Gender Analysis in Policy Research 2017 PEP Annual Conference Nairobi, Kenya June 14, 2017 Dileni Gunewardena

Gender analysis in quantitative research: need for improvement and lessons from experience How can we improve gender analysis in quantitative research using existing databases? Lessons from good gender analysis and from missed opportunities for integrating gender analysis in research What are key issues in gender and development and how do we frame those into research questions that can inform policy?

Continuum of gender inclusion in research No inclusion – all household members are alike Gender dummy – incorporates effect of being female (usually) but constrains effect to be the same as male Sample separation – same research question but exploring how it applies to men and women separately Address a critical gender related issue as a main research question and include it in the research design

Continuum of gender inclusion in research No inclusion – all household members are alike Gender dummy – incorporates effect of being female (usually) but constrains effect to be the same as male Sample separation – same research question but exploring how it applies to men and women separately Address a critical gender related issue as a main research question and include it in the research design

Some ways to think about gender in (quantitative) development research Gender as diversity/heterogeneity People are different, respond differently to incentives, face different constraints Gender as a culture-specific construct, gender roles differ among societies, and gender identity is acquired – and operates at multiple levels [Definition from Caren Grown, Gender Issues in Development, American University, 2010] How to include social norms in the model? Gender bias operates at different levels Gender as a category of conflict Bargaining models

Caren Grown, Gender Issues in Development, American University, 2010

Some issues in gender and development Gender bias in intra-household allocation of resources Girls’ education issues Adolescent girls’ health issues – with implications for child nutrition (next generation) Gender bias in skill acquisition – within formal schooling, and OJT Gender bias in the labour market Gender wage gaps Gender bias in hiring Occupational choice and gender Low female labour force participation – tradition or economics? Preferences, influenced by cultural norms Constraints – (relative) prices Gender and entrepreneurship, access to credit Unpaid work, care economy, time poverty

Gender identity is acquired and operates at multiple levels - e. g Gender identity is acquired and operates at multiple levels - e.g. FLFP decision This graphic conveys some of the complexity of the female labor force participation decision – the takeaway is that while a woman’s endowments affect the decision, so does her community – and markets and governments play a role as well – markets in how they value or reward those skills and governments in the kinds of policies they implement. Source: Author

Analysing Intra-household wellbeing with existing data Intra-household wellbeing – evidence of boy girl discrimination - data is a challenge – but there are some well-known examples of how to get around this Deaton (1989) – outlay equivalent analysis – expenditure on adult goods like alcohol and tobacco decrease more for boys of a specific age compared with girls of the same age; conversely, spending on education greater in the presence of an additional boy vs. an additional girl Jensen (2005) - fertility behaviour generates sex inequality – son preference differential stopping behaviour (SP-DSB) – if boys are preferred, girls will on average have more siblings - between one tenth to one-quarter of the large male-female differences in educational attainment in the various states can be accounted for by the differences in sibling cohort size. Deaton, Angus, 1989. Looking for boy-girl discrimination in household expenditure data. World Bank Economic Review 3:1-15 Jensen, Robert, 2005.

Occupational choice and food insecurity Does concern for household food security influence the choice of business among self-employed men and women in urban low-income households? (Floro and Swain, 2012) Endogenous switching probit regression Self-employed women in households with money shortage (proxying higher risk of food insecurity) tend to engage in food enterprise activities – men tend not to

Microenterprises and returns to capital Average return to capital 5.7% per month, but ?9% for men, and zero % for women The authors explore different explanations for the lower returns among female owners, and find no evidence that the gender gap is explained by differences in ability, risk aversion, or entrepreneurial attitudes. Differential access to unpaid family labor and social constraints limiting sales to local areas are not important. However, there is evidence that women invested grants differently from men. A smaller share of the smaller grants remained in the female owned enterprises, and men were more likely to spend the grant on working capital and women on equipment. The gender gap is largest when male-dominated sectors are compared with female- dominated sectors, although female returns are lower than male returns even for females working in the same industries as men. [De Mel, McKenzie and Woodruff, 2008]

Integrating gender using existing databases National databases – LFS, HIES – household rosters have a lot of intra-household information International databases World Bank Integrated Surveys on Agriculture – LSMS-ISA – 5 countries? standardized individual-level disaggregated data on asset ownership, including ownership, management and control of agricultural plots and livestock, as well as other assets and access to credit (Grown 2014) The Evidence and Data for Gender Equality (EDGE) initiative https://unstats.un.org/edge/index.cshtml developing methodologies and guidelines for collecting individual level information on physical and financial assets and entrepreneurship and piloting experimental work in 12 countries through 2015. The Global Financial Inclusion (Global Findex) Database – 148 countries since 2011 measures how adults – spanning basic socioeconomic levels, gender, as well as urban/rural settings – save and manage their finances, and cope with access issues. Skills toward Employment and Productivity (STEP) – 14 countries Cognitive skills and noncognitive skills (socioemotional, personality/behavioural traits), technical skills Demographic and Health Surveys DHS – can address a variety of questions, and have a lot of woman-specific data – fertility, etc. Databases needed Countries need support to implement time use surveys and conduct them on a regular basis, either as stand- alone surveys or as part of multi-purpose household surveys (Grown 2014)