1. Using EUROMOD to nowcast risk of poverty in the EU Jekaterina Navicke, Olga Rastrigina and Holly Sutherland ISER, University of Essex 2013 EUROMOD research workshop Lisbon, 2 October 2013

2. Motivation & aim Context Toolbox Results Further steps Outline:

3. Problem: 2-3-year time lag in the production of EU-SILC statistics Timely indicators would:  Promote distributional issues when assessing current socio-economic conditions  Facilitate monitoring of current policy reforms/problems  Help assess progress towards Europe 2020 target  Not a substitute for more timely data collection and processing!  As any forecast should be treated with caution Aims:  To predict what the EU-SILC will show when the data on current income are available  To develop methods that can be applied quickly and updated easily for EU27  To estimate the direction and scale of movement of key income-based indicators (median, risk-of-poverty, inequality, etc.) Motivation & aim

4. Dec 2012: paper at the NetSILC2 conference  develop the method  tested on EU-SILC 2008 (2007 income)  8 countries: Estonia, Greece, Spain, Italy, Latvia, Lithuania, Portugal, Romania  nowcast for 2010-2012  Eurostat working paper: http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-RA-13- 010/EN/KS-RA-13-010-EN.PDF http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-RA-13- 010/EN/KS-RA-13-010-EN.PDF July 2013: EUROMOD working paper  focus on validation  https://www.iser.essex.ac.uk/publications/working-papers/euromod/em11-13 https://www.iser.essex.ac.uk/publications/working-papers/euromod/em11-13 By Dec 2013: SSM research note (work in progress)  improvements to current methodology  application on EU-SILC 2010 (2009 income)  more countries (+ Germany, Finland, …)  nowcast 2011-2013 Context:

5.  EUROMOD simulation  Adjusting EUROMOD to account for employment changes  Calibration to align EUROMOD and EU-SILC  No data adjustments to account for demographic changes  Ad hoc and country specific adjustments kept to the minimum Toolbox:

6. EUROMOD - static tax-benefit microsimulation model for the EU:  Unique: consistent results across 27 Member States  Operates on anonymized EU-SILC cross-sectional micro-data  Scope: income taxes, social contributions and cash benefits  For details see EUROMOD Country Reports: https://www.iser.essex.ac.uk/euromod/resources-for-euromod-users/country- reports https://www.iser.essex.ac.uk/euromod/resources-for-euromod-users/country- reports Simulation:  Tax and benefit policies simulated up to 2012 (as of June 30 th );  Non-simulated benefits and original incomes are updated from 2007 to 2012 using indexes (earnings, CPI etc.) plus official projections. Updating disaggregated where possible (e.g. earnings by sector). Toolbox (1): EUROMOD simulation

7. Fig 1 Nominal proportional changes in average gross employment income (EUROMOD and EU- SILC) and compensation per employee (AMECO), EUR Notes: Chain growth. EU-SILC numbers are lagged by one year to correspond to the income reference year. Statistics on compensation per employee obtained from the annual macro- economic dataset of DG ECFIN (AMECO).

8. Adjusting EUROMOD input data (SILC 2008) for employment changes (2008-2012) Based on LFS data:  Trade-off between more up-to-date and more detailed data  We use published LFS employment figures (in 2012: annual up to 2011 & rolling quarterly average for 2012)  Concepts do not align perfectly between SILC and LFS =>  = > Aim is not to align LFS and SILC, but model relative changes Steps:  modelling employment transitions (net changes in employment rates modelled within 18 stratum by age, gender, educational status: random selection + 200 replications for more robust results)  modelling share of long-term unemployment to capture changes in eligibility for benefit receipt (similar method)  adjusting labour market characteristics in the EUROMOD data & simulating benefits. Toolbox (2): Employment Adjustments

9. Fig 2 Employment rates in the LFS, EU-SILC and in EUROMOD before and after labour market adjustments Notes: EU-SILC numbers are lagged by one year to correspond to the income reference year.

10. Estimates based on EUROMOD diverge from EUROSTAT even in the baseline year. Sources of discrepancy include ( Figari et al. 2012) :  Version of the SILC data  Slightly different definition of disposable income  Non take-up or leakage of means-tested benefits; tax evasion.  Reporting errors in the data or reference time period mismatches  Simulation error due to low quality or lack of information in the data  EUROMOD adjusts household composition to correspond to income year (babies born since income reference period are dropped) Calibration:  Household-specific calibration factor  Factor is calculated based on 2007 income data and applied to 2008-2012  Calibration on average improves predictions of both levels and changes Toolbox (3): Calibration to EU-SILC

