JOBS SERIES Issue No. 21 DIAGNOSTIC SRI LANKA E li z ab e t h R up p e r t B ulm e r DIAGNOSTIC SRI LANKA E li z ab e t h R up p e r t B ulm e r Some rights reserved Rights and Permissions Attribution Translations This translation was not created by The World Bank and should not be considered an official World Bank translation. The World Bank shall not be liable for any content or error in this translation. Adaptations This is an adaptation of an original work by The World Bank. Views and opinions expressed in the adaptation are the sole responsibility of the author or authors of the adaptation and are not endorsed by The World Bank. Third-party content ACKNOWLEDGMENTS i ABBREVIATIONS ASI ASS AST DCS Department of Census and Statistics ICT Neither working nor in education or training PPP PSPS Small and medium enterprise VA Value added WDI World Development Indicators ii CONTENTS PREFACE iv EXECUTIVE SUMMARY 1 1. OVERVIEW OF THE ECONOMY AND STRUCTURAL TRANSFORMATION 9 2. IMPACT OF STRUCTURAL AND DEMOGRAPHIC TRANSITION ON LABOR MARKET OUTCOMES 17 3. SHORTCOMINGS IN JOB QUALITY FOR YOUTH AND WOMEN 35 4. PRIVATE SECTOR DEMAND FOR LABOR 49 5. LABOR REGULATIONS 65 6. CONCLUSIONS AND RECOMMENDATIONS 73 REFERENCES 78 ANNEX A 80 ANNEX B 95 ANNEX C 103 PREFACE Policies that provide income support to workers who have experienced significant earnings losses. Income support—whether in cash or in kind—is essential for sustaining consumption and avoiding dissaving, Policies that target support to firms in order to minimize layoffs iv Policies that help workers and firms adjust to new working conditions. Policies that keep remittance channels open v EXECUTIVE SUMMARY KEY MESSAGES Sri Lanka has undergone significant structural transformation over the past two decades. Better-quality jobs are being created, and yet too many jobs remain informal and low-quality. Increasingly educated youth and women are underutilized, despite their rising aspirations. More and better work opportunities have been slow to materialize in the private sector, where most firms are micro and face impediments to compete and grow. A number of economic distortions restrain more and better job creation. ∫ ∫ ∫ ∫ This report outlines 5 main policy channels for interventions to help the Sri Lankan economy modernize and move beyond yesterday’s labor-intensive strategies to more skill-intensive employment in competitive firms 1 Figure 0.1 4,000 3,500 3,000 1,000 PEOPLE 2,500 2,000 1,500 1,000 500 0 AGRICULTURE INDUSTRY SERVICES 1997 2007 2017 Source: Sri Lanka’s economy is shifting from primary production to the more modern and diversified industry and services sectors. Productivity gains bring many diverse jobs to Sri Lankan cities. Job growth in Sri Lanka was strong over the last decade and sectorally diverse, and the economy added more wage than non-wage jobs. 1 Informal employment remains dominant, however, signaling continued low average job quality The sector distribution of new jobs spanned both high- and low-productivity sectors, creating a mixed picture that raises questions about the sustainability of future job growth. 1 This report defines a “formal job” as one having pension coverage—that is, a worker whose employer contributes to a pension fund (the Employees’ Provident Fund, EPF, and the Employees’ Trust Fund, ETF) or a civil servant covered by the Public Service Pension Scheme (PSPS). Employers and self-employed workers whose enterprises are registered with an employee provident fund or with inland revenue are also defined to be formally employed. All other work is considered “informal,” including unpaid family, casual, and unregistered self-employment work. 2 Figure 0.2 UNEMPLOYED FORMAL WAGE 4% UNPAID PUBLIC 8% 13% FARMER FORMAL WAGE 12% PRIVATE 14% EMPLOYER 3% L A SELF-EMPLOYED RM (NON-FARMER) O INFORMAL WAGE INF 18% 28% Note: Source: Figure 0.3 FORMAL –4.3 AGRICULTURE, CATTLE, AND FISHING INFORMAL 5.6 FORMAL 1.3 MINING AND UTILITIES INFORMAL 0.4 FORMAL 1.4 MANUFACTURING FOOD AND BEVERAGES INFORMAL 3.8 FORMAL 0.3 MANUFACTURING TEXTILE INFORMAL 4.4 FORMAL 2.6 MANUFACTURING OTHER INFORMAL 2.3 FORMAL 2.7 CONSTRUCTION INFORMAL 10.4 FORMAL 5.6 WHOLESALE AND RETAIL INFORMAL 8.4 FORMAL 1.5 TRANSPORT AND COMMUNICATIONS INFORMAL 7.9 FORMAL 8.1 FINANCE AND REAL ESTATE INFORMAL 5.0 GOVERNMENT / PUBLIC FORMAL 8.9 ADMINISTRATION INFORMAL –0.3 FORMAL 6.7 EDUCATION AND HEALTH INFORMAL 2.8 FORMAL 2.7 OTHER SERVICES INFORMAL 11.8 –6.0 –4.0 –2.0 0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 Source: 3 Several structural rigidities impede further improvement in Sri Lanka’s job outcomes. A number of other factors also represent major obstacles to creating more and better jobs. The state, prominent in Sri Lanka’s economy, both directly and indirectly distorts markets. The vast majority of firms are micro-sized and characterized by low productivity and low wages. Larger firms tend to be more productive, but a small number of very large, dominant old firms may be impeding innovation. Labor-intensive manufacturing, which facilitated structural transformation and Sri Lankan economic development, is losing relevance in today’s dynamic global economy. Women are underutilized and disincentivized to contribute to the economy. The limited attractive 4 Youth educational attainment and aspirations are rising, but improvements in their labor market outcomes are slow to materialize. Sri Lanka’s labor regulations, designed to protect workers, instead hinder better labor outcomes for the large number of workers excluded from formal jobs. Labor market distortions—including from public employment and pay policies, and the disparity between formal and informal jobs—impede private sector development and employment opportunities. Structural economic transformation—toward higher value added and product and service quality and sophistication—has stalled, which undermines prospects for improved job quality. Private Sri Lanka needs a coordinated shift in strategy and supportive policies or it will fall behind more dynamic and innovative economies that can better navigate changing global market demand. Reforms 5 Table 0.1 Short-term actions • • Strategic medium-term priorities 1. Reduce barriers to firm growth and productivity gains. • • • • Short-term actions • • • • • 2. Revise distortionary labor, • government employment, and competition policies. • Strategic medium-term priorities • • • • • 6 Short-term actions • • • • • • 3. Enhance youth and women’s • Partner with private IT companies to develop ICT training modules targeted to women to provide capacity to achieve better labor outcomes. Strategic medium-term priorities • • • support accredited childcare and eldercare, and training and entrepreneurship support to Short-term actions • • 4. Increase the productivity of the self-employed. • • Short-term actions • • 5. Improve matching of job seekers with employers. Strategic medium-term priorities • 7 1. OVERVIEW OF THE ECONOMY AND STRUCTURAL TRANSFORMATION 1.1 SNAPSHOT OF THE ECONOMY Sri Lanka’s economy experienced robust and sustained growth over the past several decades Annual GDP growth averaged over 5 percent, although with some volatility. High growth periods were This impressive GDP growth was accompanied by important reductions in poverty and improved living conditions. 2 Human development indicators also showed improvement; in fact, accelerated since 2013 compared to the previous period, it still fell short of the national average growth rate Sri Lanka’s island economy is only moderately integrated into the global economy, and in recent years has become more inward-oriented. Figure 1.1 90 4,500 80 4,000 70 3,500 BILLIONS 2010 US$ 60 3,000 2010 US$ 50 2,500 40 2,000 30 1,500 20 1,000 10 500 0 0 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 01 03 05 07 09 11 13 15 17 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20 20 20 20 GDP (BILLIONS 2010 US$) GDP PER CAPITA (2010 US$) Source: 2 Sri Lanka’s national poverty line dates to 2002 and therefore cannot be reliably applied to current consumption levels. 9 Figure 1.2 80,000 10% 70,000 8% 60,000 6% 50,000 4% 40,000 2% 30,000 0 20,000 10,000 –2% 0 –4% 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 19 19 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 AGRICULTURE INDUSTRY SERVICES VA GROWTH (%) Source: Figure 1.3 0.8000 0.7630 0.7660 0.7680 0.7700 0.7570 0.7450 0.7500 SRI LANKA 0.7000 MALDIVES 0.6500 0.6000 INDIA BHUTAN 0.5500 BANGLADESH NEPAL PAKISTAN 0.5000 AFGHANISTAN 0.4500 2010 2012 2014 2015 2016 2017 Source: These competitiveness challenges are further compromised by the prominent role of the state in Sri Lanka’s economy. The public sector exerts outsized influence, both indirectly and directly. 10 Figure 1.4 NON- TULLES ETHYLENE SYNTHETIC RUBBER REFINED NARROW CRUDE CEMENT LIGHT RETAIL SEMI-FIN. COPPER HOT-ROLLED POLYMERS RUBBER 0.48% WOVEN AND NET WIRE IRON GOLD PETROLEUM 2.1% RUBBERIZED FABRIC PURE FABRIC IRON 0.31 0.25% 0.62% 0.56% 4.4% PETROLEUM COTTON 0.65% 0.43% 2.6% KNITTED YARN 1.6% 0.51% FABRIC 9.8% 3.4% COATED FLAT-ROLLED IRON 0.59% HEAVY PURE IRON STRUCTURES 0.53% COAL BRIQUETTES WOVEN COTTON 1.1% DIAMONDS 0.45% 1.0% PACKAGED CARBON MEDICA- 0.26% RAW SUGAR CONCENTRATED UNCOATED PETROLEUM GAS MENTS MILK PAPER 0.99% 0.99% 1.4% 0.51% 1.9% BROADCASTING ELECTRICAL SEWING TRANS- MACHINES PLANES, HELICOPTERS, DELIVERY MOTOR- EQUIPMENT TRUCKS CYCLES FORMERS 0.31% AND / OR SPACECRAFT 2.1% 1.6% 1.2% 3.3% RTS 0.44% MPO AT ED I RICE D -REL 1.5% FOO SAWN COMPUTERS MEDICAL OTHER CARS BUSES INSTRUMENTS FURNITURE WOOD 0.77% 0.51% 0.26% 2.6% WHEAT PALM OIL VEHICLE PARTS 1.2% 0.67% 0.39% Source: Figure 1.5 OTHER WOMEN'S EXCAVATION OTHER PROCESSEDANIMAL NON-KNIT MEN'S KNIT T-SHIRTS KNIT GLOVES TEA MACHINERY 1.1% EDIBLE TOBACCO FOOD PREPARA- 0.56% 0.51% UNDERGARMENTS SUITS 3.3% 2.9% 11% TIONS 0.64% 5.5% 3.3% INSULATED WIRE RAW TOBACCO 0.41% ELECTRICAL CONTROL BOARDS ELECTRICAL TRANSFORMERS KNIT WOMEN'S NON-FILLET NON-FILLET UNDERGARMENTS KNIT SWEATERS NON-KNIT KNIT COCONUT FRESH FISH FROZEN FISH AND OTHER 5.0% 2.2% WOMEN'S ACTIVE VEGETABLE 0.62% 0.55% SHIRTS WEAR FIBERS CINNAMON PEPPER CLOVES WHEAT FISH FILLETS 0.93% 1.6% 0.80% 0.42% FLOURS 1.4% 1.11% 0.58% KNIT MEN'S KNIT OTHER VEGETABLE PRECIOUS STONES DIAMONDS FOOTWEAR PARTS 1.6% 0.74% KNIT WOMEN'S SUITS UNDERGARMENTS MEN'S COCONUTS, BRAZIL PRODUCTS 0.65% NUTS, AND CASHEWS 4.7% 1.7% SHIRTS 0.83% 0.59% 0.93% OTHER KNITTED NON-KNIT USED RUBBER TIRES RUBBER TIRES RUBBER HATS 2.9% 1.7% PROD. REFINED PETROLEUM 0.27% MEN'S SHIRTS 0.80% 1.4% 1.7% COCONUT OIL NON-KNIT WOMEN'S SUITS 0.83% 3.8% KNIT BABIES' ACTIVATED ESSENTIAL RUBBER CARBON OILS GARMENTS RUBBER APPAREL 1.7% 0.76% 0.44% 1.6% PLASTIC LIDS Source: 3 3 Informality can be defined from the firm perspective or the worker perspective. Informal firms can be defined using size criteria (e.g., less than 10 workers), or as those that are not registered with the government authorities, or firms that operate informally by evading certain regulations despite being registered (such as employing workers informally). Informal workers are defined in this report to include employees with no permanent employer, unregistered self-employed, wage employees who are not covered by a pension fund (the Employees’ Provident Fund (EPF) or the Employees’ Trust Fund (ETF), unpaid family workers, and employers whose enterprise is not registered with an employee provident fund or inland revenue department. Formal workers are defined to include employees whose employer contributes to a pension fund (EPF or ETF), civil servants who are covered by the Public Service Pension Scheme (PSPS), and employers and self-employed whose enterprise is registered with an employee provident fund or inland revenue department. 11 BOX 1.1: MORE TRADE TRANSLATES INTO FASTER GROWTH AND BETTER JOBS A wealth of economic literature has analyzed the country-level impacts of increasing trade—both exports and imports—on economic growth and jobs; the positive evidence is overwhelming, and the long-term effects are even stronger than the short-term effects. There are multiple channels through which these positive direct and indirect effects arise, including through export manufacturing jobs, expanded demand for exports, the import of better intermediate inputs and technology that enable increased productivity and increased product quality and sophistication which in turn requires more skilled labor, and the aggregate demand effects of increased household incomes, inter alia. A recent report on South Asia (Artuc et al. 2019) finds that export growth raised the wages of those in export-affected sectors, and that more informal workers transitioned into formal jobs. In Vietnam, a country that experienced remarkable GDP per capita growth between 1990 and 2015, the indirect positive effects of increased exports on employment are estimated to be very large; Hoang and Nguyen (2018) find that in addition to increased manufacturing employment following a trade agreement between Vietnam and the US, there were strong spillovers into the services sectors as well, mostly as a result of the higher incomes from manufacturing jobs that increased the demand for local services. The recently completed Vietnam Jobs Diagnostic report confirms that exports have been a strong source of job creation and higher wages, and part of this success stems from Vietnam’s entry into multiple global value chains (Cunningham et al., 2018). The OECD’s 2012 report on trade and employment—a compilation of research developed by various multilateral agencies under the International Collaborative Initiative on Trade and Employment—provides a comprehensive treatment of the employment effects of rising global integration, using cross-country, regional and country-level analysis to estimate the impact and transmission channels of increased trade on jobs. The research indicates that trade has played a significant role in generating better jobs, fostering higher wages, and contributing to improved working conditions, especially when the right supportive policies are in place (OECD 2012). Both exports and imports foster productivity growth and therefore GDP growth, and more productive firms pay higher wages. The reallocation of labor from less productive to more productive activities may need to be facilitated by labor policies that promote labor mobility and protect workers adversely affected by these transitions through adequate safety nets. The report concludes that “policies that embed trade reforms in a context of macroeconomic stability and a sound investment climate on the one hand, and, on the other, protection for workers, maintenance of high-quality working conditions, and facilitation of labour transitions, can play an important role in realising the potential wage, employment and income gains associated with trade” (OECD 2012, p. 7). 