Search Results

Source: Journal of Econometrics
Resulting in 24 citations.
1. Bernal, Raquel
Keane, Michael P.
Quasi-structural Estimation of a Model of Childcare Choices and Child Cognitive Ability Production
Journal of Econometrics 156,1 (May 2010): 164-189.
Also: http://www.sciencedirect.com/science/article/pii/S0304407609002140
Cohort(s): Children of the NLSY79, NLSY79
Publisher: Elsevier
Keyword(s): Aid for Families with Dependent Children (AFDC); Armed Forces Qualifications Test (AFQT); Child Care; Children, Academic Development; Maternal Employment; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading); Peabody Picture Vocabulary Test (PPVT); State Welfare; State-Level Data/Policy; Temporary Assistance for Needy Families (TANF); Welfare

This article evaluates the effects of maternal vs. alternative care providers' time inputs on children's cognitive development using the sample of single mothers in the National Longitudinal Survey of Youth. To deal with the selection problem created by unobserved heterogeneity of mothers and children, we develop a model of mother's employment and childcare decisions. We then obtain approximate decision rules for employment and childcare use, and estimate these jointly with the child's cognitive ability production function. To help identify our selection model, we take advantage of the plausibly exogenous variation in employment and childcare choices of single mothers generated by the variation in welfare rules across states and over time created by the 1996 welfare reform legislation and earlier State waivers.
Bibliography Citation
Bernal, Raquel and Michael P. Keane. "Quasi-structural Estimation of a Model of Childcare Choices and Child Cognitive Ability Production." Journal of Econometrics 156,1 (May 2010): 164-189.
2. Black, Dan A.
Smith, Jeffrey A.
How Robust Is the Evidence on the Effects of College Quality? Evidence from Matching
Journal of Econometrics 121,1-2 (July/August 2004): 99-125.
Also: http://www.sciencedirect.com/science/article/pii/S0304407603002562
Cohort(s): NLSY79
Publisher: Elsevier
Keyword(s): Colleges; Gender Differences; Modeling; Test Scores/Test theory/IRT

We estimate the effects of college quality using propensity score matching methods and the National Longitudinal Survey of Youth 1979 cohort. Matching allows us to relax the linear functional form assumption implicit in regression-based estimates. We also examine the support problem by determining whether there are individuals attending low-quality colleges similar to those attending high-quality colleges, and find that the support condition holds only weakly. Thus, the linear functional form plays an important role in regression-based estimates (and matching estimates have large standard errors). Point estimates from regression and matching are similar for men but not women. [ABSTRACT FROM AUTHOR]
Bibliography Citation
Black, Dan A. and Jeffrey A. Smith. "How Robust Is the Evidence on the Effects of College Quality? Evidence from Matching." Journal of Econometrics 121,1-2 (July/August 2004): 99-125.
3. Carneiro, Pedro M.
Lee, Sokbae
Estimating Distributions of Potential Outcomes Using Local Instrumental Variables with an Application to Changes in College Enrollment and Wage Inequality
Journal of Econometrics 149,2 (April 2009): 191-208.
Also: http://www.sciencedirect.com/science/article/pii/S0304407609000281
Cohort(s): NLSY79
Publisher: Elsevier
Keyword(s): College Enrollment; Colleges; High School Completion/Graduates; Schooling; Variables, Instrumental; Wage Equations; Wages

This paper extends the method of local instrumental variables developed by Heckman and Vytlacil [Heckman, J., Vytlacil E., 2005. Structural equations, treatment, effects and econometric policy evaluation. Econometrica 73(3), 669–738] to the estimation of not only means, but also distributions of potential outcomes. The newly developed method is illustrated by applying it to changes in college enrollment and wage inequality using data from the National Longitudinal Survey of Youth of 1979. Increases in college enrollment cause changes in the distribution of ability among college and high school graduates. This paper estimates a semiparametric selection model of schooling and wages to show that, for fixed skill prices, a 14% increase in college participation (analogous to the increase observed in the 1980s), reduces the college premium by 12% and increases the 90–10 percentile ratio among college graduates by 2%. [Copyright 2009 Elsevier]

Copyright of Journal of Econometrics is the property of Elsevier Science Publishers B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts)

Bibliography Citation
Carneiro, Pedro M. and Sokbae Lee. "Estimating Distributions of Potential Outcomes Using Local Instrumental Variables with an Application to Changes in College Enrollment and Wage Inequality." Journal of Econometrics 149,2 (April 2009): 191-208.
4. Chamberlain, Gary
Multivariate Regression Models for Panel Data
Journal of Econometrics 18,1 (January 1982): 5-46.
Also: http://www.sciencedirect.com/science/article/pii/030440768290094X
Cohort(s): Young Men
Publisher: Elsevier
Keyword(s): Heterogeneity; Regions; Research Methodology; Standard Metropolitan Statistical Area (SMSA); Unions

