We show that time variation in risk premia leads to time-varying idiosyncratic income risk for workers. Using US administrative data on worker earnings, we show that increases in risk premia lead to lower earnings for low-wage workers; these declines are primarily driven by job separations. By contrast, productivity shocks affect the earnings mainly of highly paid workers. We build an equilibrium model of labor market search that quantitatively replicates these facts. The model generates endogenous time-varying income risk in response to changes in risk premia and matches several stylized features of the data regarding unemployment and income risk over the business cycle.

For whom has earnings risk changed, and why? We answer these questions by combining the Kalman filter and EM-algorithm to estimate persistent and temporary earnings for every individual at every point in time. We apply our method to administrative earnings linked with survey data. We show that since the 1980s, persistent earnings risk rose by 12.5% for both employed and unemployed workers and the scarring effects of unemployment doubled. At the same time, temporary earnings risk declined. Using education and occupation codes, we show that rising persistent earnings risk is concentrated among high-skill workers and related to technology adoption.

U.S. employers and the federal government devote over 1.5% of GDP annually toward promoting defined contribution (DC) retirement saving. Using a new employer-employee linked dataset covering millions of Americans, we show that this system of saving incentives benefits White workers and those with richer parents more than their similar-income coworkers who are Black or Hispanic or from lower-income families. Breaking the link between contribution choices and saving subsidies—through revenue- neutral reforms—can close the gaps in DC wealth between Black and White workers, between Hispanic and White workers, and between those with the richest and those with the poorest parents by close to a third.

We develop measures of labor-saving and labor-augmenting technology exposure using textual analysis of patents and job tasks. Using US administrative data, we show that exposure to labor-saving technologies negatively affects the earnings of exposed workers. This negative effect is pervasive across both blue- and white-collar workers and across workers of different ages or earnings relative to their peers. In contrast, labor-augmenting technologies have a heterogeneous impact on exposed workers. While the wage bill paid to affected groups rises, this increase is driven primarily by an increase in employment, while earnings rise for new entrants but decline for incumbent workers. This decline is primarily present among white-collar, older, and higher-paid workers, highlighting the importance of vintage-specific human capital. Last, we find positive spillovers of both types of innovation at the industry level, benefiting other workers in the same industry who are not directly exposed to these innovations.

Administrative earnings data reveal that households are exposed to large, countercyclical idiosyncratic tail risks in labor earnings. I illustrate how these risks affect asset prices within an asset pricing framework with recursive preferences, heterogeneous agents and incomplete markets. Quantitatively, a model in which agents face a time-varying probability of experiencing a rare, idiosyncratic disaster, with parameters disciplined by data, matches the level and dynamics of the equity premium. Stock returns are highly informative about labor market event risk, and, consistent with model predictions, initial claims for unemployment, a proxy for labor market uncertainty, is a highly robust predictor of returns.

Using administrative data from the United States, we document novel stylized facts regarding technological innovation and the riskiness of labor income. Higher rates of industry innovation are associated with significant increases in labor earnings for top workers. Decomposing this result, we find that own firm innovation is associated with a modest increase in the mean, but also variance, of worker earnings growth. Innovation by competing firms is related to lower, and more negatively skewed, future earnings. We construct a structural model featuring creative destruction and displacement of human capital that replicates these patterns. In the model, higher rates of innovation by competing firms increase the likelihood that both the worker and the incumbent producer are displaced. By contrast, a higher rate of innovation by the worker's own firm increases profits, but is a mixed blessing for workers, as it increases odds that the skilled worker is no longer a good match to the new technology. Estimating the parameters of the model using indirect inference, we find significant welfare losses and hedging demand against innovation shocks. Consistent with our model, we find that these left tail effects are more pronounced for process improvements, novel innovations, and are concentrated in movers rather than continuing workers.

