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Real-time inequality and the welfare state in motion: Evidence from COVID-19 in Spain

Oriol Aspachs, Ruben Durante, Alberto Graziano, Josep Mestres, José Garcia Montalvo () and Marta Reynal-Querol ()
Additional contact information
José Garcia Montalvo: https://www.upf.edu/web/econ/faculty/-/asset_publisher/6aWmmXf28uXT/persona/id/3418887
Marta Reynal-Querol: https://www.upf.edu/web/econ/faculty/-/asset_publisher/6aWmmXf28uXT/persona/id/3418663

Economics Working Papers from Department of Economics and Business, Universitat Pompeu Fabra

Abstract: Most official economic statistics have a relatively low frequency. The measures of inequality, in particular, are not only produced with low frequency but also with significant lags. This poses an important challenge for policymakers in their objective to mitigate the effects of a rapidly moving epidemic as the COVID-19. We propose a methodology for tracking the evolution of income inequality in the aftermath of the COVID-19 pandemic using high-frequency, high-quality microdata from bank-records. Using this approach we study the evolution of inequality since the beginning of the COVID-19 pandemic, and its effect on different groups of the population. First, we show that the payroll data managed by banks are an extremely useful source of information to detect, timely and accurately, changes in the distribution of wages. Our data replicate very closely the distribution of wages from the official wage surveys. Second, we show that, in absence of public benefits schemes, inequality would have increased dramatically. The impact of the crisis on inequality is explained mostly by its effect on low-wage workers. Pre-benefits wage inequality has increased significantly among foreign-born individuals, and regions that have a heavy economic dependence on touristic activities. Finally, we show that the public benefits activated soon after the beginning of the pandemic have substantially mitigated the impact of the COVID-19 crisis on inequality.

Keywords: Inequality; COVID-19; administrative data; high frequency (search for similar items in EconPapers)
JEL-codes: C81 D63 E24 J31 (search for similar items in EconPapers)
Date: 2020-07
New Economics Papers: this item is included in nep-lma and nep-mac
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