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Nowcasting: estimating developments in the risk of poverty and income distribution in 2013 and 2014

Author

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  • Rastrigina, Olga
  • Leventi, Chrysa
  • 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 crucial for monitoring of the social situation and of the effectiveness of tax and benefit policies. However, partly due to the complexity of EU-SILC data collection, estimates of the number of people at risk of poverty are published with a significant delay. This paper extends and updates previous work on estimating (‘nowcasting’) indicators of poverty risk using the tax-benefit microsimulation model EUROMOD. The model’s routines are enhanced with additional adjustments to the EU-SILC based input data in order to capture changes in the employment characteristics of the population since the data were collected. The nowcasting method is applied to seventeen EU Member States. AROP rates are estimated up to 2014 for ten countries and 2013 for the remaining seven countries. The performance of the method is assessed by comparing the predictions with actual EU-SILC indicators for the years for which the latter are available.

Suggested Citation

  • Rastrigina, Olga & Leventi, Chrysa & 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.
  • Handle: RePEc:ese:emodwp:em12-15
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    File URL: https://www.iser.essex.ac.uk/research/publications/working-papers/euromod/em12-15.pdf
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    References listed on IDEAS

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    1. Andreas PEICHL, "undated". "The Benefits of Linking CGE and Microsimulation Models - Evidence from a Flat Tax analysis," EcoMod2008 23800106, EcoMod.
    2. François Bourguignon & Maurizio Bussolo & Luiz A. Pereira da Silva, 2008. "The Impact of Macroeconomic Policies on Poverty and Income Distribution : Macro-Micro Evaluation Techniques and Tools," World Bank Publications, The World Bank, number 6586, July.
    3. Rastrigina, Olga & Leventi, Chrysa & Sutherland, Holly, 2015. "Nowcasting risk of poverty and low work intensity in Europe," EUROMOD Working Papers EM9/15, EUROMOD at the Institute for Social and Economic Research.
    4. Mariña Fernández Salgado & Francesco Figari & Holly Sutherland & Alberto Tumino, 2014. "Welfare Compensation for Unemployment in the Great Recession," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(S1), pages 177-204, May.
    5. Francesco Figari & Andrea Salvatori & Holly Sutherland, 2011. "Economic Downturn and Stress Testing European Welfare Systems," Research in Labor Economics,in: Who Loses in the Downturn? Economic Crisis, Employment and Income Distribution, volume 32, pages 257-286 Emerald Publishing Ltd.
    6. Manos Matsaganis & Chrysa Leventi, 2014. "Poverty and Inequality during the Great Recession in Greece," Political Studies Review, Political Studies Association, vol. 12(2), pages 209-223, May.
    7. repec:bla:revinw:v:60:y:2014:i::p:s177-s204 is not listed on IDEAS
    8. Francesco Figari & Maria Iacovou & Alexandra Skew & Holly Sutherland, 2012. "Approximations to the Truth: Comparing Survey and Microsimulation Approaches to Measuring Income for Social Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 105(3), pages 387-407, February.
    9. Herwig Immervoll & Horacio Levy & Christine Lietz & Daniela Mantovani & Holly Sutherland, 2006. "The sensitivity of poverty rates to macro-level changes in the European Union," Cambridge Journal of Economics, Oxford University Press, vol. 30(2), pages 181-199, March.
    10. Tim Goedemé & Karel Van den Bosch & Lina Salanauskaite & Gerlinde Verbist, 2013. "Testing the Statistical Significance of Microsimulation Results: A Plea," International Journal of Microsimulation, International Microsimulation Association, vol. 6(3), pages 50-77.
    11. Jekaterina Navicke & Olga Rastrigina & Holly Sutherland, 2014. "Nowcasting Indicators of Poverty Risk in the European Union: A Microsimulation Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 119(1), pages 101-119, October.
    12. Essama-Nssah, B., 2005. "The poverty and distributional impact of macroeconomic shocks and policies : a review of modeling approaches," Policy Research Working Paper Series 3682, The World Bank.
    13. Mike Brewer & James Browne & Andrew Hood & Robert Joyce & Luke Sibieta, 2013. "The Short‐ and Medium‐Term Impacts of the Recession on the UK Income Distribution," Fiscal Studies, Institute for Fiscal Studies, vol. 34(2), pages 179-201, June.
    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.
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    Cited by:

    1. 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.
    2. Ekaterina Tosheva & Iva Tasseva & Dragomir Draganov & Venelin Boshnakov, 2016. "Effects of changes in tax-transfer system on households income distribution in Bulgaria: simulation analysis using EUROMOD for 2011-2015," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 5, pages 51-71,72-91.

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