An education story, not an age story

Like much of changing and exciting news in demography, the New York Times’ story about births to women under 30 appears to be largely about education. Kathryn Edin, who wrote a book I’ve lauded several times in this space and use extensively in my own research, responds in an article Harvard Magazine.

“What the article essentially got wrong is that this is aneducation story, not an age story,” explains Edin, professor of public policy and management at Harvard Kennedy School and a prominent scholar of the American family. She points out that 94 percent of births to college-educated women today occur within marriage (a rate virtually unchanged from a generation ago), whereas the real change has taken place at the bottom of the socioeconomic ladder. In 1960 it didn’t matter whether you were rich or poor, college-educated or a high-school dropout—almost all American women waited until they were married to have kids. Now 57 percent of women with high-school degrees or less education are unmarried when they bear their first child.

The statistic put forth by the Times severely undercounts the issue when we don’t take into account education. College-educated women, it seems, are waiting for marriage to have kids, and non-college-educated women are having kids before they’re married. Importantly, it’s still a large group of women that are choosing to have kids without being married, and as I argue in my dissertation, it’s a group that merits more attention. We don’t know much about them.


Betsey Stevenson and Justin Wolfers on why we study families

I’m often asked why my research is economics and not sociology. Justin Wolfers and Betsey Stevenson give one answer as part of a longer Q&A on their research:

Your other areas of research focus include marriage, divorce, and family. Why would these areas interest economists? Or business leaders?

Dr. Stevenson: Economics is about how people make decisions optimally, given that they’re facing constraints. That framework can be applied anywhere, not just to things that are about dollars and cents and the economy. Families and labor markets are intimately connected, and to understand one, it’s helpful to understand the other. That’s because decisions about labor force participation and about what kinds of jobs to take and what kind of hours to keep are made within the context of family lives. What happens in families affects the way people make those kinds of decisions. And what happens in labor markets affects the decisions people make about families. Economists are also interested in families because we have come to realize that there are many parallels between family and labor markets.

Dr. Wolfers: The first place that people notice the similarities between family and economics is in what some have called the marriage market, which looks a whole lot like the labor market. People search for partners the same way they search for jobs. When you find a spouse or a job that looks like a good fit, you take it. And you must make a decision about how much time to spend searching for the perfect spouse or the perfect job before accepting a job or a spouse.

Related Content:

  1. Anticipating Divorce
  2. For Valentine’s Day, on Love and Marriage and Economics

An introduction to spatial analysis

After my first, rather disastrous, year of graduate school in Boulder, I almost transferred to Geography. Or at least, I thought a lot about it. While the math in Economics was kind of kicking my butt, everyone working with graphs and maps seemed so blissfully happy. Ultimately, I stuck it out in Economics, and am extremely glad that I did, but I haven’t lost my love of maps and have always been curious about spatial research.

Next week, I’ll be doing a three-day workshop at the University of Colorado‘s Institute of Behavioral Science. Many of my economics professors were associated with IBS, but none really did spatial analysis, so I was left to find out some of it on my own. A few years ago, I helped design a survey on handwashing and other hygiene behaviors for a group building latrines and protecting water sources in Nepal. The data are fascinating and though we started analyzing it, everyone had limited use of one of the two tools necessary to do spatial regression. I had the Stata skills and my coauthors had limited GIS skills, but combining them wasn’t going to happen. This short course is hopefully the next step in getting those papers off the ground and into journals, but also more importantly, back to the community where we did the research. Though we’ve presented some findings to them, I’m sure there are many more insights to be had with these data.

With that, I’ll be reading a lot of spatial analysis papers over the next week. The syllabus has hundreds of pages of reading, much of which I’ve printed out and am planning for my long trip back to Colorado next week, but I’m willing to share the “lite” version with you all.

For definitional purposes, spatial analysis is “the formal quantitative study of phenomena that manifest themselves in space,” according to Luc Anselin. More informatively, I think, spatial analysis allows us to “interpret what ‘near’ and ‘distant’ mean in a particular context” and showcase whether and how proximity or location have an effect on an outcome we’re interested in.

Anselin divides spatial analysis into two categories–data-driven analysis and model-driven analysis, and highlights the challenges of each, which I imagine will get plenty of air time next week and are a little bit daunting to a student and devotee of econometrics:

Indeed, the characteristics of spatial data (dependence and heterogeneity) often void the attractive properties of standard statistical techniques. Since most EDA techniques are based on an assumption of independence, they cannot be implemented uncritically for spatial data…As a result, many results from the analysis of time series data will not apply to spatial data.

Model-driven analysis seems much more up my alley and suited to regression, but the main problem, which I encountered in my own research, “is how to formalize the role of ‘space.'”

Just like this basic the ideas and tools used in spatial regression seem fairly consistent with my view of econometrics in general. There are tradeoffs to employing different models and assumptions, and measurement error is alive and well. Notably, although this could be out of date by now: “Spatial effects in models with limited dependent variables, censored and truncated distributions, or in models that have count data have been largely ignored…multivariate dependent distributions other than the normal are highly complex.” More to come, I’m sure. My colleague has already told me I have to teach him in the Fall, and I’m hoping to be able to incorporate some of this into my Methods class, so get ready for some spatial econometrics here.

As an aside, if you happen to be in Colorado, check out these cool solar events that are happening, including a world-record-braeking attempt at the most people in one place to watch a solar eclipse together at CU’s Folsom Stadium. Or, well, you could just go look at it where you are, too.

Referenced: Anselin, Luc. 1989. “What Is Special about Spatial Data? Alternative Perspectives on Spatial Data Analysis.” Conference Proceedings, Spatial Statistics: Past, Present, and Future. Institute of Mathematical Geography, Syracuse University.

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