Gender gaps in youth employment: a spatial approach

Aslihan ArslanInternational Fund for Agricultural Development (IFAD)

Joint work with Eva-Maria Egger (IFAD) and David E. Tschirley (MSU)

Future of Work in Agriculture ConferenceWorld Bank, Washington, D.C.

20.03.2019

Motivation

• Three transformations increase the importance of rural wage employment:- Structural transformation reaching rural areas (IFAD 2016)- Transformation of agri-food systems (Reardon et al. 2015)- Employment transformation (Filmer and Fox 2014)

• Wage employment important for youth employment challenge

• Young rural women face a triple burden:- Rural areas lag behind in the transformation. Thus, connectivity & mobility

important to access wage employment. (Christiaensen, et al. 2013)- younger women’s mobility constrained by social norms and domestic work

(Chakravarty, Das and Vaillant 2017)

• Wage employment of young rural women can contribute to - the empowerment of young women.- speed up the demographic transition hence contribute to rural

transformation (Stecklov & Menashe-Oren 2019)

Global Youth over the Rural Opportunity Space (Rural Development Report 2019)

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Figure: Share of global rural youth within rural opportunity space (all low and middle income countries)

• Globally 67% of rural youth live in areas with high agricultural potential

• For welfare outcomes, commercialization potential (=connectivity) matters more

Individual employment data from 12 surveys

Total: ~420 K 4

Country Survey Name Year Sample Size (indiv.)

Geo-locationlevels

Sub-Saharan AfricaEthiopia Ethiopian Socioeconomic Survey 2015/2016 23,393 EAs

Malawi Fourth Integrated Household Survey 2016/2017 53,885 EAsNiger National Survey on Household Living

Conditions on Agriculture - Panel2014 22,671 EAs

Nigeria General Household Survey- Panel 2015/2016 24,807 EAsTanzania National Panel Survey 2014/2015 16,285 EAsUganda The Uganda National Panel Survey 2013/2014 9,376 EAs

Latin AmericaMexico Encuesta nacional de ingresos y gastos de

los hogares 2016 256,448 EAs

Nicaragua Encuesta nacional de hogares sobre medición de nivel de vida

2014 29,381 Municipality

Peru Encuesta nacional del hogares 2016 (Anual) – Condiciones de vida y pobreza

2016 134,235 EAs

AsiaCambodia Cambodia Socio-Economic Survey 2014 53,968 VillageIndonesia Indonesian Family Life Survey 2014 58,300 EAsNepal Nepal Living Standards Survey 2010 28,670 Village

Gender gaps in LFP and in wage employment

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Research questions

1. To what extent can connectivity explain the gender gap in:- youth LFP? - non-farm wage participation (NFWP)?

2. Does this differ for the AFS vs. non-AFS sector, and by region?

Two spatial aspects of connectivity matter:• Opportunities available where you live: Rural Opportunity

Space population density• How long does it take to get to nearest city? travel time

Hypotheses: If there was no mobility constraint, …1. young women would be equally likely to access non-farm wage

employment as young men with the same connectivity𝑃𝑃(𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊 = 1)𝑖𝑖

𝐽𝐽 = 𝛼𝛼 + 𝛽𝛽1𝐹𝐹𝑊𝑊𝐹𝐹𝑖𝑖𝐽𝐽 + 𝛽𝛽2𝑋𝑋𝑖𝑖

𝐽𝐽 + 𝛽𝛽3𝐼𝐼𝐼𝐼𝐼𝐼 + 𝑊𝑊𝑖𝑖𝐽𝐽

J=Rural, Semi-rural, Peri-urban, Urban𝑯𝑯𝟎𝟎: �𝜷𝜷𝟏𝟏 = 𝟎𝟎

2. longer travel time would affect young women’s likelihood to access wage jobs within the same location as much as for young men 𝑃𝑃(𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊 = 1)𝑖𝑖

𝐽𝐽

= 𝛼𝛼 + 𝛽𝛽1𝐹𝐹𝑊𝑊𝐹𝐹𝑖𝑖𝐽𝐽

+ 𝛽𝛽2𝑇𝑇𝑇𝑇𝐹𝐹𝑊𝑊𝑖𝑖𝐽𝐽

+ 𝛽𝛽3𝐹𝐹𝑊𝑊𝐹𝐹 ∗ 𝑇𝑇𝑇𝑇𝐹𝐹𝑊𝑊𝑖𝑖𝐽𝐽

+ 𝛽𝛽4𝑋𝑋𝑖𝑖𝐽𝐽 + 𝑊𝑊𝑖𝑖𝐽𝐽

𝑯𝑯𝟎𝟎: �𝜷𝜷𝟏𝟏 + �𝜷𝜷𝟑𝟑𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑱𝑱 = 𝟎𝟎

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Data Sources

• Household survey data from 12 low and middle income countries (Mexico, Nicaragua, Peru, Cambodia, Indonesia, Nepal, Ethiopia, Malawi, Niger, Nigeria, Tanzania, Uganda)

• Merged with geo-spatial data on - population density (WorldPop project)- travel time to cities/towns (Open Street Map, Google

roads database, Global Human Settlement Layer)- greenness (Enhanced Vegetation Index )

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Spatial data: Rural-urban gradient based on population density• 1km x 1km resolution population density maps for each country

globally (age and gender disaggregated)• order global sample of grids from least to most dense and define

quartiles of rural-urban gradient• Match to enumeration areas (or admin units) with available geo-codes

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Table 1: Population density based rural-urban gradient vs. admin urbanization rates: Population shares by region.

