reduce post-harvest losses and generate additional employment opportunities. • Promoting continued diversification to higher value agricultural commodities. Lifting constraints to marketing, transport, and export of agricultural commodities, and agro-processing would encourage farmers to diversify to higher value commodities, particularly in rain-fed areas with high poverty levels. The government can further incentivize the production of higher value products by expanding access to formal credit, especially for small farmers. • Promoting sustainable resource use. Fiscal subsidies for key agricultural inputs, such as for fertilizer and energy, can be repurposed to promote a more efficient and sustainable use of resources, taking into account their environmental exter- nalities. Replacing the supply of free power with direct, targeted transfers could limit water overuse. Using direct transfers in lieu of input subsidies would allow to target limited resources at small and marginal farmers who most need support. iii) Facilitating labor mobility by strengthening the “One Nation One Ration Card” (ONORC) scheme. Labor mobility— particularly inter-state—has played a limited role in improving labor productivity (see Chapter 2). Food insecurity is a significant challenge to migration, especially to urban areas (see Annex 6.2). In 2019, the ONORC scheme was introduced to improve ration portability by including migrants into the country’s largest food subsidy program — the Public Distri- bution System (PDS). However, achieving the full potential of the ONORC is hampered by insufficient awareness about the program among shop owners, requirements for additional identification cards, issues with older versions of ration cards, and insufficient deployment of electronic point-to-sale equipment in shops. Addressing these constraints would go a long way to boost labor mobility. iv) Consolidating gains in physical and digital infrastructure development. India is undertaking several reforms to strengthen its physical and digital infrastructure. • Developing an updated classification of infrastructure to inform the Harmonized List of Infrastructure.97 The goal is to ensure the sectors identified in the list are more clearly defined and key digital and environmental infrastructure services are included. This will help to strengthen infrastructure planning and monitoring and also facilitate the creation of a consolidated database on the sector that can inform infrastructure policies. In this direction, the Union Budget 2023-24 announced the setting up of a review committee to develop an updated classification of infrastructure. 97 Since 2012, India has a harmonized master list of infrastructure that is updated by the government from time to time. The sectors included in this list enjoy specific incentives. In 2022, the list was updated to include data centres and energy storage systems. 106 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 6 Program of Reforms to Achieve India’s High-Income Aspirations • Strengthening multimodal connectivity by incentivizing investments in greener transport modes (for example, policies to foster the development of green shared-mobility services (e-bus) through PPPs) and modal interchanges as well as logistic clusters would maximize the positive spillovers associated with infrastructure development. Strengthening infrastructure financing to attract long-term funding and greater private participation would complement these efforts (see Section II). • Policies to bridge the rural-urban divide in digital access and incentivizing firms to adopt advanced digital solutions would strengthen the multiplier effects associated with rising ICT investments. Boosting digital infrastructure, including expand- ing access to high-speed mobile broadband connectivity, should be a key priority. With only 500 million smartphones users for 1.2 billion telephone connections, device affordability remains a key challenge. Finally, increased adoption of existing digital products and services, such as Digilocker (digital documents wallet) and the Unified Mobile Application for New-Age Governance (UMANG)— a government super app offering more than 2000 central and state government services to citizens—through targeted digital and financial literacy initiatives could further boost the digital economy. v) Expanding GVC participation by focusing on the “whole of the supply chain”. India can leverage its strengths in sectors such as IT, pharmaceuticals, textiles and automobile to create regional and global hubs for GVCs. This would require a comprehensive approach including reducing import tariffs on intermediate inputs and capital goods (see Chapter 2) improving trade facilitation,98 and liberalizing services and FDI. vi) Facilitating the infusion of technology across domestic firms to lay the foundation for greater innovation and creative destruction in the economy. Trade facilitation, greater GVC integration, R&D focused tertiary education, and market competition create the conditions for firms to climb up the “Capabilities Escalator” (Cirera & Maloney, 2017) and transition from basic to sophisticated capabilities. In addition to these cross-cutting pre-conditions, targeted policies to promote infusion, and ultimately innovation, could include: (i) working with sector organizations on technology adoption roadmaps and skills, training needs, and improving the provision of business services and technology extension services, (ii) under- taking regulatory impact assessment to identify whether regulations enable the adoption of technologies, (iii) providing financial support (for example, subsidies to first adopters, tax incentives to technologies with large positive externalities such as green technologies) to incentivize firms to adopt new technologies, and (ii) incentivizing industry-academia R&D collaboration. vii) Reinforcing the competition policy framework to boost market dynamism. Limited competition in markets typically stems from a combination of: (i) restrictive regulations, or discretionary application of the regulatory framework, that deter entry and operation of new firms; (ii) market distortions due to the unequal treatment (through state support measures and other instruments) of firms that are directly or indirectly owned by the government; and (iii) ineffective enforcement of competition laws. Thus, addressing government regulations that restrict or increase the costs of market competition, and strengthening the general antitrust and institutional framework to prevent anticompetitive mergers and the “abuse of dominance” to usher in competitive neutrality are some of the guiding principles to make markets work better and boost productivity gains (Figure 6.1). Effective implementation of the amended competition rules would be critical.    viii) Improving learning outcomes further. Structural transformation requires a workforce with the requisite skills and human capital to transition from low- to high-productivity activities. Research indicates that the most effective way to improve learning outcomes among school students in India is by enhancing teachers’ effectiveness. This can be achieved by reduc- ing teacher absenteeism, improving the teacher-to-pupil ratio, providing proper teacher training (including on the use of ICT), increasing instructional time for remediation, measuring and tracking learning, preparing teachers to teach accord- ing to the students’ capabilities, and providing psychological support to students. Bridging the digital divide between urban and rural areas can improve the utilization of ICT in education and further expand the reach of online educational opportunities to students in remote areas. 98 Trade facilitation can be achieved through measures such as streamlining customs procedures, increasing transparency and predictability in regulations and policies, and reducing red tape. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 107 Chapter 6 Program of Reforms to Achieve India’s High-Income Aspirations FIGURE 6.1: A comprehensive competition policy framework Pillar 1: Pro-competition regulations and Pillar 2: Competitive neutrality Pillar 3: Effective competition law and government interventions: opening markets and and non- distortive public aid antitrust enforcement removing anticompetitive sectoral regulation support Reform policies and regulations that strengthen Control state aid to avoid favorit- Tackle cartel agreements that raise the dominance: restrictions to the number of firms, ism and minimize distortions on costs of key inputs and final products statutory monopolies, bans towards private invest- competition and reduce access to a broader variety of ment, lack of access regulation for essential facilities. products Eliminate government interventions conducive Ensure competitive neutrality Prevent anticompetitive mergers to collusive outcomes or increase the costs of including vis-a-vis SOEs competing: controls on prices and other market variables that increase business risk Reform government interventions that discriminate and harm competition on the Strengthen the general antitrust and insti- merits: frameworks that distort the level playing field or grant high levels of discretion tutional framework to combat anticompet- itive conduct and abuse of dominance Source: WBG-OECD (2016). Adapted from Kitzmuller M. and M. Licetti, “Competition Policy: Encouraging Thriving Markets for Development” Viewpoint Note Number 331, World Bank Group, August 2012.  ix) Enhancing expenditure efficiency to leverage limited budgetary resources available for human development. Spending efficiency varies across states, which implies that inefficient states can improve outcomes without additional spending, and efficient states can boost spending without leakage. High income-high efficiency states broadly correspond to convergence groups 3 and 4. Low income - low efficiency states largely correlate with convergence group 1 and 2, with some Northeastern states such as Meghalaya and Manipur demonstrating relatively high efficiency despite being low-income states (Chapter 3). Low-income states with low expenditure-efficiency scores require maximum policy attention, with a combination of initiatives that ensure effective implementation of education and health-related policies and increased but conditional funding. Low-income states with high expenditure-efficiency can benefit from greater allocations into their education and health systems. High income states with low expenditure-efficiency scores should focus on strengthening their manage- ment capabilities and systems for education, health, and infrastructure, including by learning from better performing peers. II. Turbocharging and Sustaining Investment i) Deepening financial sector reforms to boost credit and investment. To support a sustained credit boom, reforms to diversify the sources of funding available to firms and improve the efficiency of credit allocation are needed. They can build on important recent initiatives to bolster risk assessment frameworks and tighten NBFC regulations (see Annex 3.4). • Facilitating the timely recognition of financial risks. Given the evolution of financial linkages between NBFCs and the rest of the financial system, establishing a centralized systemic risk database and developing advanced risk assessment tools will enable better and more timely supervision of financial sector risks. The current macro-financial environment, characterized by low NPA ratios and sound balance sheets, provides an opportunity to expand countercyclical capital buffers (that are currently zero) to preserve banks’ ability to lend in potential future episodes of financial stress. The RBI has continued to strengthen banks’ compliance with the NPA classification, including through automation requirements. However, there is still room for progress, for instance by addressing exemptions for certain exposures and relatively low minimum provi- sioning rates through the adoption of International Financial Reporting standards (IFRS) 9 with prudential backstops99. 99 Prudential backstops can be introduced to allow regulators to require that if regulatory provisions are higher than provisions under IFRS 9, the shortfall is deducted from regulatory capital of banks, or regulatory provisions supersede accounting provisions. 