Category: Development

Female empowerment, decisionmaking, and how to measure it

How we define women’s empowerment or autonomy using decisionmaking questions in the DHS surveys (and similar questions in other surveys) has always bothered me. I’m glad someone decided to look into it rigorously.

While there has been little evidence explicitly testing indicators and sources of bias in conventional intrahousehold decisionmaking, the literature does discuss a number of reoccurring limitations. The first is around the treatment of jointness in decisionmaking. Although questions are typically sensitive enough to identify whether a decision is made solely by the woman or jointly by the woman and someone else, how should we treat these distinctions? Whereas it is tempting to assume for all cases that an autonomous decision, relative to a joint decision, is the one in which the woman has more power, the rationale for that possible ranking must clearly be conditioned on household composition. In a household with several adult members, a woman is more likely to make joint decisions based on sharing of resources and responsibilities. In addition, in such cases, it is often difficult to understand in the presentation of indicators with whom the decision is being made jointly and how much that matters for rankings. The implications for women’s empowerment may be very different if the woman is making a decision jointly with her spouse or if she is making it jointly with her father, mother-in-law, or son. Further, in western societies, we often think that in the most equitable partnerships, decisions are discussed through open communication and made jointly. Therefore, it could be claimed that joint 5 decisions should be ranked equal to or preferred to sole decisions; however, the actual dynamic may vary case by case. The issue of jointness further interacts with the importance of the decisionmaking domain. For example, one woman may make a sole decision on a relatively less important domain (for example, daily food preparation) and another woman a joint decision on a relatively more important domain (for example, purchase of a house). In this case, how would we rank or interpret their decisionmaking power relative to each other?

From a new paper by Amber Peterman and colleagues on women’s decisionmaking indicators and their usefulness. (Emphasis added by me).

Inheritance law and suicide in India

I started to send this out as a series of tweets, but decided it was worth something a bit longer. I haven’t had much time to blog over the last 9 months, but perhaps this summer will get me writing again…

A new Anderson & Genicot paper finds that codifying inheritance rights to property for women in India lead to increased suicide rates for both men and women. The paper is based on an intrahousehold bargaining framework and rests on the mechanism whereby if women are seemingly arbitrarily given more power in relationships via more access to capital, that might cause stress and thus lead to suicide by men. It also might be that as men inherit smaller shares of their parents’ assets, it is essentially an unexpected shock and could cause financial stress that could lead to suicide. There is precedent for this interpretation in the literature, particularly in sociology.

For women, the argument to me is less clear. The inherited property, though perhaps causing additional marital discord or stress, is also 1) an increase in potential income–which should theoretically decrease overall stress levels, and 2) a better outside option, leaving women more free to leave a relationship. If either of these hold, they should actually lead to a decrease in the suicide rate.

Also, suicide rates are not just going up for married men and women. The WHO recently announced that suicide is the biggest killer of adolescent girls worldwide. Even though adolescent girls can inherit property in India (from what I can tell, there is no bar based on age of majority), they’re probably not the largest group of inheritors. So, do we believe that suicide rates for adolescent girls are totally unrelated to suicide rates for older women and men? I doubt it, especially given a large body of work that posits that suicide rates may be influenced by media coverage of suicide (for example). That suicide is driven by the inheritance law requires us to believe they are mostly unrelated. Or that girls are so stressed about the idea of one day owning and running a farm that they check out early.

While the empirical work appears to be very strong in the Anderson and Genicot paper, I’m not sold on the theoretical mechanism. Moving towards gender equality in places with strong traditional gender roles and norms is likely to put stress on many individuals. Reallocation of profits and assets will also understandably cause unexpected wealth shocks for both men and women and could lead to marital discord, but it could also lead to stronger, more independent women. Further, higher rates of suicide among groups that are likely unaffected by the law change suggest something unobserved is affecting suicide rates.

Agricultural technology adoption and persistence

A new paper (gated) by Michael Carter, Rachid Laagja and Dean Yang shows, using a randomized fertilizer subsidy, that reducing costs increases adoption, but also, somewhat in opposition to previous research and importantly, that adoption is persistent into the following season.

First, we provide one of the 􏰄first randomized controlled trials of the impact of an input subsidy program, and the 􏰄first to measure impacts on a range of important household outcomes beyond fertilizer use itself. The only previous study using randomized methods is Dufl􏰅o et al. (2011), who estimate impacts of fertilizer subsidies on fertilizer use alone (in rural Kenya). We show positive impacts of input subsidies (in Mozambique) on a range of outcomes beyond input use, including farm output, household consumption, assets, and housing quality.

