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Hausman (1978) represented a tectonic shift in inference related to the specification of econometric models. The seminal insight that one could compare two models which were both consistent under the null spawned a test which was both simple and powerful. The so-called ‘Hausman test’ has been applied and extended theoretically in a variety of econometric domains. This paper discusses the basic Hausman test and its development within econometric panel data settings since its publication. We focus on the construction of the Hausman test in a variety of panel data settings, and in particular, the recent adaptation of the Hausman test to semiparametric and nonparametric panel data models. We present simulation experiments which show the value of the Hausman test in a nonparametric setting, focusing primarily on the consequences of parametric model misspecification for the Hausman test procedure. A formal application of the Hausman test is also given focusing on testing between fixed and random effects within a panel data model of gasoline demand.

An adverse health event can affect women’s work capacity as they need time to recover. The institutional framework in the Netherlands provides employment protection during the first two years after the diagnosis. In this study, we have assessed the extent to which women’s employment is affected in the short- and long term by an adverse health event. We have used administrative Dutch data which follow women aged 25 to 55 years for four years after a medical diagnosis. We found that diagnosed women start leaving employment during the protection period and four years later they were about one percentage point less likely to be employed. Women in permanent employment did not reduce their employment during the protection period and reduced their employment with less than 0.5 percentage points thereafter. Furthermore, we found minor adjustments in the working hours in the short term and no adjustments in the long term. Lastly, we found that for wages, and not for employment and hours, adjustments could be related to the severity of the health condition: women diagnosed with temporary health conditions experienced a short-term wage penalty of about 0.5–1.7 percent and those diagnosed with chronic and incapacitating conditions experienced a long-term wage penalty of about 0.5 percent, while women diagnosed with some chronic and nonincapacitating conditions, such as respiratory conditions, experienced no wage changes in the short or long term.

In this chapter, we are among the first to investigate the actual course of affairs in AGMs with respect to shareholder forum rights. In the first part of the chapter, we provide descriptive statistics on the use of the right to ask questions and speak in AGMs in the Netherlands. We find that in an average meeting there are around 42 questions and remarks made by around 8 shareholders. Most of these questions and remarks seem to be relevant; with a categorization framework of 14 topics, we could already identify over 50% of these questions and remarks. However, we also find that the average number of shareholders that physically ask questions is only 8. Next, we consider the determinants of the use of these forum rights. In several panel data analyses with a Poisson distribution and a negative binomial distribution, we, inter alia, found that the ‘importance of the meeting’ generally contributes to the amount of questions and remarks and the number of shareholders that actively engage in discussions. We have also found that the number of speakers – and the number of private investors – that actively attend the AGM depends on previous attendance numbers. This may imply that there is a small base of very active (private) investors in the Netherlands. We conclude that the forum function of AGMs is definitely relevant, but given the low number of shareholders that make use of these rights, amendments may be considered.

We investigate the question of whether investing in a child’s development by having a parent stay at home when the child is young is correlated with the child’s adult outcomes. Specifically, do children with stay-at-home mothers have higher adult earnings than children raised in households with a working mother? The major contribution of our study is that, unlike previous studies, we have access to rich longitudinal data that allows us to measure both the parental earnings when the child is very young and the adult earnings of the child. Our findings are consistent with previous studies that show insignificant differences between children raised by stay-at-home mothers during their early years and children with mothers working in the market. We find no impact of maternal employment during the first five years of a child’s life on earnings, employment, or mobility measures of either sons or daughters. We do find, however, that maternal employment during children’s high school years is correlated with a higher probability of employment as adults for daughters and a higher correlation between parent and daughter earnings ranks.

