ETHIOPIAN DEVELOPMENT RESEARCH INSTITUTE

Rural youth and employment in Ethiopia

Emily Schmidt and Firew BekeleIFPRI-ESSP and EDRI

Transformation and vulnerability in Ethiopia: New evidence to inform policy and investmentsGetfam Hotel, Addis AbabaMay 27, 2016Addis Ababa

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OverviewPrimary research question: How are rural youth engaging in the on and off-farm labor market, and what are the potential factors that influence their decisions to diversify out of agriculture?

1. Background

2. Discuss youth employment trends in Ethiopia using National Labor Force Survey (CSA, 2005 and 2013) – main occupation

3. Discuss youth in agriculture

4. Analyze youth engagement in non-farm activities - using the Ethiopia Socioeconomic Survey (CSA, 2013/14) – labor portfolio

5. Conclusions

Background• Ethiopia focused on Agricultural Development Led

Industrialization (ADLI) strategy over the last several decades –> 11% increase in GDP/year over the last decade

– Major emphasis on achieving high rates of agricultural growth –> which would, ideally, lead to growth in rural non-farm sector

• Little labor transition from agriculture to more high-value sectors

– More than ¾ of working population report ‘main occupation’ in agriculture (NLFS, 2013)

– Approximately 68% of working population engaged in agriculture according to the Ethiopia Socio-economic Survey (ESS, 2013/14)

Employment in Ethiopia (NLFS 2005, 2013)Employment shares by industry of economically active Industry 2005 2013a 2013bAgriculture 80.21 72.67 76.64Manufacturing 4.87 4.38 4.87Construction 1.42 1.95 2.16Trade 5.21 5.54 6.19Education 0.9 1.61 1.82Private households 0.79 7.3 1.6

• Change in NLFS questionnaire in 2013 to classify wood and water collectors as private household service work

• Water and wood collectors: • 89% female • 94% rural • 88% unpaid family work

• National labor force survey data (modified) suggest very little labor movement out of agriculture.

Youth and employment (NLFS, 2013)• Youth (15-34) comprise almost 1/3 of the total population and ½ of

economically active in Ethiopia • Youth make up 42% of economically active labor force in rural areas

• 10% decrease of youth working agriculture –> However – likely due to the adjustment of the 2013 labor definitions.

• 12% increase of youth ‘not in the labor force’ reflects individuals that stated their primary occupation as wood and water collector

• Very little change of youth in non-agriculture sector

72.5

12.13.1

12.3

63.2

10.7 2.5

23.7

0

20

40

60

80

100

Agric NonAgr Unemp Not in Laborforce

Rural youth employment

2005 2013

• Given that a majority of youth are currently working in agriculture – what opportunities does agriculture provide for rural youth?

• Analyze youth headed households (15-34) compared to mature headed households (35-64):

• Youth headed households own and operate significantly less land

• Youth headed households don’t implement significantly greater agricultural enhancing technologies

• However, youth headed households have more diversified household labor portfolios compared to mature headed households.

Youth in agriculture

Rural youth in agriculture

• Youth headed households (age 15-34) own significantly less land

• Although youth rent-in more agricultural land, total operational land remains significantly lower than mature headed households (age 35-64)

1.74

1.381.50

0.82

0.00

0.50

1.00

1.50

2.00

Mature headed households Youth headed households

Hect

ares

Agricultural land by youth and mature households

Total operated Total owned

Rural youth in agriculture

• Youth headed households are not adopting greater agricultural enhancing technologies

• Smaller share of youth headed household apply/cultivate: fertilizer, improved seed, row planting, cash crops

• Youth headed households have less access to agricultural credit and ag extension

0%

20%

40%

60%

80%

100%

Shar

e of

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seho

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Agricultural intensification and inputs

Mature headedhouseholds

Youth headedhouseholds

Rural household labor composition

Labor portfolio

Mature headed

household

Youth headed

household t-testOwn-farm 83.8 75.5 ***Mixed own-farm and off-farm 12.9 16.3 ***Off-family farm 3.4 8.1 ***N observations 2410 942

• Less youth headed households comprised solely of own-farm workers

• Greater share of youth headed households have a diversified labor portfolio among household members

• A majority of the off-farm work that is rural workers engage in the non-agricultural sector – at least 90% of off-family farm work is in the non-agricultural sector (limited demand for agricultural wage work).

