This paper is part of the Global Repository of Income Dynamics (GRID) project cross‐country comparison of earnings inequality, volatility, and mobility. Using data from the U.S. Census Bureau's Longitudinal Employer‐Household Dynamics (LEHD) infrastructure files, we produce a uniform set of earnings statistics for the U.S. From 1998 to 2019, we find U.S. earnings inequality has increased and volatility has decreased. The combination of increased inequality and reduced volatility suggest earnings growth differs substantially across different demographic groups. We explore this further by estimating 12‐year average earnings for a single cohort of age 25–54 eligible workers. Overall, differences in labor supply (hours paid and quarters worked) are found to explain almost 90% of the variation in worker earnings at the mean, although substantial earnings differences across and within groups remain unexplained. Using a quantile regression approach, we estimate counterfactual earnings distributions for each demographic group. We find that at the bottom of the earnings distribution differences in characteristics such as hours paid, geographic division, industry, and education explain almost the entire earnings gap; however, above the median the contribution of the differences in the returns to characteristics is the dominant component.
{"title":"U.S. long‐term earnings outcomes by sex, race, ethnicity, and place of birth","authors":"Kevin McKinney, John M. Abowd, Hubert Janicki","doi":"10.3982/qe1908","DOIUrl":"https://doi.org/10.3982/qe1908","url":null,"abstract":"This paper is part of the Global Repository of Income Dynamics (GRID) project cross‐country comparison of earnings inequality, volatility, and mobility. Using data from the U.S. Census Bureau's Longitudinal Employer‐Household Dynamics (LEHD) infrastructure files, we produce a uniform set of earnings statistics for the U.S. From 1998 to 2019, we find U.S. earnings inequality has increased and volatility has decreased. The combination of increased inequality and reduced volatility suggest earnings growth differs substantially across different demographic groups. We explore this further by estimating 12‐year average earnings for a single cohort of age 25–54 eligible workers. Overall, differences in labor supply (hours paid and quarters worked) are found to explain almost 90% of the variation in worker earnings at the mean, although substantial earnings differences across and within groups remain unexplained. Using a quantile regression approach, we estimate counterfactual earnings distributions for each demographic group. We find that at the bottom of the earnings distribution differences in characteristics such as hours paid, geographic division, industry, and education explain almost the entire earnings gap; however, above the median the contribution of the differences in the returns to characteristics is the dominant component.","PeriodicalId":46811,"journal":{"name":"Quantitative Economics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49369086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kotlarski's identity has been widely used in applied economic research based on repeated‐measurement or panel models with latent variables. However, how to conduct inference for these models has been an open question for two decades. This paper addresses this open problem by constructing a novel confidence band for the density function of a latent variable in repeated measurement error model. The confidence band builds on our finding that we can rewrite Kotlarski's identity as a system of linear moment restrictions. Our approach is robust in that we do not require the completeness. The confidence band controls the asymptotic size uniformly over a class of data generating processes, and it is consistent against all fixed alternatives. Simulation studies support our theoretical results. Deconvolution measurement error robust inference uniform confidence band C14 C57
{"title":"Robust inference in deconvolution","authors":"Kengo Kato, Yuya Sasaki, T. Ura","doi":"10.3982/QE1643","DOIUrl":"https://doi.org/10.3982/QE1643","url":null,"abstract":"Kotlarski's identity has been widely used in applied economic research based on repeated‐measurement or panel models with latent variables. However, how to conduct inference for these models has been an open question for two decades. This paper addresses this open problem by constructing a novel confidence band for the density function of a latent variable in repeated measurement error model. The confidence band builds on our finding that we can rewrite Kotlarski's identity as a system of linear moment restrictions. Our approach is robust in that we do not require the completeness. The confidence band controls the asymptotic size uniformly over a class of data generating processes, and it is consistent against all fixed alternatives. Simulation studies support our theoretical results. \u0000 \u0000Deconvolution measurement error robust inference uniform confidence band C14 C57","PeriodicalId":46811,"journal":{"name":"Quantitative Economics","volume":"12 1","pages":"109-142"},"PeriodicalIF":1.8,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41820793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose to add ranking restrictions on impulse‐responses to sign restrictions to narrow the identified set in vector autoregressions (VARs). Ranking restrictions come from micro data on heterogeneous industries in VARs, bounds on elasticities, or restrictions on dynamics. Using both a fully Bayesian conditional uniform prior and prior‐robust inference, we show that these restrictions help to identify productivity news shocks in the data. In the prior‐robust paradigm, ranking restrictions, but not sign restrictions alone, imply that news shocks raise output temporarily, but significantly. This holds both in an application with rankings in the form of heterogeneity restrictions and in another applications with slope restrictions as rankings. Ranking restrictions also narrow bounds on variance decompositions. For example, the bound of the contribution of news shocks to the forecast error variance of output narrows by about 30 pp at the one‐year horizon. While misspecification can be a concern with added restrictions, they are consistent with the data in our applications. Structural VAR set‐identification sign restrictions ranking restrictions heterogeneity posterior bounds Bayesian inference sampling methods productivity news C32 C53 E32
