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

Author

Listed:
  • Oriol Aspachs
  • Ruben Durante
  • Alberto Graziano
  • Josep Mestres
  • Jose G Montalvo
  • Marta Reynal-Querol

Abstract

SUMMARYG. Montalvo and Marta Reynal-Querol?>Official statistics on economic inequality are only available at low frequency and with considerable delay. This makes it challenging to assess the impact on inequality of fast-unfolding crises like the COVID-19 pandemic, and to rapidly evaluate and tailor policy responses. We propose a new methodology to track income inequality at high frequency using anonymized data from bank records for over three million account holders in Spain. Using this approach, we analyse how inequality evolved between February and November 2020 (compared to the same months of 2019). We first show that the wage distribution in our data matches very closely that from official labour surveys. We then document that, in the absence of government intervention, inequality would have increased dramatically, mainly due to job losses and wage cuts experienced by low-wage workers. The increase in pre-transfer inequality was especially pronounced among the young and the foreign-born, and in regions more dependent on services. Public transfers and unemployment insurance schemes were effective at providing a safety net to the most affected segments of the population and at offsetting most of the increase in inequality. Increased inequality is primarily driven by differential changes in employment rate. Indeed, using individual-level regressions, we find that, over the course of the pandemic, the probability of being employed decreased drastically for workers in the lower part of the pre-COVID wage distribution, young cohorts and foreign-born.

Suggested Citation

  • Oriol Aspachs & Ruben Durante & Alberto Graziano & Josep Mestres & Jose G Montalvo & Marta Reynal-Querol, 2022. "Real-time inequality and the welfare state in motion: evidence from COVID-19 in Spain," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 37(109), pages 165-199.
  • Handle: RePEc:oup:ecpoli:v:37:y:2022:i:109:p:165-199.
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    File URL: http://hdl.handle.net/10.1093/epolic/eiac008
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    Cited by:

    1. Ali B. Barlas & Seda Guler Mert & Berk Orkun Isa & Alvaro Ortiz & Tomasa Rodrigo & Baris Soybilgen & Ege Yazgan, 2024. "Big data financial transactions and GDP nowcasting: The case of Turkey," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 227-248, March.

    More about this item

    JEL classification:

    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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