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Real-Time Inequality and the Welfare State in Motion: Evidence from COVID-19 in Spain

Author

Listed:
  • Aspachs, Oriol
  • Durante, Ruben
  • Graziano, Alberto
  • Mestres, Josep
  • García-Montalvo, José
  • Reynal-Querol, Marta

Abstract

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 analyze 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 labor 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

  • Aspachs, Oriol & Durante, Ruben & Graziano, Alberto & Mestres, Josep & García-Montalvo, José & Reynal-Querol, Marta, 2022. "Real-Time Inequality and the Welfare State in Motion: Evidence from COVID-19 in Spain," CEPR Discussion Papers 15118, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:15118
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    Citations

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    Cited by:

    1. Ainaa, Carmen & Brunetti, Irene & Mussida, Chiara & Scicchitano, Sergio, 2021. "Who lost the most? Distributive effects of COVID-19 pandemic," GLO Discussion Paper Series 829, Global Labor Organization (GLO).
    2. Mr. Andrew Berg & Lahcen Bounader & Nikolay Gueorguiev & Hiroaki Miyamoto & Mr. Kenji Moriyama & Ryota Nakatani & Luis-Felipe Zanna, 2021. "For the Benefit of All: Fiscal Policies and Equity-Efficiency Trade-offs in the Age of Automation," IMF Working Papers 2021/187, International Monetary Fund.
    3. Davide Furceri & Prakash Loungani & Jonathan D. Ostry & Pietro Pizzuto, 2022. "Will COVID-19 Have Long-Lasting Effects on Inequality? Evidence from Past Pandemics," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(4), pages 811-839, December.
    4. Monica Martinez-Bravo & Carlos Sanz, 2021. "Inequality and psychological well-being in times of COVID-19: evidence from Spain," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(4), pages 489-548, December.
    5. Stefanie Stantcheva, 2022. "Inequalities in the times of a pandemic," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 37(109), pages 5-41.
    6. Juan Laborda & Pilar Rivera-Torres & Vicente Salas-Fumas & Cristina Suárez, 2021. "Is there life beyond the Spanish government’s aid to furloughed employees by COVID-19?," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-21, June.
    7. José Garcia Montalvo & Marta Reynal-Querol, 2020. "Distributional effects of COVID-19 on spending: A first look at the evidence from Spain," Economics Working Papers 1740, Department of Economics and Business, Universitat Pompeu Fabra.
    8. Stefan Pollinger, 2023. "Optimal Contact Tracing and Social Distancing Policies to Suppress A New Infectious Disease," The Economic Journal, Royal Economic Society, vol. 133(654), pages 2483-2503.
    9. Oriol Aspachs & Ruben Durante & Alberto Graziano & Josep Mestres & Marta Reynal-Querol & Jose G Montalvo, 2021. "Tracking the impact of COVID-19 on economic inequality at high frequency," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-14, March.
    10. Francesca Carta & Marta De Philippis, 2021. "The impact of the COVID-19 shock on labour income inequality: evidence from Italy," Questioni di Economia e Finanza (Occasional Papers) 606, Bank of Italy, Economic Research and International Relations Area.

    More about this item

    Keywords

    Inequality; Covid-19; Administrative data; High frequency data;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • 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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