108 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 6 Program of Reforms to Achieve India’s High-Income Aspirations • Strengthening NPA resolution mechanisms. The Corporate Insolvency Resolution Process (CIRP) provides a transparent framework for addressing insolvency. However, the recovery process continues to be slow: the recovery rate is estimated at only 35 percent and 22 percent of the original loan amount for financial and operational creditors respectively (Indian Institute of Management Ahmedabad, 2023). National Company Law Tribunals (NCLT), which focus on company and insolvency issues, are reportedly experiencing delays of up to one year during the pre-admission phase, along with adjournments and stays of proceedings. Alternative dispute resolution mechanisms are not well developed and are rarely utilized. The introduction of specialized insolvency regulations and the creation of a dedicated bench for insolvency applications and pre-admission matters within the NCLTs can improve case management. Another key measure will be to promote the resolution of the small-scale bankruptcies. Since the pandemic, commercial banks and FIs have significantly increased their exposure to retail and personal loans. This is a cause for concern, because the accumulation of business and household credit prior to a credit peak can subsequently have an adverse impact on growth. Retail bankruptcies are often more difficult to resolve. Many SMEs are likely to be owned and operated by families that have pledged their personal assets for business credit. Hence, business insolvency can lead to personal insolvency, even where the business is a separate legal entity. The introduction of the Pre-Packaged Insolvency Resolution Process (PPIRP) is a positive step, but its utilization has been low so far. It could be enhanced by introducing “prepack” procedure for debtor-in-possession restructuring and a simplified procedure for MSME financial distress. Furthermore, IBC provisions for personal bankruptcy provisions have not been notified (with the exception of personal guarantors to corporate debtors). • Deepening corporate bond markets (CBMs). India could leverage the CBM more by: (a) increasing access to issuers —particularly lower rated corporates, through tailor-made instruments and guarantees; (b) widening the investor base by focusing on regulatory measures targeted at institutional investors; and (c) enhancing market efficiency through targeted improvements in market infrastructure and enhancements in risk-management frameworks. • Facilitating financing for MSMEs. MSMEs a critical contributors to output and employment ( they account for 36 percent of manufacturing output, and 45 percent of exports), but their growth is constrained by substantial financing gaps (the credit gap of MSMEs was estimated at INR 20-25 trillion in 2019, about 60 percent of the demand), and limited access to formal sources (accounting for only 30 percent of total MSME financing in 2019) (IFC, 2021). Constraints to MSME finance include low formality, difficulty in assessing cash flows and profit, lack of collateral, and risk aversion of banks. MSME financing can be improved by strengthening the ability of NBFC to lend to them. This could be done by removing the existing interest cap on NBFCs to be eligible for guarantees provided by the Credit Guarantee Fund Trust for Micro and Small Enterprises (CGTMSE) and introducing risk-sharing mechanisms for bank lending to NBFCs. Other steps to improve the flow of credit to MSMEs would include scaling up the TReDS online platform by operationalizing a new window (“second window”) to allow factoring without the need for explicit buyer confirmation of invoices, as well as the credit guarantee scheme and trade credit insurance for factoring100. • Improving NBFC access to long term funding. Post-pandemic, banks and FIs have become more exposed to NBFCs. In response, the RBI has increased the risk weights for most bank exposures to NBFCs from 100 percent to 125 percent. However, the NPA ratio of the NBFC sector has declined and they remain well capitalized. The recently adopted scale- based regulatory framework for NBFCs has introduced proportionality to NBFC regulation, and as a result the largest NBFCs are more stringently regulated. The introduction of ownership-neutral supervision of NBFCs101 will ensure better regulatory oversight of the sector. Steps to tighten NBFC regulation should be complemented by measures to improve their access to liquidity (especially for small and medium NBFCs that face challenges in accessing financing). Access to government long-term lending support during the pandemic was limited to the healthier NBFCs (see Annex 3.4). The government and RBI could consider a more permanent liquidity arrangement for NBFCs in the form of periodic liquidity facilities through DFIs, TLTROs, and partial credit guarantee schemes. • Strengthening digital financial services. Digital payments have grown significantly but some challenges remain in the payments ecosystem. The UPI payment system is now the dominant retail payments system and it accounts for a large part of overall digital payments. However, important issues remain such as: concentration in the UPI ecosystem (two 100 Factoring refers to transactions where the business sells its receivables or outstanding invoices at a discount to a third party (or, “factor”) to meet immediate liquidity needs, with the third-party then responsible for the collection of the receivables. 101 Few large and state-owned NBFCs face less stringent supervisory requirements with respect to exposure and corporate governance. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 109 Chapter 6 Program of Reforms to Achieve India’s High-Income Aspirations Third-Party Application Providers handle more than 80 percent of customer-initiated transactions, operating mostly through four Payment System Providers (PSPs) banks), and consumer protection. The open finance framework could be developed further through integrating liability data, including payment initiation services and multi-party arrange- ments supported by open application programming architecture (API) connections (instead of bilateral connections through sponsor banks). ii) Removing barriers to FDI. Policy improvements could be considered as follows • Rationalizing minimum investment requirements: Foreign investment in multi-brand retail trading, for instance, is subject to a minimum investment of USD100 million. At least 50 percent of the first tranche of this amount needs to be invested in “back-end infrastructure” within three years; • Assisting companies to meet mandatory local sourcing requirements: Foreign investors, with FDI exceeding 51 percent, need to source at least 30 percent of the value of goods purchased (for resale to the ultimate customer, and now exports) from India • Resolving inconsistencies between national and state level policies. These inconsistencies along with weak state capacity to formulate and implement investment policies impede the ability to attract FDI. In areas such as tourism and manufacturing, governing legislation is the responsibility of both the states and the central government. Although 100 percent FDI is allowed in tourism under the automatic route, some tourism-related areas are included in restricted lists. iii) Unlocking financing for infrastructure. India is currently making a big push on infrastructure. But the needs are such that public financing alone will be insufficient, and the participation of the private sector needs to increase. • Strengthening project preparation systems. Weak project preparation systems mean that projects are sometimes taken to procurement without the requisite readiness; this eventually causes cost and time overruns during implementation.  The reasons for time overruns as reported by project implementing agencies include delays in finalizing detailed engineering, changes in the scope of the project, and issues in tendering, ordering and equipment supply. These are issues that can be addressed through appropriate project planning and development and the deployment of project screening toolkits. • Scaling up blended finance tools. Blended finance has emerged as a useful financing structure, especially to enhance private participation in sustainable development initiatives. In the recent Union Budget, the government announced setting up thematic funds for blended finance for climate action, Deep-Tech, Digital Economy, Pharma and Agri-Tech. Recently, the Securities and Exchange Board of India (SEBI) also altered the guidelines for AIFs to enable waterfall struc- tures, allowing different class of investors to have different return/loss profiles. It would be critical that these blended finance structures are implemented swiftly, and the guidelines are reviewed frequently to include new class of investors to take up first loss/capped return positions in the blended finance structures. • Guarantee Fund and Credit Enhancement Tools. Credit enhancement mechanisms can encourage institutional inves- tors to fund infrastructure projects. Currently, strict regulatory requirements require such investors to invest only in safe government and public sector bonds. Most infrastructure projects are rated below “AA”; for institutional investors to access these projects through capital market instruments, it is critical to enhance their rating. To enable this market, the RBI can review the credit enhancement guidelines, allow DFIs to offer such products in the market, and introduce guidelines (including capital adequacy rules) in line with international best practices. iv) Targeting public investments in key sectors crowd in private investments, particularly in sectors such as agriculture and allied activities, urban development, and transport. The government’s capacity to target public investments has been strengthened by significant progress in digitalization across all levels of government. Reforms to enhance fiscal space for the states to undertake more productive expenditures would also enhance the capacity of the government to sustainably increase targeted public investments (see Section IV). 110 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 6 Program of Reforms to Achieve India’s High-Income Aspirations III. Creating Nabling Conditions for More and Better Jobs The analysis in chapter 4 shows that boosting job creation requires a dedicated focus on growth in labor intensive sectors. Indeed, some subsectors of the economy (such as agro-processing, intermediate manufacturing, and traditional market services) are rela- tively less productive than others but have a significant potential to absorb low skilled labor; conversely subsectors like modern market services and advanced manufacturing can create skill-intensive and high productivity jobs but not at the scale required to absorb India’s vast labor force102. i) To support job creation in relatively low-productivity sectors, cross-cutting priorities include easing factor market regulations and providing basic infrastructure facilities. Improving learning outcomes and training workers in market-rel- evant skills will increase labor productivity in the long-term. Steps to make the business environment more competitive (by easing factor market regulations and improving access to basic infrastructure, such as water supply and reliable power supply), can support the creation and growth of firms in these sectors. • Agro-processing industry. For this sector, human capital interventions should focus on improving healthcare infra- structure and and achieving better learning outcomes at primary and secondary levels. In the case of food processing, binding constraints include restrictive labor regulations, unavailability of adequate storage and distribution facilities, land constraints, unreliable supply of power and water and inadequate quality of road infrastructure. Firms in the textiles industry are impeded by the inverted duty structure in the man-made fibre (MMF) segment, unscalable business model of subcontracting, insufficient access to credit and less-advanced technology. Creating industrial clusters (through the geographic concentration of companies, suppliers, service providers, institutions and supply-chain relevant infrastructure for industries), especially of agro-processing MSMEs, would help alleviate these constraints. • Traditional market services and intermediate manufacturing. Between 40-45 percent of workers in these sectors have secondary or higher-secondary level of schooling: human capital interventions could consist of private-public partner- ships in training programs. According to the World Bank Enterprise Survey (2022), only 8.7 percent of firms in traditional market services and 8.9 percent of firms in intermediate manufacturing provided formal training to their employees, while 17.8 percent and 14 percent of firms in these sectors, respectively, reported an inadequately educated workforce as a major constraint. For firms in traditional market services, other important determinants of expansion are governance and quality of institutions, access to finance, and physical infrastructure. In contrast, factors constraining intermediate manufacturing include restrictive labor regulations, inadequate logistics support, inverted duty structure on key inputs, and insufficient investment in R&D and technology adoption. These factors are believed to have limited FDI inflows into the intermediate manufacturing industry. ii) To support high-productivity sectors, policy can focus on tertiary education, improving the capabilities of firms and workers to adopt technology, and creating an innovation ecosystem. Modern market services and machinery manufacturing cumulatively account for only 5.8 percent of total employment but employ the most high-skilled workers (graduate degree and diploma holders). The most important policy human capital intervention is therefore to improve the tertiary education network. The government aims to increase enrollment at tertiary-level to 50 percent by 2035 while enhancing the quality of tertiary education, making graduates more employable, and increasing funding of research in tertiary schools targeting priority sectors. • Modern market services would benefit from: (i) further expansion in the quality of the ICT infrastructure, especially in rural areas; (ii) further FDI liberalization; (iii) the removal of barriers to competition such as the inability to appeal decisions by the regulatory body, restrictions on advertising, and the ability of the government to overrule regulatory decisions. 102 Annex 6.3 discusses selected sector-wise proposals in more detail. