Knowledge that Transforms

Abstract The Lorenz curve and Gini index are standard tools for the evaluation of inequality in one dimension. However, inequality is inherently multi-dimensional. Extending the Lorenz curve and Gini index to a multidimensional context has proved controversial. This paper proposes a new multivariate extension based on multivariate rearrangements of optimal transport theory, which shares many of the desirable properties of their univariate counterparts. In particular, the corresponding multivariate inequality ordering is equivalent to preference by any social planner with inequality averse multivariate rank dependent social evaluation functional.

We use linked survey and administrative data to document differences across multiple socio-economic and demographic groups in the extent of adverse economic and health impacts of the first two years of the COVID-19 pandemic in the United States. Across a wide set of characteristics-including race/ethnicity, education, industry, and occupation-the impacts of the pandemic on all-cause mortality and on employment were disproportionately concentrated in the same groups in the population. As the pandemic progressed, disparities in the pandemic's mortality impacts narrowed substantially between Black and White Americans and between Hispanic and White Americans, but persisted along the educational divide. For economic damages, only Hispanic-White disparities narrowed; Black-White and educational disparities persisted for the first two years of the pandemic. We also document greater mortality impacts for lower income individuals, with this negative income-excess mortality gradient becoming steeper in the pandemic's second year. Together our findings-using a consistent set of methods and measures on nationally representative data with a wide set of measures of socio-economic status-paint a detailed picture of the heterogeneous impacts of the first two years of the COVID-19 pandemic on health and economic well-being.

Abstract We examine the intergenerational effects of an integration program that increased language training and improved labor market outcomes of adult immigrants in Finland. Exploiting a discontinuity in the phase-in rule of a reform, we find that parents' participation in the program improved their children's grades by 0.5 standard deviations and extended their educational attainment by over a year. Two decades post-arrival, children of the affected immigrants earned 42% more than their counterparts whose parents narrowly missed the policy's implementation.

Abstract We show how random subspace methods can be adapted to estimating local projections with many controls. Random subspace methods have their roots in the machine learning literature and are implemented by averaging over regressions estimated over different combinations of subsets of these controls. We document three key results: (i) Our approach can successfully recover the impulse response functions across Monte Carlo experiments representative of different macroeconomic settings and identification schemes. (ii) Our results suggest that random subspace methods are more accurate than other dimension reduction methods if the underlying large dataset has a factor structure similar to typical macroeconomic datasets such as FRED-MD. (iii) Our approach leads to differences in the estimated impulse response functions relative to benchmark methods when applied to two widely studied empirical applications.

Abstract We augment Henderson et al. (2012)'s two-signal model of true GDP growth with a third signal to overcome its underidentification problem. The additional moment conditions from the third signal help fully identify all model parameters without ad-hoc calibrations of the GDP's signal-to-noise ratio. We characterize the necessary properties of the third signal. Using the model, we recover the optimal weight of official GDP in the composite true GDP growth estimates, which varies with the quality of the national statistics. The model improves on existing methodologies that use signals to measure true income.

Abstract This paper shows that the bunching of wages at round numbers is partly driven by firm coarse wage-setting. Using data from over 200 million new hires in Brazil, I first establish that contracted salaries tend to cluster at round numbers. Then, I show that firms that tend to hire workers at round-numbered salaries have worse market outcomes. Next, I develop a wage-posting model in which optimization costs lead to the adoption of coarse rounded wages and provide evidence supporting two model predictions using two research designs. Finally, I examine some consequences of coarse wage-setting for relevant economic outcomes.

Abstract This paper introduces the random discounted expected utility (R-DEU) model, which we have developed as a means to deal with heterogeneous risk and time preferences. The R-DEU model provides an explicit linkage between preference and choice heterogeneity. We prove it has solid comparative statics, discuss its identification, and demonstrate its computational convenience. Finally, we use two distinct experimental datasets to illustrate the advantages of the R-DEU model over common alternatives for estimating heterogeneity in preferences across individuals.

Abstract This paper presents an alternative perspective to a longstanding empirical puzzle: that most entrepreneurs persevere despite persistently low earnings. Because entrepreneurial earnings are notoriously difficult to measure, I approach the question from an expenditure angle. I look at how switching into self-employment corresponds to changes in reported earnings versus expenditure. Using 45 years of longitudinal data, I find that individuals report earning 27.7% less in self-employment, while spending 3.8% more. This household expenditure premium accrues with entrepreneurial experience and is not offset by lower savings or longer work hours. The results hold in highly educated and incorporated business owner subsamples.

Abstract Rational respondents to economic surveys may report as a point forecast any measure of the central tendency of their predictive distribution, e.g. the mean, median, or mode. We propose tests of forecast rationality when the measure used by the respondent is unknown. We overcome an identification problem that arises when the centrality measures are in a local neighborhood of each other, as is the case for approximately symmetric distributions. We apply our tests to income forecasts from the FRBNY's Survey of Consumer Expectations. We find these forecasts are rationalizable as mode forecasts, but not as mean or median forecasts.

Abstract I estimate impulse responses to a 100 basis point US monetary policy shock using setidentified structural vector autoregressions. Identified sets for these responses may be unbounded when the identifying restrictions admit zero impact response of the federal funds rate following a standard-deviation shock. Such a zero response is always admissible when there are fewer sign and zero restrictions than endogenous variables. This is the case under existing restrictions on the systematic component of monetary policy, which consequently yield uninformative identified sets. Additional sign and narrative restrictions yield informative identified sets and imply smaller output responses than some previous estimates.