Search Results

Source: Quantitative Economics
Resulting in 6 citations.
1. Bhattacharya, Debopam
Mazumder, Bhashkar
A Nonparametric Analysis of Black-White Differences in Intergenerational Income Mobility in the United States
Quantitative Economics 2,3 (November 2011): 335-379.
Also: http://onlinelibrary.wiley.com/doi/10.3982/QE69/abstract
Cohort(s): NLSY79
Publisher: Wiley Online
Keyword(s): Armed Forces Qualifications Test (AFQT); Income; Intergenerational Patterns/Transmission; Mobility; Mobility, Economic; Racial Differences; Wage Gap

Permission to reprint the abstract has not been received from the publisher.

Lower intergenerational income mobility for blacks is a likely cause behind the persistent interracial gap in economic status in the United States. However, few studies have analyzed black–white differences in intergenerational income mobility and the factors that determine these differences. This is largely due to the absence of appropriate methodological tools. We develop nonparametric methods to estimate the effects of covariates on two measures of mobility. We first consider the traditional transition probability of movement across income quantiles. We then introduce a new measure of upward mobility which is the probability that an adult child's relative position exceeds that of the parents. Conducting statistical inference on these mobility measures and the effects of covariates on them requires nontrivial modifications of standard nonparametric regression theory since the dependent variables are nonsmooth functions of marginal quantiles or relative ranks. Using National Longitudinal Survey of Youth data, we document that blacks experience much less upward mobility across generations than whites. Applying our new methodological tools, we find that most of this gap can be accounted for by differences in cognitive skills during adolescence.
Bibliography Citation
Bhattacharya, Debopam and Bhashkar Mazumder. "A Nonparametric Analysis of Black-White Differences in Intergenerational Income Mobility in the United States." Quantitative Economics 2,3 (November 2011): 335-379.
2. Bohm, Michael J.
The Price of Polarization: Estimating Task Prices under Routine‐biased Technical Change
Quantitative Economics 11,2 (May 2020): 761-799.
Also: https://onlinelibrary.wiley.com/doi/10.3982/QE1031
Cohort(s): NLSY79, NLSY97
Publisher: Wiley Online
Keyword(s): Armed Forces Qualifications Test (AFQT); Male Sample; Skills; Wage Growth

Permission to reprint the abstract has not been received from the publisher.

This paper proposes a new approach to estimate task prices per efficiency unit of skill in the Roy model. I show how the sorting of workers into tasks and their associated wage growth can be used to identify changes in task prices under relatively weak assumptions. The estimation exploits the fact that the returns to observable talents will change differentially over time depending on the changes in prices of those tasks that they predict workers to sort into. In the generalized Roy model, also the average non‐pecuniary amenities in each task are identified. I apply this approach to the literature on routine‐biased technical change, a key prediction of which is that task prices should polarize. Empirical results for male workers in U.S. data indicate that abstract and manual tasks' relative prices indeed increased during the 1990s and 2000s.
Bibliography Citation
Bohm, Michael J. "The Price of Polarization: Estimating Task Prices under Routine‐biased Technical Change." Quantitative Economics 11,2 (May 2020): 761-799.
3. Chalak, Karim
Identification of Average Effects under Magnitude and Sign Restrictions on Confounding
Quantitative Economics 10,4 (November 2019): 1619-1657.
Also: https://onlinelibrary.wiley.com/doi/10.3982/QE689
Cohort(s): Young Men
Publisher: Wiley Online
Keyword(s): Educational Returns; Modeling, Nonparametric Regression; Modeling, Structural Equation; Racial Differences; Statistical Analysis; Wage Gap

Permission to reprint the abstract has not been received from the publisher.

This paper studies measuring various average effects of X on Y in general structural systems with unobserved confounders U, a potential instrument Z, and a proxy W for U. We do not require X or Z to be exogenous given the covariates or W to be a perfect one‐to‐one mapping of U. We study the identification of coefficients in linear structures as well as covariate‐conditioned average nonparametric discrete and marginal effects (e.g., average treatment effect on the treated), and local and marginal treatment effects. First, we characterize the bias, due to the omitted variables U, of (nonparametric) regression and instrumental variables estimands, thereby generalizing the classic linear regression omitted variable bias formula. We then study the identification of the average effects of X on Y when U may statistically depend on X and Z. These average effects are point identified if the average direct effect of U on Y is zero, in which case exogeneity holds, or if W is a perfect proxy, in which case the ratio (contrast) of the average direct effect of U on Y to the average effect of U on W is also identified. More generally, restricting how the average direct effect of U on Y compares in magnitude and/or sign to the average effect of U on W can partially identify the average effects of X on Y. These restrictions on confounding are weaker than requiring benchmark assumptions, such as exogeneity or a perfect proxy, and enable a sensitivity analysis. After discussing estimation and inference, we apply this framework to study earnings equations.
Bibliography Citation
Chalak, Karim. "Identification of Average Effects under Magnitude and Sign Restrictions on Confounding." Quantitative Economics 10,4 (November 2019): 1619-1657.
4. Okumura, Tsunao
Usui, Emiko
Concave-Monotone Treatment Response and Monotone Treatment Selection: With an Application to the Returns to Schooling
Quantitative Economics 5,1 (March 2014): 175-194.
Also: http://onlinelibrary.wiley.com/doi/10.3982/QE268/abstract
Cohort(s): NLSY79
Publisher: Wiley Online
Keyword(s): Educational Returns; Statistical Analysis; Treatment Response: Monotone, Semimonotone, or Concave-monotone

