co-authored with Alaka Holla
Everyone always says that great things happen when you give money to women. Children start going to school, everyone gets better health care, and husbands stop drinking as much. And we know from impact evaluations of conditional cash transfers programs that a lot of these things are true (see for example this review of the evidence by colleagues at the World Bank). But, aside from just giving them cash with conditions, how do we get money in the hands of women? Do the programs we use to increase earnings work the same for men and women? And do the same dimensions of well-being respond to these programs for men and women?
The answer is we don’t know much. And we really should know more. If we don’t know what works to address gender inequalities in the economic realm, we can’t do the right intervention (at least on purpose). This makes it impossible to economically empower women in a sustainable, meaningful way. We also don’t know what this earned income means for household welfare. While the evidence from CCTs for example might suggest that women might spend transfers differently, we don’t know whether more farm or firm profits for a woman versus a man means more clothes for the kids and regular doctor visits. We also don’t know much about the spillover effects in non-economic realms generated by interventions in the productive sectors and whether these also differ across men and women. Quasi-experimental evidence from the US for example suggests that decreases in the gender wage-gap reduce violence against women (see this paper by Anna Aizer), but some experimental evidence by Fernald and coauthors from South Africa suggests that extending credit to poor borrowers decreases depressive symptoms for men but not for women.
In fact, a large majority of impact evaluations of projects that target economic activities don’t have any insight into differences across the sexes. Why? One person we talked to didn’t a collect a sex variable at all. OK, so that’s a minority. But a lot of folks probably don’t even try to test for differences. And for a bigger set of people, the differences are just not reported.
This is frustrating, because sometimes looking at program impacts across sexes can really matter. Take David and his co-author’s work on Sri Lanka (ungated version here) – he finds that women don’t benefit from a cash infusion to their business whereas men do. While this might make us want to rethink the whole giving them money thing (at least in the business context), he gets different results in Ghana (as he discussed here) – for some women’s businesses, in-kind transfers can make a difference for profits (but money may still be a bad idea). On the other hand, we borrowed somebody’s experimental data to look at the impact of improved access to finance for agriculture. The rough structure of this program was that people had to join groups and then groups were randomized into treatment and control. What we found was that while the average treatment effect looked promising (as per the original paper), the effects on profits for women were strikingly larger and significantly higher than for men – but the authors hadn’t run this regression (and thus shall remain anonymous not least because they were nice enough to give us the data).
So clearly more folks should be interacting treatment with gender when the set up of the intervention makes this feasible. This then gives rise to two other issues. The first is power – there often aren’t enough females (or males) in the dataset to statistically detect a reasonable difference. We’ll come back to this point next week.
The second issue is: who are these women who do participate? As our colleague from the project discussed above pointed out, the women who were participating (and who were a distinct minority) were quite different both from the male participants and from women who did not participate. This isn’t surprising if you think about it since the underlying barriers that women face mean that the ones who are economically active may be quite exceptional – they could, for example, be more entrepreneurial, or more desperate. And the things that make them different could matter a lot for how the program plays out. So in the case above, while we can conclude that the program benefitted eligible women who got to participate (by a heck of a lot) relative to those who were eligible but did not get to participate, it doesn’t tell us about what’s driving the difference between women and men. That would take some more structural work to understand what drives the difference in initial participation between men and women. This opens up a much larger research agenda, and we’ll come back to one piece of how to tackle this in two weeks.