Business and games

This paper responds to the popular argument that business is like a game and is thus insulated from the demands of morality. In the first half of the paper, I offer objections to this argument as it is put forward by John Ladd in his well-known article, Morality and the Ideal of Rationality in Formal Organizations. I argue that Ladd's analysis is flawed both because it deprives us of the ability to assert that a business is acting badly or that its goals are irrational, and because it is internally inconsistent. In the second half of the paper, I give reasons for thinking that business is not like a game.Peter Heckman teaches business ethics at Santa Clara University. His publications on Nietzsche can be found inThe British Journal of Aesthetics andPhilosophy and Rhetoric.     

Introduction to internally consistent modeling,aggregation, inference,and policy

This special issue of the Journal of Econometrics honors William A. Barnett’s exceptional contributions to unifying economic theory with rigorous statistical inference to interpret economic data and inform public policy. It is devoted to papers that advance microeconometrics, macroeconometrics, and financial econometrics to build models to interpret evidence.     

Tests of Hypotheses Arising In the Correlated Random Coefficient Model

This paper examines the correlated random coefficient model. It extends the analysis of Swamy (1971), who pioneered the uncorrelated random coefficient model in economics. We develop the properties of the correlated random coefficient model and derive a new representation of the variance of the instrumental variable estimator for that model. We develop tests of the validity of the correlated random coefficient model against the null hypothesis of the uncorrelated random coefficient model.     

Separating uncertainty from heterogeneity in life cycle earnings

This paper develops and applies a method for decomposing crosssection variability of earnings into components that are forecastableat the time students decide to go to college (heterogeneity)and components that are unforecastable. About 60% of variabilityin returns to schooling is forecastable. This has importantimplications for using measured variability to price risk andpredict college attendance.     

Econometric Causality

This paper presents the econometric approach to causal modelling. It is motivated by policy problems. New causal parameters are defined and identified to address specific policy problems. Economists embrace a scientific approach to causality and model the preferences and choices of agents to infer subjective (agent) evaluations as well as objective outcomes. Anticipated and realized subjective and objective outcomes are distinguished. Models for simultaneous causality are developed. The paper contrasts the Neyman–Rubin model of causality with the econometric approach.     

Comparing IV with structural models: What simple IV can and cannot identify

This paper compares the economic questions addressed by instrumental variables estimators with those addressed by structural approaches. We discuss Marschak’s Maxim: estimators should be selected on the basis of their ability to answer well-posed economic problems with minimal assumptions. A key identifying assumption that allows structural methods to be more informative than IV can be tested with data and does not have to be imposed.     

Bayesian exploratory factor analysis

This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements.     

Dynamic discrete choice and dynamic treatment effects

This paper considers semiparametric identification of structural dynamic discrete choice models and models for dynamic treatment effects. Time to treatment and counterfactual outcomes associated with treatment times are jointly analyzed. We examine the implicit assumptions of the dynamic treatment model using the structural model as a benchmark. For the structural model we show the gains from using cross-equation restrictions connecting choices to associated measurements and outcomes. In the dynamic discrete choice model, we identify both subjective and objective outcomes, distinguishing ex post and ex ante outcomes. We show how to identify agent information sets.     

Repeated payment delinquency among young adults in the United States

There is concern in the United States about young adults falling behind financially due to the increased use of student loans and low wages. This study investigates payment delinquency as a measure of financial distress to better understand how young adults might be struggling. Personality traits are incorporated into the model to determine the extent to which behavioural factors are correlated with financial behaviors and if they predict a habit trend of payment delinquency. The 1997 National Longitudinal Survey of Youth, a nationally representative longitudinal data set, is used in the study. A random effects probit model and a dynamic random effects probit model are used to examine late bill pay (harassed by bill collectors) and late rent or mortgage payments (more than 60 days late) over a period of 8 years (2007–2015). Results from the analysis indicate that payment delinquency in a previous period increases the likelihood of payment delinquency by 10 percentage points in a subsequent period. Conscientiousness decreases the likelihood by 2.1 percentage points, while neuroticism increases the likelihood by 1.6 percentage points.     

Matching As An Econometric Evaluation Estimator

This paper develops the method of matching as an econometric evaluation estimator. A rigorous distribution theory for kernel-based matching is presented. The method of matching is extended to more general conditions than the ones assumed in the statistical literature on the topic. We focus on the method of propensity score matching and show that it is not necessarily better, in the sense of reducing the variance of the resulting estimator, to use the propensity score method even if propensity score is known. We extend the statistical literature on the propensity score by considering the case when it is estimated both parametrically and nonparametrically. We examine the benefits of separability and exclusion restrictions in improving the efficiency of the estimator. Our methods also apply to the econometric selection bias estimator.