Do experts adjust their policy recommendations when the facts change? We conduct a large-scale randomized experiment among 1,224 economic experts across 109 countries that includes two treatments. The first treatment is the geographic and temporal variation in the initial spread of Covid-19...

We provide a comprehensive overview of the literature on the measurement of democracy and present an extensive update of the Machine Learning indicator of Gründler and Krieger (2016, European Journal of Political Economy). Four improvements are particularly notable: First, we produce a...

We compile data for 186 countries (1919 - 2016) and apply different aggregation methods to create new democracy indices. We observe that most of the available aggregation techniques produce indices that are often too favorable for autocratic regimes and too unfavorable for democratic regimes....

We examine how structural reforms relate to income inequality. We employ many indicators of structural reforms and use data for market and net income inequality. The dataset includes up to 135 countries since 1960. The results do not suggest that market-oriented structural reforms were...

Scholars have been active in investigating causes and consequences of austerity policies. We examine how economists use the term "austerity" in scientific studies and measure austerity in empirical analyses. The sample includes around 3,500 journal articles published in the top 400 journals...

We present a new aggregation method - called SVM algorithm - and use this technique to produce novel measures of democracy (186 countries, 1960-2014). The method takes its name from a machine learning technique for pattern recognition and has three notable features: it makes functional...

We propose that crisis experience influences preferences towards COVID-19 vaccination and the speed of vaccination during the initial phase when vaccines became available. We use macro and micro data to empirically investigate our theory and introduce a novel crisis experience index. Evidence...

We examine the deep roots of preferences for vaccination against COVID-19, moving beyond proximate factors which can only account for part of the observable heterogeneity in the willingness to get vaccinated. Our model on experience-based learning predicts that exposure to past disruptive crises...