Intergenerational mobility: New evidence from consumption data

The intergenerational persistence of consumption describes the extent to which children inherit the living standards of their parents. Evidence on this parameter is scarce due to limited data on the joint consumption of parents and children. This paper identifies parents who participated in the Danish Expenditure Survey, links them to their children through population‐wide Danish registries, and estimates the intergenerational elasticity of consumption in Denmark. The results suggest that, consistent with intergenerational consumption smoothing, the persistence of consumption across generations is higher than the persistence of earnings and income.     

Set identification via quantile restrictions in short panels

This paper studies the identifying power of conditional quantile restrictions in short panels with fixed effects. In contrast to classical fixed effects models with conditional mean restrictions, conditional quantile restrictions are not preserved by taking differences in the regression equation over time. This paper shows however that a conditional quantile restriction, in conjunction with a weak conditional independence restriction, provides bounds on quantiles of differences in time-varying unobservables across periods. These bounds carry observable implications for model parameters which generally result in set identification. The analysis of these bounds includes conditions for point identification of the parameter vector, as well as weaker conditions that result in point identification of individual parameter components.     

Multiplicative-error models with sample selection

This paper presents a simple approach to deal with sample selection in models with multiplicative errors. Models for non-negative limited dependent variables such as counts fit this framework. The approach builds on a specification of the conditional mean of the outcome only and is, therefore, semiparametric in nature. GMM estimators are constructed for both cross-section data and for panel data. We derive distribution theory and present Monte Carlo evidence on the finite-sample performance of the estimators.     

Bayesian inference in a sample selection model

This paper develops methods of Bayesian inference in a sample selection model. The main feature of this model is that the outcome variable is only partially observed. We first present a Gibbs sampling algorithm for a model in which the selection and outcome errors are normally distributed. The algorithm is then extended to analyze models that are characterized by nonnormality. Specifically, we use a Dirichlet process prior and model the distribution of the unobservables as a mixture of normal distributions with a random number of components. The posterior distribution in this model can simultaneously detect the presence of selection effects and departures from normality. Our methods are illustrated using some simulated data and an abstract from the RAND health insurance experiment.     

Treatment effects in sample selection models and their nonparametric estimation

In a sample-selection model with the ‘selection’ variable Q$Q$ and the ‘outcome’ variable Y^∗$Y^{\ast }$, Y^∗$Y^{\ast }$ is observed only when Q=1$Q=1$. For a treatment D$D$ affecting both Q$Q$ and Y^∗$Y^{\ast }$, three effects are of interest: ‘participation  ’ (i.e., the selection) effect of D$D$ on Q$Q$, ‘visible performance  ’ (i.e., the observed outcome) effect of D$D$ on Y≡QY^∗$Y\equiv Q{Y}^{\ast }$, and ‘invisible performance  ’ (i.e., the latent outcome) effect of D$D$ on Y^∗$Y^{\ast }$. This paper shows the conditions under which the three effects are identified, respectively, by the three corresponding mean differences of Q$Q$, Y$Y$, and Y|Q=1$Y|Q=1$ (i.e., Y^∗|Q=1$Y^{\ast }|Q=1$) across the control (D=0$D=0$) and treatment (D=1$D=1$) groups. Our nonparametric estimators for those effects adopt a two-sample framework and have several advantages over the usual matching methods. First, there is no need to select the number of matched observations. Second, the asymptotic distribution is easily obtained. Third, over-sampling the control/treatment group is allowed. Fourth, there is a built-in mechanism that takes into account the ‘non-overlapping support problem’, which the usual matching deals with by choosing a ‘caliper’. Fifth, a sensitivity analysis to gauge the presence of unobserved confounders is available. A simulation study is conducted to compare the proposed methods with matching methods, and a real data illustration is provided.     

Binary response panel data models with sample selection and self‐selection

We consider estimating binary response models on an unbalanced panel, where the outcome of the dependent variable may be missing due to nonrandom selection, or there is self‐selection into a treatment. In the present paper, we first consider estimation of sample selection models and treatment effects using a fully parametric approach, where the error distribution is assumed to be normal in both primary and selection equations. Arbitrary time dependence in errors is permitted. Estimation of both coefficients and partial effects, as well as tests for selection bias, are discussed. Furthermore, we consider a semiparametric estimator of binary response panel data models with sample selection that is robust to a variety of error distributions. The estimator employs a control function approach to account for endogenous selection and permits consistent estimation of scaled coefficients and relative effects.     