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 111 Chapter 6 Program of Reforms to Achieve India’s High-Income Aspirations • Transport and electrical equipment and machinery (TEEM) activities are currently constrained by an inverted duty structure in automobile and machinery manufacturing (which results in higher costs of domestic production using imported inputs), inadequate skills among educated workers, and restrictive factor market regulations. iii) Boosting women’s participation in paid activities requires better access to child and elderly care, skilling, formal credit and infrastructure. Subsidizing informal childcare provision. There are different approaches to subsidizing informal childcare provision, including the provision of tax incentives and allowances for care givers and recipients (World Bank Group, 2015). • Customizing skills training. There is growing evidence that training programs focusing on soft skills (self-esteem, decision-making) are most effective in increasing female employment and the bargaining power of women within the household (Yousefy, 2011). • Improving access to finance. Policy options to improve access to finance for women include tailored outreach approaches to women-owned MSMEs, technology-enabled risk assessment, enforcement of nondiscrimination laws for access to financing, and financial products targeting women and preferential lending programs (World Bank, 2022). • Making laws more gender-neutral. Maternity and childcare laws can make hiring women costlier, while gender-neutral policies, such as mandatory paternity benefits and creches at workplaces reduce bias by promoting shared responsi- bilities. Labeling women as “vulnerable,”, may limit job opportunities, as seen in the Factories Act’s work restrictions on several factory processes across industries without scientific evidence on health risks to women. Expanding family planning access empowers women, enabling delayed childbirth and greater workforce participation. • Facilitating safer mobility. Enhancing women’s workforce participation in India requires targeted improvements in transportation, safety, and housing infrastructure. Women face greater mobility challenges than men due to heightened safety risks while commuting, limited access to secure public transit, and norm-related restrictions on traveling alone or at late hours. Poorly lit roads and inadequate transportation options further discourage their movement, restricting job opportunities. Additionally, the lack of affordable and safe housing for single women, such as hostels and boarding houses, limits their ability to migrate for work. Expanding transportation networks, ensuring well-lit and secure public spaces, and increasing gender-responsive housing options—integrated into initiatives like AMRUT—can create a more inclusive urban environment, enabling more women to access and sustain employment. • Improving access to technology and undertaking targeted recruitment initiatives. Increasing access to technology and targeted recruitment initiatives is crucial for expanding women’s workforce participation. Digital infrastructure and literacy empower women to overcome household and geographical constraints, and provide remote and flexible work opportunities. Targeted recruitment improves job awareness, while policies supporting female entrepreneurs in male-dominated sectors help break gender segregation. IV. Facilitating the States to Grow Together Reducing the gap between the higher and lower income convergence groups by accelerating the catch-up growth of less developed states will be critical to achieving India’s high- income aspirations. Some degree of convergence is essential since the less developed states are the most populous and home to most of India’s poor (see Chapter 5). A differentiated policy approach, rather than a “one size fits all”, can support the states in the low-income groups to achieve more rapid growth and those in the high-income groups to sustain their growth. i) Less developed states have significant potential to realize productivity and employment gains by promoting the manufacturing sector. The convergence analysis (see Chapter 5) shows an overlap in states with relatively higher per capita income levels and higher share of manufacturing in GVA. For the less developed states, which are characterized by a relatively higher share of the population employed in agriculture, creating jobs in the manufacturing sector is a priority due to the relatively higher productivity gains and greater potential for job creation. The productivity gains from additional 112 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Chapter 6 Program of Reforms to Achieve India’s High-Income Aspirations investment in manufacturing are likely to be higher than creating high productivity jobs in services which are likely to have higher skill requirements. These states can focus on improving their investment climate to attract manufacturing firms not only by improving the quality of infrastructure but also through factor (land and labor) market reforms, given that most of these states score low on policy-related variables (see Chapter 5). Many less developed states are disadvantaged by their location and economic geography, by being landlocked or having difficult terrain. However, states such as Sikkim, Himachal Pradesh, and Uttarakhand have managed to compensate for these disadvantages through policy choices, for example by providing reliable and inexpensive energy. ii) Relatively developed states can emphasize higher-productivity, modern marketable services to attract more private investment. These states would need to focus on the next generation of reforms. Policies that facilitate access to interna- tional markets, improve trade facilitation, reduce regulatory burdens, and barriers to achieving scale for firms would help to sustain growth in these states. iii) National level interventions can assist districts to do better. States also need to push the districts in the lowest district- groups out of a low-level equilibrium trap. Most of the districts belonging to the same district-group are geographically contiguous and are in located in clusters. A successful example of a nationally led policy intervention is the NITI Aayog’s “Aspirational Districts” program, launched in 2018, aimed at measuring incremental progress across various socio-economic indicators in 112 least-developed districts and focusing on areas where rapid progress could be made. An early appraisal of the program found that program districts outperformed non-program districts across several indicators of health and nutrition, as well as financial inclusion (UNDP, 2020). Another approach to boost growth in less developed regions is to focus on direct interventions by the central government on the top-performing districts in the low-income district groups. Such interventions could be helpful in attracting more investments in the district that is already performing relatively well. This can, in turn, help the district-group to grow through agglomeration effects. Other “spatially connective” interventions by the central government, including strengthening of the GST framework, incentivizing internal migration, and reducing the digital divide, would complement this initiative and reduce spatial disparities. iv) Availability and quality of public services for private sector development are also key requirements. The availability and quality of support services such as investment promotion and facilitation, single-window clearance, customs and trade facilitation and telecommunication facilities, have a positive impact on exports, FDI and employment. Cluster-based programs for manufacturing such as industrial parks, estates and SEZs do not guarantee improvements in firm performance. The success of spatial policies is also dependent on the overall macroeconomic policy framework as well as factors such as the quality of local infrastructure and governance (see Chapter 2). The quality of public support services matters for firm performance both inside and outside these designated zones. These public services include governance, trade, connectivity, and infrastructure support and fiscal incentives. There are significant differences in the availability and perceived quality of services across states. Thus, enhancing the availability of the missing support services and improving their quality can improve growth performance and complement efforts to improve physical infrastructure. v) Increasing own tax revenue collections and enhancing subnational fiscal frameworks can contribute to increased subnational fiscal space for productive expenditures.103 Given the existence of large vertical imbalances and expen- diture-side budget rigidities, increasing own revenue generating potential of states remains a priority. Subsequently, enhancing subnational fiscal frameworks to embed specific incentives for improving the quality of expenditure can bolster the long-term growth prospects of the states. 103 Chapter 5 shows, for example, that increasing the share of government development spending, particularly on urban development, transport, and agriculture and allied activities is estimated to boost the growth impact of state fiscal policy. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 113 Chapter 6 Program of Reforms to Achieve India’s High-Income Aspirations References Chaurey, R., R. Manghnani, R., V. M. Perego, and S. Sharma. 2022. Firm Level Input Distortions in Indian States. World Bank Policy Research Working Paper 10048. May, 2022. International Finance Corporation (IFC). 2021. “Opportunities and Constraints of Women Owned Very Small Enterprises in India.” accessed at https://www.ifc.org/en/insights-reports/2022/opportunities-and-constraints-of-wvses-in-india Gupta, D. B., Shashanka Bhide, Deepak Sanan, Charu Jain, Falak Naz, Somnath Sen, Prerna Prabhakar, Aswani Munnangi, Chandni Mishra, Disha Saxena, Arundhati Sharma, Vijay Singh Bangari, Apoorva, and Rupal Taneja. 2021. “Land Records and Services Index (N-LRSI) 2021”, NCAER. United Nations Development Program. 2020. Aspirational Districts Program: An Appraisal. Accessed at https://www.undp.org/ sites/g/files/zskgke326/files/2023-06/adp_report.pdf OECD. 2019. “Innovation, productivity and sustainability in food and agriculture: Main findings from country reviews and policy lessons” accessed at https://one.oecd.org/document/TAD/CA/APM/WP(2018)15/FINAL/En/pdf Yousefi, A., 2011. The impact of information and communication technology on economic growth: evidence from developed and developing countries. Economics of Innovation and New Technology, 20(6), pp.581-596. 114 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation © 2021 Talukdar David/Shutterstock VOLUME II Technical Annexes India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 115 Volume II Technical Annexes Annexes to Chapter 1 Annex 1.1: Estimating Potential Long-term Growth using the Long-Term Growth Model (LTGM) The LTGM is a Excel-based neoclassical model based on the Solow Swan growth model (Loayza and Pennings 2022; https://www. worldbank.org/LTGM). The growth rate of GDP (Y) depends on assumed exogenous growth rates of Total Factor Productivity (TFP, A), labor supply (L, based on population growth, demographics and participation rates), human capital per worker (h, based on schooling), as in Equation 1. It also depends on physical capital stock (K), which evolves over time based on investment (I) (which is assumed to be a fixed share of GDP) and depreciation rates ( in Equation 2. Time is in years, with further details in the appendix. (1) Yt​ ​  ​ At​ ​   =  ​  ​ ​Kt​  ​ ​   β​  1− (​  ​  ​ ​  ​ ht​ ​ ​Lt​)​  ​ ​  β​ (​β​=52.2% (labor share) from PWT10) ( (labor share) from PWT10) ​ K​  ​ ​ (​δ​=5.7% from PWT10; ​ _ ​ ​ Kt​ (2) ​​  ​ ​ (1 −  δ)​   =  ​ Kt​  ​  −1​ ​ It​   + ​  ​ Y​ ​ ​= 3.18 from MFMOD database)     ​ ​ 2020 2020 Growth fundamentals are assumed to evolve based on a continuation of historical trends, adjusted for the COVID-19 shock. Most important (affecting GDP growth 1:1) is TFP growth, which is expected to grow at a rapid 2-2.5 percent based on historically fast TFP growth in India. Next most important in investment, which averages 32 percent of GDP (very similar to the post-2000 average). Human capital growth is expected to average 1.1 percent going forward, based on rapid growth from Penn World Tables (PWT10) over the past decade. Investment, TFP growth and human capital growth are expected be slower in the short term due to effects of COVID-19, before recovering by 2030. Population growth (UN projections) is expected to slow by 0.8 ppts due to declining fertility, with a positive demographic dividend in the early 2020s, turning into a drag by the late 2050s. Labor force participation (LFP) is assumed to be flat, with substantial upside potential weighing against a recent history of falling female LFP. Potential GDP growth is simulated to recover to 6.5 percent by 2035 before slowing to 5.5 percent by 2050. Short and medium run potential growth in the LTGM is below IMF (WEO) and World Bank GDP growth forecasts of around 6.5%, as some of the latter reflects strong post-pandemic demand. Longer term slowing of growth reflects the maturing of the economy (with lower assumed TFP and investment growth), slowing population growth and less favorable demographics. While per capita growth follows the same pattern, it is boosted by slowing population growth. 