Research

Working Papers

Media Coverage and Blogs: Knowledge at Wharton  Regional Economist  Timely Topics Podcast  OTE Blog  Bloomberg 
We use Norwegian administrative panel data on wealth and income and follow the same individuals for 23 years between 1993 and 2015 to empirically study their lifecycle wealth dynamics, focusing on the wealthiest.  On average, the wealthiest start their lives substantially richer than other households in the same cohort, own mostly private equity in their portfolios, earn higher returns, derive most of their income from dividends and capital gains, and save at higher rates.  We empirically decompose the roles of different factors behind their wealth. At age 50, the excess wealth accumulation of the top 0.1% group relative to mid-wealth households is accounted for in about equal terms by higher saving rates (34%), higher initial wealth (32%), and higher returns (27%), while higher labor income (5%) and inheritances (1%) account for the small residual (Figure A). 
We also document significant heterogeneity among the wealthiest: around one-fourth of them—which we dub the “New Money”—start below median wealth but experience rapid wealth growth early in life. Relative to households who started their life rich—the “Old Money”—the New Money are characterized by even higher saving rates and returns and also by higher labor income. Their excess wealth at age 50 is mainly explained by higher saving rates (46%), followed by higher returns (34%) and higher labor income (16%) (Figure B).
The values on  y-axes are in multiples of the economy-wide average wealth (AW).
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Bibtex file   (An earlier version of this paper was circulated under the title “Income Differences and Health Care Expenditures over the Life Cycle.”) 

Using data from the Medical Expenditure Panel Survey (MEPS) I find that early in life the rich spend significantly more on health care, whereas from middle to very old age the poor outspend the rich by 25% in the US. Furthermore, while low-income individuals are less likely to incur medical expenses, they are more prone to experiencing extreme expenses when they do seek care. To account for these facts, I develop and estimate a life-cycle model of two types of health capital: physical and preventive. Physical health capital determines survival probabilities, whereas preventive health capital governs the distribution of shocks to physical health capital, thereby controlling life expectancy. Moreover, I incorporate key features of the US health care system, including private and public health insurance. Because of their lower marginal utility of consumption, the rich spend more on preventive care, resulting in milder health shocks (and lower curative medical expenditures) in old age compared to the poor. Notably, public insurance, which by design covers large expenditures, amplifies these differences by hampering the poor's incentives to invest in preventive health. Therefore, the model also implies a widening life expectancy gap between income groups in response to rising inequality. Policy experiments suggest that expanding health insurance coverage and subsidizing preventive care to encourage health care use by the poor early in life can generate substantial welfare gains, even when accounting for the higher taxes required to finance them.
Recent empirical studies document that the distribution of earnings changes displays substantial deviations from lognormality: in particular, earnings changes are negatively skewed with extremely high kurtosis (long and thick tails), and these non-Gaussian features vary substantially both over the life cycle and with the earnings level of individuals. Furthermore, earnings changes display nonlinear (asymmetric) mean reversion. In this paper, we embed a very rich “benchmark earnings process” that captures these non-Gaussian and nonlinear features into a lifecycle consumption-saving model and study its implications for consumption dynamics, consumption insurance, and welfare. We show four main results. First, the benchmark process essentially matches the empirical lifetime earnings inequality—a first-order proxy for consumption inequality—whereas the canonical Gaussian (persistent-plus-transitory) process understates it by a factor of five to ten. Second, the welfare cost of idiosyncratic risk implied by the benchmark process is between two-to-four times higher than the canonical Gaussian one. Third, the standard method in the literature for measuring the pass-through of income shocks to consumption—can significantly overstate the degree of consumption smoothing possible under non-Gaussian shocks. Fourth, the marginal propensity to consume out of transitory income (e.g., from a stimulus check) is higher under non-Gaussian earnings risk.

Publications

Published version    NBER EF&G SLIDES  2021 Draft  Data and Code   Bibtex file

Media Coverage and Blogs:  OTE Blog #1   OTE Blog #2  OTE Blog #3  Bloomberg 


We study the determinants of lifetime earnings (LE) inequality in the United States by focusing on latent heterogeneity in job-ladder dynamics and on-the-job learning. We use administrative data to document a novel set of moments on job mobility and earnings growth across the LE distribution. We then estimate a structural model featuring a rich set of worker types and firm heterogeneity. We find vast ex ante differences in job-loss, job-finding, and contact rates across worker types. These differences account for 75% of the lifetime wage growth differential among the bottom half of the LE distribution. Above the median, almost all lifetime wage growth differences are a result of Pareto-distributed learning ability.

