My research is in applied microeconomics, with projects spanning
marriage and the family, education, and economic history. Each project below is
presented by its central question, main figure, and finding.
Published
If you marry your cousin, do your children pay for it their whole lives — not just in infancy?
Figure 2. Left: share of offspring surviving beyond each age, when parents are first cousins vs. not. Right: the estimated survival gap, with 95% confidence band.
Yes. Tracing couples married over a century ago through U.S. genealogical records, the children of first cousins live about 2.2 fewer years (measured from age 5) than the children of those same parents’ siblings — and once under-reported infant deaths are counted, the loss is at least 3.2 years. The figure makes the pattern visible: the left panel plots the share still alive at each age, and the first-cousin curve stays below the comparison curve the whole way; the right panel plots the estimated survival gap, which keeps widening with age (to roughly 4–5 percentage points) rather than closing after childhood.
Can banning cousin marriage loosen the grip of family ties and push people into the modern economy?
Figure 4. Effect of cousin-marriage bans by birth decade relative to the ban, comparing people from high- vs. low-cousin-marriage surnames — urbanization, farm residence, urban residence, and interstate migration (95% confidence bands).
Using 19th-century U.S. state bans and a genetics-based measure of cousin marriage built from millions of marriage records, the bans moved people from high-cousin-marriage families off farms (−8 pp), into cities (+3 pp), and into higher-paying, more varied occupations (~+4% income). Each panel of the figure is an event study — one outcome plotted by birth decade relative to the ban (0 marks the ban year). The estimates sit flat and near zero before the ban and only move after it, exactly the signature you’d expect if the ban itself caused the shift: farm residence turns down while urbanization, urban living, and interstate migration turn up. The pattern looks cultural — the effect of loosened kinship ties — rather than genetic.
Does a census-taker’s handwriting quietly decide which people history lets us trace?
Figure 3. Both the linkage rate (left) and the share of links later validated (right) rise with handwriting legibility — consistently across all eight linking algorithms.
It does. Measuring handwriting legibility across the 1940 U.S. Census and using enumeration-district boundaries for identification, moving from illegible to perfectly legible records would raise the rate at which people can be linked across censuses by 5–10 percentage points. In the figure, each line is one of eight different linking algorithms, and legibility runs along the x-axis: both panels slope steadily upward, so as handwriting gets clearer, both the linkage rate (left) and the share of links later validated (right) improve — for every algorithm. Legibility, not just the past itself, shapes the historical record we can recover.
How much measurement error is buried in historical census data — and is it random?
Figure 5. Correcting name and age errors with genealogical profiles raises the linkage rate (left) and lowers the share of invalid links (right), across census years — before vs. after correction.
Comparing census records against genealogical profiles, the paper finds that about one in two records has an error in a name or age — and, crucially, the errors aren’t random noise: they’re systematically more common for people with less human capital. The figure shows what correcting them buys, decade by decade: in the left panel the “after correction” line sits above “before,” so more people get linked, and in the right panel it sits below, so fewer of those links are false — a net 20–36% more valid links. (Reassuringly, the errors have only a negligible effect on estimates of intergenerational mobility.)
Why does the very same apartment sell for less just because it’s on the top floor?
Figure 1. Resale price by floor level (relative to the building), for 5-, 10-, 15-, 20-, and 25-story buildings. Prices rise with height, then drop sharply at the top floor.
Across 1.9 million Korean apartment resales, top-floor units sell at a 1.3–8.3% discount. Each panel of the figure is a different building height, and the shape repeats in all of them: the price premium climbs steadily floor by floor and then drops sharply at the very top floor — that last point, sitting well below the trend, is the discount. The reason isn’t the view or the climb; it’s energy: top floors swing through more extreme indoor temperatures and cost more to heat and cool (their residents even own more air conditioners).
Can paying couples to marry across caste lines reduce caste divisions — and who pays the price?
Illustration of the reported result — original figure to be added.
A ₹10,000 incentive raises marriages between Scheduled-Caste men and non-Scheduled-Caste women by about 4% in rural India — the upward bar in the figure. But the chart’s second, downward bar captures the catch: the same policy lowers the welfare of rural women. So integration is bought partly at their expense — a trade-off the paper models and then measures.
