Is there a glass ceiling over Europe? : exploring the gender pay gap across the wage distribution

Using harmonised data from the European Union Household Panel, we analyse gender pay gaps by sector across the wages distribution for ten countries. We find that the mean gender pay gap in the raw data typically hides large variations in the gap across the wages distribution. We use quantile regression (QR) techniques to control for the effects of individual and job characteristics at different points of the distribution, and calculate the part of the gap attributable to differing returns between men and women. We find that, first, gender pay gaps are typically bigger at the top of the wage distribution, a finding that is consistent with the existence of glass ceilings. For some countries gender pay gaps are also bigger at the bottom of the wage distribution, a finding that is consistent with sticky floors. Third, the gender pay gap is typically higher at the top than the bottom end of the wage distribution, suggesting that glass ceilings are more prevalent than sticky floors and that these prevail in the majority of our countries. Fourth, the gender pay gap differs significantly across the public and the private sector wages distribution for each of our EU countries

A Note on Estimated Coefficients in Random Effects Probit Models.

This note points out to applied researchers what adjustments are needed to the coefficient estimates in a random effects probit model in order to make valid comparisons in terms of coefficient estimates and marginal effects across different specifications. These adjustments are necessary because of the normalization that is used by standard software in order to facilitate easy estimation of the random effects probit model.ECONOMIC MODELS ; EVALUATION

Inequality in Infant Survival Rates in India: Identification of State-Dependence Effects

Data from a number of regions indicate that childhood deaths are unequally distributed across families. This has been identified, in previous research, with (observed and unobserved) heterogeneity between families. In this paper, we investigate whether, on top of these correlated risks, there is a causal process at work within families, whereby the death of a child elevates the risk of death of the succeeding sibling. Borrowing language from the unemployment literature, the causal process is termed state dependence or scarring. To the extent that scarring exists, a social multiplier comes into play, raising the payoff to policies that reduce infant mortality. Acknowledging scarring effects is also potentially relevant to understanding the relation of mortality and fertility behaviour within families. The analysis is conducted using data for the 15 major states of India. Large scarring effects are observed in 14 of the 15 states.Death clustering, infant mortality, state dependence, scarring, unobserved heterogeneity, dynamic random effects logit, India

Infant Death Clustering in India: Genuine Scarring vs Unobserved Heterogeneity

Data from a range of different environments indicate that the incidence of death is not randomly distributed across children or households but, rather, that there is death clustering within households. A hypothesis of considerable interest for both theory and policy is that there is a causal process whereby the death of a child influences the risk of death of the succeeding child in the family. This causal effect which, drawing language from the literature on unemployment, we term scarring or genuine state dependence tends to be confounded with both observable and unobservable inter-family heterogeneity. In this paper, we investigate the extent of genuine scarring in three Indian states, controlling for these confounding factors. The paper offers a number of methodological innovations upon previous research in the area and, thereby, offers what we expect are more robust estimates of the scarring effect. Classification-O12, I12, C25, J13death clustering, state dependence, unobserved heterogeneity, random effects probit, dynamic model

Effects of in-class variation and student rank on the probability of withdrawal : cross-section and time-series analysis for UK university students

From individual-level data for nine entire cohorts of undergraduate students in UK universities, we estimate the probability that an individual will drop out of university during their first-year. We examine the 1984-85 to 1992-93 cohorts of students enrolling full-time for a three or four-year course, and focus on the sensitivity of the probability of withdrawal to the individual’s prior qualifications relative to those of the other students in their university course. We show not only that weaker students are more likely to withdraw but also that the extent of variation in prior qualifications within the student’s university degree course exerts an influence on the individual's probability of withdrawal in a way that varies with the individual’s own in-class rank

Dropping out of medical school in the UK : explaining changes over 10 years

For the 1990-92 and 1998-2000 cohorts we find the probability of dropping out of medical school is lower for students (i) with better prior qualifications, (ii) with a parent who is a doctor, (iii) living on campus

Are there asymmetries in the effects of training on the conditional male wage distribution?

