Title page
Contents
Acknowledgments 8
Abbreviations 9
Executive Summary 10
1. Growth and Jobs Context 12
Growth, poverty, and inequality 12
Demographic change and rate of urbanization 13
The sectoral and spatial jobs transformation 15
Factor accumulation and labor productivity growth 18
2. Labor Supply 23
Labor force participation 24
School-to-work transition 27
Unemployment and underemployment 29
Employment 32
Gender differences 37
Evolution of employment over time 38
The possibility of services-led structural transformation 40
3. Labor Demand 44
Characteristics of firms 44
Job creation 51
Spotlight: Early Impact of COVID-19 on Kenya's Labor Market 53
4. Labor Market Constraints and Jobs Programs 58
Demand-side constraints 58
Supply-side constraints 63
Social constraints 65
Review of jobs programs implemented by the government 66
5. Recommendations 70
Addressing long-term structural constraints 71
Short-term productive engagement 73
Improve data to support evidence-based policy making 77
Appendix: Data Tables 78
Factors influencing female labor force participation 78
Education levels 78
References 81
Table 2.1. Younger cohorts have more education compared to older workers (percentage of population) 25
Table 3.1. Sources of firm-level data in Kenya 45
Table 3.2. Breakdown of MSME data by firm size and formality 48
Table 3.3. Distribution of firms across sectors 49
Figure 1.1. Kenya has experienced robust economic growth in the last decade 12
Figure 1.2. The largest-ever cohorts are about to reach working age 13
Figure 1.3. Historical job creation has been of low quality 13
Figure 1.4. NEDI counties have much higher fertility rates compared to the rest of the country 14
Figure 1.5. A majority of Kenya's population lives in rural areas 14
Figure 1.6. Services contribute the most to value added... 15
Figure 1.7. ...but agriculture value added is still well above the LMIC average 15
Figure 1.8. Employment moved from agriculture to services... 16
Figure 1.9. ...but Kenya continues to rely more on agricultural employment compared to peers 16
Figure 1.10. Economic transformation occurred mainly in the central and eastern regions 17
Figure 1.11. In some counties, a majority of those employed work in subsistence agriculture 18
Figure 1.12. Increases in labor force participation led to growth over 2010-14 19
Figure 1.13. Increases in labor productivity led to growth over 2015-19 19
Figure 1.14. Between 2010 and 2014, productivity decrease within services offset increases due to structural change 20
Figure 1.15. Between 2015 and 2019, productivity increased due to increase in services sector productivity and structural change 20
Figure 1.16. Some sectors experienced both productivity and employment growth 21
Figure 1.17. Contribution of productivity to output per capita was higher, and demographic change lower, in comparator countries, 2015-19 21
Figure 2.1. A large majority of those not in school or education are women, 2019 24
Figure 2.2. Some groups have labor force participation rates that are well above average; others are more often inactive 25
Figure 2.3. There are large variations in the labor force participation across Kenyan counties 26
Figure 2.4. Labor force participation declined between 2016 and 2019... 26
Figure 2.5. ...with the fall being sharper for those already vulnerable 26
Figure 2.6. School-to-work transition in Kenya continues well into the 20s, and very few youths enter formal employment 28
Figure 2.7. School-to-work transition is more difficult for young women compared to young men 28
Figure 2.8. Rural youth enter employment early, but are predominantly in lower-tier employment 29
Figure 2.9. Unemployment declined between 2006 and 2019 30
Figure 2.10. Unemployment in Kenya is lower than the LMIC average 30
Figure 2.11. Unemployment declined among all groups between 2006 and 2019, except those with tertiary education 30
Figure 2.12. The number of unemployed and discouraged workers has increased in recent years 31
Figure 2.13. The proportion of people citing lack of suitable jobs as the reason for not seeking work increased between 2016 and 2019 31
Figure 2.14. Underemployment is high among females, those in rural areas, and those with lower levels of education 31
Figure 2.15. Underemployment is high in the agricultural sector 32
Figure 2.16. Employment rates are high for prime-age adults and the better educated 32
Figure 2.17. A majority of women and individuals living in rural areas work in agriculture 32
Figure 2.18. Individuals with low educational attainment are often in lower-tier informal employment, as are those in rural areas and women 33
Figure 2.19. A majority of females, those living in rural areas, and individuals with low educational attainment are employed in agriculture 34
Figure 2.20. The trade subsector employs the second- largest share following agriculture 34