The relationship between heterogeneity bias and strict exogeneity is examined in a distributed lag regression of y on x. The relationship is very strong when x is continuous, weaker when x is discrete, and non-existent as the order of the distributed lag becomes infinite. The individual specific random variables introduce nonlinearity and heteroskedasticity, so a framework suitable for the estimation of multivariate linear predictors is provided. A minimum distance estimator is used to impose restrictions, being generally more efficient than the conventional estimators, such as quasi-maximum likelihood. Computationally simple generalizations of 2- and 3-stage least squares exist to accomplish this efficiency gain. The sample of Young Men in the NLS is used to illustrate some of these ideas. Regressions on leads and lags of variables measuring union coverage, Standard Metropolitan Statistical Areas (SMSAs), and regions are reported. The results suggest that the leads and lags could have been brought about just by a random intercept, which gives some support for analysis of covariance type estimates. These estimates point to a substantial heterogeneity bias in the union, SMSA, and region coefficients. (ABI/Inform)
Bibliography Citation
Chamberlain, Gary. "Multivariate Regression Models for Panel Data." Journal of Econometrics 18,1 (January 1982): 5-46.
5. Chen, Xirong
Li, Degui
Li, Qi
Li, Zheng
Nonparametric Estimation of Conditional Quantile Functions in the Presence of Irrelevant Covariates
Journal of Econometrics 212,2 (October 2019): 433-450.
Also: https://www.sciencedirect.com/science/article/pii/S0304407619301034
Cohort(s): NLSY97
Publisher: Elsevier
Keyword(s): Dating; Modeling, Nonparametric Regression; Wages, Men; Wages, Women

Allowing for the existence of irrelevant covariates, we study the problem of estimating a conditional quantile function nonparametrically with mixed discrete and continuous data. We estimate the conditional quantile regression function using the check-function-based kernel method and suggest a data-driven cross-validation (CV) approach to simultaneously determine the optimal smoothing parameters and remove the irrelevant covariates. When the number of covariates is large, we first use a screening method to remove the irrelevant covariates and then apply the CV criterion to those that survive the screening procedure. Simulations and an empirical application demonstrate the usefulness of the proposed methods.
Bibliography Citation
Chen, Xirong, Degui Li, Qi Li and Zheng Li. "Nonparametric Estimation of Conditional Quantile Functions in the Presence of Irrelevant Covariates." Journal of Econometrics 212,2 (October 2019): 433-450.
6. D'Haultfoeuille, Xavier
Maurel, Arnaud
Zhang, Yichong
Extremal Quantile Regressions for Selection Models and the Black-White Wage Gap
Journal of Econometrics 203,1 (March 2018): 129-142.
Also: https://www.sciencedirect.com/science/article/pii/S0304407617302269
Cohort(s): NLSY79, NLSY97
Publisher: Elsevier
Keyword(s): Armed Forces Qualifications Test (AFQT); Family Background; Racial Differences; Wage Gap

We consider the estimation of a semiparametric sample selection model without instrument or large support regressor. Identification relies on the independence between the covariates and selection, for arbitrarily large values of the outcome. We propose a simple estimator based on extremal quantile regression and establish its asymptotic normality by extending previous results on extremal quantile regressions to allow for selection. Finally, we apply our method to estimate the black-white wage gap among males from the NLSY79 and NLSY97. We find that premarket factors such as AFQT and family background play a key role in explaining the black-white wage gap.
Bibliography Citation
D'Haultfoeuille, Xavier, Arnaud Maurel and Yichong Zhang. "Extremal Quantile Regressions for Selection Models and the Black-White Wage Gap." Journal of Econometrics 203,1 (March 2018): 129-142.
7. Das, Mitali
Identification and Sequential Estimation of Panel Data Models with Insufficient Exclusion Restrictions
Journal of Econometrics 114,2 (June 2003): 297-329.
Also: http://www.sciencedirect.com/science/article/pii/S0304407603000861
Cohort(s): Older Men
Publisher: Elsevier
Keyword(s): Data Analysis; Modeling; Modeling, Fixed Effects; Monte Carlo; Statistical Analysis