{"title":"Identification and inference with ranking restrictions","authors":"Pooyan Amir-Ahmadi, Thorsten Drautzburg","doi":"10.3982/QE1277","DOIUrl":"https://doi.org/10.3982/QE1277","url":null,"abstract":"We propose to add ranking restrictions on impulse‐responses to sign restrictions to narrow the identified set in vector autoregressions (VARs). Ranking restrictions come from micro data on heterogeneous industries in VARs, bounds on elasticities, or restrictions on dynamics. Using both a fully Bayesian conditional uniform prior and prior‐robust inference, we show that these restrictions help to identify productivity news shocks in the data. In the prior‐robust paradigm, ranking restrictions, but not sign restrictions alone, imply that news shocks raise output temporarily, but significantly. This holds both in an application with rankings in the form of heterogeneity restrictions and in another applications with slope restrictions as rankings. Ranking restrictions also narrow bounds on variance decompositions. For example, the bound of the contribution of news shocks to the forecast error variance of output narrows by about 30 pp at the one‐year horizon. While misspecification can be a concern with added restrictions, they are consistent with the data in our applications. \u0000 \u0000Structural VAR set‐identification sign restrictions ranking restrictions heterogeneity posterior bounds Bayesian inference sampling methods productivity news C32 C53 E32","PeriodicalId":46811,"journal":{"name":"Quantitative Economics","volume":"12 1","pages":"1-39"},"PeriodicalIF":1.8,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48133742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We investigate if, and why, an initial success can trigger a string of successes. Using random variations in success in a real-effort laboratory experiment, we cleanly identify the causal effect of an early success in a competition. We confirm that an early success indeed leads to increased chances of a later success. By alternatively eliminating strategic features of the competition, we turn on and off possible mechanisms driving the effect of an early success. Standard models of dynamic contest predict a strategic effect due to asymmetric incentives between initial winners and losers. Surprisingly, we find no evidence that they can explain the positive effect of winning. Instead, we find that the effect of winning seems driven by an information revelation effect, whereby players update their beliefs about their relative strength after experiencing an initial success.
{"title":"How success breeds success","authors":"Ambroise Decamps, Changxia Ke, Lionel Page","doi":"10.31219/osf.io/kb5ag","DOIUrl":"https://doi.org/10.31219/osf.io/kb5ag","url":null,"abstract":"We investigate if, and why, an initial success can trigger a string of successes. Using random variations in success in a real-effort laboratory experiment, we cleanly identify the causal effect of an early success in a competition. We confirm that an early success indeed leads to increased chances of a later success. By alternatively eliminating strategic features of the competition, we turn on and off possible mechanisms driving the effect of an early success. Standard models of dynamic contest predict a strategic effect due to asymmetric incentives between initial winners and losers. Surprisingly, we find no evidence that they can explain the positive effect of winning. Instead, we find that the effect of winning seems driven by an information revelation effect, whereby players update their beliefs about their relative strength after experiencing an initial success.","PeriodicalId":46811,"journal":{"name":"Quantitative Economics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45954526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose generalized DWH specification tests which simultaneously compare three or more likelihood‐based estimators in multivariate conditionally heteroskedastic dynamic regression models. Our tests are useful for Garch models and in many empirically relevant macro and finance applications involving Var s and multivariate regressions. We determine the rank of the differences between the estimators' asymptotic covariance matrices under correct specification, and take into account that some parameters remain consistently estimated under distributional misspecification. We provide finite sample results through Monte Carlo simulations. Finally, we analyze a structural Var proposed to capture the relationship between macroeconomic and financial uncertainty and the business cycle. Durbin–Wu–Hausman tests partial adaptivity semiparametric estimators singular covariance matrices uncertainty and the business cycle C12 C14 C22 C32 C52