Population density based rural-urban gradient Administrative

Regions Rural Semi-Rural Peri-Urban Urban urbanization rate

LAC 42 16 20 22 67SSA 47 14 14 25 38APR 33 23 24 20 38NEN 43 13 18 25 57Global Average 43 16 18 24 47

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Summary statistics of sample (15 to 24 year old men and women)

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Note: Point estimates are population means. Level of statistical significance of Wald-test of difference in means .01 - ***; .05 - **; .1 - *;

Table 1: Summary statistics of main variables for women and men aged 15 to 24 yearsMean Difference in means

Female MaleNo. of observations 61 899 61 411In labor force 0.51 0.65 -0.14***Of those in labor force, work in:Off-farm wage employment 0.24 0.28 -0.03***

off-farm AFS wage 0.09 0.10 -0.01non-AFS wage 0.20 0.24 -0.04***

Age 19.08 18.94 0.14***Currently in school 0.44 0.47 -0.04***Secondary school completed 0.39 0.39 -0.00Married 0.20 0.06 0.14***Household characteristics:Household size 6.24 6.63 -0.40***Dependency ratio 0.63 0.58 0.05***

Gender gap in LFP: marriage matters (IV for first step)

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Table: Marginal effects from Probit estimation of LFP, separately for men and women, by age groups

Married Population density

Travel time to cities

15-24 years M 0.164*** -0.026***F -0.088*** -0.016M 0.048*** 0.023*F -0.070** 0.018

25-64 years M 0.169*** -0.007***F -0.084*** -0.008M 0.049*** 0.005F -0.068** 0.012

LFP (First step): Marginal effect of being a young female along the rural-urban gradient and by travel time (vs. young males)

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By region:

Non-farm wage participation (NFWP, Second step): Marginal effect of being a young female – by sector (compared to young males)

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NFWP (Second step): Marginal effect of being a young female by region & sector (compared to young males)

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Rural-urban gradient

Travel time

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How does the mobility constraint affect young women’s NFWP within R-U categories? (compared to young males)

Conclusion

• With innovative combination of household and geo-spatial data, we show that the gender gap in NFWP significantly changes over the rural-urban gradient

• Young women’s mobility constraint exists mostly for non-AFS wage employment, presumably because more AFS jobs are available in more remote areas

• Positive spill-overs of young women in employment: empowerment, faster demographic transition & rural transformation

Policy:» invest in connectivity!» Spatially differentiated approach to young women’s

employment challenge 17

THANK YOU!

Aslıhan Arslana.arslan@ifad.org

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References

• Chakravarty, S., Das, S. and J. Vaillant (2017), Gender and Youth Employment in Sub-Saharan Africa. A Review of Constraints and Effective Interventions, Policy Research Working Paper Series, 8245, Washington, DC: Worldbank.

• Christiaensen, L., De Weerdt, J., and Todo, Y. (2013). Urbanization and poverty reduction: The role of rural diversification and secondary towns. Agr. Econ., 44(4–5), pp. 435-447.

• Filmer, D. and Louise Fox (2014).Youth employment in sub-Saharan Africa: The World Bank.

• IFAD (2016). Rural Development Report 2016: Fostering Inclusive Rural Transformation. Rome: IFAD.

• Stecklov G and A Menashe-Oren (2018), The Demography of Rural Youth in Developing Countries, Background paper for Rural Development Report 2019, Rome: IFAD.

• Tschirley DL, Snyder J, Dolislager M, Reardon T, Haggblade S, Goeb J, Traub L, Ejobi F, Meyer F (2015), Africa ' s unfolding diet transformation: implications for agrifood system employment, Journal of Agribusiness in Developing and Emerging Economies, 5(2): 102-136.

