It’s a question of power
What we know from impact evaluation about
gender in ag & psd interventions in Africa and
why we don’t know more
Markus Goldstein
Alaka Holla
Michael O’Sullivan
Africa region gender team/DECRG
How we might think about the gender
impacts of policy/interventions
A. Male vs female beneficiaries (i.e. who gets
the program)
B. Spillover benefits: effects on those of
different genders w/in the household of the
beneficiary (whoever the beneficiary is)
C. Differences in male versus female
sensitivity/responsiveness to an intervention
How we think about our IE results
• We are testing the hypothesis: the gender
difference is not statistically different from
zero
• Two ways this can happen:
– 1. There might or might not be a gender
difference - we can’t tell – the estimates are so
noisy as to be indistinguishable  NO
information for policy
– 2. The difference is actually zero (well estimated)
 policy relevant result
We don’t know much
• Scattered evaluations
• Methods are often improvised ex-post, making it
harder to show causal links or they are experiments
to test a particular point rather than policy
• Agricultural & PSD interventions are harder to
evaluate than, e.g. health and education
– More data intensive (crop/plot data, firms)
– Interventions often multi-sectoral/multi-faceted
– For ag: Infrastructure placement does not make finding a
control group easy
We do know a bit about PSD
(although not much)
•
I’ll discuss some evidence from impact evaluations in
small business finance
–
–
–
–
•
Credit: access and take-up
Investment and income responses
Impacts within the household
Suggestive evidence, but not designed for gender effects
Know less about
–
–
–
–
Effectiveness of BDS for women entrepreneurs
Effects on enterprise outcomes (by gender)
Spillovers to non-enterprise outcomes and total welfare effects
What policy interventions can close gaps? Is it desirable to do
so?
The interventions
• Interest free savings accounts in rural banks in
Kenya (Dupas & Robinson)
• Better information for lenders, and the
borrowers know this in Malawi (Gine, et al)
• Changing advertising content, interest rates,
and extending credit to marginal borrowers in
South Africa (Karlan, et al)
Savings accounts in Kenya
Outcome
Credit and
borrowing
ROSCA
contributio
ns
Income &
investment
Business
investment
Household
Food
expenditure
Impact for
women
Gender
difference in
impact
Smallest
detectable
difference
18.33
-17.94
|23.23|
252.89
-152.83
|420.97|
24.13
-16.24
|26.24|
Numbers in red = statistically significant
Fingerprinting borrowers in Malawi
Outcome
Credit and
borrowing
Loan size
Income &
investment
Market
sales
Profits
Impact for
women
Gender
difference in
impact
309.27
-1253.14
32248.16
-29698.637
97690.57
-98806.78
Numbers in red = statistically significant
Smallest
detectable
difference
|1190.32|
|39629.29|
|96649.46|
South Africa: Content of loan advertisements
Intervention
Outcome
Credit and Offered
Apply for
borrowing interest rates loan
Impact for
women
Gender
difference in
impact
Smallest
detectable
difference
-0.003
0.0002
|0.002|
0.034
-0.039
|0.033|
0.076
-0.020
|0.024|
Default
Longer
deadline to
apply
Apply for
loan
Numbers in red = statistically significant
South Africa: Extending credit to marginally
ineligible applicants
Outcome
Household
Impact for
men
Gender
difference in
impact
Smallest
detectable
difference
-1.18
1.53
|1.40|
Impact for
women
Impact for men
Statistically
distinguishable?
Have micro
loan
0.129
0.119
No
Have loan
from formal
source
-0.083
0.008
No
Food
consumption
-0.023
0.232
No
Control and
outlook
0.159
0.196
No
Depression
Outcome
Credit and
borrowing
Household
And agriculture?
Land certification- Ethiopia
• Large land certification program, joint ownership
for spouses
• Reductions in perceived insecurity, big increases
in land investment, and increased rental market
activity
• Gender: Female-headed HHs with certificates
were 20% more likely than male headed hh to
make soil & water conservation investments in
land & spent 72% more time on these
investments
• (Deininger, Ali, Alemu, 2008)
Reducing exposure to risk - Malawi
• Rainfall insurance, tied to loans for HYV
• Fewer farmers take loan when tied to
insurance
• Gender: No significant difference
• (Giné and Yang 2008)
Technological adoption - Kenya
• Provided credit, agricultural extension and
export facilitation services to farmers to adopt
and market export crops
• Farmers more likely to grow export crops but
no overall impact on farm input usage, HH
income or harvest value
• Gender: No significant difference
• (Ashraf, Giné, and Karlan 2008)
Increasing fertilizer use - Kenya
• Put in place mechanisms to get farmers to
commit to buying fertilizer at harvest rather
than later
• Small, well-timed discounts can outperform
large (not-timed) subsidies
• Gender: No significant difference
• (Duflo, Kremer and Robinson, 2009)
So that’s a lot of insignificant
results
Why?
Unpacking gender insignificance
• We are testing the hypothesis: the gender
difference is not statistically different from
zero
• Two ways this can happen:
– 1. We can’t tell – the estimates are so noisy as to
be indistinguishable  NO information for policy
– 2. The difference is actually zero (well estimated)
 policy relevant result
What separates the two is statistical power
Variable
Country
25%
detectable
impact
50%
larger
female
impact
Notes
Loan take-up
S. Africa
7042
28196
No baseline
Loan take-up
S. Africa
18046
With baseline &
autocorrelation=0.6
4508
Land market
participation
Ghana
Land market
participation
Ghana
No baseline
1112
712
4448
2846
With baseline &
autocorrelation=0.6
So where we do we go?
• To do this right, to really build the evidence on
gender differences, we need bigger surveys,
gender incorporated in the design of evaluations
• This means:
a) that people doing the evaluations pay attention to
this (not obvious)
b) more money
c) The results get used
• And thus, it is all a question of power…