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Change and Rigidity in Youth Employment Patterns in Malawi

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CGIAR Research Program on Policies, Institutions, and Markets Workshop on Rural Transformation in the 21st Century (Vancouver, BC – 28 July 2018, 30th International Conference of Agricultural Economists). Presentation by Bob Baulch, Todd Benson, Alvina Erman, and Yanjanani Lifeyo.

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Change and Rigidity in Youth Employment Patterns in Malawi

  1. 1. 1 Change and Rigidity in Youth Employment Patterns in Malawi PIM Workshop on Rural Transformation Vancouver 28 July 2018 Bob Baulch, Todd Benson, Alvina Erman*, and Yanjanani Lifeyo IFPRI and *World Bank
  2. 2. 2 Agriculture in Malawi’s economy  Agriculture contributed 26 percent of Malawi’s GDP in 2017.  Down from 50 percent of the economy 50 years ago. Growing production of services.  Malawi is among the 15 most agriculture- dependent countries in the world  Small manufacturing sector; few non-agricultural natural resources to exploit  88 percent of those of working age (15 to 64 years) and employed work in agriculture (2016 IHS)
  3. 3. 3 Population growth & education in Malawi  Malawi’s population projected to be 43.1 million by 2050, up from 19.1 million in 2018  Malawi has one of the youngest age structures in the world: 45% of population <15 years old  Result is increasing pressure to use all available land for agriculture  Primary education has been free since 1994  Program has been subject to continual criticism for poor quality of education provided  But years of education completed for the 15 to 24 year old age-cohort increased from 5.0 in 1998 to 7.3 in 2016
  4. 4. 4 Motivation for this study  How have changes in, and the interplay of these factors, affected the employment choices of Malawians, particularly for youth?  Do we see some movement of labor out of agriculture into other sectors?  Are youth central to any changes occurring in employment patterns in Malawi?  Are Malawi’s youth entering the work force in a different manner than did previous generations?
  5. 5. 5 Analytical approach  Use Malawi Integrated Household Survey data series - IHS-2 (2004), IHS-3 (2010), & IHS-4 (2016)  Focus is on working-age population (aged 15 to 64 years)  Further distinguish younger youth (15 to 24 years), older youth (25 to 34), and non-youth (35 to 64)  Three principal analyses  Cross-sectional analysis of employment of working-age population in 2016  Temporal analysis of changes in employment patterns between 2004, 2010, and 2016  Multivariate analysis of determinants of employment and type of employment in 2016 IHS-2 IHS-3 IHS-4 Sample size, households 11,280 12,271 12,0447 Working age (15 - 64 years of age) sample size, ind. 25,144 27,842 27,475 Survey administration period March 2004 to March 2005 March 2010 to March 2011 April 2016 to April 2017
  6. 6. Structure of employment in 2016  Dominance of agriculture for those employed  88 percent of those employed work in agriculture  Over 60 percent of older youth and non-youth work in agriculture  45% of younger youth are students (so, not economically active) while 33% work in agriculture 6
  7. 7. Structural change in employment? 2004 2010 2016 Annual growth, 2004-16, % Working age population, ‘000s 5,975 6,871 8.264 2.7 Employed, % share of working age population 76.7 72.8 60.7 0.8 Agriculture, % share of employed 85.3 87.1 87.8 0.7 Industry, % share of employed 5.8 3.2 2.3 -6.8 Services, % share of employed 8.9 9.7 9.9 1.3 Not economically active, % share of working age pop. 8.6 10.1 19.2 9.8 Students, % share of not economically active 13.9 15.7 17.7 4.8 7  Services - growth in share of employed  Industry – absolute decline in workers employed  Agriculture – share of workers stable to slightly down (lower growth than that of working age population)  No strong evidence that process of structural change in employment now gaining momentum
  8. 8. 8 Structural change in employment? – disaggregated (1)  Agriculture  94 percent of all employed women worked on-farm between 2004 to 2016; 80 percent of men. Stable pattern  No sign of FISP induced changes in agricultural employment  Services  Non-youth especially account for growth in employment in services  Suggests that capital accumulation and work experience, rather than educational attainment, may be more important driving factors in movement of labor out of agriculture into services  Industry  Significant drop in employment, despite national accounts data over period showing performance of sector to be generally positive
  9. 9. 9 Structural change in employment? – disaggregated (2)  Students – Largest jump seen in share of working age individuals who are students  Particularly among younger youth (ages 15 to 24 years): Share who are students rose from 35 percent in 2004 to 45 percent in 2016  Reduced share of younger youth who are employed over this period. But if employed, work on-farm  Puzzle that 2.7 percent growth rate of working age population is lower than 3.0 percent population growth rate  Some suggestion in data that emigration of male older male youth from Malawi part of explanation. But only limited data on this.
