AN OVERVIEW OF THE INDIAN HEALTH LABOR MARKETS Edson C. Araujo, Bernardo D. P. Coelho, Gumilang Aryo Sahadewo, Navneet Kaur Manchanda, Barbara McPake, Ajay Mahal, Lakshmi Murthy Sripada, Deepak Santhanakrishnan, Aarushi Bhatnagar June 2025 i Health, Nutrition and Population (HNP) Discussion Paper © 2025 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW Washington DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. 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Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for noncommercial purposes as long as full attribution to this work is given. 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; e-mail: pubrights@worldbank.org. ii Health, Nutrition and Population (HNP) Discussion Paper AN OVERVIEW OF THE INDIAN HEALTH LABOR MARKETS Edson C. Araujo1, Bernardo D. P. Coelho2, Gumilang Aryo Sahadewo3, Navneet Kaur Manchanda4, Barbara McPake5, Ajay Mahal6, Lakshmi Murthy Sripada7, Deepak Santhanakrishnan8, Aarushi Bhatnagar9 1 Senior Economist, World Bank, Washington, DC, USA 2 Consultant, World Bank, Nice, France 3 Associate Professor, Department of Economics, Universitas Gadjah Mada, Yogyakarta, Indonesia 4 Economist, Health, World Bank, New Delhi, India 5, 6 Professor, Nossal Institute for Global Health, University of Melbourne, Melbourne, Australia 7 Health Specialist, World Bank, New Delhi, India 8 Consultant, World Bank, New Delhi, India 9 Senior Economist, World Bank, New Delhi, India ABSTRACT: This report presents a comprehensive analysis of India’s health labor markets using data from the Periodic Labor Force Survey, the country’s largest labor dataset. It updates estimates of the size, composition, and characteristics of the active health workforce and examines employment dynamics in the sector, including estimates of labor supply elasticities, job quality, and projections of workforce needs and demand. India’s health workforce comprises about 5 million active workers when considering only core occupations. Although workforce density remains below global, regional, and structural benchmarks, efforts to scale up training capacity have led to a 60 percent increase in the number of health workers since 2017. Despite this progress, persistent shortages remain in key medical specialties, and informality is still widespread—both in employment arrangements and training requirements. The workforce also remains concentrated in urban areas, although rural representation has improved, particularly among associate nurses and midwives. The health sector plays a vital role in providing quality employment, especially for women, accounting for 1.3 percent of the total workforce and 2 percent of female employment in the country. Medical training increases female labor force participation by 24.6 percentage points. However, challenges persist: a 30 percent gender pay gap and the disproportionate burden of household responsibilities continue to limit women’s participation—over one-third of women with undergraduate-level medical training are outside the labor force. Labor supply elasticity estimates suggest that wage increases alone do not significantly increase the working hours of health workers. This underscores the importance of compensation structure and non-pecuniary benefits—such as training opportunities, career advancement, housing, and access to childcare, healthcare, and maternity benefits—especially for female workers. While projections indicate India may need iii up to 10 million additional health workers by 2035 (depending on the benchmark), current trends suggest the supply-demand gap is gradually narrowing, largely due to major healthcare reforms. As India expands health coverage and mobilizes resources to fund healthcare services, more healthcare jobs will be needed to support progress toward universal health coverage. KEYWORDS: Health Labor Markets, Health Workforce, Human Resources for Health, India DISCLAIMER: The findings, interpretations, and conclusions expressed in the paper are entirely those of the authors, and do not represent the views of the World Bank, its Executive Directors, or the countries they represent. CORRESPONDENCE DETAILS: Edson C. Araujo, Senior Economist, World Bank,1818 H Street, Washington, DC, USA Telephone: 202-290-8150; E-mail: earaujo@worldbank.org; website: www.worldbank.org/hnp. iv TABLE OF CONTENTS TABLE OF CONTENTS ................................................................................................................................ V ACKNOWLEDGMENTS ........................................................................................................................... VIII EXECUTIVE SUMMARY ............................................................................................................................ IX LIST OF ACRONYMS............................................................................................................................... XIV INTRODUCTION........................................................................................................................................ 1 HEALTH WORKFORCE PROFILE ................................................................................................................. 4 HEALTH WORKFORCE SIZE AND COMPOSITION ...................................................................................................... 5 DEMOGRAPHIC PROFILE.................................................................................................................................. 11 GEOGRAPHICAL DISTRIBUTION ......................................................................................................................... 13 SECTORAL DISTRIBUTION ................................................................................................................................ 18 DEMAND AND SUPPLY FOR HEALTH WORKERS IN INDIA ....................................................................... 21 CHARACTERISTICS OF LABOR SUPPLY AND DEMAND ............................................................................................. 21 INTERNATIONAL MIGRATION OF INDIAN PHYSICIANS AND NURSES .......................................................................... 26 QUALITY OF JOBS IN THE HEALTH SECTOR........................................................................................................... 30 MODELING LABOR FORCE PARTICIPATION, WAGE DYNAMICS, AND LABOR SUPPLY ELASTICITIES OF HEALTH WORKERS ................................................................................................................................. 32 LABOR FORCE PARTICIPATION AMONG THOSE WITH MEDICAL TRAINING ................................................................. 32 LABOR SUPPLY ELASTICITIES AMONG HEALTH WORKERS ....................................................................................... 36 EMPIRICAL SPECIFICATION ............................................................................................................................... 37 WAGES ANALYSIS .......................................................................................................................................... 42 HEALTH WORKFORCE PROJECTIONS: SUPPLY, DEMAND, AND NEEDS FOR HEALTH WORKERS .............. 47 INTRODUCTION ............................................................................................................................................. 47 RESULTS ...................................................................................................................................................... 48 PROSPECTIVE SCENARIOS ................................................................................................................................ 49 DISCUSSIONS AND POLICY RECOMMENDATIONS .................................................................................. 53 POLICY RECOMMENDATIONS ........................................................................................................................... 54 REFERENCES ........................................................................................................................................... 57 APPENDIX A – CLASSIFICATION OF CATEGORIES OF HEALTH WORKERS ................................................. 62 APPENDIX B – QUALIFICATION ADJUSTMENT ........................................................................................ 63 APPENDIX C – WEIGHT ESTIMATES ........................................................................................................ 67 v APPENDIX D – REGRESSION ANALYSIS SUPPLEMENTARY MATERIALS .................................................... 68 List of Figures Figure 2.1 – Density of Physicians, India and Its Peers 6 Figure 2.2 – Density of Nurses and Midwives, India and Its Peers 7 Figure 2.3 – Health Workforce and Population Dynamics, India, 2017/18 and2022/23 8 Figure 2.4 – Health Sector Employment and General Labor Market, India, 2017/18–2022/23 9 Figure 2.5 – Health Workforce Adjusted by Qualification Criteria, India, 2022/23 10 Figure 2.6 – Physician and Nursing Workforce Age Distribution, India, 2022/23 12 Figure 2.7 – Female Concentration Across Cadres, India, 2017/18 and 2022/23 13 Figure 2.8 – Urban-Rural Distribution of Health Workers and Population, India, 2017/18 and 2022/23 14 Figure 2.9 – Health Workers Density by State, India, 2022/23 15 Figure 2.10 – Density of Health Workers, Total Health Expenditure, Gross Domestic Product by State, India (INR) 16 Figure 2.11 – Physician and Nurses per 10,000 Population Across States, India, 2022/23 17 Figure 2.12 – Health Workforce Distribution by Sectors, India, 2022/23 19 Figure 2.13 – Share of Health Personnel by Sector in Selected Countries 20 Figure 3.1 – Monthly Wages for Selected Health and Non-Health Occupations, India 22 Figure 3.2 – Wages and GDP Per Capita for Health Workers and Selected Occupations, India, (INR) 23 Figure 3.3 – Box Plot Monthly Wages, Selected Occupations,2017/18–2022/23 (INR) 24 Figure 3.4 – Percentage of Workers Receiving Employment Benefits, India, 2022/23 26 Figure 3.5 – Indian-Trained Health Workers Practicing in OECD Countries 27 Figure 3.6 – Hours Worked by Health and Non-Health Occupations, India, 2017/18–2022/23 28 Figure 3.7 – Distribution of Hours Worked per Week Selected Health Workers, India, 2017/18–2022/23 30 Figure 5.1 – Static Model of the Labor Market for Nurses, India 50 Figure 5.2 – Projected Supply and Need Scenarios for Physicians, Nurses, and Midwives, India 51 Figure 5.3 – Projected Supply and Demand Scenarios for Physicians, Nurses, and Midwives, India 52 Figure B.1 – Health Worker Qualification by Gender, India, 2022/23 65 Figure B.2 – Health Worker Qualification by Urban/Rural, India, 2022/23 66 Figure B.3 – Health Worker Qualification by Age Structure, India, 2022/23 66 List of Tables vi Table 2.1 – Indian Health Workforce by Survey Instrument 5 Table 2.2 – Physician and Nurse/Midwive Densities and Nurse-Physician Ratio, India, YEAR 18 Table 3.1 – Health Sector Monthly Wages by Sector, Urban/Rural, and Gender, 2022/23 (INR ) 25 Table 3.2 – Hours of Work per Week by Public/Private and Urban/Rural, India, 2022/23 29 Table 3.3 – Job Quality Index, Selected Health and Non-Health Sector, India, 2022/23 31 Table 4.1 – Determinants of Labor Force Participation and Employment Probability 34 Table 4.2 – Estimates of Labor Supply Elasticity 39 Table 4.3 – Labor Supply Elasticities Among Health Workers by Public and Private Sector, India 41 Table 4.4 – Estimates of Labor Supply Elasticities Among Female and Male Health Workers, India 42 Table 4.5 – Determinants of Health Workers’ Wages, India 43 Table 5.1 – Projections for Skilled Health Worker Supply, Demand, and Need, India 49 Table B.1 – Health Worker Qualification Rate by Criteria, India, 2017/18–2022/23 64 Table B.2 – Stock of Health Workers by Qualification, India, 2022/23 65 Table D.1 – Determinants of Labor Force Participation and Employment, India 68 Table D.2 – Estimates of Labor Supply Elasticities by Labor Supply Percentiles, India 69 Table D.3 – Estimates of Labor Supply Elasticities by Economic Sectors, India 70 Table D.4 – Labor Supply Elasticities Among Health Workers by Receipt of Medical Training, India 70 Table D.5 – Determinants of Wages by Healthcare Professions, India 71 Table D.6 – Determinants of Wages by Healthcare Professions and Gender, India 73 Table D.7 – List of Variables for the Regression Analyses, India 75 Boxes Box 2.1: Informal Health Providers in India 11 vii ACKNOWLEDGMENTS The report was prepared under the activity Supporting Health Professional Education Market Reforms in India and was developed by a team composed of Edson C. Araujo (Senior Economist, World Bank), Bernardo Dantas Pereira Coelho (Economist, Consultant at the World Bank), Gumilang Aryo Sahadewo (Associate Professor, Department of Economics, Universitas Gadjah Mada), Navneet Kaur Manchanda (Economist, World Bank), Barbara McPake (Professor, Nossal Institute for Global Health, University of Melbourne), Ajay Mahal (Professor, Nossal Institute for Global Health, University of Melbourne), Lakshmi Murthy Sripada (Public Health Specialist, World Bank), Deepak Santhanakrishnan (Public Health Specialist, Consultant at the World Bank), and Aarushi Bhatnagar (Senior Economist, World Bank). The report benefited from comments and invaluable insights received from Michael Weber (Senior Economist, World Bank), Himanshu Negandhi (Director Academics, Public Health Foundation of India), Christophe Lemiere (Lead Health Specialist, World Bank), Nandini Krishnan (Lead Economist, World Bank), and Dr. Anup Karan (Independent Researcher). The preparation of this report received support from the Novo Nordisk Foundation. The authors are grateful to the World Bank for publishing this report as an HNP Discussion Paper. viii EXECUTIVE SUMMARY The report provides an in-depth analysis of India’s health labor markets (HLMs) using the country’s largest labor force survey, the Periodic Labor Force Survey (PLFS) . PLFS offers a wide range of variables for descriptive and causal HLM analysis, it provides updated estimates of the active health workforce size and composition and to explore key health workers’ characteristics (such as demographics, gender), the characteristics of their household, and job-related variables (wages, working hours, geographical and sectoral distribution). These variables help analyze labor supply responses to policy changes and compare HLM trends with other sectors. There are shortcomings to using the PLFS for HLM analysis, (i) the sample sizes for health workers are often insufficient, particularly at state level, and (ii) health worker categories may be aggregated into broader occupational groups, limiting detailed analysis, such as across specialties within a given cadre. Despite progress, India continues to face critical shortages in its health workforce relative to global standards. According to 2022/23 PLFS data, India has 4.96 million active health workers. That includes 1.06 million physicians and 2.84 million nurses (two-thirds professional nurses and midwives), 251,241 AYUSH practitioners (the traditional systems of medicine practiced in India: Ayurveda, Yoga & Naturopathy, Unani, Siddha, Sowa Rigpa and Homeopathy), and 768,607 allied health workers. India’s health workforce density is 28.2 per 10,000, below the World Health Organization (WHO) recommended 44.5 and peer countries like Indonesia (46.7), the Philippines (55.4). Physician density is 7.68 per 10,000, similar to the South Asian Region average (7.63) but below Organization for Economic Co-operation and Development (OECD) (33.94) and the average among Brazil, Russia, India, China, and South Africa (BRICS countries) (19.87). Nursing and midwifery density is 20.49 per 10,000, higher than the South Asian Region average (14.89) but well below OECD (93.92) and BRICS standards (44.25). The nurse-to-physician ratio is 2.6:1, slightly under the OECD average of 2.76:1. Health workforce expansion in India has outpaced both population and overall labor force growth since 2017/18. The country’s active health workforce grew by 60% between 2017/18 and 2022/23, significantly outpacing population growth (6.3%) and overall workforce growth (4.7%). During this period, the number of physicians increased by 47%, professional nurses and midwives (N&MWs) grew by 41%, and associate N&MWs increased by 151%. In contrast, the number of AYUSH practitioners declined by 28%. This growth was driven by the rapid expansion of medical and nursing education, particularly in the private sector. By 2023, India had 612 medical schools (up from 335 in 2010/11), with 92,127 medical school seats. Nursing colleges grew from 2,904 in 2010 to over 5,200 in 2023, accompanied by an 186% increase in nursing program seats. The expansion of training capacity has been primarily concentrated in the southern and western states. ix The health care industry accounts for 1.3% of the total workforce in the country and 2% of the total female workforce. These figures include both core health occupations and other roles within the sector, such as support staff, administrative personnel, and care and social workers. This share remains significantly lower than in countries like Brazil (8%), the United States (10.8%), the average across OECD countries (10.1%), and countries in the Latin America and Caribbean region (6–9%). This pattern may reflect underlying differences in health care financing levels between India and these countries. It also highlights the potential to expand employment in the sector as India continues to expand health coverage and mobilize additional resources to fund health care services for its population. India’s health workforce remains predominantly urban -based and privately employed, though recent years have seen a notable increase in rural representation. Over half of India’s active health workers practice in urban areas, where 36% of the population lives. 64% of physicians, 65% of AYUSH practitioners, and 62% of professional N&MWs. Dentists and pharmacists are almost exclusively urban-based. In contrast, 60% of associate N&MWs work in rural areas, making up 50% of the rural health workforce. Between 2017/18 and 2022/23, the share of rural health workers grew by 14 percentage points, reducing the urban-rural imbalance. This growth was driven by the increase in the rural associate N&MWs workforce. Most health workers are employed in the private sector. For example, 63% of physicians, 56% of professional N&MWs, and 85% of AYUSH practitioners work privately. India’s health workforce is predominantly female, young, and characterized by high labor force participation—especially among women with medical training . Medical training significantly increases labor force participation (LFP), particularly for women. In 2022/23, individuals with medical training had an LFP of 76% (compared to 57% for the general workforce). For women, LFP reached 68%, compared to the overall female LFP of 36%. However, health workers faced higher unemployment rates (7.2%) than the national average (3.4%). India’s health workforce is relatively young, with over 50% under the age of 39, and mostly females (63%). Certain cadres have an even younger workforce: 71% of professional N&MWs, 86% of dentists, and 87% of pharmacists are below 39. 