This chapter investigates which factors contribute to (small) shareholder attendance using a hand-collected panel data set with information about turnout rates, voting behaviour and ownership structures of companies that are listed in seven Member States. We document how ownership concentration positively affects total shareholder turnout, but has a negative effect on small shareholder turnout. Voting power also affects small shareholder turnout rates; the greater small shareholder voting power, the greater their eagerness to vote. In addition, total and small shareholder turnout is higher the more important the meeting agenda. And, small shareholders tend to free-ride on large institutional shareholders and corporate insiders, but the magnitude of the free-rider effect is larger for the latter category of blockholders. Our results provide some important insights for the debate on shareholder rights and the role of the AGM in corporate governance. The results show that, despite the criticism, the AGM still plays an important role in small shareholder monitoring. Some topics seem to clearly motivate small shareholders to attend, while others are less relevant. Policy makers can stimulate shareholder monitoring by focusing on the factors that are determined in this study, but it is important to consider possible endogeneity issues as well.

This chapter extends the work of Baltagi, Bresson, Chaturvedi, and Lacroix (2018) to the popular dynamic panel data model. The authors investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, the authors consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner’s (1986) g-priors for the variance–covariance matrices. The authors propose a general “toolbox” for a wide range of specifications which includes the dynamic panel model with random effects, with cross-correlated effects à la Chamberlain, for the Hausman–Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using a Monte Carlo simulation study, the authors compare the finite sample properties of the proposed estimator to those of standard classical estimators. The chapter contributes to the dynamic panel data literature by proposing a general robust Bayesian framework which encompasses the conventional frequentist specifications and their associated estimation methods as special cases.

We examine the roles of sample initial conditions and unobserved individual effects in consistent estimation of the dynamic binary response panel data model. Different specifications of the model are estimated using female welfare and labor force participation data from the Survey of Income and Program Participation. These include alternative random effects (RE) models, in which the conditional distributions of both the unobserved heterogeneity and the initial conditions are specified, and fixed effects (FE) conditional logit models that make no assumptions on either distribution. There are several findings. First, the hypothesis that the sample initial conditions are exogenous is rejected by both samples. Misspecification of the initial conditions results in drastically overstated estimates of the state dependence and understated estimates of the short- and long-run effects of children on labor force participation. The FE conditional logit estimates are similar to the estimates from the RE model that is flexible with respect to both the initial conditions and the correlation between the unobserved heterogeneity and the covariates. For female labor force participation, there is evidence that fertility choices are correlated with both unobserved heterogeneity and pre-sample participation histories.

The authors use a reduced form state-dependent labor participation decision model to illustrate that parameter stability is achieved only if a model properly takes account the observed sample heterogeneity and unobserved sample heterogeneity provided (external) conditions of a model stay constant. Our analysis of the dynamic response path to a health shock using Australian HILDA panel data from 2002 to 2009 shows that experiencing an event by itself can only have a temporary effects. The long-run equilibrium condition is independent of initial conditions or shocks that do not last. In other words, if experiencing an event does not lead to changes in the response parameters such as the real business cycle (Kydland & Prescott, 1977, 1982) or dynamic stochastic general equilibrium model (DSGE, e.g., Sbordone et al., 2010) assumed, policy change may only change the short-run response path. There is no long-term impact for a policy change. On the other hand, if a policy change leads to changes in the decision rules (e.g., the recent US–China trade friction) as the Lucas critique (1976) implies, then there is no other way to evaluate the impact of a policy except to explicitly model how agents respond to the policy change.

We present Bayesian models for finding the longitudinal causal effects of a randomized two-arm training program when compliance with the randomized assignment is less than perfect in the training arm (but perfect in the non-training arm) for reasons that are potentially correlated with the outcomes. We deal with the latter confounding problem under the principal stratification framework of Sommer and Zeger (1991) and Frangakis and Rubin (1999), and others. Building on the Bayesian contributions of Imbens and Rubin (1997), Hirano et al. (2000), Yau and Little (2001) and in particular Chib (2007) and Chib and Jacobi (2007, 2008), we construct rich models of the potential outcome sequences (with and without random effects), show how informative priors can be reasonably formulated, and present tuned computational approaches for summarizing the posterior distribution. We also discuss the computation of the marginal likelihood for comparing various versions of our models. We find the causal effects of the observed intake from the predictive distribution of each potential outcome for compliers. These are calculated from the output of our estimation procedures. We illustrate the techniques and ideas with data from the 1994 JOBS II trial that was set up to test the efficacy of a job training program on subsequent mental health outcomes.