  10. 10. Determinants of employment 10  Examine factors associated with working and sector of employment at individual level:  Logit followed by Multinomial logit regression  Use different employment categories than ILO scheme used earlier  Categories allow for individ- uals to be employed in more than one sector (inter- section in diagram)  Also distinguish informal (household enterprises) from formal (wage labor) employ- ment (not shown in diagram) n=25,384 individuals Employed in agriculture only Industry or services only Agriculture and industry or services Not economically active
  11. 11. Logit on Labor Force Participation  Males significantly more likely to be employed or looking for work than females  Younger youth (<24 years) less likely but older youth (30-34 years) are more likely to be working than non-youth (35-64 years)  Higher levels of education associated with higher probabilities of employment  Other northern ethnic groups and residents of Lower Shire Valley also more likely to be economically active 11
  12. 12. Multinomial logit (MNL) regression 12  Five category dependent variable:  Explanatory variables used in MNL include: 1. Employed in agricultural sector only; 2. Employed both in agricultural sector and in household enterprise(s) in the industry or services sectors; 3. Employed both in agricultural sector and in wage employment in the industry or services sectors; 4. Only employed in household enterprise(s) in industry or services sectors; 5. Only employed for wages in the industry or services sector; o Demographic characteristics, including youth age ranges; o Ethnicity; o Educational attainment, o Household wealth; o Agriculture-related factors; o Physical access to markets; and o Recent experiences of economic shocks.
  13. 13. 13 Multivariate analysis on employment (1)  Youth:  Up to 24 years, either in agriculture or are not economically active  Those aged 25 to 29 years are in a transitional period in terms of the nature of their employment  Oldest youth aged 30 to 34 years more likely to be employed in both agriculture and the non-farm sectors  However, youth are not in the vanguard of those Malawians taking up employment, whether informal or formal, in the services and industrial sectors and abandoning agriculture.  Sex: Males dominate employment outside of agriculture  Dependents: dependents within a household, less likely to be economically active (primarily students) or works outside agriculture
  14. 14. 14 MNL results on employment (2)  Education: Greater educational attainment results in much higher probabilities of working outside of agriculture and in formal, wage- based employment  Household wealth: Strong association between the level of household wealth and engagement in non-farm employment.  Land: Larger agricultural landholdings associated with a lower propensity to be in non-farm wage employment  Market access: strong inverse association between distance to largest urban centers and whether individual engaged in non-farm employment.  Shocks: Individuals in communities that experience idiosyncratic shocks more likely to engage in some non-farm employment
  15. 15. 15 Summary of analyses on youth and employment in Malawi  Little evidence of change in how youth enter the work force:  Pattern of employment of older youth similar to the non-youth  Younger youth extending period remain in school, but generally enter the work force through agriculture  Structural transformation?  Share of those of working age in agriculture grew from 2004 to 2016. Increase in share of older youth and non-youth in services, but decline in industry.  Only faint indications of structural transformation processes  The structure of employment in Malawi remains dominated by agriculture, as it has been for generations
  16. 16. 16 Policy implications  Maintain level of investments in education – Good returns, both socially and individually  But the now better trained Malawians not finding good jobs  Such jobs needed to pull people out of farming and to grow and diversify the economy.  Public investment needed to supply such job opportunities  Provide incentives to private sector for the supply of such jobs  Foreign direct investment likely a principal channel for providing the associated technology and creating demand for such jobs  To attract such investment requires good transport infrastructure, reliable energy supplies, and significant urban development  Agriculture probably will remain at core of economy  So need to continue to invest to increase agricultural productivity  Growth in industry and services likely to be most readily achieved by strengthening linkages of those sectors to a vibrant agricultural sector
  17. 17. Additional Slides (Not for presentation) 17
  18. 18. Employment categories by age cohort % share of population, 2016-17 18
  19. 19. Population Projections for Malawi 19 1964 2018 2050 Population (estimated) 3,963,423 18,860,963 43,154,607 Source: https://populationpyramid.net/malawi/2018/
  20. 20. Population Pyramid for Malawi, 2017 20 Source: https://populationpyramid.net/malawi/2017/

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