Embedded Files
Working Papers
Working Papers
Scalable versus Productive Technologies UPDATED DRAFT!!!
(with Mons Chan, Guangbin Hong, Joachim Hubmer, and S. Salgado)Abstract. Do larger firms have more productive technologies, are their technologies more scalable, or both? We use administrative data on Canadian and US firms to estimate a joint distribution of output elasticities of capital, labor, and intermediate inputs—thus, returns to scale (RTS)—along with total factor productivity (TFP). We find significant heterogeneity in RTS across firms within industries. Furthermore, larger firms operate technologies with higher RTS, whereas the largest firms do not exhibit the highest TFP. Higher RTS for large firms are entirely driven by higher intermediate input elasticities. Descriptively, these align with higher intermediate input revenue shares. We also show that high-RTS firms grow faster, pay higher wages, and are owned by wealthier households. We then incorporate RTS heterogeneity into the workhorse model of endogenous entrepreneurship that matches the observed heterogeneity in TFP and RTS. We find that the efficiency losses from financial frictions are more than twice as large compared to a conventional calibration that attributes all heterogeneity to TFP and assumes a common RTS parameter.
Why Are the Wealthiest So Wealthy? New Longitudinal Empirical Evidence and Implications for Theories of Wealth Inequality (revision requested by Econometrica)
(with Joachim Hubmer, E. Halvorsen, and S. Salgado)Media Coverage and Blogs: Bloomberg Knowledge at Wharton Regional Economist Timely Topics Podcast OTE Blog
An earlier version of this paper focused on the empirical part and was circulated under the title "Why Are the Wealthiest So Wealthy? A Longitudinal Empirical Investigation."
Abstract. We use 1993—2015 Norwegian administrative panel data on wealth and income to study lifecycle wealth dynamics. By employing a novel budget constraint approach, we show that at age 50 the excess wealth of the top 0.1%, relative to mid-wealth households, is accounted for by higher saving rates (38%), inheritances (34%), returns (23%), and labor income (5%) (Figure A). One-fourth of the wealthiest—the "New Money"—start with negative wealth but experience rapid wealth growth early in life. Relative to the "Old Money," the New Money are characterized by even higher saving rates, returns, and labor income (Figure B). We use these dynamic facts to test six commonly used models of wealth inequality. Although these models can generate the high concentration of wealth seen in the cross-section, they tend to put too much weight on (accidental) bequests and fail to capture the contribution of the New Money. A model with heterogeneous returns that decrease in wealth, and non-homothetic preferences is consistent with the new facts on the dynamics of wealth accumulation.
The values on y-axes are in multiples of the economy-wide average wealth (AW).
An earlier version of this paper focused on the empirical part and was circulated under the title "Why Are the Wealthiest So Wealthy? A Longitudinal Empirical Investigation."
Abstract. We use 1993—2015 Norwegian administrative panel data on wealth and income to study lifecycle wealth dynamics. By employing a novel budget constraint approach, we show that at age 50 the excess wealth of the top 0.1%, relative to mid-wealth households, is accounted for by higher saving rates (38%), inheritances (34%), returns (23%), and labor income (5%) (Figure A). One-fourth of the wealthiest—the "New Money"—start with negative wealth but experience rapid wealth growth early in life. Relative to the "Old Money," the New Money are characterized by even higher saving rates, returns, and labor income (Figure B). We use these dynamic facts to test six commonly used models of wealth inequality. Although these models can generate the high concentration of wealth seen in the cross-section, they tend to put too much weight on (accidental) bequests and fail to capture the contribution of the New Money. A model with heterogeneous returns that decrease in wealth, and non-homothetic preferences is consistent with the new facts on the dynamics of wealth accumulation.
The values on y-axes are in multiples of the economy-wide average wealth (AW).
