The local Solow growth model

growth models

Barriers to price convergence

This paper uncovers novel empirical patterns in the cross-country price mechanism using a nonlinear factor model and threshold regression analysis based on individual goods retail price data for a large panel of countries. To our knowledge, this is the first paper to find strong evidence for club convergence of retail prices. These clubs emerge due to the interaction of traded and non-traded factors. For example, countries physically closer to potential trade partners converge faster than countries in the high distance regime as long as they have low initial labor productivity or low initial income. Moreover, we find an asymmetry in the extent that arbitrage opportunities related to international trade are exploited, with low initial price regime countries exhibiting faster convergence from below than high initial price regime countries exhibit from above, consistent with less resistance to exporting than to importing due to political economy considerations. We interpret our findings as evidence of a local law of one price due to barriers to price convergence influencing the duration of the effect of price shocks

Data for: Robust determinants of intergenerational mobility in the land of opportunity

Abstract of associated article: This paper revisits the influential work by Chetty et al. (2014) who attempt to explain the variation in intergenerational mobility across commuter zones in the US (i.e., spatial mobility) using nine classes of variables. We employ Bayesian model averaging methods that allow for model uncertainty to identify robust predictors of spatial mobility. In doing so we pay special attention to the specification of model and parameter priors. We also investigate the heterogeneous effects of these predictors on spatial mobility across commuter zones in different average income quintiles. Our findings suggest a more nuance and complex characterization of the spatial mobility process than that proposed by Chetty, Hendren, Kline, and Saez

Socio-economic inequalities and organized crime: An empirical analysis

In this chapter we contribute to the recent literature that provides evidence that inequality is high and social mobility is low in the Italian regions and provinces where organized crime is widespread such as those of Southern Italy. We provide a theoretical justification for these pieces of evidence and, by using a novel panel dataset at the regional level for the period 1985-2014, we investigate the relationship between inequality and organized crime at the regional level, and assess the role of social mobility in organized crime. Our main finding is that higher inequality leads to higher organized crime development. The results are robust for different organized crime measures and inequality indices. For all measures the ratio of 90th quintile over the 10th in the distribution is a strong predictor of organized crime, even if we control for other covariates that capture economic development, education etc. We also find that consumption inequality performs better that income inequality as the relevant inequality measure. Finally, we conduct a provincial level analysis to study the effect of socio-economic mobility on organized crime, using three alternative measures of social mobility. We find that lower socio-economic mobility display a robust association with organized crime development

Schools opening and Covid-19 diffusion: Evidence from geolocalized microdata

Are schools triggering the diffusion of the Covid-19? This question is at the core of an extensive debate about the social and long-run costs of stopping the economic activity and human capital accumulation from reducing the contagion. In principle, many confounding factors, such as climate, health system treatment, and other forms of restrictions, may impede disentangling the link between schooling and Covid-19 cases when focusing on a country or regional-level data. This work sheds light on the potential impact of school opening on the upsurge of contagion by combining a weekly panel of geocoded Covid-19 cases in Sicilian census areas with a unique set of school data. The identification of the effect takes advantage of both a spatial and time-variation in school opening, stemming from the flexibility in opening dates determined by a Regional Decree, and by the occurrence of a national referendum, which pulled a set of poll-station schools towards opening earlier or later September 24th. The analysis finds that census areas where schools opened earlier observed a significant and positive increase in the growth rate of Covid-19 cases between 2.5–3.7%. This result is consistent across several specifications, including accounting for several determinants of school opening, such as the number of temporary teachers, Covid-19 cases in August, and pupils with special needs. Finally, the analysis finds lower effects in more densely populated areas, on younger population, and on smaller class size. The results imply that school reopening generated an increase of one third in cases