1.2 STRUCTURAL TRANSFORMATION AND PRODUCTIVITY Sri Lanka is undergoing a fundamental shift from primary production to the more modern sectors of industry and services. Sri Lanka has experienced strong productivity growth in per capita terms since 1997, but little of this came from a reallocation effect. As the economy has shifted from primary activities to industrial activities and, especially, into services, the sectoral composition of employment has become increasingly dominated by service sector jobs. Sri 12 Figure 1.6 4,000 3,500 3,000 1,000 PEOPLE 2,500 2,000 1,500 1,000 500 0 AGRICULTURE INDUSTRY SERVICES 1997 2007 2017 Source: Figure 1.7 2007–2017 TOTAL=5.2% 1997–2007 TOTAL=3.3% –1 0 1 2 3 4 5 6 % YEARLY CONTRIBUTION TO GROWTH WITHIN-SECTOR PRODUCTIVITY STATIC REALLOCATION DYNAMIC REALLOCATION EMPLOYMENT RATE PARTICIPATION RATE DEMOGRAPHIC CHANGE Source: Agriculture work and manufacturing are relatively more important among female workers Structural transformation and urbanization go hand in hand; productivity gains are bringing more diversified jobs to cities. 13 Figure 1.8 A. MEN B. WOMEN 100% 100% 90% 90% SERVICES SERVICES 80% 80% 70% 70% 60% 60% 50% 50% MANUF. 40% 40% MANUF. 30% 30% 20% 20% 10% 10% 0 0 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 OTHER SERVICES TRANSPORT AND COMMUNICATIONS MANUFACTURING TEXTILE EDUCATION AND HEALTH WHOLESALE AND RETAIL MANUFACTURING FOOD AND BEVERAGES GOVERNMENT / PUBLIC ADMINISTRATION CONSTRUCTION MINING AND UTILITIES FINANCE AND REAL ESTATE MANUFACTURING OTHER AGRICULTURE, CATTLE, AND FISHING Source: Figure 1.9 A. EMPLOYMENT CONCENTRATION (’000s) B. POVERTY RATES (%) (800,1000) 36% (600,800) (400,600) (200,400) (0,200) 3% Source: 4 4 Measurement challenges stem from outdated definitions of rural and urban in the household survey statistics. 14 Figure 1.10 6.00 5.00 4.00 3.00 2.00 1.00 0 0 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 5 –3 –4 –4 –5 –5 –6 –6 –7 –7 –8 –8 –9 –9 –0 –0 –1 –1 –2 –3 -2 20 30 35 40 45 50 55 25 60 65 70 75 80 85 90 95 00 05 10 15 20 20 20 20 20 20 20 19 19 19 19 19 19 19 19 20 20 20 20 20 TOTAL FERTILITY (BIRTHS PER WOMAN) REPLACEMENT RATE Source: Some regions are missing out on these employment gains. 1.3 DEMOGRAPHIC TRANSITION Sri Lanka has largely completed its demographic transition These simultaneous and offsetting trends of declining fertility and rising life expectancy brought a “youth bulge” into the population pyramid, creating a demographic window The remainder of this report is organized as follows. Also referred to as a demographic dividend. 15 16 STARTING AND ENDING YEARS Source: Source: UMICs 1995 2030 Figure 1.12 Figure 1.11 LMICs 2020 2065 LICs 2060 2100 FERTILITY RATE, TOTAL (BIRTHS PER WOMAN) 0 1 2 3 4 5 6 7 8 SSA 2065 2100 52 54 CHN 1990 2025 56 KOR 1990 2015 58 60 BGD 2015 2045 62 BTN 2015 2045 64 66 IND 2015 2050 68 MLD 2010 2045 70 72 NPL 2020 2055 74 LIFE EXPECTANCY AT BIRTH, TOTAL (YEARS) PAK 2035 2075 76 78 SRI LANKA LKA 1995 2025 80 KHM 2025 2055 82 84 IDN 2010 2055 LAO 2025 2055 OECD MYS 2010 2045 SOUTH ASIA PHL 2025 2070 THA 1995 2020 VNM 2005 2035 2. IMPACT OF STRUCTURAL AND DEMOGRAPHIC TRANSITION ON LABOR MARKET OUTCOMES 6 2.1 CHANGING LABOR FORCE COMPOSITION AND YOUTH PARTICIPATION Sri Lanka’s economic growth over the last decade—although strong—has not been very employment- intensive. Labor force participation (LFP) in Sri Lanka is quite low compared to other countries in the region. This Labor force participation rates have changed little over the last decade (Figure 2.5), but this apparent inertia belies a major shift in youth’s7 labor supply behavior. Figure 2.1 5 4 3 EMPLOYMENT GROWTH (%) 2 SRI LANKA 1 0 –1 E IN OECD °L 45 –2 SOUTH ASIA –3 –2 –1 0 1 2 3 4 5 6 7 8 GDP GROWTH (%) Source: The analysis in this chapter relies primarily on the Sri Lanka Labor Force Surveys (LFS) from 2006 to 2017. 7 For this report, we define youth to include those aged 15–29 years. 17 Figure 2.2 5 MALDIVES 4 AFGHANISTAN PAKISTAN LABOR FORCE GROWTH (%) 3 BANGLADESH 2 1 INDIA SRI LANKA 0 –1 E IN °L 45 –2 –2 –1 0 1 2 3 4 5 EMPLOYMENT GROWTH (%) Source: Figure 2.3 NEPAL AFGHANISTAN MALDIVES BANGLADESH PAKISTAN INDIA SRI LANKA 74.4 BHUTAN 68.0 70.0 72.0 74.0 76.0 78.0 80.0 82.0 84.0 86.0 Source: Figure 2.4 NEPAL BHUTAN AFGHANISTAN MALDIVES SRI LANKA 36.5 BANGLADESH INDIA PAKISTAN 0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 Source: 18 Figure 2.5 80.0 75.0 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0 30.0 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 ALL MALE FEMALE Source: Figure 2.6 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 STUDY STUDY AND WORK WORK UNEMPLOYED NO STUDY AND INACTIVE Source: 8 Older workers are remaining active longer. Education is a key driver of labor supply decisions about when to enter work or even whether to work. 8 For youth ages 15–24 year olds, the increase was even more marked, from 39 percent in 2006 to 48 percent in 2017. 19 Figure 2.7 A. MALE B. FEMALE 100.0 100.0 90.0 90.0 80.0 80.0 70.0 70.0 60.0 60.0 50.0 50.0 40.0 40.0 30.0 30.0 20.0 20.0 10.0 10.0 0 0 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 ALL PRIMARY INCOMPLETE PRIMARY COMPLETE MORE THAN SECONDARY NO EDUCATION SECONDARY INCOMPLETE SECONDARY COMPLETE Source: 10 Less educated females commonly choose to remain outside the labor force, although the high share of female NEETs is falling. The urban/rural divide in participation rates is widening, as urban youth are participating more while rural youth are participating less. Once youth decide to look for work, they do not always find it. Probabilistic regressions, in this case linear probability models, estimate which factors are statically correlated with entering the labor force or being in a specific work status. See Annex Table AA.1 for details on the specification and results for youth ages 15–29. 10 Education categories are defined using reported years of education as follows: no education if zero years of education; primary incomplete if 1 to 4 years of education; primary complete if 5 years of education; secondary incomplete if 6–12 years of education; secondary complete if 13 years of education and passing A levels or HNCE; more than secondary if 14 or more years of education. 20 Figure 2.8 A. MALE B. FEMALE 100% 100% 90% 90% 80% 80% 70% 70% 60% 60% 50% 50% 40% 40% 30% 30% 20% 20% 10% 10% 0 0 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 AGE AGE STUDY STUDY AND WORK WORK UNEMPLOYED NO STUDY AND INACTIVE Note: Source: Figure 2.9 A. MALE B. FEMALE 20.0 20.0 18.0 18.0 16.0 16.0 14.0 14.0 12.0 12.0 10.0 10.0 8.0 8.0 6.0 6.0 4.0 4.0 2.0 2.0 0 0 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 PRIMARY INCOMPLETE PRIMARY COMPLETE MORE THAN SECONDARY SECONDARY INCOMPLETE SECONDARY COMPLETE NO EDUCATION Source: The persistence of youth unemployment suggests that many young job seekers are unable to find jobs that meet their minimum threshold of “acceptable” work. 21 2.2 YOUTH’S MOTIVATIONS TO WORK Attitudes among Sri Lanka’s youth are shifting, and these changes significantly affect youth’s labor supply behavior and employment outcomes. BOX 2.1: YOUTH PERCEPTIONS OF JOB OPPORTUNITIES AND THE MOTIVATION TO WORK: FINDINGS FROM THE SRI LANKA YOUTH AND GENDER QUALITATIVE STUDY This study, commissioned as a background analysis to this Jobs Diagnostic report, aims to discover the factors that influence youth’s labor supply and demand using qualitative exploratory methodology through small group discussions with male and female youth ages 18–29 in selected urban, semi-urban and rural communities across Sri Lanka (see Figure B2.1.1). Note that this qualitative methodology is not intended to be statistically representative like a traditional survey, but instead allows a more nuanced and expansive treatment of particular issues within the framework of less structured discussions in which multiple voices are heard and various reactions are captured. A total of 27 focus group discussions were carried out, with participants grouped by gender, employment status, migration status, and education level. Each focus group comprised 6–8 participants. These group discussions were complemented by one-on-one interviews with selected youth, parents of female youth, community leaders and private sector employers. Topics of discussion included youth life aspirations, the role of work in these aspirations, most valued job attributes, degree to which parents and their children agree on what constitutes an acceptable or appealing job for male and female youth, how gender norms vary between home and work settings, and degree to which youth’s expectations align with skills that employers seek. Focus group participants were asked to rank job preferences through a blind voting exercise in which a series of job profiles were presented two at a time, with job characteristics varying by formality status, sector and compensation, among others. Participants selected preferred options through confidential balloting, and the votes were tallied. Figure B2.1.1 NORTH-WESTERN KURUNEGALA EASTERN BATTICALOA WESTERN GAMPAHA COLOMBO UVA BADULLA 22 The main conclusions of this qualitative study are: ¬ Although gender norms are changing among the younger generation, these are not yet translating into labor market outcomes. Large gender gaps in work opportunities and job quality persist. Urban females are more ambitious and focused on advancement, but both rural and urban females seek flexible hours, consistent with their greater home/care responsibilities. Females tend to be more risk averse, remain in a job longer, and exhibit more soft skills that employers seek. Female employees earn less than males, and female occupations are inherently accorded lower status. ¬ Parents interviewed for the study exert a strong influence over their children’s job choices, but the set of job options that parents deem acceptable is very narrow and highly colored by social status. As a result, fewer youth work or try jobs that have an uncertain payoff in the near term (many of which actually pay well), ultimately leading to worse job outcomes for youth. ¬ Participants in the study are optimistic about job opportunities, especially in urban areas, but perceive impediments relating to inadequate English proficiency, insufficient education or formal qualifications, and the lack of social capital. Youth are motivated by inspirational role models, parental expectations, a desire to overcome economic hardship, and a wish to support their families, inter alia. Whereas male youth in the study appear to be more self-focused and face individualistic challenges, female youth appear especially motivated to help their families, but they face sanctions or limitations based on their gender. ¬ Interviewed youth are willing to trade higher wages in favor of long-term stability. Youth are more risk averse than they appear, preferring long-term income security over less formal work options that have opportunities for advancement and better earnings. In most blind voting scenarios, the tipping point was provision of Employees’ Provident Fund (EPF) / Employees’ Trust Fund (ETF) coverage. Youth—especially females—assign the highest value to a civil service job compared to a private sector quasi-formal fixed-term job, even one with a substantially higher salary. ¬ Wide gaps exist between youth’s preferred job attributes and those of available entry-level jobs. Even unskilled youth indicate an unwillingness to take up physically strenuous work, and most youth would not work long hours. Youth value jobs requiring less time at work, more leave and holidays, and the freedom to take off as and when they wish. Youth also expect frequent raises and promotions. Few jobs have these attributes, especially in manufacturing and in the growth sectors of tourism and elder care services. ¬ Internal and external migration are widely considered alternatives for accessing better jobs, and these options have already been factored into youth’s employment decisions. Internal migrants prefer their current jobs— mostly in Colombo—compared to the limited alternatives in their home villages. Non-migrants, by contrast, were mostly unwilling to move for work, except for a high- paying formal job. ¬ Participating youth expressed unrealistic career goals, and the demotivating mismatch between career expectations and reality discourages youth from entering the labor force. ¬ Despite the vast supply of underutilized Sri Lankan youth labor, interviewed employers report difficulty recruiting necessary skills, especially soft skills. The cultural generation gap between Gen X middle managers on one side and modern, tech-savvy youth with outsized expectations on the other side creates tensions in the workplace. Managers must rely on the digital expertise and up-to-date knowledge of the youth, but younger employees struggle to communicate clearly and employers say they “lack commitment”. The most important skills employers identified were being target-driven, highly committed, hard-working, timely, flexible in extending work hours, willing to take on responsibility, while possessing language proficiency and ability to “think outside the box”. Employers generally believe that technical skills can be taught on-the-job, but soft skills require early school interventions to shape youth attitudes. Source: “Youth and Gender Qualitative Study on Explaining Mismatch between Labor Supply and Demand in Sri Lanka” by M. Dissanayake, Survey Research Lanka (Pvt) Ltd (2019). Youth value independent and flexible work, and yet entrepreneurship is not widely pursued. 