63% of the health workforce are women, that is mostly due to the female dominance in nursing and midwifery. While medical training significantly increases labor force participation —especially for women—caregiving duties and larger household size disproportionately hinder women’s employment prospects. Regression analysis results show that having medical training increases LFP by 20.2 percentage points, and the effect is stronger for females (24.6 percentage points). The positive relationship between education and labor force participation is more pronounced among females: among women, diplomas or certificates in medical fields increase LFP by 10.4 percentage points, compared to 6.36 percentage points for males. Additionally, females with medical training are 5 percentage points more likely to be employed, while males are 2.66 percentage points more likely. Having kids and larger household size x negatively impacts employment likelihood, especially for females. Each additional household member reduces a woman’s employment probability by 0.66 percentage points, suggesting caregiving responsibilities disproportionately affect women. Job Quality Index (JQI) data indicate relatively high job quality in India’s health sector, though disparities remain across occupational groups and between genders. The JQI measures job quality based on four dimensions: (i) sufficient income to sustain a family above the purchasing power parity (PPP) $3.65 poverty line, (ii) employment benefits such as health insurance or pensions, (iii) job stability via written contracts, and (iv) job satisfaction with appropriate work hours. Physicians have the highest JQI (3.3), similar to other professionals such as lawyers (3.2) and engineers (3.4). Professional N&MWs have a JQI of 2.6 due to limited availability of employment benefits and lower job satisfaction. Associate N&MWs have a JQI of 1.7, primarily due to income levels (only 57% earning above the PPP $3.65 poverty line). Gender disparities exist with male health workers generally having higher JQI scores, except among physicians. The largest gender gap is observed among AYUSH practitioners, within this group females JQI is 1.7 compared to 3.3 for males. Labor supply elasticity estimates suggest that higher wages and extended work hours limit responsiveness to wage increases, while gender-based pay gaps remain substantial in many health occupations. The analysis of labor supply elasticity shows a negative and significant relationship between working hours and real hourly wages. For all health workers, a 10% increase in hourly wages leads to a 1.66% decrease in working hours. Higher wages and long working hours for health workers (46.14 hours/week, 14% above the average) likely drive the income effect, reducing labor supply as wages increase. Physicians' wages are 3.22 times gross domestic product (GDP) per capita, while professional N&MWs and AYUSH practitioners earned 1.16 and 1.43 times, respectively. Public sector health workers earn 40.35% more than their private-sector counterparts. There is also a significant gender wage gap: on average, women earn 29.69% less than men. This gap is statistically significant among professional N&MWs (19.84% less) and associate N&MWs (23.24% less). Among physicians, the gender wage gap is not statistically significant. While estimates show India may need up to 10 million additional health workers by 2035 depending on the benchmark, trends suggest the country is gradually closing the supply-demand gap. The report estimates the future supply, needs, and demand of the health workforce using projection techniques adapted to the Indian context. Three scenarios are estimated using needs and supply projections: the first scenario, using the proposed WHO norm of 4.45 per 1,000 population, India will face a persistent shortage, with a shortfall of 2.21 to 3.30 million in 2025-26, narrowing to 282,415 to 79,997 by 2035-36. The second scenario adapts the WHO Sustainable Development Goal (SDG) composite index methodology to the Indian context. In this scenario, the estimated health worker need is 1.37 per 1,000 population, which represents the median threshold value of the composite index. This scenario predicts a xi surplus of health workers in the country, ranging from 1.08 to 2.17 million in 2025-26, and growing to 4.40 million by 2035-36. The third scenario uses the 75th percentile value of health worker density (9.27 per 1,000) based on the adapted WHO SDG methodology. It projects severe shortages, ranging from 9.08 to 10.18 million in 2025-26, improving slightly but remaining significant at 7.41 to 7.61 million by 2035-36. From a projected surplus of 1 to 2.1 million health workers in 2025/26 and 1.1 to 1.94 million in 2030/31, to a shortfall of 13,000 to 215,000 in 2035/36, the estimates indicate India is closing the health worker supply-demand gap. The alignment between demand and supply seems to be driven by the major healthcare reforms in India over the last decades. Major publicly funded programs intensified the need and the demand for health workers in the country and major efforts have been made, in public and private sectors, to scale up training capacity. More attention is needed to effectively regulate the country’s diverse HLM and the growing influence of global HLM through increased health worker migration. The fact that the private sector employs majority of the health workforce, reflecting the private sector dominance in healthcare financing in the country, highlights the importance of government regulation and design of appropriate incentives to attract and retain health workers where they are needed the most (and not only based on market signals). In such circumstances, a HLM approach is paramount. Such an approach emphasizes that workforce outcomes arise from the individual decisions of millions of healthcare workers. These individual decisions are based on their assessment of how to best achieve their career objectives, whether those emphasize job satisfaction, social contribution, financial returns or other lifetime goals. These decisions guide health workers among alternative job opportunities that are shaped by thousands of employers and by national and state government investments and regulations. Governments influence outcomes primarily through investments and regulations and only marginally by mandates through which healthcare workers are directed to public sector roles. The report addresses key challenges in India’s health workforce, focusing on the need for strategic policy interventions to manage a complex labor market where most health professionals work in the private sector and are in high demand globally. Priorities identified for public policy include: • Enhance health workforce composition and skill mix: Health workforce policies are not only about the increasing number of health professionals but also their skill sets and competencies. Expanding roles for allied health professionals, essential for managing non-communicable diseases (NCDs) in primary health care (PHC), is crucial for improving service delivery and addressing growing public health concerns. Likewise, expanding mid-level health worker programs, such as for Accredited Social Health Activists (ASHAs) and associate N&MWs, and implementing task-shifting xii models can help alleviate pressure on higher-level professionals and improve service delivery in resource-constrained settings. • Improve quality of training and practice: Despite an increase in the number of health workers, there is a need for stronger quality assurance mechanisms and a more robust regulatory framework, especially for informal health providers (IHPs), to ensure high standards of training and practice. Healthcare workers need to be incentivized to pursue and complete medical training, as receipt of medical training is associated with higher labor market returns. • Reform compensation policies to address HLM imbalances: Wage increases alone do not necessarily improve working hours or labor supply due to the income effect. Instead, the structure and composition of compensation, including non-pecuniary benefits such as access and funding to training, career advancement, and housing, can be an important part of healthcare worker incentive design. Access to childcare, healthcare, and maternity benefits can be influential, especially for female workers. • Aligning health financing policies with workforce objectives : Health financing policies, such as Pradhan Mantri Jan Arogya Yojana (PM-JAY), can influence both public and private sector remuneration. These policies must be carefully designed to avoid unintended consequences, such as reinforcing gender gaps or bolstering the private sector’s role at the expense of public health services. • Leveraging the health sector potential for job creation and improving female labor force participation: Medical training increases labor force participation, especially among women, but significant wage gaps and the underrepresentation of women in the workforce remain. Policies should focus on reducing household responsibilities, improving workplace safety, and expanding access to healthcare benefits. This could potentially create 4 to 6 million new quality jobs for women in the next decade. • Improved health workforce data information systems: Reliable health workforce data is critical for understanding labor market dynamics and designing effective policies. The report emphasizes the need for improved data systems to address workforce shortages, wage disparities, and regional inequalities. Current data systems are insufficient, with a nearly two-decade gap in health workforce accounting, making it difficult to assess the role of allied health professionals and track their emergence. xiii LIST OF ACRONYMS ASHA Accredited Social Health Activists AYUSH Ayurveda, Yoga & Naturopathy, Unani, Siddha, Sowa Rigpa, and Homeopathy BRICS Brazil, Russia, India, China, and South Africa CPF Contributory Provident Fund GDP Gross Domestic Product GPF General Provident Fund HLM Health Labor Markets HWC Health and Wellness Center IHME Institute for Health Metrics and Evaluation IHP Informal Health Providers INR Indian Rupees JQI Job Quality Index LFP Labor Force Participation MoSPI Ministry of Statistics and Programme Implementation N&MW Nurses and Midwives NCD Non-Communicable Diseases NCO National Classification of Occupations NHWA National Health Workforce Accounts NIC National Industry Classification NPS National Pension System OECD Organization for Economic Co-operation and Development OLS Ordinary Least Squares PHC Primary Health Care PLFS Periodic Labor Force Survey PM-JAY Pradhan Mantri Jan Arogya Yojana PPF Public Provident Fund PPP Purchasing Power Parity SAR South Asian Region SDG Sustainable Development Goal THE Total Health Expenditure WHO World Health Organization WTP Willingness-to-Pay xiv INTRODUCTION India has undertaken major healthcare reforms over the last decades, and the success of these reforms relies on having a well-trained and adequately resourced health workforce to deliver quality care across all regions of the country. The demand for a fit-for-purpose health workforce has grown in response to major publicly funded programs launched and implemented over the past decade, particularly Ayushman Bharat. This program has two main components: the Pradhan Mantri Jan Arogya Yojana (PM-JAY), a publicly funded insurance scheme for the poor, and the Ayushman Bharat Health and Wellness Centers (HWCs), which aim to strengthen the existing primary healthcare (PHC) service delivery system and establish 150,000 HWCs across the country. PM-JAY provides public health insurance coverage for inpatient secondary and tertiary hospital care to poor and near-poor households, approximately 40 percent of the country’s population. By expanding healthcare coverage, PM-JAY is expected to increase the demand for health services at secondary and tertiary care providers, which depends critically on availability of health personnel at both public and private facilities. Implementing comprehensive PHC will only be successful if HWCs are staffed by qualified health personnel. These new demands highlight the need for national and state-level health workforce strategies to address existing constraints and prepare and deploy a health workforce that enables the achievement of reform objectives and UHC. Despite rapid growth in the number of institutions training physicians, nurses, midwives, and allied health workers, the available evidence indicates a continued overall shortage of qualified health workers in India, particularly in rural areas. The most recent analyses of census and household survey data indicate that India faces a significant shortage of physicians, nurses, and midwives when assessed against population density benchmarks (Rao et al. 2011; Rao, Bhatnagar, and Berman 2012; Karan et al. 2021). Additionally, evidence points to shortages of specialists in areas such as mental health and ophthalmology ( Ginneken et al. 2017). A critical challenge is the recruitment and retention of qualified health workers in rural areas, although this issue is less pronounced for nurses and ayurvedic practitioners than for allopathic physicians (Ramani et al. 2013; Goel et al. 2016). Evidence also highlights retention challenges faced by public sector health facilities in both rural and urban areas, with an estimated 80 percent of physicians and 70 percent of nurses in India employed in the private sector (Bhattacharya and Ramachandran 2015; Karan et al. 2019). Numerous studies have noted that the quality of health professional training and practice falls short when assessed against national and international benchmarks. For example, Rao and colleagues (2013) found that 61 percent of prescriptions of Medical Officers and Rural Medical Assistants were appropriate in comparison to 51 percent of AYUSH medical officers who are trained as ayurvedic medicine practitioners but reported practicing a mix of ayurvedic and allopathic medicine (Rao et al. 2013). Joshi and colleagues (2019) identified retraining needs for both nurses and physicians, if they are to effectively manage non- communicable diseases (NCD). Additionally, a significant proportion of the health workforce does not have the required qualifications, estimated at 37 percent by Rao, Bhatnagar, (2012) and 25 percent by Karan and colleagues (2019). Looking ahead, India’s health workforce will need to respond to the needs of a population that is simultaneously ageing and experiencing a rapidly growing burden of NCDs . India’s population aged 65 1 years and over is expected to grow from approximately 7 percent at present to more than 20 percent by the year 2050 (UNFPA India 2023). The share of NCDs in India’s burden of disease (as measured by disability- adjusted life years) almost doubled between 1990 and 2019, rising from 30.5 percent to 57.2 percent, and is expected to rise further ( ICMR, PHFI, and IHME 2017). The National Family Health Survey data 2019–21 suggests that among people with diabetes, only 21.5 percent were under treatment and only 7 percent had their diabetes under control (Maiti et al., 2023); and among people with hypertension, only 13.7 percent were under treatment and 7.8 percent had their blood pressure under control (Guthi et al., 2024). These findings indicate shortcomings in the ability of PHC providers to effectively manage patients with chronic conditions, which is directly linked to existing health workforce constraints. This report provides an overview of the Indian health labor markets (HLMs) using data from six consecutive years of Periodic Labor Force Survey (PLFS) . It aims to offer an updated picture of the active health workforce by presenting new estimates of its size, composition, demographic profile, and distribution across urban and rural areas as well as the public and private sectors. By utilizing household survey data from the PLFS, the report describes the characteristics of health workers and their households and compares HLM outcomes with those in other sectors and the broader labor market trends in India. Additionally, the report explores the determinants of key HLM outcomes, by providing estimates of the factors influencing health workers' decisions to enter or leave the workforce (elasticity of labor force participation), and the determinants of their labor supply (measured by the number of hours worked in health-related occupations) in response to changes in wages or other economic, household, and job-related factors (elasticities of labor supply). The report presents an analysis of wages among health workers, with a particular focus on testing the existence of a gender pay gap in the health sector. Lastly, the report explores how demographic shifts, health system priorities, and economic and epidemiological changes will influence the supply and demand of and the need for health professionals in the coming years. Based on this evidence, the report identifies strategic policy directions for the national and state government(s) by leveraging the HLM approach. This approach looks at investment and market regulation opportunities to enhance health workforce outcomes. It clarifies market regulations, such as subsidizing health professional training and shaping markets for new cadres of allied health professionals, and identifies applications of these tools to address the specific issues that are identified in chapters 2 to 5. The report is structured as follows: Chapter 2 presents updated estimates of the size, composition, and demographics of India's active health workforce, exploring the distribution of health workers across urban and rural areas as well as across the public and private sectors. Chapter 3 provides a descriptive analysis of key variables related to the demand and supply of health workers in India. It begins with an analysis of wage rates for different categories of health workers, comparing them to similar non-health sector occupations, and discusses wage differentials across urban and rural areas, public and private sectors, and age and gender groups. This chapter also explores the distribution of weekly working hours among selected categories of health workers and compares it to non-health sector occupations as well as the broader trends in the Indian labor markets. Chapter 4 presents findings from regression analyses on the determinants of labor force participation among individuals with medical training, estimates labor supply elasticities for various health 2 worker categories, and discusses the determinants of wages and wage differentials across groups of health workers. Chapter 5 provides a forward-looking perspective on the Indian health workforce. It presents estimates of health workforce supply, demand, and need using new health workforce projection techniques. The final chapter discusses the main findings in the context of broader labor and health sector changes in the country and lists policy recommendations to strengthen the country’s health workforce. 3 HEALTH WORKFORCE PROFILE This chapter presents updated estimates of the size, composition, and demographics of the Indian active health workforce. The estimates use data from the PLFS, a nationally representative labor force survey conducted by the National Sample Survey Office under the Ministry of Statistics and Programme Implementation (MoSPI). The PLFS collects information on labor force participation, unemployment, the nature of employment (formal and informal), sector (public and private), wages, weekly working hours, and individual and household characteristics, across demographic and geographic groups. This report uses data from the six PLFS annual reports from 2017/18 to 2022/23. Seven categories of active health workers are defined using the three-digit occupation classification and the five-digit industry classification. 1 The active health worker categories are physicians, dentists, professional nurses and midwives (N&MW), associate N&MWs, AYUSH (traditional systems of medicine practiced in India: Ayurveda, Yoga & Naturopathy, Unani, Siddha, Sowa Rigpa, and Homeopathy), and a group of workers with a diploma/certificate in fields associated with medicine defined as “allied health workers.�2 Since health worker categories in the PLFS are identified by their occupation, it is not possible to distinguish those who are out of the labor market, unemployed, or employed in other sectors. On the other hand, these labor market indicators can be estimated for the group of workers who reported having medical training. 3 The main advantage of using a labor force survey is that the wide range of variables enables descriptive and causal analysis. In addition to estimating the number of active health workers, the PLFS provides data on their demographics and household characteristics as well as information about their jobs, including associated wages and working hours. Those variables can be used as controls to assess labor supply decisions resulting from changes in the health sector or labor market policies and to compare HLM trends and outcomes with trends and outcomes in other sectors and in the general labor market. Labor force surveys often do not contain sufficient sample sizes of health workers or the definitions of health workers may be aggregated into larger occupational groups such that specific cadres of health workers cannot be identified or only identified as an aggregate group. Both limitations do exist in the PLFS case. For instance, the health workforce size estimates from using the PLFS and existing administrative data, such as the National Health Workforce Accounts (NHWA) produced by the World Health Organization (WHO), are quite different (see next section). These differences are partially explained by the sampling of health workers in the PLFS and by the fact that the survey relies on self-report information. The NHWA estimates the stock of health workers based on official records from health professional associations (which may not efficiently capture attrition in the workforce), while the PLFS estimates the current flow of health workers. 1 National Classification of Occupations (NCO) 2004 and NCO 2015, National Industry Classification (NIC) 2008. appendix A provides a full list of NCO and NIC codes used to identify the categories of health workers in the PLFS. 2 Includes physiotherapists, health assistants, dental assistants, physiotherapy associates, pharmacist assistants, occupational therapists, chiropodists, masseurs, and others. Appendix A presents a full list of the occupations. 3 Medical training is defined as either reporting “healthcare and life sciences training� as field of training or having technical education: "diploma or certificate (below graduate level) in medicine," "diploma or certificate (graduate and above level) in medicine," or "technical degree in medicine." 