This paper presents estimators for nonparametric panel data models with additive fixed effects. We analyze a model in which the entire regressor vector, consisting of time-varying as well as time-invariant regressors, is correlated with the individual fixed effect but there are insufficient exclusion restrictions to permit direct instrumental variables estimation of the model. It is shown that when the model satisfies the Additive Interactive Regression (AIR) representation of Andrews and Whang (Economic theorpy 6 (1990) 466-480), a sequential estimator consistently estimates the model. The limiting distribution of scalar nonlinear functionals of the model is shown to be standard normal. Finite sample properties of the estimator are considered in a Monte Carlo simulation study, and used to estimate the returns to education in a cohort of mature men from the National Longitudinal Survey. [Copyright 2003 Elsevier]
Bibliography Citation
Das, Mitali. "Identification and Sequential Estimation of Panel Data Models with Insufficient Exclusion Restrictions." Journal of Econometrics 114,2 (June 2003): 297-329.
8. Deza, Monica
Is There a Stepping Stone Effect in Drug Use? Separating State Dependence from Unobserved Heterogeneity Within and Between Illicit Drugs
Journal of Econometrics 184,1 (January 2015): 193-207.
Also: http://www.sciencedirect.com/science/article/pii/S030440761400181X
Cohort(s): NLSY97
Publisher: Elsevier
Keyword(s): Alcohol Use; Drug Use; Geocoded Data; Heterogeneity; Modeling; Substance Use

Empirically, teenagers who use soft drugs are more likely to use hard drugs in the future. This pattern can be explained by a causal effect (i.e., state dependence between drugs or stepping-stone effects) or by unobserved characteristics that make people more likely to use both soft and hard drugs (i.e., correlated unobserved heterogeneity). I estimate a dynamic discrete choice model of alcohol, marijuana and hard drug use over multiple years, and separately identify the contributions of state dependence (within and between drugs) and unobserved heterogeneity. I find statistically significant "stepping-stone" effects from softer to harder drugs, and conclude that alcohol, marijuana and hard drugs are complements in utility.
Bibliography Citation
Deza, Monica. "Is There a Stepping Stone Effect in Drug Use? Separating State Dependence from Unobserved Heterogeneity Within and Between Illicit Drugs." Journal of Econometrics 184,1 (January 2015): 193-207.
9. Flinn, Christopher Jay
Heckman, James J.
New Methods for Analyzing Structural Models of Labor Force Dynamics
Journal of Econometrics 18,1 (January 1982): 115-168.
Also: http://www.sciencedirect.com/science/article/pii/0304407682900975
Cohort(s): Young Men
Publisher: Elsevier
Keyword(s): Labor Force Participation; Labor Turnover; Unemployment; Work Histories

New econometric methods are presented for the analysis of labor force dynamics. The economic models discussed assume that rational agents make choices about their employment and labor force activity in the presence of uncertainty about fundamental aspects of their labor market environment. The economic theory of decision-making under uncertainty is used to produce three econometric models of dynamic discrete choice: 1. for a single spell of unemployment, 2. for an equilibrium 2-state model of employment and non-employment, and 3. for a general 3-state model with a non-market sector. A fundamental condition required in this analysis is a recoverability condition that is implicit in all econometric analyses of truncated data. This condition must be fulfilled in order to recover an untruncated distribution from a truncated distribution with a known point of truncation. A recoverability condition will be fulfilled only if the untruncated distribution is assumed to belong to a parametric family. Most econometric models for the analysis of truncated data are non-parametrically underidentified, and the structural estimators often violate standard regularity conditions. The standard asymptotic theory is altered to explain this crucial characteristic of many structural models of labor force dynamics. [ABI/Inform]
Bibliography Citation
Flinn, Christopher Jay and James J. Heckman. "New Methods for Analyzing Structural Models of Labor Force Dynamics." Journal of Econometrics 18,1 (January 1982): 115-168.
10. Graham, Bryan
Hahn, Jinyong
Poirier, Alexandre
Powell, James L.
A Quantile Correlated Random Coefficients Panel Data Model
Journal of Econometrics 206,2 (October 2018): 305-335.
Also: https://www.sciencedirect.com/science/article/pii/S0304407618300952
Cohort(s): NLSY79
Publisher: Elsevier
Keyword(s): Collective Bargaining; Earnings; Heterogeneity; Modeling, Fixed Effects

