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...

This paper shows that the matching function and the Beveridge curve in the United States exhibit strong nonlinearities over the business cycle. These patterns can be replicated by enhancing a search and matching model with idiosyncratic productivity shocks for new contacts. Large negative...

To measure income inequality with right censored (topcoded) data, we propose multiple imputation for censored observations using draws from Generalized Beta of the Second Kind distributions to provide partially synthetic datasets analyzed using complete data methods. Estimation and inference...

We examine the determinants of low income transitions using first-order Markov models that control for initial conditions effects (those found to be poor in the base year may be a nonrandom sample) and for attrition (panel retention may also be non-random). Our econometric model is a form of...

The socio-economic mosaic of urban neighbourhoods changes under the influence of three distinctive distributional processes: reordering of the socio-economic position of urban neighbourhoods; changing levels of inequality between neighbourhoods; and an overall growth or decline in income levels...

Using the new AWFP dataset that covers all German establishments, we document a substantial cross-sectional heterogeneity of establishments' average real wages over the business cycle. While the median establishments' real wages are procyclical, there is a large fraction of establishments with...

This paper proposes a new approach for analyzing the relationship between macroeconomic factors and the income distribution. The conventional method of analysis is regression of summary inequality indices on variables such as the unemployment and inflation rates. Building on the lessons from...