4 Health Workforce Size and Composition According to the PLFS 2022/23, there are 4.96 million active health workers. The physician workforce is estimated at 1.06 million, while the nursing workforce, the largest category of health workers in the country, is estimated at 2.84 million (of whom two-thirds are associate N&MWs). For the same year, the estimated health workforce includes 251,241 AYUSH practitioners, 35,126 dentists, 7,825 pharmacists, and 768,607 allied health workers. The latest available NHWA data (2018)4 estimates the total health workforce in India at 5.7 million. The estimate of the active health workforce based on the PLFS corresponds to 86 percent of the NHWA stock estimates. This is higher than previously available estimates; for instance, Karan and colleagues (2021) found that PLFS-based estimates accounted for 67.2 percent of the NHWA health workforce stock estimates. The discrepancies can be attributed to: (i) differences in the classification of health worker categories; for example, the NHWA does not have information on allied health workers; and (ii) the PLFS largely underestimates dentists and pharmacists because both occupations are likely to report as allied health workers.5 Table 0.1 – Indian Health Workforce, Latest NHWA Available and PLFS 2022/23 Health Worker Category PLFS 2022/23 NHWA 2018 Physician 1,065,038 1,160,000 Dentist 35,126 270,000 Nurse/Midwife 2,837,968 2,340,000 AYUSH 251,241 790,000 Allied Health Worker 768,607 - Pharmacist 7,825 1,190,000 Total 4,965,805 5,760,000 Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2022/23), using Census of India (2011) population projections to adjust weights, 6 and National Health Systems Resource Centre National Health Workforce Accounts Estimates for India, 2018-19 (2022). In India, the population density of physicians, nurses, and midwives is 28.2 per 10,000 population, according to the PLFS 2022/23, which is lower than the WHO recommended density and below that of its peers. The WHO bases its estimate on the number of health workers required to achieve Target 3 (Good Health and Wellbeing) of the Sustainable Development Goals (SDG); it recommends a density of 44.5 physicians, nurses, and midwives per 10,000 population. 7 In India, the estimated density is two-thirds of this 4 Available at https://apps.who.int/nhwaportal/. 5 Dentists and pharmacists are identified in the four-digit occupation code in NCO 2015 (2261 for dentist and 2262 for pharmacist, while occupation is observed in the three-digit code in the PLFS (226 for allied health workers). 6 The PLFS underestimates the current Indian population, thus the recalibration. (See appendix C for details.) 7 See chapter 5 for details on the WHO methodology. 5 recommendation. Compared to its peers, India’s density of health workers is below of that of Indonesia (46.7), the Philippines (55.4), the Organization for Economic Co-operation and Development (OECD) average (127.9), and the Brazil, Russia, India, China, and South Africa (BRICS) average (64.1). The exceptions are its regional peers, Pakistan (15.5) and Bangladesh (12.8), with lower population densities. The physician-to-population ratio in India is 7.68 per 10,000 population, which is similar to the South Asian Region (SAR) average of 7.63. However, it is less than one-quarter of the average among OECD countries (33.94) and less than half of the average among BRICS countries (19.87). The density of N&MWs in India is 20.49 per 10,000 population, which is higher than the SAR average of 14.89. However, it is one-fifth lower than the average among OECD countries (93.92) and less than half of the BRICS average (44.25). The nurse-to-physician ratio in India is estimated at 2.6:1, meaning there are approximately 2.6 nurses for every physician in the country. This is slightly lower than the OECD average of 2.76:1 in 2019 (OECD 2023). Figure 0.1 – Density of Physicians, India and Its Peers 70 60 Physician density per 10,000 50 40 30 20 10 0 India Indonesia India Spain India China Philippines China Sweden Italy Ireland Australia Latvia Hungary Colombia Turkey France Belgium Brazil Brazil Greece Switzerland Estonia Poland Chile Korea, Rep. Turkey Mexico Norway Bangladesh Germany Slovenia Luxembourg Japan Mexico Sri Lanka South Africa Denmark Canada Portugal Iceland Czechia United Kingdom Pakistan Austria Lithuania Finland Netherlands Slovak Republic New Zealand Israel Costa Rica Russian Federation United States Peers BRICS OECD Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on OECD Health Statistics (2023), Periodic Labor Force Survey of India data (2022/23), and WHO Global Workforce Statistics database (2022 for Indonesia, Austria, Canada, Chile, Iceland, Israel, Italy, Lithuania, New Zealand, United Kingdom; 2021 for Philippines, China, Mexico, Brazil, Turkey, Bangladesh, Sri Lanka, South Africa, Belgium, Colombia, Czechia, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Latvia, Netherlands, Norway, Poland, Portugal, Slovak Republic, Slovenia, Korea Rep., Spain, Switzerland, United States; 2020 for Russian Federation, Japan, Denmark, Sweden, Australia; 2019 for Pakistan; 2017 for Luxembourg). 6 Figure 0.2 – Density of Nurses and Midwives, India and Its Peers 250 200 Nurses density per 10,000 150 100 50 0 Philippines Indonesia India India India Ireland Colombia China Switzerland Sweden Italy Spain Hungary Brazil Turkey Chile Brazil China Belgium Australia France Korea, Rep. Estonia Poland Latvia Greece Turkey Norway Mexico Slovenia Mexico Sri Lanka Bangladesh Luxembourg Canada South Africa Germany Iceland Japan New Zealand Denmark Czechia United Kingdom Pakistan Netherlands Portugal Slovak Republic Finland Austria Lithuania Costa Rica Israel United States Russian Federation Peers BRICS OECD Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on OECD Health Statistics (2023), Periodic Labor Force Survey of India data (2022/23), and WHO Global Workforce Statistics database (2022 for Indonesia, Belgium, Canada, Chile, Costa Rica, Iceland, Ireland, Israel, Italy, New Zealand, United Kingdom, United States; 2021 for Philippines, China, Mexico, Brazil, Turkey (Türkiye), Bangladesh, Sri Lanka, Australia, Austria, Colombia, Czechia, Estonia, France, Germany, Greece, Hungary, Latvia, Lithuania, Netherlands, Norway, Poland, Portugal, Slovak Republic, Slovenia, Korea, Rep., Spain, Switzerland; 2020 for Russian Federation, South Africa, Denmark, Finland, Japan, Sweden; 2019 for Pakistan; 2017 for Luxembourg). India's active health workforce grew by 60 percent from 2017/18 to 2022/23, far exceeding the estimated population growth of 6.3 percent during the same period.8 This trend also outpaced the overall growth of India's total workforce, which increased approximately 4.7 percent over the same six-year period. Between 2017/18 and 2022/23, the physician workforce increased by 47 percent, the professional N&MW workforce grew by 41 percent, and the associate N&MW workforce increased by 151 percent. The only category that experienced a decline was AYUSH practitioners, which decreased by 28 percent. The growth of the active health workforce is driven by the rapid expansion of medical and nursing schools over the past decade, in both the public and private sectors. India has the largest number of medical schools in the world. By 2023, India had 612 medical schools, of which 53 percent in the public sector accounting for 52 percent of Bachelor of Medicine and Bachelor of Surgery seats; this represents an increase from 335 medical schools in 2010/11. The number of seats in medical schools more than doubled, from 40,775 in 2010 to 92,127 in 2022/23 (Agarwal, Balani, and Venkateswaran 2023).9 The number of nursing colleges increased from 2,904 in 2010 to over 5,200 by 2023, accompanied by a 186 percent rise in undergraduate nursing program seats. Unlike medical colleges, the growth in nursing education in India has been mainly driven by the private sector, with government institutions accounting for only about 14 percent of the total nursing colleges and 14 percent 8 Census population projections, Ministry of Home Affairs, Government of India. 9 The role of private sector institutions is especially noticeable in the training of allied health staff, especially pharmacists. According to the list of 1,750 institutions approved to train pharmacists (Bachelor of Pharmacy) across India, almost 90% are in the private sector and account for a similar share of student seats. 7 of the available seats.10 As for medical colleges, the expansion was concentrated in the southern and western states (Agarwal, Balani, and Venkateswaran 2023). Figure 0.3 – Health Workforce and Population Dynamics, India, 2017/18 and 2022/23 2.3 (a) Indexed Growth of Health Workforce vs. Population 2.3(b) Health Workforce, Selected Cadres, 2017/18 and 2022/23 170 10.00 4.97 9.00 Population/Health Workforce Index (2017/18 = 100) 160 160 8.00 150 Health Workers - selected cadres 7.00 140 6.00 3.11 130 136 5.00 (millions) 1.89 120 126 4.00 3.00 0.76 110 103 104 105 101 102 100 2.00 100 0.67 0.94 1.00 90 0.73 1.07 93 - 92 2017-18 2022-23 80 2017-18 2018-19 2019-20 2020-21 2021-22 2022-23 Physicians Professional N&MW Population Health workforce Associate N&MW Total health workers Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2017/18 and 2022/23), using Census of India (2011) population projections to adjust weights. The health care industry accounts for 1.3 percent of the total workforce in the country, and 2.0 percent of the total female workforce. These figures include both core health workforce occupations and other roles within the health care industry, such as support or administrative roles, care, and social workers. This share remains significantly lower than in countries such as Brazil (8 percent), the United States (10.8 percent), the average across OECD countries (10.1 percent), and countries in the Latin America and Caribbean region (6-9 percent). This pattern may reflect underlying differences in the levels of health care financing. It also highlights the potential to expand employment in the sector as India continues to expand health coverage and mobilize additional resources to fund health care services for its population. Medical training significantly boosts labor force participation (LFP), especially for women. In 2022/23, LFP among individuals aged 15 and older with medical training was just over 76 percent, compared to 57 percent for the general workforce. For women, medical training increased LFP by 32 percentage points, reaching 68 percent, compared to the overall female LFP of 36 percent. Unemployment rates among health workers have been consistently higher than the national average, with a rate of 7.2 percent in 2022/23, compared to 3.4 percent for the total workforce. 10 Indian Nursing Council (available at: https://indiannursingcouncil.org/statistics). 8 Figure 0.4 – Health Sector Employment and General Labor Market, India, 2017/18–2022/23 2.4(a) Share of Employment by Sector * 2.4(b) Labor Force Participation by Sector ** 0.90 Health 1.3 0.79 0.80 0.76 0.74 0.75 Education 3.4 0.73 0.72 Labor Force Participation 0.70 Transportation and storage 4.7 0.57 Wholesale and retail trade 11.9 0.60 0.54 0.55 0.53 0.50 0.50 Construction 12.2 0.50 Manufacturing 12.8 0.40 Agriculture 39.7 0.30 - 10.0 20.0 30.0 40.0 50.0 2017-18 2018-19 2019-20 2020-21 2021-22 2022-23 Share of Total Worforce LFP - All Workers LFP - Workers with Medical Training Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2017/18–2022/23), using Census of India (2011) population projections to adjust weights. Unemployment and labor force participation are defined by individuals 15 years and older. * Share of employment by sector is defined by the industry classification, using the NIC 2008 code. ** Active health workers are defined by those having medical training. A challenge in estimating the supply of active health workers using labor force surveys is aligning occupation self-reports with the workers' educational qualifications. The PLFS collects the working status of individuals based on self-reports, which can lead to unqualified workers identifying as qualified without meeting the educational requirements set by health professionals' associations. Additionally, evidence indicates a considerable number of informal care providers operating within the Indian healthcare system (see Box 2.1). As a result, informal care providers may report across PLFS occupation health worker categories because they perform tasks typically related to these roles, even though they lack formal training. To adjust the size of the active health workforce based on qualifications, this report applies the following criteria: (i) general education, (ii) technical education, (iii) years of schooling, and (iv) field of training. For example, a physician is considered qualified with at least 18 years of schooling to meet the qualification standards, while an associate N&MW requires a minimum of 14 years of schooling to meet the qualification standards.11 Applying these criteria, most physicians (89.8 percent) and dentists (95.9 percent) are qualified, whereas only 55 percent of professional N&MWs and 43.1 percent of associate N&MWs fulfill the criteria. 11 Appendix B presents a full description of the qualification adjustment criteria. 9 Figure 0.5 – Health Workforce Adjusted by Qualification Criteria, India, 2022/23 2.5(a) Qualified and Unqualified, Selected Cadres 2.5(b) Percentage of Qualified Health Workers, Urban/Rural 1.00 0.04 0.73 0.10 Total health workers 0.47 0.90 0.28 0.79 0.39 Other health workers Percentage of Qualified Health Workes 0.80 0.40 0.61 0.45 0.70 0.57 0.80 AYUSH 0.23 0.60 0.56 Associate N&MW 0.50 0.34 0.96 0.90 0.40 0.59 Professional N&MW 0.72 0.48 0.30 0.60 0.61 0.55 0.96 Dentist 0.20 0.43 1.00 0.10 0.96 Physician 0.79 - - 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Physician Dentist Professional Associate AYUSH Other health Total health N&MW N&MW worker worker Percentage of Qualified Health Workes Qualified Unqualified Urban Rural Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2022/23), using Census of India (2011) population projections to adjust weights. 10 Box 2.1: Informal Health Providers in India Informal Health Providers (IHPs) in India account for 55 percent of all healthcare providers in India, and in rural areas, this figure can be as high as 68 percent (Thapa et al. 2024). These providers include traditional healers, unregistered practitioners, and individuals with paramedical training but no formal degrees. Despite their lack of official recognition, state and local governments often collaborate with them, especially in underserved areas. For instance, in Mumbai, local hospitals work with IHPs in slums to manage tuberculosis cases. Role and Importance: IHPs are crucial in rural and underserved areas where formal healthcare is limited. They often serve as the first point of contact, operating small clinics and providing treatments for a fee. Most have secondary education and informal paramedical training, enabling them to manage common conditions. They are trusted by local communities for their accessibility, willingness to provide home visits, and credit facilities (Gautham et al. 2014). Services Provided: IHPs offer basic medical consultations and treatments for common ailments, prescribe and administer medications including antibiotics and injections, refer patients to formal healthcare facilities for serious conditions, and participate in public health initiatives such as immunization drives and family planning programs (Sudhinaraset et al. 2013). Quality of Care: Studies indicate that the care provided by IHPs can be comparable to that of formal public healthcare providers. However, the quality of care can vary significantly between regions. South Indian IHPs often demonstrate better organization and knowledge management compared to their North Indian counterparts. This variation is likely due to differences in local healthcare markets and the level of interaction with formal healthcare providers in each area. For example, in Madhya Pradesh, IHPs spent more time with patients and adhered more closely to medical checklists. Training programs have improved their ability to manage medical cases, though challenges remain in reducing the overuse of medications (NASEM 2018, 2021). Challenges and Skepticism: While often viewed skeptically due to their lack of formal qualifications, IHPs are indispensable in India's healthcare system, particularly in rural areas. With proper training and regulation, they can significantly improve healthcare access and quality for underserved populations. However, IHPs are not recognized by the country's regulatory and legal framework, and they usually have little or no officially recognized training. Due to their legal status as informal health providers, IHPs are not officially recognized, and their numbers are not systematically enumerated or estimated. Demographic Profile The Indian health workforce is relatively young, with over 50 percent under the age of 39, mirroring the general population, where 53 percent fall into this age group. However, some health cadres, such as professional N&MWs, dentists, and pharmacists, have an even larger proportion of their workforce under the 11 age of 39: 71 percent for professional N&MWs, 86 percent for dentists, and 87 percent for pharmacists. In contrast, physicians, associate N&MWs, and AYUSH practitioners tend to be older, with only 34 percent, 58 percent, and 51 percent of their workforces, respectively, under the age of 39. The median age varies across professions: professional N&MWs have a median age of 33 (32 for male, 35 for female), associate N&MWs have a median of 38 (32 for male, 38 for female workers), and physicians have a median age of 45 (49 for male, 38 for female physicians). Figure 0.6 – Physician and Nursing Workforce Age Distribution, India, 2022/23 100% 90% 80% Cumulative distribution (%) 70% 60% 50% 40% 30% 20% 10% 0% 18 24 30 36 42 48 54 60 66 72 78 Age Physicians Professional N&MW Associate N&MW Population over 18 Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2022/23), using Census of India (2011) population projections to adjust weights. Women make up approximately 63 percent of the health workforce, with overall female participation increasing across the six rounds of the PLFS survey. The total number of female health workers increased by 90 percent between 2017/18 and 2022/23. This growth was largely driven by a 199 percent increase in the number of female associate N&MWs over the same period (increasing the concentration of women in this occupation by 15 percentage points). Certain occupations are predominantly female: 78 percent of professional N&MWs are women, 93 percent of associate N&MWs, 84 percent of pharmacists, and 60 percent of dentists. In contrast, the physician and AYUSH workforces remain male-dominated, with 76 percent and 72 percent male representation, respectively. Between 2017/18 and 2022/23, the share of women in the physician workforce decreased by 4.7 percentage points, while female participation among AYUSH practitioners increased by 11.4 percentage points. 12 Figure 0.7 – Female Concentration Across Cadres, India, 2017/18 and 2022/23 1.00 0.93 0.90 0.82 0.80 0.78 0.78 0.70 Share of Female Workers 0.60 0.50 0.43 0.40 0.36 0.30 0.29 0.28 0.20 0.24 0.17 0.10 - 2017-18 2022-23 Physician Professional N&MW Associate N&MW AYUSH Other health worker Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2017/18 and 2022/23), using Census of India (2011) population projections to adjust weights. Geographical Distribution Over half of India’s active health workers practice in urban areas, where 36 percent of the population lives. About two-thirds of physicians (64 percent), AYUSH practitioners (65 percent), and professional N&MWs (62 percent) are based in urban locations. Nearly all dentists and 87 percent of pharmacists are based in urban locations. Almost two-thirds of associate N&MWs (60 percent) are based in rural locations, comprising the primary rural health cadre and accounting for 50 percent of all rural health workers in the country. Between 2017/18 and 2022/23, the share of India’s rural population declined by 3 percentage points, while the proportion of active health workers in rural areas grew by nearly 14 percentage points, helping to reduce the urban-rural health workforce imbalance. This increase was primarily driven by the growth of associate N&MWs in rural areas, which rose by 243 percent between 2017/18 and 2022/23 (from 331,000 to 1.1 million). In comparison, the associate N&MWs workforce in urban areas increased by 78 percent over the same period (from 423,000 to 753,000). The share of rural health workers also increased across other cadres between 2017/18 and 2022/23: the share of physicians in rural areas grew by 16 percentage points, while that of professional N&MWs increased by 9 percentage points and AYUSH practitioners by 2.5 percentage points. 13 Figure 0.8 – Urban-Rural Distribution of Health Workers and Population, India, 2017/18 and 2022/23 2.8(a) Health Workers Density, Urban-Rural 2022/23 2.8(b) Share of Workers in Rural Areas, 2017/18 and 2022/23 16.0 15.0 0.60 0.60 13.8 14.0 12.6 Health Workers per 1,000 Population 11.8 12.0 0.50 Share of Workers in Rural Areas 0.44 9.7 10.0 0.40 0.38 8.0 0.36 0.33 6.0 0.35 0.30 4.2 4.0 0.29 4.0 3.3 3.2 0.20 2.0 0.20 1.0 0.0 Physician Professional Associate AYUSH Other health 0.10 N&MW N&MW worker 2017-18 2022-23 Physician Professional N&MW Associate N&MW AYUSH Rural Urban Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2017/18 and 2022/23), using Census of India (2011) population projections to adjust weights. The supply of active health workers varies considerably. Southern states like Kerala, Tamil Nadu, and Karnataka have higher densities of active health workers, particularly for physicians and professional N&MWs. In contrast, northern and eastern states like Bihar, Jharkhand, Uttar Pradesh, and Odisha have lower densities of active health workers. The largest disparities across states are observed among associate N&MWs. Meghalaya (62), Goa (47.7), and Kerala (33.6) have the highest densities of associate N&MWs per 10,000 population, while Tripura (2.8), Jharkhand (3.9), and Bihar (4.1) have much lower densities of associate N&MWs. Considering the physician workforce, Uttarakhand (18 physicians per 10,000 population), Delhi (14.7), and Meghalaya (14.4) have the highest densities, whereas Bihar (3.2), Assam and Manipur (2.4), Mizoram (2.1), and Odisha (0.2) have the lowest densities. 