Dissecting Idiosyncratic Income Risk, Journal of European Economic Association, 2023

(with E. Halvorsen,  H. Holter  and K. Storesltetten )

Published version   2020 Draft   SLIDES  Data and Code  JEEA Teaching Material  


This paper examines whether nonlinear and non-Gaussian features of earnings dynamics are caused by hours or hourly wages. Our findings from the Norwe- gian administrative and survey data are as follows: (i) Nonlinear mean reversion in earnings is driven by the dynamics of hours worked rather than wages since wage dynamics are close to linear, while hours dynamics are nonlinear—negative changes to hours are transitory, while positive changes are persistent. (ii) Large earnings changes are driven equally by hours and wages, whereas small changes are associated mainly with wage shocks. (iii) Both wages and hours contribute to negative skewness and high kurtosis for earnings changes, although hour-wage interactions are quantitatively more important. (iv) When considering household earnings and disposable household income, the deviations from normality are miti- gated relative to individual labor earnings: changes in disposable household income are approximately symmetric and less leptokurtic.
Published version  Replication files   2020 Draft - Longer version 
Abstract: Using administrative data from Norway, we first present stylized facts on labor earnings dynamics between 1993 and 2017 and its heterogeneity across narrow population groups. We then investigate the parents' role in children's income dynamics—the intergenerational transmission of income dynamics. We find that children of high-income, high-wealth fathers enjoy steeper income growth over the life cycle and face more volatile but more positively skewed income changes, suggesting that they are more likely to pursue high-return, high-risk careers. Children of poorer fathers also face more volatile incomes, but theirs grow more gradually and are more left skewed. Furthermore, the income dynamics of fathers and children are strongly correlated. In particular, children of fathers with steeper life-cycle income growth, more volatile incomes, or higher downside risk also have income streams of similar properties. We also confirm that fathers' significant role in workers' income dynamics is not simply spurious because of omitted variables, such as workers' own permanent income. These findings shed new light on the determinants of intergenerational mobility.
Part of the Global Repository of Income Dynamics Project (by F. Guvenen, L. Pistaferri and G. Violante), which aims to produce a harmonized cross-country database containing detailed and relevant statistics on individual- and household-level wages, earnings, and related labor market measures. The STATA code used to produce the harmonized statistics in all countries is prepared by S. Salgado and myself. 
Working paper  Online appendix  Downloadable data moments    Parameter estimates for the income processes   Replication files    Bibtex file    2020 Draft   
2019 Draft   Downloadable data moments in 2019 draft  Parameter estimates for the income processes  Older Version (2016) (based on wage/salary + self employment income over 1994-2013): Data Appendix: Moments For Men  Moments For WomenOlder Version (2015) (based on wage/salary income over 1978-2013): Data Appendix
Media Coverage: A nontechnical summary blog post on VoxEU  Washington Post  Slate   The Telegraph Bloomberg/Business   Bloomberg II   CNBC

We study individual male earnings dynamics over the life cycle using panel data on millions of U.S. workers. Using nonparametric methods, we first show that the distribution of earnings changes exhibits substantial deviations from lognormality, such as negative skewness and very high kurtosis. Further, the extent of these non-normalities varies significantly with age and earnings level, peaking around age 50 and between the 70th and 90th percentiles of the earnings distribution. Second, we estimate nonparametric impulse response functions and find important asymmetries: Positive changes for high-income individuals are quite transitory, whereas negative ones are very persistent; the opposite is true for low-income individuals. Third, we turn to long-run outcomes and find substantial heterogeneity in the cumulative growth rates of earnings and the total number of years individuals spend nonemployed between ages 25 and 55. Finally, by targeting these rich sets of moments, we estimate stochastic processes for earnings that range from the simple to the complex. Our preferred specification features normal mixture innovations to both persistent and transitory components and includes state-dependent long-term nonemployment shocks with a realization probability that varies with age and earnings.