23 Higher educational attainment is pushing job expectations beyond the current realities of the labor market. Youth recognize the merits of hard work and high skill level, but believe people with more social capital get the best jobs. Figure 2.10 BOTH MALE AND FEMALE YOUTH INTERVIEWED BELIEVE THAT THE BEST JOBS IN THEIR AREAS ARE GIVEN TO PEOPLE WITH CONNECTIONS TO LOCAL HIGH-LEVEL OFFICIALS, WHILE TALENTED AND EDUCATED PEOPLE ALSO GET OPPORTUNITIES FOR THEIR ACADEMIC AND PROFESSIONAL ACHIEVEMENTS PEOPLE WITH CONNECTIONS RICH PEOPLE EVEN IF NOT EDUCATED BUT CAN PAY FOR FAVORS EXPERIENCED HARD-WORKING WELL-EDUCATED AND TALENTED BUSINESS-MINDED WITH ENGLISH LANGUAGE PROFICIENCY Source: 24 Interviewed youth were nevertheless optimistic about current employment opportunities, especially in urban and semi-urban areas Internal and external migration act as a pressure valve to relieve excess local labor supply.11 either in favor of internal migration and had already migrated, or were opposed to migrating and would not consider it in the future Internal migration delivers better job outcomes on average, which is driving urbanization. The arrival External migration holds mixed appeal. Figure 2.11 Employed Employed NEET Tuk tuk Employed Migrants-employed < A/L (incomplete) (mixed education) drivers (mixed education) (mixed education) Male Female Male Female Male Female Male Male Female Male Female Urban Colombo AVERAGE > AVERAGE > AVERAGE > AVERAGE > AVERAGE > AVERAGE EXCELLENT > AVERAGE > AVERAGE Semi-urban Gampaha > AVERAGE > AVERAGE > AVERAGE AVERAGE > AVERAGE EXCELLENT < AVERAGE/ AVERAGE/ Rural 1 Kurunegala BAD > AVERAGE > AVERAGE AVERAGE Rural 2 Batticaloa VERY BAD AVERAGE < AVERAGE < AVERAGE Plantation Badulla AVERAGE AVERAGE AVERAGE AVERAGE Source: 11 Note that the LFS does not capture data on internal or external labor mobility. Data on external migration are based on administrative data and estimates from the Sri Lanka Bureau of Foreign Employment. 25 12 13 2.3 WORK STATUS AND JOB QUALITY Informality is a dominant feature of Sri Lanka’s labor market. 14 Women are slightly more likely to be informal compared to men 17 Figure 2.12 UNEMPLOYED FORMAL WAGE 4% UNPAID PUBLIC 8% 13% FARMER FORMAL WAGE 12% PRIVATE 14% EMPLOYER 3% L A SELF-EMPLOYED RM (NON-FARMER) O INFORMAL WAGE INF 18% 28% Note: Source: 12 Female outmigration for housemaid positions was 56,000 in 2017, down from 125,000 in 2005. Sri Lanka Bureau of Foreign Employment (2017). 13 Sri Lanka Bureau of Foreign Employment (2017). 14 We define formal workers to include (a) employees whose employer contributes to a pension fund (the Employees’ Provident Fund (EPF) and the Employees’ Trust Fund (ETF)) or civil servants who are covered by the Public Service Pension Scheme (PSPS); and (b) employers and self-employed whose enterprise is registered with an employee provident fund or inland revenue department. All other workers are considered to be informally employed, including unpaid family workers, employees with no permanent employer, and unregistered self-employed. Most self-employed and farmers are not covered by an employee provident fund, even though independent workers can register and pay for themselves. In 2017, 40 percent of employers were formal. Note that employers may be considered formal but still lack pension coverage for themselves. 17 Includes public administration and public education and health sector jobs, inter alia. 26 Figure 2.13 A. MALE B. FEMALE 100% 100% 90% 90% 80% 80% 70% 70% 60% 60% 50% 50% 40% 40% 30% 30% 20% 20% 10% 10% 0 0 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 UNEMPLOYED UNPAID FARMER EMPLOYER SELF-EMPLOYED (NON-FARMER) INFORMAL WAGE FORMAL WAGE PRIVATE FORMAL WAGE PUBLIC Source: For men, educational attainment increases the likelihood of being formally employed ; even men For women, however, a secondary education does not increase their access to formal work; only tertiary level education increases the likelihood of formal employment for women, while an incomplete Informal jobs are—by definition and character—“lower” quality than formal jobs BOX 2.2: WHAT DETERMINES JOB QUALITY Defining job quality is subjective. Many factors feed into what is meant by a “good” job or a good quality job. It depends on perspective: the worker’s, the family’s, or the government’s. Workers may value high wages and working conditions more than they value productivity. Families may place more weight on sector or social status. Governments may prioritize safe working standards and social insurance coverage to ensure worker welfare and their capacity to weather income shocks. From a development perspective, many of these job attributes are relevant for defining a good-quality job. In this analysis, the best jobs are formal (and thus covered by social insurance and labor protections), have above- average productivity, and pay a wage commensurate with labor productivity. 27 Informal workers earn less than formally employed workers. Net earnings18 Public sector employees earn the most 20 Wages do at least partly reflect worker productivity 21 Earnings vary significantly across sectors, even when controlling for work status and education. 22 Jobs in the Western region pay better than elsewhere. Figure 2.14 700 600 500 400 300 200 100 0 2013 2014 2015 2016 2017 FARMER EMPLOYER SELF-EMPLOYED (NON-FARMER) INFORMAL WAGE FORMAL WAGE PRIVATE FORMAL WAGE PUBLIC Note: Source: 18 All wages reported in the LFS are gross wages; informal gross wages are equal to informal net wages, because there is no labor tax paid. Formal gross wages in the private sector are 8 percent higher than formal private net wages, due to the employee contribution to EPF. Public sector employees do not make a salary-based pension contribution, so that their gross and net wages are the same. Note that wage data are self-reported in the LFS and are not necessarily consistent with administrative data on public sector salaries, for example, or employers’ income reported to tax authorities. 20 Coefficient values are somewhat different with respect to monthly earnings (i.e., not accounting for differences in hours worked). For example, the gender gap jumps from 34 to 54 percent, reflecting that women work fewer hours. Details are in Annex Table AA.5. 21 Note that completed secondary education is defined as completing 13 years of school and passing GCE (A levels) or Higher National Certificate in Education (HNCE). 22 Note that we controlled for selection bias using a Heckman correction, and the results were nearly identical to the OLS coefficient estimates reported here. 28 Figure 2.15 FEMALE –0.345 PRIMARY INCOMPLETE* 0.037 PRIMARY COMPLETE* 0.055 EDUCATION EDUCATION LEVEL MATTERS FOR (RELATIVE TO NO SECONDARY INCOMPLETE 0.177 EARNINGS EDUCATION) SECONDARY COMPLETE 0.453 MORE THAN SECONDARY 0.837 AGE 0.032 AGE AGE SQUARED 0.000 FARMER –0.155 EMPLOYER 0.723 (RELATIVE TO LABOR STATUS SELF-EMPLOYED (NON-FARMER) 0.086 INFORMAL WAGE) FORMAL WAGE PRIVATE 0.283 FORMAL WAGE PUBLIC 0.549 A GOVERNMENT JOB PAYS MORE! CENTRAL –0.346 SOUTHERN –0.138 NORTHERN –0.352 REGION (RELATIVE EASTERN –0.243 TO WESTERN) NORTH-WESTERN –0.194 NORTH-CENTRAL –0.174 UVA –0.447 SABARAGAMUVA –0.204 AGRICULTURE, CATTLE, AND FISHING –0.055 MINING AND UTILITIES –0.173 MANUFACTURING FOOD AND BEVERAGES –0.057 MANUFACTURING TEXTILE –0.080 SECTOR (RELATIVE MANUFACTURING OTHER* 0.001 TO RETAIL) CONSTRUCTION 0.290 TRANSPORT AND COMMUNICATIONS 0.073 FINANCE AND REAL ESTATE 0.099 OTHER SERVICES 0.059 –0.700 –0.500 –0.300 –0.100 0 0.100 0.300 0.500 0.700 0.900 1.100 Note: Source: Average job quality is low, but there are bright spots. 23 23 Public wage employees worked an average 45 hours per week in 2017, compared to 50 hours for private sector wage employees. 29 2.4 TRENDS IN JOB CREATION AND JOB QUALITY Job growth over the last decade was strong, and the economy added more wage than non-wage jobs. Job creation was diversified across sub-sectors. Self-employment increased, partly absorbing the decline in farming and unpaid work. The share of the Several sectors created formal jobs at a robust rate, but the public sector dominated. 24 The sectoral distribution of job creation paints a mixed productivity picture. 24 Note that 81 percent of education sector workers and 70 percent of health sector workers were in the public sector in 2017. Note that this data differs from administrative data on the civil service. Note that other services includes both low and high-skilled activities. Over a third of women employed in other services are domestic workers, who have an average 6.7 years of education and earn less than farmers. 30 Table 2.1 Share of total net Sector Employment 2006 2017 Net job creation job creation (%) Formal 230,483 Informal Formal Informal Formal Manufacturing, food and bev. Informal Formal Manufacturing, textile Informal Formal Informal 33,144 Formal Informal Formal Informal 124,183 Formal 144,134 Informal 334,221 Formal Informal Formal Informal –4,240 Formal Informal 130,402 Formal 100,107 Informal Formal 2,016,995 2,568,963 551,968 Total Informal 4,720,629 5,639,215 918,586 Source: 31 Figure 2.16 0.5 MINING AND PRODUCTIVITY UTILITIES FINANCE AND HIGH AVG. 0.4 REAL ESTATE LOG (SECTOR PRODUCTIVITY / TOTAL PRODUCTIVITY), 2017 TRANSPORT 0.3 AND COMMUNICATIONS OTHER SERVICES 0.2 CONSTRUCTION 0.1 MANUFACTURING 0 LOW AVG. PRODUCTIVITY –0.1 GOVERNMENT / PUBLIC AGRICULTURE, ADMINISTRATION –0.2 CATTLE, AND EDUCATION FISHING WHOLESALE AND HEALTH –0.3 AND RETAIL –0.4 –0.5 ADDING JOBS –0.6 –0.7 –5.5 –4.5 –3.5 –2.5 –1.5 –0.5 0.5 1.5 2.5 CHANGE IN EMPLOYMENT SHARE, 2007–2017 (%) Note: Source: With respect to earnings, job quality improved across the board during the past five years. Average 27 Aside The public sector stands out for its sustained job growth and rising wages. 28 Government employment policies distort the labor market. 27 Note that LFS did not collect data on non-wage income prior to 2013. 28 Graduates in some disciplines easily find a civil service job. In medicine, the Ministry of Health absorbs all medical graduates as staff. EPF is a defined-contribution scheme under which employers and employees both contribute a percentage of gross wages (12 percent by employers, 8 percent by employees) to finance workers’ retirement. It is available at a young age (50 for women, 55 for men) and paid in a lump sum to beneficiaries to manage. Although this benefit is highly desired, its value falls short of providing adequate income to sustain retirees. Converting the average payout to an annuity would generate a modest monthly income ranging between Rs. 2,130 for women and Rs. 2,650 for men, well below the poverty line (World Bank 2019b). ETF is a complementary non-contributory scheme financed by an employer contribution equal to 3 percent of gross wage, paid out in a lump sum at retirement or separation, or before retirement based on eligible need (Premaratna et al., 2019). 32 Figure 2.17 9.0% 8.4% 8.0% 7.0% 6.4% 6.6% 6.0% 5.0% 4.5% 4.0% 4.0% 2.9% 3.0% 2.0% 1.0% 0 FARMER EMPLOYER SELF-EMPLOYED INFORMAL WAGE FORMAL WAGE FORMAL WAGE (NON-FARMER) PRIVATE PUBLIC Source: Labor market distortions—whether due to public employment and pay policies or the disparity in the nature of formal and informal jobs—impede private sector development. And this, in turn, limits Figure 2.18 SAME WAGE, SAME WAGE, MALE PREFERENCES FEMALE PREFERENCES PRIVATE FORMAL PRIVATE FORMAL FIXED TERM FIXED TERM 44% 24% CIVIL SERVICE CIVIL SERVICE 56% 76% 50% HIGHER PRIVATE WAGE, 50% HIGHER PRIVATE WAGE, MALE PREFERENCES FEMALE PREFERENCES PRIVATE FORMAL PRIVATE FORMAL FIXED TERM FIXED TERM 52% 36% CIVIL SERVICE CIVIL SERVICE 48% 64% Source: 33 3. SHORTCOMINGS IN JOB QUALITY FOR YOUTH AND WOMEN 3.1 YOUTH ACCESS TO FORMAL EMPLOYMENT Few male youth find formal work soon after entering the labor market; more educated males fare better. Figure 3.1 30 A. MALES (AGES 15–29) B. FEMALES (AGES 15–29) 100% 100% 90% 90% 80% 80% 70% 70% 60% 60% 50% 50% 40% 40% 30% 30% 20% 20% 10% 10% 0 0 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 AGE AGE FORMAL WAGE PUBLIC INFORMAL WAGE EMPLOYER UNPAID FORMAL WAGE PRIVATE SELF-EMPLOYED (NON-FARMER) FARMER Source: 30 This is not a dynamic analysis but a plot of the 2017 work status by age for youth between 15 and 29 years old. 35 Young females’ access to formal work is much greater than their male counterparts, but is not affected by their educational attainment.31 This may be because a large share of young women across all education levels have an exclusive preference for formal work and are willing to queue. Female youth’s preference for formal and especially public employment comes out very strongly in our qualitative study. Figure 3.2 Female youth Shared Male youth • Recreation and celebrations—annual trips, • A job to serve people • Can earn lots of money / salary New Year celebrations increments / salary advance in an emergency Urban / Semi-urban • Safety and stability—job security / a • Tea, gym, and other facilities to relax permanent job • Job with a career path / job specialization— employee’s mind • Less stress availability of promotions • Provide boarding facilities (migrant) • Bonus pay • Shouldn’t be sweaty / hard work • Cooperation from people • Suitable work environment • Freedom—to choose when to work, when • A disciplined job which maintains a certain not to work • Good management and good employees level or standard of work, e.g. airport • Job position—power • Se ar ime and ni h ime • Pension, pay gratuity • Attractive and socially acceptable / job with a • Paid leave • Entitled to EPF / ETF reputation / job recognition • Can gain good experience • Medical insurance / hospital or medical • ra i e alary ed alary • Enough workload facilities / company support in emergencies • Vehicle / transportation Shared • Good working environment, safety and • Freedom—can take leave, can leave the health protection workplace a little early in the evening (migrants, too, demanded this) • No poli i al in uen e • Safety and stability—job security / a • Freedom—to do the job freely permanen o ed in ome • Promotions / opportunities to go up the ladder • Welfare—food, accommodation facilities • Sponsor for foreign trips to relax • Reliable job Rural / Plantation • Training facilities • Bene for hildren • Job matched with talents and skills • Have set rules and regulations • Bene in en i e for a endan e for performance Note: Source: 31 There is no statistically significant increase in the probability of formal employment for post-secondary educated female youth, although there is a 20 percent higher probability for females with post-secondary education when both youth and non-youth are considered together. 