We propose a generalization of the linear quantile regression model to accommodate possibilities afforded by panel data. Specifically, we extend the correlated random coefficients representation of linear quantile regression (e.g., Koenker, 2005; Section 2.6). We show that panel data allows the econometrician to (i) introduce dependence between the regressors and the random coefficients and (ii) weaken the assumption of comonotonicity across them (i.e., to enrich the structure of allowable dependence between different coefficients). We adopt a "fixed effects" approach, leaving any dependence between the regressors and the random coefficients unmodelled. We motivate different notions of quantile partial effects in our model and study their identification... We apply our methods to study the effects of collective bargaining coverage on earnings using the National Longitudinal Survey of Youth 1979 (NLSY79). Consistent with prior work (e.g., Chamberlain, 1982; Vella and Verbeek, 1998), we find that using panel data to control for unobserved worker heterogeneity results in sharply lower estimates of union wage premia. We estimate a median union wage premium of about 9 percent, but with, in a more novel finding, substantial heterogeneity across workers.
Bibliography Citation
Graham, Bryan, Jinyong Hahn, Alexandre Poirier and James L. Powell. "A Quantile Correlated Random Coefficients Panel Data Model." Journal of Econometrics 206,2 (October 2018): 305-335.
11. Ham, John C.
Li, Xianghong
Reagan, Patricia Benton
Matching and Semi-parametric IV Estimation, a Distance-Based Measure of Migration, and the Wages of Young Men
Journal of Econometrics 161, 2 (April 2011): 208-227.
Also: http://www.sciencedirect.com/science/article/pii/S0304407610002460
Cohort(s): NLSY79
Publisher: Elsevier
Keyword(s): College Graduates; Male Sample; Migration; School Dropouts; Statistical Analysis; Wage Growth

Our paper estimates the effect of US internal migration on wage growth for young men between their first and second job. Our analysis of migration extends previous research by: (i) exploiting the distance-based measures of migration in the National Longitudinal Surveys of Youth 1979 (NLSY79); (ii) allowing the effect of migration to differ by schooling level and (iii) using propensity score matching to estimate the average treatment effect on the treated (ATET) for movers and (iv) using local average treatment effect (LATE) estimators with covariates to estimate the average treatment effect (ATE) and ATET for compliers.

We believe the Conditional Independence Assumption (CIA) is reasonable for our matching estimators since the NLSY79 provides a relatively rich array of variables on which to match. Our matching methods are based on local linear, local cubic, and local linear ridge regressions. Local linear and local ridge regression matching produce relatively similar point estimates and standard errors, while local cubic regression matching badly over-fits the data and provides very noisy estimates.

We use the bootstrap to calculate standard errors. Since the validity of the bootstrap has not been investigated for the matching estimators we use, and has been shown to be invalid for nearest neighbor matching estimators, we conduct a Monte Carlo study on the appropriateness of using the bootstrap to calculate standard errors for local linear regression matching. The data generating processes in our Monte Carlo study are relatively rich and calibrated to match our empirical models or to test the sensitivity of our results to the choice of parameter values. The estimated standard errors from the bootstrap are very close to those from the Monte Carlo experiments, which lends support to our using the bootstrap to calculate standard errors in our setting.

From the matching estimators we find a significant positive effect of migration on the wage growth of college graduates, and a marginally significant negative effect for high school dropouts. We do not find any significant effects for other educational groups or for the overall sample. Our results are generally robust to changes in the model specification and changes in our distance-based measure of migration. We find that better data matters; if we use a measure of migration based on moving across county lines, we overstate the number of moves, while if we use a measure based on moving across state lines, we understate the number of moves. Further, using either the county or state measures leads to much less precise estimates.

We also consider semi-parametric LATE estimators with covariates (Frolich 2007), using two sets of instrumental variables. We precisely estimate the proportion of compliers in our data, but because we have a small number of compliers, we cannot obtain precise LATE estimates.

Bibliography Citation
Ham, John C., Xianghong Li and Patricia Benton Reagan. "Matching and Semi-parametric IV Estimation, a Distance-Based Measure of Migration, and the Wages of Young Men." Journal of Econometrics 161, 2 (April 2011): 208-227.
12. Heckman, James J.
Raut, Lakshmi K.
Intergenerational Long-term Effects of Preschool--Structural Estimates from a Discrete Dynamic Programming Model
Journal of Econometrics 191,1 (March 2016): 164-175.
Also: http://www.sciencedirect.com/science/article/pii/S0304407615002493
Cohort(s): Children of the NLSY79, NLSY79
Publisher: Elsevier
Keyword(s): Armed Forces Qualifications Test (AFQT); Children, Academic Development; Children, Behavioral Development; Earnings; Head Start; Intergenerational Patterns/Transmission; Mobility, Schools; Noncognitive Skills; Pearlin Mastery Scale; Preschool Children; Rosenberg Self-Esteem Scale (RSES) (see Self-Esteem); Sociability/Socialization/Social Interaction; Socioeconomic Status (SES)

