This paper examines long-run development effects of regional productivity shocks in the United States. We exploit the timing and location of large resource discoveries to measure exogenous variation in the demand for labor and consider heterogeneous effects created by environmental and geographic features of the discovery site. Doing so requires developing novel, geographically delineated measures of both amenity value and geographic isolation. Using a dynamic event-study analysis we find that resource discoveries cause population to swell both in the short and long-run (e.g., fifty years), though this effect is largely driven by discoveries in unfavorable locations that either suffer from low natural amenity value or geographic isolation. The event study is complemented by a cross-sectional analysis of a broader set of modern-day economic outcomes that generally offers consistent results. This paper highlights the importance of considering environmental characteristics of a place when examining the influence of regional economic shocks and offers insights about the observed spatial pattern of development in the United States.
There is surprisingly little evidence on how terror attacks impact elections. With only a few exceptions, previous studies in this literature have focused on a particular country or attack, limiting their generalizability. Ours is the first comprehensive, multi-country examination of the effects of terror attacks on political opinions and election outcomes. The results provide little evidence that terror attacks are systematically related to Europeans’ attitudes towards immigrants and how much trust they have in government. International terror attacks are, however, associated with an increase in the vote share received by nationalistic parties in Europe. These results are relevant to the ongoing debate among academics over the effectiveness of terror attacks.
Sudden shocks to labor demand have sometimes been shown to increase local crime rates. We build on this literature by estimating the causal effect of labor-intensive seasonal agricultural activity on crime. We analyze a unique data set that describes criminal activity and fruit, vegetable, and horticultural (FVH) employment by month and U.S. county from 1990 to 2016. We find that the FVH labor share is associated with reduced property and violent crime rates, and possibly the number of property crimes committed within county years. Examining heterogeneities based on ethnicity, labor-intensive FVH activity decreases the rate of non-Hispanic arrests and victimization, and increases the number of Hispanic arrests and victims (consistent with rising local Hispanic populations). Taken together, results are broadly consistent with the idea that agricultural harvest of labor-intensive crops enhances local labor market opportunities that reduce incentives to commit crimes. Results are robust to a battery of alternative specifications that address the inherent challenges associated with measuring seasonal agricultural labor.
This paper examines the impact of the U.S. fracking boom on local STI transmission rates and prostitution activity as measured by online prostitution review counts. We first document significant and robust positive effects on gonorrhea rates in fracking counties at the national level. But we find no evidence that fracking increases prostitution when using our national data, suggesting sex work may not be the principal mechanism linking fracking to gonorrhea growth. To explore mechanisms, we then focus on remote, high-fracking production areas that experienced large increases in sex ratios due to male in-migration. For this restricted sample we find enhanced gonorrhea transmission effects and moderate evidence of extensive margin effects on prostitution markets. This study highlights public health concerns relating to economic shocks and occupational conditions that alter the local demographic composition.
Feyrer, Mansur and Sacerdote (2017) estimate the spatial dispersion of the effects of the recent shale-energy boom by unconditionally regressing income and employment on energy production at various levels of geographic aggregation. However, producing counties tend to be located near each other and receive inward spillovers from neighboring production. This inflates the estimated effect of own-county production and spatial aggregation does not address this. We propose an alternative estimation strategy that accounts for these spillovers and identify reduced propagation effects. The proposed estimation strategy can be applied more generally to estimate the dispersion of multiple, simultaneously occurring economic shocks. click to download "geographic dispersion..." replication files
Measuring Inequality, with with Thomas McGregor and Samuel Wills, Oxford Review of Economic Policy, 2019.
Inequality is important, both for its own sake and for its political, social, and economic implications. However, measuring inequality is not straightforward, as it requires decisions to be made on the variable, population, and distributional characteristics of interest. These decisions will naturally influence the conclusions that are drawn so they must be closely linked to an underlying purpose, which is ultimately defined by a social welfare function. This paper outlines important considerations when making each of these decisions, before surveying recent advances in measuring inequality and suggesting avenues for future work. Dutch Disease and the Oil Boom and Bust, Canadian Journal of Economics, 2019. Winner of the Robert Mundell Prize for best paper published in the CJE authored by a young economist in the previous two years. (working paper version)
This paper examines the impact of the oil price boom in the 1970s and the subsequent bust on non-oil economic activity in oil-dependent countries. During the boom, manufacturing exports and value added increased significantly relative to non-oil dependent countries, along with wages, employment, and capital formation. These measures decreased, though to a lesser and more gradual extent, during the bust and subsequent period of low prices, displaying a positive relationship with oil prices. However, exports of agricultural products sharply decreased during the boom. Imports of all types of goods displayed strong pro-cyclicality with respect to oil prices. The results suggest that increased local demand and investment spillovers induced by the oil revenue windfall resulted in increased manufacturing activity.