• Moving forward, we examine individual youth labor decisions to evaluate potential opportunities and constraints:

We run 3 models:

1) Evaluate the whole sample of working individuals (ages 15-64)

2) Youth (ages 15-24) 3) Youth (ages 25-34)

• Sample is divided into 3 categories:

• Individuals that work solely on their own family farm• Individuals that work in a mix of own-family farm and NFE• Individuals that work in a mix of own-family farm and wage work

Youth working in off-farm labor

Split the sample to focus on:

Rural youth: Individual-level labor diversification• Use a multinomial logit model to compare decisions of individuals

to engage in non-farm enterprise or wage work relative to working solely on their own family farm

• Individual characteristics: gender, education, marital status, whether individual is a head / student, etc.

• Household level factors: household size, wealth, land holdings, shocks etc.

• Location: distance to a market, distance to a road, agricultural potential

• Limit the sample to individuals that:• Live in rural and small towns (under population of 10,000 people)

• Are between the ages of 15-64

• Work in at least one activity (own farm, non-farm enterprise, or wage work)

Rural working population (age 15-64): marginal effects of selected variables

• Youth do not have a greater probability of working in wage work compared to non-youth (age 35-64).

• Older youth (age 25-34) have a 5% greater probability of working in a non-farm enterprise.

• Females are less likely to engage in wage work (4 %), but more likely to engage in non-farm enterprise work (4 %)

• Primary education increases the probability of engaging in wage work by 4%

• Located in a good agricultural potential area have a 5% greater probability of engaging in NFE activities

Rural working young youth (age 15-24): marginal effects of selected variables

• Household heads have a 2% greater probability of working in wage work

• Females are more likely to work in NFE (by 4%), but no significant difference between males and females in wage work

• Completing primary education increases the probability of working in wage work compared to on-farm work by 3%

• Good ag potential increases probability of engaging in NFE activities by 4 %, and decreases probability of being in wage work by 2 %

Rural working older youth (age 25-34): marginal effects of selected variables

• Females are 6% less likely to work in a mix of farm and wage, but 7% more likely to work in a mix of NFE compared to solely on-farm.

• Completing primary education increases the probability of wage work activity by 5% compared to own-farm work

• Access to ag credit decreases probability of diversifying into wage by 7% compared to own-farm work

• Good agricultural potential increases probability of engaging in NFE activities by 8%

Conclusions

• Large growth in GDP / year in Ethiopia over the last decade (11%)

• However, limited transition out of agriculture sector into higher value sectors (foundation of structural change)

• Given youth’s limited access to agricultural land and services (credit, extension, etc.), youth will play a large role in the agricultural and labor transformation within Ethiopia

Conclusions• Youth aged 25-34: greater probability of engaging in non-farm

enterprise work relative to working solely on their own farm

• Women youth: less likely to work in wage labor and more likely to work in a non-farm enterprise.

• Primary education: increases the probability of working in wage work relative to working solely on own family farm.

• Located in a good agricultural potential area: increases the probability of engaging in a non-farm enterprise in all age cohorts

• However, proximity to a market or road does not increase the probability of diversifying off-farm

• Limited demand for off-farm work in rural markets may be constraining labor transitions to the off-farm sector

Conclusions

• Given the majority of Ethiopia’s population remains in rural areas, investments in agriculture enhancing technology and services remains important to increasing agricultural productivity, and ultimately sparking greater off-farm demand for goods and services

• Creating greater work opportunities in the non-farm sector via generating demand for rural non-farm products is crucial for increasing off-farm labor opportunities

• Continuing to evaluate constraints for youth and women in the on and off-farm sectors as the economy continues to grow will remain important to ensuring healthy, sustainable economic growth moving forward.