This paper formulates a structural dynamic programming model of preschool investment choices of altruistic parents and then empirically estimates the structural parameters of the model using the NLSY79 data. The paper finds that preschool investment significantly boosts cognitive and non-cognitive skills, which enhance earnings and school outcomes. It also finds that a standard Mincer earnings function, by omitting measures of non-cognitive skills on the right-hand side, overestimates the rate of return to schooling. From the estimated equilibrium Markov process, the paper studies the nature of within generation earnings distribution, intergenerational earnings mobility, and schooling mobility. The paper finds that a tax-financed free preschool program for the children of poor socioeconomic status generates positive net gains to the society in terms of average earnings, higher intergenerational earnings mobility, and schooling mobility.
Bibliography Citation
Heckman, James J. and Lakshmi K. Raut. "Intergenerational Long-term Effects of Preschool--Structural Estimates from a Discrete Dynamic Programming Model." Journal of Econometrics 191,1 (March 2016): 164-175.
13. Horowitz, Joel L.
Lee, Sokbae
Semiparametric Estimation of a Panel Data Proportional Hazards Model with Fixed Effects
Journal of Econometrics 119,1 (March 2004): 155-198.
Also: http://www.sciencedirect.com/science/article/pii/S0304407603002033
Cohort(s): NLSY79
Publisher: Elsevier
Keyword(s): Modeling, Fixed Effects; Modeling, Hazard/Event History/Survival/Duration; Monte Carlo; Statistical Analysis; Work History

This paper considers a panel duration model that has a proportional hazards specification with fixed effects. The paper shows how to estimate the baseline and integrated baseline hazard functions without assuming that they belong to known, finite-dimensional families of functions. Existing estimators assume that the baseline hazard function belongs to a known parametric family. Therefore, the estimators presented here are more general than existing ones. This paper also presents a method for estimating the parametric part of the proportional hazards model with dependent right censoring, under which the partial likelihood estimator is inconsistent. The paper presents some Monte Carlo evidence on the small sample performance of the new estimators.
Bibliography Citation
Horowitz, Joel L. and Sokbae Lee. "Semiparametric Estimation of a Panel Data Proportional Hazards Model with Fixed Effects." Journal of Econometrics 119,1 (March 2004): 155-198.
14. Horowitz, Joel L.
Manski, Charles F.
Censoring of Outcomes and Regressors Due to Survey Nonresponse: Identification and Estimation Using Weights and Imputations
Journal of Econometrics 84,1 (May 1998): 37-58.
Also: http://www.sciencedirect.com/science/article/pii/S0304407697000778
Cohort(s): NLSY79
Publisher: Elsevier
Keyword(s): Data Quality/Consistency; Longitudinal Data Sets; Longitudinal Surveys; Nonresponse

Survey nonresponse makes identification of population parameters problematic. Except in special cases, identification is possible only if one makes untestable assumptions about the distribution of the missing data. However, nonresponse does not preclude identification of bounds on parameters. This paper shows how identified bounds on unidentified population parameters can be obtained under several forms of nonresponse. Organizations conducting major surveys commonly release public-use data files that provide nonresponse weights or imputations to be used for estimating population parameters. The paper shows how to bound the asymptotic bias of estimates using weights and imputations. The results are illustrated with empirical examples based on the National Longitudinal Survey of Youth. Photocopy available from ABI/INFORM.
Bibliography Citation
Horowitz, Joel L. and Charles F. Manski. "Censoring of Outcomes and Regressors Due to Survey Nonresponse: Identification and Estimation Using Weights and Imputations." Journal of Econometrics 84,1 (May 1998): 37-58.
15. Kennan, John
Walker, James R.
Wages, Welfare Benefits and Migration
Journal of Econometrics 156,1 (May 2010): 229-238.
Also: http://www.sciencedirect.com/science/article/pii/S0304407609002188
Cohort(s): NLSY79
Publisher: Elsevier
Keyword(s): Benefits; Economics, Regional; Job Search; Life Cycle Research; Migration; Welfare

Differences in economic opportunities give rise to strong migration incentives, across regions within countries, and across countries. In this paper we focus on responses to differences in welfare benefits across States within the United States. We apply the model developed in Kennan and Walker (2008), which emphasizes that migration decisions are often reversed, and that many alternative locations must be considered. We model individual decisions to migrate as a job search problem. A worker starts the life-cycle in some home location and must determine the optimal sequence of moves before settling down. The model is sparsely parameterized.We estimate the model using data from the National Longitudinal Survey of Youth (1979). Our main finding is that income differences do help explain the migration decisions of young welfare-eligible women, but large differences in benefit levels provide surprisingly weak migration incentives. [Copyright c. Elsevier]

