Category: Culture

A totally different country

A New York Times article yesterday details the growth of craft brewing around the US, but particularly in Denver. I, naturally, loved this article, but my favorite line in it has nothing to do with beer. The article quotes a craft brewer, who enjoys working for himself despite the long hours, and

who opened the Strange Brewing Company in 2010 in an old medical marijuana growing warehouse.

Colorado has a lot of quirky laws about alcohol. And it also has some quirky laws about marijuana. But what is so telling about this quote is that the medical marijuana growers moved on. In my limited experience with the industry (I know a guy who owns a medical marijuana store), the industry is growing like gangbusters. These growers probably went and got a bigger place.

But regardless, the nonchalance with which it’s included is priceless.

H/t @price_laborecon

Duflo and Female Empowerment

When you volunteer with a left-leaning organization that requires forty-two hours of training on social justice and examining your own privilege and sensitivity, one of the first things you are taught is that empowerment is a silly word. Empowering, by definition, involves giving someone your power, which is, by this understanding of power, impossible. The idea is that we each have privilege and power that we didn’t necessarily earn, by way of our gender, skin color, or height, for example, and as we can’t give those things to another person; we can’t actually “empower” them.

The distinction seems like semantics, but it actually creates a very different outlook in social justice terms. There is a difference between trying to give someone your power–which is patriarchal in addition to futile–and creating an environment in which more people have access to power.

Hence, when I saw the title of Esther Duflo’s latest NBER working paper, I cringed a bit in anticipation of what might lie within. She and Abhijit Banerjee also sprinkle the term throughout their recent book Poor Economics, which I’ve recently finished, enjoyed, am excited to hear my students’ reactions. But I’m a proponent of presenting and discussing Duflo’s work, even if not always a proponent of the work itself, so I was willing to give it a try, hoping it was just a vocabulary issue.

Though I still think the term should be used more carefully, Duflo largely seems to be addressing issues of equality of treatment, investment, education, and salary in the developing world. It is a literature review, and a rather comprehensive one at that, covering the status of women all over the world and a number of experiments and papers that have sought to tease out the directionality of the relationship between gender equality and development.

For anyone interested in the state of women in the developing world and the relationship between equality and development, it’s a must-read.

MLK Day and Race

Today is Martin Luther King, Jr. Day, as I’m sure you know. MLK Day was the only federal holiday we got off at Duke, or at least the only one that fell during the semester. It was always marked with a big celebration and my dance group often performed. I always liked that celebration.

But I’ve gotten totally off-topic. An article this week in the NYT highlighted the issue of choosing a race, particularly on census forms, for Latinos in the US. Latinos, who are incredibly diverse in physiognomy and heritage, are, according to the article, choosing to mark ‘other’ instead of one or more of the categories provided.

The issue is of particular importance to economists because in most microeconomic work, we control for race. The implication of this, of course, is that by including someone’s race in a regression, we are separating out some aspect that is predictive of whatever behavior or outcome we’d like measure. And not only are we separating it out, we’re separating it out in a measured, specific way such that we think it applies to all respondents.

For example, we might see a regression that says, all other things equal, the average black person receives one more year of education than a white person. (I saw a statistic like this the other day, saying that black people of similar wealth and socio-economic status get more education than their white peers, I wish I could remember where it came from.) Though the statement is necessarily couched with “on average”, if a number of people are choosing other instead of white or black or some combination of these, we’re not actually seeing the true average. This is called measurement error, and can have pretty significant effects on esimation.

In my own work, for instance, black mothers and white mothers in the Fragile Families Data display different characteristics and decisions regarding investments in children when controlling for whether they’ve received a promise of financial support. But if I were able to capture more of the group that self-identifies their race as other, this effect may be reduced or even disappear.

The question of whether to even ask about race, or ethnicity, is a sticky one. It may give us information that gives different groups more “clout” as the NYT article argues, or it may reinforce stereotypes and feed the flames. Regardless, if research continues the way it currently goes, having a large group of people opt out because they don’t find something that fits them is problematic.

There’s still lots of thinking to be done about it, and perhaps today is a good day to mull it over a bit. I hope you enjoy your MLK Day!

–“The arc of the moral universe is long, but it bends towards justice.” -MLK, Jr.

Public Randomization

A significant issue in conducting randomized control trials in a community is the issue of fairness. The idea behind RCTs is to mimic the medical model by scientifically ascertaining just how useful a treatment might be. In the case of development economics, this could be a subsidy or an extra year of education, for example. In order to eliminate (or at least reduce) the effect of confounding factors, the researcher randomizes over the population, picking a representative sample to receive the treatment and compares their results to those who did not receive the treatment.

While in theory this should give us the best answer as to how to combat poverty, or get children to school, or determine the effect on whatever outcome we hope to affect, it’s also problematic. The process of randomization necessarily leaves some people out, essentially denying them help that could be life-saving or life-transforming. It might also provide benefits that researchers view as small, but that are capable of creating divisions in a community, or perhaps jealousy, suspicion, or bitterness.

Different RCTs deal with this in different ways. Some do nothing. Some hope the treatment group doesn’t notice. Some tell the control group that they will get the treatment after the analysis is done, some take this course but without informing the treatment or control groups. All of these solutions have their issues, which are dependent on the type of treatment. In some cases, control respondents might change their answers to certain questions to appear more sympathetic, or deserving of the treatment. Or they might anticipate how the treatment is going to affect them in the future and have their answers reflect their hopes rather than their actual state.