Permission to reprint the abstract has not been received from the publisher.

This paper identifies sharp bounds on the mean treatment response and average treatment effect under the assumptions of both the concave-monotone treatment response (concave-MTR) and the monotone treatment selection (MTS). We use our bounds and the U.S. National Longitudinal Survey of Youth 1979 to estimate mean returns to schooling. Our upper-bound estimates are substantially smaller than (i) estimates using only the concave-MTR assumption of Manski (1997), and (ii) estimates using only the MTR and MTS assumptions of Manski and Pepper (2000). Our upper-bound estimates fall in the range of the point estimates given in previous studies that assume linear wage functions.
Bibliography Citation
Okumura, Tsunao and Emiko Usui. "Concave-Monotone Treatment Response and Monotone Treatment Selection: With an Application to the Returns to Schooling." Quantitative Economics 5,1 (March 2014): 175-194.
5. Song, Suyong
Schennach, Susanne M.
White, Halbert
Estimating Nonseparable Models With Mismeasured Endogenous Variables
Quantitative Economics 6,3 (November 2015): DOI: 749-794.
Also: https://qeconomics.org/ojs/index.php/qe/article/view/299
Cohort(s): Children of the NLSY79, NLSY79
Publisher: Econometric Society
Keyword(s): Children, Academic Development; Family Income; Monte Carlo; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading); Statistical Analysis

Permission to reprint the abstract has not been received from the publisher.

We study the identification and estimation of covariate‐conditioned average marginal effects of endogenous regressors in nonseparable structural systems when the regressors are mismeasured. We control for the endogeneity by making use of covariates as control variables; this ensures conditional independence between the endogenous causes of interest and other unobservable drivers of the dependent variable. Moreover, we recover distributions of the underlying true causes from their error‐laden measurements to deliver consistent estimators. We obtain uniform convergence rates and asymptotic normality for estimators of covariate‐conditioned average marginal effects, faster convergence rates for estimators of their weighted averages over instruments, and root‐n consistency and asymptotic normality for estimators of their weighted averages over control variables and regressors. We investigate their finite‐sample behavior using Monte Carlo simulation and apply new methods to study the impact of family income on child achievement measured by math and reading scores, using a matched mother-child subsample of the National Longitudinal Survey of Youth. Our findings suggest that these effects are considerably larger than previously recognized, and depend on parental abilities and family income. This underscores the importance of measurement errors, endogeneity of family income, nonlinearity of income effects, and interactions between causes of child achievement.
Bibliography Citation
Song, Suyong, Susanne M. Schennach and Halbert White. "Estimating Nonseparable Models With Mismeasured Endogenous Variables." Quantitative Economics 6,3 (November 2015): DOI: 749-794.
6. Williams, Benjamin
Identification of a Nonseparable Model under Endogeneity Using Binary Proxies for Unobserved Heterogeneity
Quantitative Economics 10,2 (May 2019): 527-563.
Also: https://onlinelibrary.wiley.com/doi/10.3982/QE674
Cohort(s): NLSY79
Publisher: Wiley Online
Keyword(s): Armed Forces Qualifications Test (AFQT); Cognitive Ability; Educational Attainment; Heterogeneity; Modeling; Test Scores/Test theory/IRT

Permission to reprint the abstract has not been received from the publisher.

In this paper, I study identification of a nonseparable model with endogeneity arising due to unobserved heterogeneity. Identification relies on the availability of binary proxies that can be used to control for the unobserved heterogeneity. I show that the model is identified in the limit as the number of proxies increases. The argument does not require an instrumental variable that is excluded from the outcome equation nor does it require the support of the unobserved heterogeneity to be finite. I then propose a nonparametric estimator that is consistent as the number of proxies increases with the sample size. I also show that, for a fixed number of proxies, nontrivial bounds on objects of interest can be obtained. Finally, I study two real data applications that illustrate computation of the bounds and estimation with a large number of items.
Bibliography Citation
Williams, Benjamin. "Identification of a Nonseparable Model under Endogeneity Using Binary Proxies for Unobserved Heterogeneity." Quantitative Economics 10,2 (May 2019): 527-563.