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Nowcasting indicators of poverty risk in the European Union: a microsimulation approach

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  • Navicke, Jekaterina
  • Rastrigina, Olga
  • Sutherland, Holly

Abstract

The at-risk-of-poverty rate is one of the three indicators used for monitoring progress towards the Europe 2020 poverty and social exclusion reduction target. Timeliness of this indicator is critical for monitoring the effectiveness of policies. However, due to complicated nature of the European Union Statistics on Income and Living Conditions (EU-SILC) poverty risk estimates are published with a 2 to 3 year delay. This paper presents a method that can be used to estimate (“nowcast†) the current at-risk-of-poverty rate for the European Union (EU) countries based on EU-SILC microdata from a previous period. The EU tax-benefit microsimulation model EUROMOD is used for this purpose in combination with up to date macro-level statistics. We validate the method by using EU-SILC data for 2007 incomes to estimate at-risk-of-poverty rates for 2008-2012, and where possible compare our predictions with actual EU-SILC and other external statistics. The method is tested on eight EU countries which are among those experiencing the most volatile economic conditions within the period: Estonia, Greece, Spain, Italy, Latvia, Lithuania, Portugal and Romania.

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  • Navicke, Jekaterina & Rastrigina, Olga & Sutherland, Holly, 2013. "Nowcasting indicators of poverty risk in the European Union: a microsimulation approach," EUROMOD Working Papers EM11/13, EUROMOD at the Institute for Social and Economic Research.
    • Handle: RePEc:ese:emodwp:em11-13
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    2. Michal Myck & Mateusz Najsztub, 2015. "Data and Model Cross-validation to Improve Accuracy of Microsimulation Results: Estimates for the Polish Household Budget Survey," International Journal of Microsimulation, International Microsimulation Association, vol. 8(1), pages 33-66.
    3. Mathias Dolls & Clemens Fuest & Dirk Neumann & Andreas Peichl, 2018. "An unemployment insurance scheme for the euro area? A comparison of different alternatives using microdata," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 25(1), pages 273-309, February.
    4. Olga Cantó & Francesco Figari & Carlo V. Fiorio & Sarah Kuypers & Sarah Marchal & Marina Romaguera‐de‐la‐Cruz & Iva V. Tasseva & Gerlinde Verbist, 2022. "Welfare Resilience at the Onset of COVID‐19 Pandemic in a Selection of European Countries: Impact on Public Finance and Household Incomes," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(2), pages 293-322, June.
    5. Cathal O'Donoghue & Denisa M. Sologon & Iryna Kyzyma & John McHale, 2020. "Modelling the Distributional Impact of the COVID‐19 Crisis," Fiscal Studies, John Wiley & Sons, vol. 41(2), pages 321-336, June.
    6. Dimpfl, Thomas & Langen, Tobias, 2015. "A Cross-Country Analysis of Unemployment and Bonds with Long-Memory Relations," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112921, Verein für Socialpolitik / German Economic Association.
    7. Giuseppe Mastromatteo & Sergio Rossi, 2015. "The economics of deflation in the euro area: a critique of fiscal austerity," Review of Keynesian Economics, Edward Elgar Publishing, vol. 3(3), pages 336-350, July.
    8. Petr Janský & Klára Kalíšková & Daniel Münich, 2016. "Does the Czech Tax and Benefit System Contribute to One of Europe’s Lowest Levels of Relative Income Poverty and Inequality?," Eastern European Economics, Taylor & Francis Journals, vol. 54(3), pages 191-207, May.
    9. J. Muñoz & E. Álvarez-Verdejo & R. García-Fernández & L. Barroso, 2015. "Efficient Estimation of the Headcount Index," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 123(3), pages 713-732, September.