116 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Volume II Technical Annexes Annexes to Chapter 2 Annex 2.1: Firm-level technology adoption TABLE 2.11: Access to ICT Technology for Firms Technology Mean Std. Dev. Small Medium Large Manufacturing Services Having Telephone 86.9% 0.34 82.0% 90.8% 98.8% 83.4% 90.1% Having Mobile Phone 95.9% 0.20 96.3% 95.2% 96.1% 94.9% 96.7% Having Computer 96.5% 0.18 93.4% 99.7% 100.0% 98.3% 94.8% Having Smartphone 86.0% 0.35 83.4% 88.0% 91.9% 85.2% 86.6% Having Internet 97.8% 0.15 95.9% 99.8% 100.0% 98.8% 96.9% Type: Dial Up Internet 3.1% 0.17 3.4% 2.9% 2.2% 1.9% 4.3% Type: DSL Internet 20.0% 0.40 19.6% 17.8% 34.2% 23.2% 17.1% Type: Wireless Internet 70.2% 0.46 72.5% 71.3% 49.9% 66.7% 73.4% Type: BPL Internet 0.8% 0.09 1.1% 0.3% 1.9% 0.7% 0.9% Type: Other 5.0% 0.22 2.5% 6.9% 11.5% 6.7% 3.5% Internet disruption per month 1.68 1.54 1.53 1.69 2.51 1.76 1.61 Number of Telephones 2.06 2.27 1.42 2.51 3.88 2.04 2.08 Number of Mobile Phones 4.22 10.03 2.62 4.01 15.59 4.98 3.52 Number of Computer 6.10 41.35 2.49 6.13 29.25 5.76 6.41 Number of Smartphone 2.68 5.51 1.68 2.66 9.22 2.91 2.46 Source: Firm Level Technology Adoption in India: Evidence from Uttar Pradesh and Tamil Nadu, World Bank, 2022. Cirera, et. al. (2021). India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 117 Volume II Technical Annexes Annex 2.2: Trade Integration104 The model underlying the simulations is the Global Trade Analysis Project–Foreign Direct Investment Regional Integration between India and South Asia and Southeast Asia. The scenarios are as follows: • Scenario 1 (Shallow Integration). A uniform 90 percent cut in bilateral tariffs between India and South Asian and Southeast Asian countries across all sectors. • Scenario 2. Scenario 1 plus a 50 percent reduction in bilateral non-tariff barriers (NTBs) between India and South Asian and Southeast Asian countries. It is assumed that 50 percent of the total non-tariff trade costs are actionable (possible to be affected by policy measures), and 50 percent of these actionable barriers are liberalized – a conservative assumption for potential integration scenarios. The assumption of 50 percent liberalization is aligned with the literature that assumes that only about half of the NTMs are actionable. • Scenario 3. Scenario 2 plus improvement in trade facilitation implying a 15.5 percent reduction in trade costs between South Asia and Southeast Asia. According to Moïsé and Sorescu (2013)105, implementing the WTO TFA could reduce trade costs by up to 15.5 percent. Bilateral trade costs come from the United Nations Economic and Social Commission for Asia and the Pacific–World Bank trade costs database. • Scenario 4 (Deep Integration) Scenario 3 plus South Asia–Southeast Asia FDI liberalization, closing half the bilateral FDI gap. • Scenario 5. Sustained US-China trade tensions – as specified in Petri and Plummer (2020)106, this scenario assumes an increase in US-China tariffs in place by December 2019, 10 percent increase in agricultural and manufacturing US-China NTBs, 50 percent increase in US-China services NTBs, and US-China FDI barriers double. • Scenario 6 Open regionalism – India integrates with an open South Asia and Southeast Asia region that extends the same liberalization to the rest of the world and regionally within South Asia and Southeast Asia. • Scenario 7. India integrates with South Asia and Southeast Asia without intraregional South-Southeast Asia integration. The GTAP-FDI model—a multi-region, multi-sector, and multi-factor computable general equilibrium model (Lakatos and Fukui, 2013).107 The model is calibrated using the GTAP 11 database representing the global economy in 2017 (Aguiar, et. al., 2022)108 and endogenously updated to 2021 to capture the latest macroeconomic developments. To capture the FDI and multinational linkages of the GTAP-FDI model, the GTAP database has been complemented with an explicit breakdown of FDI stocks and cross-border operations of multinational companies. The model is ideal for measuring the impact of policies that have wide-ranging effects as it takes into consideration general equilibrium linkages. These include interactions between consumers, producers, and governments; inter- and intra-industry links; interactions between domestic and foreign markets; investment decisions; and resource constraints. For simulations, the GTAP database has been aggregated into 17 sectors and 31 economies/regions. The 17 sectors are: vege- tables and fruits, other agriculture, energy, meat, other food, beverages and tobacco, textiles, wearing apparel, motor vehicles, machinery, other manufactures, construction, trade, transportation, public administration, business services, and other services. The 31 economies/regions are: Australia; New Zealand; China; Hong Kong SAR, China SAR, China; Japan; the Republic of Korea; Mongolia; Taiwan, China; Brunei; Cambodia; Indonesia; the Lao People’s Democratic Republic; Malaysia; the Philippines; Singapore; 104 This annexure uses material from the World Bank Report titled “Deepening Linkages Between South Asia and Southeast Asia: Synthesis Report, June 2022” prepared by Csilla Lakatos. 105 Moïsé, Evdokia and Silvia Sorescu. 2013. Trade Facilitation Indicators: The Potential Impact of Trade Facilitation on Developing Countries’ Trade. OECD Trade Policy Papers 144. 106 Petri, Peter A., and Michael G. Plummer. 2020. East Asia Decouples from the United States: Trade War, COVID-19, and East Asia’s New Trade Blocs. Working Paper Series WP20-09, Peterson Institute for International Economics. 107 Lakatos, Csilla, and Tani Fukui. 2013. Liberalization of Retail Services in India: a CGE Model. Office of Economics Working Paper No. 2013-03A, US International Trade Commission. 108 Aguiar, angel, Maksym Chepeliev, Erwin Corong, and Dominique Van Der Mensbrugghe. 2022. The Global Trade Analysis Project (GTAP) Data Base: Version 11. Journal of Global Economic Analysis, Volume 7 (2022), No. 2, pp. 1-37. 118 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Volume II Technical Annexes Thailand; Vietnam; the rest of East Asia and Pacific; India; India; Nepal; Pakistan; Sri Lanka; the rest of South Asia; the United States; the rest of North America; Latin America and the Caribbean; the European Union; Europe and Central Asia; the Middle East and North Africa; and Sub-Saharan Africa. The important innovation in the GTAP-FDI model concerns the explicit treatment of FDI. In the model, FDI is associated with the international mobility of capital and firms operating across national borders. As in the pioneering work of Petri (1997),109 the GTAP-FDI model employs the Armington assumption of national product differentiation to distinguish between product variet- ies differentiated not only by firm location, but also by firm ownership. In comparison, the standard GTAP model distinguishes product varieties by firm location only. In addition, as in Petri (1997), the model defines investor preferences as a nested imperfect transformation function that allocates a given investment budget across sectors and regions. On the supply side, compared with the standard GTAP model where in a given region and sector there is one representative firm, the GTAP-FDI model differentiates between domestic firms and foreign-owned affiliates of multinational companies that also produce goods and services. Further, each of these firms combines value added and intermediate inputs using a Leontief production technology. The specification implies that intermediate inputs (just as final demand) are differentiated not only by the region of firm location, but also by the region of firm ownership. Although the model is not dynamic, the closure has been adapted to capture the effects of policy reforms. This implies that invest- ment adds to the existing capital stock and is available for production (Walmsley 1998).110 The modeling of FDI and foreign affiliates requires the following data: capital stocks disaggregated by region of ownership/location and sector, and cost and sales structure of domestic firms and foreign affiliates. The global FDI stock data documented in Bekkers, et. al. (2021)111 is used to disaggregate capital stocks by industry, host, and source country in the GTAP database. Using the foreign affiliates sales data described in Bekkers, et. al. (2021), the production side of the GTAP database is disaggregated. This analysis uses the World Bank–UNESCAP trade costs database (Arvis, et. al. 2016).112 This database is the only one available that provides estimates of non-tariff trade costs at the bilateral level, which is essential for the analysis of deeper South Asia–Southeast Asia regional integration described in this report. In the scenarios, it is assumed that 50 percent of the total non-tariff trade costs are actionable (possible to be affected by policy measures), and 50 percent of these actionable barriers are liberalized—a conservative assumption for potential integration scenarios. 109 Petri, P. 1997. The Case of Missing Foreign Investment in the Southern Mediterranean. OECD Development Centre Working Papers No. 128, OECD, Paris. 110 Walmsley, T. L. 1998. Long-Run Simulations with GTAP: Illustrative Results from APEC Trade Liberalisation. Centre for Policy Studies, London. 111 Bekkers, E., E. Corong, C. Lakatos, I. Macskasi, J. Metivier, and S. Wettstein. 2021. Foreign Affiliate Sales Data. Working Paper presented at the 24th Annual Conference on Global Economic Analysis. Global Trade Analysis Project, Purdue University. 112 Arvis, Jean-François; Saslavsky, Daniel; Ojala, Lauri; Shepherd, Ben; Busch, Christina; Raj, Anasuya; Naula, Tapio. 2016. Connecting to Compete 2016: Trade Logistics in the Global Econo- my--The Logistics Performance Index and Its Indicators. World Bank, Washington, DC. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 119 Volume II Technical Annexes Annexes to Chapter 3 Annex 3.1: Domestic savings The decline in investments post- GFC was accompanied by a decline in household saving. Within domestic sources of savings, the household sector contributes the most to capital formation (~40 percent on average over 2012-2020) followed by private non-financial corporations (36 percent). The decline in investment was accompanied by a decline in gross domestic savings by about 6 percentage points of GDP between 2012 and 2020. Among peer countries, China and Indonesia have witnessed declines of smaller magnitudes, with only Philippines registering a greater decline over this period (Figure 3.11). The decline in saving was driven primarily by a drop in household savings by 4.1 pps, while the savings of non-financial corporations increased marginally. Households significantly reduced savings held in physical assets over this period, but this did not translate into higher savings in financial instruments (Figure 3.12). Despite this decline, domestic savings in India are on par with most peers (excluding China). FIGURE 3.11: Gross Domestic Savings FIGURE 3.12: Components of Gross Domestic Savings (percent of GDP) (percent of GDP) 60 20 50 15 10 40 5 30 0 20 (5) 10 (10) 0 (15) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Non- nancial corporations Bangladesh China India Financial corporations Indonesia Philippines Viet Nam General Government Households net nancial saving Households saving in physical assets Households saving in the form of gold & silver ornaments Source: MOSPI and WDI, 2022. 120 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Volume II Technical Annexes Annex 3.2: Estimating India’s ICT capital stock and investment share Decomposing ICT capital stock from the aggregate capital stock entails using data from various sources and involves several steps. The methodology employed follows the procedure outlined in Erumban and Das (2020) with minor modifications. Constructing Software and Hardware Investment Shares The analysis starts with determining the investment share of ICT capital. The methodology assumes that ICT capital comprises two distinct components—software, and hardware— with communication equipment being a subset of the latter. Additionally, the methodology assumes that ICT capital was non-existent before 1970. The input-output database (OECD 2021) of the OECD serves as the primary source for hardware investment data113. These data are obtainable from 1995 onwards. To extrapolate the hardware investment (as share of GDP) series back to 1970, the trends observed in US hardware investment share, as reported in the EUKLEMS database (Jager, 2017), are utilized. The estimation of software investment share requires data on fiscal year investments in Intellectual Property products, as reported in India’s National Accounts (Statement 1.11, Gross Fixed Capital Formation by asset and institutional sector). Unfortunately, these data are only available from the fiscal year ending 2012, which presents a challenge in extending the series backward. To overcome this limitation, the methodology leverages data from the World Information Technology and Services Alliance (WITSA) reports (WITSA 2010), which provide aggregate spending Figures for software and hardware114. By utilizing the trend in software-to-hard- ware spending as observed in the WITSA reports, the software investment share series is extrapolated back to 1993, the last year for which data are available in the WITSA reports. Subsequently, to arrive at the final series, trends in software-to-hardware spending in the US, as obtained from EU KLEMS, are applied to years prior to 1993. Constructing Capital Stock To derive the real capital stock, two inputs are required – capital-specific price deflators and depreciation rates. The price deflators are adjusted for inflation differentials between the US and India by employing the US hedonic deflators (BEA 2022) as follows: ∆ ln​ ​ ​ Pxt ​  ​  IND  = ∆ ln​ ​ PYt ​  ​  IND +  ∆ ln​ ​ US ​  ​ Pxt Pxt ​ − ∆ ln​ ​ US ​  ​ {​  ,  x ∊  ​ ​ }​ C, N​  ​ ​ ​   (1) where ∆ ​J   Pxt ​  ln​   ​​denotes the change in price of capital x in country J, and ∆   ​ ​  ln​ ​​represents the change in aggregate prices in country J. ​J   PYt     ​ To obtain the capital stock series, the perpetual inventory method is employed, starting from a baseline of zero software and hardware capital in 1969 ​(​ Kx​   =  0)​  ,1969​ ​ .