Functional form,sample selection,and housing demand

This paper addresses issues of functional form and sample selection in estimating housing demand. Urban economic theory suggests that income and price elasticities be allowed to vary with reference incomes and housing prices. A generalized Box-Cox estimator rejects linear and log-log forms; semi-log forms are accepted in some cases. Sample truncation leads to downward biases in permanent income regressions, but to slightly upward-biased income elasticities. Price elasticities are generally unchanged.     

Distribution-free and link-free estimation for the sample selection model

The prevalent estimation methods for the sample selection model rely heavily on parametric assumptions and are sensitive to departures from the underlying parametric assumptions [see, e.g., Goldberger (1983)]. We propose an alternative estimation method, the corrected maximum likelihood estimate, which is consistent for the slope vector in the outcome equation up to a multiplicative scalar, even through the parametric model on which the estimate is based might be misspecified. As an important corollary, it follows from our result that Olsen's (1980) corrected ordinary least squares estimate is consistent if the outcome equation is linear, without requiring Olsen's assumptions on the joint error distribution.     

Estimation of sample selection models with spatial dependence

We consider the estimation of a sample selection model that exhibits spatial autoregressive errors (SAE). Our methodology is motivated by a two‐step strategy where in the first step we estimate a spatial probit model and in the second step (outcome equation) we include an estimated inverse Mills ratio (IMR) as a regressor to control for selection bias. Since the appropriate IMR under SAE depends on a parameter from the second step, both steps are jointly estimated employing the generalized method of moments. We explore the finite sample properties of the estimator using simulations and provide an empirical illustration. Copyright © 2010 John Wiley & Sons, Ltd.     

Nonparametric identification and estimation of sample selection models under symmetry

Under a conditional mean restriction Das et al. (2003) considered nonparametric estimation of sample selection models. However, their method can only identify the outcome regression function up to a constant. In this paper we strengthen the conditional mean restriction to a symmetry restriction under which selection biases due to selection on unobservables can be eliminated through proper matching of propensity scores; consequently we are able to identify and obtain consistent estimators for the average treatment effects and the structural regression functions. The results from a simulation study suggest that our estimators perform satisfactorily.     

Semiparametric and nonparametric estimation of sample selection models under symmetry

This paper considers the semiparametric estimation of binary choice sample selection models under a joint symmetry assumption. Our approaches overcome various drawbacks associated with existing estimators. In particular, our method provides root-n$n$ consistent estimators for both the intercept and slope parameters of the outcome equation in a heteroscedastic framework, without the usual cross equation exclusion restriction or parametric specification for the error distribution and/or the form of heteroscedasticity. Our two-step estimators are shown to be consistent and asymptotically normal. A Monte Carlo simulation study indicates the usefulness of our approaches.     

Simple tests for sample selection bias in censored and discrete choice models

This paper presents two simple tests of sample selection bias for models where the primary equation of interest has a censored or discrete dependent variable. The first test is derived as a conditional moment test and can be implemented in a regression-based framework. The second test is an extension of the testing procedures proposed by Heckman (1979) and Vella (1993) and is a t-test on a constructed variable in an auxiliary equation. The utility of the tests is illustrated in a model determining the receipt of work conditioned nonwage labour income over a subsample of working women.     

A stochastic frontier model with correction for sample selection

Heckman’s (Ann Econ Soc Meas 4(5), 475–492, 1976; Econometrica 47, 153–161, 1979) sample selection model has been employed in three decades of applications of linear regression studies. This paper builds on this framework to obtain a sample selection correction for the stochastic frontier model. We first show a surprisingly simple way to estimate the familiar normal-half normal stochastic frontier model using maximum simulated likelihood. We then extend the technique to a stochastic frontier model with sample selection. In an application that seems superficially obvious, the method is used to revisit the World Health Organization data (WHO in The World Health Report, WHO, Geneva 2000; Tandon et al. in Measuring the overall health system performance for 191 countries, World Health Organization, 2000) where the sample partitioning is based on OECD membership. The original study pooled all 191 countries. The OECD members appear to be discretely different from the rest of the sample. We examine the difference in a sample selection framework.     

