Are trade wars class wars? The importance of trade-induced horizontal inequality

What is the nature of the distributional effects of trade? This paper demonstrates conceptually and empirically the importance of "trade-induced horizontal inequality," i.e., inequality brought about by trade shocks that occurs among workers with the same level of earnings prior to the shock. While this type of inequality does not affect the income distribution, it generates winners and losers at all income levels and may thus affect political support for trade policy. To quantify the horizontal inequality and changes in the income distribution induced by trade in a data-driven way, we develop a characterization of the welfare impacts, governed by simple and intuitive statistics of labor market and consumption exposure to trade. This characterization holds in a class of quantitative trade models allowing for a broad set of preferences, including non-homothetic, and production functions. Taking this framework to U.S. data, we find substantial heterogeneity in exposure and thus in the welfare effects of trade shocks across workers, with horizontal inequality as the dominant force. Over 99% of the variance of welfare changes from trade shocks arise within income deciles, rather than across. This finding runs against a popular narrative that "trade wars are class wars"

The distributional effects of trade: theory and evidence from the United States

How much do consumption patterns matter for the impact of international trade on inequality? In neoclassical trade models, the effects of trade shocks on consumers' purchasing power are governed by the shares of imports in consumer expenditures, under no parametric assumptions on preferences and technology. This paper provides in-depth measurement of import shares across the income distribution in the United States, using new datasets linking expenditure and customs microdata. Contrary to common wisdom, we find that import shares are flat at throughout the income distribution: the purchasing-power gains from lower trade costs are distributionally neutral. Accounting for changes in wages in addition to prices in a unified nonparametric framework, we find substantial distributional effects that arise within, but not across, income and education groups. There is little impact of a fall in trade costs on inequality, even though trade shocks generate winners and losers at all income levels, via wage changes

Revisiting Event Study Designs: Robust and Efficient Estimation

We develop a framework for difference-in-differences designs with staggered treatment adoption and heterogeneous causal effects. We show that conventional regression-based estimators fail to provide unbiased estimates of relevant estimands absent strong restrictions on treatment-effect homogeneity. We then derive the efficient estimator addressing this challenge, which takes an intuitive "imputation" form when treatment-effect heterogeneity is unrestricted. We characterize the asymptotic behavior of the estimator, propose tools for inference, and develop tests for identifying assumptions. Extensions include time-varying controls, triple-differences, and certain non-binary treatments. We show the practical relevance of these insights in a simulation study and an application. Studying the consumption response to tax rebates in the United States, we find that the notional marginal propensity to consume is between 8 and 11 percent in the first quarter -- about half as large as benchmark estimates used to calibrate macroeconomic models -- and predominantly occurs in the first month after the rebate

The role of schools in transmission of the SARS-CoV-2 virus: quasi-experimental evidence from Germany

This paper considers the role of school closures in the spread of the SARS-CoV-2 virus. To isolate the impact of the closures from other containment measures and identify a causal effect, we exploit variation in the start and end dates of the summer and fall school holidays across the 16 federal states in Germany using a difference-in-differences design with staggered adoption. We show that neither the summer closures nor the closures in the fall had a significant containing effect on the spread of SARS-CoV-2 among children or a spill-over effect on older generations. There is also no evidence that the return to school at full capacity after the summer holidays increased infections among children or adults. Instead, we find that the number of children infected increased during the last weeks of the summer holiday and decreased in the first weeks after schools reopened, a pattern we attribute to travel returnees

Revisiting event-study designs: robust and efficient estimation

We develop a framework for difference-in-differences designs with staggered treatment adoption and heterogeneous causal effects. We show that conventional regression-based estimators fail to provide unbiased estimates of relevant estimands absent strong restrictions on treatment-effect homogeneity. We then derive the efficient estimator addressing this challenge, which takes an intuitive “imputation” form when treatment-effect heterogeneity is unrestricted. We characterize the asymptotic behaviour of the estimator, propose tools for inference, and develop tests for identifying assumptions. Our method applies with time-varying controls, in triple-difference designs, and with certain non-binary treatments. We show the practical relevance of our results in a simulation study and an application. Studying the consumption response to tax rebates in the U.S., we find that the notional marginal propensity to consume is between 8 and 11% in the first quarter—about half as large as benchmark estimates used to calibrate macroeconomic models—and predominantly occurs in the first month after the rebate

Design-based identification with formula instruments: a review

Many studies in economics use instruments or treatments that combine a set of exogenous shocks with other predetermined variables via a known formula. Examples include shift-share instruments and measures of social or spatial spillovers. We review recent econometric tools for this setting, which leverage the assignment process of the exogenous shocks and the structure of the formula for identification. We compare this design-based approach with conventional estimation strategies based on conditional unconfoundedness, and contrast it with alternative strategies that leverage a model for unobservables

Are trade wars class wars? The importance of trade-induced horizontal inequality

What is the nature of the distributional effects of trade? This paper demonstrates conceptually and empirically the importance of “trade-induced horizontal inequality,” i.e. inequality that occurs among workers with the same level of earnings before the trade shock. This type of inequality does not affect the income distribution but generates winners and losers at all income levels. To quantify the horizontal inequality and changes in the income distribution induced by trade in a data-driven way, we develop a characterization of the welfare impacts, governed by simple and intuitive statistics of labor market and consumption exposure to trade. In the U.S., we find substantial heterogeneity in exposure and thus in the welfare effects of trade shocks across workers. Over 99% of the variance of welfare changes from trade shocks arises within income deciles. These findings run against a popular narrative that “trade wars are class wars.

Non-random exposure to exogenous shocks: Theory and application

We develop new tools for causal inference in settings where exogenous shocks affect the treatment status of multiple observations jointly, to different extents. In these settings researchers may construct treatments or instruments that combine the shocks with predetermined measures of shock exposure. Examples include measures of spillovers in social and transportation networks, simulated eligibility instruments, and shift-share instruments. We show that leveraging the exogeneity of shocks for identification generally requires a simple but nonstandard recentering, derived from the specification of counterfactual shocks that might as well have been realized. We further show how specification of counterfactual shocks can be used for finite-sample inference and specification tests, and we characterize the recentered instruments that are asymptotically efficient. We use this framework to estimate the employment effects of Chinese market access growth due to high-speed rail construction and the insurance coverage effects of expanded Medicaid eligibility

Are trade wars class wars? The importance of trade-induced horizontal inequality

What is the nature of the distributional effects of trade? This paper demonstrates conceptually and empirically the importance of 'trade-induced horizontal inequality,' i.e. inequality brought about by trade shocks that occurs among workers with the same level of earnings prior to the shock. While this type of inequality does not affect the income distribution, it generates winners and losers at all income levels and may thus affect political support for trade policy. To quantify the horizontal inequality and changes in the income distribution induced by trade in a data-driven way, we develop a characterization of the welfare impacts, governed by simple and intuitive statistics of labor market and consumption exposure to trade. This characterization holds in a class of quantitative trade models allowing for a broad set of preferences, including non-homothetic, and production functions. Taking this framework to U.S. data, we find substantial heterogeneity in exposure and thus in the welfare effects of trade shocks across workers, with horizontal inequality as the dominant force. Over 99% of the variance of welfare changes from trade shocks arise within income deciles, rather than across. This finding runs against a popular narrative that 'trade wars are class wars.