36 Job security is the biggest motivation behind youth’s preferences for formal employment. BOX 3.1: LACK OF SECURITY: LIFE STORIES OF TWO SRI LANKAN YOUTH Sajith is a 22-year-old from Colombo who has held a series of informal jobs in his short life since leaving school. He had a comfortable life in a well-off family until his father met misfortune, sending the family into financial difficulties that spurred Sajith to enter the workforce after O levels. He started working in a garment factory and then his father found a vacancy in a pharmacy. Despite lacking specific knowledge or skills, he learned on the job and was well paid, but the firm faltered and was sold. He then joined another pharmacy for a monthly salary of Rs.45,000/— three times to wages of his friends who had passed A levels. At age 19, he joined another pharmacy and ran the business very successfully alone but by age 21 was let go. With the help of his father, who took out a loan because Sajith could not qualify himself, he tried to start his own pharmacy, but government agents never < A/L (incomplete) considered his registration application. As a result, the pharmacy did not open Employed—Informal but the loan remained outstanding. After that incident, Sajith struggled, working Urban a series of odd jobs (construction, security, Pick Me taxi driver similar to Uber) to earn money to pay back his father’s loan. When he found a job to keep the accounts of a construction company, he was dismissed without cause and without pay for work already completed. Sajith subsequently found another pharmacy job for a monthly salary of Rs.30,000/—and he was content, since his father had settled his loan by selling the family home. Personnel conflicts led him to leave that job as well, following which he bought a three-wheeler and started driving Pick me Taxi. But even this did not meet his earning expectations (and his father had an accident that damaged the three-wheeler!). Sajith currently works in a pharmacy, earning Rs.45,000/—per month, but this is inadequate given his high level of debt obligations. Nimali is a 27-year-old single mother who was forced to leave school after grade 6 to care for her younger siblings and the household chores. Her mother had migrated abroad, and her father’s income was inadequate. She left home for work, but at age 19, became pregnant. Abandoned by her fiancé, she returned to live in her father’s house. She works in a fish exporting company, weighing prawns and cuttlefish and packaging for export, and her daughter is looked after by her father and brother. The unpleasant semi-frozen work environment causes her chest pain and she has to take leave often, but she does not want to leave this job because she can manage her house work and it covers her expenses, although does not allow her to save for her daughter. She earns Rs.35,000–40,000/—per month, taken as daily wages, which she prefers over a monthly salary because it < A/L (incomplete) helps her meet her day-to-day expenses. Employed—Informal Semi-urban Source: Youth and Gender Qualitative Study (Dissanayake 2019). 37 Youth express some optimism about work opportunities but some pessimism about finding stable employment. Figure 3.3 ු ෑගල | குரண KURUNEGALA | කුරණ ு ாகல் Overall perception is that there are average levels of job opportunities in the area. ு BATTICALOA | මඩකලපුව | மட்டக்களப்ப  sometimes people offer bribes to obtain a job. Very poor job opportunities have discouraged the youth in the area, and many Male youth identify that most available job opportunities are informal jobs. have discontinued education despite a high level of interest in higher studies. Both male and female see that there aren’t good and suitable job opportunities for The opinions of male and female youth were not very different. Self-employment is perceived to be the most common solution that the community has chosen for a living. OPPORTUNITY FOR CAREER PROGRESSION IN THE AREA: If one is determined, he/she has opportunities to go up the ladder, but not to the OPPORTUNITY FOR CAREER PROGRESSION IN THE AREA: top as there are not many big companies in the area and most available jobs are informal jobs and lack promotion opportunities. Some opportunities are in public Lack of job opportunities is the key issue and hence can not think about career progression opportunities for people in the ladder. Current country situation doesn’t promise anything solid for youth to this area. Hoping for a change in the future, but it’s unclear have any hope for a change in the future. for youth in the area. NORTH-WESTERN ் ு COLOMBO | කොළඹ | கொழுமப KURUNEGALA EASTERN Youth believe that the job opportunities in BATTICALOA Colombo are the best in the country and most competitive. WESTERN and experience wo GAMPAHA good job in Colombo. COLOMBO UVA BADULLA OPPORTUNITY FOR CAREER PROGRESSION IN COLOMBO: It’s a very competitive market. The most talented and honest will get opportunities to go up the career ladder. Seniors should leave at their retirement age for the next rank of employees to advance. Colombo youth believe that the competitiveness prevalent today in Colombo will extend to other areas of the country in the future. BADULLA | බදුල්ල | பதுளை  GAMPAHA | ගම්පහ | கம்பஹா  Youth in the area have mixed opinions. Some believe that there are opportunities in the area; others are unaware of opportunities, but when they Perception of many job opportunities in the area, both formal and informal jobs due to fast development in the area and the Trade Zone labor demand. However, hinders job supply in the area. People who are not selected for the University there is a trend that youth in the village tend to go to Gampaha town or Colombo need more guidance to pursue their higher education. one of the reasons for this migration of area people to Colombo for work. OPPORTUNITY FOR CAREER PROGRESSION IN THE AREA: a hing uali ed you h la or upply i al o a on ern in Gampaha No career guidance for youth in the area and few see any opportunities for a career path. Area people sometimes don't know of the existence of opportunities, as such information is not shared. If not guided, youth believe the situation will be worse in the future. OPPORTUNITY FOR CAREER PROGRESSION IN THE AREA: Have opportunities to go up the ladder but not to the top; most top positions are in Colombo. The youth in this area expect government attention towards creating career opportunities in the future, but this is missing today. Source: 38 3.2 GENDER GAPS IN JOB QUALITY Women’s earnings are significantly lower than men’s. Several factors contribute to this quality gap: some direct, some indirect, some observable, some implicit. Women work fewer hours than men. 32 Occupational segregation pulls down women’s job quality. Women’s employment has lower social value and is paid less. The unexplained33 wage gap between men and women is very large. 34 We find implicit discrimination reflected in lower sectoral returns in sectors dominated by women such as textiles and food and beverage manufacturing. The public sector wage premium is especially high for women, a key factor behind women’s preference for a government job. Women are falling further behind; the past decade of job creation benefited men much more than women. 32 Data on primary occupation only. 33 The gap is “unexplained” by observable characteristics, and could reflect lower productivity or work effort, but is more likely to reflect gender-based discrimination. 34 The wage regressions control for individual characteristics such as age, education level, work status, sector of work and region. Complete results are reported in Annex Tables AA.4 and AA.5. Civil service jobs also offer a pension benefit, shorter work hours, and high social status. The average educational attainment of men in public sector jobs is 12 years, compared to almost 14 years for women. But in the formal private sector, the pattern is reversed: women have slightly less education on average (10 years) compared to men (almost 11 years). 39 Figure 3.4 A. MALE B. FEMALE AGRICULTURE, WAGE 10.1 AGRICULTURE, WAGE –8.0 CATTLE, AND CATTLE, AND FISHING NON-WAGE –5.1 FISHING NON-WAGE 2.5 MINING WAGE 2.3 MINING WAGE –0.2 AND AND UTILITIES NON-WAGE 0.4 UTILITIES NON-WAGE 0.0 MANUF. WAGE 2.2 MANUF. WAGE 2.8 FOOD AND FOOD AND BEV. NON-WAGE 2.0 BEV. NON-WAGE 4.2 WAGE 1.4 WAGE –0.6 MANUF. MANUF. TEXTILE TEXTILE NON-WAGE 1.0 NON-WAGE 9.8 WAGE 5.8 WAGE –0.2 MANUF. MANUF. OTHER OTHER NON-WAGE 2.4 NON-WAGE –1.1 WAGE 15.0 WAGE 2.0 CONSTR. CONSTR. NON-WAGE 4.0 NON-WAGE 0.4 WAGE 9.3 WAGE 7.3 WHOLESALE WHOLESALE AND RETAIL AND RETAIL NON-WAGE 1.0 NON-WAGE 13.7 WAGE 2.4 WAGE 1.4 TRANSP. AND TRANSP. AND COMM. COMM. NON-WAGE 10.5 NON-WAGE 1.2 WAGE 10.9 WAGE 12.7 FINANCE AND FINANCE AND REAL ESTATE REAL ESTATE NON-WAGE 1.7 NON-WAGE 1.5 GOVT. / WAGE 4.2 GOVT. / WAGE 16.3 PUBLIC PUBLIC ADMIN. NON-WAGE 0.2 ADMIN. NON-WAGE –0.1 WAGE 2.1 WAGE 18.8 EDUC. AND EDUC. AND HEALTH HEALTH NON-WAGE 1.0 NON-WAGE 2.2 WAGE 10.7 WAGE 6.2 OTHER OTHER SERVICES SERVICES NON-WAGE 4.4 NON-WAGE 7.2 –10.0 –5.0 0 5.0 10.0 15.0 20.0 –10.0 –5.0 0 5.0 10.0 15.0 20.0 Note: Source: 3.3 LIMITED PROSPECTS FOR YOUTH SHAPED BY GENDER NORMS Beyond wages, there are other factors youth deem important; some of these are similar for male and female youth, while others differ by gender. Traditional gender roles at home—where wives and daughters perform household tasks and care for children, sometimes in addition to being employed, while husbands and sons only work or study— exacerbate inferior labor market outcomes for women. This is manifested through informal work with 40 Female youth’s personal ambitions are fully intertwined with their family’s ambitions Male youth tend to display self-interested attitudes in their work choices. Those interviewed in our study describe life aspirations that are very specific and individualistic in nature, and center on creating a wealthy life Traditional gender norms are integral to the social fabric and are reinforced in many ways beyond household responsibilities—notably in the workplace and in the community. The high degree of sectoral segregation is the result of numerous factors that include cultural norms and societal pressures that lead women Figure 3.5 A. FEMALE YOUTH B. MALE YOUTH BUILD A NEW HOUSE MALES MARRY SOMEONE WHO BUY A VEHICLE ALLOWS ME TO FOR FAMILY WORK “I want to become an inspiring cricketer, CREATE A coach, and umpire. My parents support me CAREER IN in all possible ways to achieve my dream.” CRICKET (NEET in Badulla) SUPPORT TRAVEL FAMILY FEMALES ABROAD ECONOMICALLY “Teaching of Buddhism has guided me to understand CALM AND what is worth spending time on and what is not in RELAXED life. My family is very religious. I just wish for a FUTURE peaceful life in the future.” GOOD (Employed in Gampaha) TAKE CARE OF EDUCATION FOR CHILDREN THEIR CHILD “I want to create my own business, earn CONTINUE BUY A VEHICLE money, and buy a vehicle on my own.” STUDIES FOR SELF (Self-employed in Kurunegala) Source: 41 Youth’s unrealistic job expectations can create a downward spiral toward worse labor outcomes. For both male and female youth, accessing formal employment takes time, and many workers are willing to wait, whether outside the labor force, unemployed but searching, or engaged in informal work. 37 For males, it is easier to access well-paid informal jobs in construction and transport and communications, for example. Figure 3.6 Source: 37 Most self-employed women are engaged in wholesale and retail trade or in textile manufacturing. 42 BOX 3.2: THE PROMISE OF BEING A SELF-EMPLOYED “TUK TUK” DRIVER A large share of youth opt for the most convenient and appealing work available, including driving a three-wheeler or “tuk tuk”. The qualitative study included a focus group discussion with 8 male tuk tuk drivers in Colombo with an average age of 27. High earnings and the freedom and flexibility inherent to the job are the two main reasons for taking up the work. Drivers reported earning Rs.45,000 to 50,000/- per month, significantly more than their counterparts in the public or private sector, especially those with low qualifications like themselves. It would be extremely rare to earn such high incomes in their home villages or cities. Most of the three wheeler drivers interviewed had completed GCE O levels (O/Ls) but were not interested in further education. Some considered technical colleges, but those in rural settings offer low quality training. They regret their low proficiency in English as it could help them in their interactions with tourists. The group is very happy with the freedom of being a tuk tuk driver, and strongly believe that self-employment is a good job, requiring commitment and responsibility. While on the job, they also engage in other income-generating activities such as selling food or other goods through connections established with regular clients. Some drivers indicated plans to start another business. All agreed on the importance of saving. The easy entry requirements of becoming a driver and the availability of credit to purchase a vehicle (subsequently tightened) led to an influx of male youth to the commercial centers within Colombo. This has given rise to negative reactions by some residents, parents, and even the government, stemming from concerns about congestion, an under-regulated industry that is not capturing tax receipts, the presence of young unsupervised males roaming the streets, and the emergence of crime, drug use, and other illicit activities. Source: Youth and Gender Qualitative Study (Dissanayake 2019) Few youth are interested in or suited to entrepreneurship, and fewer are entering self-employment (with the exception of tuk tuk drivers). Sri Lanka’s youth are an underutilized labor resource. 