Publications
Publications
Working paper CRNY SLIDES Bibtex file
(An earlier version of this paper was circulated under the title “Income Differences and Health Care Expenditures over the Life Cycle.”)I show that while the rich spend more on healthcare early in life, the poor outspend them by 25% from middle to old age in the US. Furthermore, the poor seek medical care less frequently but face higher risks of extreme expenses when they do. I develop a life-cycle model, incorporating physical and preventive health capital, along with features of the US healthcare system. Preventive health capital governs the distribution of health shocks, thereby controlling life expectancy. The model suggests that the rich spend more on preventive care due to lower marginal utility of consumption, resulting in milder health shocks and lower curative expenses in old age. Public insurance---covering large curative expenditures---inadvertently widens the life expectancy gap by hampering the poor’s incentives to invest in preventive health. Policy experiments suggest that expanding insurance coverage and subsidizing preventive care to encourage the poor to use healthcare early in life yield substantial welfare gains.
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.
Anatomy of Lifetime Earnings Inequality: Heterogeneity in Job Ladder Risk vs Human Capital, Journal of Political Economy: Macroeconomics, 2023
(with J. Song and F. Karahan)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.
Earnings Dynamics and Its Intergenerational Transmission: Evidence from Norway, Quantitative Economics, 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.
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.
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.
What Do Data on Millions of U.S. Workers Say About Lifecycle Labor Income Risk?, Econometrica, 2021
(with F. Guvenen, F. Karahan, and J. Song)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.
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.
Heterogeneous Scarring Effects of Full-Year Nonemployment, American Economic Review P&P, 2017
(with F. Guvenen, F. Karahan and J. Song)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.
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.
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%.
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%.
Taxation of Human Capital and Wage Inequality: A Cross-Country Analysis, Review of Economic Studies, 2014
(with F. Guvenen and B. Kuruscu) 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.
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.
On the Persistence of Income Shocks over the Life Cycle: Evidence, Theory, and Implications, Review of Economic Dynamics, 2013
(with F. Karahan)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.
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.
Work In Progress
Work In Progress
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..
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..
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.
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.
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
Discussions
”The Life-Cycle Dynamics of Wealth Mobility” by Audoly, McGee, Ocampo, and Paz-Pardo
”Why Are Returns to Private Business Wealth So Dispersed?” by Boar, Gorea, and Midrigan
”Cyclical Earnings, Career and Employment Transitions” by Carrillo-Tudela, Visschers, and Wiczer
”Firm Heterogeneity in Skill Returns” by Böhm, Esmkhani, and Gallipoli
“Income and Wealth Inequality in America,1949-2016” by Kuhn, Schularick, and Steins
Code & Data
Code & Data
A Rich Set of Moments on Labor Income Dynamics from the US SSA's Master Earnings File
The empirical moments on labor income dynamics we documented in my papers using the SSA's Master Earnings file.
There are more than 10,000 moments reported in these files ranging from very simple average life-cycle earnings profiles to higher-order moments of long-term earnings growth by year, age and past income. These moments are intended to be used by researchers in their calibration exercises.
Moments documented in "What Do Data on Millions of U.S. Workers Say About Lifecycle Labor Income Risk?", Econometrica 2021 (with F. Guvenen, F. Karahan, and J. Song).
- Downloadable data moments in 2019 draft
- Older Version (2016): Moments For Men Moments For Women
- Older Version (2015) (based on wage/salary income over 1978-2013): Data Appendix
Moments documented in "The Nature of Countercyclical Income Risk", Journal of Political Economy 2014 (with F. Guvenen and J. Song).
Very efficient and flexible global optimization algorithm.
This algorithm is evolved out of Fatih Guvenen's joint projects with me and Tony Smith, Fatih Karahan, Tatjana Kleineberg, and Antoine Arnaud.
You can read the description of this algorithm in this paper and find its applications in my papers here, here and there.
You can find my version on GitHub.
Great care was taken to make it as compliant with Fortran 90 as possible, but there may be a couple of invocations to Fortran 95 intrinsics.
Parallel performance. Here is an example of the scaling performance of TikTak on an SMM estimation problem with 1200+ moments and 7 parameters (taken from Guvenen, Karahan, Ozkan, Song (2021, specification in Table IV, column 2).
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.
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