This paper analyzes gender differences in agricultural productivity in Nigeria and Uganda using household survey data. It finds that productivity is significantly lower on plots owned or managed by females compared to males. However, results vary by crop, region, and whether the analysis considers sex of the household head or plot owner. Productivity is lowest on plots with mixed female-male ownership, indicating bargaining difficulties. The paper concludes that addressing gender differences requires considering local contexts and improving the representation of data.

1. Understanding the Complexities Surrounding Gender Differences in
Agricultural Productivity in Nigeria and Uganda
AMBER PETERMAN,
AGNES QUISUMBING,
JULIA BEHRMAN, AND
EPHRAIM NKONYA
IFPRI
AFRICA GROWTH FORUM
JANUARY 19-20, 2011
Harvesting in Nigeria, Credit: Yosef Hadar

2. Outline of presentation
 Framing the issue
 Methods
 Findings
 Policy implications
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3. This paper:
 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
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4. Methods: Data
Nigeria 2005 Uganda 2003
 Collected to evaluate Fadama II, a  Collected to study natural resource
national agricultural welfare management and poverty
program  Plot level data: 3,625 plots in 851
 Household level data: 3,750 hhs hhs
 Gender variable: Sex of hh head  Gender variable: Sex of crop
ownership for plot, also allows for
mixed ownership; sex of hh head
also collected
 Biophysical plot characteristics
Both countries: Large agricultural sectors, diversity in agro- ecological zones, crop
choice, ethnic variation and low women’s status and property rights.
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5. Methods: Empirics, tobit model
 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
 Li labor input (hired or family)
 Ti vector of land, capital, and other conventional inputs
 Ei 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
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6. Methods: More on empirics
 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).
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7. Findings: Plotting productivity
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8. Findings: Summary of tobit estimation results
Variable
Nigeria Full Maize Rice Cassava Tomato Leafy Cowpea
veg
FHH=1 -0.32*** -0.25 -0.03 -0.49 -2.08** -0.34 -0.06
Uganda Full Banana Beans & Maize Sweet Cassava Sorghum
peas potato
Female -0.27** 0.23 0.07 -0.06 -0.80* -0.27 -0.93**
crop
owner=1
Mixed -0.29* 0.21 -0.82** -0.65 -0.98*** -0.66 -0.38
owners=1
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9. Findings
 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?
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10. Policy implications—part 1
 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
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11. 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
Credit: ILRI
representativeness of data
collection and analytical efforts

12. Questions, Comments?
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)
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