​. Starting with no software and hardware capital in 1969 the capital stock series can be constructed using the perpetual inventory method: Kxt ​   ​​ ​ Kx   = ​ (1 − ​ ​ , t−1​​ )​ δx​ ​​ + ​ ​  ​​ Ixt ​   (2) Previous studies (Jorgenson & Vu 2005, Erumban & Das 2020) have suggested an annual depreciation rate of 31.5 percent for software capital and 11.5 percent for hardware capital, which is adopted in this case. 113 OECD sector Computer, Electronic, and Optical Equipment serves as the counterpart for hardware goods. 114 The WITSA reports spending under three heads: software, hardware, and communication equipment. Expenditure on communication equipment is subsumed under the hardware category to ensure consistency with the aggregation scheme. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 121 Volume II Technical Annexes Annex 3.3: Trends in NBFCs since the IL&FS Crisis NBFCs advances moderated following shocks in quick succession in 2019. Advances of NBFCs accelerated post AQR, demonetization, and the modification of regulations by the Securities and Exchange Board of India (SEBI)115 in 2017 (Figure 3.31 and Figure 3.32). Incremental growth116 slowed 2018 onwards with the default of IL&FS in September 2018. The IL&FS defaulted on debt worth INR 910 billion (of which INR 570 billion was from public sector banks). This was followed by the default of the Diwan Housing Finance Limited (DHFL) in 2019. In the aftermath of the defaults, mutual funds drastically reduced their exposure to NBFCs, in turn leading to a moderation in their advances and borrowings. This was largely a “flight to quality” evident from the fact that mutual funds did not reduce exposure to government securities despite lower yields. FIGURE 3.31: NBFC advances and borrowings (INR FIGURE 3.32: Incremental growth (INR trillion) trillion) Demon IL&FS DHFL 25 Demon IL&FS DHFL 14 20 In INR trillion 12 15 In INR trillion 10 10 8 5 0 6 FY2016 FY2017 FY2018 FY2019 FY2020 FY2021 Loans and Advances Borrowing FY2015 FY2016 FY2017 FY2018 FY2019 FY2020 FY2021 Loans and Advances Borrowing Source: CRISIL data, Ganesh, Bhargava, Ghosh and Kulkarni (2023). Dependence of NBFCs on banks and financial institutions has increased. Non-HFC NBFCs as well as HFCs increasingly resorted to non-market sources of funding after the IL&FS and COVID-19 crises. In the case of non-HFC NBFCs, debentures comprised the largest share of borrowings in 2018 (39 percent), followed by banks and financial institutions (32 percent) and commercial paper (CP, 10 percent). In 2021, the latest year for which detailed data from CRISIL was available, the share of banks and FIs increased to 40 percent and that of debentures and CP declined to 35 and 4 percent, respectively. A similar trend is noticed for HFCs. Their share in bank and FI borrowings increased from 19 percent in 2018 to 27 percent in 2021, with the shares of debentures and CP declining as well. 115 In August 2016 and February 2017, the SEBI introduced two regulations that relaxed limits on the exposure of mutual funds to HFCs. 116 The difference in levels of advances and borrowings in a given fiscal year compared to the previous year. The results are based on Ganesh, Bhargava, Ghosh, and Kulkarni (2023) that analyzes data on a sample of NBFCs obtained from CRISIL Limited. The data is at annual frequency on operational and financial parameters for 180 large and systemically important NBFCs and HFCs. It covers 54 out of the 74 large (asset sizes greater than INR 100 crores) HFCs identified by the National Housing Board. The rest non-HFC NBFCs encompass 10 categories: auto finance, wholesale finance, diversified, diversified SME (Small and Medium Enterprises), Diversified LAP (Loans Against Property), microfinance, infrastructure finance, gold loans, educational finance, and construction equipment finance. The average lending across these 180 companies in FY 2019 is INR 19,333 crores. 122 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Volume II Technical Annexes FIGURE 3.33: Financing patterns of NBFCs based on Financial Health (2015=1) Non-HFC NBFCs HFCs A. Commercial Paper Demon IL&FS DHFL Demon IL&FS DHFL 25 4 (Normalized to 1 in 2015) (Normalized to 1 in 2016) 2 3 Total CP borrowing Total CP borrowing 1.5 2 1 1 0 0 FY2015 FY2016 FY2017 FY2018 FY2019 FY2020 FY2021 FY2015 FY2016 FY2017 FY2018 FY2019 FY2020 FY2021 Weak Healthy B. Short-term Loans Demon IL&FS DHFL Demon IL&FS DHFL 16 2 Normalized to 1 in 2015 Normalized to 1 in 2015 14 1.5 1.2 1 1 .8 .5 .6 0 FY2015 FY2016 FY2017 FY2018 FY2019 FY2020 FY2021 FY2015 FY2016 FY2017 FY2018 FY2019 FY2020 FY2021 Weak Healthy C. Debentures Demon IL&FS DHFL Demon IL&FS DHFL 3.5 2 Total debenture borrowing Total debenture borrowing (Normalized to 1 in 2015) (Normalized to 1 in 2015) 3 1.5 2.5 1 2 1.5 .5 1 0 FY2015 FY2016 FY2017 FY2018 FY2019 FY2020 FY2021 FY2015 FY2016 FY2017 FY2018 FY2019 FY2020 FY2021 Healthy D. Term Loans Demon IL&FS DHFL Demon IL&FS DHFL 5 15 Normalized to 1 in 2015 Normalized to 1 in 2015 4 10 3 5 2 0 0 FY2015 FY2016 FY2017 FY2018 FY2019 FY2020 FY2021 FY2015 FY2016 FY2017 FY2018 FY2019 FY2020 FY2021 Weak Healthy Source: CRISIL Limited. Ganesh, Bhargava, Ghosh, and Kulkarni (2023). India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 123 Volume II Technical Annexes Change in financing pattern of NBFCs since the IL&FS crisis After the IL&FS crisis, healthier NBFCs have had more access to long-term funding compared to the weaker ones. Figure 3.33 shows the changing financing patters of weak as well as healthy NBFCs over 2015 and 2021 (with the 2015 level normalized to 1)117. i) Long-term financing: In the case of non-HFC NBFCs, funding via debentures continued to increase, and did so to a greater extent for healthy NBFCs. HFCs witnessed a similar increase with healthy HFCs increasing funding via debentures 2018 onwards. Higher access to the corporate bond market, especially by healthy NBFCs, indicates the impact of the various RBI and government measures that targeted this market segment. This is also reflected in the much larger access to long- term borrowing from banks and FIs by healthy NBFCs since some of these measures were rolled out via the banks as well (Figure 3.33D). ii) Short-term financing: With the onset of the IL&FS crisis, NBFCs witnessed a decline in financing through commercial paper (CP) as mutual funds started to withdraw. The decline was steeper for weaker NBFCs (Figure 3.33A). For non-HFC NBFCs, the decline continued up to 2021. Healthy HFCs also reduced CP exposure to a greater extent than weaker ones over 2019 and 2021. NBFCs were able to compensate the deficit in CP funding, to some extent, by borrowing more short-term loans from banks and FIs (Figure 3.33B). However, this was the case for weaker non-HFC NBFCs up to 2020. 117 Analyzing the Capital Adequacy Ratio (CAR) just prior to the crisis episodes, ‘weak’ NBFCs are identified as those with below median values of CAR as of March 31st, 2018. 124 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Volume II Technical Annexes Annex 3.4: M ajor regulatory measures to manage stress in the banking sector and increase credit flow to the NBFCs Recent reforms were undertaken to resolve the stock of stressed assets and to stem future sustained rise in NPA ratios across the banking and non-banking segments. Major Government initiatives to improve stress-management in the financial system have been taken since 2016. • The RBI carried out an AQR in 2016 to measure the seriousness of banks NPAs. • A new Insolvency and Bankruptcy Code introduced in 2016 facilitates time-bound process to resolve insolvency. The law was amended in April 2021 to include a Pre-Packaged Insolvency Resolution Process (PPIRP) to facilitate speedier resolution of the debts of medium, small and micro enterprises (MSMEs) that were disproportionately affected by the COVID-19 pandemic. • NBFCs were included in the Insolvency and Bankruptcy Code (IBC) to aid NBFCs that were struggling to raise money post IL&FS, especially after the rating downgrades by credit rating agencies. The Ministry of Corporate Affairs issued a notification whereby NBFCs (along with HFCs) with an asset size of INR 500 crore or more were to be included under the IBC, and their insolvency resolution and liquidation proceedings to be carried out as per the provisions of the IBC. For NBFCs, the RBI was mandated to initiate the CIRP proceedings, per the FSP rules 2019 Insolvency and Bankruptcy (Insolvency and Liquidation Proceedings of Financial Service Providers and Application to Adjudicating Authority). • To expedite the resolution of the stock of NPAs, the government set up the National Asset Reconstruction Company Limited (NARCL) in 2021, complemented by the India Debt Resolution Company Limited, to support asset resolution, and provided capital infusions to support bank solvency. • Reforms also were undertaken to improve stress management in banks to stem the flow of new NPAs, including special mention accounts (SMA) classification for early stress recognition, automated Early Warning Systems (EWS) in banks using third party data and workflow for time-bound remedial actions, segregation of monitoring and sanctioning functions, and the establishment of stressed asset management verticals. • The Prompt Corrective Action (PCA) Framework for banks was revised in 2017 and extended to NBFCs (including deposit taking NBFCs and non-deposit taking NBFCs in the middle, upper and top layers) in 2022 to enable timely supervisory intervention by the RBI and the supervised entity to restore financial health. • The NBFC regulatory framework was overhauled to reduce regulatory arbitrage, given the increasing significance of NBFCs and linkages with the banking sector. • The number of public sector banks was reduced from 27 in 2017 to 12 in 2020 to lower costs, bolster the capital base and increase risk appetite. Enhanced Access and Service Excellence (EASE) reforms were also introduced to improve the oper- ational performance and governance of public sector banks. Measures to Increase Flow of Credit to the NBFCs IL&FS Crisis • A bank’s exposure to a single NBFC was increased to 20 percent of its Tier 1 capital from 15 percent of its Tier 1 Capital, effective April 1, 2019, harmonizing bank exposure to single NBFCs with other entities. • The limit of exposure of a bank to NBFCs was also harmonized with that of other sectors, from 10 percent of banks’ capital to 15 percent. The risk weights for banks’ exposure to NBFCs were harmonized with those of other corporates. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 125 Volume II Technical Annexes • A special FALLCR (Facility to Avail Liquidity for Liquidity Coverage Ratio) of 0.5 percent exclusively for lending to NBFCs was introduced in October 2018. • Credit to the Priority Sector – banks were permitted to on-lend through NBFCs: “Bank lending to registered NBFCs (other than MFIs) for on-lending to Agriculture (investment credit) up to INR10 lakhs; Micro and Small Enterprises up to INR 20 lakh and housing up to INR 20 lakh per borrower (up from INR 10 lakh) to be classified as priority sector lending”. COVID-19 • Targeted Long-Term Repo Operations (TLTRO): Funds received by banks were to be invested in investment grade corporate debt. • TLTRO 1.0, March 27, 2020: INR 1,00,000 crore (in four tranches of 25,000 crore each) for loans up to 3 years tenor, floating rate linked to repo. • TLTRO 2.0, 17 April 2020: Sought to address liquidity constraints faced by small and mid-sized corporates, including NBFCs and micro finance institutions (MFIs). INR 50,000 crore, up to 3 years tenor, floating rate linked to repo. In the first tranche, total bids received amounted to INR 12,850 crore, with a bid to cover ratio of 0.54. • TLTRO 3.0, October 9, 2020: To revive economic activity in certain important sectors like agriculture, micro, small and medium enterprises (MSMEs) and secured retail, amongst others. On-tap TLTRO of up to three years tenor for a total amount of up to INR 1,00,000 crore at a floating rate linked to the policy repo rate; extended till December 31, 2021. 126 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Volume II Technical Annexes Annex 3.5: Credit cycles and the implications of declining NPLs and changing credit composition Credit cycles and NPLs To trace credit cycles, quarterly data on total credit to the non-financial private sector from banks as well as non-banks is consid- ered over 1980Q1 and 2021Q2 for 23 advanced and 15 emerging market economies from the Bank for International Settlements (BIS). The nominal values are transformed into real values using the consumer price index. Determining credit cycles involves identification of turning points in the real log-level values. The methodology follows seminal work by Claessens et. al. (2011),118 to identify turning points or the local maxima and minima based on a censoring criteria. Applying this criterion to the cross-country data shows that the average (total) credit cycles in EMEs are shorter compared to those in AEs—implying that they encounter frequent turning points given that financial development is a work-in-progress in many EMEs, relatively more frequent shocks to trend growth, greater exposure to volatility in capital flows and frequent policy shifts that impact macroprudential norms and financial stability. The length of the credit cycle has shortened significantly in AEs post-GFC (Figure 3.51 and Figure 3.52). The length of the average cycle in EMEs—that was 16 quarters (4 years) prior to the GFC, has reduced marginally to 14.6 quarters (about 3.7 years) over the same period. But the change in the cycle length for India before and after 2009 has been starker. In the pre-2009 period, the length was about 7.8 years. It has reduced to below 3 years. Also, since 2009, the duration of both upturns and downturns has declined. Thus, credit in India is subjected to much shorter cycles in recent years compared to both AEs and EMEs (Figure 3.53). FIGURE 3.51: Pre-2009: Total credit cycle length, 1980- FIGURE 3.52: Post-2009: Total credit cycle length, 2009 (years) 2009-2021 (years) 9 10 8 9 7 8 6 7 6 5 5 4 4 3 3 2 2 1 1 0 0 AE EME India AE EME India Upturn duration Downturn duration Upturn duration Downturn duration Source: BIS and staff calculations. A “bad” peak is defined as a peak associated with both of the following: (i) The average real growth in per-capita income over the 5-years following a peak is lower by at least 2 percentage points compared to the average 5 years prior: and (ii) The investment rate (nominal investment as a share of nominal GDP) is lower by at least 2 percentage points compared to the average in the 5 years preceding the peak. 118 Claessens, S., M. Ayhan Kose, and M. E. Terrones. 