14 Figure 0.9 – Health Workers Density by State, India, 2022/23 Assam 2.4 4.7 6.6 0.12.9 Bihar 3.2 4.8 4.1 0.8 4.8 Jharkhand 4.7 5.5 3.9 1.6 4.4 Delhi 14.7 - 3.4 0.0 2.0 West Bengal 3.5 2.0 13.6 1.4 3.9 Rajasthan 3.7 8.2 11.4 0.8 0.6 Madhya Pradesh 3.1 2.5 17.2 1.2 4.6 Uttar Pradesh 12.4 3.8 10.0 1.8 3.2 Tamil Nadu 6.9 9.1 13.9 1.1 2.2 Nagaland 3.1 4.1 15.1 3.3 9.3 India 7.7 6.8 13.7 1.8 5.5 Haryana 14.0 2.4 10.8 1.5 7.9 Odisha 0.2 1.0 33.3 0.42.6 Himachal Pradesh 11.9 1.3 20.4 0.9 3.9 Karnataka 7.0 10.3 13.1 2.8 6.5 Sikkim 4.1 20.9 8.6 3.2 4.2 Gujarat 6.5 3.6 20.9 2.0 8.8 Tripura 7.7 8.8 2.8 9.4 13.6 Chhattisgarh 7.0 9.7 20.3 2.3 4.8 Telangana 12.9 9.9 12.0 6.1 4.9 Andhra Pradesh 8.2 7.7 20.8 0.5 10.7 Punjab 13.0 8.2 7.9 5.1 17.7 Mizoram 2.1 11.1 8.1 11.7 19.8 Maharashtra 11.1 18.7 14.8 4.0 6.3 Goa 0.0 7.4 47.7 - 4.3 Uttrakhand 18.0 6.2 26.2 - 9.3 Manipur 2.4 27.5 18.9 1.1 9.5 Arunachal Pradesh 5.0 21.6 34.2 - 11.9 Meghalaya 14.4 1.3 62.0 0.5 3.5 Kerala 12.2 15.7 33.6 1.4 23.2 0 10 20 30 40 50 60 70 80 90 Physician Professional N&MW Associate N&MW AYUSH Other health workers Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2022/23), using Census of India (2011) population projections to adjust weights. 15 The density of active health workers is positively associated with total health expenditure (THE) per capita and, to a lesser extent, with gross domestic product (GDP) per capita at the state level . Generally, as state THE per capita increases, so does the availability of physicians, professional N&MWs, and associate N&MWs, measured in terms of the density of these workers per 10,000 population (with a few outliers, such as Kerala, Uttarakhand, and Odisha). States with higher GDP per capita tend to have a higher density of physicians, though the correlation is weaker than the observed between density of active health workers and THE per capita. The density of professional N&MWs also shows wider variance among states with similar GDP levels compared to that of physicians. The distinct effects of THE per capita and GDP per capita suggest that prioritization of healthcare spending, both public and private, does not necessarily correlate with GDP per capita. Additionally, state efforts to train and attract health workers appear to operate independently of overall state economic development. Figure 0.10 – Density of Health Workers, Total Health Expenditure, Gross Domestic Product by State, India (INR) 2.10(a) Physicians per 10,000 Population and THE per Capita 2.10(b) Physicians per 10,000 Population and GDP per Capita 20.00 20.00 Uttrakhand 18.00 Uttarakhand 18.00 16.00 16.00 y = 0.0012x + 2.5955 Meghalaya Delhi R² = 0.2262 Haryana 14.00 Haryana 14.00 Punjab Telangana Physicians per 10,000 Population Uttar Pradesh Physicians per 10,000 Population Punjab Kerala Himachal Pradesh Uttar Pradesh Kerala 12.00 Himachal Pradesh 12.00 Maharashtra Telangana Maharashtra y = 0.0004x + 6.4171 10.00 10.00 R² = 0.0147 Karnataka Andhra Pradesh Andhra Pradesh 8.00 Chhattisgarh 8.00 Chhattisgarh Tamil Nadu Tamil Nadu Gujarat 6.00 Karnataka 6.00 Jharkhand Rajasthan Sikkim Jharkhand West Bengal 4.00 Bihar RajasthanWest Bengal 4.00 Assam Nagaland Bihar Madhya Pradesh Mizoram Manipur Assam 2.00 2.00 Odisha Goa - Odisha - - 2,000.00 4,000.00 6,000.00 8,000.00 10,000.00 12,000.00 - 1,000.00 2,000.00 3,000.00 4,000.00 5,000.00 6,000.00 THE per Capita (INR 2020) GDP per Capita (INR 2022) Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2022/23), using Census of India (2011) population projections to adjust weights; Reserve Bank of India Handbook of Statistics on the Indian Economy, 2022-23 (2023); National Health Systems Resource Centre National Health Accounts Estimates for India, 2020-21 (2024). The nurse-to-physician ratio across Indian states varies significantly, with the national average ratio being 2.6:1 (which means 2.6 nurses for every physician). This is below the WHO recommendation of 3 nurses for every physician, but slightly above the average ratio across OECD countries (2.76 nurses per physician in 2021) (OECD 2023). States with lowest densities of physicians and N&MWs are Assam (2.42 physicians per 10,000 population and 11.38 N&MWs per 10,000), Bihar (3.2 physicians per 10,000 and 8.98 N&MWs per 10,000 population), and Jharkhand (4.7 physicians per 10,000 and 9.48 N&MWs per 10,000 population). States with the highest densities of physicians and N&MWs are Meghalaya (with a density of 14.44 physicians per 10,000 population and 63.25 N&MWs per 10,000 population) and Kerala (12.19 16 physicians per 10,000 and 49.24 N&MWs per 10,000), well above the national averages. States with relatively more physicians than N&MWs include Delhi, Haryana, and Punjab; the nurse-to-physician ratios in these states are 0.14, 0.95, and 1.24, respectively. Manipur has more N&MWs than physicians, with a nurse-to- physician ratio of 19.66. Figure 0.11 – Physician and Nurses per 10,000 Population Across States, India, 2022/23 70.00 Meghalaya 60.00 Goa Kerala 50.00 Manipur Nurses and Midwives per 10,000 40.00 Odisha Maharashtra Uttrakhand Sikkim Chhattisgarh Andhra Pradesh 30.00 Gujarat Karnataka Himachal Pradesh Telangana Nagaland 20.00 Mizoram Tamil Nadu Rajasthan Tripura Punjab Madhya Pradesh West Bengal Uttar Pradesh Haryana 10.00 Assam Bihar Jharkhand Delhi - - 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 Physicians per 10,000 Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2022/23), using Census of India (2011) population projections to adjust weights and Reserve Bank of India Handbook of Statistics on the Indian Economy, 2022-23 (2023). The nurse-to-physician ratio has traditionally been low in South Asia, with a significant improvement observed in India over the past decades. Available regional data suggest that while the extreme case of a higher number of physicians than nurses persist in Bangladesh (0.71) and Pakistan (0.45), South Asian ratios remain substantially lower than those in Indonesia (6.67) and the Philippines (6.0). While there is no universally ideal ratio, it significantly affects the roles that physicians and nurses fulfill. In contexts with high nurse-to-physician ratios, physicians can focus on specialist tasks for which they are trained, and nurses can assume a broader range of clinical responsibilities at a higher level of skill and specialization. Conversely, in settings with a low ratio, physicians often take on more routine tasks due to the relative shortage of nurses. The optimal ratio may also depend on resource constraints within the healthcare system. In resource-limited environments, having physicians perform lower-skill tasks may be inefficient. Based on this reasoning, India and other South Asian countries may still have an imbalance where physicians are less supported by the nursing staff, limiting their ability to specialize; while nurses are confined to more basic, "handmaiden" roles. 17 This imbalance shapes the medical practice culture over time, suggesting that the legacy of historically low nurse-to-physician ratios may influence healthcare dynamics for years to come. Table 0.2 – Physician and Nurse/Midwife Densities and Nurse-Physician Ratio Country Year Physicians per 1,000 Nurses and Nurses per Population Midwives per 1,000 Physician Population India 1991 1.2 0.4 0.33 2020 0.7 1.7 2.42 Bangladesh 2020 0.7 0.5 0.71 Indonesia 2020 0.6 4.0 6.67 Pakistan 2019 1.1 0.5 0.45 Philippines 2020 0.8 4.8 6.0 Sri Lanka 2020 1.2 2.5 2.08 Source: World Bank Development Indicators database (World Bank, various years). Sectoral Distribution Active health workers are more likely to be employed in the private sector and, on average, work longer hours per week in their private sector jobs. Over two-thirds of physicians (63 percent), more than half of professional N&MWs (56 percent), and a large majority of AYUSH practitioners (85 percent) are employed in the private sector. All categories of active health workers working in the private sector report longer average weekly hours than their public sector counterparts. For example, private sector physicians work an average of 50 hours per week, roughly two hours more than those in the public sector (47.8 hours). Among professional N&MWs, the difference is about three hours. The gap in average weekly hours between private and public sector jobs is even more pronounced for associate N&MWs and AYUSH practitioners. On average, associate N&MWs and AYUSH practitioners work approximately eight hours more per week in the private sector compared to their public sector counterparts. 18 Figure 0.12 – Health Workforce Distribution by Sectors, India, 2022/23 2.12(a) Share of the Workforce by Sector 2.12(b) Average Number of Hours (Weekly) by Sector 60.0 Other health worker 0.35 0.65 51.5 50.1 49.0 50.1 50.0 47.8 48.1 47.5 46.0 Average Number of Hours per Week 41.7 39.8 AYUSH 0.15 0.85 40.0 30.0 Associate N&MW 0.71 0.29 20.0 Professional N&MW 0.44 0.56 10.0 Physician 0.37 0.63 0.0 Physician Professional Associate AYUSH Other health - 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 N&MW N&MW worker Share of Health Workers Public Private Public Private Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2022/23), using Census of India (2011) population projections to adjust weights. The proportion of health workers working in the private sector in India is much higher than the proportion observed in other Asian middle-income countries. This feature of the Indian health workforce landscape means that policy related to public sector human resource management will have less influence over health workforce outcomes than it does in comparator countries and that the issues of regulating the private sector to achieve health policy goals assume a central importance. More than in countries like the Philippines and Malaysia where 40-60 percent of physicians and nurses work in the private sector and far more than in countries like China and Thailand where the proportion is closer to 20 percent, a central planning style approach to the health workforce will not be effective in shaping health workforce outcomes. Under these conditions, it is paramount to examine the key labor market variables influencing health workers’ employment decisions in both sectors to understand HLM outcomes and design appropriate policies. 19 Figure 0.13 – Share of Health Personnel by Sector in Selected Countries 100 18 14 14 18 18 90 20 30 Percentage of Health Workers 33 80 42 39 53 49 70 56 55 60 78 81 50 100 82 86 86 82 82 40 80 70 67 30 58 61 47 51 20 44 45 10 22 19 0 Physicians Physicians Physicians Dental nurses Pharmacists 2010 Nurses Pharmacists Pharmacists Nurses Nurses Pharmacists Midwives Dentists 2014 Nurses & Midwives 2015 Dentists Physicians 2010 Dental practitioners China* Malaysia** Philippines*** Thailand**** Public Private Sources: *WHO (2016), Human resources for health country profiles: China. ** WHO (2014), Human Resources for Health Country Profiles: Malaysia. *** WHO (2013), Human Resources for Health Country Profiles: Philippines. **** WHO International Health Policy Program Thailand (2016), Progress Report: Strengthening Human Resources for Health through Transformative Education and Rural Retention in Thailand. 20 DEMAND AND SUPPLY FOR HEALTH WORKERS IN INDIA This chapter provides a descriptive analysis of key variables related to the demand and supply of health workers in India. The demand for health workers in a country is largely influenced by the willingness- to-pay (WTP) of government entities, the private sector, and international actors, such as donors and multinational corporations (McPake, Scott and Edoka 2014). The level and composition of healthcare financing is a key influence of WTP for hiring health workers. However, there is often a mismatch between this demand and the actual need for health workers—either due to insufficient resources to cover needed health services or because available resources are allocated to non-priority services or distributed unequally across geographical regions, resulting in imbalances in the health workforce. The supply of health workers refers to the number of trained individuals willing to work in the health sector as well as the number of hours current health workers are willing to provide, given wages and employment packages. Labor market variables—such as wage rate, employment conditions and benefits, job security, and job quality—play a significant role in shaping the demand for and supply of health workers. This chapter explores these variables within the Indian HLMs. The analysis sets the foundation and is complemented by the regression analyses presented in the next chapter. The value added of such an approach lies in its focus on these key labor market variables affecting health worker inflows and outflows, with an emphasis on establishing causality whenever possible and observing changes across demographic groups, sectors, and regions. This approach contrasts with alternative methods, which often center on simple counts of workers entering and leaving the labor market, without providing insights into the drivers of these movements. Characteristics of Labor Supply and Demand Wage disparities are significant across categories of workers in the health sector. In the period analyzed, physicians consistently earned the highest wages, both when compared to other health sector occupations and occupations in other sectors, such as lawyers and engineers. In 2022/23, the average monthly wages of physicians were 2.8 times those of professional N&MWs, 2.3 times those of AYUSH practitioners, and nearly five times the average monthly wages of associate N&MWs. Physicians’ average monthly wages were also 48 percent higher than the average wages among lawyers and 22 percent higher than the average among engineers. All four categories of health workers experienced a decrease in their average monthly wages between 2017/18 and 2022/23: Associate N&MWs experienced a 37 percent drop in real average monthly wages (from INR 19,448 to INR 12,226); physicians’ average monthly wages fell by 11 percent (from INR 64,360 to INR 54,456); professional N&MWs average monthly wages dropped by 16 percent (from INR 24,633 to INR 20,632); and AYUSH practitioners’ average monthly wages declined by 5 percent (from INR 26,479 to INR 25,334). 21 Figure 3.1 – Monthly Wages for Selected Health and Non-Health Occupations, India 3.1(a) Trends 2017/18–2022/23* 3.1(b) Health and Selected Occupations, 2022/23 70,000 64,360 Physicians 57,218 56,084 57,218 60,000 55,207 54,456 Engineers 46,942 49,641 50,000 Lawyers 38,682 40,000 Montlhy Wages in INR 33,268 30,559 AYUSH 25,344 28,747 26,749 30,000 25,344 23,510 Professional N&MW 20,632 27,746 20,000 24,633 25,764 24,249 19,092 20,632 19,448 18,545 Associate N&MW 12,226 16,910 10,000 15,512 14,588 12,226 0 10,000 20,000 30,000 40,000 50,000 60,000 0 2017-18 2018-19 2019-20 2020-21 2021-22 2022-23 Physicians Professional N&MW Associate N&MW AYUSH Mointhly Wages in INR Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2017/18 and 2022/23), using Census of India (2011) population projections to adjust weights. * At constant process of July 2024. The downward trend in the health sector wages is also observed relative to GDP per capita . The ration of wages to GDP per capita decreased between 2017/18 and 2022/23 for all four categories of health workers, with the largest decrease seen among associate N&MWs (47 percent), followed by professional N&MWs (29 percent), physicians (25 percent), and AYUSH practitioners (20 percent). Professional categories from other sectors also experienced a decline in the ratio of wages to GDP per capita, although at a slower rate: engineers experienced a 19 percent decrease, while lawyers experienced a 4 percent decrease. In 2022/23, the average yearly wages were higher than GDP per capita for physicians (3.22 times), professional N&MWs (1.16 times), and AYUSH practitioners (1.43 times), but lower for associate N&MWs (0.69). Overall, it suggests that the relative purchasing power of all these workers has diminished over time. 22 Figure 3.2 – Wages and GDP Per Capita for Health Workers and Selected Occupations, India 3.2 (a) Yearly Wage Relative to GDP per Capita, 2022/23 3.2(b) Variation 2017/18–2022/23 4.29 4.50 Physicians 3.22 4.00 3.22 Yearly Wages/GDP per Capita 3.50 3.26 Engineers 2.64 3.00 2.64 2.50 2.26 Lawyers 2.18 2.18 2.00 1.78 1.43 AYUSH 1.43 1.50 1.64 1.00 1.30 1.16 Professional 1.16 N&MW 0.50 0.69 - Associate N&MW 0.69 2017-18 2022-23 - 0.50 1.00 1.50 2.00 2.50 3.00 3.50 Physicians Professional N&MW Associate N&MW Yearly Wages/GDP per Capita AYUSH Lawyers Engineers Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2017/18 and 2022/23), using Census of India (2011) population projections and Ministry of Statistics and Programme Implementation National Statistical Office GDP per capita data for 2018- 2023 to adjust weights. Significant wage disparities exist within high-skilled health professions, particularly among physicians, compared to the relatively consistent wages found in lower-paid health sector roles. Although physicians earn the highest wages in the sector, they also show the greatest variation in wages within their workforce. Figures 3.3a and b display the distribution of monthly real wages (for those earning less than INR 300,000) for selected health and non-health sector occupations in India, based on PLFS data for 2022/23. Physicians’ wages have the highest variability, evidenced by the wide interquartile range and numerous outliers, reflecting the presence of high-wage earners in this profession. Occupations such as engineers and lawyers also show considerable wage dispersion, though their interquartile ranges are narrower than those of physicians, indicating less variation within these groups. In contrast, professional N&MWs, associate N&MWs, and AYUSH practitioners have a more concentrated wage distribution, with fewer high earners compared to physicians, lawyers, and engineers. 23 Figure 3.3 – Box Plot Monthly Wages, Selected Occupations,2017/18–2022/23 (INR) Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2017/18–2022/23), using Census of India (2011) population projections and Ministry of Statistics and Programme Implementation National Statistical Office GDP per capita data for 2018-2023 to adjust weights. Prices at level of July 2024. There is a wage premium for physicians and professional N&MWs working in the public sector, and that is consistent for both male and female workers. Average monthly wages for public sector physicians are 63 percent higher than the average monthly wages for physicians working in the private sector (INR 74,965 compared to INR 45,948), and this difference is consistent for both male and female physicians. Female physicians earn, on average, 12 percent more than their male counterparts, regardless of the sector. A similar pattern is observed among professional N&MWs, for this group the average monthly wages in the public sector are 83 percent higher than in the private sector (INR 27,755 compared to INR 15,193). Female professional N&MWs enjoy a similar wage premium in the public sector, while for male professional N&MWs, the premium is 63 percent. In contrast, associate N&MWs and AYUSH practitioners experience a wage premium in the private sector of 4 percent and 24 percent, respectively. Within these groups, female workers earn more in the private sector, 6 percent higher among associate N&MWs and 189 percent higher among AYUSH practitioners. Conversely, male associate N&MWs and AYUSH practitioners have higher average wages in the public sector, with premiums of 79 percent and 111 percent, respectively. Unlike physicians and professional N&MWs, male associate N&MWs and AYUSH practitioners generally earn, on average, more than their female counterparts in both sectors, except for female AYUSH practitioners in the private sector, who earn 54 percent more than their male counterparts. Active health workers across all four categories earn significantly more in urban areas than in rural areas. The urban wage premium is highest among AYUSH practitioners, with a 70 percent difference (INR 29,272 compared to INR 17,195), followed by professional N&MWs at 62 percent (INR 24,233 compared to INR 24 14,976), physicians at 61 percent (INR 66,281 compared to INR 41,058), and associate N&MWs at 57 percent (INR 15,627 compared to INR 9,978). This trend holds across genders, with both male and female urban health workers earning more, on average, than their rural counterparts. The largest wage differences are observed among female AYUSH practitioners in urban areas, who earn on average 325 percent more than their rural counterparts, female professional N&MWs (83 percent higher than those in rural areas), and male physicians (64 percent more than male physicians in rural areas). Pro-female wage gaps are notable among rural physicians (17 percent higher for females), urban professional N&MWs (58 percent), and urban AYUSH practitioners (32 percent). In contrast, pro-male wage gaps appear among rural AYUSH practitioners (157 percent) and among both urban (35 percent) and rural (100 percent) associate N&MWs. Table 3.1 – Health Sector Monthly Wages by Sector, Urban/Rural, and Gender, 2022/23 (INR) Occupation by Gender Total Sectors Location (INR) Public Private Urban Rural (INR) (INR) (INR) (INR) Physicians 57,218 74,965 45,948 66,281 41,058 Female 63,864 81,380 50,093 65,209 47,548 Male 55,393 72,893 44,930 66,764 40,747 Professional N&M 20,632 27,755 15,193 24,233 14,976 Female 21,683 28,580 15,666 27,153 14,871 Male 16,920 23,375 13,948 17,171 15,847 Associate N&M 12,226 12,111 12,617 15,627 9,978 Female 11,679 11,529 12,196 15,034 9,638 Male 19,994 27,274 15,236 20,310 19,325 AYUSH 25,344 21,097 