Child-custody reform, marital investment in children, and the labor supply of married mothers

Research on child custody primarily focuses on the well-being of children following divorce. We extend this literature by examining how the prospect of joint child custody affects within-marriage investment in children through changes in household bargaining power. Variation in the timing of joint-custody reforms across states provides a natural-experiment framework with which to examine within-marriage investment in children. The probability of children's private school attendance declines by 12% in states that adopt joint-custody laws. We also find evidence linking joint-custody reform to higher rates of labor force participation for married mothers, which may indicate less time devoted household production.     

Sexual orientation and household decision making.: Same-sex couples' balance of power and labor supply choices.

This study estimates the effect of intra-household-bargaining on gay and lesbian couples' labor supplies, in order to determine how homosexual and heterosexual decision making compare, in a collective-household framework. Data from the 2000 US Census show that couples of all types exhibit a significant response to bargaining power shifts, as measured by age and non-labor-income differences between partners. Among gay, lesbian, and heterosexual cohabiting couples, a relatively young or rich partner has more bargaining power and supplies less labor, the opposite being true for his/her mate. Among married couples, the older spouse is instead more powerful, or the richer. No such patterns are found among same-sex roommates.     

Social Security and elderly labor supply: Evidence from the Health and Retirement Study

This study uses panel data from the Health and Retirement Study (HRS) to estimate the effects of Social Security income on elderly labor supply in the 1990s and early 2000s. The identification strategy takes advantage of the 1977 amendments to the Social Security Act, which led to a large, unanticipated reduction in Social Security benefits for those born after January 1, 1917. Despite the advanced age of the notch cohorts, there is a significant, negative and surprisingly elastic relationship between Social Security income and hours of work. This suggests that currently proposed reductions in benefits would induce Social Security recipients to work more hours in retirement, even through their 70s and early 80s.     

Semiparametric efficient estimation of dynamic panel data models

This paper extends the semiparametric efficient treatment of panel data models pursued by Park and Simar [Park, B.U., Simar, L., 1994. Efficient semiparametric estimation in stochastic frontier models. Journal of the American Statistical Association 89, 929–936] and Park et al. [Park, B.U., Sickles, R.C., Simar, L., 1998. Stochastic frontiers: a semiparametric approach. Journal of Econometrics 84, 273–301; Park, B.U., Sickles, R.C., Simar, L., 2003. Semiparametric efficient estimation of AR(1) panel data models. Journal of Econometrics 117, 279–309] to a dynamic panel setting. We develop a semiparametric efficient estimator under minimal assumptions when the panel model contains a lagged dependent variable. We apply this new estimator to analyze the structure of demand between city pairs for selected U.S. airlines during the period 1979 I–1992 IV.     

A semiparametric panel model for unbalanced data with application to climate change in the United Kingdom

This paper is concerned with developing a semiparametric panel model to explain the trend in UK temperatures and other weather outcomes over the last century. We work with the monthly averaged maximum and minimum temperatures observed at the twenty six Meteorological Office stations. The data is an unbalanced panel. We allow the trend to evolve in a nonparametric way so that we obtain a fuller picture of the evolution of common temperature in the medium timescale. Profile likelihood estimators (PLE) are proposed and their statistical properties are studied. The proposed PLE has improved asymptotic property comparing the sequential two-step estimators. Finally, forecasting based on the proposed model is studied.     

Fixed effects estimation of structural parameters and marginal effects in panel probit models

Fixed effects estimators of nonlinear panel models can be severely biased due to the incidental parameters problem. In this paper, I characterize the leading term of a large-T expansion of the bias of the MLE and estimators of average marginal effects in parametric fixed effects panel binary choice models. For probit index coefficients, the former term is proportional to the true value of the coefficients being estimated. This result allows me to derive a lower bound for the bias of the MLE. I then show that the resulting fixed effects estimates of ratios of coefficients and average marginal effects exhibit no bias in the absence of heterogeneity and negligible bias for a wide variety of distributions of regressors and individual effects in the presence of heterogeneity. I subsequently propose new bias-corrected estimators of index coefficients and marginal effects with improved finite sample properties for linear and nonlinear models with predetermined regressors.     

Dragon Children: Identifying the Causal Effect of the First Child on Female Labour Supply with the Chinese Lunar Calendar*

Instrumental variables (IV) estimates of the effect of fertility on female labour supply have only been able to identify the causal effect of second and higher parity children. This study uses exogenous variation in fertility caused by the Chinese lunar calendar to identify the effect of the first child. Additionally, weighting formulas are presented to interpret IV estimates as weighted average treatment effects in the case of multiple endogenous variables, which are useful when children vary in intensity by both number and age. The effect of the first child is found to be much greater than that of other children.     