    10. Francesca Carta, 2020. "Timely Indicators for Inequality and Poverty Using the Italian Labour Force Survey," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 149(1), pages 41-65, May.
    11. Navicke, Jekaterina & Kump, Nataša, 2014. "Re-weighting EUROMOD for demographic change: an application on Slovenian and Lithuanian data," EUROMOD Working Papers EM13/14, EUROMOD at the Institute for Social and Economic Research.
    12. Gijs Dekkers & Ekaterina Tarantchenko & Karel Van den Bosch, 2019. "Working Paper 03-19 - Medium-term projection for Belgium of the at-risk-of-poverty and social exclusion indicators based on EU-SILC [Working Paper 03-19 - Prévisions à moyen terme des indicateurs d," Working Papers 1903, Federal Planning Bureau, Belgium.
    13. Pasi Moisio & Kirsi-Marja Lehtelä & Susanna Mukkila, 2014. "Estimating the poverty reduction effect of tax and benefit policies in Finland 1993-2013 using a microsimulation method," ImPRovE Working Papers 14/06, Herman Deleeck Centre for Social Policy, University of Antwerp.
    14. Tim Goedemé & Karel Van den Bosch & Lina Salanauskaite & Gerlinde Verbist, 2013. "Testing the Statistical Significance of Microsimulation Results: Often Easier than You Think. A Technical Note," ImPRovE Working Papers 13/10, Herman Deleeck Centre for Social Policy, University of Antwerp.
    15. O'Donoghue, Cathal & Sologon, Denisa Maria, 2023. "The Transformation of Public Policy Analysis in Times of Crisis – A Microsimulation-Nowcasting Method Using Big Data," IZA Discussion Papers 15937, Institute of Labor Economics (IZA).
    16. Jinjing Li & Yogi Vidyattama & Hai Anh La & Riyana Miranti & Denisa M. Sologon, 2022. "Estimating the Impact of Covid-19 and Policy Responses on Australian Income Distribution Using Incomplete Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 162(1), pages 1-31, July.
    17. Domingo Morales & María del Mar Rueda & Dolores Esteban, 2018. "Model-Assisted Estimation of Small Area Poverty Measures: An Application within the Valencia Region in Spain," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(3), pages 873-900, August.
    18. O'Donoghue, Cathal & M. Sologon, Denisa & Kyzyma, Iryna & McHale, John, 2020. "Modelling the distributional impact of the Covid-19 crisis in Ireland," Centre for Microsimulation and Policy Analysis Working Paper Series CEMPA4/20, Centre for Microsimulation and Policy Analysis at the Institute for Social and Economic Research.
    19. Jason Loughrey & Fiona Thorne & Thia Hennessy, 2016. "A Microsimulation Model for Risk in Irish Tillage Farming," International Journal of Microsimulation, International Microsimulation Association, vol. 9(2), pages 41-76.
    20. Chrysa Leventi & Olga Rastrigina & Holly Sutherland, 2016. "The importance of income-tested benefits in good times and bad: lessons from EU countries," ImPRovE Working Papers 16/01, Herman Deleeck Centre for Social Policy, University of Antwerp.
    21. Leventi, Chrysa & Rastrigina, Olga & Sutherland, Holly, 2015. "Nowcasting: estimating developments in the risk of poverty and income distribution in 2013 and 2014," EUROMOD Working Papers EM12/15, EUROMOD at the Institute for Social and Economic Research.
    22. Denisa M. Sologon & Cathal O’Donoghue & Iryna Kyzyma & Jinjing Li & Jules Linden & Raymond Wagener, 2022. "The COVID-19 resilience of a continental welfare regime - nowcasting the distributional impact of the crisis," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(4), pages 777-809, December.
    23. Manos Matsaganis & Chrysa Leventi, 2014. "Distributive Effects of the Crisis and Austerity in Seven EU Countries," ImPRovE Working Papers 14/04, Herman Deleeck Centre for Social Policy, University of Antwerp.
    24. Gary Cornwall & Marina Gindelsky, 2024. "Nowcasting Distributional National Accounts for the United States: A Machine Learning Approach," BEA Papers 0130, Bureau of Economic Analysis.

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