{"title":"Specification tests for non‐Gaussian maximum likelihood estimators","authors":"G. Fiorentini, Enrique Sentana","doi":"10.3982/TE1585","DOIUrl":"https://doi.org/10.3982/TE1585","url":null,"abstract":"We propose generalized DWH specification tests which simultaneously compare three or more likelihood‐based estimators in multivariate conditionally heteroskedastic dynamic regression models. Our tests are useful for Garch models and in many empirically relevant macro and finance applications involving Var s and multivariate regressions. We determine the rank of the differences between the estimators' asymptotic covariance matrices under correct specification, and take into account that some parameters remain consistently estimated under distributional misspecification. We provide finite sample results through Monte Carlo simulations. Finally, we analyze a structural Var proposed to capture the relationship between macroeconomic and financial uncertainty and the business cycle. \u0000 \u0000Durbin–Wu–Hausman tests partial adaptivity semiparametric estimators singular covariance matrices uncertainty and the business cycle C12 C14 C22 C32 C52","PeriodicalId":46811,"journal":{"name":"Quantitative Economics","volume":"12 1","pages":"683-742"},"PeriodicalIF":1.8,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47521546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Unlike linear ones, nonlinear business cycle models can generate sustained fluctuations even in the absence of shocks (e.g., via limit cycles/chaos). A popular approach to solving nonlinear models is perturbation methods. I show that, as typically implemented, these methods are incapable of finding solutions featuring limit cycles or chaos. Fundamentally, solutions are only required not to explode, while standard perturbation algorithms seek solutions that meet the stronger requirement of convergence to the steady state. I propose a modification to standard algorithms that does not impose this overly strong requirement.
{"title":"Saddle cycles: Solving rational expectations models featuring limit cycles (or chaos) using perturbation methods","authors":"Dana Galizia","doi":"10.3982/qe1491","DOIUrl":"https://doi.org/10.3982/qe1491","url":null,"abstract":"Unlike linear ones, nonlinear business cycle models can generate sustained fluctuations even in the absence of shocks (e.g., via limit cycles/chaos). A popular approach to solving nonlinear models is perturbation methods. I show that, as typically implemented, these methods are incapable of finding solutions featuring limit cycles or chaos. Fundamentally, solutions are only required not to explode, while standard perturbation algorithms seek solutions that meet the stronger requirement of convergence to the steady state. I propose a modification to standard algorithms that does not impose this overly strong requirement.","PeriodicalId":46811,"journal":{"name":"Quantitative Economics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46686553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bullying cannot be tolerated as a normal social behavior portraying a child's life. This paper quantifies its negative consequences allowing for the possibility that victims and nonvictims differ in unobservable characteristics. To this end, we introduce a factor analytic model for identifying treatment effects of bullying in which latent cognitive and noncognitive skills determine victimization and multiple outcomes. We use early test scores to identify the distribution of these skills. Individual‐, classroom‐ and district‐level variables are also accounted for. Applying our method to longitudinal data from South Korea, we first show that while noncognitive skills reduce the chances of being bullied during middle school, the probability of being victimized is greater in classrooms with relatively high concentration of boys, previously self‐assessed bullies and students that come from violent families. We report bullying at age 15 has negative effects on physical and mental health outcomes at age 18. We also uncover heterogeneous effects by latent skills, from which we document positive effects on the take‐up of risky behaviors and negative effects on schooling attainment. Our findings suggest that investing in noncognitive development should guide policy efforts intended to deter this problematic behavior.
{"title":"Bullying among adolescents: The role of skills","authors":"M. Sarzosa, Sergio S. Urzúa","doi":"10.3982/qe1215","DOIUrl":"https://doi.org/10.3982/qe1215","url":null,"abstract":"Bullying cannot be tolerated as a normal social behavior portraying a child's life. This paper quantifies its negative consequences allowing for the possibility that victims and nonvictims differ in unobservable characteristics. To this end, we introduce a factor analytic model for identifying treatment effects of bullying in which latent cognitive and noncognitive skills determine victimization and multiple outcomes. We use early test scores to identify the distribution of these skills. Individual‐, classroom‐ and district‐level variables are also accounted for. Applying our method to longitudinal data from South Korea, we first show that while noncognitive skills reduce the chances of being bullied during middle school, the probability of being victimized is greater in classrooms with relatively high concentration of boys, previously self‐assessed bullies and students that come from violent families. We report bullying at age 15 has negative effects on physical and mental health outcomes at age 18. We also uncover heterogeneous effects by latent skills, from which we document positive effects on the take‐up of risky behaviors and negative effects on schooling attainment. Our findings suggest that investing in noncognitive development should guide policy efforts intended to deter this problematic behavior.","PeriodicalId":46811,"journal":{"name":"Quantitative Economics","volume":"1 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43300435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We introduce a novel simulated certainty equivalent approximation (SCEQ) method for solving dynamic stochastic problems. Our examples show that SCEQ can quickly solve high‐dimensional finite‐ or infinite‐horizon, stationary or nonstationary dynamic stochastic problems with hundreds of state variables, a wide state space, and occasionally binding constraints. With the SCEQ method, a desktop computer will suffice for large problems, but it can also use parallel tools efficiently. The SCEQ method is simple, stable, and can utilize any solver, making it suitable for solving complex economic problems that cannot be solved by other algorithms.
