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

Motivation & aim Context Toolbox Results Further steps Outline:

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

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  Eurostat working paper: 010/EN/KS-RA EN.PDF 010/EN/KS-RA EN.PDF July 2013: EUROMOD working paper  focus on validation  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 Context:

 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:

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: reports 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

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).

Adjusting EUROMOD input data (SILC 2008) for employment changes ( ) 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 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

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.

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  Calibration on average improves predictions of both levels and changes Toolbox (3): Calibration to EU-SILC

Results (1): POVERTY RISK Fig 3 EUROMOD and EU-SILC : 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

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

Results (3): NOWCASTED CHANGE Change in (i.e. since the income year of latest SILC statistics) Notes : *** p < 0.001, ** p < 0.01, * p < 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: 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%*** ***-1.77*-1.57**9.79*** Greece-21.0%***1.38**1.85** ***4.08***-8.95*** Spain-3.2%*** * ***1.47***-4.54*** Italy1.6%*** Latvia15.6%***1.38** *** *** Lithuania9.2%*** * *** Portugal-3.0% *** Romania1.8%-0.63*-0.71*-0.55*

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, Greece 8, Spain 12, Italy 16, Latvia 4, Lithuania 4, Portugal 8, Romania 2, Notes: Household incomes are equivalized using the modified OECD scale. Median income in Euro per year. Change in applied on the latest EU-SILC statistics.

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)

Thank you! Your comments are welcomed! Jekaterina Navicke:

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

Fig 5 EUROMOD and EU-SILC : 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 (Available at: page/portal/income_social_inclusion_li ving_conditions/quality/eu_quality_rep orts).

Fig 6 EUROMOD : 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

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