This document summarizes research on changes in youth employment patterns in Malawi between 2004-2016. The key findings are: 1) Agriculture remains the dominant sector, employing 88% of working Malawians. However, the share of older youth and non-youth working in services has increased slightly while industry employment has declined. 2) Younger youth are more likely to be students than employed, while older youth's employment patterns are similar to non-youth. Education increases the likelihood of non-agricultural employment. 3) There is little evidence of structural transformation in employment away from agriculture. Maintaining education investments alongside private sector job creation and infrastructure development are needed to pull people out of farming

1. Change and Rigidity in Youth Employment
Patterns in Malawi, 2004-2016
Bob Baulch, Todd Benson, Alvina Erman*,
and Yanjanani Lifeyo
International Food Policy Research Institute and *World Bank
PIM Workshop on Rural Transformation
Vancouver | 28 July 2018

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)
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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
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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?
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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
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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. 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
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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
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 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. 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
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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.
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10. Determinants of employment
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 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. 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
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12. Multinomial logit (MNL) regression
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 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. 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
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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
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15. Summary
• 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
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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
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