Copyright of Journal of Econometrics is the property of Elsevier Science Publishers B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Bibliography Citation
Kennan, John and James R. Walker. "Wages, Welfare Benefits and Migration." Journal of Econometrics 156,1 (May 2010): 229-238.
16. Koop, Gary
Tobias, Justin L.
Semiparametric Bayesian Inference in Smooth Coefficient Models
Journal of Econometrics 134,1 (September 2006): 283-315.
Also: http://www.sciencedirect.com/science/article/pii/S0304407605001491
Cohort(s): NLSY79
Publisher: Elsevier
Keyword(s): Bayesian; Cognitive Ability; Education; Labor Supply; Modeling; Variables, Independent - Covariate

We describe procedures for Bayesian estimation and testing in cross-sectional, panel data and nonlinear smooth coefficient models. The smooth coefficient model is a generalization of the partially linear or additive model wherein coefficients on linear explanatory variables are treated as unknown functions of an observable covariate. In the approach we describe, points on the regression lines are regarded as unknown parameters and priors are placed on differences between adjacent points to introduce the potential for smoothing the curves. The algorithms we describe are quite simple to implement—-for example, estimation, testing and smoothing parameter selection can be carried out analytically in the cross-sectional smooth coefficient model. We apply our methods using data from the National Longitudinal Survey of Youth (NLSY). Using the NLSY data we first explore the relationship between ability and log wages and flexibly model how returns to schooling vary with measured cognitive ability. We also examine a model of female labor supply and use this example to illustrate how the described techniques can been applied in nonlinear settings. [ABSTRACT FROM AUTHOR; Copyright 2006 Elsevier]
Bibliography Citation
Koop, Gary and Justin L. Tobias. "Semiparametric Bayesian Inference in Smooth Coefficient Models." Journal of Econometrics 134,1 (September 2006): 283-315.
17. Li, Kai
Poirier, Dale J.
An Econometric Model of Birth Inputs and Outputs for Native Americans
Journal of Econometrics 113,2 (April 2003): 337-361.
Also: http://www.sciencedirect.com/science/article/pii/S0304407602002063
Cohort(s): NLSY79
Publisher: Elsevier
Keyword(s): Alcohol Use; Bayesian; Birthweight; Cigarette Use (see Smoking); Endogeneity; Ethnic Groups; Modeling, Multilevel; Pre-natal Care/Exposure; Simultaneity; Weight

This paper presents a new model of the birth process of Native Americans with seven endogenous variables: four birth inputs maternal smoking (S), drinking (D), prenatal care (PC), and weight gain (WG), and three birth outputs gestational age (G), birth length (BL), and birth weight (BW). The model is a seven-equation simultaneous model with three endogenous dummies S, D, and PC. The data are taken from the National Longitudinal Survey of Youth (NLSY). We find that the four birth inputs are determined jointly and dependently among S, D, and PC, but independently of WG. S has negative systematic correlation with G. D and PC appear to have no sizeable systematic effect on G, BL, or BW. Except for the sizeable and positive correlation between the unexplained parts of S and G, there seem to be no unexplained common effects between the birth inputs and outputs. Moreover, G appears dependent on the exogenous size of the mother. BL is affected by the inputs mainly through WG. BW is affected by the inputs through their effects on G. Except for maternal weight, there is little correlation between the remaining exogenous variables and BW. Finally, the predictive density of BW for a typical pregnancy gives a mean weight of 3.240 kg. [Copyright 2003 Elsevier]
Bibliography Citation
Li, Kai and Dale J. Poirier. "An Econometric Model of Birth Inputs and Outputs for Native Americans." Journal of Econometrics 113,2 (April 2003): 337-361.
18. Liu, Echu
Hsiao, Cheng
Matsumoto, Tomoya
Chou, Shin-Yi
Maternal Full-Time Employment and Overweight Children: Parametric, Semi-Parametric, and Non-Parametric Assessment
Journal of Econometrics 152,1 (September 2009): 61-69.
Also: http://www.sciencedirect.com/science/article/pii/S0304407609000542
Cohort(s): Children of the NLSY79, NLSY79
Publisher: Elsevier
Keyword(s): Birthweight; Body Mass Index (BMI); Child Health; Children; Fathers, Presence; Maternal Employment; Mothers, Education; Obesity; Weight

We use the matched mother-child data from the 2000 wave of the US National Longitudinal Survey of Youth 79 (NLSY79) to assess the impact of full-time working mothers on children's body mass index (BMI) and the likelihood of becoming overweight. Parametric, semi-parametric and non-parametric methods are employed to correct the bias of selection on observables and unobservables. Pros and cons of various methods are discussed and specification tests are conducted. In general, we find that a mother's full-time employment does have some impact on her children's BMI and likelihood of becoming overweight across models and inference procedures. [Copyright 2009 Elsevier]

