Top

Social Indicators Research

Published in:

01-10-2014

Nowcasting Indicators of Poverty Risk in the European Union: A Microsimulation Approach

Authors: Jekaterina Navicke, Olga Rastrigina, Holly Sutherland

Published in: Social Indicators Research | Issue 1/2014

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

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 2–3 years 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. The method is validated by using EU-SILC data for 2007 incomes to estimate at-risk-of-poverty rates for 2008–2012 and to compare the 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.

previous article Measuring Advances in Equality of Opportunity: The Changing Gender Gap in Educational Attainment in Canada in the Last Half Century

next article A Concept Mapping Study on Social Inclusion in Hong Kong

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

For the critique of the current measures of economic development and an argument of the importance of bringing distributional issues into policy debates see e.g. Atkinson (2013); Stiglitz (2012).

For further information on EUROMOD and its applications see Sutherland and Figari (2013).

Note that EU-SILC data collected at time t (e.g. 2011) contains income information collected for time t − 1 (e.g. 2010).

A detailed discussion of the method is available in Navicke et al. (2013).

In the LFS employed persons are persons aged 15 and over (15–74 years in Estonia and Latvia) who performed work, even for just 1 hour per week, for pay, profit or family gain during the reference week or were not at work but had a job or business from which they were temporarily absent because of illness, holidays, industrial dispute, and education or training (Eurostat 2006).

In the EU-SILC a person is considered to be employed or self-employed in a given month if he or she worked (or was in paid apprenticeship or training) the majority of the weeks in that month. Information on every month is collected. If a person had a job, but was temporarily absent because of maternity leave, injury or temporary disability, slack work for technical or economic reasons, he or she is considered employed (Eurostat 2010).

Among many examples, see Atkinson and Sutherland (1988).

Available at: http://ec.europa.eu/economy_finance/db_indicators/ameco/.

Employment income in EU-SILC/EUROMOD is defined as gross employee cash or near cash income. It refers to the monetary component of the compensation of employees in cash payable by an employer to an employee. It includes the value of any social contributions and income taxes payable by an employee or by the employer on behalf of the employee to social insurance schemes or tax authorities (EUROSTAT 2010).

This holds under the assumption that compensation in kind remains proportional to compensation in cash and no major reforms in employer social insurance contributions take place.

See for details: https://www.iser.essex.ac.uk/euromod/resources-for-euromod-users/country-reports.

The comparison for the base year 2007 is not reported because it coincides with the SILC by construction.

The results of the comparison are not reported in the paper but are available upon request.

For Latvia and Lithuania 2007 was an exceptional year of high economic growth. But the following years brought severe economic decline. It is plausible that dramatic changes in macroeconomic circumstances triggered behavioural responses which are not accounted for in the model (e.g. changes in tax evasion or benefit take-up behaviour).

EU-SILC Quality reports available at: http://epp.eurostat.ec.europa.eu/portal/page/portal/income_social_inclusion_living_conditions/quality.

Gender differences in income poverty in the pre-crisis period and after are discussed in Bettio et al. (2013), among others.

Ajwad, M. I. (2013). Poverty prospects in Europe: assessing progress towards the Europe 2020 poverty and social exclusion targets in new European union member states. Washington DC: World Bank.

Atkinson, A. B. (2013). Ensuring social inclusion in changing labour and capital markets. Economic Papers 481. DG ECFIN: Brussels.

Atkinson, A. B., & Sutherland, H. (eds.) (1988). Tax Benefit Models. STICERD Occasional Paper 10, LSE.

Avram, S., & Sutherland, H. (2012). Baseline results from EUROMOD: 2006-2009 policies EUROMOD Working Paper EM1/12 Colchester: ISER, University of Essex.

Avram, S., Sutherland, H., Tasseva, I., & Tumino, A. (2011). Income protection and poverty risk for the unemployed in Europe. Research Note 1/2011 of the European observatory on the social situation and demography, European Commission.

Bettio, F., Corsi, M., D’Ippoliti, C., Lyberaki, A., Lodovici, S. M., & Verashchagina, A. (2013). The impact of the economic crisis on the situation of women and men and on gender equality policies. Luxembourg: European Commission.

Bourguignon, F., Bussolo, M., & Pereira da Silva, L. (2008). Evaluating the impact of macroeconomic policies on poverty and income distribution. In Bourguignon, F., Bussolo, M., Pereira da Silva, L (eds.) The impact of macroeconomic policies on poverty and income distribution: Macro–micro evaluation techniques and tools, (pp. 1–26). Washington, DC: The World Bank.

Brewer, M., Browne, J., Hood, A., Joyce, R., & Sibieta, L. (2013). The Short- and Medium-Term Impacts of the Recession on the UK Income Distribution, FISCAL STUDIES (Vol. 34, no. 2, pp. 179–201). http://www.ifs.org.uk/docs/FSBrewerEtAl.pdf.

Brewer, M., Browne, J., & Joyce, R. (2011). Child and working-age poverty from 2010 to 2020, IFS Commentary C121. London: The Institute for Fiscal Studies. http://www.ifs.org.uk/publications/5711.

Dekkers, G., & Zaidi, A. (2011). The European network for dynamic microsimulation (EURODYM): A vision and the state of affairs. International Journal of Microsimulation, 4(1), 100–105.

Essama-Nssah, B. (2005). The poverty and distributional impact of macroeconomic shocks and policies: A review of modeling approaches. World Bank Policy Research Working Paper 3682, Washington, DC, p. 77.

