We present evidence that the traditional structure of society is an important determinant of the scope of trust today. Within Africa, individuals belonging to ethnic groups that organized society using segmentary lineages exhibit a more limited scope of trust, measured by the gap between trust in relatives and trust in non-relatives. This trust gap arises because of lower levels of trust in non-relatives and not higher levels of trust in relatives. A causal interpretation of these correlations is supported by the fact that the effects are primarily found in rural areas where these forms of organization are still prevalent.
It is estimated that about one quarter of the global disease burden in terms of healthy life years lost and about one quarter of all premature deaths can be attributed to modifiable environmental factors (Pruss-Ustun and Corvalan 2006). Three infectious diseases--diarrhea, respiratory infections, and malaria--account for the largest absolute burden in developing countries with children facing the greatest impacts. There is a growing body of evidence demonstrating the health burden of air and water pollution, as well as important productivity and income effects (see, for example, reviews of the literature in Pattanayak and Pfaff 2009 and Greenstone and Jack 2016). Studies that focus on the impacts of natural resource degradation are fewer. Notably, Garg (2016) provides the first causal estimates of the impact of sustained forest cover on reduced malarial incidence in Indonesia, demonstrating a large and previously understudied cost of forest cover loss. In this paper, we extend this new literature on the health impacts of environmental degradation by estimating the causal impact of forest loss on infectious disease incidence in young children using temporal and spatial variation in the last decade in Nigeria. Our estimation strategy involves geolinking a new high-resolution dataset of global forest change to child-level health data from the Nigeria Demographic and Health Surveys from 2008 and 2013. We find that forest loss significantly increases the incidence of malaria, though it does not affect the incidence of diarrhea and respiratory diseases. The impact of forest loss on malaria is large (one standard deviation of forest loss increases malaria incidence by around 4.5 percent in children under five) and the dynamic pattern of the impact suggests a temporary ecological disturbance consistent with findings in Garg (2016) and the tropical medicine literature.
Honesty is the best policy in the face of a strategy-proof mechanism--irrespective of others' behavior, the best course of action is to report one's preferences truthfully. We review evidence from different markets in different countries and find that a substantial percentage of participants do not report their true preferences to the strategy-proof Deferred Acceptance mechanism. Two recurring correlates of preference misrepresentation are lower cognitive ability and the expectation of stronger competition. We evaluate possible explanations, which we hope will inform practicing market designers.
Rainfall measures may be imperfect proxies for floods, given factors such as upstream water balance, proximity to rivers, and topography. We check the robustness of flooding-migration relationships by combining nationally-representative survey data with measures of flooding derived from weather stations, gridded products, and remote sensing tools. Linear probability models reveal that extreme flooding is negatively associated with out-migration. Rainfall-based proxies produce results qualitatively similar to those using the satellite-based measure of inundation, but only the latter is able to discern non-monotonic effects throughout the distribution. Moreover, estimates differ widely across areas, suggesting that households respond differently to rainfall and flooding.
We study the relation between the distribution of labor earnings and the distribution of wealth. We show, theoretically as well as empirically, that while labor earnings and precautionary savings are important determinants of wealth inequality factors, they cannot by themselves account for the thick tail of (the large top shares in) the observed distribution of wealth. Other determinants, like stochastic returns to wealth, as well as savings rates and rates of returns increasing in wealth, need to be accounted for.
The German economy exhibits rising service and declining manufacturing employment, but this decline is much sharper in import-competing than export-oriented branches. We first document the individual-level job transitions behind those trends. They are not driven by manufacturing workers who smoothly switch to services. The observed shifts are entirely due to young entrants and returnees from non-employment. We then investigate if rising trade with China and Eastern Europe causally affected those labor flows. Exploiting variation across industries and regions, we find that globalization did not speed up the manufacturing decline in Germany. It even retained those jobs in the economy.
We discuss firm-level evidence based on UK data showing that within-firm pay inequality--wage differentials between top- and bottom-level jobs--increases with firm size. Moreover, within-firm pay inequality rises as firms grow larger over time. Lastly, using wage data from 15 developed countries, we document a positive association between aggregate wage inequality at the country level and growth by the largest firms in the country. We conclude that part of what may be perceived as a global trend toward more wage inequality may be driven by an increase in the size of the largest firms in the economy.
We build on the analysis in Akcigit, Grigsby, and Nicholas (2017) by using US patent and census data to examine the relationship between immigration and innovation. We construct a measure of foreign born expertise and show that technology areas where immigrant inventors were prevalent between 1880 and 1940 experienced more patenting and citations between 1940 and 2000. The contribution of immigrant inventors to US innovation was substantial. We also show that immigrant inventors were more productive than native born inventors; however, they received significantly lower levels of labor income. The immigrant inventor wage-gap cannot be explained by differentials in productivity.
To estimate treatment heterogeneity in two randomized controlled trials of a youth summer jobs program, we implement Wager and Athey's (2015) causal forest algorithm. We provide a step-by-step explanation targeted at applied researchers of how the algorithm predicts treatment effects based on observables. We then explore how useful the predicted heterogeneity is in practice by testing whether youth with larger predicted treatment effects actually respond more in a hold-out sample. Our application highlights some limitations of the causal forest, but it also suggests that the method can identify treatment heterogeneity for some outcomes that more standard interaction approaches would have missed.
How is credit for group work allocated when individual contributions are not observed? I use data on academics' publication records to test whether demographic traits like gender influence how credit is allocated under such uncertainty. While solo-authored papers send a clear signal about ability, coauthored papers are noisy, providing no specific information about each contributor's skills. I find that men are tenured at roughly the same rate regardless of coauthoring choices. Women, however, are less likely to receive tenure the more they coauthor. The result is much less pronounced among women who coauthor with other women.