26,212 29,272 17,195 Female 25,950 12,587 36,343 35,424 8,340 Male 25,086 49,849 23,637 26,780 21,396 Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2022/23), using Census of India (2011) population projections to adjust weights. Prices at level of July 2024. Physicians and AYUSH practitioners have greater access to employment benefits compared to professional and associate N&MWs. Seventy-nine percent of physicians and 76 percent of AYUSH practitioners have a written contract, compared to 63 percent of professional N&MWs and 64 percent of associate N&MWs. While eligibility for paid leave is relatively consistent across all cadres, disparities exist in access to healthcare, maternity benefits, and pensions. Only 29 percent of associate N&MWs, 44 percent of professional N&MWs, and 45 percent of AYUSH practitioners receive healthcare and maternity benefits; among women, the rates in these categories are 29 percent, 51 percent, and 28 percent, respectively. Pensions are available to a larger share of AYUSH practitioners (64 percent), physicians (84 percent), and professional N&MWs (57 percent), while only 35 percent of associate N&MWs are entitled to this benefit. Qualified active health workers enjoy greater access to employment benefits than their unqualified counterparts, with the 25 most notable differences seen in pensions (65 percent of qualified workers compared to 30 percent of unqualified) and healthcare and maternity benefits (56 percent compared to 24 percent). A higher proportion of qualified workers have a written contract (69 percent compared to 58 percent among unqualified active health workers) and more likely to receive paid leave (78 percent) than unqualified workers (63 percent). Figure 3.4 – Percentage of Workers Receiving Employment Benefits, India, 2022/23 100% 89% 89% 90% 84% 78% 79% 80% 76% 73% Share of Workers with Benefits 70% 67% 63% 64% 64% 57% 60% 50% 44% 45% 40% 35% 29% 30% 20% 10% 0% Physicians Professional N&MW Associate N&MW AYUSH Pension* Health care & maternity benefits Eligible for paid leave Has written contract Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2022/23), using Census of India (2011) population projections to adjust weights. * Pension: Pension includes benefits from the National Pension System (NPS), Employee Pension Scheme, and Provident Fund, including General (GPF), Contributory (CPF), and Public (PPF). International Migration of Indian Physicians and Nurses India is a major exporter of health workers, with 75,000 physicians and 104,000 nurses trained in India currently practicing in OECD countries. These numbers represent 7 percent of India’s active physician workforce and 3 percent of the total active nursing workforce (or 11 percent of the active professional N&MW workforce, who are more likely to migrate due to their qualifications). Most Indian-trained physicians work in the United States (61.2 percent), the United Kingdom (25.3 percent), Canada (8 percent), and Australia (2.7 percent). The distribution of Indian-trained nurses across OECD countries is more diverse, with the largest share in the United Kingdom (43.4 percent), followed by the United States (15.9 percent), Canada (13.9 percent), Ireland (13 percent), and New Zealand (4.3 percent). 26 Figure 3.5 – Indian-Trained Health Workers Practicing in OECD Countries (a) Physicians (b) Nurses Other, 2.7% Other, 2.1% Canada, 2.9% Australia, 8.0% Australia, 13.9% United States, 15.9% Canada, 7.5% United Kingdom, 25.3% Ireland, United 13.0% States, 61.2% United Kingdom, 43.4% New Zealand, 4.3% Source: OECD Health Statistics 2025, Health workforce migration (2022 for Australia, Canada, Ireland, New Zealand, and nurses in the United Kingdom; 2021 for physicians in the United Kingdom; 2020 for nurses in the United States; and 2016 for physicians in the United States). In 2022/23, health workers reported working an average of 46.14 weekly hours, 14 percent higher than the 40.53 hours for the average Indian worker. Weekly hours vary considerably across health worker categories. Physicians, for instance, reported an average of approximately 50 hours per week —more than engineers (approximately 48 hours on average) and lawyers (approximately 45 hours on average). AYUSH practitioners reported an average of 48 hours per week, while associate N&MWs and professional N&MWs reported 47 and 42 hours, respectively. The distribution of weekly hours worked also varies within each group. Physicians have the most concentrated distribution of work hours, with 50 percent reporting between 48 and 56 hours per week. In contrast, associate N&MWs have the most spread distribution, with 50 percent reporting between 42 and 56 hours weekly. When compared to monthly wages, physicians generally earn the highest wages and work the longest hours. AYUSH practitioners and professional N&MWs work comparable hours, though with lower monthly wages. However, these differences do not account for factors such as experience, education, sector, and location, which will be further analyzed in chapter 4. 27 Figure 3.6 – Hours Worked by Health and Non-Health Occupations, India, 2017/18–2022/23 3.5(a) Distribution of Weekly Hours of Work 3.5(b) Weekly Hours of Work and Monthly Wages 60,000 Physician 50,000 Engineer Lawyer Montly Wages (Rs) 40,000 Other Health 30,000 Health sector Workers AYUSH Non-health workers 20,000 Professional N&MW 10,000 Associate N&MW - 35.00 37.00 39.00 41.00 43.00 45.00 47.00 49.00 51.00 Hours per Week Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2017/18–2022/23), using Census of India (2011) population projections to adjust weights. Active health workers employed in the private sector report higher weekly work hours than those employed in the public sector. The largest differences were observed among associate N&MWs and AYUSH practitioners, who report an average of 20 percent and 19 percent more hours per week, respectively, in the private sector. Private sector physicians work on average 4 percent more hours per week than their public sector counterparts, while professional N&MWs in the private sector report 7 percent more hours on average than those in the public sector. While the total hours worked by physicians and professional N&MWs in rural areas are comparable to those in urban areas, associate N&MWs report, on average, fewer weekly hours in rural areas (7 percent less than their urban counterparts), whereas AYUSH practitioners report, on average, longer hours in rural areas (8 percent more than those in urban settings). Male AYUSH practitioners report, on average, 24 percent more hours per week than females (51 percent more in rural areas). Similarly, male associate N&MWs report 22 percent more hours per week than their female counterparts, with the gap increasing to 30 percent in rural areas. Rural female physicians report, on average, 26 percent fewer hours per week compared to male physicians. 28 Table 3.2 – Hours of Work per Week by Public/Private and Urban/Rural, India, 2022/23 Occupations Total Sectors Location Public Private Urban Rural Physicians 49 48 50 49 49 Female 47 48 48 49 39 Male 50 48 51 50 49 Professional N&MW 48 46 49 48 48 Female 47 45 48 46 47 Male 52 49 53 52 50 Associate N&M 42 40 48 44 41 Female 41 39 48 43 40 Male 50 51 51 50 52 AYUSH 48 42 50 47 51 Female 41 39 47 43 37 Male 51 49 51 48 56 Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2022/23), using Census of India (2011) population projections to adjust weights. The distribution of weekly work hours varies significantly across health worker cadres and between the public and private sectors. In both sectors, associate N&MWs and allied health workers show greater variability in the reported weekly work hours, with a wider interquartile range and numerous outliers. Physicians tend to have a more concentrated distribution, with most working between 40 and 60 hours per week and fewer extreme outliers, although some in the public sector exceed 80 hours. AYUSH practitioners and professional N&MWs exhibit a more concentrated distribution, with AYUSH practitioners showing slightly more variability but fewer outliers than other worker categories. Across both sectors, there is a trend toward less variability in the reported hours worked per week among physicians and more variability in nursing, midwifery, and allied health professions. This variability is especially pronounced in the private sector, where outliers—particularly at the upper end of the distribution—are more frequent. 29 Figure 3.7 – Distribution of Hours Worked per Week Selected Health Workers, India, 2017/18–2022/23 3.6(a) Distribution of Hours per Week – Public Sector 3.6(b) Distribution of Hours per Week – Private Sector Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2017/18–2022/23), using Census of India (2011) population projections to adjust weights. Quality of Jobs in the Health Sector In this section, the Job Quality Index (JQI) methodology is applied to assess the quality of health sector jobs.12 Utilizing PLFS data for 2022/23, the JQI measures job quality based on four dimensions: (i) sufficient income to sustain a family above the Purchase Power Parity (PPP) $3.65 poverty line; (ii) employment benefits such as health insurance or pensions; (iii) job stability via written contracts; and (iv) job satisfaction with appropriate work hours. Each dimension contributes equally to a maximum score of 4, though failure to meet the income threshold results in a score of 0. Health sector jobs have a higher JQI compared to the Indian labor market average, consistently across all four categories of health worker. The average JQI for non-health workers is 1.6, significantly lower than the JQI for physicians (3.3), and professional N&MWs (2.5), and AYUSH practitioners (2.5). The JQI for physicians is comparable to other professional occupations, such as lawyers (3.2) and engineers (3.4). Among health workers, physicians report the highest job quality, with an average Job Quality Index (JQI) of 3.3. This reflects relatively high-income levels, access to employment benefits, and job stability. Notably, female physicians score slightly higher (3.5) than their male counterparts (3.2). Professional N&MWs have a moderate JQI of 2.6, constrained primarily by limited access to employment benefits and lower levels of job satisfaction. Associate N&MWs face more pronounced challenges, with an average JQI of 1.7. This disparity is largely driven by income: only 57 percent of associate N&MWs earn enough to keep their families above the PPP $3.65 poverty line, compared to 82 percent of professional N&MWs. Male health workers generally have higher JQI scores than females, apart from physicians. The most significant gender gap is 12 This methodology has been applied to India under the “Earnings poverty estimates based on World Bank Poverty and Equity team’ s Economic Activity for Welfare� ASA (P179994), including the India annual labor market updates series. 30 observed among AYUSH practitioners, where female workers have a JQI of 1.7, compared to 3.3 for males. Both in the health sector and the broader labor market in India, job quality tends to be higher among professionals in urban areas and in the public sector. Table 3.3 – Job Quality Index, Selected Health and Non-Health Sector, India, 2022/23 Occupation Average Gender Location Sector Job Female Male Rural Urban Public Private Quality Index Health Occupations Physicians 3.3 3.5 3.2 3.3 3.3 3.6 2.8 Professional N&MW 2.6 2.6 2.6 2.1 2.8 2.7 2.4 Associate N&MW 1.7 1.6 2.5 1.6 1.9 1.5 2.1 AYUSH 2.6 1.7 3.3 1.1 3.3 1.5 3.3 Allied health workers 2.5 2.3 2.6 2.1 2.7 2.9 2.1 Non-Health Occupations Lawyer 3.2 3.2 3.2 3.3 3.1 3.5 3 Engineer 3.4 3.2 3.4 3.4 3.4 3.6 3.4 General labor market in 1.6 1.2 1.7 1.3 2 2.8 1.5 India Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2022/23), using Census of India (2011) population projections to adjust weights. 31 MODELING LABOR FORCE PARTICIPATION, WAGE DYNAMICS, AND LABOR SUPPLY ELASTICITIES OF HEALTH WORKERS This chapter presents regression analyses to identify factors associated with key health labor market outcomes in India. It begins by examining the determinants of labor force participation (LFP) among individuals with medical training. Then, it presents an empirical analysis of labor elasticities among health workers, broken down by categories of health workers, sectors of employment (public and private), and gender. Finally, it explores factors influencing wages among health workers, also exploring differentials across workers’ categories, sectors, and genders. Labor Force Participation Among Those with Medical Training This section examines the factors associated with the likelihood of LFP and employment among individuals with medical training. The analysis aims to estimate the correlation between having medical training and the likelihood of participating in the labor force and the likelihood of being employed. As shown in chapter 2, the LFP among individuals with medical training is higher than those without it: The LFP for individuals with medical training is 19 percentage points higher. Among women with medical training, LFP is 36 percentage points higher. To estimate this probability, the following binary response models were estimated: 𝑃𝑟(𝑙𝑎�𝑓𝑜𝑟�𝑒𝑖𝑗𝑞𝑡 = 1) = 𝐺(𝛼 + 𝛾𝑋𝑖𝑗𝑞𝑡 + 𝛿𝑗 + �𝑞𝑡 + 𝜀𝑖𝑗𝑞𝑡 ), 𝑃𝑟(𝑒𝑚�𝑖𝑗𝑞𝑡 = 1) = 𝐺(𝛼 + 𝛾𝑋𝑖𝑗𝑞𝑡 + 𝛿𝑗 + �𝑞𝑡 + 𝜀𝑖𝑗𝑞𝑡 ), where G is the logistic function. The vector X includes individuals’ characteristics such as binary indicators for highest educational degree—up to higher secondary school, diploma or certificate, university graduate, and postgraduate, a binary indicator for females, binary indicators of age groups, a binary indicator of having medical training, a binary indicator of spouse’s working status, a log of spouse’s wage, and household size. We do not include wages among those who work, as wages are endogenous to labor force participation and wages are an outcome of labor force participation. The model controls for state fixed effects and quarter-by- year fixed effects. With the results of the logistic regression, average marginal effects for each of the individuals’ characteristics were also estimated. Table 4.1 presents the regression results. Aligned with the results from the descriptive analysis (chapter 2), regression results show that individuals with medical training are more likely to participate in the labor force, and that is true for both males and females. As background, the share of the health workforce adjusted by qualification— including medical training—is about 61 percent, with the highest among physicians. As shown in columns A, B, and C of table 4.1, individuals with medical training are associated with a higher likelihood of being engaged in the labor force by about 20.2 percentage points. This finding is consistent with the findings in chapter 2. Additionally, the relationship between medical training and the likelihood of being engaged in 32 the labor force is stronger among females than males: females with medical training are associated with a higher likelihood of being engaged in the labor force by 24.6 percentage points. Among males with medical training, that probability is 19.8 percentage points higher. The relationship between educational attainment and the likelihood of being engaged in the labor force is higher among females than males. For example, females with a diploma or a certificate in medical fields are more likely to participate in the labor force by about 10.4 percentage points compared to those without education. Among males, the estimated correlation is only 6.36 percentage points. The same pattern holds for females and males with bachelor’s and postgraduate degrees. Those with medical training are more likely to be employed, and the effect of medical training on employment status is stronger among females. Females with medical training are associated with a higher likelihood of being employed by approximately 5 percentage points. The estimated correlation among males with medical training is 2.66 percentage points. Those married are more likely to be employed than those who are single and divorced. Household size is negatively correlated with the probability of being employed, and the estimated correlation is higher among females, including females with medical training. An additional household member is associated with a lower likelihood of being employed among women by about 0.66 percentage points (which suggests that females bear the burden of caregiving more than males). 33 Table 4.1 – Determinants of Labor Force Participation and Employment Probability Labor Force Participation Employment A: All Individuals B: Female C: Male D: All Individuals E: Female F: Male 1 if received medical training 0.202*** 0.246*** 0.0198* 0.0314*** 0.0499*** 0.0266*** (33.98) (28.53) (2.40) (0.00225) (0.00352) (0.00346) 1 if completed higher secondary -0.0550*** -0.0826*** -0.0469*** -0.0220*** -0.0446*** -0.0118*** school (-38.93) (-44.26) (-18.10) (0.00135) (0.00253) (0.00172) 1 if completed diploma/certificate 0.0900*** 0.104*** 0.0636*** -0.0705*** -0.115*** -0.0543*** (23.42) (14.74) (15.83) (0.00267) (0.00636) (0.00295) 1 if completed bachelor 0.0741*** 0.0712*** 0.0469*** -0.0858*** -0.130*** -0.0665*** (41.65) (28.04) (16.67) (0.00157) (0.00302) (0.00194) 1 if completed postgraduate and 0.145*** 0.199*** 0.0534*** -0.0897*** -0.136*** -0.0634*** above (59.64) (55.87) (15.10) (0.00204) (0.00359) (0.00258) 1 if male 0.510*** 0.0258*** (602.35) (0.000948) 30-39 0.219*** 0.169*** 0.217*** 0.0795*** 0.117*** 0.0565*** (161.30) (83.25) (163.36) (0.00125) (0.00284) (0.00127) 40-49 0.224*** 0.171*** 0.189*** 0.111*** 0.174*** 0.0758*** (156.54) (81.95) (101.59) (0.00118) (0.00256) (0.00135) 50-59 0.162*** 0.0864*** 0.0540*** 0.115*** 0.190*** 0.0769*** (102.14) (39.75) (19.86) (0.00119) (0.00238) (0.00143) 34 60+ -0.194*** -0.0971*** -0.473*** 0.119*** 0.191*** 0.0832*** (-124.06) (-66.22) (-217.21) (0.00133) (0.00259) (0.00159) 1 if married 0.0564*** -0.123*** 0.266*** 0.0801*** 0.0545*** 0.0993*** (46.75) (-74.92) (165.87) (0.000994) (0.00209) (0.00134) Observations 847,310 419,438 427,872 408,806 95,127 313,679 Note: Signs *, **, *** indicate significance at the 10, 5, and 1 percent level. Control covariates also include an indicator of spouse’s working status, a log of spouse’s wage, state fixed effect, and quarter-by-year fixed effect. Standard errors are clustered at the health worker level. in the appendix D, table D.1 shows the full regression results. 35 Labor Supply Elasticities Among Health Workers This section presents estimates of labor supply elasticities with respect to wages among active health workers in India. Understanding how individuals adjust their work hours in response to wage changes is of great interest for public policy. Such knowledge is instrumental in designing effective policies such as compensation setting, tax policies, incentives, and pay for performance systems (Blundell, Duncan, and Meghir 1998; Blundell and Macurdy 1999; Blundell, Brewer, and Francesconi 2008; McClelland and Mok 2012; Bargain and Peichl 2016). For instance, estimated labor supply elasticities will enable the government to predict how the labor supply of health workers, measured by the number of hours they are willing to work, might change in response to wage increases. This is especially relevant in the context of health worker shortages, which makes the efficient use of the available labor resources critical. The effect of wage changes on labor supply elasticity is ambiguous, as wage increases are associated with income and substitution effects. The substitution effect occurs when higher wages make work more appealing relative to leisure, prompting workers to increase their working hours (thereby raising the labor supply). The income effect occurs when higher wages allow workers to maintain the same standard of living while working fewer hours, leading them to choose more leisure time (thereby reducing their labor supply). Thus, the substitution effect would cause working hours to increase as wages increase, while the income effect would cause working hours to decrease as wages increase (Blundell and Macurdy 1999). Estimating labor supply elasticity is challenging due to a data censoring problem, as workers have fixed working hours stipulated in their contracts. Thus, they do not necessarily adjust working hours in response to wage changes (Blundell, Brewer, and Francesconi 2008).Other challenges are potential measurement errors in the reported wage rate and the simultaneity of labor supply and wage earnings (Qin, Li, and Hsieh 2013).13 The literature estimating labor supply elasticities among health workers presents mixed findings. Studies focusing on the nursing workforce typically find positive estimates of labor supply elasticities, which suggests that improving wages can induce nurses to work longer hours (Askildsen, Baltagi, and Holmås 2003; Shields 2004; Qin, Li, and Hsieh 2013; Hanel, Kalb, and Scott 2014). One study, however, suggests that allowing for a quadratic specification, a backward-bending labor supply among nurses is observed (i.e., increases in wages lead to a decrease in labor supply due to the income effect) (Shields 2004). A study in Norway found positive but small estimates of labor supply elasticity among registered nurses but concluded that a policy to increase wages may not necessarily induce a higher labor supply. The study also highlighted the importance of the non-pecuniary characteristics of the profession in shaping nurses’ supply decisions (Di Tommaso, Strøm and Sæther 2009). As for physicians, a recent study estimated negative labor supply elasticities among Australian physicians. The authors argue that the results are driven by the fact that Australian physicians typically 13 Because wages and labor supply are jointly determined, it's challenging to disentangle the direction of causality between these two variables. 