43 Figure 3.7 55.0 50.0 45.0 40.0 35.0 30.0 25.0 20.0 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 ALL MALE FEMALE Source: 3.4 BREAKING WITH OLD SOCIAL NORMS Youth attitudes are evolving alongside economic modernization and cultural integration. A majority of youth is nevertheless employed in paid work. Many youth are willing to work hard to succeed. Although youth are aware of the prevailing social norms around male and female jobs, they claim not to feel bound by them. Interviewed parents recognized that social norms limit job options for their daughters, but most believed that anyone should be able to do any type of job, and that work should depend on ability. 44 BOX 3.3: YOUNG WOMEN BREAKING WITH SOCIAL NORMS Darshini is a 29-year-old teacher in Badulla, completed an English diploma course after her A/L (science stream) and currently earns Rs. 34,000/- monthly salary. She is married and has a small son. Her husband migrated to Jaffna, and her mother provides childcare while Darshini teaches. She wants to save money to buy a house first, then a car for her husband, then a small “scooty” for herself and then travel around. She is unwilling to migrate, and feels like she needs to be an inspiration for her generation. Vilashini is 23 years old and the youngest in a family of three children in Colombo. She is a hard worker who depends on her job to help her parents. She has held a series of jobs since completing A levels, and took extra accounting courses subsidized by her employer. She subsequently found a better position as an account clerk earning Rs. 20,000/- monthly (more than double her old salary). She works very carefully and with great commitment to protect her job. She wanted to get a government job, but without any connections, she could not find one. She likes to work and is not interested in marrying. Vilashini refused to marry someone who was proposed by her parents because he wanted Vilashini to stop working after the marriage. Source: Youth and Gender Qualitative Study (Dissanayake 2019) Some sectors remain socially off-limits. 3.5 EMPLOYERS’ PERCEPTIONS OF YOUTH Employer attitudes are not changing as fast as those of the youth. One key behavioral trait exhibited by many youth today, as observed in our qualitative study, is limited personal agency or self-motivation and a dependence on regular affirmation by adults. 45 Employers noted two challenges that illustrate conflicting perspectives between employers and their young employees. Employers place a higher value on soft skills. 46 Figure 3.8 Semi-/ Unskilled Workers Skilled Workers • • • Undergraduates and graduates • Technical knowledge of the products • Diploma in HR • • • NVQ 3 • • • • • • • • Computer skills • • • • • • • Committed to work development • • • Hard working • • • • Who can work overnight • Who can work overnight • • With pleasing personalities Source: 47 4. PRIVATE SECTOR DEMAND FOR LABOR 38 4.1 SNAPSHOT OF THE PRIVATE SECTOR Firm size Most firms in Sri Lanka are informal, having fewer than 10 employees. 40 Formal firms—that is, registered firms employing 10 or more employees—number close to 27,600 firms (3 percent of all firms) but account for over half of firm-based employment. And very large firms play a disproportionate role. 41 38 The analysis in this chapter was carried out jointly by the World Bank and the Department of Census and Statistics, Sri Lanka. For the purpose of the Economic Census, an establishment is defined as formal if it maintains documented accounts or has more than 10 employees or is incorporated. All others are considered to be informal establishments. It is sometimes the case that firms with more than 10 employees are not registered; for example, there are some cooperatives and non-profits that are not required to be registered. For this analysis, firms with fewer than 10 employees are defined as informal. 40 For this analysis, there is no distinction between permanent and temporary employment. All reported firm-level employment includes permanent, temporary, unpaid and family employment. 41 Albeit down from close to 60 percent of the total manufacturing workforce in 2007. 49 Figure 4.1 A. SHARE OF FIRMS B. SHARE OF WORKERS C. SHARE OF VALUE ADDED 1.2% 1.9% 15.8% 26.6% 33.8% 7.0% 44.7% 9.9% 21.2% 12.5% 22.3% 96.7% 6.3% INFORMAL VERY LARGE 1−9 10−19 20−99 100−499 500+ Source: Sectoral patterns Sri Lanka’s shift toward a services-based economy is reflected in the sectoral distribution of firms and firm-based employment. The retail42 Figure 4.2 A. TOTAL WORKERS, BY SIZE B. SHARE OF WORKERS, BY SIZE 100 2007 10−19 2015 80 2007 20−99 2015 60 PERCENTAGE FIRM SIZE 2007 100−499 40 2015 20 2007 500+ 2015 0 0 100,000 200,000 300,000 400,000 500,000 2007 2015 WORKERS 10−19 20−99 100−499 500+ Source: 42 Note that the retail sector (also referred to as the commerce sector) includes both wholesale and retail activities. 50 Figure 4.3 A. SHARE OF FIRMS B. SHARE OF WORKERS C. SHARE OF VALUE ADDED 1.2% 3.0% 3.6% 6.6% 7.6% 15.3% 19.3% 9.9% 18.8% 8.6% 5.0% 1.9% 10.7% 4.9% 17.5% 0.9% 11.2% 8.7% 13.2% 0.8% 1.4% 12.2% 4.4% 30.0% 49.5% 1.9% 32.1% MINING, QUARRYING, AND UTILITIES TEXTILES, APPAREL, AND LEATHER OTHER MANUFACTURING WHOLESALE AND RETAIL OTHER SERVICES FOOD AND BEVERAGE MANUFACTURING METALS CONSTRUCTION TRANSPORT., COMM., AND STORAGE Source: Within manufacturing, the textiles, apparel, and leather subsector leads the way, employing 17 percent of all firm-based workers, many in very large firms. Other manufacturing and the food and beverages (F&B) subsectors also account for large shares of total firms and jobs. The petroleum and chemicals sector saw a rapid expansion, albeit from a low base, with remarkable growth in its contribution to economic growth. Spatial distribution The highest geographic concentration of firms is in the Western region, which comprises Colombo, but manufacturing activity is beginning to shift outside the capital region. 51 Figure 4.4 A. SHARE OF FIRMS IN ENTIRE ECONOMY (2013) B. TOTAL FIRMS IN MANUFACTURING SECTOR SABARAGAMUWA 2007 8.0% WEST 2015 UVA 4.8% 2007 WEST CENTRAL 2015 NORTH-CENTRAL 31.9% 6.4% 2007 SOUTH 2015 2007 NORTH 2015 2007 NORTH-WEST EAST 2015 13.3% 2007 NORTH-WEST 2015 2007 NORTH-CENTRAL 2015 EAST 2007 8.1% CENTRAL UVA 10.6% 2015 NORTH 2007 4.9% SABARAGAMUWA SOUTH 2015 12.1% 0 10,000 20,000 30,000 40,000 50,000 60,000 Source: Foreign ownership Foreign firms play a disproportionate role given their small number. have some foreign ownership,43 44 Figure 4.5 A. SHARE OF FIRMS B. SHARE OF WORKERS C. SHARE OF VALUE ADDED 1.0% 18.7% 43.0% 57.0% 99.0% 81.3% DOMESTIC FOREIGN Source: 43 We define firms as foreign owned if they report having foreign ownership; no minimum ownership threshold is determined. 44 This includes the wholesale and retail sector and the other services sector. 52 In addition to services, foreign firms have a significant presence in textile manufacturing, but only a small footprint in the food and beverages sector. 4.2 PATTERNS IN FIRM PERFORMANCE Firm entry and survival Firm creation in Sri Lanka is dynamic, but young, mostly micro-sized firms struggle to compete with old established firms. For formal firms, entry rates are comparatively low. Figure 4.6 A. FORMAL AND INFORMAL B. FORMAL MANUFACTURING ONLY 100 14.7% 80 41.7% 60 PERCENTAGE 26.7% 40 20 16.8% 0 2007 2015 1–5 6–9 10–19 20+ Source: Note that formal firm entry may be capturing existing smaller and/or informal firms that became formal. 53 Figure 4.7 60 50 40 PERCENTAGE 30 20 10 0 CA N RU A KA Y E UE O SH VA VO UA NE NA A A O M A A DA LA E AN RD IR UA NY TA NI NI BI DI TH S NA O Q DE O PE O FA ST VO RI SO G HA AN N M O ZA BO VE IS G KE AG BI SO LE LD RA A AF ET NI ED LA ZA IK KO AN A DI G UG M N IL M O LE BO RA HA VI IN R CA J TA NG AC H ZA TA CA M TE PA SR RK UT ER CA G NI O M CO BA AF BU SO SI M % ENTERING FIRMS % EMPLOYMENT AT ENTRY Source: Most new firms are in services, and 20 percent are in manufacturing. But new entrants to manufacturing Compared to other developing countries, Sri Lanka has a high share of old firms This persistence of micro firms and the dichotomy between young and old firms and a missing middle in Sri Lanka suggest there are impediments to growth. For new firms entering other services, average employment in the year of start-up was 4.4. 54 Figure 4.8 A. SHARE OF FIRMS B. SHARE OF EMPLOYMENT 100 100 80 80 PERCENTAGE PERCENTAGE 60 60 40 40 20 20 0 0 V UA SO E P O M K ERU M DO A L A UT LA VA AF A CA PA E A AN A N M R DI LA G NIA NG SO A NI LA HO B N RK AG IA A Y R A O O L A G B E NI UE S N G O NG TN A LA AM NI ZA ESH RA BIA TA OT A NZ HO KE IA CA G NYA TE VE A VO E AC P RE ED ERU S L A UT LA VA AF A CA ET N RR AM IA NZ AY E HA FA E CA NIS SO B N TA GU E CA AR ESH KO ERD AF ZAM EON DI RD JIK U R RA ON IN UA E Y O NI A BI UG AN BA LE D M TA M RA ND KO STA BA VIE OL LE GU CO BO AN O NI VI ISTA M TA V H NK SIE UG FAS AN OV H NK BU PAR OD AN SIE Z OD CO A NA I AF KIN VOI BU TE GO HA IQ TA BIQ N SO SRI DO SO RI DO BO AG RI RI T CA M A M O D C D CA IKIS AC E H H L A S M J ZA TA G O M M YOUNG (<6) OLD (10+) Note: Source: 47 makes Firm productivity 48 Larger firms tend to be more productive. In most sectors this positive link is strongest among the most But the very largest firms (500+), and those with majority female workforces, have relatively lower productivity, other things being equal 47 This includes food & beverages; textiles, apparel & leather; metals; and other manufacturing sectors, formal and informal. 48 Firm productivity is measured as firm value added divided by the total number of paid and unpaid employees in the firm. Since differences in firms’ characteristics may affect the performance and productivity of individual firms, we use regression analysis to test which variables are statistically significant correlates. Because firms with less than 10 employees (informal firms) report a limited number of variables and exclude key information such as capital, the regression analysis considers only firms with 10 or more paid employees, unless otherwise indicated. It is notable that even for informal firms, which are micro-sized by definition, firm size and productivity are positively correlated. Recall, however, that the number of very large firms in the sample is small. 55 Figure 4.9 500 THRESHOLD EFFECT 400 NUMBER OF EMPLOYEES 300 200 100 0 RI ES N S ES G , TI , RI ER HE , TA E RA . AL ILI NG AT EL O O M RE AL TU AG IC E NG ES NG R IL TU TH TI LE AR ET ST M D ES RV UT YI UC AC ER D , CO AC O M D PP D RR AN OL SE UF BEV TR AN S, A AN UA H AN R T ER NS W ,Q AN D ILE O TH CO UF M AN SP G XT O AN AN IN D TE IN M O TR M FO PRODUCTIVITY LOWEST 2ND 3RD HIGHEST QUARTILES Source: Firms in the Western region have a productivity advantage, as do firms with at least some foreign ownership. foreign owners tend to have additional resources to invest in initial capital endowments or capital upgrading Older firms are not necessarily more productive than young firms, even though they have survived longer. Larger and older firms may be successful for a variety of reasons, including lower unit production costs Competition and market contestability Sri Lanka’s economy appears relatively competitive when comparing national-level market concentration When we dig deeper into sectors and subsectors, however, we find that many are highly concentrated 56 Table 4.1 Share of Top 4 firms’ Market share Top 4 firms’ Number of Manuf./ Serv. Labor share of labor share of of market share Description ISIC firms Manuf./ Serv. subsector Manuf./ Serv. of subsector Manufacturing 14 Food 10 Non-Metallic Mineral 23 31,417 13 17,133 8 16 20 25 22 2,371 Furniture 31 Services Retail Trade 47 56 46 Financial 64 Accommodation 55 Motor Retail 45 42,877 49 Security 80 61 82 Note: Source: Given that a significant share of apparel production is exported, the inflationary impact on domestic apparel prices may be modest. 57 Many sectors are traditionally uncompetitive, such as utilities where quasi-public provision is the norm; but Sri Lanka also has several sectors that appear to be abnormally uncompetitive. and even wholesale trade, Wages More productive sectors pay higher wages, and more productive firms within a given sector pay higher wages. There is a strong positive correlation between firms’ average wage54 and firm productivity. Regression Figure 4.10 5,000 PETROLEUM AND VALUE ADDED PER WORKER (THOUSAND LCU) CHEMICALS 4,000 3,000 MACHINERY 2,000 FOOD AND BEVERAGES TEXTILES, APPAREL, AND NON-MET. LEATHER PLASTICS 1,000 METALS WOOD AND OTHER PAPER 0 150 200 250 300 350 REAL AVERAGE WAGE (THOUSAND LCU) Note: Source: The calculations exclude tuk tuks. Firm productivity is calculated here as a firm’s value added (sales minus labor, production and service charges) divided by the number of paid and unpaid workers in the firm. Firm average wages are calculated here as a firm’s total labor costs divided by the number of paid and unpaid workers in the firm. For the purposes of comparability, all regression analysis is conducted on formal firms with at least 10 paid employees, and the productivity, employment and average wage variables used in the regression analysis are defined using only paid employment. 58 Figure 4.11 A. SHARES OF VALUE ADDED B. CAPITAL-TO-LABOR RATIOS C. CAPITAL (LOG) CAPITAL−LABOR RATIO THOUSAND LCU 100 8 20 CAPITAL (LOG) THOUSAND LCU 80 6 15 PERCENTAGE 60 4 10 40 2 5 20 0 0 07 15 07 15 07 15 07 15 07 15 07 15 20 20 20 20 20 20 20 20 20 20 20 20 0 2007 2015 FOOD, BEVERAGES, TOBACCO FOOD, BEVERAGES, TOBACCO LABOR SHARE OF VALUE ADDED OTHER MANUFACTURING OTHER MANUFACTURING CAPITAL SHARE OF VALUE ADDED TEXTILES, APPAREL, LEATHER TEXTILES, APPAREL, LEATHER Note: Source: Capital plays a central role for both firm productivity and job quality. 