Quasi-maximum likelihood estimation of volatility with high frequency data

This paper investigates the properties of the well-known maximum likelihood estimator in the presence of stochastic volatility and market microstructure noise, by extending the classic asymptotic results of quasi-maximum likelihood estimation. When trying to estimate the integrated volatility and the variance of noise, this parametric approach remains consistent, efficient and robust as a quasi-estimator under misspecified assumptions. Moreover, it shares the model-free feature with nonparametric alternatives, for instance realized kernels, while being advantageous over them in terms of finite sample performance. In light of quadratic representation, this estimator behaves like an iterative exponential realized kernel asymptotically. Comparisons with a variety of implementations of the Tukey–Hanning₂ kernel are provided using Monte Carlo simulations, and an empirical study with the Euro/US Dollar future illustrates its application in practice.     

Sieve estimation of panel data models with cross section dependence

In this paper we consider the problem of estimating semiparametric panel data models with cross section dependence, where the individual-specific regressors enter the model nonparametrically whereas the common factors enter the model linearly. We consider both heterogeneous and homogeneous regression relationships when both the time and cross-section dimensions are large. We propose sieve estimators for the nonparametric regression functions by extending Pesaran’s (2006) common correlated effect (CCE) estimator to our semiparametric framework. Asymptotic normal distributions for the proposed estimators are derived and asymptotic variance estimators are provided. Monte Carlo simulations indicate that our estimators perform well in finite samples.     

Partial identification of probability distributions with misclassified data

This paper addresses the problem of data errors in discrete variables. When data errors occur, the observed variable is a misclassified version of the variable of interest, whose distribution is not identified. Inferential problems caused by data errors have been conceptualized through convolution and mixture models. This paper introduces the direct misclassification approach. The approach is based on the observation that in the presence of classification errors, the relation between the distribution of the ‘true’ but unobservable variable and its misclassified representation is given by a linear system of simultaneous equations, in which the coefficient matrix is the matrix of misclassification probabilities. Formalizing the problem in these terms allows one to incorporate any prior information into the analysis through sets of restrictions on the matrix of misclassification probabilities. Such information can have strong identifying power. The direct misclassification approach fully exploits it to derive identification regions for any real functional of the distribution of interest. A method for estimating the identification regions and construct their confidence sets is given, and illustrated with an empirical analysis of the distribution of pension plan types using data from the Health and Retirement Study.     

Identification and nonparametric estimation of a transformed additively separable model

Let r(x,z)$r(x,z)$ be a function that, along with its derivatives, can be consistently estimated nonparametrically. This paper discusses the identification and consistent estimation of the unknown functions H$H$, M$M$, G$G$ and F$F$, where r(x,z)=H[M(x,z)]$r(x,z)=H\left[M(x,z)\right]$, M(x,z)=G(x)+F(z)$M(x,z)=G\left(x\right)+F\left(z\right)$, and H$H$ is strictly monotonic. An estimation algorithm is proposed for each of the model’s unknown components when r(x,z)$r(x,z)$ represents a conditional mean function. The resulting estimators use marginal integration to separate the components G$G$ and F$F$. Our estimators are shown to have a limiting Normal distribution with a faster rate of convergence than unrestricted nonparametric alternatives. Their small sample performance is studied in a Monte Carlo experiment. We apply our results to estimate generalized homothetic production functions for four industries in the Chinese economy.     

Pseudo conditional maximum likelihood estimation of the dynamic logit model for binary panel data

We show how the dynamic logit model for binary panel data may be approximated by a quadratic exponential model. Under the approximating model, simple sufficient statistics exist for the subject-specific parameters introduced to capture the unobserved heterogeneity between subjects. The latter must be distinguished from the state dependence which is accounted for by including the lagged response variable among the regressors. By conditioning on the sufficient statistics, we derive a pseudo conditional likelihood estimator of the structural parameters of the dynamic logit model, which is simple to compute. Asymptotic properties of this estimator are studied in detail. Simulation results show that the estimator is competitive in terms of efficiency with estimators recently proposed in the econometric literature.     

All in the family: why non-democratic leaders have more children

Economists have come to learn that politics matters. But survival matters the most to those involved in politics. We provide a theory whereby non-benevolent, non-democratic leaders increase their expected family size to raise the likelihood that a child will be a match at continuing the regime’s survival. As a consequence, having a larger family size raises the non-democratic leader’s expected rents that they can exploit from the citizenry. In contrast, democratic leaders have a lower desire to appropriate rents from the citizenry, and therefore have a diminished desire to have additional children for these purposes. We construct a data set of the number of children of country leaders as of August 31, 2005. We find that in a sample of 221 country leaders, fully non-democratic leaders have approximately 1.5–2.5 more actual children as compared to if they are fully democratic. This empirical relationship is established controlling for a full array of country specific as well as individual specific variables. Our finding also continues to hold when using alternative measures of family size. This paper has been prepared for a conference honoring Herschel I. Grossman at Brown University in April of 2006. We thank our discussant, Murat Iyigun, an anonymous referee, Michelle Garfinkel and conference participants for excellent suggestions.     

