with Leonid Kogan, Dimitris Papanikolaou, and Bryan Seegmiller. Revised July 2022
We develop a granular, occupation-specific measure of technological progress that relies only on textual descriptions of patent documents and the tasks performed by workers in an occupation. Our measure primarily identifies labor-saving innovations and is broadly available from the 19th century to the present. Examining the type of worker tasks most exposed to innovation, we find that while non-routine manual (physical) and routine-manual tasks have been highly exposed throughout the last 150 years, the innovations of the information technology revolution in the post-1980 period saw an increased relationship with cognitive tasks. Using a panel of administrative data on worker earnings, we show that the earnings of older and more highly-paid workers are more responsive to our technology exposure measure, a pattern consistent with skill displacement. Our calibrated model fits these facts and emphasizes the importance of movements in skill quantities, not just skill prices, for the link between technology and inequality.
with Carter Braxton, Kyle Herkenhoff, and Jonathan Rothbaum. Revised June 2022
For whom has earnings risk changed, and why? To answer these questions, we develop a filtering method that estimates parameters of an income process and recovers persistent and temporary earnings for every individual at every point in time. Using linked SSA-CPS earnings records, we show that since the 1980s persistent earnings risk has risen for both employed and unemployed workers and declines in persistent earnings during unemployment spells have become larger. We show that rising persistent earnings risk is concentrated among high-skill workers and related to technology adoption. Finally, we show the increase in persistent risk generates large welfare losses.
Job Market Paper, Revised February 2022. Winner of 2015 AQR Top Finance Graduate Award and 2015 Cubist Systematic Strategies Ph.D. Candidate Award for Outstanding Research
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.
with Leonid Kogan, Dimitris Papanikolaou, and Jae Song.
We examine the relation between technological progress and the riskiness of labor income. Motivated by a simple model of creative destruction, we draw a distinction between technological innovation advanced by the firm, or its competitors. Using administrative data from the United States, we find that own firm innovation is associated with a modest increase in worker earnings growth, while innovation by competing firms is related to lower future worker earnings. Importantly, these earnings changes are asymmetrically distributed across workers: both gains and losses are concentrated on a subset of workers, which implies that the distribution of worker earnings growth rates becomes more right- or left-skewed following innovation by the firm, or its competitors, respectively. These effects are particularly strong for the highest-paid workers. Our results therefore suggest innovation is associated with a substantial increase in the labor income risk, especially for workers at the top of the earnings distribution. Our simulations reveal that the increased disparity in innovation outcomes across firms in the 1990s can account for a significant part of the recent rise in income inequality.
with Taha Choukhmane, Jorge Colmenares, Cormac O'Dea, and Jonathan Rothbaum. Draft coming soon!
In 2021, U.S. private employers and the federal government devoted a combined $300bn to encourage contributions to retirement savings plans. In this paper, we study the distributional impact of these tax and employer match incentives across racial groups using a new linked employer-employee data set covering millions of Americans. On average, White workers contribute 4.6% of their salary to employer-sponsored retirement accounts whereas Black (Hispanic) workers contribute 2.9% (3.3%) of their salary. Differences in income across racial groups explain only one-third of this gap in retirement saving, and large disparities remain even after further controlling for education, occupation, county of residence, employer fixed effects, and homeownership. This gap in contributions, amplified by the tax and matching incentives, implies that the average Black (Hispanic) participant in a DC plan would retire with 51% (37%) less than the average White participant in their account. We explore two mechanisms driving differences in contribution between employees with similar individual-level characteristics. First, household composition and parental characteristics can explain nearly half of the residual gap in retirement contributions across racial groups. Second, we find evidence that Black and Hispanic individuals face tighter liquidity constraints. Black retirement savers are twice as likely as Whites to take an early withdrawal from their retirement account in any given year, a sign of limited access to alternative means of liquidity. These findings suggest that the institutional design of U.S. retirement plans, which rewards those who can and do save more, exacerbates racial wealth gaps and propagates wealth inequality across generations.
with Sung Je Byun and Johnathan Loudis. Revised August 2022.