Second, we 􏰄find positive e􏰃ffects of input subsidies that persist up to two annual agricultural seasons beyond the season in which the subsidies were off􏰃ered. This result contrasts with Du􏰅flo et al. (2011), who 􏰄find no persistent impact of either 􏰀heavy􏰁 (50%) subsidies for fertilizer or the 􏰀well-timed nudge􏰁 of o􏰃ffering free delivery at the time of the previous harvest. Both treatments raise fertilizer use in the season they are provided, but impacts are very close to zero and not statistically signi􏰄ficantly di􏰃fferent from zero in the next season.

Having spent a lot of time lately with a friend writing a book on fertilizer and the apparent failure to launch of Africa’s Green Revolution, my thoughts immediately go to whether the fertilizer available on the market is real and how perceptions of fake fertilizer are affecting the decisions of farmers to continue (or not) using fertilizer in their fields.

Luckily, a few people are looking into this and maybe we’ll have some answers soon.

Workin’ for a livin’ in Bangladesh: Garment workers and outcomes for women

The garment industry in Bangladesh has received a lot of bad press in the last few years with the collapse of factories and threats of boycotts by workers’ rights groups. The question of whether employment in these industries is beneficial to workers, and particularly female workers, remains open. Economists tend to emphasize the effects on female empowerment (bargaining power, buying power, delayed childbearing, for instance), while rights groups enumerate the safety concerns and potential human rights abuses (long hours, low pay, no overtime pay, etc.).

While by no means offering a definitive answer the question, a new paper by Rachel Health and Mushfiq Mobarak (NBER gated or not gated) attempts to show that the economist are right. The paper shows that exposure to garment sector jobs increases age at marriage and first birth for girls and women in Bangladesh. Child marriage and early childbirth are common in Bangladesh, outcomes which expose women and girls to abuse, early mortality and morbidity, domestic violence, low educational attainment and more. If the garment industry is avoiding or delaying some of these outcomes by providing different opportunities, that’s certainly something to note.

Perhaps more importantly, the paper shows that there are significant returns to education within the garment sector. More educated employees receive higher pay and opportunities for advancement. Subsequently, knowledge of these additional returns to education may actually increase educational attainment in addition to these other desirable outcomes. There’s some concern about endogenous factory placement in the paper and how that might affect their results, but the authors do a nice job addressing it.

What works for women and girls, redux

Woman ironing clothes in Chandni Chowk, Delhi, India
Woman ironing clothes in Chandni Chowk, Delhi, India

Last week, I wrote a little about my contiuous struggle with the word “empowerment” and what it means in the context of improving the lives of women and girls. In particular, I mentioned a few World Bank studies that examine “what works?” and how can we incorporate the knowledge of local context into our understanding of empowerment. Then, a survey by DFID came across my desk asking a similar kind of “what works” question, but posing it to researchers, practitioners, and funders. If you’re involved in research, funding, or implementation of programs that target violence against women and girls, I encourage you to take the survey and be involved in the subsequent discussion groups. For my part, I can say that my involvement with DFID (through the partnership with the Nike Foundation—Girl Hub) was extremely informative and worthwhile.

Because the survey asks about rigorous evidence, I think it’s also worth mentioning some of my own work on the subject (with Laurie Ball Cooper). While the programmatic mapping is a bit old by now (I know plenty of new programs have been put in place), I think the overarching takeaway is the same. We need more evidence about what works to reduce violence and discrimination against women and girls. Whether that’s accomplished through increased impact evaluations, RCTs, use of secondary or administrative data, or experimental ethnography, great, but we need more evidence.

All of the papers that came out of that DFID workshop are worth a read. Here’s a link to one a linkt to one more from IFPRI’s Agnes Quisumbing and Chiara Kovarik.

What works for girls?

Last week, I had the pleasure of meeting with Ratna Sudarshan, an economist here in Delhi who is currently a fellow at the National University Education Planning Administration. We had a long discussion about how to look at female employment in India and then about the cultural dimensions of women’s empowerment and agency. I’ve often said that I really dislike the term empowerment. First, because the word literally means to give someone power and you can’t really give someone your own power, but secondly because it’s a term that’s quite jargon-y and steeped in a Western sense of what it means to be independent, have agency, and make one’s own decisions. 

Ratna asked me what I meant by empowerment and I gave a litany of possible answers, ending with, but it all depends on where you are. And she responded with a story of girls in Rajasthan, an arid, desert-y state in Western India, where age of marriage is very early, but girls tend not to live with their husbands until they’ve finished their formal schooling. So while the outward measure of “empowerment” bodes poorly for women, their age at first birth is actually quite high, so the health risks normally associated with child marriage aren’t really present.

It was with this in mind that I read about a series of World Bank reports summarizing impact evaluations on what works to reduce maternal mortality, delay age of marriage, and generally improve the lot of girls in the developing world. The reports were released this month in anticipation of the coming Millenium Development Goals deadlines. 

So, while I highly recommend you read them, I also urge you to think about context. 

Context, context, context, and how important it is in determining the effectiveness of policy or programmatic interventions.

