Upper and Lower Bound Estimates of Inequality of Opportunity: A Cross-National Comparison for Europe

I provide lower and upper bound estimates of inequality of opportunity (IOp) for 32 European countries, between 2005 and 2019. Lower bound estimates use machine learning methods to address sampling variability. Upper bound estimates use longitudinal data to capture all-time invariant factors. Across all years and countries, lower bound estimates of IOp account from 6 percent to 60 percent of total income inequality, while upper bound estimates account from 20 percent to almost all income inequality. On average, upper bound IOp saw a slight decrease in the aftermath of the Great Recession, recovering and stabilizing at around 80 percent of total inequality in the second half of the 2010s. Lower bound estimates for 2005, 2011, and 2019 show a similar pattern. My findings suggest that lower and upper bound estimates complement each other, corroborating information and compensating each other's weaknesses, highlighting the relevance of a bounded estimate of IOp.     

The external cost of prostitution: Evidence from shutting down red light districts in the Netherlands

This article examines the value of externalities created by the presence of sex workers in the city. Although a legal paid-sex industry might contribute to the economy, it may also generate negative externalities. To identify the net impact of overt prostitution, we estimate changes in housing prices following the sudden closure of the two red light districts (RLDs) in the Dutch City of Utrecht. Our results show that the capitalization effect of RLDs is spatially heterogeneous. While some areas are unaffected, others are up to 12% more expensive if far from operating brothels. Interestingly, though, evidence also shows that RLDs increase local employment in a variety of sectors. All the aversion to living near RLDs is instead explained by petty crimes.     

Top Income Adjustments and Inequality: An Investigation of the EU-SILC†

This paper provides a novel assessment of how the World Inequality Database (WID) top income adjustment applied by Blanchet, Chancel, and Gethin (2021) to European Union Statistics on Income and Living Conditions (EU-SILC) data for 26 countries over 2003–2017 for Distributional National Accounts purposes affects inequality in equivalized gross and disposable household income. On average, the Gini is increased by around 2.4 points for both gross and disposable income, with notable differences across countries but limited impact on trends. EU-SILC countries that rely on administrative register data see relatively small effects on inequality. Comparing with two other recent studies, differences in impacts on measured inequality depend less on the adjustment method and more on whether external data sources are used.