Thank you

Extra slides

Understanding importance of secondary occupation

• NLFS reports on primary / ‘main’ occupation only.

• Ethiopia Socioeconomic Survey (ESS 2013/14) collected specific data on time spent on planting, harvesting, non-farm enterprise, and wage activities– Construct work portfolios for each individual– Unable to identify individuals that are ‘not in the labor force’

• Labor portfolio of working age population (15-64)– 58% of working age population works solely on own-farm

activities– 10% does a mix of own-farm and off-farm– 9% work exclusively in off-farm activities

Age cohortYouth aged 15-24Youth aged 25-34Mature aged 35-64 (omitted)

Individual characteristicsHead (1=yes)Female (1=yes)Married (1=yes)Completed primary (1=yes)Student (1=yes)

Household characteristicsHousehold size (number)Adult males in household (numberTotal expenditure/capita (Birr/capita)Total area owned (hectares)Total area operated (hectares)Household received ag extension support (1=yes)Household received ag credit (1=yes)Tropical livestock unitsHousehold experienced drought (1=yes)Household experienced flood (1=yes)

Location characteristicsGood agricultural potential (1=yes)Distance to market (km)Distance to road (km)

Comprehensive list of control variables for MNL

Farm and wage

Farm and NFE

Youth (age 15-24) -0.011 0.018(0.010) (0.019)

Youth (age 25-34) -0.001 0.048***(0.006) (0.015)

Female -0.035*** 0.037**(0.010) (0.015)

Completed primary school 0.044*** 0.001(0.008) (0.018)

Good ag potential -0.009 0.053***(0.009) (0.020)

Rural working population (age 15-64): marginal effects of selected variables • Youth do not have a greater

probability of working in wage work compared to non-youth (age 35-64).

• Older youth (age 25-34) have a 5% greater probability of working in a non-farm enterprise.

• Females are less likely to engage in wage work, but more likely to engage in non-farm enterprise work

• Primary education increases the probability of engaging in wage work by 4%

• Located in a good agricultural potential area have a 5% greater probability of engaging in NFE activities

Note: A selection of covariates are displayed for presentation purposesN observations: 7,567Standard errors in parentheses

Rural working youth (age 15-24): marginal effects of selected variables

Farm and wage

Farm and NFE

Household head 0.016* 0.027(0.009) (0.035)

Female 0.001 0.036*(0.008) (0.021)

Completed primary 0.031*** 0.016(0.010) (0.020)

Good ag potential -0.019* 0.044*(0.011) (0.025)

Focusing on youth aged 15-24

• Household heads have a 2% greater probability of working in wage work

• Females are more likely to work in NFE, but no significant difference between males and females in wage work

• Completing primary education increases the probability of working in wage work compared to on-farm work

• Good ag potential increases probability of engaging in NFE activities by 4%, and decreases probability of being in wage work

Note: A selection of covariates are displayed for presentation purposesN observations: 2,526Standard errors in parentheses

Rural working youth (age 25-34): marginal effects of selected variables

Focusing on youth aged 25-34

• Females are 6% less likely to work in a mix of farm and wage, but 7% more likely to work in a mix of NFE compared to solely on-farm.

• Completing primary education increases the probability of wage work activity by 5% compared to own-farm work

• Access to ag credit decreases probability of diversifying into wage

• Good agricultural potential increases probability of engaging in NFE activities by 8%

Note: A selection of covariates are displayed for presentation purposesN observations: 1,600Standard errors in parentheses

Farm and wage

Farm and NFE

Household head -0.015 0.052(0.017) (0.035)

Female -0.061** 0.071**(0.028) (0.029)

Completed primary 0.054*** -0.020(0.012) (0.040)

Ag credit -0.073** -0.039(0.031) (0.034)

Good ag potential -0.010 0.079**(0.018) (0.032)