11. Results (1): POVERTY RISK Fig 3 EUROMOD 2007- 2012 and EU-SILC 2007- 2010: At risk of poverty rates (using 60% median as the threshold) Notes: EU-SILC numbers are lagged by one year to correspond to the income reference year

12. We focus on direction and scale of movement in indicators relative to the latest available EU-SILC estimates (not on 2012 levels). 3 main reasons for this: Discrepancies between the EUROMOD and EU-SILC estimates still remain after adjusting for employment and calibration. Wide confidence intervals around AROP point estimates in the EU-SILC: (standard errors vary from 0.4 pp for IT, ES to 0.9 pp for LT). Nowcasts of direction and scale of change are more reliable: reduction in the standard errors due to covariance in the data. Results (2): NOWCAST

13. Results (3): NOWCASTED CHANGE Change in 2010-2012 (i.e. since the income year of latest SILC statistics) Notes : *** p < 0.001, ** p < 0.01, * p < 0.05. Information on the sample design of EU-SILC 2008 used for calculations was derived following Goedemé (2010) and using do files Svyset EU-SILC 2008 provided at: http://www.ua.ac.be/main.aspx?c=tim.goedeme&n=95420. Standard errors around AROP indicators are based on the Taylor linearization using the DASP module for Stata. Poverty rates (60% of median) Median IncomeAllMalesFemales Children (<18) Prime- age Elderly (65+) Estonia13.3%***0.62-0.771.80***-1.77*-1.57**9.79*** Greece-21.0%***1.38**1.85**0.913.94***4.08***-8.95*** Spain-3.2%***0.320.65*0.001.89***1.47***-4.54*** Italy1.6%***-0.19-0.22-0.16-0.10-0.05-0.15 Latvia15.6%***1.38**0.232.35***-0.28-0.789.20*** Lithuania9.2%***0.980.811.13*2.820.001.81*** Portugal-3.0%-0.52-0.49-0.55-0.02-0.29-2.25*** Romania1.8%-0.63*-0.71*-0.55*-0.80-0.84-0.40

14. Results (4): NOWCASTED LEVELS What EU-SILC 2013 will show (2012 income) Poverty rates (60% of median) Median Income (€ per year)AllMalesFemales Children (<18) Prime- age Elderly (65+) Estonia 6,34018.116.819.217.714.322.9 Greece 8,68322.8 27.622.714.7 Spain 12,11122.121.822.429.121.916.3 Italy 16,22519.418.120.626.219.116.9 Latvia 4,79620.520.220.824.718.518.1 Lithuania 4,37321.020.621.227.119.813.9 Portugal 8,15517.517.117.822.414.717.7 Romania 2,15521.621.221.932.121.013.7 Notes: Household incomes are equivalized using the modified OECD scale. Median income in Euro per year. Change in 2010-2012 applied on the latest EU-SILC statistics.

15. Select cases for employment transitions based on estimated conditional probabilities of being in a particular employment status  occupational decision models (e.g. Habib et.al. 2010, Ferreira et.al. 2004)  estimation based on the latest EU-SILC microdata  Logit model for predicting employment/non-employment estimated separately for those with low/high education, age frame 15-64: predictors: sex, age, years of education, occupational status as measured by ISCO, dependency ratio in a household, participation rate, dummies for hh head, employed partner, small children under 4; squares, interactions.  combine with published LFS employment statistics (as currently) Modeling new wages needs refinement:  Currently based on average wage within the stratum  Refining this using wage equations Reweighting  for changes in employment and/or demographics Other? Further steps (methodological improvements)

16. Thank you! Your comments are welcomed! Jekaterina Navicke: jnavicke@essex.ac.uk

17. Fig 4 EUROMOD 2007-2012 and EU- SILC 2007-2010: Median equivalized household disposable income (EUR per year) Note: SILC data corresponds to the income reference period.

18. Fig 5 EUROMOD 2007- 2012 and EU-SILC 2007- 2010: At risk of poverty rates (using 60% median as the threshold) Notes: Confidence intervals for EUROMOD estimates are due to a random element in the simulation of employment transitions and do not account for sampling variability. Confidence intervals for EU-SILC estimates of at risk of poverty rates are constructed based on the standard errors provided in Comparative EU Intermediate Quality Reports for EU- SILC 2008-2010 (Available at: http://epp.eurostat.ec.europa.eu/portal/ page/portal/income_social_inclusion_li ving_conditions/quality/eu_quality_rep orts).

19. Fig 6 EUROMOD 2007 -2012: At risk of poverty rates by household type (using 60% of the 2007 median as the threshold) Note: The poverty threshold is 60% of median 2007 equivalised household income, indexed by the HCPI

20. Growth incidence curves Change in real income by percentile, 2010-2012 Note: based on re-ranked distribution