Copyright of Journal of Econometrics is the property of Elsevier Science Publishers B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Bibliography Citation
Liu, Echu, Cheng Hsiao, Tomoya Matsumoto and Shin-Yi Chou. "Maternal Full-Time Employment and Overweight Children: Parametric, Semi-Parametric, and Non-Parametric Assessment." Journal of Econometrics 152,1 (September 2009): 61-69.
19. Liu, Haiyong
Mroz, Thomas
van der Klaauw, Wilbert
Maternal Employment, Migration, and Child Development
Journal of Econometrics 156,1 (May 2010): 212-228.
Also: http://www.sciencedirect.com/science/article/pii/S0304407609002176
Cohort(s): Children of the NLSY79, NLSY79
Publisher: Elsevier
Keyword(s): Armed Forces Qualifications Test (AFQT); Child Development; Common Core of Data (CCD); Maternal Employment; Migration; Mobility; Modeling, Hazard/Event History/Survival/Duration; Peabody Individual Achievement Test (PIAT- Math); Residence; School Characteristics/Rating/Safety; School Quality; Test Scores/Test theory/IRT

We analyze the roles of and interrelationships among school inputs and parental inputs in affecting child development through the specification and estimation of a behavioral model of household migration and maternal employment decisions. We integrate information on these decisions with observations on child outcomes over a 13-year period from the National Longitudinal Study of Youth (NLSY). We find that the impact of our school quality measures diminishes by factors of 2 to 4 after accounting for the fact that families may choose where to live in part based on school characteristics and labor market opportunities. The positive statistical relationship between child outcomes and maternal employment reverses sign and remains statistically significant after controlling for its possible endogeneity. Our estimates imply that when parental responses are taken into account, policy changes in school quality end up having only minor impacts on child test scores.
Bibliography Citation
Liu, Haiyong, Thomas Mroz and Wilbert van der Klaauw. "Maternal Employment, Migration, and Child Development." Journal of Econometrics 156,1 (May 2010): 212-228.
20. Moretti, Enrico
Estimating the Social Return to Higher Education: Evidence from Longitudinal and Repeated Cross-sectional Data
Journal of Econometrics 121,1-2 (July-August 2004): 175-212.
Also: http://www.sciencedirect.com/science/article/pii/S0304407603002653
Cohort(s): NLSY79
Publisher: Elsevier
Keyword(s): Census of Population; College Graduates; Data Linkage (also see Record Linkage); Education; Geocoded Data; Modeling, Instrumental Variables; Wage Rates

Economists have speculated for at least a century that the social return to education may exceed the private return. In this paper, I estimate spillovers from college education by comparing wages for otherwise similar individuals who work in cities with different shares of college graduates in the labor force. A key issue in this comparison is the presence of unobservable characteristics of individuals and cities that may raise wages and be correlated with college share. I use longitudinal data to estimate a model of non-random selection of workers among cities. I account for unobservable city-specific demand shocks by using two instrumental variables: the (lagged) city demographic structure and the presence of a land-grant college. I find that a percentage point increase in the supply of college graduates raises high school drop-outs’ wages by 1.9%, high school graduates’ wages by 1.6%, and college graduates wages by 0.4%. The effect is larger for less educated groups, as predicted by a conventional demand and supply model. But even for college graduates, an increase in the supply of college graduates increases wages, as predicted by a model that includes conventional demand and supply factors as well as spillovers.
Bibliography Citation
Moretti, Enrico. "Estimating the Social Return to Higher Education: Evidence from Longitudinal and Repeated Cross-sectional Data." Journal of Econometrics 121,1-2 (July-August 2004): 175-212.
21. Rosenzweig, Mark R.
Wolpin, Kenneth I.
Inequality at Birth: The Scope for Policy Intervention
Journal of Econometrics 50,1-2 (October-November 1991): 205-225.
Also: http://www.sciencedirect.com/science/article/pii/030440769190096V
Cohort(s): Children of the NLSY79, NLSY79
Publisher: Elsevier
Keyword(s): Birthweight; Child Health; Childbearing; Cigarette Use (see Smoking); Fertility; Mothers, Behavior; Mothers, Health; Parental Influences; Sons; Substance Use

