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Poverty and informal employment are often regarded as correlated phenomena. Many empirical studies have shown that informal employment has a causal impact on household poverty, mainly through low wages. Yet other studies focus on the reverse causality from poverty to informality, arising from a range of constraints that poverty poses to jobholders. Only recently have empirical researchers tried to study the simultaneous two-way relationship between poverty and informality. However, existing studies have relied upon cross-sectional data and static econometric models.

This chapter takes the next step and studies the dynamics of poverty and informality using longitudinal data. Our empirical analysis is based on a bivariate dynamic random-effect probit model and recent panel data from Argentina. The method used provides a means of assessing the persistence over time of poverty and informal employment at the individual level, while controlling for both observed and unobserved determinants of the two processes. The results show that both poverty and informal employment are highly persistent processes. Moreover, positive spillover effects are found from past poverty on current informal employment and from past informality to current poverty status, corroborating the view that the two processes are also shaped by interrelated dynamics in segmented labor markets.

It is well-known that the majority of women work in a limited number of occupations characterized by a proportionately high number of female workers. Moreover, workers in these female-dominated (FD) occupations earn less, on average, than workers in traditionally male or integrated occupations (McPherson & Hirsch, 1995). This occupational wage differential is widely accepted as a partial explanation for the pervasive gender wage-differential. However, it is unclear why an individual would enter into a FD occupation if the wages are lower than in nonfemale-dominated (NFD) occupations. It is also unclear if women who choose FD occupations could earn more in occupations that are NFD. Therefore, attributing a portion of the gender wage differential to occupational differences may be incorrect. Indeed, differences in the occupational choices of men and women will only explain the wage differential between genders if females in FD occupations could expect to earn higher wages elsewhere.

The purpose of the chapter is to test the hypothesis that food safety (chemical) standards act as barriers to international seafood imports. We use zero-accounting gravity models to test the hypothesis that food safety (chemical) standards act as barriers to international seafood imports. The chemical standards on which we focus include chloramphenicol required performance limit, oxytetracycline maximum residue limit, fluoro-quinolones maximum residue limit, and dichlorodiphenyltrichloroethane (DDT) pesticide residue limit. The study focuses on the three most important seafood markets: the European Union’s 15 members, Japan, and North America.Our empirical results confirm the hypothesis and are robust to the OLS as well as alternative zero-accounting gravity models such as the Heckman estimation and the Poisson family regressions. For the choice of the best model specification to account for zero trade and heteroskedastic issues, it is inconclusive to base on formal statistical tests; however, the Heckman sample selection and zero-inflated negative binomial (ZINB) models provide the most reliable parameter estimates based on the statistical tests, magnitude of coefficients, economic implications, and the literature findings. Our findings suggest that continually tightening of seafood safety standards has had a negative impact on exporting countries. Increasing the stringency of regulations by reducing analytical limits or maximum residue limits in seafood in developed countries has negative impacts on their bilateral seafood imports. The chapter furthers the literature on food safety standards on international trade. We show competing gravity model specifications and provide additional evidence that no one gravity model is superior.

Researchers in economics and other disciplines are often interested in the causal effect of a binary treatment on outcomes. Econometric methods used to estimate such effects are divided into one of two strands depending on whether they require unconfoundedness (i.e., independence of potential outcomes and treatment assignment conditional on a set of observable covariates). When this assumption holds, researchers now have a wide array of estimation techniques from which to choose. However, very little is known about their performance – both in absolute and relative terms – when measurement error is present. In this study, the performance of several estimators that require unconfoundedness, as well as some that do not, are evaluated in a Monte Carlo study. In all cases, the data-generating process is such that unconfoundedness holds with the ‘real’ data. However, measurement error is then introduced. Specifically, three types of measurement error are considered: (i) errors in treatment assignment, (ii) errors in the outcome, and (iii) errors in the vector of covariates. Recommendations for researchers are provided.

This chapter enquires whether family migration experience affects the probability of high school graduation of children once unobserved heterogeneity is properly accounted for. Bivariate dynamic random effects probit models for cluster data are estimated to control for the potential endogeneity of education and migration outcomes of elder members of a family in a regression for the education and migration of younger children. Correlation of unobservables across migration and education decisions as well as within groups of individuals such as the family are explicitly modeled. Results show that children from households headed by a migrant are less likely to graduate from high school than children from households headed by a non-migrant. However, as the number of migrants in the family increase, a larger number of migrants in the family is associated with a higher probability of graduation from high school in México. Negative migrant selection in unobservables is detected.

We discuss a class of copula-based ordered probit models with endogenous switching. Such models can be useful for the analysis of self-selection in subjective well-being equations in general, and job satisfaction in particular, where assignment of regressors may be endogenous rather than random, resulting from individual maximization of well-being. In an application to public and private sector job satisfaction, and using data on male workers from the German Socio-Economic Panel for 2004, and using two alternative copula functions for dependence, we find consistent evidence for endogenous sector selection.

In the early days of the COVID-19 pandemic, an area could only report its first positive cases if the infection had spread into the area and if the infection was subsequently detected. A standard probit model does not correctly account for these two distinct latent processes but assumes there is a single underlying process for an observed outcome. A similar issue confounds research on other binary outcomes such as corporate wrongdoing, acquisitions, hiring, and new venture establishments. The bivariate probit model enables empirical analysis of two distinct latent binary processes that jointly produce a single observed binary outcome. One common challenge of applying the bivariate probit model is that it may not converge, especially with smaller sample sizes. We use Monte Carlo simulations to give guidance on the sample characteristics needed to accurately estimate a bivariate probit model. We then demonstrate the use of the bivariate probit to model infection and detection as two distinct processes behind county-level COVID-19 reports in the United States. Finally, we discuss several organizational outcomes that strategy scholars might analyze using the bivariate probit model in future research.

We consider the Bayes estimation of a multivariate sample selection model with p pairs of selection and outcome variables. Each of the variables may be discrete or continuous with a parametric marginal distribution, and their dependence structure is modeled through a Gaussian copula function. Markov chain Monte Carlo methods are used to simulate from the posterior distribution of interest. The methods are illustrated in a simulation study and an application from transportation economics.