In addition to a priced, dominant market factor (DMF), the value-weighted market stock return contains an “idiosyncratic financial factor” (IFF) related to overweighting of large-cap stocks. The IFF carries no risk premium, is unrelated to macroeconomic factors and returns in other markets, and significantly impacts systematic risk estimates. Size factors separate exposures to the DMF from the IFF. Consistent with a model with nontraded assets, using the DMF as an alternative market factor resolves the size anomaly and obviates the need for size factors in multifactor models. Finally, the DMF features a stronger intertemporal risk-return tradeoff.

Employer-sponsored health insurance is a significant component of labor costs. We examine the causal effect of health insurance premiums on firms’ employment and employment outcomes of low- versus high-income workers. To address endogeneity concerns, we instrument for insurance premiums using idiosyncratic variation in insurers’ recent losses, which is plausibly exogenous to their customers who are employers. Using Census microdata, we show that following an exogenous increase in premiums, firms reduce employment. Lower-income workers become more likely to be separated from their jobs, become unemployed, experience a large earning reduction upon job separation, and be part-time (ineligible for health insurance benefits).

We propose criteria that employers can use to evaluate their match formula: equity, efficiency, and cost. Recognizing that plan sponsors have different objectives and constraints, we offer the criteria to help sponsors make the trade­offs in plan design explicit and help them meet their goals.

In two-thirds of plans, employer contributions exacerbate pay inequity. Employer contributions are highly concentrated, with 44% of dollars accruing to the top 20% of earners. Many common formulas, including safe harbor designs, disproportionately benefit higher-income employees. An employer match is efficient if it encourages workers to save more. Employee saving rates vary little across plans with different levels of employer matches. The majority (59%) of employer contributions accrue to the 41 % of employees who save more than the match cap, suggesting they would have saved just as much without the match.

Employer contribution costs vary widely. No single formula is a clear winner in terms of efficiency, but dollar caps are more equitable and contain costs. Nonelective contributions that decouple employer contributions from employee choices can also be designed to achieve equity objectives.

Policymakers could do more to promote equity. Adopting additional safe harbor standards with equity considerations could nudge plans toward more equitable designs.

Accurately measuring household income and poverty is essential to understanding the nation’s overall economic wellbeing. Many studies show that measurement error stemming from unit nonresponse, item nonresponse and misreporting biases key official statistics such as mean or median income and the official poverty rate. The direction of bias differs between these sources of measurement error. Since these error components are typically studied in isolation, their overall impact on the accuracy of survey estimates remains unclear. This paper summarizes the National Experimental Wellbeing Statistics (NEWS) Project, which integrates this research and address each of these sources of bias simultaneously in order to produce more accurate estimates of household income and poverty. The NEWS project makes three unique contributions. First, we address as many sources of measurement error as we can simultaneously – including unit and item nonresponse and underreporting in surveys as well as the various challenges in administrative data such as measurement error, conceptual misalignment, and incomplete coverage. Second, we bring together all of the available survey and administrative data, which allows to address many of the shortcomings of individual data sources. Third, we propose a model to combine survey and administrative earnings data given measurement error in both sources, replacing ad hoc assumptions that have been used in prior work.

We propose a simple alternative to linear-in-parameters quantile regressions for the modeling of conditional distributions. We parameterize the conditional quantile function in terms of a single "location" quantile (usually the median), to which we add or subtract sums of exponentially affine functions (quantile spacings) to obtain a finite number of other quantiles. Our generalized location-scale specification preserves the computational tractability of standard linear quantile regression, is not subject to the quantile crossing problem, and the separability restriction we impose on scale can be motivated by the non-parametric generalization of differences-in-differences of Athey and Imbens (2006). Thus, under some assumptions, an application of our method extends nonlinear differences-in- differences to allow for many, potentially continuous covariates. We illustrate the utility of the method by considering impacts of mass layoffs on the distribution of displaced workers’ earnings using employer-employee matched data from the US. We find that average effects, which are in line with established literature, are driven by a substantial fattening of the left tail, a phenomenon which is further exacerbated during macroeconomic downturns, suggesting that the welfare costs of cyclical variation in income losses from job displacement are even higher than considering average effects alone.

Sample R code which uses our spacing estimator and includes slides with discussion of D-i-D and new Census results.

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