The Nature of Countercyclical Income Risk , Journal of Political Economy, 2014

(with F. Guvenen and J. Song)
Working Paper    Downloadable data moments   Code   Bibtex fileWeb Appendices:  Appendix A: Data (excel file)   Appendix B: Sensitivity Analysis    Appendix C: Parametric EstimationFEDS Notes  NBER Digest    Bloomberg Article    Business Insider Article

This paper studies business cycle variation in individual earnings risk using  administrative SSA dataset. We document two sets of results. First, contrary to past research, we find that the variance of idiosyncratic earnings shocks is not countercyclical. Instead, it is the left-skewness of shocks that is strongly countercyclical: during recessions, the upper end of the shock distribution collapses—large upward earnings movements become less likely—whereas the bottom end expands–-large drops in earnings become more likely. Thus, while the dispersion of shocks does not increase, shocks become more left skewed and, hence, risky during recessions. Second, we find that the fortunes during recessions are predictable by observable characteristics before the recession. For example, prime-age workers that enter a recession with high earnings suffer substantially less compared with those who enter with low earnings. Finally, the cyclical nature of earnings risk is dramatically different for the top 1% compared with all other individuals—even those in the top 2% to 5%.
 Code & Data   Working Paper with Online Appendix  Bibtex file    Summary in the Region Magazine    Forbes Article

Wage inequality has been significantly higher in the U.S. than in continental European countries (CEU) since the 1970s. Moreover, this inequality gap has further widened during this period as the U.S. has experienced a large increase in wage inequality, whereas the CEU has seen only modest changes. This article studies the role of labour income tax policies for understanding these facts, focusing on male workers. We construct a life cycle model in which individuals decide each period whether to go to school, work, or stay non-employed. Individuals can accumulate human capital either in school or while working. Wage inequality arises from differences across individuals in their ability to learn new skills as well as from idiosyncratic shocks. Progressive taxation compresses the (after-tax) wage structure, thereby distorting the incentives to accumulate human capital, in turn reducing the cross-sectional dispersion of (before-tax) wages. Consistent with the model, we empirically document that countries with more progressive labour income tax schedules have (i) significantly lower before-tax wage inequality at different points in time and (ii) experienced a smaller rise in wage inequality since the early 1980s. We then study the calibrated model and find that these policies can account for half of the difference between the U.S. and the CEU in overall wage inequality and 84% of the difference in inequality at the upper end (log 90–50 differential). In a two-country comparison between the U.S. and Germany, the combination of skill-biased technical change and changing progressivity of tax schedules explains all the difference between the evolution of inequality in these two countries since the early 1980s.
Code & Data     Working Paper     Bibtex file

How does the persistence of earnings change over the life cycle? Do workers at different ages face the same variance of idiosyncratic earnings shocks? This paper proposes a novel specification for residual earnings that allows for an age profile in the persistence and variance of labor income shocks. We show that the statistical model is identified, and we estimate it using Panel Study of Income Dynamics data. We find that shocks to earnings are only moderately persistent (around 0.75) for young workers. Persistence rises with age, up to unity, until midway through life. The variance of persistent shocks exhibits a U-shaped profile over the life cycle (with a minimum of 0.01 and a maximum of 0.05). These results suggest that the standard specification in the literature (with age-invariant persistence and variance) cannot capture the earnings dynamics of young workers. We also argue that a calibrated job turnover model can account for these nonflat profiles. The key idea is that workers sort into better jobs and settle down as they age; in turn, magnitudes of wage growth rates decline, thereby decreasing the variance of shocks. Furthermore, the decline in job mobility results in higher persistence. Finally, we investigate the implications of age profiles for consumption-savings behavior. The welfare cost of idiosyncratic risk implied by the age-dependent income process is up to 1.6 percent of lifetime consumption lower compared with its age-invariant counterpart. This difference is mostly due to a higher degree of consumption insurance for young workers, for whom persistence is moderate. These results suggest that age profiles of persistence and variances should be taken into account when calibrating life-cycle models.
Code & Data   Online Appendix   Downloadable data in figures  Bibtex file
We use administrative data from the SSA to study not just the average scarring effects but investigate how the scarring effects vary with worker characteristics, such as age, skill level, and work history. Rather than focusing on involuntary job losses during mass layoffs, we consider more broadly the long lasting effects of spending at least one year nonemployment regardless of the reasons behind it. We find that the scarring effects of nonemployment vary greatly across workers with different recent earnings. In particular, low-recent-earnings workers as well as those in the top 5 percent suffer larger scarring effects of nonemployment. Furthermore, the large losses mostly result from the higher incidence of future nonemployment for the treatment group, rather than their lower earnings conditional on working. This effect is especially strong for low-income individuals.

