Econometrica最新文献

Weak environmental regulation has global consequences. When domestic regulation fails, the international community can target emitters with trade policy. I develop a dynamic empirical framework for evaluating trade policy as a substitute for domestic regulation, and I apply the framework to the market for palm oil, a major driver of deforestation and global CO₂ emissions. Relative to business as usual, a domestic production tax of 50% reduces CO₂ emissions by 7.4 Gt from 1988 to 2016, amounting to 0.26 Gt annually. Coordinated, committed import tariffs of similar magnitude reduce emissions by 5.4 Gt over the same period. The cost of these import tariffs is only $15 per ton of CO₂, even accounting for compensating transfers that recognize welfare losses for producing countries. Without coordination and commitment, import tariffs have more limited effects. Alternative policies include domestic export taxes, which are fiscally appealing independent of emission concerns, and a carbon border adjustment mechanism, which encourages domestic regulation.

We derive a novel decomposition of the Gini coefficient into within- and between-group inequality terms that sum to the aggregate Gini coefficient. This decomposition is derived from a set of axioms that ensure desirable behavior for the within- and between-group inequality terms. The decomposition of the Gini coefficient is unique given our axioms, easy to compute, and can be interpreted geometrically.

We study the problem of a partisan gerrymanderer who assigns voters to equipopulous districts to maximize his party's expected seat share. The designer faces both aggregate, district-level uncertainty (how many votes his party will receive) and idiosyncratic, voter-level uncertainty (which voters will vote for his party). Segregate-pair districting, where weaker districts contain one type of voter, while stronger districts contain two, is optimal for the gerrymanderer. The optimal form of segregate-pair districting depends on the designer's popularity and the relative amounts of aggregate and idiosyncratic uncertainty. When idiosyncratic uncertainty dominates, a designer with majority support pairs all voters, while a designer with minority support segregates opposing voters and pairs more favorable voters; these plans resemble uniform districting and “packing-and-cracking,” respectively. When aggregate uncertainty dominates, the designer segregates moderate voters and pairs extreme voters; this “matching slices” plan has received some attention in the literature. Estimating the model using precinct-level returns from recent U.S. House elections shows that, in practice, idiosyncratic uncertainty dominates. We discuss implications for redistricting reform, political polarization, and detecting gerrymandering. Methodologically, we exploit a formal connection between gerrymandering—partitioning voters into districts—and information design—partitioning states of the world into signals.

We study how criminal organizations affect economic development. We exploit a natural experiment in El Salvador, where these criminal organizations emerged due to an exogenous shift in American immigration policy that led to the deportation of gang leaders from the United States to El Salvador. Using a spatial regression discontinuity design that focuses on the gang-created system of borders, we find that individuals in gang-controlled neighborhoods have less material well-being, income, and education than individuals living only 50 meters away but outside of gang territory. None of these discontinuities existed before the arrival of the gangs. A key mechanism behind the results is that gangs restrict individuals' mobility, affecting their labor-market options by preventing them from commuting to other parts of the city. The results are not determined by high rates of selective migration, differential exposure to extortion and violence, or differences in public goods provision.

Empirical research typically involves a robustness-efficiency tradeoff. A researcher seeking to estimate a scalar parameter can invoke strong assumptions to motivate a restricted estimator that is precise but may be heavily biased, or they can relax some of these assumptions to motivate a more robust, but variable, unrestricted estimator. When a bound on the bias of the restricted estimator is available, it is optimal to shrink the unrestricted estimator towards the restricted estimator. For settings where a bound on the bias of the restricted estimator is unknown, we propose adaptive estimators that minimize the percentage increase in worst-case risk relative to an oracle that knows the bound. We show that adaptive estimators solve a weighted convex minimax problem and provide lookup tables facilitating their rapid computation. Revisiting some well-known empirical studies where questions of model specification arise, we examine the advantages of adapting to—rather than testing for—misspecification.

We offer a theory of changing dimensions of political polarization based on endogenous social identity. We formalize voter identity as in Bonomi, Gennaioli, and Tabellini (2021), but add parties that compete on policy and spread stereotypes to persuade voters. Parties are historically connected to different social groups, whose members are more receptive to the party messages. An endogenous switch from class to cultural identity accounts for three major changes: (i) growing cultural conflict between voters and parties; (ii) dampening of redistributive conflict, despite rising inequality; (iii) a realignment of lower class voters from the left to the right. The incentive of parties to spread stereotypes is a key driver of identity-based polarization. Using survey data and congressional speeches, we show that—consistent with our model—there is evidence of (i) and (ii) in the voting realignment induced by the “China Shock” (Autor, Dorn, Hanson, and Majlesi (2020)).