4.3 TIME TRENDS IN FIRM PERFORMANCE AND JOB QUALITY Average sector productivity has declined in many manufacturing subsectors, as employment growth outpaced gains in value added. These sector-level trends mask some important within-sector heterogeneity. In petroleum and chemicals, 59 Figure 4.12 MACHINERY 10 VALUE ADDED PER WORKER CHANGE (%) NON-MET. PETROLEUM AND CHEMICALS 5 OTHER 0 ΔL=3 WOOD AND PAPER FOOD AND PLASTICS BEVERAGES ΔL=215 −5 TEXTILES AND APPAREL METALS −10 −5 0 5 10 15 REAL AVERAGE WAGE CHANGE (%) Note: ΔL (blue bubble) ΔL (grey bubble) Source: The declining trend in firm productivity across the majority of manufacturing subsectors is partly due to a structural shift in production and inter-sectoral reallocations of capital and labor. Using Outliers are defined as subsectors whose values lie outside of 3 standard deviations from the yearly variable mean of the aggregate values of firms in the sector. Productivity can be decomposed into factors capturing changes in the relative share of each sector as compared to other sectors (between component), and changes in the overall value of the sector itself (within component). When the size of the sector share changes, we refer to this as a “reallocation of resources” or “structural transformation”. When there is an overall increase in the value of the sector, we refer to this as sectoral upgrading. 60 Figure 4.13 FOOD BEVERAGES TOBACCO TEXTILES APPAREL LEATHER WOOD, PAPER, AND PRINTING PETROLEUM CHEMICALS PLASTICS NON-METALLIC CEMENT AND CLAY BASIC METALS MACHINERY, VEHICLES, AND TRANSPORT −400 −300 −200 −100 0 100 200 VALUE ADDED PER WORKER (LCU) WITHIN BETWEEN Note: Source: Figure 4.14 500 REAL WAGES (THOUSAND LCU) 400 300 200 100 0 CC ES, HE L, TI D AL D CL NT O S, RI ER CS S AL IN AN IC AN AT RE SP LE TU TH O R NG S AY RT NG D ME BA AG TI ET AN HIC LE PA AS AC O PR ER, EM M AN CE M TO R D AP CH LEU PL D EVE TR E P D Y, V C . PA ET AN ES, SI O AN , B M BA AN ER D, TR IL UF D N- XT N O PE O AN NO HI O FO TE W AC M M 2007 2015 Source: The apparel, food, and tobacco subsectors all had sizeable within-sector productivity losses, suggesting At the firm level, real wages rose across most manufacturing subsectors, regardless of whether average firm productivity declined, suggesting that job quality has improved since 2007 61 4.4 IMPLICATIONS FOR WOMEN’S JOB QUALITY AND FUTURE SUSTAINABILITY Where women work Women are highly concentrated in the low productivity sectors of textiles, retail, other services, and food and beverage (F&B) manufacturing The second and third most important sectors for employing women are retail and other services. The four sectors with a large female presence are all characterized by high rates of informality/ micro-size, low productivity and low average wages at the firm level. Figure 4.15 A. SHARE OF FEMALE WORKERS B. TOTAL FEMALE WORKERS MINING, QUARRYING, MINING, QUARRYING, UTILITIES UTILITIES FOOD AND OTHER 0.7% BEVERAGES FOOD AND BEVERAGES SERVICES 11.4% 18.3% TEXTILES, APPAREL, LEATHER TRANSPORT., METALS COMM., STORAGE 2.0% OTHER MANUFACTURING TEXTILES, CONSTRUCTION APPAREL, WHOLESALE LEATHER 32.5% WHOLESALE AND RETAIL AND RETAIL 25.4% TRANSPORT., COMM., STORAGE CONSTRUCTION METALS OTHER SERVICES 0.4% OTHER 0.4% MANUFACTURING 0 100,000 200,000 300,000 400,000 8.9% FEMALE WORKERS Source: Note that data on employment by gender is not disaggregated by paid and unpaid work. As such, we cannot exclude unpaid work from this part of the analysis. 62 Figure 4.16 A. ALL SECTORS (FORMAL AND INFORMAL FIRMS) B. MANUFACTURING (FORMAL FIRMS ONLY) MINING, QUARRYING, UTILITIES MINING, QUARRYING, UTILITIES FOOD AND BEVERAGES FOOD AND BEVERAGES TEXTILES, APPAREL, LEATHER METALS TEXTILES, APPAREL, LEATHER OTHER MANUFACTURING METALS CONSTRUCTION OTHER MANUFACTURING WHOLESALE AND RETAIL TRANSPORT., COMM., STORAGE CONSTRUCTION OTHER SERVICES 0 100,000 200,000 300,000 400,000 0 200,000 400,000 600,000 800,000 1.0E+06 EMPLOYMENT HEADCOUNT (2013) EMPLOYMENT HEADCOUNT (2013) FEMALE NON-PROD. FEMALE PROD FEMALE MALE MALE NON-PROD. MALE PROD Note: Source: The large presence of women in these low productivity, low paying sectors means that women are more dependent on low quality jobs, even when employed in formal firms. In other words, female Prospects for future job quality and sustainability The sizeable gains in female employment in Sri Lanka’s firm sector may represent progress, but still fall short in terms of job quality, both today and in the future. These labor demand trends further relegate women to jobs with limited prospects to improve their job quality. The mixed performance of Sri Lanka’s manufacturing sector—particularly expansion of F&B and textile production despite falling productivity and low wages—does not bode well for dynamic growth or sustaining competitiveness. 63 Figure 4.17 2007 FOOD, BEVERAGES, TOBACCO 2015 2007 TEXTILES, APPAREL, LEATHER 2015 2007 WOOD, PAPER, PRINTING 2015 2007 PETROLEUM AND CHEMICALS 2015 2007 PLASTICS 2015 2007 NON-MET. CEMENT AND CLAY 2015 2007 BASIC METALS 2015 2007 MACHINERY, VEHICLES, AND TRANSP. 2015 2007 OTHER MANUFACTURING 2015 0 200,000 400,000 600,000 EMPLOYMENT HEADCOUNT (2015) FEMALE NON-PROD. MALE NON-PROD. FEMALE PROD. MALE PROD. Source: 64 5. LABOR REGULATIONS 58 Firms in Sri Lanka face a series of complex rules and associated costs for hiring and retaining workers. Sri Lanka lacks a single unified labor law, although the Government is seeking to streamline existing regulations into one law. ∫ ∫ ∫ ∫ ∫ ∫ ∫ ∫ ∫ ∫ The Ministry of Labor, Trade Union Relations and Sabaragamuwa Development is responsible for formulating and implementing national policy on labor, trade union relations, and other subjects under its purview. This chapter draws from the more detailed treatment in Kuddo and Ruppert Bulmer’s 2018 paper “Assessment of Labor Regulations in Sri Lanka.” 65 Several aspects of this legal framework may be particularly onerous, related to restrictions on overtime, women’s work hours, the large number of paid holidays, maternity benefits, dismissal rules and severance pay, labor taxes, and the vast number of wage boards involved in setting sectoral minimum wages. Restrictions on night and Sunday work for women Limits on overtime can restrain labor demand. are not out of line with international norms, and are much less onerous than in Bangladesh and India, which Annual and sick leave provisions are similar to comparator countries, but paid casual leave is an added benefit to workers. For more information, see http://www.ilo.org/dyn/normlex/en/f?p=NORMLEXPUB:11200:0::NO::P11200_COUNTRY_ID:103172 ILO’s Committee of Experts on the Application of Conventions and Recommendations, however, recommends that such limits be reasonable and in line with goals of averting fatigue and ensuring workers have sufficient time to spend outside of paid work. See ILO (2007); ILO (2005). http://www.desaram.com/general-labour-laws-in-sri-lanka/#work-hours-overtime 66 Table 5.1 Annual leave Sick leave Maternity leave There are 12–13 Full Moon Poya Days per year, generally treated as paid holidays for formal workers, and requiring overtime pay for workers who cannot take leave. The cost of maternity leave is fully borne by employers, reducing the incentive to hire younger women. Dismissal rules are relatively onerous. Corporate restructuring is an important element of maintaining EC (2008). https://www.ilo.org/dyn/normlex/en/f?p=NORMLEXPUB:12100:0::NO::P12100_ILO_CODE:C183 https://www.ilo.org/dyn/normlex/en/f?p=NORMLEXPUB:12100:::NO:12100:P12100_ILO_CODE:R191:NO Additional provisions may be considered in case of mass redundancies (Kuddo et al., 2015). 67 Outdated requirement for third-party approval of redundancies. Excessive severance pay and gratuity rules are distorting firms’ hiring decisions. 70 http://www.employers.lk/labour-law-reforms/11-the-shop-and-office-employees-act-no15-of-1954- According to the Termination of Employment of Workmen (Special Provisions) Act 1971, “the following provisions shall apply in case of the exercise of powers conferred on the Commissioner to grant or refuse his approval to an employer to terminate scheduled employment of any workman: (a) such approval may be granted or refused on application made in that behalf by such employer, a copy of which application shall be served on the workman concerned, who shall be afforded an opportunity of being heard; (b) the Commissioner may, in his absolute discretion, decide to grant or refuse such approval; (c) the Commissioner shall grant or refuse such approval within three months from the date of receipt of an application in that behalf made by such employer; (d) the Commissioner shall give notice in writing of his decision on the application to both the employer and the workman; (e) the Commissioner may, in his absolute discretion, decide the terms and conditions subject to which his approval should be granted, including any particular terms and conditions relating to the payment by such employer to the workman of a gratuity or compensation for the termination of such employment; and (f) any decision made by the Commissioner under the preceding provisions of this subsection shall be final and conclusive, and shall not be called in question whether by way of writ or otherwise— (a) in any court, or (b) in any court, tribunal or other institution established under the Industrial Disputes Act.” Heltberg and Vodopivec (2009). http://www.lexmundi.com/images/lexmundi/PracticeGroups/Labor/Surveys/DeskBook/Sri%20Lanka.pdf 70 Note that “salary” includes the basic salary or wages plus cost of living allowances or any other similar allowances. 68 BOX 5.1: LABOR REFORMS THAT HELPED STIMULATE EMPLOYMENT— LESSONS FROM PORTUGAL In the decade leading up to the 2008 global financial crisis, Portugal’s economic growth was among the slowest in the OECD, unemployment was high and long-term in nature rather than frictional, and labor costs were growing at a relatively fast pace. The labor market was highly segmented between formal workers on permanent contracts and those on temporary contracts. This duality is largely ascribed to Portugal’s restrictive employment protections, notably a generous severance pay requirement that is costly to firms, and narrowly-defined criteria for legitimate adjustments to employment through dismissals. The dire impact of the 2008 crisis—reflected in a rapid doubling of the unemployment rate to 17 percent and a crippling deficit—triggered a series of reforms that included labor market reforms to engender more efficient labor (re)allocation and boost employment. Among the reforms adopted beginning in 2011 was a significant reduction in severance pay for new hires to rates that are lower for workers on permanent contracts compared to those on temporary contracts (and the latter were also reduced). Early evidence suggests that the reforms are succeeding in encouraging more efficient labor flows and employment. The effects are observed through increased job search while on-the-job (i.e., job-to-job transitions) and increased hiring on permanent rather than temporary contracts. Small firms seemed to be impacted more than larger firms. The evidence also suggests that because the old severance rates were retained for those on existing contracts (i.e., grandfathered), the number of job separations that may have otherwise taken place following the reform was effectively reduced. Source: OECD (2017). High labor taxes squeeze labor demand and encourage informality. 71 72 73 74 Minimum wage pros and cons. 71 http://ec.europa.eu/economy_finance/db_indicators/tax_benefits_indicators/definitions_en.htm 72 The tax wedge is the sum of personal income tax and employee plus employer social security contributions together with any payroll tax less cash transfers, expressed as a percentage of labor costs. The tax wedge is a so-called synthetic measurement, meaning it is purely based on legislation and therefore measures what individuals are supposed to pay, not what they actually pay, in taxes and social security contributions. 73 Comparable data not available for Bangladesh or Vietnam; see World Bank, 2012. 74 SSA (2017). 69 Sri Lanka’s framework for setting minimum wage levels is complex. set minimum wages and working conditions by sector and occupation in consultation with unions 77 78 The prevalence of informal employment contracts suggests that regulations are not fully enforced. 80 81 Though not the most binding constraint on businesses, dissatisfaction with labor laws in Sri Lanka was higher than in comparator countries. 82 83 See for example Doucouliagos and Stanley (2009), Leonard et al. (2013), and OECD (2006). Kuddo et al. (2015) provide a comprehensive review of minimum wage effects and design issues. Sri Lanka has two main laws relating to payment and fixing of wages: the Wages Board Ordinance of 1941, and the Shop and Office Employees Act of 1954. The Wages Board Ordinance mandates the Minister of Labor to establish a Wages Board for any trade to which provisions of the Ordinance have been applied. The number of representative members of a Wages Board is determined by the Minister, and is divided equally between employers’ representatives and workers’ representatives. 77 See: https://wageindicator.org/main/salary/minimum-wage/sri-lanka/faq-minimum-wages-in-sri-lanka-1 78 There was a Cabinet Decision in April 2019 to increase the national minimum wage to Rs12,500 per month, but this is still awaiting approval by Parliament. ILO (2005). 80 https://www.dol.gov/agencies/ilab/resources/reports/child-labor/sri-lanka 81 The ILO’s suggested ratio of labor inspectors to workers should approach the following: 1/10,000 in industrial market economies; 1/15,000 in rapidly industrializing economies; 1/20,000 in transition economies; and 1/40,000 in least developed countries. 82 www.enterprisesurveys.org 83 The survey interviewed business owners and top managers in 610 firms in Sri Lanka from June to November 2011 (World Bank 2011). 