Eurostat. (2006). Basic concepts and definitions: The European Union labour force survey.

Eurostat. (2010). EU-SILC 065 (2008 operation). Description of target variables. Version January 2010. Luxembourg: Eurostat.

Fernandez, S. M., Figari, F., Sutherland, H., & Tumino, A. (2013). Welfare compensation for unemployment in the great recession. Review of Income and Wealth. doi:10.1111/roiw.12035.

Ferreira, F. H. G., Leite, P. G., Pereira da Silva, L. A., & Picchetti, P. (2004). Can the distributional impacts of macroeconomic shocks be predicted? A comparison of the performance of macro–micro models with historical data for Brazil. World Bank Policy Research Working Paper 3303. World Bank, Washington, DC.

Figari, F., Iacovou, M., Skew, A., & Sutherland, H. (2012). Approximations to the truth: comparing survey and microsimulation approaches to measuring income for social indicators, Social Indicators Research 105, doi:10.1007/s11205-010-9775-4.

Figari, F., Salvatori, A., & Sutherland, H. (2011). Economic downturn and stress testing European welfare systems. Research in Labor Economics, 32, 257–286.CrossRef

Goedemé, T. (2010). The standard error of estimates based on EU-SILC. An exploration through the Europe 2020 poverty indicators. Working Paper No. 10/09 (p. 36). Antwerp: Herman Deleeck Centre for Social Policy, University of Antwerp.

Goedemé, T. (2013). How much confidence can we have in EU-SILC? Complex sample designs and the standard error of the Europe 2020 poverty indicators. Social Indicators Research, 110(1), 89–110. doi:10.1007/s11205-011-9918-2.CrossRef

Goedemé, T., Van den Bosch., K., Salanauskaite, L., & Verbist, G. (2013). Tetsing the statistical significance of microsimulation results: often easier than you think. A technical note. ImPRovE Methodological Paper No. 13/10. Antwerp, p. 24.

Habib, A., Narayan, A., Olivieri, S., & Sanchez-Paramo, C. (2010). Assessing ex ante the poverty and distributional impact of the global crisis in a developing country: A micro-simulation approach with application to Bangladesh. Policy Research Working Paper 5238. Washington DC: The World Bank. doi: 10.1596/1813-9450-5238 http://elibrary.worldbank.org/content/workingpaper/10.1596/1813-9450-5238.

Immervoll, H., Levy, H., Lietz, C., Mantovani, D., & Sutherland, H. (2006). The sensitivity of poverty rates in the European Union to macro-level changes. Cambridge Journal of Economics, 30, 181–199.CrossRef

Isaacs, J. B., & Healy, O. (2012). The Recession’s ongoing impact on children, 2012. Indicators of children’s economic well-being. Washington, DC: The Urban Institute.

Jenkins, S. P., Brandolini, A., Micklewright, J., & Nolan, B. (2013). The great recession and the distribution of household income. Oxford: Oxford University Press.

Keane, C., Callan, T., Savage, M., Walsh, J. R., & Timoney, K. (2013). Identifying policy impacts in the crisis: Microsimulation evidence on tax and welfare. Journal of the Statistical and Social Inquiry Society of Ireland (forthcoming).

Lelkes, O., & Sutherland, H. (2009). Tax and benefit policies in the enlarged Europe. Assessing the impact with microsimulation models. Aldershot: Ashgate.

Lietz, C., & Mantovani, D. (2007). A short introduction to EUROMOD: an integrated European tax-benefit model in Microsimulation in action: policy analysis in Europe using EUROMOD. Bargain, O (eds.). Research in Labor Economics 25. Amsterdam: Elsevier (pp. 1–26).

Navicke, J., Rastrigina, O., & Sutherland, H. (2013). Using EUROMOD to “nowcast” poverty risk in the European Union. Eurostat Methodologies and Working papers series. http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-RA-13-010/EN/KS-RA-13-010-EN.PDF.

O’Donoghue, C. (1999). Dynamic microsimulation: A methodological survey, Brazilian Electronic Economic Journal 4(2).

Stiglitz, J. E. (2012). The price of inequality: How today’s devided society endangers our future (1st ed.). New York: W. W. Norton & Co.

Sutherland, H., & Figari, F. (2013). EUROMOD: the European Union tax-benefit microsimulation model, International Journal of Microsimulation, 6(1), 4–26.

Title: Nowcasting Indicators of Poverty Risk in the European Union: A Microsimulation Approach
Authors: Jekaterina Navicke
Olga Rastrigina
Holly Sutherland
Publication date: 01-10-2014
Publisher: Springer Netherlands
Published in: Social Indicators Research / Issue 1/2014
Print ISSN: 0303-8300
Electronic ISSN: 1573-0921
DOI: https://doi.org/10.1007/s11205-013-0491-8

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 1/2014

Assessing Adjustment to Aging: A Validation Study for the Adjustment to Aging Scale (AtAS)

Family Functioning and Life Satisfaction and Happiness in South African Households

Work-to-Family Enrichment and Employees’ Well-Being: High Performance Work System and Job Characteristics

The Association Between the Fear of Crime, and Mental and Physical Wellbeing in New Zealand

Patterns of Residential Adjustment for Older Person: Who will Recover and How Do They Recover? A Study in Different Residential Environments in Hong Kong

The Relationship Between Serious Leisure Characteristics and Subjective Well-Being of Older Adult Volunteers: The Moderating Effect of Spousal Support

Premium Partner