Recent studies have used quantile regression (QR) techniques to estimate the impact of education on the location, scale and shape of the conditional wage distribution. In our paper we investigate the degree to which work-related training – another important form of human capital – affects the location, scale and shape of the conditional wage distribution. Using the first six waves of the European Community Household Panel, we utilise both ordinary least squares and QR techniques to estimate associations between work-related training and wages for private sector men in ten European Union countries. Our results show that, for the majority of countries, there is a fairly uniform association between training and hourly wages across the conditional wage distribution. However, there are considerable differences across countries in mean associations between training and wages

Simplified Implementation of the Heckman Estimator of the Dynamic Probit Model and a Comparison with Alternative Estimators

This paper presents a convenient shortcut method for implementing the Heckman estimator of the dynamic random effects probit model and other dynamic nonlinear panel data models using standard software. It then compares the estimators proposed by Heckman, Orme and Wooldridge, based on three alternative approximations, first in an empirical model for the probability of unemployment and then in a set of simulation experiments. The results indicate that none of the three estimators dominates the other two in all cases. In most cases all three estimators display satisfactory performance, except when the number of time periods is very small.Dynamic discrete choice models ; initial conditions ; dynamic probit ; panel data ; dynamic nonlinear panel data models

Factors affecting the probability of first-year medical student dropout in the UK : a logistic analysis for the entry cohorts of 1980-1992

Objectives: To assess the extent to which various factors influence the probability that anindividual medical student will drop out of medical school during their first year of study, focussing on the influence both of prior qualifications, such as A-level subjects taken and scores attained, and of type of school and family background. Design: Individual-level administrative data for entire cohorts of medical students from 1980/81 to 1992/93 (that is, the graduating cohorts of 1985/86 to 1997/98), yielding data on 51,810 students in 20 medical schools in the UK. The nature of the data permits two different approaches to the modelling of medical school dropout. First, by focussing on the determinants of first-year dropout we are able to exploit data for cohorts up to and including the entry cohort of 1992. Second, as an alternative, for any given cohort one can analyse the probability of a student dropping out in any one of the 5 years of their programme: but in this case one is restricted to data on cohorts no more recent than the 1986 entry cohort.1 In the current paper, we follow the first of these research strategies. Statistical analysis is by logistic regression. Main outcome measures First-year dropout from medical school versus continuation into second year of study. A (first-year) dropout is anyone who left their medical school programme before the end of their first year of study. Results The probability that a student will drop out of medical school during their first year of study is influenced significantly both by the subjects studied at A-level and by the scores achieved. Among students who took Biology, Chemistry and Physics at A-level, each extra grade achieved reduces the probability of dropping out by about one-third of a percentage point. There is an additional effect for students with the maximum A-level score of 30 points in their best 3 A-levels (that is, three grade As): such a student is almost one percentage point less likely to drop out of medical school, ceteris paribus, compared to a student with 28 points. Furthermore, this estimated effect of A-level performance on dropout behaviour is very similar for each of the 13 cohorts. In general, indicators of both the social class and the previous school background of the student are largely insignificant, with the exception that students with a parent who is a medical doctor are significantly less likely to drop out. There are significant differences by gender, with males more likely to drop out. There is also evidence of significant age effects, with a tendency for the dropout probability to fall with age. Conclusions: Policies aimed at increasing the size of the medical student intake in the UK and of widening access to students from non-traditional backgrounds should be informed by evidence that student dropout probabilities are sensitive to measures of A-level attainment such as subject studied and scores achieved. If traditional entry requirements or standards are relaxed, then this is likely to have detrimental effects on medical schools’ retention rates unless accompanied by appropriate measures such as targeted admissions and focussed student support

Maternal Autonomy and the Education of the Subsequent Generation: Evidence from Three Contrasting States in India

This paper makes a significant contribution on both conceptual and methodological fronts, in the analysis of the effect of maternal autonomy on school enrolment age of children in India. The school entry age is modelled using a discrete time duration model where maternal autonomy is entered as a latent characteristic, and allowed to be associated with various parental and household characteristics which also conditionally affect school entry age. The model identification is achieved by using proxy measures collected in the third round of the National Family Health Survey of India, on information relating to the economic, decision-making, physical and emotional autonomy of a woman. We concentrate on three very different states in India – Andhra Pradesh, Kerala and Uttar Pradesh. Our results indicate that female autonomy is not associated with socio-economic characteristics of the woman or her family in Kerala (except maternal education), while it is strongly correlated to these characteristics in both Andhra Pradesh and Uttar Pradesh. Secondly, while female autonomy is significant in influencing the school starting age in UP, it is less important in AP and not significant at all in Kerala.latent factor models, structural equation models, female autonomy, school enrolment decisions, India, National Family Health Survey