Estimation of dynamic panel data models with sample selection

We propose a new method for estimating dynamic panel data models with selection. The method uses backward substitution for the lagged dependent variable, which leads to an estimating equation that requires correcting for contemporaneous selection only. The estimator is valid under relatively weak assumptions about errors and permits avoiding the weak instruments problem associated with differencing. We also propose a simple test for selection bias that is based on the addition of a selection term to the first‐difference equation and subsequent testing for significance of this term. The methods are applied to estimating dynamic earnings equations for women. Copyright © 2011 John Wiley & Sons, Ltd.     

Education and Intergenerational Mobility in Occupations

A bstract The problem of measuring the intergenerational transmission of inequality and its implications for social welfare is studied. A possible decomposition of relevant factors–namely, educational attainments and other factors–is proposed and applied to three individual data sets regarding Germany, Italy, and the United States. The main result is that educational attainment is responsible for almost half of observed immobility The possibility that increasing equality of opportunity in entering the educational system may result in less inequality in income distribution is considered.     

Monte Carlo evidence on the choice between sample selection and two-part models

This Monte Carlo study examines the relative performance of sample selection and two-part models for data with a cluster at zero. The data are drawn from a bivariate normal distribution with a positive correlation. The alternative estimators are examined in terms of means squared error, mean bias and pointwise bias. The sample selection estimators include LIML and FIML. The two-part estimators include a naive (the true specification, omitting the correlation coefficient) and a data-analytic (testimator) variant.In the absence of exclusion restrictions, the two-part models are no worse, and often appreciably better than selection models in terms of mean behavior, but can behave poorly for extreme values of the independent variable. LIML had the worst performance of all four models. Empirically, selection effects are difficult to distinguish from a non-linear (e.g., quadratic) response. With exclusion restrictions, simple selection models were significantly better behaved than a naive two-part model over subranges of the data, but were negligibly better than the data-analytic version.     

A fixed sample size selection procedure for negative binomial populations

Summary A fixed sample size procedure for selecting the ‘best’ ofk negative binomial populations is developed. Selection is made in such a way that the probability of correct selection is at leastP* whenever the distance between the probabilities of success is at leastδ*. The exponentr is assumed to be known and the same for all populations. Extensive computer calculations* were employed to obtain the exact least favorable configuration. The smallest sample sizes needed to meet specifications (P*,δ*) are tabulated forr=1 (1)5;δ*=0.05 (0.05) 0.55 andP*=0.75, 0.80, 0.90, 0.95, 0.98, 0.99 involvingk=3 (1) 6, 8, 10 populations. All the computations were carried out on the Alabama Supercomputer. Part of this work was completed when the authors were at the Department of Statistics, Oklahoma State University, Stillwater, OK 74078.     

Reducing estimation risk in optimal portfolio selection when short sales are allowed

The issue of estimation risk is of particular interest to the decision‐making processes of portfolio managers who use long–short investment strategies. Accordingly, our paper explores the question of whether a VaR constraint reduces estimation risk when short sales are allowed. We find that such a constraint notably decreases errors in estimates of the expected return, standard deviation, and VaR of optimal portfolios. Furthermore, optimal portfolios in the presence of the constraint are substantially closer to the ‘true’ efficient frontier than those in its absence. Finally, we provide VaR bounds and confidence levels for the constraint that lead to the best out‐of‐sample performance. Copyright © 2008 John Wiley & Sons, Ltd.     

The nonrandom selection of don't knows in binary and ordinal responses: Corrections with the bivariate probit model with sample selection

On social surveysdon't knows are a common answer to attitudinal questions, which often have binary or ordinal response categories.Don't knows can be nonrandomly selected according to certain demographic or socioeconomic characteristics of the respondent. To model the sample selection and correct for its bias, this paper discusses two types of bivariate models —binary-probit and the ordinal probit model with sample selection. The difference between parameter estimates and predicted probabilities from the analysis modelling the sample selection bias ofdon't knows and those from the analysis not modellingdon't knows is emphasized. Two empirical examples using the 1989 General Social Survey data demonstrate the necessity to correct for the bias in the nonrandom selection ofdon't knows for binary and ordinal attitudinal response variables. A replication of the analyses using the 1990 and 1991 General Social Survey data helps demonstrate the reliability of the sample selection bias ofdon't knows.