Are Educational Mismatches Responsible for the ‘Inequality Increasing Effect’ of Education?

This paper asks whether educational mismatches can account for the positive association between education and wage inequality found in the data. We use two different data sources, the European Community Household Panel and the Portuguese Labour Force Survey, and consider several types of mismatch, including overqualification, underqualification and skills mismatch. We test our hypothesis using two different measurement methods, the ‘statistical’ and the ‘subjective’ approach. The results are robust to the different choices and unambiguously show that the positive effect of education on wage inequality is not due to the prevalence of educational mismatches in the labour market.     

Describing poverty in an ultraperipheral region: The case of the Canary Islands

Based on data from the Survey on Social Conditions in the Canary Islands of the Instituto Canario de Estadística, this study analyzes poverty in Canary households by island of residence, age, employment and the educational level of the main provider. In addition to accepted objective measures, we also estimate poverty using subjective methods, in which the poverty line is defined or obtained by the opinion of household members of their own level of poverty.     

Coordination in Games with Strategic Complementarities: An Experiment on Fixed vs. Random Matching

In this article, we study behavior in a series of two‐player supply chain game experiments. Each player simultaneously chooses a capacity before demand is realized, and sales are given by the minimum of realized demand and chosen capacities. We focus on the differences in behavior under fixed pairs and random rematching. Intuition suggests that long‐run relations should lead to more profitable outcomes. However, our results go against this intuition. While subjects' capacity choices are better aligned (i.e., closer together) under fixed pairs, average profits are more variable. Moreover, learning is slower under fixed pairs—so much so that over the last five periods, average profits are actually higher under random rematching. The underlying cause for this finding appears to be a “first‐impressions” bias, present only under fixed matching, in which the greater the misalignment in initial choices, the lower are average profits.     

Changes in Economic Hardship and Intimate Partner Violence: A Family Stress Framework

While recent studies underscore how financial stress and lack of tangible assets significantly increase the odds of intimate partner violence (IPV) among couples, little is known about these effects over time. Theoretically informed by family stress theory, this study examines the influence of changing subjective reports of economic hardship over time on the risk for IPV. Specifically, we test two hypotheses: (1) change in economic hardship significantly predicts IPV; and, specifically, (2) increased economic hardship or unstable economic conditions increases the risk of IPV. Using longitudinal data from the Fragile Families and Child Well-being Study, we assess the incidence of IPV for 941 women in committed relationships. Results from our logistic regression analyses suggest that after controlling for numerous demographic factors, women who never experienced economic hardship had lower odds of experiencing IPV than those who did. Further, women who experienced high levels of economic hardship over time had the highest odds of experiencing IPV. However, the effects of changing economic hardship on IPV were attenuated once maternal depression and parenting stress were controlled. Results are discussed in terms of practice and policy implications.     

A Note on Collinearity Diagnostics and Centering

The usual approach for diagnosing collinearity proceeds by centering and standardizing the regressors. The sample correlation matrix of the predictors is then the basic tool for describing approximate linear combinations that may distort the conclusions of a standard least-square analysis. However, as indicated by several authors, centering may eventually fail to detect the sources of ill-conditioning. In spite of this earlier claim, there does not seem to be in the literature a fully clear explanation of the reasons for this bad potential behavior of the traditional strategy for analyzing collinearity. This note studies this issue in some detail. Results derived are motivated by the analysis of a well-known real dataset. Practical conclusions are illustrated with several examples.     

Child and Household Deprivation: A Relationship Beyond Household Socio-demographic Characteristics

This paper aims to contribute to the literature on poverty and social exclusion by analysing the type of deprivation of the household where the child lives and the level of deprivation that child experiences. Using the EU-SILC 2009 module on deprivation for Spain, we find that the level of child deprivation varies among household types, that is, even after controlling for the socio-economic characteristics of the household and parents, the lack of certain items at the household level induces a more intense child deprivation. Therefore, we can conclude that there exists an association between child deprivation and the household deprivation profile that surpasses the socio-demographic characteristics of the household and parents.     

Percentiles of sums of heavy-tailed random variables: beyond the single-loss approximation

A perturbative approach is used to derive approximations of arbitrary order to estimate high percentiles of sums of positive independent random variables that exhibit heavy tails. Closed-form expressions for the successive approximations are obtained both when the number of terms in the sum is deterministic and when it is random. The zeroth order approximation is the percentile of the maximum term in the sum. Higher orders in the perturbative series involve the right-truncated moments of the individual random variables that appear in the sum. These censored moments are always finite. As a result, and in contrast to previous approximations proposed in the literature, the perturbative series has the same form regardless of whether these random variables have a finite mean or not. For high percentiles, and specially for heavier tails, the quality of the estimate improves as more terms are included in the series, up to a certain order. Beyond that order the convergence of the series deteriorates. Nevertheless, the approximations obtained by truncating the perturbative series at intermediate orders are remarkably accurate for a variety of distributions in a wide range of parameters.