A M B E R P E T E R M A N ,

A G N E S Q U I S U M B I N G ,

J U L I A B E H R M A N , A N D

E P H R A I M N K O N Y A

IFPRI

AFRICA GROWTH FORUM

JANUARY 19-20, 2011

Understanding the Complexities Surrounding Gender Differences in Agricultural Productivity in Nigeria and Uganda

Harvesting in Nigeria, Credit: Yosef Hadar

Outline of presentation

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Framing the issue

Methods

Findings

Policy implications

This paper:

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Provides new estimates of gender differences in agricultural productivity using IFPRI household survey data from Nigeria (2005) and Uganda (2003)

Address some complexities by looking at:

Crop choice

Sensitivity of productivity estimates to choice of stratifying ‘gender’ variable (sex of hh head, sex of plot owner, mixed ownership)

Heterogeneity within agro-ecological zones

Controlling (where possible) for hh-level unobservables

Controlling (where possible) for biophysical characteristics of plot

Nigeria 2005 Uganda 2003

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Collected to evaluate Fadama II, a national agricultural welfare program

Household level data: 3,750 hhs

Gender variable: Sex of hh head

Collected to study natural resource management and poverty

Plot level data: 3,625 plots in 851 hhs

Gender variable: Sex of crop ownership for plot, also allows for mixed ownership; sex of hh head also collected

Biophysical plot characteristics

Methods: Data

Both countries: Large agricultural sectors, diversity in agro- ecological zones, crop choice, ethnic variation and low women’s status and property rights.

Methods: Empirics, tobit model

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ln Yi = α0 + α1ln Li + α2 ln Ti + ß ln Ei + γ EXTi+ δ Genderi + ε

Yi ith hh or plot value of crop yield per unit area

L i labor input (hired or family)

T i vector of land, capital, and other conventional inputs

E i educational attainment

EXT i index of extension services

Gender i dummy variable for the sex or gender of the farm manager or household head

ε error term

Methods: More on empirics

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Allow for mass point at zero using tobit

Treatment of zero as either fallow or no output

Crop choice modeled using probit and Cragg’s two-tiered unconditional tobit

Uganda: explore robustness to inclusion of fixed effects using Honoré’s fixed effects tobit estimator

All regressions control for age, education of head, hh size; land, irrigation, fertilizer and seeds, extension, labor (previous season inputs);

All full sample regressions control for primary crop indicators (results are robust to inclusion of secondary crop indicators).

Findings: Plotting productivity

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Findings: Summary of tobit estimation results

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Variable

Nigeria Full Maize Rice Cassava Tomato Leafy veg

Cowpea

FHH=1 -0.32*** -0.25 -0.03 -0.49 -2.08** -0.34 -0.06

Uganda Full Banana Beans &peas

Maize Sweet potato

Cassava Sorghum

Femalecrop owner=1

-0.27** 0.23 0.07 -0.06 -0.80* -0.27 -0.93**

Mixed owners=1

-0.29* 0.21 -0.82** -0.65 -0.98*** -0.66 -0.38

Findings

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Productivity significantly lower on plots owned or managed by females; results hold taking into account farm and hh characteristics and crop choice

Results vary across crops, agro-ecological zones, and with inclusion of biophysical characteristics

Type of gender indicator matters: extent of productivity differential diluted when headship is used as stratifying variable

Productivity lowest on mixed ownership plots, but not robust to inclusion of hh fixed effects. Indicates bargaining difficulty with mixed ownership/decision making?

Policy implications—part 1

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Headship as a stratifying variable underestimates productivity differences => need to pay attention to level of aggregation in collecting sex-disaggregated data

Productivity lowest on female-owned plots =>pay attention to gender differences in control of resources in research and program implementation

Policy implications--2

Variation by region, crop, biophysical characteristics => address gender in context of regional ecological and biophysical needs, cultural context

Avoid extrapolation of policy findings from very localized studies; increase geographical representativeness of data collection and analytical efforts

Credit: ILRI

Questions, Comments?

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Paper funded by the FAO as a background paper for the State of Food and Agriculture (2010) and we gratefully acknowledge funding. Thanks to Edward Kato for assistance with data and understanding of local context and to Andre Croppenstedt and two anonymous reviewers for helpful comments on an earlier draft.

Paper is forthcoming in the Journal of Development Studies (2011)