Empirical Welfare Maximization (EWM) is a framework that can be used to select welfare program eligibility policies based on data. This paper extends EWM by allowing for uncertainty in estimating the budget needed to implement the selected policy, in addition to its welfare. Due to the...

Nonlinear estimation of the gravity model with Poisson/negative binomial methods has become popular to model international trade flows, as it permits a better accounting for large numbers of zero flows. Nevertheless, as trade flows are not independent of each other due to spatial...

This paper provides a systematic review of the literature that employs stochastic frontier analysis to measure the efficiency of higher education institutions. The overview opens with a look at the general development of the literature, before emphasis is laid on the methodical aspects. Focus is...

This paper investigates the impact of individual bank fundamental variables on stock market returns using data from a panel of 235 European banks from 1991 to 2005. The sample period marks a significant transition in the European banking sector, characterized by higher competition, lower profit...

This research introduces an innovative GDP nowcasting strategy tailored for developing countries, specifically addressing challenges related to limited data timeliness. The study centers on Bolivia, where the official monthly indicator of economic growth is released with a substantial delay of...

Die vorliegende Dissertation besteht aus fünf voneinander unabhängigen Papieren, die zwei größeren Forschungsfelder abdecken: die Ökonomie des Alterns und die Innovationsökonomik. Die ersten drei Kapitel betrachten den Einfluss der Interaktion zwischen Generationen auf die Eltern...

This paper provides a detailed decomposition analysis of rising lifetime earnings inequality in Germany using individual employment biographies constructed from high-quality administrative data. The results show that significant parts of rising lifetime earnings inequality among West German men...