In this paper, the authors utilize information on birthweight and gestational age among siblings and maternal behaviors relevant to birth outcomes to decompose the inequality (variance) in child health at birth into those components associated with variance in endowments, the correlation between health-relevant behaviors and endowments, and the correlation between health endowments and the environmental variables influencing the household choice set. Estimations are made of: (1) the effects of maternal behaviors, including substance abuse, cigarette smoking, prenatal care, birth spacing and timing, and weight gain on the two birth outcomes; (2) the variance in the health endowment common to the two measures and to siblings; (3) the covariances between the maternal behaviors and health endowments; and (4) the variance in measurement errors for each outcome variable. The results indicate that, despite the importance of many maternal behaviors in influencing birthweigh t, a substantial fraction of its variance is due to endowment variation. This result appears to be robust to what is assumed about the relative importance of the correlations between household constraints and the responsiveness of health-related parental behavior to endowments. For birthweight, it was found, moreover, that endowment variation is on net reinforced by parental resource allocations, although this effect is small. It was also found that for the NLSY sample most of the variance in gestation is measurement error, while for birthweight the "noise" component is only one-third of the total variance. The authors reject the hypothesis that gestation and birthweight measure a single health factor, with parental behaviors influencing each in distinctly different ways.
Bibliography Citation
Rosenzweig, Mark R. and Kenneth I. Wolpin. "Inequality at Birth: The Scope for Policy Intervention." Journal of Econometrics 50,1-2 (October-November 1991): 205-225.
22. Sasaki, Yuya
Xin, Yi
Unequal Spacing in Dynamic Panel Data: Identification and Estimation
Journal of Econometrics 196,2 (February 2017): 320-330.
Also: http://www.sciencedirect.com/science/article/pii/S0304407616301932
Cohort(s): Older Men
Publisher: Elsevier
Keyword(s): Data Quality/Consistency; Modeling, Fixed Effects; Wage Dynamics

We propose conditions under which parameters of fixed-effect dynamic models are identified with unequally spaced panel data. Under predeterminedness, weak stationarity, and empirically testable rank conditions, AR(1) parameters are identified given the availability of "two pairs of two consecutive time gaps," which generalizes "two pairs of two consecutive time periods." This result extends to models with multiple covariates, higher-order autoregressions, and partial linearity. Applying our method to the NLS Original Cohorts: Older Men, where personal interviews took place in 1966, 67, and 69, we analyze the earnings dynamics in the old time, and compare the results with more recent ones.
Bibliography Citation
Sasaki, Yuya and Yi Xin. "Unequal Spacing in Dynamic Panel Data: Identification and Estimation." Journal of Econometrics 196,2 (February 2017): 320-330.
23. Su, Liangjun
Ura, Takuya
Zhang, Yichong
Non-separable Models with High-dimensional Data
Journal of Econometrics 212,2 (October 2019): 646-677.
Also: https://www.sciencedirect.com/science/article/pii/S0304407619301447
Cohort(s): NLSY79
Publisher: Elsevier
Keyword(s): Fathers and Sons; Income; Intergenerational Patterns/Transmission; Modeling; Statistical Analysis

This paper studies non-separable models with a continuous treatment when the dimension of the control variables is high and potentially larger than the effective sample size. We propose a three-step estimation procedure to estimate the average, quantile, and marginal treatment effects. Using simulated and real datasets, we demonstrate that the proposed estimators perform well in finite samples.
Bibliography Citation
Su, Liangjun, Takuya Ura and Yichong Zhang. "Non-separable Models with High-dimensional Data." Journal of Econometrics 212,2 (October 2019): 646-677.
24. Torgovitsky, Alexander
Minimum Distance from Independence Estimation of Nonseparable Instrumental Variables Models
Journal of Econometrics 199,1 (July 2017): 35-48.
Also: http://www.sciencedirect.com/science/article/pii/S0304407617300441
Cohort(s): NLSY79
Publisher: Elsevier
Keyword(s): Educational Returns; Modeling, Instrumental Variables

I develop a semiparametric minimum distance from independence estimator for a nonseparable instrumental variables model. An independence condition identifies the model for many types of discrete and continuous instruments. The estimator is taken as the parameter value that most closely satisfies this independence condition. Implementing the estimator requires a quantile regression of the endogenous variables on the instrument, so the procedure is two-step, with a finite or infinite-dimensional nuisance parameter in the first step. I prove consistency and establish asymptotic normality for a parametric, but flexibly nonlinear outcome equation. The consistency of the nonparametric bootstrap is also shown. I illustrate the use of the estimator by estimating the returns to schooling using data from the 1979 National Longitudinal Survey.
Bibliography Citation
Torgovitsky, Alexander. "Minimum Distance from Independence Estimation of Nonseparable Instrumental Variables Models." Journal of Econometrics 199,1 (July 2017): 35-48.