2011. Financial Cycles: What? How? When?. IMF Working Paper WP/11/76. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 127 Volume II Technical Annexes FIGURE 3.53: India: Turning points in the total credit cycle 15.5 14.5 Peak Trough 13.5 12.5 11.5 10.5 9.5 8.5 1980q1 1981q1 1982q1 1983q1 1984q1 1985q1 1986q1 1987q1 1988q1 1989q1 1990q1 1991q1 1992q1 1993q1 1994q1 1995q1 1996q1 1997q1 1998q1 1999q1 2000q1 2001q1 2002q1 2003q1 2004q1 2005q1 2006q1 2007q1 2008q1 2009q1 2010q1 2011q1 2012q1 2013q1 2014q1 2015q1 2016q1 2017q1 2018q1 2019q1 2020q1 2021q1 Source: BIS and staff calculations. ‘Bad peaks’ are bad since real GDP growth and investment rate may not return to their previous peaks in their aftermath. In cases where they do, it takes 11-12 years for the growth and investment rate, on average, to revert to their respective pre-peak ‘highs’. The analysis shows that ‘bad peaks’ are rare. For India, out of the 7 peaks total credit peaks, only 2016 is detected as a ‘bad peak’. Using various databases, a cross-country pooled probit regression is run based on annual data over 2000-20 including both AEs and EMEs, the probability of ending up on a ‘bad peak’ given the 1-period change in the NPL ratio is estimated controlling for banking sector indicators (bank capital and concentration), private credit to GDP, the net international investment position, macro variables (inflation and real GDP growth) and an EME dummy. In this regression, the dependent variable takes the value 1 for each ‘bad peak’ identified in the sample and zero otherwise. The independent variables are measured in terms of 1-period changes leading up to a peak. The results of the regression indicate that a higher NPL ratio is associated with a higher probability of a cyclical peak turning ‘bad’. The probability estimate is obtained by calculating the Average Marginal Effect (AME) using the z-scores. The AME measures the probability of ending up on a ‘bad peak’ for every one-unit acceleration in NPLs (given the independent variables are in their first differences) keeping the changes in other variables at their averages. A 1 percentage point acceleration in the NPL ratio increases the probability of a cyclical peak turning ‘bad’ by 27-30 percentage points. The magnitude of the AME is robust to the alternative specification as well. Thus, the trend of declining bank NPL in recent years in India is a welcome development in that it dampens the likelihood of bad macroeconomic outcomes. Credit Composition and Growth To estimate the manner in which household and business credit expansion impacts the growth, a local projections model is used, following the specification in Jorda et. al. (2020),119 to estimate the effect of the expansion in business and household credit as percent of GDP on real per capita GDP growth after a peak. ​ ​ ∆​ In Table 3, ​  5​ ​χ​  ​  B ​  )​ (​ p​ it​ ∆​ 5​​ ​is the 5-year change in business credit as percent of GDP level over the 5 years after the peak, similarly ​ χ​ B ​   ​ )​ (​ p​ it​ ​​ is the change in household credit as percent of GDP over a similar period. The dependent variable for each regression for (1)- (5) is the approximate percentage change, i.e. deviations measured in log points times 100, in per-capita GDP from the year of peak till 119 Jorda, O., M. Kornejew, M. Schularik, and A. M. Taylor. 2020. Zombies at Large? Corporate Debt Overhang and the Macroeconomy. Federal Reserve Bank of San Francisco Working Paper 2020-36. 128 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Volume II Technical Annexes horizon h. Other macro variables that are controlled for include the current and past two year lagged values of real GDP growth rate, inflation, and real investment growth. In the case of the household shock there isn’t a significant drop in output growth in the short run; a significant drop in output growth is seen with a lag of 3 years. While the business credit shock leads to a decline in output immediately. Beyond 3 years, both shocks are seen to have similar impact on output growth. TABLE 3: Credit composition and cycle responses (1) (2) (3) (4) (5) h=1 h=2 h=3 h=4 h=5 Average cycle 0.01*** 0.03*** 0.04*** 0.06*** 0.07** (0.00) (0.00) (0.01) (0.01) (0.01) ​ ∆​ Business credit/GDP expansion ​  5​  ​ ​χ​ B ​​   (​ ​p​)​​ it​ -0.14*** -0.16*** -0.28*** -0.29*** -0.28*** (0.04) (0.04) (0.05) (0.07) (0.07) ​ Household credit/GDP expansion ​∆​  5​ ​χ​  ​  H ​​   i ( ​​p​)​​ t​ 0.04 -0.07 -0.22** -0.32*** -0.41*** (0.05) (0.06) (0.09) (0.09) (0.09) Macro controls Yes Yes Yes Yes Yes β​ ​  ​= ​β​  ​ B h B h  ​ ​ ​ (p-value) 0.016 0.212 0.566 0.824 0.334 R2 0.32 0.30 0.52 0.52 0.52 Cycles 116 116 116 116 116 Source: WB staff calculations using the Mueller and Verner (2020) database, following Jorda et. al. (2020). India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 129 Volume II Technical Annexes Annex 3.6: Expenditure efficiency analysis TABLE 3.61: Education Sector Multiple Output-Outcomes (percent of GDP) State Expenditure as GER Schools ASER Read ASER Math Teacher-Stu- Technical percent of GDP Computers dent Efficiency AP 0.02 100.10 4.12 64.70 51.80 0.04 1.00 1 BH 0.05 96.20 2.50 69.70 58.00 0.02 1.00 1 GJ 0.02 92.40 58.31 52.10 31.30 0.03 1.00 1 HP 0.04 106.00 8.37 87.60 48.20 0.07 1.00 1 HR 0.02 103.20 12.74 72.50 49.50 0.04 1.00 1 KE 0.02 101.00 49.56 81.80 39.90 0.04 1.00 1 MG 0.06 155.70 3.82 73.30 18.70 0.05 1.00 1 MN 0.06 117.60 11.01 77.50 53.70 0.08 1.00 1 MZ 0.08 137.50 14.25 86.00 41.30 0.09 1.00 1 NG 0.06 87.30 17.04 79.10 37.30 0.11 1.00 1 PN 0.02 109.60 33.11 82.60 44.50 0.04 1.00 1 SK 0.04 92.90 13.96 65.90 43.20 0.12 1.00 1 TN 0.02 98.80 32.70 62.80 43.50 0.04 1.00 1 TS 0.01 110.20 5.15 58.10 40.20 0.04 1.00 1 UT 0.03 113.20 14.36 81.00 40.00 0.06 1.00 1 KR 0.02 107.10 10.76 58.70 33.40 0.04 0.97 16 MH 0.02 104.30 8.18 75.20 38.10 0.04 0.95 17 CG 0.05 95.90 3.98 81.10 38.60 0.04 0.93 18 TR 0.05 109.10 2.80 65.50 43.20 0.05 0.91 19 OD 0.03 95.40 6.35 73.20 42.50 0.04 0.89 20 JH 0.03 97.00 2.86 62.70 43.20 0.02 0.88 21 UP 0.03 98.10 4.99 62.60 41.80 0.03 0.88 22 RJ 0.04 101.80 11.25 67.10 29.10 0.04 0.83 24 AS 0.05 109.80 5.38 63.60 21.70 0.04 0.81 25 MP 0.03 88.70 4.14 60.20 39.00 0.03 0.81 26 JK 0.05 90.10 3.94 50.20 26.30 0.07 0.75 27 130 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Volume II Technical Annexes TABLE 3.62: Multiple Output-Outcome Health Sector Efficiency (percent of GSDP) State Expenditure as Infant Survival General Survival Immunization TB Recovery Technical Rank percent of GDP Rate Rate Coverage Rate Efficiency KE 0.01 0.99 0.99 92.44 88.21 1.000 1 MH 0.01 0.98 0.99 98.94 81.33 1.000 1 MZ 0.03 1.00 1.00 100.00 87.58 1.000 1 NG 0.03 1.00 1.00 55.97 78.55 1.000 1 SK 0.02 0.99 1.00 62.85 84.55 0.999 5 GA 0.01 0.99 0.99 92.70 70.09 0.999 6 TN 0.01 0.99 0.99 85.16 84.35 0.997 7 PN 0.01 0.98 0.99 89.59 83.32 0.995 8 MN 0.02 0.99 1.00 83.65 79.70 0.995 9 KR 0.01 0.98 0.99 94.11 79.80 0.994 10 TS 0.01 0.98 0.99 100.00 90.17 0.989 11 GJ 0.01 0.97 0.99 90.97 81.99 0.988 12 HP 0.02 0.98 0.99 87.82 88.68 0.987 13 HR 0.01 0.97 0.99 93.46 82.41 0.987 14 JK 0.01 0.98 1.00 100.00 83.81 0.986 15 TR 0.02 0.98 0.99 95.38 87.31 0.983 16 AP 0.01 0.97 0.99 98.87 90.81 0.983 17 UT 0.01 0.97 0.99 93.63 85.24 0.981 18 JH 0.01 0.97 0.99 96.54 83.37 0.979 19 BH 0.01 0.97 0.99 94.50 73.56 0.976 20 MG 0.03 0.97 0.99 100.00 75.78 0.972 22 RJ 0.01 0.97 0.99 75.05 76.89 0.971 23 OD 0.01 0.96 0.99 85.61 88.40 0.968 24 AS 0.02 0.96 0.99 85.80 83.03 0.966 25 CG 0.01 0.96 0.99 94.69 83.59 0.966 26 UP 0.01 0.96 0.99 95.99 78.93 0.965 27 MP 0.01 0.95 0.99 90.98 81.24 0.960 28 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 131 Volume II Technical Annexes Annexes to Chapter 4 Annex 4.1: Challenges in calculating female labor force participation in India Reference periods: There are important discrepancies in data on the FLFP in India, which come from the definition of effective demand for work adopted in labor force surveys. Different definitions and methodologies are used to collect labor force data. The NSS classifies individuals as being in the labor force (either working or not), based on their activities over three reference periods. These are the usual status, which uses a combination of 365 days and 30 days reference period; current weekly status based on 7 days; and daily status based on a 1-day reference period. As can be expected, FLFPR and WPR are higher under usual status than under current weekly or daily status, but the differences are much smaller for males (NSS, 2011-12). Survey questions using shorter reference periods (weekly and daily) also use the priority criterion whereby the status of ‘working’ gets priority over other activity classifications even if the person did not spend a majority of the reference period working. Exclusion of productive activity from women’s work: The literature on Indian FLFP attributes the downward trend in LFP to discrepancies in the definition of women’s work (Hirway, 2015; Mondal, et. al., 2018).120 There are disagreements over the ‘true’ rate of FLFP. Research found that that fewer women may be participating because more of them are attending (and remaining in) school (Bhalla and Kaur, 2011),121 however the data on female participation overwhelming suggests household unpaid care duties prevent women’s participation. This may be attributed to some extent to methodological issues in the NSS as well as the PLFS. For more than 30 years, key economic indicators have been based on a framework adopted at the 13th International Conference of Labor Statisticians (ICLS) in 1982, classified individuals as employed, unemployed, or economically inactive (Mehrotra, 2022).122 These categories, and their accompanying defini- tions, were later considered insufficient to fully capture how individuals — women in particular — contributed to the economy or the well-being of their households. At the 19th ICLS in 2013, new, more gender-sensitive standards were adopted with refined definitions and additional categories. As these changes were introduced, a discussion arose on how this would affect data collection and impact women. However, India’s PLFS, which started in 2018, avoided adopting the upgraded international standard of the 19th ICLS defini- tions and questionnaire. Currently, whether an individual is participating in the labor force depends on the following survey question: i) working or being engaged in economic activity (employed): a) worked in household enterprise (self-employed) as an own-account worker b) worked in household enterprise (self-employed) as an employer c) worked in household enterprise (self-employed) as ‘helper’ d) worked as regular wage/ salaried employee e) worked as casual wage labour in public works other than Mahatma Gandhi National Rural Employment Guarantee (MGNREG) works f) worked as casual wage labour in MGNREG works g) worked as casual wage labour in other types of works h) did not work due to sickness though there was work in household enterprise (self-employed) i) did not work due to other reasons though there was work in household enterprise (self-employed) j) did not work due to sickness but had regular wage/ salaried employment k) did not work due to other reasons but had regular wage/ salaried employment 120 Hirway, I., 2015. Unpaid Work and the Economy: Linkages and their Implications. Indian Journal of Labour Economics, 58(1), pp.1-21. Mondal, B., Ghosh, J., Chakraborty, S. and Mitra, S., 2018. Women workers in India: Labour Force Trends, Occupational Diversification and Wage Gaps.Azim Premji University. 121 Bhalla, S. S., and R. Kaur. Labor Force Participation of Women in India: Some Facts, Some Queries. Asia Research Centre Working Paper 40, the London School of Economics. 122 Mehrotra, S. 2022. Reconceptualising Women’s Work in the National Sample Survey. The India Forum. 