36 earn high incomes and work long hours with no scope to further increase labor supply (Kalb et al. 2018). Nicholson and Propper (2011) review the empirical literature on labor supply elasticities for physicians and nurses. They report that most empirical studies conclude that the labor supply of physicians and nurses is not more responsive to wages than that of other professions. Furthermore, studies using microdata also conclude that changes in income have little impact on physicians' work effort, indicating that the income effect is small (Nicholson and Propper 2011). Empirical Specification This section presents an empirical estimate of labor supply elasticity for Indian active health workers using PLFS panel data. The panel nature of the PLFS allows us to overcome the limitations (as discussed above) to estimate the labor supply elasticity among active health workers using a fixed- effects model. One caveat of panel data analyses using the PLFS data is the exclusion of the rural sample, as the rotational panel aspect is limited to urban areas. The current design of the PLFS does not involve revising the same households in rural areas over time. The estimate of labor supply elasticity is limited to health workers in the urban areas. Thus, the panel analyses cannot capture rural-urban differentials such as variations in infrastructure, service delivery platforms and quality, and labor market structure. This fixed-effects specification accounts for unobserved health worker-specific heterogeneity that may be correlated with wage changes. The analysis focuses on physicians, professional N&MWs, and associate N&MWs. As discussed in chapter 2, the share of physicians and professional and associate N&MWs accounts for most health professionals in India, approximately 80 percent of the active health workforce. The specification for estimation using the fixed-effects model is: ln(ℎ𝑜𝑢𝑟𝑠)𝑖𝑗𝑞𝑡 = 𝛼 + 𝛽𝑓(ln(𝑤𝑎𝑔𝑒)𝑖𝑗𝑞𝑡 ) + 𝛾𝑋𝑖𝑗𝑞𝑡 + 𝜇𝑖 + 𝛿𝑗 + �𝑞𝑡 + 𝜀𝑖𝑗𝑞𝑡 where hours denotes the total hours of work within a week; wage is the hourly real wage rate; X is a vector of time-varying variables including years of schooling completed, dual contract status, an indicator of spouse’s working status, and log of spouse’s wage; μ is a health worker fixed effect; δ is the state fixed effect; and τ is a quarter-by-year fixed effect. The analysis allows a flexible function of the log wages, allowing for non-linear specification, to accommodate different labor supply elasticities across the wage distribution and cluster the standard errors at the health worker level to account for the correlation of unobserved characteristics across waves within the health worker categories. Assuming a linear relationship between working hours and the real hourly wage, the estimated labor supply elasticity is negative and significant (columns A and B of table 4.2). The results suggest that a 10 percent increase in hourly wages results in a decrease in working hours by 1.66 percent. The analysis also estimated the labor supply elasticities in different quantiles of labor supply or working hours: 25th percentile, 50th percentile, and 75th percentile. The results, presented in column 37 A of table 4.2, are consistent across selected percentiles. 14 A potential explanation is that health workers earn relatively high wages and, therefore, the income effect occurs. As shown in figure 3.1, wages among physicians are generally higher than wages for lawyers and engineers. Similarly, the average wages of professional N&MWs are typically higher than the average in the economy. Another explanation is that active health workers work more hours per week than the average worker in the Indian labor market. Specifically, as discussed in chapter 3, active health workers work 46.14 hours per week, approximately 14 percent above the average workers. 14 In a supplementary analysis presented in appendix D, table D.3, the estimated labor supply elasticity of health workers is comparable to that of other professionals. 38 Table 4.2 – Estimates of Labor Supply Elasticity A: Fixed Effect B: Fixed Effect C: Fixed Effect D: Fixed Effect Log of real wage -0.178*** -0.166*** 0.155*** 0.144*** (0.0354) (0.0335) (0.0304) (0.0297) Log of real wage, squared -0.0347*** -0.0325*** (0.00417) (0.00410) Observations 11,070 11,070 11,070 11,070 Adj. R-sq 0.170 0.220 0.267 0.305 R-sq Overall 0.00507 0.00524 0.00658 0.00760 F-stats 25.25 39.18 74.69 40.85 Controls N Y N Y Cluster Individual Individual Individual Individual Note: Signs *, **, *** indicate significance at the 10, 5, and 1 percent level. Control covariates in the fixed-effects model include educational attainment, dual contract status, an indicator of spouse’s working status, a log of spouse’s wage, state fixed effect, and quarter-by-year fixed effect. Standard errors are clustered at the health worker level. 39 When relaxing the model's linearity assumption to introduce a quadratic specification, the estimated negative labor supply elasticity increases in magnitude as active health workers earn higher wages (columns C and D of table 4.2 above). Marginal effects analyses are conducted to gain a better understanding of health workers' labor supply responses across the real wage distribution. Due to the non-linear relationship, the estimated coefficients in columns C and D do not directly indicate how labor supply responds to wage changes. The results from the marginal effects analysis, shown in figure 4.1, indicate that estimated labor supply elasticities are negative and significant. Furthermore, these elasticities decrease as wages rise, suggesting that health workers reduce their working hours even more substantially at higher wage levels. Figure 4.1 – Estimated Labor Supply Elasticity Across the Wage Distribution, India Note: The estimated marginal effects are derived from a regression of the fixed-effects model with a quadratic function of the log of real wages. Additional analyses focus on estimating labor supply elasticities across various categories of active health workers, as well as among those employed in the public and private sectors .15 The estimated labor supply elasticities across categories of health workers and sectors are negative and statistically significant (table 4.4). In the public sector, a 10 percent increase in hourly wages is estimated to reduce working hours by approximately 1.58 percent. In the private sector, this effect is estimated at 1.76 percent. For professional N&MWs in both the public and private sectors, a 10 percent increase in hourly wages is estimated to reduce working hours by approximately 1.29 percent in the public sector and 1.36 percent in the private sector. As for associate N&MWs, the estimated elasticities suggest that a 10 percent increase in the hourly wage is associated with a decrease in their work hours by about 3 percent in both public and private sectors. Labor supply is not responsive to 15 We also estimate labor supply elasticities by healthcare professionals and receipt of medical training as presented in table D.4 in appendix D. 40 wage changes among physicians in the public sector. A potential explanation is that physicians (as well as other health workers) in the public sector work with fixed wages and working hours. Another potential explanation is that physicians (and other health workers) in the public sector already earn higher wages (as discussed in chapter 3). Finally, health workers also work 14 percent more hours per week than the average workers in the Indian economy. Given the relatively high wages and fixed working hours, additional wages may not incentivize health workers to change their labor supply. Table 4.3 – Labor Supply Elasticities Among Health Workers by Public and Private Sector, India Dependent Public: All Public: Public: Public: Private: All Private: Private: Private: variable: Log Physicians Nurses & Assoc. Physicians Nurses & Assoc. of working Midwives Nurses & Midwives Nurses & hours Midwives Midwives Log of real -0.158*** -0.0712 -0.129** -0.343*** -0.176*** -0.158*** -0.136** -0.315*** wage (0.0532) (0.0464) (0.0540) (0.0586) (0.0339) (0.0420) (0.0550) (0.0598) Observations 5775 1568 1886 2321 5295 1995 1482 1818 Adj. R-sq 0.224 0.151 0.179 0.425 0.220 0.188 0.219 0.355 R-sq Overall 0.00348 0.0708 0.00845 0.00960 0.0339 0.0421 0.0423 0.0588 F-stats 22.49 7.748 6.216 20.70 20.19 7.565 8.102 12.36 Controls Y Y Y Y Y Y Y Y State fixed Y Y Y Y Y Y Y Y effect Year-by- quarter fixed Y Y Y Y Y Y Y Y effect Health Health Health Health Health Health Health Health Cluster Worker Worker Worker Worker Worker Worker Worker Worker Note: Signs *, **, *** indicate significance at the 10, 5, and 1 percent level. Control covariates in the fixed-effects model include educational attainment, dual contract status, an indicator of spouse’s working status, a log of spouse’s wage, state fixed eff ect, and quarter-by-year fixed effect. Standard errors are clustered at the health worker level. Labor supply elasticities among male and female health workers are further estimated to understand differences in labor supply responses between genders. Female health workers' estimated labor supply elasticities are generally lower, particularly among physicians and associate N&MWs. Among female physicians, a 10 percent increase in the hourly wage is associated with a decrease in their working hours by about 2.80 percent. The estimated decrease in working hours among male physicians is only 0.85 percent for a 10 percent increase in hourly wages. Among associate N&MWs, a 10 percent increase in hourly wages is associated with a 2.15 percent decrease in working hours for male associate N&MWs and a 3.50 percent decrease for female associate N&MWs. The estimated labor supply elasticities for male and female N&MWs suggest that a 10 percent increase in hourly wages would reduce working hours by 1.26 percent for female professional N&MWs and 1.52 percent for male professional N&MWs. 41 Table 4.4 – Estimates of Labor Supply Elasticities Among Female and Male Health Workers, India Dependent Male: All Male: Male: Male: Female: Female: Female: Female: variable: Log of Physicians Nurses & Assoc. All Physicians Nurses & Assoc. working hours Midwives Nurses & Midwives Nurses & Midwives Midwives -0.101*** -0.085** -0.152** -0.216*** -0.229*** -0.280*** -0.126*** -0.350*** Log of real wage (0.0370) (0.0372) (0.0646) (0.0602) (0.0413) (0.0690) (0.0432) (0.0502) Observations 3843 2410 620 813 7227 1153 2748 3326 Adj. R-sq 0.153 0.141 0.185 0.336 0.286 0.305 0.204 0.413 R-sq Overall 0.0581 0.0526 0.0763 0.113 0.00250 0.0582 0.0233 0.000656 Controls Y Y Y Y Y Y Y Y State fixed effect Y Y Y Y Y Y Y Y Year-by-quarter Y Y Y Y Y Y Y Y fixed effect Health Health Health Health Health Health Health Health Cluster Worker Worker Worker Worker Worker Worker Worker Worker Note: Signs *, **, *** indicate significance at the 10, 5, and 1 percent level. Control covariates in the fixed-effects model include educational attainment, dual contract status, an indicator of spouse’s working status, a log of spouse’s wage, state fixed ef fect, and quarter-by-year fixed effect. Standard errors are clustered at the health worker level. Wages Analysis This section examines the relationship between various characteristics of health workers— such as years of education, work experience, and level of medical training—and their wages. The analysis estimates the following Mincer equation specification: ln(𝑤𝑎𝑔𝑒)𝑖𝑗𝑞𝑡 = 𝛼 + 𝛽education𝑖𝑗𝑞𝑡 + 𝛾𝑋𝑖𝑗𝑞𝑡 + 𝛿𝑗 + �𝑞𝑡 + 𝜀𝑖𝑗𝑞𝑡 where wage is the log of hourly wage rate; X is a vector of time-varying variables including estimated years of working experience, age group, an indicator of public sector worker, having medical training, spouse’s working status, and spouse’s wage in log; δ is the state fixed effect; and τ is a quarter-by-year fixed effect. Standard errors are clustered at the health worker level to account for the correlation of unobserved characteristics across waves. The Mincer equation is estimated to use pooled cross- sectional data from the PLFS, allowing for estimations of time-invariant variables such as educational attainment, medical training, and public sector worker status. Sub-group analyses of wage regressions are conducted for different cadres of health workers, namely physicians, professional N&MWs, and associate N&MWs. Additionally, analyses of wage differentials by gender and by public and private sector are performed. It is important to note that the estimated coefficients from the regression do not necessarily imply causal effects; however, they provide valuable information on the correlates of wages across these observable characteristics. The parameter of interest, β, represents the return to education, indicating the percentage increase in earnings associated with an additional year of schooling. The literature suggests that the estimated return to education to a typical worker is around 9.7 percent, higher among females (11.5 percent) than males (9.1 percent) (Card 2000; Montenegro and Patrinos 2014; Patrinos and Psacharopoulos 2020). By including work experience in the model, it also estimates the human capital 42 accumulation post-education (Polachek 2007). McPake and colleagues (2015) reviews the estimates of private rates of return and net present value among health workers. They point out that estimated private rates of return to education are higher among health workers than average workers (McPake et al. 2015). Table 4.5 – Determinants of Health Workers’ Wages, India A: All B: All C: All D: All E: All F: All No. of years in 0.130*** 0.156*** 0.0965*** education (0.00386) (0.00370) (0.00521) Secondary school 0.264*** 0.345*** 0.207*** (0.0580) (0.0573) (0.0552) Higher secondary 0.399*** 0.621*** 0.407*** school (0.0518) (0.0520) (0.0511) Diploma/certificate 0.620*** 0.913*** 0.533*** (0.0534) (0.0527) (0.0548) Graduate 0.931*** 1.251*** 0.697*** (0.0457) (0.0471) (0.0523) Postgraduate and 1.521*** 1.746*** 0.937*** above (0.0503) (0.0500) (0.0613) Years of working 0.0389*** 0.0432*** 0.0257*** 0.0207*** experience (0.00290) (0.00290) (0.00492) (0.00491) Working experience, -0.000327*** -0.000505*** -0.0000891 -0.000279*** squared (0.0000716) (0.0000688) (0.0000911) (0.0000903) 1 if in a dual contract 0.107 0.150* (0.0783) (0.0856) 1 if received medical 0.164*** 0.199*** training (0.0207) (0.0221) Nurses & Midwives -0.351*** -0.371*** (0.0279) (0.0291) assoc. Nurses & -0.506*** -0.523*** Midwives (0.0291) (0.0303) Public worker 0.363*** 0.378*** (0.0207) (0.0207) Male 0.106*** 0.116*** (0.0237) (0.0242) 30-39 -0.00185 0.0626 43 (0.0428) (0.0425) 40-49 -0.120* 0.0489 (0.0704) (0.0685) 50-59 -0.0744 0.256*** (0.0992) (0.0927) 60+ -0.447*** 0.0895 (0.143) (0.132) Log of spouse's wage 0.244*** 0.245*** (0.0157) (0.0160) Observations 11070 11070 11070 11070 10983 10983 Adj. R-sq 0.332 0.330 0.436 0.413 0.569 0.561 Controls N N N N Y Y State fixed effects Y Y Y Y Y Y Year-by-quarter fixed Y Y Y Y Y Y effects Cluster Health Worker Health Worker Health Worker Health Worker Health Worker Health Worker Note: Signs *, **, *** indicate significance at the 10, 5, and 1 percent level. The wage regression specification was estimated using ordinary least squares (OLS) and includes covariates such as quarter-by-year and state fixed effects. Standard errors are clustered at the health worker level. Results show significant returns to education, particularly for health workers with bachelor’s or postgraduate degrees. The estimated return to education (column E of table 4.5) is 10.13 percent, which suggests that an additional year of schooling would improve health workers’ wages by about 10.13 percent. Consistent with findings in the labor economics literature, there are also positive returns to working experience (Polachek 2007). Health workers with dual contracts and those with medical training earned significantly higher wages. Dual practitioners, health workers working in both public and private sectors simultaneously, are associated with higher wages by about 16.18 percent, while those with medical training are associated with higher wages by 17.82 percent to 22.02 percent.16 The analyses show that physicians experience the highest return to medical training (figure 4.2(a)). Physicians earn the highest wages, followed by professional N&MWs and associate N&MWs. There is also a significant public sector wage premium. Public sector health workers earn about 40.35 percent more than their private sector counterparts (figure 4.2(b)). The subgroup analyses by health professionals suggest that the public sector premium is the highest among professional N&MWs, followed by associate N&MWs and physicians. 16 Further analyses by health professionals and gender are shown in appendix D, tables D.5 and D.6. 44 Figure 4.2 – Medical Training and Public Sector Premia by Health Professions, India 4.2 (a) Medical Training Premia 4.2(b) Public Sector Premia Note: The estimated marginal effects are derived from the ordinary least squares (OLS) regression of the Mincer equation. There is a significant gender wage gap among health professionals, except for physicians. The estimated gender wage gap among all active health workers is about 29.69 percent, which means women earn on average 30 percent less than their males. The estimated gender wage gap among physicians is quite low at 1.78 percent and it is not statistically significant. Among professional N&MWs, the estimated gender wage gap is 19.84 percent, which is statistically significant. The highest estimated gender wage gap is among associate N&MWs, 23.24 percent. These results mean that female professional N&MWs and associate N&MWs earn approximately 20 percent and 23 percent less, respectively, than their male counterparts in the same occupation. The gender wage gap among professional N&MWs and associate N&MWs is an important policy discussion, as these health workers are predominantly females. 45 Figure 4.3 – Gender Wage Gap by Health Professions, India Note: The estimated marginal effects are derived from the ordinary least squares (OLS) regression of the Mincer equation. Estimated returns to working experience are also positive among males and females (figure 4.4). Specifically, estimated returns to working experience are positive for the first 30 years, and the returns to experience peak when health professionals are around 50 years. The results suggest that health workers further accumulate human capital as they accumulate working experience. Figure 4.4 – Estimated Returns to Experience Among Health Professionals, India Note: The estimated marginal effects are derived from the ordinary least squares (OLS) regression of the Mincer equation. 