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Meijer, Rohwedder, and Wansbeek (MRW, Journal of Business & Economic Statistics, 2012) develop methods for prediction of a single earnings figure per worker from mixture factor models fitted using earnings data from multiple linked data sources. MRW apply their method using parameter estimates of Kapteyn and Ypma's mixture factor model (KY, Journal of Labour Economics 2007) fitted to earnings data for Swedish workers aged 50+. First, we replicate MRW's empirical analysis using the Swedish model estimates. Second, we check the generality of their empirical finding with a new application. Using estimates of a KY model fit to a linked dataset on earnings for UK employees of all ages, we confirm that MRW's principal findings about the performance of their various predictors of true earnings also hold in this different setting.

Kapteyn and Ypma (Journal of Labour Economics 2007) is an influential study of errors in survey and administrative data on employment earnings. To fit their mixture models, Kapteyn and Ypma assume a specific fraction of their sample have error-free earnings. Using a new UK dataset, we assess the sensitivity of model estimates and post-estimation statistics to variations in this fraction and find some lack of robustness.

This paper uses tax and student loan administrative data to measure how the earnings of English graduates around 10 years into the labour market vary with gender, institution attended subject and socioeconomic background. The English system is competitive to enter, with some universities demanding very high entrance grades. Students specialise early, nominating their subject before they enter higher education (HE). We find subjects like Medicine, Economics, Law, Maths and Business deliver substantial premiums over typical graduates, while disappointingly, Creative Arts delivers earnings which are roughly typical of non-graduates. Considerable variation in earnings is observed across diff erent institutions. Much of this is explained by student background and subject mix. Based on a simple measure of parental income, we see that students from higher income families have median earnings which are around 25% more than those from lower income families. Once we control for institution attended and subject chosen this premium falls to around 10%.

This paper compares survey based labour earnings data for English graduates, taken from the UK's Labour Force Survey (LFS), with the UK Government administrative sources of official individual level earnings data. This type of administrative data has few sample selection issues, is substantially longitudinal and its large samples mean the earnings of subpopulations can be potentially studied (e.g. those who study a specific subject at a specific university and graduate in a speci?c year). We find that very broadly the LFS and administrative data show a similar distribution of graduates' earnings. However, the administrative data has considerably less gender disparity, higher high quantiles and more time series persistence. We also report on how the distribution of graduate and non-graduate earnings fell during each year of the Great Recession.

This paper presents analysis of urban areas in the Tanzania Integrated Labour Force Survey (ILFS) for 2000/01 and 2006 and the Urban Household Worker Survey (UHWS) for 2004, 2005 and 2006. The main aims are to estimate returns to education and to identify, conditioned on education and labour market experience, earnings differentials by gender and across sectors (public, private and informal). We confirm the general pattern that returns to education are increasing in level and years of education but note differences across sector of employment and the earnings distribution. Public sector workers (who tend to be more educated with longer tenure) and the self-employed with employees (small and micro enterprises) have the highest earnings whereas informal sector (self-employed without employees) and private sector wage earners have similar earnings on average (except for wage earners in large firms who have considerably higher earnings). Post-primary education is important in determining selection into wage employment, especially for the public sector. Allowing for selection, education has no additional effect on public sector wages, returns to education are concave for the self-employed but non-concave for the private wage sector. Quantile regressions reveal differential returns to education across the earnings distribution: primary and secondary education are inequality-reducing (more beneficial to those on lower earnings) whereas tertiary education is inequality-increasing.

Based on the affirmation that internal migration in Chile has become increasingly less important as an equalizing mechanism for regional disparities, this paper aims to look at the causes of such immobility. The estimation procedure allows for obtaining a non-endogenous potential wage differential which controls for the selectivity process involved in the migration decision (based on observed and unobserved characteristics). This study finds that the productivity differential is the leading factor explaining migration. However, migration not only depends on individual characteristics, but strongly relies on the level of household education. Unfortunately, the initial disadvantages related to the household background determine that the one who faces attractive potential wage differentials is at the same time constrained by its household. The conclusion is that household-related migration costs are a source of inefficiency in labour allocation. Consequently, supporting the infrastructure in the rural economy is not the only way to achieve convergence across the territory. Subsidies aimed to reduce migration costs can be also considered in a framework oriented towards encouraging functional migration flows.

This paper assesses the potential impacts of the removal of agricultural and other trade distortions using a newly developed dataset and methodological approach for evaluating the global poverty and inequality effects of policy reforms. It finds that liberalization of agriculture will increase global extreme poverty (US$1 a day) slightly and by almost 1 percent if other goods trade is also liberalized; but the number of people living on less than $2 a day will fall by almost 1 percent. Beneath these small aggregate changes, most countries witness a substantial reduction in poverty while South Asia where half of the world's poor reside will experience an increase in extreme (but not moderate) poverty incidence due to high rates of protection afforded to its unskilled labor-intensive agricultural sectors. The distributional changes also are projected to be mild, but again exhibit a strong regional pattern: inequality falls in Latin America, which is characterized by high initial inequality, and rises in South Asia, has relatively low income inequality.

A first order autoregressive non-Gaussian model for analysing panel data is proposed. The main feature is that the model is able to accommodate fat tails and also skewness, thus allowing for outliers and asymmetries. The modelling approach is designed to gain sufficient flexibility, without sacrificing interpretability and computational ease. The model incorporates individual effects and covariates and we pay specific attention to the elicitation of the prior. As the prior structure chosen is not proper, we derive conditions for the existence of the posterior. By considering a model with individual dynamic parameters we are also able to formally test whether the dynamic behaviour is common to all units in the panel. The methodology is illustrated with two applications involving earnings data and one on growth of countries.

Why do people work unpaid overtime? We show that remarkable long-term labor earnings gains are associated with unpaid overtime in West Germany. A descriptive analysis suggests that over a 10-year period workers with unpaid overtime experience on average at least a 10 percentage points higher increase in real labor earnings than their co-workers. Applying panel data models this result generally holds. Furthermore, we find some evidence for gender specific differences with respect to the effects of unpaid overtime work. Our results point to the importance of investment in current working hours beyond the standard work week to enhance real earnings prospects.