A Mechanism-Based Approach to the Identification of Age–Period–Cohort Models

This article offers a new approach to the identification of age-period-cohort (APC) models that builds on Pearl's work on nonparametric causal models, in particular his front-door criterion for the identification of causal effects. The goal is to specify the mechanisms through which the age, period, and cohort variables affect the outcome and in doing so identify the model. This approach allows for a broader set of identification strategies than has typically been considered in the literature and, in many circumstances, goodness of fit tests are possible. The authors illustrate the utility of the approach by developing an APC model for political alienation.Sociolog

Undocumented Worker Employment and Firm Survivability

Do firms employing undocumented workers have a competitive advantage? Using administrative data from the state of Georgia, this paper investigates the incidence of undocumented worker employment across firms and how it affects firm survival. Firms are found to engage in herding behavior, being more likely to employ undocumented workers if competitors do. Rivals' undocumented employment harms firms' ability to survive while firms' own undocumented employment strongly enhances their survival prospects. This finding suggests that firms enjoy cost savings from employing lower-paid undocumented at workers wages less than their marginal revenue product. The herding behavior and competitive effects are found to be much weaker in geographically broad product markets, where firms have the option to shift labor-intensive production out of state or abroad

Advancements in marginal modeling for categorical data

Very often the data collected by social scientists involve dependent observations, without, however, the investigators having any substantive interest in the nature of the dependencies. Although these dependencies are not important for the answers to the research questions concerned, they must still be taken into account in the analysis. Standard statistical estimation and testing procedures assume independent and identically distributed observations, and they need to be modified for observations that are clustered in some way. Marginal models provide the tools to deal with these dependencies without having to make restrictive assumptions about their nature. In this paper, recent developments in the (maximum likelihood) estimation and testing of marginal models for categorical data will be explained, including marginal models with latent variables. The differences and commonalities with other ways of dealing with these nuisance dependencies will be discussed, especially with GEE and also briefly with (hierarchical) random coefficient models. The usefulness of marginal modeling will be illuminated by showing several common types of research questions and designs for which marginal models may provide the answers, along with two extensive real world examples. Finally, a brief evaluation will be given, including a discussion of shortcomings and strong point