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Source: Econometric Reviews

Resulting in 4 citations.

1.	Dong, Yan Gan, Li Wang, Yingning
1.	Residential Mobility, Neighborhood Effects, and Educational Attainment of Blacks and Whites Econometric Reviews 34, 6-10 (2015): 762-797. Also: http://www.tandfonline.com/doi/full/10.1080/07474938.2014.956586#tabModule Cohort(s): NLSY79 Publisher: Taylor & Francis Group Keyword(s): Educational Attainment; Geocoded Data; Mobility, Residential; Neighborhood Effects; Racial Differences Permission to reprint the abstract has not been received from the publisher. This paper proposes a new model to identify if and how much the educational attainment gap between blacks and whites is due to the difference in their neighborhoods. In this model, individuals belong to two unobserved types: the endogenous type, which may move in response to the neighborhood effect on their education; or the exogenous type, which may move for reasons unrelated to education. The Heckman sample selection model becomes a special case of the current model in which the probability of one type of individuals is zero. Although we cannot find any significant neighborhood effect in the usual Heckman sample selection model, we do find heterogeneous effects in our two-type model. In particular, there is a substantial neighborhood effect for the movers who belong to the endogenous type. No significant effects exist for other groups. We also find that the endogenous type has more education and moves more often than the exogenous type. On average, we find that the neighborhood variable, the percentage of high school graduates in the neighborhood, accounts for about 28.96% of the education gap between blacks and whites.
	Bibliography Citation Dong, Yan, Li Gan and Yingning Wang. "Residential Mobility, Neighborhood Effects, and Educational Attainment of Blacks and Whites." Econometric Reviews 34, 6-10 (2015): 762-797.

2.	Hirukawa, Masayuki Murtazashvili, Irina Prokhorov, Artem
2.	Yet Another Look at the Omitted Variable Bias Econometric Reviews published online (8 February 2023): DOI: 10.1080/07474938.2022.2157965. Also: https://www.tandfonline.com/doi/full/10.1080/07474938.2022.2157965 Cohort(s): Young Men Publisher: Taylor & Francis Group Keyword(s): Cognitive Ability; Missing Data/Imputation; Panel Study of Income Dynamics (PSID); Statistical Analysis; Wages; World of Work Test Permission to reprint the abstract has not been received from the publisher. When conducting regression analysis, econometricians often face the situation where some relevant regressors are unavailable in the data set at hand. This article shows how to construct a new class of nonparametric proxies by combining the original data set with one containing the missing regressors. Imputation of the missing values is done using a nonstandard kernel adapted to mixed data. We derive the asymptotic distribution of the resulting semiparametric two-sample estimator of the parameters of interest and show, using Monte Carlo simulations, that it dominates the solutions involving instrumental variables and other parametric alternatives. An application to the PSID and NLS data illustrates the importance of our estimation approach for empirical research.
	Bibliography Citation Hirukawa, Masayuki, Irina Murtazashvili and Artem Prokhorov. "Yet Another Look at the Omitted Variable Bias." Econometric Reviews published online (8 February 2023): DOI: 10.1080/07474938.2022.2157965.

3.	Richey, Jeremiah Alexander
3.	An Odd Couple: Monotone Instrumental Variables and Binary Treatments Econometric Reviews 35,6 (2016): 1099-1110. Also: http://tandfonline.com/doi/abs/10.1080/07474938.2014.977082 Cohort(s): NLSY97 Publisher: Taylor & Francis Group Keyword(s): Crime; Modeling, Instrumental Variables; Occupations; Treatment Response: Monotone, Semimonotone, or Concave-monotone Permission to reprint the abstract has not been received from the publisher. This paper investigates Monotone Instrumental Variables (MIV) and their ability to aid in identifying treatment effects when the treatment is binary in a nonparametric bounding framework. I show that an MIV can only aid in identification beyond that of a Monotone Treatment Selection assumption if for some region of the instrument the observed conditional-on-received-treatment outcomes exhibit monotonicity in the instrument in the opposite direction as that assumed by the MIV in a Simpson's Paradox-like fashion. Furthermore, an MIV can only aid in identification beyond that of a Monotone Treatment Response assumption if for some region of the instrument either the above Simpson's Paradox-like relationship exists or the instrument's indirect effect on the outcome (as through its influence on treatment selection) is the opposite of its direct effect as assumed by the MIV. The implications of the main findings for empirical work are discussed and the results are highlighted with an application investigating the effect of criminal convictions on job match quality using data from the 1997 National Longitudinal Survey of the Youth. Though the main results are shown to hold only for the binary treatment case in general, they are shown to have important implications for the multi-valued treatment case as well.
	Bibliography Citation Richey, Jeremiah Alexander. "An Odd Couple: Monotone Instrumental Variables and Binary Treatments." Econometric Reviews 35,6 (2016): 1099-1110.

4.	Richey, Jeremiah Rosburg, Alicia
4.	Decomposing Joint Distributions via Reweighting Functions: An Application to Intergenerational Economic Mobility Econometric Reviews 39,6 (2020): 541-558. Also: https://www.tandfonline.com/doi/full/10.1080/07474938.2019.1697088 Cohort(s): NLSY79 Publisher: Taylor & Francis Group Keyword(s): Household Income; Intergenerational Patterns/Transmission; Male Sample; Mobility, Economic; Statistical Analysis Permission to reprint the abstract has not been received from the publisher. We introduce a method that extends the traditional Oaxaca-Blinder decomposition to both the full distribution of an outcome of interest and to settings where group membership varies along a continuum. We achieve this by working directly with the joint distribution of outcome and group membership and comparing it to an independent joint distribution. Like all decompositions, we assume the difference is partially due to differences in characteristics between groups (a composition effect) and partially due to differences in returns to characteristics between groups (a structure effect). We use reweighting functions to estimate a counterfactual joint distribution representing the hypothetical if characteristics did not vary according to group while returns to characteristics did. The counterfactual allows us to decompose differences between the empirical and independent distributions into composition and structure effects. We demonstrate the method by decomposing multiple measures of immobility for white men in the U.S.
	Bibliography Citation Richey, Jeremiah and Alicia Rosburg. "Decomposing Joint Distributions via Reweighting Functions: An Application to Intergenerational Economic Mobility." Econometric Reviews 39,6 (2020): 541-558.

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