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Interview with Seema Jayachandran: Women and Development, Deforestation, and Other Topics

Summary:
Douglas Clement and Anjali Nair have collaborated to produce a "Seema Jayachandran interview: On deforestation, corruption, and the roots of gender inequality" (Federal Reserve Bank of Minneapolis, February 12, 2021). Here are a couple of samples: The U-shaped relationship between economic development and women's labor force participationThere’s a famous U-shaped relationship in the data between economic development and female labor force participation. ... Historically, in richer countries, you’ve seen this U-shape where, initially, there are a lot of women working when most jobs are on the family farm. Then as jobs move to factories, women draw out of the labor force. ... But then there’s an uptick where women start to enter the labor market more and not just enter the labor market, but

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Douglas Clement and Anjali Nair have collaborated to produce a "Seema Jayachandran interview: On deforestation, corruption, and the roots of gender inequality" (Federal Reserve Bank of Minneapolis, February 12, 2021). Here are a couple of samples: 

The U-shaped relationship between economic development and women's labor force participation

There’s a famous U-shaped relationship in the data between economic development and female labor force participation. ... Historically, in richer countries, you’ve seen this U-shape where, initially, there are a lot of women working when most jobs are on the family farm. Then as jobs move to factories, women draw out of the labor force. ... But then there’s an uptick where women start to enter the labor market more and not just enter the labor market, but earn more money. There are several reasons why we think that will happen.

One is structural transformation, meaning the economy moves away from jobs that require physical strength like in agriculture or mining towards jobs that require using brains. ... For example, the percentage of the economy in services is higher in the U.S. compared to Chad, and service jobs are going to advantage women. So that’s one reason that economic development helps women in the labor market.

The second reason is improvement in household production. Women do the lion’s share of household chores and, as nations develop, they adopt technology that reduces the necessary amount of labor. Chores like cooking and cleaning now use a lot more capital. We use machines like vacuum cleaners, washing machines, or electric stoves rather than having to go fetch wood and cook on a cookstove. This labor-saving technology frees up a lot of women’s time because those chores happen to be disproportionately women’s labor. Some of those technological advances are in infrastructure. Piped water, for instance, where we’re relying on the government or others to build that public good infrastructure. And some is within households; once piped water is available, households invest in a washing machine.

The third reason is fertility. When countries grow richer, women tend to have fewer kids and have the ability to space their fertility. For example, both the smaller family size and the ability to choose when you have children allows women to finish college before having children.  ... Less on the radar is that childbearing has also gotten a lot safer over time. There’s some research on the U.S. by Stefania Albanesi and Claudia Olivetti suggesting that reduction in the complications from childbirth are important in thinking about the rise in female labor force participation.

Paying landowners in western Uganda to prevent deforestation
In many developing countries, people are clearing forests to grow some cassava or other crop to feed their family. Obviously, that’s really important to them. You wouldn’t want to ban them from doing that. They’d go hungry! But if we think about it in absolute terms and global terms, the income people are generating by clearing forests is small. If we can encourage them to protect the forest and compensate them for the lost income, then protecting the forest actually makes them better off than clearing it. And because the income they’re forgoing is small in global terms, that could cost a lot less than other ways of reducing carbon emissions. ...

This is a truly interdisciplinary project. One of my collaborators is a specialist in remote sensing, which is analyzing satellite data to measure land use and forests. It’s similar to the machine learning that economists use often. But here we use high-resolution satellite imagery, where a single pixel covers 2.4 meters by 2.4 meters of surface area. 


If I showed you one of our images, you could spot every tree with your eye. Of course, there are 300 million pixels in the area we have imagery for, so you don’t want to go and hand-classify all those trees. But we have the algorithms and the techniques to classify all of those pixels into whether there’s a tree or not. We have this imagery for both the control villages where the program wasn’t in place and the treatment villages where it was, where landowners were paid to not cut their trees. So we could see before-and-after images of what happened in both control and treatment villages.

By doing that, we could see that in the control villages over this two-year period, 9 percent of the tree cover that existed at the beginning was gone. That’s a really rapid rate of deforestation. ... By comparison, in the villages with this program, the rate of tree loss was cut in half, closer to 4 to 5 percent. There’s still tree loss—not everybody wanted to participate in the program—but the program made a pretty big dent in the problem.

Another thing the high-resolution imagery shows is the pattern of tree-cutting, and that showed that we’ve been underestimating the rate of deforestation in poor countries. On relatively low-resolution satellite imagery, we could see clear-cutting of acres and acres of land. That is an important problem. But recent estimates suggest that, especially in Africa, half of the deforestation is smaller landholders who are cutting four or five trees in a year to pay for a hospital bill, say. That adds up.

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