Using the shadow insurance setting, we study the interplay between tax and nontax incentives in income shifting. Shadow insurance involves intercompany transactions designed to help firms meet regulatory capital requirements. However, prior to the Tax Cuts and Jobs Act of 2017 (TCJA), foreign-owned life insurance firms could save taxes by using shadow insurance to shift U.S. profits to tax havens. Consistent with expectations, we find that while nontax incentives appear to be the dominant factor behind firms' use of shadow insurance, tax considerations also played a role for certain firms. We also find that shadow insurance is associated with lower liquid asset holdings and increased credit risk. Overall, our results suggest that taxes were an important incentive for foreign-owned life insurance firms to use shadow insurance pre-TCJA. Moreover, in this setting, nontax considerations appeared to have motivated U.S.-owned firms' use of tax havens.

Macroeconomic news announcements are elaborate and multidimensional. We consider a framework in which jumps in asset prices around announcements reflect both the response to observed surprises in headline numbers and to latent factors, reflecting other news in the release. Non-headline news, for which there are no expectations surveys, is unobservable to the econometrician but nonetheless elicits a market response. We estimate the model by the Kalman filter, which efficiently combines OLS and heteroskedasticity-based event study estimators in one step. With the inclusion of a single latent surprise factor, essentially all yield curve variance in event windows are explained by news.

Social network data are often prohibitively expensive to collect, limiting empirical network research. We propose an inexpensive and feasible strategy for network elicitation using Aggregated Relational Data (ARD): responses to questions of the form "how many of your links have trait k ?" Our method uses ARD to recover parameters of a network formation model, which permits sampling from a distribution over node- or graph-level statistics. We replicate the results of two field experiments that used network data and draw similar conclusions with ARD alone.

Lemoine and Rudik (2017) argues that it is efficient to delay reducing carbon emissions, due to supposed inertia in the climate system’s response to emissions. This conclusion rests upon misunderstanding the relevant earth system modeling: there is no substantial lag between CO 2 emissions and warming. Applying a representation of the earth system that captures the range of responses seen in complex earth system models invalidates the original article’s implications for climate policy. The least-cost policy path that limits warming to 2°C implies that the carbon price starts high and increases at the interest rate. It cannot rely on climate inertia to delay reducing and allow greater cumulative emissions.