70 Whereas Sri Lanka’s labor regulations were designed to protect workers, they ultimately hinder better labor outcomes for the large number of workers currently excluded from formal jobs. 71 6. CONCLUSIONS AND RECOMMENDATIONS Despite Sri Lanka’s significant progress in improving job outcomes over the last decade, more progress is needed. The economy’s structural transformation toward higher productivity activities and more sophisticated product and service quality has stalled; this, in turn, undermines the prospects for further improving job quality. There are multiple policy entry points for addressing these various challenges Policies to improve employment outcomes in Sri Lanka can be considered through five main channels: POLICY CHANNEL 1: REDUCE BARRIERS TO FIRM GROWTH AND PRODUCTIVITY GAINS Short-term actions ∫ ∫ 73 Strategic medium-term priorities ∫ ∫ ∫ ∫ POLICY CHANNEL 2: REVISE DISTORTIONARY LABOR, GOVERNMENT EMPLOYMENT AND COMPETITION POLICIES Short-term actions ∫ ∫ ∫ ∫ ∫ ∫ 74 ∫ ∫ Strategic medium-term priorities ∫ ∫ ∫ ∫ ∫ POLICY CHANNEL 3: ENHANCE YOUTH AND WOMEN’S CAPACITY TO ACHIEVE BETTER LABOR OUTCOMES Short-term actions ∫ ∫ ∫ ∫ ∫ 75 ∫ ∫ Partner with private IT companies and trainers to develop ICT training modules targeted to women to Strategic medium-term priorities ∫ ∫ ∫ support accredited childcare and eldercare, and training and entrepreneurship support to childcare and POLICY CHANNEL 4: INCREASE THE PRODUCTIVITY OF THE SELF-EMPLOYED Short-term actions ∫ ∫ ∫ ∫ POLICY CHANNEL 5: IMPROVE MATCHING OF JOB SEEKERS WITH EMPLOYERS Short-term actions ∫ 76 ∫ Strategic medium-term priorities ∫ Some knowledge gaps remain. 77 REFERENCES Exports to Jobs: Boosting the Gains from Trade in South Asia Vietnam’s Future Jobs : Leveraging Mega-Trends for Greater Prosperity Balancing Regulations to Promote Jobs: From Employment Contracts to Unemployment Benefits Policy Priorities for International Trade and Jobs Labour Market Reforms in Portugal 2011–15: A Preliminary Assessment Getting to Work: Unlocking Women’s Potential in Sri Lanka’s Labor Force 2019 Revision of World Population Prospects, United Nations, 78 Enterprise Surveys: Sri Lanka Country Profile 2011 Doing Business 2020: Comparing Business Regulation in 190 Economies 79 ANNEX A Table AA.1 Male Female "Unemployed "Unemployed "Unemployed "Unemployed (0) "Informal (0) (0) (0) "Informal (0) (0) "Inactive (0) to Employed to Formal to inactive "Inactive (0) to Employed to Formal to inactive to Active (1)" (1)" (1)" (1)" to Active (1)" (1)" (1)" (1)" Age 0.095*** 0.057*** 0.031 –0.002 0.061*** 0.025** 0.068* –0.029*** (0.010) (0.011) (0.023) (0.018) (0.012) (0.012) (0.041) (0.008) –0.001*** –0.000* –0.000 –0.000 –0.001*** –0.000 –0.001 0.001*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001) (0.000) Attending any education –0.583*** –0.498*** –0.094** 0.276*** –0.312*** –0.246*** –0.091* 0.121*** level (0.014) (0.014) (0.038) (0.016) (0.013) (0.012) (0.048) (0.011) Highest education level (relative to primary incomplete) No education 0.495*** 0.465*** 0.006 –0.175*** 0.254*** 0.215** –0.140 –0.067** (0.070) (0.070) (0.121) (0.062) (0.089) (0.089) (0.203) (0.030) 0.500*** 0.451*** –0.109 –0.210 0.084 0.047 –0.266 –0.047 (0.078) (0.077) (0.117) (0.139) (0.105) (0.102) (0.230) (0.044) Secondary 0.537*** 0.464*** 0.005 –0.358*** 0.152** 0.100 –0.221 –0.084*** (0.059) (0.059) (0.111) (0.021) (0.063) (0.064) (0.157) (0.010) 0.492*** 0.380*** 0.217* –0.367*** 0.309*** 0.182*** –0.094 –0.208*** (0.060) (0.060) (0.112) (0.025) (0.064) (0.065) (0.157) (0.015) 0.551*** 0.355*** 0.432*** –0.542*** 0.582*** 0.335*** 0.027 –0.543*** (0.063) (0.067) (0.117) (0.061) (0.067) (0.069) (0.160) (0.042) –0.002 –0.001 –0.007 0.004 –0.012*** –0.008*** 0.004 0.007*** (0.003) (0.003) (0.004) (0.004) (0.003) (0.003) (0.007) (0.002) 0.004* 0.007*** –0.000 0.001 –0.025*** –0.021*** –0.011 0.009*** (0.002) (0.003) (0.004) (0.003) (0.003) (0.003) (0.007) (0.002) 0.003 –0.000 –0.000 –0.006* 0.016*** 0.013*** 0.021*** –0.005** (0.003) (0.003) (0.005) (0.003) (0.003) (0.003) (0.007) (0.002) –0.010*** –0.014*** 0.004 0.003 0.010** 0.008* –0.006 –0.005* (0.003) (0.004) (0.006) (0.005) (0.004) (0.004) (0.009) (0.003) Urban 0.001 0.000 –0.004 –0.002 0.022* 0.013 –0.013 –0.009 (0.010) (0.011) (0.020) (0.012) (0.012) (0.012) (0.031) (0.008) Region (relative to Western) Central 0.010 –0.019 0.035 –0.036** –0.018 –0.057*** 0.006 –0.033*** (0.012) (0.014) (0.026) (0.017) (0.016) (0.015) (0.037) (0.011) –0.003 –0.043*** –0.073*** –0.043*** –0.020 –0.065*** –0.074** –0.045*** (0.012) (0.014) (0.024) (0.016) (0.015) (0.015) (0.035) (0.011) –0.011 –0.032** –0.141*** –0.010 –0.062*** –0.146*** –0.196*** –0.071*** (0.013) (0.015) (0.022) (0.019) (0.018) (0.017) (0.042) (0.016) 80 Male Female "Unemployed "Unemployed "Unemployed "Unemployed (0) "Informal (0) (0) (0) "Informal (0) (0) "Inactive (0) to Employed to Formal to inactive "Inactive (0) to Employed to Formal to inactive to Active (1)" (1)" (1)" (1)" to Active (1)" (1)" (1)" (1)" –0.028** –0.037** –0.140*** 0.015 –0.132*** –0.156*** –0.208*** –0.004 (0.014) (0.015) (0.022) (0.016) (0.016) (0.015) (0.046) (0.010) 0.027** 0.016 –0.146*** –0.026 –0.037** –0.056*** –0.194*** –0.016 (0.012) (0.014) (0.023) (0.017) (0.017) (0.017) (0.036) (0.011) 0.025 0.020 –0.108*** –0.005 –0.014 –0.060*** –0.118*** –0.043** (0.019) (0.021) (0.026) (0.022) (0.024) (0.022) (0.044) (0.018) Uva –0.018 –0.018 –0.152*** 0.014 –0.054*** –0.085*** –0.065 –0.023* (0.017) (0.018) (0.028) (0.020) (0.020) (0.020) (0.047) (0.014) Sabaragamuva –0.023* –0.032** –0.019 0.007 –0.024 –0.060*** –0.010 –0.028** (0.013) (0.015) (0.026) (0.017) (0.017) (0.017) (0.037) (0.012) Secondary sector (relative to Retail) Agriculture, cattle, –0.081*** –0.035 (0.020) (0.036) 0.078 0.293** (0.054) (0.114) Manuf. food and 0.186*** 0.079 bev. (0.044) (0.067) Manuf. textile 0.437*** 0.418*** (0.035) (0.034) 0.099*** 0.177*** (0.028) (0.057) –0.096*** 0.067 (0.021) (0.086) –0.014 0.150** (0.025) (0.066) Finance and real 0.249*** 0.299*** (0.035) (0.046) 0.570*** 0.506*** (0.032) (0.040) Education and 0.159*** 0.173*** (0.054) (0.039) 0.038 –0.049 (0.031) (0.050) –1.105*** –0.767*** –0.199 1.210*** –0.459*** –0.113 –0.513 1.229*** (0.131) (0.133) (0.285) (0.175) (0.147) (0.141) (0.505) (0.088) 0.644 0.560 0.307 0.271 0.255 0.191 0.235 0.151 8,082 8,082 4,081 4,001 8,903 8,903 2,100 6,803 Notes: Source: 81 Table AA.2 Male Female "Unemployed "Unemployed "Unemployed "Unemployed (0) "Informal (0) (0) (0) "Informal (0) (0) "Inactive (0) to Employed to Formal to inactive "Inactive (0) to Employed to Formal to inactive to Active (1)" (1)" (1)" (1)" to Active (1)" (1)" (1)" (1)" Age 0.041*** 0.047*** 0.013*** 0.011*** 0.021*** 0.026*** 0.004** 0.007*** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.002) (0.000) –0.001*** –0.001*** –0.000*** –0.000*** –0.000*** –0.000*** –0.000*** –0.000*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Attending any education –0.648*** –0.575*** –0.033 0.327*** –0.298*** –0.232*** –0.141*** 0.114*** level (0.009) (0.009) (0.032) (0.016) (0.010) (0.009) (0.036) (0.007) Highest education level (relative to primary incomplete) No education 0.154*** 0.145*** –0.015 –0.069*** 0.025* 0.021 –0.078*** –0.006* (0.021) (0.021) (0.017) (0.009) (0.014) (0.014) (0.025) (0.003) 0.152*** 0.145*** –0.003 –0.062*** 0.001 –0.005 –0.049* –0.007* (0.022) (0.022) (0.018) (0.009) (0.016) (0.016) (0.028) (0.004) Secondary 0.153*** 0.143*** 0.050*** –0.085*** –0.036*** –0.037*** –0.101*** 0.002 (0.020) (0.020) (0.017) (0.009) (0.013) (0.013) (0.023) (0.003) 0.159*** 0.133*** 0.270*** –0.147*** 0.105*** 0.070*** 0.038 –0.064*** (0.020) (0.020) (0.019) (0.014) (0.015) (0.014) (0.026) (0.006) 0.201*** 0.167*** 0.406*** –0.224*** 0.395*** 0.335*** 0.195*** –0.245*** (0.022) (0.022) (0.023) (0.027) (0.017) (0.018) (0.028) (0.023) –0.001 –0.000 0.003 0.004** –0.017*** –0.016*** 0.008*** 0.002*** (0.001) (0.001) (0.002) (0.002) (0.002) (0.002) (0.003) (0.001) 0.001 0.005*** 0.000 0.004** –0.017*** –0.013*** –0.011*** 0.006*** (0.001) (0.001) (0.002) (0.002) (0.002) (0.002) (0.003) (0.001) 0.007*** 0.004*** –0.004** –0.006*** 0.013*** 0.011*** 0.007*** –0.004*** (0.001) (0.001) (0.002) (0.002) (0.002) (0.002) (0.002) (0.001) –0.012*** –0.014*** 0.001 –0.001 0.002 –0.000 0.006* –0.003*** (0.002) (0.002) (0.002) (0.003) (0.002) (0.002) (0.004) (0.001) Urban 0.008 0.013** –0.013 0.013* 0.042*** 0.038*** 0.010 –0.007** (0.006) (0.006) (0.009) (0.007) (0.007) (0.007) (0.012) (0.003) Region (relative to Western) Central 0.017** 0.009 0.007 –0.018* 0.059*** 0.045*** 0.075*** –0.019*** (0.007) (0.007) (0.010) (0.009) (0.009) (0.009) (0.014) (0.004) 0.024*** 0.012* –0.076*** –0.033*** 0.041*** 0.028*** –0.050*** –0.018*** (0.007) (0.007) (0.009) (0.009) (0.009) (0.009) (0.013) (0.004) –0.010 –0.017* –0.109*** –0.008 –0.071*** –0.100*** –0.095*** –0.030*** (0.008) (0.009) (0.010) (0.011) (0.010) (0.009) (0.016) (0.005) –0.005 –0.010 –0.081*** –0.005 –0.125*** –0.139*** –0.140*** –0.011*** (0.008) (0.008) (0.010) (0.011) (0.009) (0.009) (0.017) (0.004) 0.037*** 0.035*** –0.100*** –0.013 0.050*** 0.036*** –0.075*** –0.021*** (0.007) (0.007) (0.010) (0.010) (0.009) (0.009) (0.014) (0.005) 82 Male Female "Unemployed "Unemployed "Unemployed "Unemployed (0) "Informal (0) (0) (0) "Informal (0) (0) "Inactive (0) to Employed to Formal to inactive "Inactive (0) to Employed to Formal to inactive to Active (1)" (1)" (1)" (1)" to Active (1)" (1)" (1)" (1)" 0.024*** 0.022** –0.091*** –0.005 0.059*** 0.038*** –0.107*** –0.032*** (0.009) (0.010) (0.011) (0.013) (0.013) (0.012) (0.015) (0.007) Uva 0.024*** 0.024** –0.064*** 0.008 0.092*** 0.077*** –0.023 –0.022*** (0.009) (0.009) (0.012) (0.012) (0.012) (0.012) (0.016) (0.006) Sabaragamuva 0.019*** 0.016** –0.008 –0.007 0.057*** 0.046*** 0.006 –0.012*** (0.007) (0.008) (0.011) (0.010) (0.010) (0.010) (0.014) (0.004) Secondary sector (relative to Retail) Agriculture, cattle, –0.084*** –0.003 (0.009) (0.012) 0.067*** 0.295*** (0.025) (0.067) Manuf. food and 0.136*** 0.055*** (0.020) (0.019) Manuf. textile 0.334*** 0.248*** (0.020) (0.016) 0.039*** 0.114*** (0.013) (0.021) –0.127*** 0.159*** (0.010) (0.050) –0.010 0.196*** (0.012) (0.038) Finance and real 0.273*** 0.365*** (0.017) (0.024) 0.616*** 0.630*** (0.011) (0.016) Education and 0.356*** 0.447*** (0.020) (0.018) –0.011 –0.064*** (0.014) (0.015) –0.014 –0.185*** –0.014 0.567*** 0.100*** –0.049* 0.258*** 0.783*** (0.029) (0.029) (0.033) (0.031) (0.026) (0.026) (0.049) (0.015) 0.474 0.449 0.353 0.206 0.165 0.150 0.371 0.094 29,149 29,149 21,073 8,076 34,231 34,231 11,515 22,716 Notes: Source: 83 Table AA.3 "Inactive (0) "Unemployed (0) "Informal (0) "Unemployed (0) to Active (1)" to Employed (1)" to Formal (1)" to inactive (1)" Age 0.030*** 0.035*** 0.010*** 0.009*** (0.001) (0.001) (0.001) (0.000) –0.000*** –0.000*** –0.000*** –0.000*** (0.000) (0.000) (0.000) (0.000) Male 0.380*** 0.383*** 0.040*** –0.050*** (0.003) (0.003) (0.005) (0.003) Attending any education level –0.463*** –0.394*** –0.081*** 0.179*** (0.007) (0.007) (0.024) (0.007) Highest education level (relative to no education) 0.049*** 0.043*** –0.060*** –0.010*** (0.012) (0.012) (0.015) (0.003) 0.033** 0.027** –0.045*** –0.008** (0.013) (0.013) (0.016) (0.004) 0.012 0.008 –0.025* –0.003 (0.011) (0.011) (0.015) (0.003) 0.095*** 0.065*** 0.169*** –0.066*** (0.012) (0.012) (0.016) (0.006) 0.271*** 0.224*** 0.314*** –0.213*** (0.013) (0.014) (0.018) (0.018) –0.009*** –0.008*** 0.005*** 0.003*** (0.001) (0.001) (0.001) (0.001) –0.008*** –0.005*** –0.003** 0.008*** (0.001) (0.001) (0.001) (0.001) 0.011*** 0.008*** –0.000 –0.006*** (0.001) (0.001) (0.001) (0.001) –0.007*** –0.008*** 0.002 –0.004*** (0.002) (0.002) (0.002) (0.001) Urban 0.027*** 0.027*** –0.006 –0.002 (0.005) (0.005) (0.007) (0.003) Region (relative to Western) Central 0.039*** 0.028*** 0.032*** –0.018*** (0.006) (0.006) (0.008) (0.004) 0.034*** 0.021*** –0.073*** –0.021*** (0.006) (0.006) (0.008) (0.004) –0.045*** –0.063*** –0.114*** –0.023*** (0.007) (0.007) (0.009) (0.005) –0.075*** –0.086*** –0.105*** –0.006 (0.006) (0.006) (0.008) (0.004) 0.043*** 0.035*** –0.096*** –0.017*** (0.006) (0.006) (0.008) (0.004) 84 "Inactive (0) "Unemployed (0) "Informal (0) "Unemployed (0) to Active (1)" to Employed (1)" to Formal (1)" to inactive (1)" 0.039*** 0.027*** –0.103*** –0.024*** (0.008) (0.008) (0.009) (0.006) Uva 0.060*** 0.052*** –0.053*** –0.012** (0.008) (0.008) (0.010) (0.006) Sabaragamuva 0.039*** 0.032*** –0.006 –0.010** (0.006) (0.006) (0.009) (0.004) Secondary sector (relative to Retail) –0.051*** (0.007) 0.096*** (0.023) Manuf. food and bev. 0.090*** (0.014) Manuf. textile 0.262*** (0.012) 0.061*** (0.011) –0.110*** (0.009) 0.015 (0.011) 0.302*** (0.014) 0.616*** (0.009) 0.388*** (0.013) –0.034*** (0.010) –0.084*** –0.245*** 0.081*** 0.713*** (0.019) (0.019) (0.028) (0.014) 0.360 0.351 0.352 0.120 63,380 63,380 32,588 30,792 Notes: Source: 85 Table AA.4 2013 2014 2015 2016 2017 Female –0.311*** –0.317*** –0.323*** –0.355*** –0.345*** (0.017) (0.012) (0.013) (0.013) (0.013) Education level (relative to no education) 0.043 0.035 0.066* 0.069* 0.037 (0.047) (0.032) (0.039) (0.036) (0.043) 0.087* 0.064 0.029 0.089** 0.055 (0.052) (0.041) (0.044) (0.039) (0.044) 0.186*** 0.187*** 0.134*** 0.198*** 0.177*** (0.044) (0.030) (0.038) (0.033) (0.039) 0.494*** 0.454*** 0.420*** 0.445*** 0.453*** (0.047) (0.033) (0.040) (0.036) (0.041) 0.734*** 0.815*** 0.769*** 0.806*** 0.837*** (0.059) (0.035) (0.048) (0.038) (0.043) Age 0.037*** 0.029*** 0.031*** 0.036*** 0.032*** (0.004) (0.002) (0.002) (0.002) (0.002) –0.000*** –0.000*** –0.000*** –0.000*** –0.000*** (0.000) (0.000) (0.000) (0.000) (0.000) Labor status (relative to informal wage) Farmer –0.171*** –0.124*** –0.109*** –0.129*** –0.155*** (0.031) (0.023) (0.024) (0.026) (0.026) 0.640*** 0.668*** 0.710*** 0.851*** 0.723*** (0.043) (0.032) (0.023) (0.030) (0.032) 0.054** 0.076*** 0.069*** 0.165*** 0.086*** (0.022) (0.015) (0.015) (0.014) (0.016) 0.303*** 0.325*** 0.287*** 0.311*** 0.283*** (0.022) (0.014) (0.016) (0.014) (0.014) 0.432*** 0.455*** 0.548*** 0.573*** 0.549*** (0.022) (0.016) (0.018) (0.016) (0.016) Region (relative to Western) Central –0.305*** –0.314*** –0.314*** –0.353*** –0.346*** (0.030) (0.020) (0.019) (0.018) (0.018) –0.144*** –0.066*** –0.062*** –0.034** –0.138*** (0.026) (0.014) (0.015) (0.014) (0.017) –0.017 –0.187*** –0.233*** –0.249*** –0.352*** (0.021) (0.019) (0.019) (0.018) (0.026) –0.219*** –0.181*** –0.196*** –0.179*** –0.243*** (0.032) (0.019) (0.019) (0.017) (0.020) –0.132*** –0.182*** –0.175*** –0.273*** –0.194*** (0.021) (0.018) (0.018) (0.021) (0.018) –0.287*** –0.220*** –0.173*** –0.156*** –0.174*** (0.042) (0.028) (0.024) (0.022) (0.027) 