Agricultural productivity,shadow wages and off-farm labor decisions in Nicaragua

The objective of this study is to analyze the decision to work off-farm by male and female farmers in Nicaragua using a three-year unbalanced panel dataset. Shadow income and shadow wages are also estimated. Moreover, to mitigate biases from unobserved individual and farm time-invariant characteristics as well as from sample selection, we apply a semiparametric approach for panel data. Our main findings suggest that shadow wages play a major role in off-farm labor decisions for both males and females. This implies that less labor is allocated to off-farm activities as the opportunity cost for agricultural work goes up. In addition, as the on-farm marginal productivity of households (i.e., shadow income) rises, both males and females reduce the hours they allocate to off-farm activities.     

Off-farm labor supply and labor markets in rapidly changing circumstances: Bulgaria during transition

This study examines off-farm labor supply in the rapidly changing conditions of Bulgaria during the 1990s. In doing so, we make use of three different waves of the Bulgarian Integrated Household Survey, each reflecting remarkably different environmental conditions. The results suggest that standard theories of off-farm labor supply provide little guidance in situations characterized by chronic excess supply in the off-farm labor market and/or rapidly changing circumstances. In particular, the results show (1) that off-farm employment throughout the transition was predominantly determined by demand rather than by supply, and (2) that the magnitude and statistical significance of the various determinants are very sensitive to changing environmental conditions. As such, the results can be extremely relevant for both theory and policy for the many countries which may still need to go through privatization and painful restructuring as a result of financial crises and globalization.     

Quasi-structural estimation of a model of childcare choices and child cognitive ability production

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.     

Estimating the Consequences of Unintended Fertility for Child Health and Education in Romania: An Analysis Using Twins Data*

We use the natural experiment of twins at first birth to estimate the effects of unplanned fertility on the nutritional status and school enrolment of children in Romania, a country with a unique fertility history. A first‐birth twins shock has negative impacts on children's human capital investments, particularly for later‐born siblings. We infer that harsh pronatalist policies prior to the 1989 Revolution had adverse consequences for the human capital of Romanian children, and that policies to make fertility control easier will have significant positive impacts on children's health and schooling.     

Estimation of a nonlinear panel data model with semiparametric individual effects

This paper investigates identification and estimation of a class of nonlinear panel data, single-index models. The model allows for unknown time-specific link functions, and semiparametric specification of the individual-specific effects. We develop an estimator for the parameters of interest, and propose a powerful new kernel-based modified backfitting algorithm to compute the estimator. We derive uniform rates of convergence results for the estimators of the link functions, and show the estimators of the finite-dimensional parameters are root-N$N$ consistent with a Gaussian limiting distribution. We study the small sample properties of the estimator via Monte Carlo techniques.     

Identification and estimation of nonlinear dynamic panel data models with unobserved covariates

This paper considers nonparametric identification of nonlinear dynamic models for panel data with unobserved covariates. Including such unobserved covariates may control for both the individual-specific unobserved heterogeneity and the endogeneity of the explanatory variables. Without specifying the distribution of the initial condition with the unobserved variables, we show that the models are nonparametrically identified from two periods of the dependent variable _Yit$Y_{it}$ and three periods of the covariate _Xit$X_{it}$. The main identifying assumptions include high-level injectivity restrictions and require that the evolution of the observed covariates depends on the unobserved covariates but not on the lagged dependent variable. We also propose a sieve maximum likelihood estimator (MLE) and focus on two classes of nonlinear dynamic panel data models, i.e., dynamic discrete choice models and dynamic censored models. We present the asymptotic properties of the sieve MLE and investigate the finite sample properties of these sieve-based estimators through a Monte Carlo study. An intertemporal female labor force participation model is estimated as an empirical illustration using a sample from the Panel Study of Income Dynamics (PSID).     

A Lagrange Multiplier test for cross-sectional dependence in a fixed effects panel data model

It is well known that the standard Breusch and Pagan (1980) LM test for cross-equation correlation in a SUR model is not appropriate for testing cross-sectional dependence in panel data models when the number of cross-sectional units (n)$\left(n\right)$ is large and the number of time periods (T)$\left(T\right)$ is small. In fact, a scaled version of this LM test was proposed by Pesaran (2004) and its finite sample bias was corrected by Pesaran et al. (2008). This was done in the context of a heterogeneous panel data model. This paper derives the asymptotic bias of this scaled version of the LM test in the context of a fixed effects homogeneous panel data model. This asymptotic bias is found to be a constant related to n$n$ and T$T$, which suggests a simple bias corrected LM test for the null hypothesis. Additionally, the paper carries out some Monte Carlo experiments to compare the finite sample properties of this proposed test with existing tests for cross-sectional dependence.