Like every Friday, based on Raj Nallari  and Breda Griffith's lecture notes.
Measuring Gender Inequalities
In order to fully appreciate gender as an analytical category in macroeconomics and macroeconomic policy, one needs appropriate data and tools – statistics and modeling. For the most part, gender measurement issues have only been addressed in the past thirty years or so and remain a work-in-progress. Data collection methods are not always gender sensitive for a number of reasons. First, managers, researchers and technical staff may not be aware of gender issues in the policies and programs and/or lack experience with gender issues and methods. Second, surveys frequently interview the household head, which in most cases is male. Third, the nature of gender is often sensitive and formal interviews are not the best way to capture information on sensitive topics (domestic violence for example) and finally, women may not be able to speak freely in interviews or to attend or speak at community meetings. (World Bank, 2001 ). Thus for all of these reasons and not withstanding that, as noted in previous posts, the emergence of gender as an analytical category is relatively recent, the measurement of gender is an ongoing issue. Yet the case for measuring gender is a strong one.
In the past 20 years national statistics have sought to include gender statistics programs and there is an increased awareness of the importance to provide gender analysis for the development and monitoring of policy. However for the most part, these programs have often been confined to social and demographic statistics. Recognizing that gender is an issue related to all statistics where the individual – male and female – is concerned requires that gender be mainstreamed, i.e. for all statistical departments collect data on gender and not just one office. This requires the commitment of top managers and the establishment of gender advisor officers reporting directly to the chief statistician with a designated unit overseeing the mainstreaming process.
Why measure gender?
Gender statistics is important for at least three reasons. First, it brings to the public domain the inequalities facing women and men and the magnitude of those. Second, it provides valuable information for policy-makers, to more fully inform policy. And third, it provides an important baseline from which to measure the effectiveness of policy and monitor the progress being made by government policies on the lives of women and men.
The failure to measure gender results in economic invisibility and statistical underestimation of work and output, in particular women’s work. The invisibility and statistical underestimation of women’s work was a result of traditional economic analysis that concentrated on the market and thus income-earning activities. Unpaid work, such as domestic work for the family and voluntary work, occupations dominated by women, did not count, literally. The battle to include unpaid domestic production in GNP accounts was hard-fought, even with theoretical and empirical evidence from New Household Economics and the domestic labor debate respectively as discussed in previous posts that demonstrated the economic significance of household production and women’s work. An historical revaluation of women’s work and an improved system of accounting in the 1970s, spearheaded by academics, government and the international organizations (International Labor Organization (ILO) and United Nations (UN)) lead to a conceptual definition of “economically active” that included unpaid production. At the methodological level, many countries committed to improving the accuracy with which women’s participation in the labor force was counted and different methods were designed to estimate the value of home production.