86 2013 2014 2015 2016 2017 Uva –0.158*** –0.216*** –0.201*** –0.215*** –0.447*** (0.026) (0.020) (0.021) (0.023) (0.034) Sabaragamuva –0.201*** –0.086*** –0.144*** –0.213*** –0.204*** (0.029) (0.016) (0.015) (0.017) (0.017) Secondary sector (relative to Retail) 0.014 –0.014 –0.090*** –0.081*** –0.055*** (0.030) (0.019) (0.020) (0.020) (0.021) 0.093* –0.256*** –0.238*** –0.182*** –0.173*** (0.048) (0.051) (0.048) (0.060) (0.054) Manuf. food and bev. 0.070** –0.005 –0.040 –0.012 –0.057* (0.035) (0.033) (0.029) (0.025) (0.033) Manuf. textile –0.092** –0.116*** –0.116*** –0.118*** –0.080*** (0.037) (0.022) (0.022) (0.023) (0.023) 0.008 0.045** 0.029 –0.010 0.001 (0.037) (0.021) (0.020) (0.020) (0.025) 0.329*** 0.288*** 0.260*** 0.271*** 0.290*** (0.033) (0.019) (0.020) (0.017) (0.022) 0.148*** 0.104*** 0.086*** 0.085*** 0.073*** (0.032) (0.019) (0.019) (0.017) (0.020) 0.184*** 0.110*** 0.127*** 0.088*** 0.099*** (0.043) (0.026) (0.025) (0.028) (0.025) 0.128*** 0.061*** 0.069*** 0.065*** 0.059*** (0.027) (0.017) (0.018) (0.017) (0.019) –1.137*** –0.846*** –0.749*** –0.835*** –0.732*** (0.098) (0.059) (0.064) (0.061) (0.067) 0.138 0.240 0.252 0.262 0.241 25,956 26,612 27,240 28,823 27,115 Notes: Source: 87 Table AA.5 2013 2014 2015 2016 2017 Female Education level (relative to no education) Age Labor status (relative to informal wage) Farmer Region (relative to Western) Central 88 2013 2014 2015 2016 2017 Uva Sabaragamuva Secondary sector (relative to Retail) Manuf. food and bev. Manuf. textile 27,037 27,778 Notes: Source: 89 Table AA.6 Share of total net Sector Employment 2006 2017 Net job creation job creation (%) Wage 430,230 Wage 14,240 Manufacturing food and Wage Manufacturing textile Wage 144,201 Wage 134,321 Wage 142,472 70,131 108,010 37,880 Wage Wage 280,203 287,407 Wage 104,207 Wage 40,270 Wage 20,041 Wage 122,321 102,007 73,347 Total Wage 728,078 Source: Table AA.7 Share of total net Sector Employment 2006 2017 Net job creation job creation (%) Wage 12,811 Wage –1,048 –4 Manufacturing food and Wage Manufacturing textile Wage Wage –1,003 90 Share of total net Sector Employment 2006 2017 Net job creation job creation (%) Wage 10,307 832 2,807 Wage 37,831 Wage 12,833 Wage Wage 104,248 1,388 –271 Wage 24,078 Wage 80,083 Total Wage 303,431 Source: Table AA.8 Share of total net Sector Employment 2006 2017 Net job creation job creation (%) Formal Informal Formal 31,032 Informal Manufacturing food and Formal Informal Manufacturing textile Formal Informal Formal 121,330 30,034 Informal 47,733 Formal 23,000 34,323 Informal Formal 204,277 Informal Formal 123,472 143,474 20,003 Informal Formal 84,811 Informal Formal Informal 91 Share of total net Sector Employment 2006 2017 Net job creation job creation (%) Formal 101,300 123,782 22,482 Informal Formal 48,020 32,471 Informal 111,833 Total Formal Informal Source: Table AA.9 Share of total net Sector Employment 2006 2017 Net job creation job creation (%) Formal –40,271 Informal Formal 3,021 Informal 10,132 –4,073 Manufacturing food and Formal 32,187 Informal Manufacturing textile Formal Informal Formal 43,844 7,782 Informal 122,783 Formal Informal Formal Informal Formal 22,111 Informal 13,248 Formal Informal Formal Informal 2,402 Formal 272,217 Informal 33,010 Formal 7,401 Informal 120,231 182,414 Total Formal Informal Source: 92 Table AA.10 Male Female Education level (relative to no education) Primary 0.031 0.044 0.113** 0.026 –0.030 0.078 0.012 0.025 0.112** 0.158** (0.068) (0.043) (0.049) (0.046) (0.052) (0.066) (0.050) (0.065) (0.057) (0.072) Primary 0.090 0.081 0.096* 0.076 0.003 0.079 0.019 –0.066 0.071 0.145** (0.073) (0.052) (0.053) (0.048) (0.054) (0.076) (0.065) (0.082) (0.075) (0.073) Secondary 0.186*** 0.196*** 0.196*** 0.158*** 0.126*** 0.188*** 0.194*** 0.073 0.258*** 0.268*** (0.063) (0.040) (0.047) (0.043) (0.047) (0.063) (0.047) (0.061) (0.052) (0.064) Secondary 0.460*** 0.460*** 0.455*** 0.371*** 0.385*** 0.480*** 0.419*** 0.351*** 0.496*** 0.516*** (0.068) (0.044) (0.050) (0.048) (0.050) (0.072) (0.052) (0.066) (0.056) (0.067) 0.701*** 0.811*** 0.799*** 0.753*** 0.798*** 0.657*** 0.712*** 0.632*** 0.766*** 0.795*** (0.089) (0.047) (0.064) (0.049) (0.054) (0.082) (0.054) (0.076) (0.059) (0.072) Age 0.042*** 0.034*** 0.038*** 0.044*** 0.037*** 0.024*** 0.017*** 0.018*** 0.022*** 0.022*** (0.005) (0.003) (0.003) (0.003) (0.003) (0.007) (0.005) (0.006) (0.005) (0.005) –0.000*** –0.000*** –0.000*** –0.000*** –0.000*** –0.000*** –0.000*** –0.000*** –0.000*** –0.000*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Labor status (relative to informal wage) Farmer –0.223*** –0.137*** –0.124*** –0.126*** –0.173*** –0.031 –0.094* –0.055 –0.128** –0.064 (0.035) (0.024) (0.026) (0.028) (0.030) (0.064) (0.054) (0.052) (0.058) (0.052) 0.583*** 0.615*** 0.645*** 0.783*** 0.658*** 0.907*** 0.820*** 0.877*** 1.077*** 0.901*** (0.047) (0.033) (0.024) (0.032) (0.034) (0.083) (0.102) (0.071) (0.092) (0.098) 0.080*** 0.097*** 0.075*** 0.166*** 0.100*** 0.011 0.038 0.062** 0.193*** 0.086** (0.025) (0.016) (0.016) (0.015) (0.018) (0.047) (0.030) (0.031) (0.030) (0.035) 0.234*** 0.244*** 0.181*** 0.209*** 0.175*** 0.431*** 0.476*** 0.472*** 0.500*** 0.474*** (0.024) (0.017) (0.018) (0.018) (0.017) (0.044) (0.024) (0.030) (0.025) (0.027) 0.307*** 0.303*** 0.397*** 0.406*** 0.400*** 0.661*** 0.709*** 0.803*** 0.850*** 0.821*** (0.026) (0.019) (0.019) (0.019) (0.019) (0.039) (0.027) (0.038) (0.027) (0.030) Region (relative to Western) Central –0.234*** –0.330*** –0.320*** –0.358*** –0.353*** –0.469*** –0.305*** –0.325*** –0.363*** –0.342*** (0.029) (0.024) (0.020) (0.022) (0.022) (0.069) (0.037) (0.040) (0.030) (0.031) –0.183*** –0.057*** –0.067*** –0.037** –0.136*** –0.081** –0.093*** –0.078*** –0.043 –0.154*** (0.033) (0.015) (0.018) (0.016) (0.019) (0.039) (0.028) (0.030) (0.028) (0.033) 0.019 –0.147*** –0.199*** –0.215*** –0.337*** –0.179*** –0.304*** –0.369*** –0.371*** –0.402*** (0.024) (0.020) (0.021) (0.019) (0.029) (0.047) (0.047) (0.042) (0.044) (0.051) –0.219*** –0.178*** –0.170*** –0.167*** –0.225*** –0.211*** –0.190*** –0.283*** –0.215*** –0.263*** (0.038) (0.021) (0.021) (0.019) (0.022) (0.057) (0.046) (0.043) (0.034) (0.039) –0.113*** –0.189*** –0.154*** –0.236*** –0.182*** –0.182*** –0.161*** –0.239*** –0.354*** –0.228*** (0.025) (0.021) (0.021) (0.023) (0.021) (0.038) (0.031) (0.034) (0.042) (0.031) 93 Male Female –0.275*** –0.208*** –0.187*** –0.144*** –0.181*** –0.304*** –0.253*** –0.129*** –0.190*** –0.139*** (0.051) (0.031) (0.028) (0.025) (0.032) (0.075) (0.058) (0.048) (0.040) (0.049) Uva –0.160*** –0.232*** –0.215*** –0.206*** –0.484*** –0.179*** –0.186*** –0.186*** –0.271*** –0.373*** (0.030) (0.023) (0.024) (0.026) (0.042) (0.054) (0.038) (0.040) (0.048) (0.058) Sabaragamuva –0.230*** –0.112*** –0.169*** –0.254*** –0.219*** –0.151*** –0.020 –0.093*** –0.134*** –0.162*** (0.019) (0.018) (0.021) (0.020) (0.049) (0.028) (0.026) (0.028) (0.031) Secondary sector (relative to Retail) Agriculture, 0.010 –0.042* –0.116*** –0.134*** –0.122*** 0.054 0.071* –0.043 0.040 0.091** cattle, and (0.034) (0.021) (0.022) (0.022) (0.025) (0.064) (0.040) (0.042) (0.041) (0.044) Mining and 0.063 –0.319*** –0.282*** –0.256*** –0.241*** 0.316** 0.256*** 0.096 0.328*** 0.214** (0.052) (0.055) (0.051) (0.066) (0.060) (0.129) (0.090) (0.126) (0.092) (0.090) Manuf. food 0.074** –0.032 –0.002 –0.023 –0.045 0.126* 0.115** –0.010 0.104** 0.052 (0.038) (0.047) (0.031) (0.028) (0.036) (0.066) (0.049) (0.057) (0.048) (0.062) Manuf. textile –0.005 –0.064** –0.075** –0.099** 0.040 –0.055 –0.043 –0.052 –0.028 0.006 (0.046) (0.028) (0.031) (0.039) (0.030) (0.057) (0.037) (0.036) (0.035) (0.041) 0.101*** 0.085*** 0.088*** 0.050** 0.043 –0.325*** –0.113** –0.220*** –0.262*** –0.167*** (0.039) (0.022) (0.021) (0.021) (0.027) (0.092) (0.050) (0.050) (0.047) (0.051) 0.290*** 0.235*** 0.202*** 0.195*** 0.216*** 0.378** 0.515*** 0.538*** 0.585*** 0.456*** (0.035) (0.020) (0.020) (0.017) (0.022) (0.161) (0.087) (0.090) (0.073) (0.128) 0.100*** 0.057*** 0.038* 0.020 –0.000 0.409*** 0.330*** 0.310*** 0.379*** 0.349*** comm. (0.035) (0.020) (0.021) (0.018) (0.021) (0.067) (0.064) (0.068) (0.065) (0.061) Finance and 0.218*** 0.052 0.112*** 0.067** 0.041 0.194** 0.305*** 0.222*** 0.208*** 0.306*** (0.048) (0.032) (0.028) (0.032) (0.029) (0.088) (0.046) (0.054) (0.057) (0.048) 0.068** 0.021 0.023 –0.003 –0.045** 0.275*** 0.200*** 0.198*** 0.243*** 0.287*** (0.032) (0.020) (0.020) (0.020) (0.021) (0.051) (0.036) (0.038) (0.035) (0.042) –1.204*** –0.912*** –0.893*** –0.872*** –0.698*** –1.341*** –1.119*** –0.925*** –1.199*** –1.219*** (0.128) (0.072) (0.073) (0.075) (0.077) (0.151) (0.103) (0.133) (0.105) (0.133) 0.109 0.196 0.206 0.219 0.203 0.203 0.321 0.331 0.326 0.309 18,259 18,775 19,034 19,847 18,446 7,697 7,837 8,206 8,976 8,669 Notes: Source: 94 ANNEX B DESCRIPTION OF FIRM-LEVEL DATA 84 2013 Economic Census on Industry, Construction, Trade and Services Economic Census Annual Survey of Industries ASI Annual Survey of Services ASS Annual Survey of Trade Industries AST 84 Annual Survey of Industries 2016 Final Report, Department of Census and Statistics (April 2018). 95 ADDITIONAL FIGURES AND TABLES ON FIRM-LEVEL DATA Table AB.1 Mean p50 Standard dev. 2 1 1 8 Labor Productivity Value Added Note: Table AB.2 Sector Number of workers 414,241 Total Table AB.3 Employment (log) 2013 (1) (2) (3) (4) (5) (6) Variables Total Total Total Female Manufacturing Services 0.0418 0.0646 0.0465 0.0840 0.0233 0.0564 (0.0880) (0.0889) (0.0881) (0.0907) (0.122) (0.129) 0.194** 0.205*** 0.196** 0.0772 0.217** 0.187 (0.0782) (0.0777) (0.0783) (0.0772) (0.105) (0.116) 0.541*** 0.590*** 0.546*** 0.131* 0.615*** 0.556*** (0.0764) (0.0771) (0.0765) (0.0773) (0.106) (0.114) 1.014*** (0.0572) 2.461*** (0.0721) 96 Employment (log) 2013 (1) (2) (3) (4) (5) (6) Variables Total Total Total Female Manufacturing Services 4.330*** (0.140) 0.646*** 0.350*** 1.044*** 0.117 (0.107) (0.119) (0.132) (0.170) Majority Female 0.321*** (0.0695) Foreign 0.647*** 0.644*** 0.657*** 0.199*** 0.844*** 0.567*** (0.0711) (0.0712) (0.0717) (0.0643) (0.126) (0.0851) 0.149* –0.212 0.129 –0.287*** 0.265*** –0.0745 (0.0800) (0.217) (0.0801) (0.0735) (0.100) (0.132) 0.294*** –0.242 0.275*** –0.480*** 0.562*** –0.0543 (0.0829) (0.205) (0.0838) (0.0773) (0.113) (0.127) 0.467*** –0.0781 0.451*** –0.666*** 0.744*** 0.158 (0.0890) (0.210) (0.0898) (0.0828) (0.119) (0.139) Central –0.383*** –0.337*** –0.374*** 0.121 –0.273** –0.529*** (0.0886) (0.0897) (0.0894) (0.0901) (0.136) (0.108) N_Central –0.549*** –0.503*** –0.544*** 0.148 –0.230 –0.684*** (0.124) (0.124) (0.124) (0.135) (0.246) (0.135) –0.386*** –0.425*** –0.395*** 0.133 –0.210* –0.724*** (0.0919) (0.0911) (0.0926) (0.0881) (0.111) (0.138) –0.910*** –0.861*** –0.922*** –0.0614 –0.943*** –0.938*** (0.0983) (0.0973) (0.0968) (0.118) (0.156) (0.124) –0.683*** –0.634*** –0.666*** 0.259*** –0.543*** –0.742*** (0.0913) (0.0927) (0.0908) (0.0885) (0.124) (0.134) –0.144* 0.615** –0.161** –0.244*** 0.345*** (0.0823) (0.270) (0.0819) (0.0720) (0.114) –0.453*** 0.565 –0.483*** –0.427*** (0.101) (0.344) (0.102) (0.0990) –0.656*** 0.264 –0.684*** –0.231*** –0.134 (0.0718) (0.318) (0.0717) (0.0731) (0.109) 4.155*** 3.833*** 4.299*** 1.689*** 3.738*** 4.124*** (0.0941) (0.220) (0.0876) (0.0853) (0.121) (0.160) No Yes No No No No 1,848 1,848 1,848 1,638 931 917 0.194 0.230 0.188 0.596 0.220 0.198 Note: Source: 97 Table AB.4 Labor productivity (log) 2013 (1) (2) (3) (4) (5) Variables Total Total Total Manufacturing Services Foreign Majority Female Central N_Central No No No No 1,803 1,803 1,803 Note: Source: 98 Table AB.5 Average wage (log) 2013 (1) (2) (3) (4) Variables Total Total Manufacturing lnrwage Foreign Majority Female Central N_Central 99 Average wage (log) 2013 (1) (2) (3) (4) Variables Total Total Manufacturing lnrwage 1,787 1,787 910 877 0.440 0.443 0.362 0.466 Note: Source: Figure AB.1 A. SHARE OF WORKERS, BY SECTOR B. SHARE OF VALUE ADDED, BY SECTOR 100% 100% 90% 90% 80% 80% 70% 70% 60% 60% 50% 50% 40% 40% 30% 30% 20% 20% 10% 10% 0 0 2007 2015 2007 2015 FOOD, BEVERAGES, TOBACCO TEXTILES, APPAREL, LEATHER WOOD, PAPER, PRINTING PETROLEUM AND CHEMICALS PLASTICS NON-MET. CEMENT AND CLAY BASIC METALS MACHINERY, VEHICLES, TRANSP. OTHER MANUFACTURING Source: 100 Figure AB.2 A. TOTAL WORKERS B. SHARE OF WORKERS 100 2007 WEST 2015 2007 CENTRAL 80 2015 2007 SOUTH 2015 60 PERCENTAGE 2007 NORTH 2015 2007 EAST 2015 40 2007 NORTH-WEST 2015 2007 20 NORTH-CENTRAL 2015 2007 UVA 2015 0 2007 2015 2007 SABARAGAMUWA 2015 WEST CENTRAL SOUTH 0 200,000 400,000 600,000 800,000 NORTH EAST NORTH-WEST WORKERS NORTH-CENTRAL UVA SABARAGAMUWA Source: Figure AB.3 A. SHARE OF TOTAL ENTERING FIRMS B. SHARE OF EMPLOYMENT CREATED .8 .8 .6 .6 .4 .4 .2 .2 0 0 MINING AND UTILITIES MANUFACTURING CONSTRUCTION SERVICES Source: 101 Figure AB.4 ANGOLA ZAMBIA TANZANIA LESOTHO CAMBODIA VIETNAM SIERRA LEONE MOZAMBIQUE PARAGUAY AFGHANISTAN CABO VERDE NICARAGUA COTE D’IVOIRE UGANDA SOUTH AFRICA BANGLADESH GHANA BURKINA FASO MACEDONIA KOSOVO TAJIKISTAN MOLDOVA PERU SRI LANKA KENYA 0 20 40 60 80 100 SHARE Source: 102 ANNEX C Table AC.1: Bangladesh India Morocco Sri Lanka Vietnam (Dhaka) (Delhi) 8 8 8 8 44 72 overtime No No No No No limit No limit 12 No limit 72 N/A 183 Ratio of minimum wage to value added per worker N/A 0 0 0 0 30 0 0 0 0 0 100 100 No No Restrictions on night work No No No No No No No Restrictions on overtime work No No No No 17 18 14 12 17 14 13 17 21 14 14 3 3 N/A 1 No consult No No No No No No No No No No No No 0 0 103 Bangladesh India Morocco Sri Lanka Vietnam (Dhaka) (Delhi) 0 No No No No No 112 182 84 180 100 100 100 100 100 No No No No No No Source: Table AC.2 After 1 year of continuous After 5 years of continuous After 10 years of continuous employment employment employment High income Upper middle income Ecuador Lower middle income Zambia Sri Lanka Low income Sierra Leone Source: 104 Address: 17 76 G S t NW, Washington, DC 20006 Website: ht tp://w w w.worldbank .org/en/topic/jobsanddevelopment Twit ter : @W BG_ Jobs Blog: ht tp://blogs.worldbank .org/jobs