Work In Progress

A Quantitative Exploration of Wealth Inequality: New Insights from Panel Data SED Slides

(with Joachim Hubmer,  S. Salgado, and E. Halvorsen )
In a companion paper, Ozkan et al. (2023), we use a long panel dataset on wealth and income to document new empirical facts on lifecycle wealth dynamics, focusing on the wealthiest. We conclude that higher labor income and higher returns on wealth—commonly considered as the primary sources of wealth inequality by the quantitative literature—account for only a third of the top 0.1% wealth accumulation at age 50 and that capturing the heterogeneity in initial wealth at age 25 and saving rates is quantitatively crucial. In this paper, by targeting this rich set of moments we develop and estimate a structural overlapping generations model. We start with the familiar framework that features incomplete markets with heterogeneity in labor market efficiency, entrepreneurial ability, and bequests and build on it incrementally until we arrive at a benchmark model that can capture the crucial aspects of the data. Our benchmark model incorporates two key features to the standard framework. First, to account for the vast heterogeneity in savings rates, we allow for wealth in the utility function. Second, children receive inheritances from their parents stochastically over the life cycle consistent with the data. Using the estimated model, we quantify the importance of different drivers for top wealth concentration.

High Risk Workers and High Risk Firms SED Slides

(with Marlène Koffi,  Sergio Salgado, and  Marco Weißler )
Recent literature documented large heterogeneity in earnings dynamics individuals experience, in particular, in average income profiles and higher order moments of income shocks as well as in unemployment risk and job finding rates. Using administrative social security data from Germany, we decompose heterogeneity in earnings dynamics into observable and unobservable worker and firm components. First, we document salient features of earnings risk conditional on observable worker and firm characteristics. Next, in order to identify unobservable heterogeneity we employ machine learning algorithms to cluster workers and firms by features of their earnings dynamics. Finally, we estimate an individual income process that allows for ex-ante worker and firm heterogeneity. 
We find that workers in smaller and shrinking firms experience lower earnings growth with more volatile and left skewed earnings changes as well as higher unemployment risk. When we control for unobservable worker and firm types jointly, we conclude that person effects explain majority of differences in earnings dynamics while firm effects explain relatively little. These findings have important implications for public policy such as unemployment insurance..

Heterogeneity in Production Technology and Wealth Inequality, (with  M Chan, G Hong,  J Hubmer, and  S. Salgado)

Why do some firms have persistently much higher profits relative to their assets than others? Do they have more productive technologies (i.e., which allows them to produce more for a given amount of capital) or are their technologies more scalable (i.e., marginal return to capital diminishes more slowly for them), or both? Are the wealthy households more likely to invest in more productive or more scalable technologies? We use Canadian administrative data on firms and their owners to investigate these questions.

Labor Market Risk and Racial Disparities in Lifetime Earnings: The Roles of Worker and Firm Heterogeneity

(with V. Gregory, F. Leenders,  and D. Wiczer )
We investigate the factors contributing to racial disparities in earnings in the United States by focusing on two-sided latent heterogeneity in job-ladder dynamics and on-the-job learning. For this purpose, we develop and estimate a structural model featuring firm heterogeneity and a rich set of worker types. Firms differ in productivity, unemployment risk, and learning-opportunities, while workers vary  in productivity, learning ability, and job ladder risk—job-loss and job-finding rates. We estimate the model using a novel set of moments derived from the US Longitudinal Employer-Household Dynamics dataset. In particular, we target variation in job mobility and earnings growth across the earnings distribution, between black and white workers, and between firms. We then use the estimated model to decompose the black-white earnings gap.

Monetary Policy, Heterogeneity and Housing Channel

(with A. Hedlund,  K. Mitman  and K. Larkin )
We investigate the role of housing and mortgage debt in the transmission and effectiveness of monetary policy. First, monetary policy induced-movements in house prices translate into consumption changes because of wealth effects. Second, a contractionary monetary shock raises the cost of borrowing which reduces the demand and as a result the liquidity of the housing market, further depressing house prices and further increases the cost of borrowing. Furthermore, nominal long-term mortgage debt implies that changes in monetary policy result in redistribution between lenders and borrowers and generate cash-flow effects that are larger for borrowing constrained households. We build a heterogenous agent New Keynesian model with a frictional housing market to quantify the various mechanisms. The model is able to match the rich empirical heterogeneity in home ownership, leverage and MPC across households. In particular, our model is consistent with the significant difference in MPC between low- and high-LTV households that we document in the data. Our quantitative findings are as follows: First, we find that about 20% of the drop in aggregate consumption against a contractionary monetary shock is due to declining house prices. Second, we find asymmetric responses of the economy to shocks, with contractionary shocks yielding a larger response of all variables. Finally, we investigate how the transmission of monetary policy depends on the distribution of mortgage debt and find that monetary policy is more effective in stimulating the economy in an high-LTV environment.