A value-weighted portfolio of US stocks is not a well-diversified portfolio. While a substantial amount of the variation in the index can be explained by a single dominant factor (the first principal component of a large set of characteristic-sorted portfolios), index returns are also driven by nontrivial, time-varying exposures to weaker factors and “granular residuals” – idiosyncratic shocks to large firms that aren’t diversified away. We argue, both theoretically and empirically, that these additional components can generate instability in tests of the risk-return tradeoff. Then, we reevaluate the current consensus for a weak market risk-return tradeoff in the US stock market using an alternative index unaffected by them. In the time series, we find stronger evidence of a relation between the risk premium and variance of the market after these corrections. In the cross-section, we find evidence that making these corrections generates larger cross-sectional variation in market betas, and that this exposure to market risk explains a much larger share of variation in expected returns. Finally, in line with our theory, correcting for these errors eliminates the ability of size factors to improve pricing within a large set of standard factor models.
with Yinchu Zhu, Walter P. Heller Memorial Award Winner. New draft coming soon!
with Joel Flynn and Alexis Toda, Forthcoming at Theoretical Economics
We study a general class of consumption-savings problems with recursive preferences. We characterize the sign of the consumption response to arbitrary shocks in terms of the product of two sufficient statistics: the elasticity of intertemporal substitution between contemporaneous consumption and continuation utility (EIS), and the relative elasticity of the marginal value of wealth (REMV). Under homotheticity, the REMV always equals one, so the propensity of the agent to save or dissave is always signed by the relationship of the EIS with unity. We apply our results to derive comparative statics in classical problems of portfolio allocation, consumption-savings with income risk, and entrepreneurial investment. Our results suggest empirical identification strategies for both the value of the EIS and its relationship with unity.
with Leland Farmer and Allan Timmermann, Forthcoming at the Journal of Finance
For many benchmark predictor variables, short-horizon return predictability in the U.S. stock market is local in time as short periods with significant predictability (‘pockets’) are interspersed with long periods with little or no evidence of return predictability. We document this result empirically using a flexible time-varying parameter model which estimates predictive coefficients as a nonparametric function of time and explore possible explanations of this finding, including time-varying risk-premia for which we only find limited support. Conversely, pockets of return predictability are consistent with a sticky expectations model in which investors only slowly update their beliefs about a persistent component in in the cash flow process.
with Klakow Akepanidtaworn, Rick Di Mascio, and Alex Imas. Forthcoming at the Journal of Finance
Are market experts prone to heuristics, and if so, do they transfer across closely related domains---buying and selling? We investigate this question using a unique dataset of institutional investors with portfolios averaging $573 million. A striking finding emerges: while there is clear evidence of skill in buying, selling decisions underperform substantially---even relative to random selling strategies. This holds despite the similarity between the two decisions in frequency, substance and consequences for performance. Evidence suggests that an asymmetric allocation of cognitive resources such as attention can explain the discrepancy: we document a systematic, costly heuristic process when selling but not when buying.
with Dimitris Papanikolaou. Review of Asset Pricing Studies, March 2022
We analyze the supply-side disruptions associated with Covid-19 across firms and workers. To do so, we exploit differences in the ability of workers across industries to work remotely using data from the American Time Use Survey (ATUS). We find that sectors in which a higher fraction of the workforce is not able to work remotely experienced significantly greater declines in employment, significantly more reductions in expected revenue growth, worse stock market performance, and higher expected likelihood of default. In terms of individual employment outcomes, lower-paid workers, especially female workers with young children, were significantly more affected by these disruptions. Last, we combine these ex-ante heterogeneous industry exposures with daily financial market data to create a stock return portfolio that most closely replicates the supply-side disruptions resulting from the pandemic.
with Emily Gallagher, Allan Timmermann, and Russ Wermers, Review of Financial Studies, July 2019.
