Non-Response in Dynamic Panel Data Models

This paper stresses the links that exist between concepts that are used in the theory of model reduction and concepts that arise in the missing data literature. This connection motivates the extension of the missing at random (MAR) and the missing completely at random (MCAR) concepts from a static setting, as introduced by Rubin (1976), to the case of dynamic panel data models. Using this extension of the MAR and MCAR definitions, we emphasize the limits of some tests and procedures, proposed by Little (1988), Diggle (1989), Park and Davis (1993), Taris (1996) and others, to verify the ignorability of the missing data mechanism.dynamic panel model, attrition, non-response, missing at random, missing completely at random, statistical model reduction

Survey response and survey characteristics: Micro-level evidence from the ECHP

This paper presents some micro-level evidence on the role of the socio-demographic characteristics of the population and the characteristics of the data collection process as predictors of survey response. Our evidence is based on the public use files of the European Community Household Panel (ECHP), a longitudinal household survey covering the countries of the European Union, whose attractive feature is the high level of comparability across countries and over time. We use individual-level information to predict response in the next wave given response in the current wave, focusing on how the probabilities of contact failure and refusal to cooperate vary with the socio-demographic composition of the national populations and the characteristics of the data collection process. We model the response process as the outcome of two sequential events; (i) the contact between the interviewer and an eligible interviewee, and (ii) the cooperation of the interviewee. Our model allows for dependence between the ease of contact and the propensity to cooperate, taking into account the censoring problem caused by the fact that we observe whether a person is a respondent only if she has been contacted.Panel data, survey response, bivariate probit model.

The (mis)specification of discrete duration models with unobserved heterogeneity: a Monte Carlo study

Empirical researchers usually prefer statistical models that can be easily estimated using standard software packages. One such model is the sequential binary model with or without normal random effects; such models can be adopted to estimate discrete duration models with unobserved heterogeneity. But ease of estimation may come at a cost. In this paper we conduct a Monte Carlo simulation to evaluate the consequences of omitting or misspecifying the unobserved heterogeneity distribution in single-spell discrete duration models.discrete duration models, unobserved heterogeneity, Monte Carlo simulations

Estimating Income Poverty in the Presence of Missing Data and Measurement Error

Reliable measures of poverty are an essential statistical tool for public policies aimed at reducing poverty. In this paper we consider the reliability of income poverty measures based on survey data which are typically plagued by missing data and measurement error. Neglecting these problems can bias the estimated poverty rates. We show how to derive upper and lower bounds for the population poverty rate using the sample evidence, an upper bound on the probability of misclassifying people into poor and non-poor, and instrumental or monotone instrumental variable assumptions. By using the European Community Household Panel, we compute bounds for the poverty rate in ten European countries and study the sensitivity of poverty comparisons across countries to missing data and measurement error problems. Supplemental materials for this article may be downloaded from the JBES website.Misclassification error, survey non-response, partial identification

Sibling spillover effects in school achievement

This paper provides empirical evidence on direct sibling spillover effects in school achievement using administrative data on 220 thousand siblings in England. We extend previous strategies to identify peer effects by exploiting the variation in school test scores across three subjects observed at ages 11 and 16 as well as variation in peer quality between siblings. We find a statistically significant positive spillover effect from the older to the younger sibling but not vice versa. Spillover effects from high achieving older siblings are larger than from low achieving ones, but this relationship is weaker for students from disadvantaged backgrounds

Explaining personality pay gaps in the UK

Using the British Household Panel Survey we examine how the Big Five personality traits - openness to experience, conscientiousness, extroversion, agreeableness, and neuroticism - affect wages. We estimate mean and quantile pay gaps between people with low and high levels of each of the Big Five, and decompose these pay gaps in the part explained by differences in workers’ characteristics and in the residual unexplained part. We find that openness to experience is the most relevant personality trait followed by neuroticism, agreeableness and extroversion. Openness and extroversion are rewarded while agreeableness and neuroticism are penalized

Explaining personality pay gaps in the UK

Using the British Household Panel Survey we estimate the effect on pay of each of the Big Five personality traits for employed men living in the UK. We add to the existing literature by estimating the role of factors such as education and occupation in explaining personality pay gaps, by allowing the personality traits to affect wage differently across occupations, education levels and other workers characteristics, and by investigating personality pay gaps for high- and low-paid workers. We find that openness to experience is the most relevant personality trait in explaining wages, followed by neuroticism, agreeableness, extroversion and conscientiousness. Openness and extroversion are rewarded while agreeableness and neuroticism are penalized, but the openness pay gap is totally explained by differences in worker characteristics, particularly education and occupation