On the Mechanics of Wealth Inequality at the Top

(with F. Guvenen and Sergio Ocampo-Diaz)
Top-end wealth inequality is receiving increasing interest in academic research and policy debates. Despite this widespread attention, there is no consensus in the academic literature about the right framework—or model ingredients—that can generate the massive concentration of wealth at the top end. In this paper, we run a horse race between three frameworks that are commonly used for modeling top end inequality: (i) Aiygari-style models with “awesome state” shocks, (ii) Aiyagari-style models with empirically estimated non-linear and non-Gaussian income processes, (iii) Power law models with return heterogeneity. We show that the commonly used awesome state model with stochastic aging that is calibrated to match the top 1% wealth share delivers strongly counterfactual implications: 6% of population are still alive at age 170, more than half of the wealth is owned by individuals older than 100 years old, the model does not generate a Pareto tail nor anyone with more than $100 million in wealth, among others. An income process that matches many features of the income dynamics data (taken from Guvenen et al 2020) significantly understates wealth inequality with a Gini coefficient of 0.56 (vs. 0.85 and higher in the data) and a top 1% wealth share of 14% compared with more than 35% in the data. These results show that empirically well calibrated versions of the Aiyagari model cannot quantitatively match the features of very top end inequality. By contrast, the power law framework with return heterogeneity delivers a thick Pareto tail and matches the top 1% wealth share without relying on income shocks or unrealistic demographic structures.

Revisiting Gibrat’s Legacy: An Empirical Investigation of Nonlinear Firm Dynamics

(with B. Pugsley  and S. Salgado )
Using firm-level administrative panel data from the US and ten other countries, we characterize nonlinear firms' growth dynamics in terms of sales, employment, and productivity. Using nonparametric methods we first show that the distribution of firms growth exhibits substantial deviations from log normality such as negative skewness and high kurtosis. Further, the extent of these non-normalities varies significantly with age and size. Second, we estimate nonparametric impulse response functions of firm growth and find important asymmetries between positive and negative changes and small and big firms. We then investigate whether a standard model of firm dynamics featuring capital and labor adjustment costs and financial frictions can explain these empirical patterns. Our results suggest that a non-gaussian productivity process is required to capture the nonlinear and non-Gaussian features of the firms' growth dynamics.

Discussions

Code & Data

A Rich Set of Moments on Labor Income Dynamics from the US SSA's Master Earnings File


This figure shows the completion time of TikTak against the number of cores used in parallel, with the condition that the objective attained is always within 1% of the single-core value. The log-log plot is almost linear from 1 to 32 cores, with a slope of -0.976 showing almost linear scaling. Doubling the number of cores from 32 to 64 yields a final objective value that exceeds the 1% threshold.

The estimation required about N=1000 restarts (or local optimizations) in the global stage, so these results suggest a heuristic: #core ≤ sqrt(N) (sqrt(1000) ≈ 32), for the number of cores that can be used in parallel with linear scaling and no performance degradation. (Although we found that in this example, using 50 cores still delivered linear scaling without slowdown, so further experimentation is recommended).

Prepared for a special issue of the Quantitative Economics edited by F. Guvenen, L. Pistaferri and G. Violante.

Part of the Global Income Dynamics Project, which aims to produce a harmonized cross-country database containing detailed and relevant statistics on individual- and householdlevel wages, earnings, and related labor market measures. 

The STATA code used to produce the harmonized statistics in all countries is prepared by S. Salgado and myself.  Please email us to gain access to the code. Please cite the below paper if you use your code in your project. 


Here is our paper part of this project:

Earnings Dynamics and Its Intergenerational Transmission: Evidence from Norway, Quantitative Ecoomics, 2022.

(with E. Halvorsen  and S. Salgado )

Published version  Replication files   2020 Draft - Longer version 


Abstract: Using administrative data from Norway, we first present stylized facts on labor earnings dynamics between 1993 and 2017 and its heterogeneity across narrow population groups. We then investigate the parents' role in children's income dynamics—the intergenerational transmission of income dynamics. We find that children of high-income, high-wealth fathers enjoy steeper income growth over the life cycle and face more volatile but more positively skewed income changes, suggesting that they are more likely to pursue high-return, high-risk careers. Children of poorer fathers also face more volatile incomes, but theirs grow more gradually and are more left skewed. Furthermore, the income dynamics of fathers and children are strongly correlated. In particular, children of fathers with steeper life-cycle income growth, more volatile incomes, or higher downside risk also have income streams of similar properties. We also confirm that fathers' significant role in workers' income dynamics is not simply spurious because of omitted variables, such as workers' own permanent income. These findings shed new light on the determinants of intergenerational mobility.