We use 130 years of data on historical migrations to the United States to show a causal effect of the ancestry composition of US counties on foreign direct investment (FDI) sent and received by local firms. To isolate the causal effect of ancestry on FDI, we build a simple reduced-form model of...

Difference-in-Difference (DID) estimators are a valuable method for identifying causal effects in the public health researcher's toolkit. A growing methods literature points out potential problems with DID estimators when treatment is staggered in adoption and varies with time. Despite this, no...

This paper develops linear estimators for structural and causal parameters in nonparametric,nonseparable models using panel data. These models incorporate unobserved, time-varying, individual heterogeneity, which may be correlated with the regressors. Estimation is based on an approximation of...

Eligibility criteria for interventions can induce an Ashenfelter Dip, and subsequent mean-reversion may result in improvement over time even absent the intervention. We investigate these dynamics for a food-as-medicine program to treat diabetes, where eligibility required elevated hemoglobin A1c...

We study the problem of estimating the average causal effect of treating every member of a population, as opposed to none, using an experiment that treats only some. We consider settings where spillovers have global support and decay slowly with (a generalized notion of) distance. We derive the...

We study the large-scale experimental rollout of a platform that reduced search and matching frictions in Ugandan agricultural markets by connecting buyers and sellers. Market integration improved substantially: trade increased and price gaps fell. Interpreting the experiment through a trade...

Markets, likened to an invisible hand, often appear to contradict econometric assumptions that rule out spillovers of one person's treatment on another's outcomes. This paper provides a simple statistical framework highlighting that controls are indirectly affected by the treatment through the...

Time series data are widely used to explore causal relationships, typically in a regression framework with lagged dependent variables. Regression-based causality tests rely on an array of functional form and distributional assumptions for valid causal inference. This paper develops a...