As in all survey data, the mere act of asking the question affects the answer.

Last week, Kim Yi Dionne, a professor at TAMU, posted on her blog about making the randomization process public. While I don’t think it solves the problem of people changing their answers to what they think they should be (either to make the treatment look better or worse), it does deal with the bitterness and competition that can often arise out of randomly selected treatment groups.

I especially love the education component of it.

[A Malawian research supervisor] posed a question to the audience: if he wanted to know how the papayas in the village tasted, would he have to eat every papaya from every tree (pointing to the nearby papaya trees)? Some villagers laughed, many said “ayi” (no) aloud. He said, instead he would eat one or two from one tree, then take from another tree, but probably not take one from every tree in the village so that he could know more about the papayas in this village.*

Every mentor I have had for research in the developing world has been adamant that we share findings with the community whose participation was requisite to our success. But rarely do we take the opportunity to educate about how we came to our conclusions, hoping the conclusions themselves will suffice.

I think it’s brilliant.

No loo, No I do

A few weeks ago, a coauthor sent me a job market paper from an environmental economics student at Yale. Though in a very different department than me, we have similar interests and she thought I would find the paper interesting. Not only did I find it interesting, I found myself wishing it had been my job market paper. Apparently, so did a lot of people. The paper has been blowing up my twitter feed and was featured on the World Bank’s Development Impact Blog.

The paper evaluates the effects of a media campaign in Haryana, India designed to encourage women to make latrine presence a requirement for marriage. The project is particularly interesting because it allows for reasonable evaluation of a campaign targeting social norms without the the randomized control component so in vogue in economics right now. In addition, it provides real evidence as to the causal effect of skewed sex ratios. While we have speculated and reported on the effects of sex ratios, many of which I’ve discussed here, there is little statistical evidence. Now, we have some. It’s pretty great.

In summary, the paper shows that men of marrying age are more likely to build latrines when they live in areas with a more skewed sex ratio. Thus, a woman’s bargaining power in demanding a good that has an outsized benefit for her (privacy, sanitation, health) increases when she becomes relatively ‘scarce’ on the marriage market. While this doesn’t discount the other, more undesirable possible effects of a skewed sex ratio (bridenapping, increased violence against women, etc), it is certainly evidence that women are leveraging their bargaining power to improve their outcomes.

In addition, the means to test a social norms marketing campaign are huge. My own work on such campaigns directed at reducing gender-based violence showed the near impossibility of successfully and credibly evaluating their impact. The use of a sex ratio as a (somewhat?) exogenous measure of potential impact is novel, exciting, and I’m sure will be in use by many papers to come. There’s the obvious question of whether it’s plausibly exogenous, but perhaps we’ll save that conversation for another day.

The paper has two parts, one presents a theoretical model to explain the mechanism and the other presents empirical evidence from the program itself to show how a skewed sex ratio has increased women’s bargaining power, at least on this one dimension in Haryana, India. I have some nitpicky comments, like the theory section needs to be more thoroughly explained, or there are square brackets where there should be curly ones, but overall, I think it’s a great paper. It’s kind of wonkish, but you can download the paper here, if you’re interested. Good luck in Chicago, Yaniv!

More of them or more willing to say it?

Today’s NYT had an article on the cities with the highest proportion of gay couples. Interestingly, the list doesn’t include many high-density cities or the well-known gay neighborhoods. The lack of historical data and rapidly changing social norms make it difficult to differentiate between whether there are simply more gay couples living in places like Rehoboth Beach, DE, or whether they’re simply more visible and more willing to disclose their orientation.

While this limitation means we cannot make  statements about the changing demographics in these cities, I think it does say something pretty profound about standards of acceptable social behavior in small towns and, to some extent, all over the country.

Ploughs vs. sticks

There is small strain of the economics literature that deals with religion and culture and tries to take these things at face value. While much of economics (and economists) take culture out of the picture when creating models, there are whole conferences devoted to how culture influences our decision-making.

Much of the reason that culture is often excluded from economic models is that it is, or at least seems, endogenous. Culture determines our decisions which determines our culture, so we have a chicken-and-egg argument. You could say, then, that the point of the field of Economic History—which aims to bring economic reasoning to historical events and data–is to tease out which came first, the culture or the decision, the tradition or the allocation.

A recent paper by Alberto Alesina, Paola Giuliano and Nathan Nunn tackles this chicken-and-egg question by comparing places where the plough was readily adopted and places where more labor (digging with sticks, weeding by hand) than capital prevailed as the dominant agricultural tool. They argue that fertility, or how many children one decides to have, was influenced on a societal level by the adoption of the plough. The reasoning is rather straightforward. The plough, as a labor-saving device, reduced the need for women and children in the fields, thus creating a less egalitarian culture–where women stayed at home instead of working outside the home–and one where women had less children.

They note the fertility result as surprising; their original hypothesis had been that a plough would increase fertility as it increased the time mothers would have to bear children. I don’t find it particularly surprising, knowing it takes a lot of hands to run a farm, but I do think it’s an interesting attempt to identify the source of cultural norms.