We develop a new quantile-based panel data framework to study the nature of income persistence and the transmission of income shocks to consumption. Log-earnings are the sum of a general Markovian persistent component and a transitory innovation. The persistence of past shocks to earnings is...

Credible evidence from a variety of contexts suggests that student absences harm academic achievement. However, extant studies focus entirely on the average effects of student absences, and how those average effects vary by student, school, and absence type. This paper enhances our understanding...

This paper examines the effect of wage variation on individual wages. The results reveal that wage variation by educational classifications positively affects wages, while the skewness has a negative effect. As has been referred in previous literature on the issue, both results are consistent...

The paper explores the public-private wage gap in the Russian economy over time and along the whole wage distribution. Using the RLMS-HSE panel data set, we examine how gaps at various points of wage distribution changed from 2005 to 2015 and present decompositions of the gaps into components...

Recent studies have used a distributional analysis of welfare reform experiments suggesting that some individuals reduce hours in order to opt into welfare, an example of behavioral-induced participation. Using data on Connecticut's Jobs First experiment, we find no evidence of...

Using quantile regression methods, this paper analyses the gender wage gap across the wage distribution and over time (1990-2014), while controlling for changing sample selection into full-time employment. Our findings show that the selection-corrected gender wage gap is much larger than the one...

This article develops a Bayesian approach for estimating panel quantile regression with binary outcomes in the presence of correlated random effects. We construct a working likelihood using an asymmetric Laplace (AL) error distribution and combine it with suitable prior distributions to obtain...

We offer a new strategy to identify the distribution of treatment effects using data from the Infant Health and Development Program (IHDP), a relatively understudied early-childhood intervention for low birth-weight infants. We introduce a new policy parameter, QCD, which denotes quantiles of...

Quantile factor models (QFM) represent a new class of factor models for high-dimensional panel data. Unlike approximate factor models (AFM), which only extract mean factors, QFM also allow unobserved factors to shift other relevant parts of the distributions of observables. We propose a quantile...

This paper investigates the key factors affecting household energy expenditure in Egypt. Based upon the latest 2015 Egyptian HIECS Survey, we develop a quantile regression model with an innovative variable selection approach via Adaptive Lasso Regularization technique to untangle the spectrum of...