{"title":"A Simple but Powerful Simulated Certainty Equivalent Approximation Method for Dynamic Stochastic Problems","authors":"Y. Cai, K. Judd","doi":"10.3386/W28502","DOIUrl":"https://doi.org/10.3386/W28502","url":null,"abstract":"We introduce a novel simulated certainty equivalent approximation (SCEQ) method for solving dynamic stochastic problems. Our examples show that SCEQ can quickly solve high‐dimensional finite‐ or infinite‐horizon, stationary or nonstationary dynamic stochastic problems with hundreds of state variables, a wide state space, and occasionally binding constraints. With the SCEQ method, a desktop computer will suffice for large problems, but it can also use parallel tools efficiently. The SCEQ method is simple, stable, and can utilize any solver, making it suitable for solving complex economic problems that cannot be solved by other algorithms.","PeriodicalId":46811,"journal":{"name":"Quantitative Economics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48784872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We consider inference about a scalar coefficient in a linear regression model. One previously considered approach to dealing with many controls imposes sparsity, that is, it is assumed known that nearly all control coefficients are (very nearly) zero. We instead impose a bound on the quadratic mean of the controls' effect on the dependent variable, which also has an interpretation as an R 2‐type bound on the explanatory power of the controls. We develop a simple inference procedure that exploits this additional information in general heteroskedastic models. We study its asymptotic efficiency properties and compare it to a sparsity‐based approach in a Monte Carlo study. The method is illustrated in three empirical applications.
{"title":"Linear regression with many controls of limited explanatory power","authors":"Chenchuan Li, Ulrich K. Müller","doi":"10.3982/QE1577","DOIUrl":"https://doi.org/10.3982/QE1577","url":null,"abstract":"We consider inference about a scalar coefficient in a linear regression model. One previously considered approach to dealing with many controls imposes sparsity, that is, it is assumed known that nearly all control coefficients are (very nearly) zero. We instead impose a bound on the quadratic mean of the controls' effect on the dependent variable, which also has an interpretation as an R 2‐type bound on the explanatory power of the controls. We develop a simple inference procedure that exploits this additional information in general heteroskedastic models. We study its asymptotic efficiency properties and compare it to a sparsity‐based approach in a Monte Carlo study. The method is illustrated in three empirical applications.","PeriodicalId":46811,"journal":{"name":"Quantitative Economics","volume":"1 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70360848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Experimenters make theoretically irrelevant decisions concerning user interfaces and ordering or labeling of options. Reanalyzing dictator games, I first show that such decisions may drastically affect comparative statics and cause results to appear contradictory across experiments. This obstructs model testing, preference analyses, and policy predictions. I then propose a simple model of choice incorporating both presentation effects and stochastic errors, and test the model by reanalyzing the dictator game experiments. Controlling for presentation effects, preference estimates become consistent across experiments and predictive out‐of‐sample. This highlights both the necessity and the possibility to control for presentation in economic experiments. Presentation effects utility estimation counterfactual predictions laboratory experiment C10 C90
{"title":"Controlling for presentation effects in choice","authors":"Yves Breitmoser","doi":"10.3982/QE1050","DOIUrl":"https://doi.org/10.3982/QE1050","url":null,"abstract":"Experimenters make theoretically irrelevant decisions concerning user interfaces and ordering or labeling of options. Reanalyzing dictator games, I first show that such decisions may drastically affect comparative statics and cause results to appear contradictory across experiments. This obstructs model testing, preference analyses, and policy predictions. I then propose a simple model of choice incorporating both presentation effects and stochastic errors, and test the model by reanalyzing the dictator game experiments. Controlling for presentation effects, preference estimates become consistent across experiments and predictive out‐of‐sample. This highlights both the necessity and the possibility to control for presentation in economic experiments. Presentation effects utility estimation counterfactual predictions laboratory experiment C10 C90","PeriodicalId":46811,"journal":{"name":"Quantitative Economics","volume":"12 1","pages":"251-281"},"PeriodicalIF":1.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70359958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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