132 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Volume II Technical Annexes ii) not working but seeking or available for work (unemployed): a) sought work b) did not seek but was available for work iii) not working and also not available for work (not in labour force) : a) attended educational institution b) attended domestic duties only c) attended domestic duties and was also engaged in free collection of goods, tailoring, weaving, etc., for household use d) recipients of rent, pension, remittance, etc. e) notable to work due to disability f) others g) did not work due to sickness (for casual workers only). Substantial share of women who are considered out of the labor force “attended domestic duties and was also engaged in free collection of goods, tailoring, weaving, etc. for household use”. This exclusion leads to a direct underestimation of women who contribute to household enterprises. India’s definition of employment does not include all own-use activities. The Indian System of National Accounts (ISNA), which differs from the UNSNA—the UNSNA records activities such as processing of primary products for own consumption as economic activity (SNA 2008). The production boundary as defined by the UNSNA is much broader than the ISNA. The ISNA uses a narrower range of economic activity [categories (b), (c) and (d) in the Table below] and excludes various kinds of activities [categories (a), (e), (f ) and (g)] from the domain of economic engagement. This is important when calculating female workforce participation in agriculture: TABLE 4.1: Detailing the Criteria of ISNA for Economic and Non-Economic Work Nature of Work Economic Activity Non-Economic Activity a) All domestic duties (cleaning the hourse, washing utensils, XX cooking, caring for young and old b) Maintenance of kitchen gardens, orchards, etc XX c) Work in household dairy, poultry, etc. XX d) Free collection of firewood, cattle feed, fish XX e) Husking of paddy, grinding food grain, preparation of gur, XX making bskets etc. f) Preparing cow dung cakes for fuel, bringing water XX from outside the household premises for household consumption g) Sewing, tailoring, free tutoring of own/other’s children XX Source: Compiled from Participation of Women in Specified Activities along with Domestic Duties, NSS Report No. 559, 2011-12 and System of National Accounts, 2008. Mehrotra (2022) also finds that there is a possibility that the sample in urban areas may be missing out on significant parts of the population. India’s female LFPR is much lower in urban than in rural areas—the EUS and PLFS have both reported much lower female LFPR. In contrast, more locally based urban samples (for example, conducted by the Self-Employed Women’s Association) have shown much higher female LFPR than the EUS/PLFS. Thus, there may be another design issue that can be addressed, which is specific to the undercounting of women’s employment in urban areas. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 133 Volume II Technical Annexes Kapur et. al., (2021a)123 find that women’s labor force participation in India is hampered by shortcomings in data validity and data accuracy. Using granular survey data from households in four north Indian urban clusters, the authors demonstrate that the instru- ment of survey employed impacts on the reporting of female labor force participation. Furthermore, the gender composition of respondents also seems to matter although; after controlling for gender, self-reporting is indistinguishable from proxy reporting. The differences in reporting also vary between urban and rural households. Self-assessment of one’s own employment status is important in all surveys, particularly for the measurement of women’s work. For example, women working as unpaid workers in household enterprises may consider themselves only as helpers and not workers. This belief may be held by the surveyor, respondent, or the household head answering the survey questions. Further, women’s work can often be fragmented into different activities (taking care of animals, farming, selling produce) such that no single activity meets the time criteria for a specific reference period. Detailed time-use data for selected states in India find that 58 percent of rural women should be classified as working when considering the different activities women are regularly engaged in, whereas the equivalent measure from official surveys would be only 25 percent (Hirway and Jose, 2011).124 Desai and Joshi (2019)125 find that differences in WPR across surveys (NSS and the India Human Development Survey) are found mostly within household enterprises of farming and dairy where the notion of women’s work is most prone to underestimation. 123 Kapur, D., N. Sircar, and M. Vaishnav. 2021. Women, work, and the state: Service provision and female labour force. International Growth Centre Project. 124 Hirway, I., and Sunny Jose, 2011. Understanding women’s work using time-use statistics: The case of India. Feminist Economics 17.4 (2011): pp. 67-92. 125 Desai, S. and Joshi, O., 2019. The paradox of declining female work participation in an era of economic growth. The Indian Journal of Labour Economics, 62(1), pp.55-71. 134 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Volume II Technical Annexes Annex 4.2:Estimating the effects of IDA reforms in Andhra Pradesh and Telangana Exploiting the quasi-natural experiment caused by differing revisions to the IDA, the impact of labor reforms on firm growth can be studied using a difference-in-differences framework and the longitudinal data from the Annual Survey of Industries (ASI) from 1999 to 2020. The impact of the deregulation is assessed based on the evolution of employment characteristics and productivity of the establishments. The bifurcation of erstwhile Andhra Pradesh into the new states of Andhra Pradesh and Telangana was followed by IDA reform in Andhra Pradesh only. The Difference-in-Difference (DID) Analysis The DID specification for the two sets of models which exploits the longitudinal nature of the data as well as the variations across the two states of Andhra Pradesh and Telangana— is given as:   ​​ ​ Yist ​ αi​ ​​   =  ​ γt​ ​​   + ​ zist   + ​ ​  ​​  β + ​ ​  ​​ Dst  δ + ​ ​  ​​ eist Where, ​Yist ​  ​=Plant level indicator of interest ​ ​αi​ ​ ​ = Plant fixed effects  ​ ​γt​ ​= Time fixed effects ​  ​ ​zist ​=control variables ​ ​Dst  ​ ​ =Dummy variable indicating plants in the treatment group in the post-treatment period ​ The estimated δ ​ ​gives the average treatment effect on the treated group and can provide causal inference for the impact of dereg- ulation provided the parallel trends assumption is satisfied. The two models have the same treatment group but differ in their control groups. These models are described as follows: Model 1: An analysis of Establishments within the State of Andhra Pradesh In this model, the treatment group comprises establishments in Andhra Pradesh with greater than 100 but less than 300 direct workers. The control group comprises establishments in Andhra Pradesh with 300 or more direct workers. Model 2: Analysis of establishments across the states of Andhra Pradesh and Telangana In this model, the treatment group comprises establishments in Andhra Pradesh with greater than 100 but less than 300 direct workers while the control group comprises establishments with greater than 100 but less than 300 direct workers in Telangana. Since the levels of the various indicators are different, the log of the various indicators and the coefficients of the DID estimates can be interpreted as percentage changes over the base (Muralidharan & Prakash, 2017). The controls for the models are based on the neoclassical production function used in the TFP calculations. The production function is assumed to be Cobb-Douglas and a function of capital, labour, and material. Although both contract and direct workers are employed in production, they are assumed to be perfectly substitutable. In addition, controls for the age of the plant, the growth in fixed capital investment, industry controls126 and industry trends (based on 2-digit NIC-2008 industry classification), and state trends (only for model 2) are included. The TFP estimation is the residual of the production function estimation. To assess the treatment effect, the age of the plant, the growth in fixed capital investment, and include industry controls, industry trends and state trends, are used. Results 126 These are not absorbed into the establishment level fixed effects as they are note fixed in time for all establishments. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 135 Volume II Technical Annexes The treatment effects for Model 1 and Model 2 are provided in Table 4.31. TABLE 4.21: The Average Treatment Effect on Treated (ATET) due to the deregulation Indicators Model 1 Model 2 Log(Output) -.526** -.385** (.237) (.191) Log(Output/Worker) -.081 .010 (.119) (.104) Log(TFP) -.621 -.793 (.520) (.747) Investment Growth(%) 9.86 241.1 (22.9) (238.2) Log(Direct Workers) .151 .020 (.106) (.111) Log(Contract Workers) -.330 -.569 (.308) (.421) Log(Total Workers) -.011 -.075 (.075) (.138) Log(Average Wage of Direct Workers) -.163** -.31** (.067) (.127) Log(Average Wage of Contract Workers) -.355 -.548 (.256) (.394) *p<0.1; **p<0.05; ***p<0.01 Note: ATET estimate adjusted for establishment level controls, panel effects, time effects, industry controls, industry trends and state trends (only in model 2). The standard errors have been clustered at the level of the plants and are reported in parentheses. To ensure that the treatment effect has a causal interpretation the parallel trends assumption for these models is also verified. The parallel trends tests indicate that the treatment effects have a causal interpretation except for the treatment effect on contract workers in model 1. Given the treatment effects of model 1 and model 2, which provide a check of robustness for each other, it can be inferred that the only significant impact of the deregulation on the treatment group was a decline in the output growth and the real wages of the direct workers. All other treatment effects remain insignificant. The decline in output growth could be due to the bifurcation of the states hurting the performance of the treatment group. However, no decline in overall workers, contract workers (Model 2) or total workers is observed. This is probably where the relaxation of labour laws helped establishments by allowing them to adjust the real wages paid to the direct workers according to the market conditions faced by them. Hence, it is possible that the decline in output growth was matched by a decline in the wage growth of direct workers in the treatment group. The higher magnitude of decline in real wage observed in the second model indicates that while the deregulation allowed treated establishments to adjust to the output shock, it was also accompanied by loss in bargaining power when compared to their counter parts in Telangana. To confirm both hypotheses, an alternative specification is estimated that directly controls for output in the wage models. The wage functions are: βl​ ​ ω  =  A ​ ​ Ki​ ​β​ ​​​  ​ Li​ ​β​ ​−1​  ​ k l Mi​ ​β​  ​​​  ​ m This can be written in terms of output as: Q​ ​  ​ ω  =  _ ​ ​  L ​ I​  ​  ​ i  ​ 136 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Volume II Technical Annexes The log transformation of this wage function is estimated controlling for age of the plant, the growth in fixed capital investment, industry controls and industry trends (based on 2-digit NIC-2008 industry classification), and state trends (only for model 2). But first the results from testing of parallel trends show that the models conform to this assumption (Table 4.22). TABLE 4.22: Test for Parallel Trends for the alternative wage model of direct workers Indicators Model 1 (​Treat ×  Time​) Model 2 (​Treat ×  Time​) Log(Average Wage of Direct Workers) .0127 -.045 (.106) (.031) *p<0.1; **p<0.05; ***p<0.01 Note: estimate adjusted for establishment level controls, panel effects, time effects, industry controls and industry trends. The standard errors have been clustered at the level of the plants and are reported in parentheses. TABLE 4.23: The Average Treatment Effect on Treated (ATET) due to the deregulation for the alternative wage model of direct workers Indicators Model 1 Model 2 Log(Average Wage of Direct Workers) -.133 -.286 ** (.100) .139) *p<0.1; **p<0.05; ***p<0.01 Note: ATET estimate adjusted for establishment level controls, panel effects, time effects, industry controls, industry trends and state trends (only in model 2). The standard errors have been clustered at the level of the plants and are reported in parentheses. Accounting for output in the model 1 makes the decline in real wages of direct workers in the treated group insignificant, supporting the first hypothesis that the output shock faced by the treated establishments was matched by wage declines. However, in model 2, while the magnitude of decline in real wages of direct workers falls, the ATET continues to be significant. The decline in real wages of direct workers is not surprising given that one would expect the deregulation to erode the bargaining position of the regular workers who are covered in directly employed workers. However, the data on directly employed workers in ASI comprises regular as well as casual workers. An alternative scenario can be that establishments used the deregulation of the employment protection legislations as an opportunity to substitute regular workers with low-paid casual workers wherever possible. However, given the limitation of the data, it is impossible to explain which of the two reasons explains the decline in real wages of the direct workers. However, this does allow the inference that establishments were able to adjust to market conditions by adjusting wages rather than by adjusting the size of their workforce. Moreover, a substitution of contract workers for direct workers is not seen. