Alessio Giorgetta

I study whether public Italian schools discriminate against foreigners. Within province, I contact each school inquiring about enrollment procedures and welfare with either an Italian, French, or Arabic alias. I show that clerks are 5.32% less likely to answer if the sender is a foreigner. Moreover, lower income per capita increases bias: 10% of schools show biases ≥ 11%. When bureaucrats do respond, they are 17.6% less likely to send informative answers and 4.77% less likely to be polite. These results contradict the widely recognized theories of statistical discrimination and contact theory, at least in this setting. Economic theories of taste-based discrimination, jointly with psychological theories that postulate bias against outer groups depends on the feelings that group causes, are able to explain the observed discrimination.

Due to the complex dynamics of the electricity market, grid congestion is hard to prevent and therefore produces large economic costs and welfare losses. In this paper, we propose two methodologies to forecast network congestion and rationalize the role of the transmission system operator through the lenses of a toy model. After investigating the principal components that drive redispatches, we run a time series analysis using an INAR(1) model which accounts for the count nature of the data and provides an interpretable structure for forecasting. As an alternative specification, we consider a fully flexible Recurrent Neural Network (RNN) model that allows to account for the unbalancedness of the congestion frequency attaining high forecasting accuracy (0.94). RNN outperforms INAR(1), as it better captures the true negative congestion events in the test data and therefore potentially reduces the costs related to readjusting energy production. Finally, we run a counterfactual analysis in which we artificially introduce a 75% cap on the price of electricity and study the effects on congestion. We find that introducing this policy would significantly increase the frequency of redispatches.

We analyze macroeconomic and financial uncertainty as underlying determinants of business cycle fluctuations. To do so, we exploit a proxy-SVAR approach that has been used to study the dynamic effects of tax changes but that, to our knowledge, is new to the literature on uncertainty. Instrumenting both macroeconomic and financial uncertainty with an auxiliary VAR specification, we find that positive shocks in either ones have large, significant and sizeable negative effects on GDP growth. This holds true regardless of the particular relation one assumes between the two uncertainties. Moreover, we show regime dependence in pre/post-Covid effects. Our results partially align with previous results in the literature on uncertainty identification.