46 HEALTH WORKFORCE PROJECTIONS: SUPPLY, DEMAND, AND NEEDS FOR HEALTH WORKERS Introduction This chapter provides a forward-looking analysis of the Indian health workforce by presenting projections of health worker supply, demand, and need. A critical aspect of workforce planning is to ensure the health workforce supply can align with future epidemiological needs, while also ensuring sufficient fiscal capacity (i.e., labor market demand) to absorb the workforce required to meet these needs. This is particularly vital in the context of India, where a rapidly growing and aging population places increasing pressure on healthcare resources, compounded by the challenge of competing priorities for limited financial and human resources. In this context, this chapter explores how demographic changes, health system priorities, and economic and epidemiological changes influence the supply, demand, and need for health professionals and proffers insights into potential mismatches between the workforce supply, demand, and need. Supply estimates use historical data to project skilled health worker densities (per 1,000 population). The approach is static, as it assumes existing growth rates, migration patterns, entry into the health profession, retirement, and deaths of health workers will mirror the latest available data. Health workforce data are from six consecutive years of the PLFS (2017/18–2022/23). The PLFS provides data on “active� health workers; however, the projections here focus only on physicians and N&MWs. To project future supply, linear and exponential growth rate models are used. Demand-based estimates use state-level macro-economic, health expenditure, and demographic data. The model specifies skilled health worker density (dependent variable) as a function of gross state domestic product per capita, total health expenditure per capita, and size of the population over 60 years. The gross state domestic product data are drawn from the Reserve Bank of India’s Handbook of Statistics on Indian States, 2022-23, and they are converted into constant prices using 2011 as the base. The population figures and projections are taken from the 2011 Census of India; and total health expenditure is taken from the National Health Accounts estimates from 2017/18 through 2019/20. Need-based estimates use the WHO 2016 Sustainable Development Goal composite index methodology. Historically, two widely referenced estimates of the required number of physicians, nurses, and midwives per 1,000 population in global literature include: 2.28 health professionals per 1,000 population (WHO 2006) and a revised estimate of 4.45 professionals per 1,000 population (WHO 2016) using the SDG composite index methodology. The latter presents a novel approach that relates the attainment of SDG 3 to the availability of health workers.17 Need is defined as the number of health 17 The 2006 WHO report “The World Health Report 2006: Working Together for Health� propose s a needs-based threshold in the context of the Millennium Development Goals. It estimates that countries with less than 2.28 skilled health professionals (midwives, nurses, and physicians) per 1,000 population would struggle to achieve 80 percent coverage of skilled birth 47 workers required to achieve the median level of attainment for an SDG composite index, which comprises 12 tracer health indicators (i.e., proxies of health needs for universal health care and the health targets of SDG 3).18 To account for India’s specific context, with each state akin to a country in terms of its size and diversity and with an increasing burden of non-communicable diseases in a rapid epidemiological transition, the model applies state-level data to the WHO SDG composite index methodology. Since data on only 11 indicators were available at the state level in India, the need-based threshold for workers was calculated using all indicators from the WHO (2016) framework, except for cataract rates. Prior to this exercise, there have been few attempts to estimate country- or region- specific thresholds for health workforce densities needed to achieve universal health care. For example, an analysis in the Africa region found that health worker requirements differed from the standard global benchmark (Ahmat et al. 2022). Results The supply of health workers is projected to grow steadily over time. From an existing 2.3 million active health workers (physicians, nurses, and midwives) in 2022/23, the supply is projected to increase to about 4.1 million in 2025/26 and approximately 6.5 million in 2035/36, assuming a linear growth. The exponential growth model projects a supply of about 6.7 million health professionals by 2035/36. While both models indicate substantial growth, the exponential model suggests a steeper increase in the skilled health workforce in the later years of the projection period (table 5.1). Three parameters were used to estimate the health workforce need-based requirements. Using such an approach allows for a more tailored understanding of health worker needs in India's diverse and evolving healthcare landscape. The WHO 2016 threshold shows the need for around 6.3 million professionals by 2025/26, growing to 6.8 million by 2035/36. The median threshold (35 percent) shows the need for about 1.9 million professionals in 2025/26, growing to 2.1 million in 2035/36.19 Third, the 75th percentile threshold 20 shows the need for 13.2 million in 2025/26, growing to 14.1 million by 2035/36 (table 5.1). These differing thresholds highlight the gap between minimum service levels and aspirations for improved healthcare coverage and outcomes. Overall labor market demand for health workers in India is projected to increase steadily, assuming trends in gross state domestic product and total health spending continue. The HLM is projected to demand about 2.0 million physicians, nurses, and midwives in 2025/26, 1.42 health attendance. 18 SDG tracer indicators: Antenatal care, antiretroviral therapy, cataract, diabetes, DTP3 immunization, family planning, hypertension, potable water, sanitation, skilled birth attendance, tobacco smoking, and tuberculosis. 19 This threshold indicates the estimated number of skilled workers required if all states had a workforce that met at least the median composite score of SDG tracer indicators for the entire sample. The median SDG attainment score in our sample is 0.35 (on a scale of 1.0). 20 If we would like all states to have a workforce that lies at least at the 75th percentile of SDG score attainment in our sample, then solving for health workers here gives us 9.27 physicians, nurses, and midwives per 1,000. 48 workers per 1,000 population. By 2035/36, the estimated demand is 6.7 million physicians, nurses, and midwives, 4.40 health workers per 1,000 population (table 5.1). Table 5.1 – Projections for Skilled Health Worker Supply, Demand, and Need, India Projection Need Supply Demand Year WHO Median 75th Percentile Linear Model Exponential Model (4.45 per 1,000 (1.37 per 1,000 (9.27 per 1,000 Population) Population) Population) 2025/26 6,345,291 1,953,494 13,218,167 4,131,821 3,035,553 2,027,380 2030/31 6,580,549 2,025,922 13,708,244 5,296,124 4,524,800 3,357,065 2035/36 6,774,182 2,085,535 14,111,610 6,491,767 6,694,185 6,707,151 Source: Original calculations/compilations for this publication (2025). Prospective Scenarios These projections of health worker supply, demand, and need support the identification of gaps in India’s health workforce over time. Conceptually, a shortage occurs when the needs for health workers exceed the available supply in the labor market, while a health workforce shortfall (or surplus) occurs when the supply of health workers does not meet (or exceeds) the labor market demand for health workers. Figure 5.1 illustrates a static view of the labor market for nurses. Traditional labor economics suggest that in well-functioning markets, imbalances between supply and demand are typically short-lived. A key assumption is that wage rates are flexible and adjusted based on the preferences of both employers and health workers, eventually restoring balance to the labor market (McPake, Scott, and Edoka 2014). However, many countries, including India, face financial constraints that limit their ability to employ more health workers. In such markets, a shortfall occurs when the wage rate is fixed at a certain level (WA) and the quantity of health workers demanded (C) surpasses the quantity supplied (B). Furthermore, when the estimated need for health workers (E) is greater than the number of workers supplied, it results in a (need-based) shortage. 49 Figure 5.1 – Static Model of the Labor Market for Nurses, India Source: Original calculations/compilations for this publication (2025). Scenario 1: Using WHO Norms (4.45 per 1,000 Population) With no policy changes, India is projected to experience a persistent need-based shortage of physicians, nurses, and midwives using as a parameter the WHO-recommended threshold of 4.45 health professionals per 1,000 population . In 2025/26, the shortage under the linear supply model is expected to be 2.21 million, while the exponential model shows a larger shortage of 3.30 million. The situation improves slightly by 2030/31, with the shortage reducing to 1.28 million under the linear model and 2.05 million under the exponential model. By 2035/36, the shortage narrows further, reaching 282,415 under the linear model and 79,997 under the exponential model. Scenario 2: Using the Median SDG Composite Index (1.37 per 1,000 Population) Using the median threshold (1.37 health workers per 1,000 population) as a parameter, India is projected to experience a surplus of health workers. In 2025/26, a surplus of 2.17 million health professionals is expected under the linear model and 1.08 million under the exponential model. This surplus increases by 2030/31, with an estimated 3.27 million surplus under the linear model and 3.24 million under the exponential model. By 2035/36, the surplus grows, reaching 4.40 million in the linear and exponential models. Scenario 3: Using the 75th Percentile SDG Composite Index (9.27 per 1,000 Population) Using as a parameter the 75th percentile threshold (9.27 health professionals per 1,000 population, Indian would face severe health workforce shortages. In 2025/26, the shortage is projected to be 9.08 million health workers under the linear model and 10.18 million under the exponential model. While these figures decrease slightly over time, the shortage remains significant 50 by 2030/31, with gaps of 8.41 million and 9.18 million in the linear and exponential models, respectively. By 2035/36, the shortage remains substantial, with a projected shortage of 7.61 million in the linear model and 7.41 million in the exponential model. Figure 5.2 – Projected Supply and Need Scenarios for Physicians, Nurses, and Midwives, India Scenario 1 Exponential Supply-Need India Source: Original calculations/compilations for this publication (2025). Scenario 4: Supply–Demand Gap A comparison of the projected supply and demand for health workers indicates shortfalls only after 2030/31. By 2025/26, a surplus of 2.1 million health workers is expected under the linear model and approximately 1.0 million under the exponential model. By 2030/31, these surpluses remain high, 1.94 million under the linear model and 1.1 million under the exponential model. By 2035/36, the trend reverses: the linear model projects a shortfall of just over 215,000 health workers, while the exponential model projects a shortfall of about 13,000 health workers. 51 Figure 5.3 – Projected Supply and Demand Scenarios for Physicians, Nurses, and Midwives, India 2,400,000 2,104,441 1,939,059 1,900,000 Health Workers (Supply and Demand) 1,400,000 1,167,735 1,008,173 900,000 400,000 -100,000 2025-26 2030-31 2035-36 -215,384 -12,966 -600,000 Supply - Demand (linear model) Supply - Demand (exponencial model) Source: Original calculations/compilations for this publication (2025). 52 DISCUSSIONS AND POLICY RECOMMENDATIONS India’s health workforce comprises approximately 5 million active workers, with a remarkable growth over the last five years. While workforce density remains below global benchmarks and that of regional and structural peers, efforts to scale up training capacity have led to a 60 percent increase in the number of available health workers. This increase has been observed across all categories of health workers, except AYUSH workers, with significant growth in the professional N&MWs workforce. During this same period, the rural health workforce grew by 14 percentage points, helping reduce the persistent urban-rural imbalances in the distribution of health workers in the country. That reduction is driven by the increase in the number of associate N&MWs in rural areas, which is also associated with the expansion of the Ayushman Bharat HWCs, which increased the demand for PHC workers across the country. Despite significant progress, health workforce challenges persist in the country. The distribution of health workers across and within states is highly uneven, with rural areas facing significant shortages. The share of health workers not meeting the qualification criteria is high, nearly 40 percent of all health workers and over half of the workforce in nursing and midwifery occupations. The skill- mix remains imbalanced, although PLFS data do not allow breaking down occupations by specialty. Available evidence points to shortages of specialties in areas such as mental health and ophthalmology, for example. The nurse-to-physician ratio has improved but remains low. That has implications for task-sharing and efficiency, as physicians performing lower-skill tasks may be inefficient while nurses are confined to more basic "handmaiden" roles. The health sector is a significant source of quality jobs, particularly for women. It employs approximately 1 percent of the country’s total workforce and 1.8 percent of the female workforce. While these figures are lower compared to India’s peers, they highlight the sect or's potential for job creation and its substantial impact on female labor force participation (LFP). For example, medical training has a pronounced effect on LFP, particularly for women: medical training increases female LFP 24.6 percentage points. Additionally, the health sector offers jobs of higher quality consistently across all four categories of health workers, as measured by the Job Quality Index, compared to the Indian labor market average. The gender pay gap, estimated at approximately 30 percent, and the effects of household size disproportionately affecting women may influence further improvements in female LFP: more than one-third of women with undergraduate-level technical education in medicine are out of the labor force.21 An important contribution of this report is the estimation of labor supply elasticity among health workers. In most cases, higher wages lead to a slight reduction in the number of hours worked, suggesting that income effects—where workers opt to work less as their income increases— 21 See Karan et al. (2021). 53 are at play. While the elasticities highlight labor supply decisions for active health workers, broader labor market trends may influence the sector's ability to attract prospective workers. For example, health sector wages have shown a downward trend in recent years, both in terms of real average monthly wages and relative to GDP per capita. Furthermore, employment benefits remain relatively limited for certain health worker categories, particularly those that predominantly employ women (only 29 percent of associate N&MWs and 44 percent of professional N&MWs have access to healthcare and maternity benefits). Projections of short- and medium-term trends for the Indian health workforce indicate shortages only in the most ambitious needs-based scenario. This scenario assumes that a health worker density equals 9.27 per 1,000 population and projects a shortage of health workers between 7.41 million to 7.61 million by 2035/36. When applying global benchmarks (4.45 per 1,000 population), India is expected to face a short-term shortage, though this gap is projected to narrow by 2035/36. Demand-based projections suggest that India is closing the health worker supply-demand gap. This alignment between demand and supply appears to be driven by healthcare reforms over the past decades and sustained economic growth. Publicly funded programs have intensified the need for health workers, while efforts in both the public and private sectors have successfully scaled up training capacity to meet this demand. The report adopts a HLM approach to situate the health workforce in India. The approach recognizes that government mandates for health workforce outcomes are less effective than their role in influencing the market. Within that role, government investments, purchasing power, and regulating capacities shape the options available to millions of individual health workers and the attractiveness of those options, and so direct health workers increasingly toward roles that progress the country toward UHC, enhancing the health and wellbeing of the Indian population. This approach contrasts with alternative methods, which often center on simple counts of workers entering and leaving the labor market, without providing insights into the drivers of these movements. Policy Recommendations Health workforce issues in India involve managing a complex labor market where most health professionals work in the private sector and are in high demand in global HLMs. In this context, earnings and working conditions significantly influence the level and composition of training, skill mix, choice of location, decision to move abroad, and overall size of the health workforce. To address the challenges identified in the report, the following priority areas for public policy are proposed: o Enhance the composition and skill mix of the health workforce to improve health service delivery efficiency: The objective of managing the labor market is not confined to assuring the number of health professionals required but also the qualities, cadre mix and match between competencies and priority roles for health professionals. In the current India HLM context, that means developing markets for allied health professionals on both demand and supply sides of the 54 market. A lack of roles for such professionals, historically, means that the population has not developed an appreciation of their capacities, and that demand may not emerge naturally. Many types of allied health professionals are essential to efficiently manage non-communicable diseases in primary health care without which common and manageable conditions such as diabetes and hypertension will remain largely uncontrolled. This is a significant problem for health and welfare and for economic development and will result in an escalating problem of labor force exit due to ill health. o Additionally, expanding the roles of community health workers , such as Accredited Social Health Activists (ASHAs) and associate N&MWs, through mid-level health worker programs can help alleviate pressure on higher-level professionals. Implementing innovative task-shifting models, where mid-level professionals assume certain medical tasks, can further enhance healthcare service delivery, especially in resource-constrained settings. o Quality of training and practice: Despite the significant increase in the number of health workers, quality assurance mechanisms need strengthening to ensure high standards in health professional training and practice. Additionally, reinforcing the regulatory and legal framework for informal health professionals is critical, given their key role in health service delivery, particularly in rural areas. Overall, as the findings suggest medical training premiums, workers in the healthcare sector must be incentivized to complete the medical training. o Compensation Policies: Regression analysis indicates that wage increases alone will not positively impact the supply of health workers' working hours, as the income effect is at play. This suggests that the structure and composition of compensation are more important than its overall level. Additionally, non-pecuniary benefits, such as access and funding to training, career advancement, and housing, can be an important part of healthcare worker incentive design. Access to childcare, healthcare, and maternity benefits can be particularly important for female workers. There is also a need to evaluate and strengthen remuneration schemes for health workers in the private sector to address the public-private wage gap. o Align health financing policies with health workforce objectives: While governments can directly set pay in public sector roles, they can influence remuneration in the private sector through health financing policies and regulations. Such policies should be designed with a clear understanding of their potential effects to avoid unintended consequences. Governments can set pay in public sector roles and influence private sector remuneration through health financing policies like PM- JAY. These policies must be carefully designed to avoid unintended consequences, such as reinforcing gender gaps or entrenching the private sector's role in the healthcare system. o Leverage the health sector's potential for job creation, with a particular focus on increasing female labor force participation: Medical training not only increases labor force participation, but it also offers a significant wage premium across healthcare professionals in India, with these effects being 55 particularly pronounced for women. However, persistent wage gaps remain, and about one-third of women with undergraduate level technical education in medicine are not participating in the labor market or healthcare service delivery. To encourage women’s workforce participation, policies should focus on easing household responsibilities (the “child penalty �), such as providing access to childcare, improving workplace safety, and expanding access to healthcare coverage and maternity benefits. Assuming the current female share in the healthcare workforce, closing India’s healthcare workforce shortage could potentially create 4-6 million additional quality jobs for women within the next decade. o Finally, the report emphasizes the importance of improved health workforce data collection and monitoring systems. Reliable data are crucial for understanding the dynamics of the health labor market and for developing targeted policies. Improved data systems will enable policymakers to more effectively address workforce shortages, wage disparities, and regional inequalities, ultimately strengthening India’s health workforce and enhancing the quality of ca re. 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Geneva Available at: https://iris.who.int/bitstream/handle/10665/250368/9789241511131-eng.pdf?sequence=1. 61 APPENDIX A – CLASSIFICATION OF CATEGORIES OF HEALTH WORKERS For the report we classify health workers in seven groups that include physician; dentist; professional nurses and midwives; associate nurses and midwives; AYUSH; Allied health workers; and pharmacists. The PLFS dataset includes six rounds, with a classification change from the NCO 2004 to the NCO 2015 in the period. The classification in the first four PLFS rounds from 2017/18 to 2020/21 use NCO 2004 for occupation classification and NIC 2008 for industry classification. 1. Physician a. Occupation 222 & (Industry 86100 or 86201) 2. Dentist a. Occupation 222 & Industry 86202 3. Professional Nurses and Midwives a. Occupation 223 b. Occupation 222 & Industry in division 87 4. Associate Nurses and Midwives a. Occupation 323 b. Occupation 322 & Industry in division 87 5. AYUSH a. Occupation 222 & (Industry 86901; 86902; or 86903) b. Occupation 324 6. Allied health workers a. (Occupation 222 or 322) & (Industry 86904; 86905; 86906; or 86909) 7. Pharmaceutical a. (Occupation 211; 221; 323; or 324) & Industry 47721 The classification in the last two PLFS rounds from 2021/22 to 2022/23 use NCO 2015 for occupation classification and NIC 2008 for industry classification. 