Process, research ideas, and learning my way around new fields

Of the many things I’m learning in my new position is just how one individual has so many projects going at once. Rohini, and others like her with large development-oriented field projects, has a veritable army of students, RAs, research managers, field managers, enumerators, and post-docs to keep a million different parts moving. This translates into several big things for me. The first is that the work environment is much more social than any one I’ve ever been in.

I have to admit that being in India and the Philippines this summer has been a bit like being at summer camp. We all work in a big room together. We’re living in the same guesthouses and hotels. We’re mostly each other’s only friends, so there’s lots of time together. Thus far, there’s been a little bit of mentoring, lots of “how do I do this in Stata?” talk, lots of research talk exchange, and a whole lot (at least for me) of beginning to understand the process of both how this organization works and how others like it might work as well.

The other big thing about the size of the organization is that “my” research group is putting out papers on topics that I’ve never really thought about. For instance, topics in environmental economics. I took environmental economics, I think I even got an A in it, but I don’t think about environment the way I think about gender (sorry, Dr. Walsh!). When I think about gender and labor and discrimination and families and health, research ideas come out of every corner and I can barely keep up. When I think about the environment, I kind of just get depressed about how we’re destroying it. Corruption is another big space in which people are working that I just don’t know that much about.

Without any explicit instructions to do so, I kind of feel like it’s my duty to understand the papers that are coming out of this group. So when this one (gated), with an almost incomprehensible title, came across my desk for the fourth time this week, I figured I should read it. I’m starting to get my head around it and feel like I’m learning a ton, but luckily for me, we’re still talking about issues closest to my heart.

 

 

Random financial inclusion thoughts

The buildup around Prime Minister Modi’s Independence Day speech was palpable in the EPoD/BCURE office last week. My research group does quite a bit of work on financial inclusion in India and so rumors that Modi would announce a financial inclusion plan had not a few people talking.

In fact, the PM did announce a financial inclusion plan to open bank accounts for 75 million Indians by August 2018. It’s an ambitious plan, to be sure, but it struck me as rather odd. The way the papers presented the plan, Modi introduced the plan by talking about how many people have mobile phones in India, but nobody has a bank account. My head went immediately to the thought of “well, maybe he wants to expand mobile money use in India.” Despite the presence of quite a few mobile money providers in India, mobile money is used in very few transactions. This is very different than a place like Kenya, where mobile money is extremely widely used.

I’m not sure that mobile money is the best answer, but I think it’s at least an interesting use of existing infrastructure, as opposed to brick and mortar banks with minimum transactions and high withdrawal fees, for instance.

The impact of rainfall, directly

As a development and labor economist, it’s unusual to see colleagues concerned with the impact of rainfall, full stop, on anything. We’ve become so accustomed to seeing rainfall used as an instrumental variable, a pathway to causal results, rather than a driver of some effect in and of itself. A new working paper by David Levine and Dean Yang (gated), however, looks at rainfall itself, or rather deviations from mean rainfall levels, which is actually pretty important. If we’re going to use rainfall as an instrument, or think of it as an exogenous shock that can be modeled linearly (or non-linearly, but modeled nonetheless), then it’s a good idea to make sure those assumptiosn actually hold.

Abstract here:

We estimate the impact of weather variation on agricultural output in Indonesia by examining the impact of local rainfall shocks on rice output at the district level. Our analysis makes use of local meteorological data on rainfall in combination with government administrative data on district-level rice output in the 1990s. We find that deviations from mean local rainfall are positively associated with district-level rice output. 10% higher rainfall leads metric tons of rice output to be 0.4% higher on average. The impact of rainfall on rice output occurs contemporaneously (in the same calendar year), rather than with a lag. These results suggest that researchers should be justified in interpreting higher rainfall as a positive contemporaneous shock to local economic conditions in Indonesia.

The second life of RCTs and implications for child development

In the last few weeks, I’ve come upon two research programs (each with a few related papers) that utilize a combination of an RCT or phased-in intervention and follow-up data 7-10 years on to examine new research questions. They both happen to be focused on the lasting effects of childhood health and wellbeing initiatives, but I doubt that this trend will be confined to child health and literacy. Barham, Macours and Maluccio have a few papers (gated) that use the phasing in of a conditional cash transfer program in Nicaragua to test later childhood cognitive and non-cognitive outcomes, distinguishing effects by timing of the intervention. A working paper out last week shows that deworming programs in Uganda not only increased short-term anthropomorphic outcomes, but also contributed to children’s numeracy and literacy several years later.

In short, we’re seeing more evidence that these early health and wellbeing interventions can have profound impacts not just on the immediate outcomes–Under-5 mortaility, school attendance, etc–but also on future outcomes. I think it’s a neat use of experimental design to examine questions we might not have thought about when the programs were first put in place.