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LFP along the rural-urban gradient

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Travel time over the rural-urban gradient

Quartiles mean min max sdRural 120.81 3.73 1994.47 174.46Semi-Rural 66.92 3.25 584.15 68.37Peri-Urban 38.75 1.82 1050.22 39.39Urban 36.83 1.73 307.29 26.01

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Wage income and poverty

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Table: Share of total income from different sources along the rural urban gradient, by poverty status of household

Income source Rural-urban gradient Poor Non-poor Difference

Income share from off-farm wage

Rural hinterland 0.07 0.17 -0.10***Semi-rural 0.10 0.25 -0.15***Peri-urban 0.24 0.33 -0.08***Urban 0.51 0.61 -0.10***

Income share from own farming

Rural hinterland 0.64 0.44 0.20***Semi-rural 0.57 0.31 0.26***Peri-urban 0.29 0.22 0.07***Urban 0.11 0.02 0.08***

Income share from non-farm enterprise

Rural hinterland 0.17 0.22 -0.04***Semi-rural 0.23 0.29 -0.06***Peri-urban 0.22 0.23 -0.01Urban 0.28 0.23 0.05**

Sectoral participation along the rural-urban gradient (Full-time equivalent shares for those in LF)

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Summary statistics of sample (15 to 64 year old men and women)

24Note: Point estimates are population means. Level of statistical significance of Wald-test of difference in means .01 - ***; .05 - **; .1 - *;

Table 1: Summary statistics of main variables for women and men aged 15 to 64 yearsMean Difference in

meansFemale Male

No. of observations 220,074 202,010In labor force 0.65 0.81 -0.16***Of those in labor force, work in:Off-farm wage employment 0.26 0.38 -0.12***

Off-farm AFS wage 0.07 0.11 -0.04***Non-AFS wage 0.22 0.34 -0.12***

Age 34.24 33.36 0.88***Currently in school 0.15 0.19 -0.04***Secondary school completed 0.36 0.43 -0.06***Married 0.51 0.45 0.06***Household characteristics:Household size 5.75 5.92 -0.17***Dependency ratio 0.80 0.70 0.10***Any income from farming 0.55 0.56 -0.00

Hypotheses to test: If there was no mobility constraint, …

1. young women would be equally likely to access non-farm wage employment as the average person in the labor force with the same connectivity

𝑃𝑃(𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊 = 1)𝑖𝑖𝐽𝐽 = 𝛼𝛼 + 𝛽𝛽1𝐹𝐹𝑊𝑊𝐹𝐹𝑖𝑖

𝐽𝐽 + 𝛽𝛽2𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑖𝑖𝐽𝐽 + 𝛽𝛽3𝐹𝐹𝑊𝑊𝐹𝐹 ∗ 𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑖𝑖

𝐽𝐽 + 𝛽𝛽4𝑋𝑋𝑖𝑖𝐽𝐽 + 𝑊𝑊𝑖𝑖𝐽𝐽

J=connectivity𝐻𝐻0: �̂�𝛽3 = 0

2. longer travel time affects the likelihood to access wage jobs in the same location for young women as much as for the average person𝑃𝑃(𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊 = 1)𝑖𝑖

𝐽𝐽

= 𝛼𝛼 + 𝛽𝛽1𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑖𝑖𝐽𝐽

+ 𝛽𝛽2𝑇𝑇𝑇𝑇𝐹𝐹𝑊𝑊𝑖𝑖𝐽𝐽

+ 𝛽𝛽3𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌 ∗ 𝑇𝑇𝑇𝑇𝐹𝐹𝑊𝑊𝑖𝑖𝐽𝐽

+ 𝛽𝛽4𝑋𝑋𝑖𝑖𝐽𝐽 + 𝑊𝑊𝑖𝑖𝐽𝐽

J=location𝐻𝐻0: �̂�𝛽3 = 0

For both: holding everything else constant and adjusting for selection into labor force participation

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Methodology: Blinder-Oaxaca decomposition of gender gap in wage participation

Regression model: 𝑌𝑌𝑖𝑖𝐽𝐽 = 𝛼𝛼 + 𝛽𝛽𝑋𝑋𝑖𝑖

𝐽𝐽 + 𝛾𝛾𝐶𝐶𝑐𝑐 + 𝑊𝑊𝑖𝑖 𝐽𝐽 = 𝐼𝐼,𝐹𝐹Decomposition:𝐼𝐼 = 𝐸𝐸 𝑋𝑋𝑀𝑀 − 𝐸𝐸 𝑋𝑋𝐹𝐹 ′𝛽𝛽∗ + {𝐸𝐸 𝑋𝑋𝑀𝑀 ′ 𝛽𝛽𝑀𝑀 − 𝛽𝛽∗ + 𝐸𝐸 𝑋𝑋𝐹𝐹 ′ 𝛽𝛽𝐹𝐹 − 𝛽𝛽∗ }

𝛽𝛽∗ : coefficients from pooled modelY: Participation in off-farm wage employmenti: individual; k: enumeration area; c: countryX: spatial (population density in EA, population density in 50km radius, travel time to nearest city); individual (age, education) and householdcharacteristics (dependency ratio, gender of head, income diversification)C: country dummies

Control for selection into labor force participation (Heckman)

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Raw participation gap

Endowment effect Remuneration effect