1. Physician a. Occupation 221 & any Industry but 86202 2. Dentist a. (Occupation 221 or 226) & Industry 86202 3. Professional Nurses and Midwives a. Occupation 222 4. Associate Nurses and Midwives a. Occupation 322 5. AYUSH a. Occupation 223 or 323 6. Allied health workers a. (Occupation 224; 226; or 325) & Industry in division 86 or 87 7. Pharmaceutical a. (Occupation 226 or 325) & Industry 47721 62 APPENDIX B – QUALIFICATION ADJUSTMENT A health worker is defined as qualified if they fulfil one of four criteria based on general education level; years of schooling; field of training; technical education level: • Physicians: o Reported having a bachelor’s and/or postgraduate degree as general education. o Reported at least 18 years of education. o Declare having healthcare and life science training in field of training. o Diploma or certificate (graduate and above level) in medicine as technical education. • Dentist, professional nurses and midwives, and AYUSH: o Reported having a bachelor’s and/or postgraduate degree as general education. o Reported at least 16 years of education. o Declare having healthcare and life science training in field of training. o Diploma or certificate (graduate and above level) in medicine as technical education. • Associate nurses and midwives, allied health professionals, and Pharmaceutical: o Reported having diploma, certificate, bachelor and/or postgraduate degree as general education. o Reported at least 14 years of education. o Declare having healthcare and life science training in field of training. o Technical degree in medicine, diploma or certificate (below graduate level) in medicine as technical education, or diploma or certificate (graduate and above level) in medicine as technical education. 63 Applying these criteria do the PLFS 2022–23 data, 61.2 percent of health workers in India are considered qualified. Qualification rates are highest among dentists (95.9 percent) and physicians (87.3 percent), and lower among associate nurses and midwives (43.1 percent), professional nurses and midwives (55.1 percent), and AYUSH practitioners (60.3 percent). Most qualified workers are identified through general education level. Years of schooling strongly correlates with qualification status for some cadres (e.g., dentists and other health workers), but less so for physicians and AYUSH. Table B.1 – Health Worker Qualification Rate by Criteria, India, 2017/18–2022/23 Education Years of Both Criteria Qualified Level Schooling Physicians 87.3% 86.8% 86.8% 87.3% Dentist 95.9% 95.9% 95.9% 95.9% Professional N&MWs 46.4% 46.3% 43.6% 49.1% Associate N&MWs 38.2% 56.7% 37.7% 57.1% AYUSH 56.8% 56.8% 56.8% 56.8% Allied health workers 66.6% 83.7% 66.6% 83.7% Pharmaceutical 65.0% 100.0% 65.0% 100.0% Total Health Workers 55.0% 65.0% 54.2% 65.8% Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2017/18–2022/23), using Census of India (2011) population projections to adjust weights. The criteria are as follows: at least graduation or 18 years of schooling for physicians; at least graduation or 16 years of schooling for professional nurses and midwives (N&MWs) and Ayurveda, Yoga & Naturopathy, Unani, Siddha, Sowa Rigpa, and Homeopathy (AYUSH); at least a diploma/certificate or 14 years of schooling associate N&MWs, allied health workers, and pharmacists. In absolute terms, 3 million health workers are considered qualified, while 1.9 million do not meet the criteria. Nearly 1 million physicians are qualified versus 108,000 unqualified, whereas associate nurses and midwives have the highest number of unqualified workers, almost 1.1 million. Female health workers are more qualified than males across all categories except "other health workers." However, only 54 percent of female health workers are qualified compared to 73.4 percent of males. This gap reflects both the small gender difference in nurse and midwife qualifications (a female-dominated group) and the relatively low number of female physicians and AYUSH providers, which lowers the overall qualification share for women. Urban health workers are more qualified than rural counterparts across all cadres. The disparity is especially stark among AYUSH workers: only 23.2 percent are qualified in rural areas compared to 80.3 percent in urban areas. 64 Table B.2 – Stock of Health Workers by Qualification, India, 2022/23 Unqualified Qualified Qualified (Number) (Number) (%) Physicians 108,503 956,534 89.8% Dentist 1,434 33,692 95.9% Professional N&MWs 423,259 520,025 55.1% Associate N&MWs 1,077,973 816,711 43.1% AYUSH 100,106 151,134 60.2% Allied health workers 212,541 556,067 72.3% Pharmaceutical 3,037 4,788 61.2% Total health workers 1,926,854 3,038,951 61.2% Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2022/23), using Census of India (2011) population projections to adjust weights. The criteria are as follows: at least graduation or 18 years of schooling for physicians; at least graduation or 16 years of schooling for professional nurses and midwives (N&MWs) and Ayurveda, Yoga & Naturopathy, Unani, Siddha, Sowa Rigpa, and Homeopathy (AYUSH); at least a diploma/certificate or 14 years of schooling associate N&MWs, allied health workers, and pharmacists. Figure B.1 – Health Worker Qualification by Gender, India, 2022/23 Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2022/23), using Census of India (2011) population projections to adjust weights. N&Ms - nurses and midwives; AYUSH - Ayurveda, Yoga & Naturopathy, Unani, Siddha, Sowa Rigpa, and Homeopathy. 65 Figure B.2 – Health Worker Qualification by Urban/Rural, India, 2022/23 Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2022/23), using Census of India (2011) population projections to adjust weights. N&Ms - nurses and midwives; AYUSH - Ayurveda, Yoga & Naturopathy, Unani, Siddha, Sowa Rigpa, and Homeopathy. Figure B.3 – Health Worker Qualification by Age Structure, India, 2022/23 Source: Original calculations/compilations for this publication (2025). Note: The calculations are based on Periodic Labor Force Survey of India data (2022/23), using Census of India (2011) population projections to adjust weights. 66 APPENDIX C – WEIGHT ESTIMATES The Periodic Labor Force Survey (PLFS) relies on weights derived from the Census of India data (2011), which underrepresents the current population size and composition in India due to demographic shifts over the past decade. As a result, the original weights may inaccurately represent current population distributions across states, genders, and rural/urban areas. To address this discrepancy, we undertake a resampling process using the latest population projections provided by the Census Bureau. These projections are adjusted for each survey year, considering demographic variables, such as state, gender, and urban/rural status, which are critical to accurately reflect both population growth and demographic transitions across these categories. The adjustment process involves recalibrating the PLFS data to align more closely with estimated population distributions, essentially redistributing weights to reflect shifts in the population that have occurred since the last census. By integrating these projections, we ensure that underrepresented or overrepresented sub-groups in the survey data are more accurately weighted, improving the robustness and reliability of insights drawn from the PLFS data. This approach enhances the survey’s ability to reflect real-world trends in labor force participation, employment, and economic activity, offering a more accurate basis for policy analysis and economic planning. This method also helps to capture variations across regions and demographic segments that may be impacted by different rates of growth or migration, thereby producing a dataset that more accurately represents the present-day Indian population. Consequently, the adjusted PLFS weights provide a more nuanced and realistic picture of labor and employment metrics acr oss India’s diverse and dynamic population structure. 67 APPENDIX D – REGRESSION ANALYSIS SUPPLEMENTARY MATERIALS Table D.1 – Determinants of Labor Force Participation and Employment, India Labor Force Participation Employment A: All B: Female C: Male A: All B: Female C: Male Individuals Individuals 1 if received medical 0.202*** 0.246*** 0.0198* 0.0314*** 0.0499*** 0.0266*** training (33.98) (28.53) (2.40) (0.00225) (0.00352) (0.00346) 1 if completed higher -0.0550*** -0.0826*** -0.0469*** -0.0220*** -0.0446*** -0.0118*** secondary school (-38.93) (-44.26) (-18.10) (0.00135) (0.00253) (0.00172) 1 if completed 0.0900*** 0.104*** 0.0636*** -0.0705*** -0.115*** -0.0543*** diploma/certificate (23.42) (14.74) (15.83) (0.00267) (0.00636) (0.00295) 1 if completed bachelor 0.0741*** 0.0712*** 0.0469*** -0.0858*** -0.130*** -0.0665*** (41.65) (28.04) (16.67) (0.00157) (0.00302) (0.00194) 1 if completed 0.145*** 0.199*** 0.0534*** -0.0897*** -0.136*** -0.0634*** postgraduate and above (59.64) (55.87) (15.10) (0.00204) (0.00359) (0.00258) 1 if male 0.510*** 0.0258*** (602.35) (0.000948) 30-39 0.219*** 0.169*** 0.217*** 0.0795*** 0.117*** 0.0565*** (161.30) (83.25) (163.36) (0.00125) (0.00284) (0.00127) 40-49 0.224*** 0.171*** 0.189*** 0.111*** 0.174*** 0.0758*** (156.54) (81.95) (101.59) (0.00118) (0.00256) (0.00135) 50-59 0.162*** 0.0864*** 0.0540*** 0.115*** 0.190*** 0.0769*** (102.14) (39.75) (19.86) (0.00119) (0.00238) (0.00143) 60+ -0.194*** -0.0971*** -0.473*** 0.119*** 0.191*** 0.0832*** (-124.06) (-66.22) (-217.21) (0.00133) (0.00259) (0.00159) 1 if married 0.0564*** -0.123*** 0.266*** 0.0801*** 0.0545*** 0.0993*** (46.75) (-74.92) (165.87) (0.000994) (0.00209) (0.00134) 1 if spouse is not working -0.270*** -0.384*** -0.162*** -0.0290*** -0.0394*** -0.0350*** (-44.76) (-43.47) (-26.79) (0.00507) (0.0117) (0.00530) 68 Log of spouse's wage -0.0198*** -0.0148*** -0.0223*** -0.00273*** 0.000340 -0.00496*** (-25.24) (-17.73) (-23.82) (0.000637) (0.00134) (0.000719) Household size -0.00283*** -0.0103*** 0.000561* -0.00495*** -0.00658*** -0.00420*** (-13.94) (-30.20) (2.13) (0.000161) (0.000429) (0.000172) Observations 847,310 419,438 427,872 408,806 95,127 313,679 Note: Signs *, **, *** indicate significance at the 10, 5, and 1 percent level. Control covariates also include an indicator of spouse’s working status, a log of spouse’s wage, state fixed effect, and quarter-by-year fixed effect. Standard errors are clustered at the health worker level. Table D.2 – Estimates of Labor Supply Elasticities by Labor Supply Percentiles, India A: 25th percentile B: 50th percentile/median C: 75th percentile Log of real wage -0.147 -0.167** -0.183 (0.090) (0.082) (0.122) Observations 11070 State fixed effect Y Y Y Year-by-quarter fixed effect Y Y Y Note: Control covariates in the fixed-effects model include educational attainment, dual contract status, an indicator of spouse’s working status, a log of spouse’s wage, state fixed effect, and quarter-by-year fixed effect. Standard errors are clustered at the health worker level. 69 Table D.3 – Estimates of Labor Supply Elasticities by Economic Sectors, India A: B: C: D: IS E: Fin. F: Legal G: H: Ed I: Health Teleco Comp. Serv. & Acc Public Scientifi Sector m Prog. Admin c & Tech Log of real wage - - - -0.280 *** -0.288 *** -0.225 *** -0.195 *** -0.362 *** - 0.166*** 0.200*** 0.175*** 0.348*** (0.0335) (0.0432) (0.0224) (0.0327) (0.0329) (0.0259) (0.0157) (0.0140) (0.0356) Observations 11070 5138 17640 4270 21348 12003 56501 78802 5871 Adj. R-sq 0.220 0.267 0.351 0.331 0.375 0.293 0.282 0.396 0.330 R-sq Overall 0.00510 0.0962 0.156 0.0572 0.00114 0.0439 0.0450 0.00063 0.0640 3 Controls Y Y Y Y Y Y Y Y Y State fixed Y Y Y Y Y Y Y Y Y effect Year-by-quarter Y Y Y Y Y Y Y Y Y fixed effect Cluster Worker Worker Worker Worker Worker Worker Worker Worker Worker Note: Control covariates in the fixed-effects model include educational attainment, dual contract status, an indicator of spouse’s working status, a log of spouse’s wage, state fixed effect, and quarter-by-year fixed effect. Standard errors are clustered at the health worker level. Table D.4 – Labor Supply Elasticities Among Health Workers by Receipt of Medical Training, India Dependent Medical Medical Medical Medical Without Without Without Without variable: Log of Training: Training: Training: Training: Medical Medical Medical Medical working hours All Physicians Nurses & Assoc. Training: Training: Training: Training: Midwives Nurses & All Physicians Nurses & Assoc. Midwives Midwives Nurses & Midwives Log of real wage -0.146 ** -0.101 * -0.265 *** -0.348 *** -0.182 *** -0.133 *** -0.0963 *** -0.356*** (0.0588) (0.0531) (0.0614) (0.0660) (0.0350) (0.0388) (0.0368) (0.0484) Observations 4610 2210 1311 1089 6460 1353 2057 3050 Adj. R-sq 0.202 0.155 0.296 0.501 0.241 0.222 0.169 0.397 R-sq Overall 0.0450 0.0725 0.0303 0.0866 0.000698 0.0355 0.0278 0.000197 Controls Y Y Y Y Y Y Y Y State fixed Y Y Y Y Y Y Y Y effect Year-by-quarter Y Y Y Y Y Y Y Y fixed effect Health Health Health Health Health Health Health Health Cluster Worker Worker Worker Worker Worker Worker Worker Worker Note: Control covariates in the fixed-effects model include educational attainment, dual contract status, an indicator of spouse’s working status, a log of spouse’s wage, state fixed effect, and quarter-by-year fixed effect. Standard errors are clustered at the health worker level. 70 Table D.5 – Determinants of Wages by Healthcare Professions, India A: All B: Physicians C: Nurses &Midwives D: Assc. Nurses & Midwives Secondary school 0.235*** 0.0671 0.324*** 0.222*** (0.0536) (0.177) (0.0958) (0.0671) Higher secondary school 0.458*** 0.647*** 0.530*** 0.345*** (0.0508) (0.173) (0.0932) (0.0651) Diploma/certificate 0.548*** 0.421** 0.649*** 0.630*** (0.0547) (0.179) (0.101) (0.0738) Graduate 0.821*** 0.924*** 0.784*** 0.697*** (0.0516) (0.162) (0.0995) (0.0704) Postgraduate and above 1.192*** 1.127*** 0.976*** 0.804*** (0.0601) (0.164) (0.126) (0.110) Years of working experience 0.0192*** 0.00142 0.0297*** 0.0378*** (0.00520) (0.00884) (0.00907) (0.00811) Working experience, squared -0.000287*** -0.000101 -0.000280 -0.000490*** (0.0000925) (0.000181) (0.000201) (0.000126) 1 if in a dual contract 0.260*** 0.0343 0 0.408*** (0.0815) (0.0818) (.) (0.136) 1 if received medical training 0.268*** 0.203*** 0.151*** 0.159*** (0.0231) (0.0366) (0.0351) (0.0403) 1 if public worker 0.346*** 0.275*** 0.525*** 0.307*** (0.0217) (0.0356) (0.0368) (0.0352) 1 if male 0.260*** 0.0177 0.181*** 0.209*** (0.0235) (0.0344) (0.0433) (0.0449) 30-39 0.114** 0.253*** -0.0524 -0.109 (0.0450) (0.0705) (0.0726) (0.0746) 40-49 0.136* 0.364*** -0.116 -0.220* (0.0716) (0.112) (0.118) (0.116) 50-59 0.387*** 0.424*** 0.140 0.121 (0.0957) (0.145) (0.160) (0.155) 60+ 0.274** 0.517*** -0.127 -0.385 (0.136) (0.196) (0.282) (0.254) Log of spouse's wage 0.259*** 0.146*** 0.250*** 0.295*** (0.0165) (0.0234) (0.0345) (0.0257) 71 Observations 10983 3548 3342 4093 Adj. R-sq 0.528 0.453 0.479 0.472 Controls Y Y Y Y State fixed effect Y Y Y Y Year-by-quarter fixed effect Y Y Y Y Cluster Health Worker Health Worker Health Worker Health Worker Note: The wages regression specification was estimated using ordinary least squares (OLS) and includes covariates such as quarter-by-year fixed effects and state fixed effects. Standard errors are clustered at the health worker level. 72 Table D.6 – Determinants of Wages by Healthcare Professions and Gender, India Female: Male: All Female: Male: Female: Male: Female: Male: All Physicians Physicians Nurses & Nurses & Assc. Assc. Midwives Midwives Nurses & Nurses & Midwives Midwives Secondary school 0.292*** 0.0811 0.571*** 0.453*** 0.208** -0.0201 0.0228 0.196* (0.0664) (0.0876) (0.219) (0.113) (0.0816) (0.203) (0.154) (0.115) Higher secondary 0.499*** 0.285*** 1.434*** 0.612*** 0.365*** 0.445** 0.331** 0.0912 school (0.0630) (0.0844) (0.240) (0.109) (0.0776) (0.189) (0.142) (0.128) Diploma/certificate 0.607*** 0.358*** 1.127*** 0.751*** 0.669*** 0.235 0.331** 0.445*** (0.0690) (0.0946) (0.233) (0.119) (0.0879) (0.200) (0.146) (0.165) Graduate 0.848*** 0.718*** 1.555*** 0.882*** 0.719*** 0.743*** 0.489*** 0.521*** (0.0669) (0.0800) (0.202) (0.117) (0.0867) (0.176) (0.156) (0.139) Postgraduate and 1.167*** 1.129*** 1.628*** 0.898*** 0.865*** 1.004*** 1.086*** 0.665*** above (0.0819) (0.0872) (0.206) (0.150) (0.121) (0.180) (0.185) (0.243) Years of working 0.0202*** 0.0198** -0.000300 0.0224** 0.0397*** -0.00282 0.0531** 0.0345* experience (0.00652) (0.00877) (0.0139) (0.00988) (0.00915) (0.0110) (0.0220) (0.0181) Working experience, - - 0.0000097 -0.000119 - - - - squared 0.000281* 0.000317* 7 0.000527** 0.0000456 0.000901* 0.000525* * * * (0.000118 (0.000156 (0.000320) (0.000220) (0.000139) (0.000208) (0.000526) (0.000288 ) ) ) 1 if in a dual contract 0.153 -0.190 0.387*** 0.141 (0.183) (0.146) (0.146) (0.0888) 1 if received medical 0.237*** 0.288*** 0.172*** 0.121*** 0.136*** 0.208*** 0.178** 0.176 training (0.0284) (0.0386) (0.0598) (0.0380) (0.0436) (0.0444) (0.0861) (0.125) 1 if public worker 0.331*** 0.374*** 0.169*** 0.500*** 0.309*** 0.311*** 0.559*** 0.420*** (0.0266) (0.0374) (0.0521) (0.0404) (0.0391) (0.0460) (0.0801) (0.0791) 30-39 0.0729 0.193** 0.362*** -0.0211 -0.146* 0.257*** -0.0904 -0.0303 (0.0560) (0.0758) (0.108) (0.0787) (0.0841) (0.0899) (0.177) (0.149) 40-49 0.0447 0.280** 0.378** -0.0951 -0.297** 0.408*** -0.110 0.107 (0.0883) (0.119) (0.167) (0.127) (0.130) (0.140) (0.286) (0.253) 50-59 0.392*** 0.337** 0.410* 0.169 0.0768 0.466*** 0.152 0.281 (0.118) (0.155) (0.239) (0.176) (0.174) (0.177) (0.421) (0.324) 73 60+ 0.251 0.319 0.470 -0.0152 -0.288 0.554** -0.109 -0.198 (0.190) (0.206) (0.329) (0.321) (0.265) (0.234) (0.564) (0.505) Log of spouse's wage 0.330*** 0.0876*** 0.201*** 0.297*** 0.336*** 0.128*** 0.0219 -0.0358 (0.0209) (0.0236) (0.0382) (0.0384) (0.0270) (0.0298) (0.0526) (0.0586) Observations Adj. R-sq 6.227*** 1.628*** 3.776*** 5.572*** 6.304*** 2.376*** 0.309 -0.586 Controls (0.393) (0.441) (0.741) (0.723) (0.502) (0.570) (0.953) (1.056) State fixed effect 7162 3821 1153 2726 3283 2395 616 810 Year-by-quarter fixed 0.522 0.522 0.548 0.494 0.507 0.439 0.568 0.459 effect Cluster Health Health Health Health Health Health Health Health Worker Worker Worker Worker Worker Worker Worker Worker Note: The wages regression specification was estimated using ordinary least squares (OLS) and includes covariates such as quarter-by-year fixed effects and state fixed effects. Standard errors are clustered at the health worker level. 74 Table D.7 – List of Variables for the Regression Analyses, India Variable Definition Data Transformation Variable Name in Stata Panel Estimation of Labor Supply Elasticities Working hours within a Reported hours of working Log transformation ln_hours_week week within a week Real hourly wage Implied hourly wages given Log transformation ln_hourly_wage reported wage and working hours, adjusted with CPI Years of education Number of years of formal N.A. yrs_edu education completed Dual contract A dummy that indicates a N.A. dual health worker having a dual contract Quarter-by-year fixed Sets of quarter and year N.A. yr_qtr effects dummies Institution type: public A dummy that indicates N.A. public and private health workers’ main institution type Gender A dummy variable that N.A. female indicate health workers’ gender Medical training Reported participation in N.A. medical_training medical training Wage Regressions Real hourly wage Implied hourly wages given Log transformation ln_hourly_wage reported wage and working hours, adjusted with CPI Years of education Number of years of formal N.A. yrs_edu education completed Highest level of A set of dummies on the N.A. general_edu_level2 education completed highest level of education completed: up to middle school, secondary school, higher secondary school, diploma/certificate, graduate, postgraduate and above Years of working Years of reported working Quadratic transformation experience, experiencesq experience experience State A set of dummies to N.A. state indicate the state in which health workers live Dual contract A dummy that indicates a N.A. dual health worker having a dual contract Quarter-by-year fixed Sets of quarter and year N.A. yr_qtr effects dummies Institution type: public A dummy that indicates N.A. public and private health workers’ main institution type 75 Gender A dummy variable that N.A. male indicates health workers’ gender Medical training Reported participation in N.A. medical_training medical training Spouse’s wage Reported wages of spouse Log transformation ln_wage_spouse Spouse’s working status A dummy of spouse’s N.A. spouse_not_working reported working status Determinants of Labor Force Participation Labor force A dummy indicating an N.A. Labforce_upss participation individual’s labor force participation Highest level of A set of dummies on the N.A. general_edu_level2 education completed highest level of education completed: up to middle school, secondary school, higher secondary school, diploma/certificate, graduate, postgraduate and above Gender A dummy variable that N.A. male indicate health workers’ gender Age group A set of dummies on N.A. age_group2 workers’ age group Medical training Reported participation in N.A. medical_training medical training Marital status A dummy indicating a N.A. married workers’ marital status Spouse’s wage Reported wages of spouse Log transformation ln_wage_spouse Spouse’s working status A dummy of spouse’s N.A. spouse_not_working reported working status Household size The number of individuals N.A. hhsize in the household as indicated in the roster section 76 This series is produced by the Health, Nutrition, and Population Global Practice of the World Bank. The papers in this series aim to provide a vehicle for publishing preliminary results on HNP topics to encourage discussion and debate. 2