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 137 Volume II Technical Annexes Annexes to Chapter 6 Annex 6.1: Some key government initiatives to facilitate the Access to Land in Recent Years The government has enacted several measures to establish standards for better access to land. The Land Acquisition, Rehabilitation, and Resettlement Act (LARR) was enacted in 2013. In 2013, the National Land Use Policy was launched to create a comprehensive framework for land management and planning, with the objectives of promoting sustainable land use practices, the efficient use of resources, and minimizing the displacement of communities. The government came up with the Public-Private Partnership (PPP) model which laid thrust on the creation of industrial parks and Special Economic Zones (SEZs) to develop infrastructure projects. This has led to the reduction in the government’s burden for financing and managing industrial parks by allowing private sector companies to lease land from the government for industrial purposes within a specific period. Additionally, the government has given tax exemptions, subsidies, and financial assistance to industries that set up units in designated industrial areas. Furthermore, initiatives, including simplifying regulatory procedures, streamlining access to land, and offering incentives for industrial growth have led to an improvement in the ease of doing business. The Real Estate (Regulation and Development) Act (RERA) was introduced in 2016 with the objective to regulate the real estate industry, ensuring transparency and accountability during the transfer of land for industrial purposes. The Ministry of Commerce and Industry launched the Industrial Information System in 2016, an online platform which uses a geographic information system (GIS) technology to map industrial areas and give information on available land, infrastructure, and other resources for industrial estates, clusters, and land banks. The Transfer of Development Rights (TDR) program gives incentives to landowners to relinquish their land for public infrastructure development purposes. The Land Lease Policy was launched in 2017 streamlining the process of leasing land for commercial and industrial uses. The policy provides details on lease terms and conditions, lease duration, and renewal procedures. The Ministry of Commerce and Industry has introduced the National Industrial Land Bank to come up with comprehensive database of available industrial land in India. This database gives information on the land availability, ownership, and pricing to investors and entrepreneurs. Several Indian states have also launched their own State Industrial Land Banks giving similar information on industrial land availability in their given states. This has led to facilitation of land access and promoted industrial development in these states. A National GIS-enabled Land Bank system also provides a database of all industry and investment-related connectivity, plot-level information on vacant plots along with industrial land to promote the ease of doing business in the country. In 2020, the Uttar Pradesh Government introduced an online portal for industrial land allotment to provide a transparent and hassle-free process for the transfer of land. In 2021, the Maharashtra Government introduced a new policy to promote the leasing of agricultural land for non-agricultural purposes, including industrial use. The policy aimed to give a transparent and efficient process for the lease of land with clear terms and conditions for transfer. Furthermore, some states have come up with Special Purpose Vehicles (SPVs) to acquire land and develop industrial estates. These SPVs provide a one-stop solution for investors to acquire land and obtain necessary clearances for industrial development. 138 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Volume II Technical Annexes Annex 6.2: Food Insecurity. Internal Migration and the ONORC Scheme: Results from an Information Experiment The introduction of the ONORC scheme provides an opportunity to examine how food insecurity affects migration in India. The scheme was introduced in 2019 to incorporate migrants into the PDS and is likely the single biggest transfer program used by 63 percent of the population to obtain subsidized foodgrain (MicroSave, 2020). During the Covid-19 pandemic, food transfers through the PDS program reached 74 percent of households across the country (Bhattacharya and Sinha Roy, 2021). Until recently migrants were excluded from the PDS network, as beneficiaries were required to claim ration in designated PDS shops in their home localities. The ONORC initiative, imple- mented in August 2019, introduced portability features into the PDS – allowing beneficiaries to collect food ration across the entire country. However, survey data in January 2021 showed that despite several months following implementation, awareness of the program was low. Researchers randomly provided information about ONORC to a group of households and analyzed their migration behavior (Baseler, et. al., 2023).127 Researchers embedded the information experiment into three-consecutive panel surveys called the Consumer Pyramids Household Survey (CPHS) carried out three times a year by the Center for Monitoring Indian Economy (CMIE); i.e., October 2021 through December 2021 survey (baseline survey), February 2022 through April 2022 (4-month follow-up survey), and June through August 2022 (8-month follow-up survey). During baseline, a randomly selected group of households was provided the following information:(i) introduction and key features of the ONORC scheme — including contact details of nearest PDS shops in the vicinity of destination locations; (ii) barriers that households may face if they choose to migrate and attempt to claim ration at a non-designated shop; and (iii) a toll-free information hotline number that could provide further assistance or clarifications about the program. Another set of households, referred to as the control group, were not provided this information set and were instead exposed only to public ONORC campaigns by the government. As the government significantly scaled up the public campaigns over time, both groups of households were made aware of ONORC but the main information difference between the treated and control group was that the former had additional knowledge about the practical barriers that they could face while accessing the benefits. Results indicate that food security serves as a barrier to urban migration. • Firstly, the information intervention increased belief in ration portability among treated households immediately after the baseline interview, but the knowledge of practical barriers lowered knowledge of portability over time. During the initial survey immediately following the information intervention, the treated group showed a higher belief in ration portability, due to limited awareness of the ONORC scheme at that point in time. However, over time, the public campaign gradually rolled out and narrowed the awareness gap between the treated and control group about how the ONORC works de jure. As of the 4-month survey, the in-practice information on barriers to access ration, which was only provided to the treated group, dominated, and lowered the treated groups’ belief in ration portability. • Secondly, the information treatment led households to choose rural over urban emigration, with little change in the rate of emigration overall. As of the 4-month follow-up survey, treatment-group households had sent fewer emigrants to urban destinations and more emigrants to rural destinations, adding up to an insignificant impact on the overall emigration. The differential urban-and-rural emigration pattern is probably because households in urban areas do not typically grow their own food. As such, households must find stable employment to meet their food needs, exposing them to food insecurity risk if they cannot find a job quickly. The shift away from urban destinations is thus consistent with PDS access being an important consideration for prospective urban migrants. • Thirdly, the tendency for treated emigrants to avoid urban destinations was amplified if they belonged to households that reported concerns about destination food insecurity, consistent with food access driving the results above. These findings highlight the importance of reliable access to food ration not covered by PDS beneficiaries’ designated ration shops. 127 Baseler. T., A. Narayan, O. Ng, and S. Sinha Roy. 2023. Does Food Insecurity Hinder Migration? Experimental Evidence from the Indian Public Distribution System. World Bank Policy Research Working Paper forthcoming. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 139 Volume II Technical Annexes Annex 6.3:Policy matrix for employment creation in selected subsectors Sector Productivity Where should the policy intervene? Targeting capabilities of workers Targeting firms to generate jobs Agro-processing Very low 1. Improved healthcare Factor market reforms manufacturing: infrastructure. 1. Land consolidation and improved mechanism to acquire 2. Increasing the share of 1. Food Products, land. workers with basic education Beverages and 2. Reduce multiplicity of labor regulations. levels. More than half of Tobacco Product market reforms and other measures to improve the workers engaged in the 2. Textiles, Textile competitiveness sector are merely literate Products, (without formal schooling). Leather and 1. Addressing inverted duty structure issue in MMF segment Footwear of textile industry. * 3. Wood and 2. For the food processing industry, development of storage Products of and distribution infrastructure and, addressing the gaps Wood in quality and safety standards are important steps. 4. Pulp, Paper, 3. Ensuring reliable and continuous power access and water Paper Products, supply. * Printing and 4. Increasing labor productivity in agriculture, with which Publishing this sector has strong backward linkages. Incentives 1. Cluster-based policies designed and implemented in coordination with the relevant line ministries for food-processing and textiles industries, particularly targeting the MSMEs employing less than 250 workers. Traditional Low 1. To ensure better learning Factor market reforms market services: outcomes: increase the teach- 1. Improved mechanism to acquire land in urban and sub-ur- er-pupil ratio, reduce teacher ban areas. * 1. Trade absenteeism, provide better Product market reforms and measures to improve 2. Hospitality training to teachers, and competitiveness 3. Transport, increase instructional time. Storage 1. Improvement in governance and quality of institutions 2. Standardized and certified 4. Post and will have a significant positive impact on the provision of training programs with Telecommuni- infrastructure. public-private partnerships. * cation 2. Reduce regulatory opacity and barriers to competition in the trade and, transport and storage sectors. 3. Increase access to financing. Intermediate Low 1. To ensure better learning Factor market reforms goods outcomes: increase the teach- 1. Land consolidation and improved mechanism to acquire manufacturing: er-pupil ratio, reduce teacher land. absenteeism, provide better 1. Chemicals 2. Ease and reduce the multiplicity of labor regulations. training to teachers, and and Chemical Product market reforms and measures to improve increase instruction time. Products competitiveness 2. Standardized and certified 2. Rubber and training programs with Plastic Products 1. Correcting inverted duty structure in key inputs, particu- public-private partnerships. * 3. Other Non-Me- larly in the chemicals industry. tallic Mineral 2. Improving the end-to-end logistics in the country. Products Incentives 4. Basic Metals and Fabricated 1. Organizational and maintenance-related support for Metal Products firms to invest in R&D as well as adopt new processes and technology. * 140 India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation Volume II Technical Annexes Sector Productivity Where should the policy intervene? Targeting capabilities of workers Targeting firms to generate jobs Machinery Medium & 1. Increase the number and Factor market reforms manufacturing: High improve the quality of tertiary 1. Land consolidation and improved mechanism to acquire education institutes. 1. Transport land. 2. Expand the capacity of public equipment 2. Ease and reduce the multiplicity of labor regulations. management institutes and 2. Machinery Product market reforms and measures to improve facilitate a link with private 3. Electrical competitiveness businesses to help improve and Optical the managerial quality and Equipment 1. Reduce import tariffs on crucial inputs to help in GVC inte- capabilities of workers. * gration and correct inverted duty structure in key inputs. 3. Training programs with a 2. Improve governance and quality of institutions. focus on development of 3. Increase access to financing. * ICT-related skills. * 4. Improve end-to-end logistics infrastructure and services. Incentives 1. Organizational and maintenance-related support for firms to invest in R&D as well as adopt new processes and technology. * Modern market High 1. Increase the number and Product market reforms and other measures to improve services: improve the quality of tertiary competitiveness education institutes. 1. Financial and 1. Improve the diffusion and quality of the ICT infrastructure. 2. Expand the capacity of public Insurance 2. Reduce the regulatory opacity and barriers to management institutes and services (2) competition. facilitate a link with private Business 3. Liberalize FDI in some of the remaining modern market businesses to improve the services services. managerial quality and capa- bilities of workers. * Incentives 3. Training programs with a 1. Provide financial, organizational and maintenance focus on development of support to incentivize firms to adopt new technologies. * ICT-related skills. * 2. Provide incentives to corporates collaborating with tertiary education institutes to fund R&D activity. * 3. Direct and indirect financial support to firms for building innovation capacity. * Source: Bhatnagar and Gupta (2023). Note: *supported by literature review. India Country Economic Memorandum 2024  |  Becoming a High-Income Economy in a Generation 141