We develop a model of firm learning in volatile markets with noisy signals and test its predictions using historical German data. Firms' forecasts improve with age. We exploit German Reunification as a natural experiment where firms in the East are treated with ignorance about the distribution...

In this paper we conduct a counterfactual analysis and estimate the quantitative importance of demand and supply effects on wage inequality in Germany using a dynamic computable general equilibrium (CGE) model of the Auerbach-Kotlikoff (1987) type. Specifically, the methodological contribution...

In this paper we use a transparent statistical methodology – synthetic control methods – to implement data-driven comparative studies about the impact of autocratic transition on real per capita GDP. The applied methodology compares the growth of countries that experienced a transition to...

This paper argues that corruption in Russia is systemic in nature. Low wage levels of public officials provide strong incentives to engage in corruption. As corruption is illegal, corrupt officials can be exposed any time, which enforces loyalty towards the powers that be; thus corruption is a...

This paper formulates a structural empirical model of heterogeneous firms whose workers exhibit fair-wage preferences, leading to a link between a firm's operating profits and wages of workers employed by this firm. We estimate the parameters of the model in a data-set of five European...

This paper investigates the influence of the political regime on the relative importance of conspicuous consumption. We use the separation of Germany into the communist GDR and the democratic FRG and its reunification in 1990 as a natural experiment. Relying on household data that are...

Does the Internet undermine social capital or facilitate inter-personal and civic engagement in the real world? Merging unique telecommunication data with geo-coded German individual-level data, we investigate how broadband Internet affects several dimensions of social capital. One...