Arianna legovini manager development impact evaluation initiative the world bank
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Arianna Legovini Manager, Development Impact Evaluation Initiative The World Bank. Gender Policy for Development: Realizing Opportunities. Motivation. Gender matters for development E vidence from research Policy can address gender gaps

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Gender Policy for Development: Realizing Opportunities

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Arianna Legovini

Manager, Development Impact Evaluation Initiative

The World Bank

Gender Policy for Development: Realizing Opportunities


Motivation

  • Gender matters for development

    • Evidence from research

  • Policy can address gender gaps

    • Incorporate gender dimensions in policy interventions and learn from impact evaluation how to make gender policy work for development


Two questions


First question


Development improves gender balance

  • Gender gaps narrow with growth

    • Girls/boys ratio in secondary schools rose from 40/100 to 79/100 in 1970-2005

    • Female labor force participation rose

    • Female life expectancy increased more than male

  • Poverty and crises negatively affect girls

    • In poor neighborhoods in Delhi, girls are twice as likely to die of diarrhea (Khanna et al.)

    • During draughts in India girls die disproportionately (Rose)


But bias persists

  • Parental expectations (PROBE, India) (-)

    • 57% of parents wanted their sons to study as far as possible

    • Only 28% of parents wanted their daughters to study as far as possible

  • Education in English (+)

    • More lower caste girls in Mumbai study in English and have better opportunities

    • More low caste boys study in Marathi because of old boy networks and have lower opportunities


Two questions


Second question


Venues for gender to affect development

  • Women have different preferences and take different decisions than men at home and for policy

  • Position of weakness in the household may reduce household overall productivity through unequal sharing of resources

  • Rules, constraints and disadvantages may reduce productivity in the economy


Change perceptions through quotas? India (Beaman et al.)

  • Random assignment of gender quotas across Indian village councils

  • Technical audits show female presidents provide more public goods and at better quality than male presidents

  • Villagers are 1.5% less likely to pay bribes in female headed villages

  • However, villagers are 2% less satisfied with female presidents. Rate them less effective the first time they are exposed to them

  • The bias disappears for villagers that have already experienced female leaders in the past

  • Quotas for female presidents of councils may be justified to change gender perceptions (and developmental outcomes)


Money in the hands of women have different effects

  • In South Africa,

    • Girls bridge half the growth gap between South African and US children when living with female pension recipient

    • There is no effect when they live with a male pension recipient (Duflo)

    • Children 13-17 are more likely to be in school when they live with a male pension recipient (Edmonds)

  • In Cote d’Ivoire, households spend more

    • on food in years when female crops do better

    • on alcohol and tobacco when male crops do better (Duflo and Udry)

  • Many transfer and microcredit programs target women hoping to achieve more results


Create wealth by gender equality: Burkina Faso (Udry1996)

  • In households with female and male controlled plots:

  • Many more inputs are used on male than female plots

    • male plots are 30% more productive than female plots

  • But fertilizers have diminishing returns

    • if more equally shared household product would increase

  • Households could increase output by 6% if they shared resources

  • Allocation within the household is not efficient and gender inequality is a cause of poverty


Strengthening female property rights good for growth? Ghana (Goldstein & Udry 2008)

  • Women have weaker property rights on their land than men

    • Women fallow their land less than men do because they can suffer expropriation during fallowing

  • As a result women’s maize & cassava yields are much lower than men’s within the same household

  • Inefficient fallowing is costly

  • More secure property rights for women could increase Ghana’s GDP by 1%


Gender policy is development policy

  • These examples show that

    • women’s preferences can help growth, and

    • gender disparities can cause inefficiencies in household production and country growth

  • Worth investing in gender policies to support development policy


Gender factors that can be addressed through policy

  • Perceptions

  • Differential access to land, inputs, capital, output markets

  • Traditional rules on duties, movement, household decisions

  • Different formal or informal rights on property


How impact evaluation can help

  • Hypothesize factors that may induce inefficiencies in the context of your program

