This document discusses change and rigidity in youth employment patterns in Malawi based on analysis of household survey data from 2004, 2010, and 2016. The key findings are: 1) Agriculture remains the dominant sector of employment in Malawi, accounting for around 88% of those employed. 2) There is little evidence of structural change in employment patterns, with the share of those working in agriculture remaining stable. 3) Youth generally enter the workforce through agriculture like previous generations, with older youth showing some movement into other sectors like services.

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
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
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
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
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. 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. 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
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
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. 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. 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. 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.

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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
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
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
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. Additional Slides
(Not for presentation)
17

18. Employment categories by age cohort
% share of population, 2016-17
18

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. Population Pyramid for Malawi, 2017
20
Source: https://populationpyramid.net/malawi/2017/