1. CROSS-COUNTRY INCOME MOBILITY COMPARISONS UNDER PANEL ATTRITION: THE RELEVANCE OF WEIGHTING SCHEMES Luis Ayala (IEF, URJC) Carolina Navarro (UNED) Mercedes Sastre (IEF, UCM) EPUNet 2006 Barcelona 8-9 May 2006

2. Motivation Longitudinal Data: ECHP –Very detailed information –Income distribution studies: Static Analysis  Dynamic Analysis Income mobility, poverty dynamics, transitions between economic states (deprivation, unemployment…) Limits:  “sample attrition” –Is there attrition in the ECHP? –How much? –Is it selective? –Does if have effects on (dynamic) estimates? –How can it be corrected?

3. Objective Analyse the effect of sample attrition in the ECHP on different measures of income mobility Study the consequences of different weighting schemes used to correct the potential bias introduced by attrition –Extension and distribution of sample attrition –Estimation of different longitudinal weights based on the inverse selection probability (probit models) –International Comparison France, Germany, Italy, Spain, United Kingdom Differential impact of sample attrition and weighting schemes on income mobility Degree in which sample attrition can condition the results of the comparative analysis

4. Attrition in the ECHP (I) Sample attrition: lost of a percentage of the initial sample as new waves of the survey develop Several studies: Peracchi (2002), Behr et al.(2002), etc. Extension and trends of attrition in the ECHP  Substantial rates over a few years  important differences among countries May prevent to follow up a significant part of the sample and influence the distributive results (if attrition implies a lost of representativeness)

5. Attrition in the ECHP (II)

6. Attrition in the ECHP (III) Substantial attrition rates in the ECHP: –Attrition: no necessarily affects estimates, only if has a selective character If low income households more exit probability  previsible effect of inequality reduction The estimations of longitudinal processes may be biased

7. Attrition in the ECHP (IV) Incidence of attrition by socioeconomic categories: % remaining in the ECHP (balanced panel) –In general similarities in attrition patterns among countries / some divergences –Relevant Variables: Income, household main income source, age, household type, education level and housing tenure Divergences “latin model” vs. Rest of countries

8. Weighting Schemes (I) Attrition  Not totally ramdom  potential bias Estimation of longitudinal weights for each observation Intuition –Probabilistic assesment of how many attriters a particular observation represents –Indiv. with greater exit probability  greater weight Estimation –Based on the inverse selection probability obtained from different probit models using socieconomic characteristics of individuals and households (Fitzgerald et al., 1998, Gradín et al. 2004) –Probit Prob. individual not in the sample in wave 8  Pr i (Y=0) Prob. individual remains in the sample in wave 8  Pr i (Y=1) For each observation: longitudinal weight  function of the inverse selection probability of remaining in the sample

9. Weighting Schemes (II) Estimates of longitudinal weights  Alternative Models: –Type A  Household and household head characteristics (results sensitivity) Model 1 (  1 A ): Adjusted income, main income source, household head characteristics (age, sex, marital status and education level (problem) Model 2 (  2 A ): similar to Model 1 excluding education level Model 3 (  3 A ): similar to Model 1 + household size, number of children and number of full time workers in the household

10. Weighting Schemes (III) Tipo B  individual characteristics (adults with completed interview) –Model 1 (  1 B ): : Individual income, activity status, age, sex, marital status and individual education level (problem) –Model 2 (  2 B ): similar to Model 1 + age –Model 3 (  3 B ): similar to Model 2 + health status Type A and type B Models : In general, results confirm descriptive analysis  not random attrition

11. SPAIN EE 1 A1 A 2 A2 A 3 A3 A 1 B1 B 2 B2 B 3 B3 B EE 1.0000 1 A1 A 0.00821.0000 2 A2 A - 0.00050.99151.0000 3 A3 A 0.03440.99480.98591.0000 1 B1 B - 0.01250.37490.36910.37651.0000 2 B2 B - 0.02170.40120.39430.40380.93161.0000 3 B3 B - 0.00990.35430.34850.35770.80790.86801.0000 Weighting Schemes (IV)