Figure 2.21. Education, health, and social security subsector has a high share of formal wage employees 35
Figure 2.22. The majority of jobs in ICT and finance and real estate are high-skilled occupations 36
Figure 2.23. Jobs are concentrated in low-productivity sectors and subsectors 36
Figure 2.24. There are large earnings premiums for those with higher levels of education 37
Figure 2.25. Earnings are higher for males compared to females at all education levels and for almost all employment types 38
Figure 2.26. Employment moved away from agriculture to services among all groups, except for those in urban areas 38
Figure 2.27. Overall quality of employment improved between 2016 and 2019, but less so among youth and those in rural areas 39
Figure 2.28. The share of jobs requiring lower levels of skills has declined in the industry and services sectors since 2016 40
Figure 2.29. Services subsectors have different skill requirements and potential for international trade 41
Figure 2.30. Employment grew in the high-skilled services subsectors between 2016 and 2019 41
Figure 2.31. Kenya's share of employment in high-skill services subsectors is comparable to peers 42
Figure 2.32. The share of global innovator subsector exports has grown strongly in Kenya 42
Figure 3.1. Firm creation is high in Kenya for its income level 45
Figure 3.2. An overwhelming number of MSMEs are informal micro firms 48
Figure 3.3. Most MSMEs are in services subsectors 48
Figure 3.4. Informal micro firms are more often in agriculture and the accommodation and food services subsector 49
Figure 3.5. Formal micro firms are in sectors such as ICT, finance, and professional services 49
Figure 3.6. Formal micro firms are larger than informal 49
Figure 3.7. Formal firms have greater access to electricity and own computers more often 50
Figure 3.8. More formal firms have websites compared to informal; mobile phone ownership and access to credit are more mixed 50
Figure 3.9. Sales are higher for formal firms... 50
Figure 3.10. ...as are sales per worker 50
Figure 3.11. Share of employment is higher in older firms 51
Figure 3.12. The number of formal firms grew in subsectors such as retail, tourism, and other services... 51
Figure 3.13. ...as did the number of jobs 51
Figure 4.1. Lack of suitable jobs in the area has particularly discouraged men and those in rural areas 59
Figure 4.2. MSMEs in Kenya have low access to key infrastructure 59
Figure 4.3. Few informal firms use the Internet for business 60
Figure 4.4. Few informal firms use e-banking... 60
Figure 4.5. ...social media... 60
Figure 4.6. ...or the Internet 61
Figure 4.7. Access to finance is an important challenge for firms in the informal sector 61
Figure 4.8. Own funds are the main source of start-up capital 62
Figure 4.9. There is limited use of banks and microfinance institutions 62
Figure 4.10. The average start-up capital is just over K Sh 50,000 62
Figure 4.11. Kenya has a high average lending interest rate 63
Figure 4.12. Firms face regulatory burdens 63
Figure 4.13. Financial skills are the most sought-after by informal sector firms 64
Figure 4.14. Labor cost and unavailability of relevant skills are the most cited reasons for not having the right workers 64
Figure 4.15. The share of graduates in science and engineering could improve 64
Figure 4.16. The majority of women outside the labor force cite family responsibility as the reason for not seeking work; reasons for other groups are more varied 65
Figure 4.17. Government jobs programs are not comprehensive 66
Boxes
Box 2.1. Employment type 27
Box 3.1. Firm-level data in Kenya 46
Box 3.2. Analysis of Kenya's formal firms 47
Box 4.1. Informal Sectors Skills and Occupations Survey 58
Box 4.2. The Financial Inclusion Fund 67
Maps
Map 1.1. Shares of employment by sector and county 18
Box Tables
Table B3.1.1. Data set coverage 46
Box Figures
Figure B3.2.1. Small firms with four to six employees dominate the formal sector... 47
Figure B3.2.2. ...but Kenya has a larger share of formal firms compared to peers 47
Figure B3.2.3. Most formal firms are in services subsectors 47
Spotlight Figures
Figure S.1. Kenya experienced repeated waves of COVID-19 54
Figure S.2. Employment/population ratio, 2019-21 54
Figure S.3. Unemployment rate, 2019-21 54
Figure S.4. Underemployment rate, 2019-21 55
Figure S.5. Inactivity, 2019-21 55
Figure S.6. The probability of a firm reopening increased in early 2021... 56
Figure S.7. ...however, sales remain below pre-pandemic levels across all firm sizes... 56
Figure S.8. ...and across sectors 56
Figure S.9. In early 2021, firms started to become more optimistic about future sales 56
Table A.1. Probit regression estimating labor allocation decisions 78
Table A.2. Education levels across population groups and cohorts (percentage of population group) 78
Table A.3. Mincer regression results: all 79
Table A.4. Mincer regression results by sex 80