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Source: Quantitative Economics
Resulting in 4 citations.
1. 
Bhattacharya, Debopam Mazumder, Bhashkar 
A Nonparametric Analysis of BlackWhite Differences in Intergenerational Income Mobility in the United States Quantitative Economics 2,3 (November 2011): 335379. 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 BlackWhite Differences in Intergenerational Income Mobility in the United States." Quantitative Economics 2,3 (November 2011): 335379.

2. 
Chalak, Karim 
Identification of Average Effects under Magnitude and Sign Restrictions on Confounding Quantitative Economics 10,4 (November 2019): 16191657. 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): 16191657.

3. 
Okumura, Tsunao Usui, Emiko 
ConcaveMonotone Treatment Response and Monotone Treatment Selection: With an Application to the Returns to Schooling Quantitative Economics 5,1 (March 2014): 175194. 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 Concavemonotone 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 concavemonotone treatment response (concaveMTR) 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 upperbound estimates are substantially smaller than (i) estimates using only the concaveMTR assumption of Manski (1997), and (ii) estimates using only the MTR and MTS assumptions of Manski and Pepper (2000). Our upperbound 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. "ConcaveMonotone Treatment Response and Monotone Treatment Selection: With an Application to the Returns to Schooling." Quantitative Economics 5,1 (March 2014): 175194.

4. 
Williams, Benjamin 
Identification of a Nonseparable Model under Endogeneity Using Binary Proxies for Unobserved Heterogeneity Quantitative Economics 10,2 (May 2019): 527563. 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): 527563.