  • Think about what policy interventions may address them

  • Test policy alternatives rigorously

    • Impact evaluation will separately isolate the effect of a particular intervention from that of other interventions of factors

  • There is currently little impact evaluation evidence on gender differentiated program effects

    • AADAPT, in collaboration with the GAP, will support governments build the evidence


How to measure gender differentiated effects

  • Measure differential effects on men and women for the same interventions

    • Larger samples

    • Different data collection strategy

    • Additional indicators

    • For each type of intervention, measure spillover effects on the targeted individual as well as other members of the households who may be affected (wife of the head, daughters)

  • or, Target men and women with different interventions and measure effects on men and women


Measuring differential impacts: Ethiopia (Deiningeret al’s 2008)

  • Securing land property rights had significant impacts on women heads of household

  • Women heads of household who received land certificates were

    • 20% more likely to make soil & water conservation investments in land (extensive margin)

    • Spend 72% more time on these investments (intensive margin)


Are effects always different?

No difference

No difference

No difference

No difference


Unpacking “no gender difference” results

  • When we find no differences, it could mean one of two things:

    • We can’t tell – the estimates are so noisy as to be indistinguishable (sample size too small)

       NO information for policy

    • The difference is actually zero (well estimated)

       Policy relevant result


We need more and better evidence

  • A well estimated zero result is informative

    • If the policy is aimed at a documented gender gap, it did not work

    • If the policy is not aimed at a gender gap, men and women are affected equally

  • Why not report more “zero” results?

    • Gender analysis isn’t always done: requires specific sampling strategy

    • Editors’ bias for non-zero results (publication bias)


Also important is measuring externalities or unintended effects: Peru (Field 2005)

  • The impact evaluation of a national land titling program in urban Peru found:

    • No change in women’s labor supply but

    • A 21% reduction in birthrates in program areas


How to engender your impact evaluation in practice?

4 Steps:

  • IE concept stage

     whatto look for

  • Data collection: Design

     howto measure it

  • Analysis

     doing it (cf. Operational Issues , Saturday)

  • Results feed back into policy making

     what to do with it (cf. Operational Issues , Saturday)


1. IE concept stage

  • Understand what the gender issues are in your target population

    • How are the program objectives relate to them

  • Think about causal chain of the project

    • How might it be different for men and women?

  • Consider gender differentiated interventions

  • Design an evaluation that captures above


Gender differentiated results chains: e.g. Ghana


Consider bothDirect and Indirect Beneficiaries

  • Gender differences on direct beneficiaries

    • Ex.: the effects of providing irrigation on female vs. male farmers’ yields

  • Gender differences on indirect beneficiaries

    • Ex.: non-head male and female agro-processing incomes in households where the head receives the intervention


Indirect beneficiaries


Data Collection

  • Most rural surveys collect information at the household level

  • For gender, look into the structure of production within the household

  • Collect data on land and asset ownership, control over resources, use of resources, use of labor and results by class of land, type of household member

  • Gender disaggregation generally requires

    • More indicators

    • More data

      • For each indicator, what is the relevant level of data collection? (individual, household, plot, community…)

  • Bigger sample


Qual, quant and feedback


3. Impact evaluation analysis

  • Analyze direct and indirect impact by gender

  • Draw conclusions on whether policy is effective as per direct impact

  • Understand whether policy has adverse effects and what could be done to amend to them

  • Estimate whether there are significant positive externalities and spill over effects that make the policy even more effective


4. Feedback

  • Reduce the analysis to simple explanations to support

    • Scale up or down of interventions that work or do not work well

    • Modifications to interventions that have adverse effects

  • Discuss with operations and take advantage of policy cycles to introduce changes


Conclusions

  • Gender policy is development policy

  • To better influence policy in this direction, need

    • To experiment with gender differentiated interventions

    • Measure gender differentiated effects

  • Developimpact evaluations that are well designed to capture gender differences

    • Target women

    • Measure spillovers

    • Key is to understand how gender plays out in the causal chain


Thank you


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