12. Weighting Schemes (V) Wave 1 frequency distribution: initial sample vs. balanced panel –1) Initial sample, no weighting –2) Initial sample, cross section Eurostat weights –3) Initial sample (balanced panel), Eurostat longi.tudinal weights –4) Initial sample (balanced panel), estimated longitudinal weights Comparisons: 1) vs 4)  similarities –Estimated longitudinal weights  some “guarantees” –Comparisons: 2) vs 3)  high divergences –Categories with greater attrition rates  less adjustment due to the use of longitudinal weights

13. Weighting Schemes (VI) Summary: –Several attrition adjustment possibilities  longitudinal weights –Frequency distribution  important differences according to the weighting scheme Implications for dynamic analysis Income Mobility

14. Attrition and Income Mobility (I) Income Mobility Analysis –Results may be affected by the possible “balanced panel” lack of representation of the population Greater exit of low income individuals  possibly ascending mobility indicators greater than real Lower attrition of high income individuals  potential effect on wage mobility analysis Preliminary Analysis –Differential impact of attrition among countries –Not random attrition (selective)

15. Attrition and Income Mobility (II) What are the extent and type of income mobility in a society? –The answer depends greatly on how well panel data represent the population Does attrition introduce bias on income mobility estimations? –Assess the effect of attrition on income mobility measures –Estimate if the bias can be corrected using the estimated longitudinal weights

16. Attrition and Income Mobility (III) Does attrition introduce bias on income mobility estimations? (Fitzgerald et al. 1998, Behr et al. 2003) –Split the sample according to attrition behaviour of individuals on future waves and compare the mobility results for the two subsamples Estimating medium-term income mobility : Wave 1 to Wave 4 –ECHP  8 waves –Subsample P balanced panel  individuals remaining all waves (from wave 1 to wave 8) –Subsample K  individuals remaining in the sample at least the four first waves Np < Nk

17. Attrition and Income Mobility (IV) Income mobility indices  summarize changes in economic status from one time period to another Fields y Ok 1999: –Mobility as a normative concept / value judgments –Various approximations  different dimensions  No consensus Mobility as inequality reduction as the accounting period is extended  Shorrocks Rigidity Index Mobility as origin independence of last period income (statistic associación)  Hart Index Mobility as transition among different classes on the income distribution (matrices)  Shorrocks Index, Bartholomew Index Mobility as income movement (Fields and Ok Index)

18. Attrition and Income Mobility (V) Effect of attrition on income mobility measures: compare the mobility results of subsample P and subsample K –Attrition: no bias on international comparisons  No country rerankings. –Comun pattern: subsample P shows lower mobility than subsample K  greater mobility of attriters

19. Attrition and Income Mobility (VI) Mobility decomp. = Growth Mobility + Transfer Mobility –Changes in the relative contribution of each component Mobility decomposition by population subgroups –Important differences in group mob. measures and mobility contribution between subsample p and subsample k Attrition  no high influence on aggregate results Certain bias on income mobility decomposition by components and population groups ADJUSTMENT PROCEDURES

20. Mobility and weighting schemes (I) Attrition adjustment schemes: –No adjustment :  l i =1 –Eurostat longitudinal weights:  l i =  E i –Estimated longitudinal weights:  l i =  A 2i Does the weighting scheme affect the estimations? –Correlation among different weights questions the robustness of analysis ECHP Mobility from wave 1 to wave 8

21. Mobility and weighting schemes (II) ECHP Mobility Wave 1 to wave 8 Hart Index  sensitive to the weighting scheme –Country rerankings Shorrocks Rigidity Index/Transition Matrices  sensitive to the weighting scheme (moderate) –Country rerankings Fields and Ok Index Decomposition  sensitive to the weighting scheme –Growth mobility and transfer mobility –Descomp. by population subgroups  highly sensitive Income mobility measures sensitive to the weighting scheme, specially on the disaggregated analysis

22. Main Results Attrition in the ECHP  certain non- randomness Estimation of longitudinal weights to adjust for selective attrition (probit models) –Estimated longitudinal weights  dif. with Eurostat weights Results Sensitiviness –Attrition  no high effect on income mobility indices –Income mobility decomposition  Greater sensitivity to the weighting scheme  “Correction”  Adjustment throught longitudinal weights Adjustment with longitudinal weights –Some degree of sensitiviness: especially important for some population subgroups