We study investor redemptions and portfolio rebalancing decisions of prime money market mutual funds (MMFs) during the Eurozone crisis. We find evidence that investors selectively acquire and act upon information about MMFs' risk exposures. In turn, this provides strong incentives for managers to withdraw funding from issuers whose debt becomes information-sensitive. Consistent with this, we show that MMF managers, particularly those serving the most sophisticated investors, selectively adjust their portfolio risk exposures to avoid information-sensitive European risks, while maintaining or increasing risk exposures to other regions. This mechanism helps to explain the occurrence of selective dry-ups in debt markets where delegation is common and returns to information production are often low.
with Allan Timmermann and Russ Wermers, American Economic Review, September 2016
We study daily money market mutual fund flows at the individual share class level during September 2008. This fine granularity of data facilitates new insights into investor and portfolio holding characteristics conducive to run risk in cash-like asset pools. Empirically, we find that cross-sectional flow data observed during the week of the Lehman failure are consistent with key implications of a simple model of coordination with incomplete information and strategic complementarities. Similar conclusions follow from daily models fitted to capture dynamic interactions between investors with differing levels of sophistication within the same money fund, holding constant the underlying portfolio.
with Brendan Beare, Journal of Applied Econometrics, March 2016
A large class of asset pricing models predicts that securities which have high payoffs when market returns are low tend to be more valuable than those with high payoffs when market returns are high. More generally, we expect the projection of the stochastic discount factor on the market portfolio--that is, the discounted pricing kernel evaluated at the market portfolio--to be a monotonically decreasing function of the market portfolio. Numerous recent empirical studies appear to contradict this prediction. The nonmonotonicity of empirical pricing kernel estimates has become known as the pricing kernel puzzle. In this paper we propose and apply a formal statistical test of pricing kernel monotonicity. We apply the test using seventeen years of data from the market for European put and call options written on the S&P 500 index. Statistically significant violations of pricing kernel monotonicity occur in a substantial proportion of months, suggesting that observed nonmonotonicities are unlikely to be the product of statistical noise.
Journal of Mathematical Economics, January 2012
The Shapley-Folkman Theorem places a scalar upper bound on the distance between a sum of non-convex sets and its convex hull. We observe that some information is lost when a vector is converted to a scalar to generate this bound and propose a simple normalization of the underlying space which removes this loss of information. As an example, we apply this result to the Anderson (1978) core convergence theorem, and demonstrate how our normalization leads to an intuitive, unitless upper bound on the discrepancy between an arbitrary core allocation and the corresponding competitive equilibrium allocation.
Older Working Papers
with David Berger, Ian Dew-Becker, and Yuta Takahashi
The strongest predictor of changes in the Fed Funds rate in the period 1982–2008 was the layoff rate. That fact is puzzling from the perspective of representative-agent models of the economy, which imply that the welfare gains of stabilizing employment fluctuations are small. This paper augments a standard New Keynesian model with a labor market featuring countercyclical layoffs that lead to large,uninsurable, and permanent idiosyncratic wage declines. In our benchmark calibration, welfare may be increased by 1 percent of lifetime consumption or more when the central bank’s policy rule responds to the layoff rate instead of purely targeting inflation.
"Heterogeneity and Asset Prices: A Different Approach"
by Nicolae Gârleanu and Stavros Panageas, Asset Pricing Meeting, NBER Summer Institute, July 2018
"Does Precautionary Savings Drive the Real Interest Rate? Evidence from the Stock Market"
by Carolin Pflueger, Emil Siriwardane, and Adi Sunderam, NBER Asset Pricing Meeting, November 2017
"The Tail that Wags the Economy: Beliefs and Persistent Stagnation"
by Julian Kozlowski, Laura Veldkamp, and Venky Venkateswaran, ASSA meetings, Midwest Finance Association, March 2017
"Self-Fulfilling Runs: Evidence from the U.S. Life Insurance Market"
by Nathan Foley-Fisher, Borghan Narajabad, and Stéphane Verani, ASSA meetings, International Banking, Economics and Finance Association session on "Measuring and Managing Financial Stability", January 2016