How are oil revenues shared throughout society? We combine high-resolution geo-coded data on night-time lights and population to construct global measures of rural poverty from 2000-2013. We find that oil booms, due either to high prices or new discoveries, increase light intensity and GDP. However, the increase in output is limited to cities and towns, with no evidence that it benefits the rural poor. We also find that while urbanization is occurring throughout the developing world, there is no evidence that it is hastened by oil wealth.
Over the past decade, the production of tight oil and shale gas significantly increased in the United States. This paper uniquely examines how this energy boom has affected regional crime rates throughout the United States. There is evidence that, as a result of the ongoing shale-energy boom, shale-rich counties experienced faster growth in rates of both property and violent crimes including rape, assault, murder, robbery, burglary, larceny and grand-theft auto. These results are particularly robust for rates of assault, and less so for other types of crimes. Examining the migratory behavior of convicted sex offenders indicates that boomtowns disproportionately attract convicted felons. Policy makers should anticipate these effects and invest in public infrastructure accordingly. Link to "There will be blood" replication files
This paper evaluates the impact of major natural resource discoveries since 1950 on GDP per capita. Using panel fixed-effects estimation and resource discoveries in countries that were not previously resource-rich as a plausibly exogenous source of variation, I find a positive effect on GDP per capita levels following resource exploitation that persists in the long term. Results vary significantly between OECD and non-OECD treatment countries, with effects concentrated within the non-OECD group. I further test GDP effects with synthetic control analysis on each individual treated country, yielding results consistent with the average effects found with the fixed-effects model.
Gregory Clark argued in A Farewell to Alms that preindustrial societies, including England, were Malthusian. Day wages show incomes were trendless: as high in Europe in the medieval era as in 1800, even in England. The opposed view is that England and the Netherlands grew substantially from 1200 to 1800. Early day wages overestimate living standards. Here we show that preindustrial farm employment shares can be estimated from probate occupation reports. These imply only 60 percent employed in farming in England in 1560–1579 and 1653–1660, consistent with the high incomes indicated by wages. Day wages do measure preindustrial living standards.
We estimate the effect of cash transfers on voter turnout, leveraging a large-scale natural experiment, the Alaska Permanent Fund Dividend (PFD) program, which provides residents with a check of varying size one month before election day. We find that transfers cause people to vote, especially in gubernatorial elections in which a 10% increase in cash ($180) causes a 1.4 percentage point increase in turnout. Effects are concentrated among racial minorities, the young, and poor. There is little evidence that transfers reduce logistical costs of voting, but rather operate by reducing voter apathy among the low-income electorate. Matching on Noise: Bias in the Synthetic Controls Estimator, with Joseph Cummins, Douglas Miller and David Simon. Conditionally accepted at Journal of Econometric Methods
We show that the synthetic control algorithm can have a systematic bias in a set of panel data settings commonly employed in empirical research. The bias comes from matching to idiosyncratic error terms (noise) in the treated unit and the donor units' pre-treatment outcome values. This in turn leads to a biased counterfactual for the post-treatment periods. We use a Monte Carlo analysis to illustrate the determinants of the bias in terms of error term variance, sample characteristics and DGP complexity. We present two procedures to reduce the bias: one based on a specification for the matching variables that includes estimates of unit-level polynomial trend parameters, and the other a direct computational bias-correction procedure based on re-sampling from a pilot model. Both of these can reduce the bias in empirically feasible implementations. However, both corrections increase the sampling variance and mean-squared error in our simulations relative to a baseline of matching on all pre-period outcome values.
Why do some Mining Boom Towns Persist and Some Become Ghost Towns?
Understanding Heterogeneous Effects of the 1980s Oil Bust
Droughts and Intergenerational Mobility in Developing Countries