On the causal effect of schooling on smoking: evidence without exogeneity conditions

The paper explores weak monotonicity and convexity assumptions in a model for the decision to smoke with endogenous schooling. Theories of productive and allocative efficiency as well as the influence of time preferences are accounted for in order to derive testable constraints that bound the effect of schooling on smoking. Data from the Swiss Health Survey indicate that the degree of endogeneity depends on the level of schooling, and that schooling effects are likely heterogeneous with a reduction of the propensity to smoke by at most 5.9 percentage points.Smoking; education; health behavior; nonparametric bounds

Count Data Models with Unobserved Heterogeneity: An Empirical Likelihood Approach

As previously argued, the correlation between included and omitted regressors generally causes inconsistency of standard estimators for count data models. Using a specific residual function and suitable instruments, a consistent generalized method of moments estimator can be obtained under conditional moment restrictions. This approach is extended here by fully exploiting the model assumptions and thereby improving efficiency of the resulting estimator. Empirical likelihood estimation in particular has favorable properties in this setting compared to the two-step GMM procedure, which is demonstrated in a Monte Carlo experiment. The proposed method is applied to the estimation of a cigarette demand function.nonparametric likelihood, poisson model, nonlinear instrumental variables, optimal instruments, approximating functions, semiparametric efficiency

Partial Identification of Discrete Counterfactual Distributions with Sequential Update of Information

The credibility of standard instrumental variables assumptions is often under dispute. This paper imposes weak monotonicity in order to gain information on counterfactual outcomes, but avoids independence or exclusion restrictions. The outcome process is assumed to be sequentially ordered, building up and depending on the information level of agents. The potential outcome distribution is assumed to weakly increase (or decrease) with the instrument, conditional on the continuation up to a certain stage. As a general result, the counterfactual distributions can only be bounded, but the derived bounds are informative compared to the no-assumptions bounds thus justifying the instrumental variables terminology. The construction of bounds is illustrated in two data examples.nonparametric bounds, treatment effects, endogeneity, binary choice, monotone instrumental variables, policy evaluation

Empirical Likelihood in Count Data Models: The Case of Endogenous Regressors

Recent advances in the econometric modelling of count data have often been based on the generalized method of moments (GMM). However, the two-step GMM procedure may perform poorly in small samples, and several empirical likelihood-based estimators have been suggested alternatively. In this paper I discuss empirical likelihood (EL) estimation for count data models with endogenous regressors. I carefully distinguish between parametric and semi-parametric methods and analyze the properties of the EL estimator by means of a Monte Carlo experiment. I apply the proposed method to estimate the effect of women’s schooling on fertility.Nonparametric likelihood, Poisson model, endogeneity, fertility and education

Convex Treatment Response and Treatment Selection

This paper analyzes the identifying power of weak convexity assumptions in treatment effect models with endogenous selection. The counterfactual distributions are constrained either in terms of the response function, or conditional on the realized treatment, and sharp bounds on the potential outcome distributions are derived. The methods are applied to bound the effect of education on smoking.nonparametric bounds, causality, endogeneity, instrumental variables

Bounds on Counterfactual Distributions Under Semi-Monotonicity Constraints

This paper explores semi-monotonicity constraints in the distribution of potential outcomes, first, conditional on an instrument, and second, in terms of the response function. The imposed assumptions are strictly weaker than traditional instrumental variables assumptions and can be gainfully employed to bound the counterfactual distributions, even though point identification is only achieved in special cases. The bounds have a simple analytical form and thus have much practical relevance in all instances when strong exogeneity assumptions cannot be credibly invoked. The bounding strategy is illustrated in a simulated data example and applied to the effect of education on smoking.nonparametric bounds, treatment effects, causality, endogeneity, instrumental variables, policy evaluation

Income and Happiness: New Results from Generalized Threshold and Sequential Models

Empirical studies on the relationship between income and happiness commonly use standard ordered response models, the most well-known representatives being the ordered logit and the ordered probit. However, these models restrict the marginal probability effects by design, and therefore limit the analysis of distributional aspects of a change in income, that is, the study of whether the income effect depend on a person’s happiness. In this paper we pinpoint the shortcomings of standard models and propose two alternatives, namely generalized threshold and sequential models. With data of two waves of the German Socio-Economic Panel, 1984 and 1997, we show that the more general models yield different marginal probability effects than standard models.Ordered response models, marginal effects, subjective well-being

Ordered Response Models

We discuss regression models for ordered responses, such as ratings of bonds, schooling attainment, or measures of subjective well-being. Commonly used models in this context are the ordered logit and ordered probit regression models. They are based on an underlying latent model with single index function and constant thresholds. We argue that these approaches are overly restrictive and preclude a flexible estimation of the effect of regressors on the discrete outcome probabilities. For example, the signs of the marginal probability effects can only change once when moving from the smallest category to the largest one. We then discuss several alternative models that overcome these limitations. An application illustrates the benefit of these alternatives.Marginal effects, generalized threshold, sequential model, random coeffcients, latent class analysis, happiness

The Effect of Income on Positive and Negative Subjective Well-Being

Increasing evidence from the empirical economic and psychological literature suggests that positive and negative well-being are more than opposite ends of the same phenomenon. Two separate measures of the dependent variable may be needed when analyzing the determinants of subjective well-being. We argue that this conclusion reflects in part the use of too restrictive econometric models. A flexible multiple-index ordered probit panel data model with varying thresholds can identify response asymmetries in single-item measures of subjective well-being. An application to data from the German Socio-Economic Panel for 1984-2004 shows that income has only a minor effect on positive subjective well-being but a large effect on negative well-being.generalized ordered probit model, marginal probability effects, random and fixed effects, life-satisfaction