4.2: Correction for item non-response
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1 4.2: Correction for item non-response Markus M. Grabka (DIW Berlin) Meeting of providers of OECD income distribution data Paris, 21./22. February 2013, OECD
2 1 Introduction Types of Non response Unit non-response Partial Unit non-response non-response of at least one unit, or member, of an otherwise participating household Item Non-response 2
3 2 Causes of Non Response Complexity of surveyed construct (Schräpler 2003) Formulation of questions matters (Hill & Willis 2001) Interviewer-respondent matching, lacking intimicy (Schräpler & Wagner 2001) Interviewer change (Riphahn & Serfling 2003) Ignorance (Giami 2012) INR strongly related to Income Inequality and mobility (e.g. higher refusals in tails of income distribution) (Wooden & Watson 2006) INR and UNR not independent: INR in t = predictor of UNR in t+1 (Lee et al 2004, Loosfeldt et.al. 1999)... 3
4 3 How to deal with INR? Rubin (1976): Missing mechanism (MCAR, MAR, MNAR) case-wise deletion (only valid observations) weighting imputation single imputation techniques institutional imputation expert imputation mean substitution cold and hot deck regression-based row-and-column-imputation using longitudinal data (Little & Su 1989) etc. multiple imputation techniques (e.g. MICE) 4
5 4 Some Pros and Cons of Imputation Pro complete dataset with full information reduce potential bias in survey estimates if MNAR imputation makes analysis easier (less experienced users) Cons imputation cannot eliminate all non-response bias in case of variance reduction significant results more likely may produce additional bias (e.g., regression to the mean, non-harmonization across surveys) 5
6 5 Incidence of INR Incidence of INR in selected SOEP income components Sample A-B Sample A-C Sample A-F Share of persons with INR (%) Labor income from main job Income from self-employment Christmas Gratification Pension Income (own) Child Benefit Interest & Dividends Source: SOEP, Survey years 1986, 1993, 2001; unweighted results. 6
7 6 Selectivity of INR Estimating the probability for INR on total annual household income (pre-tax, post-transfer) Results from a Random Effects Probit Model
8 7 Selectivity of INR Probability of INR in Total Income, ,3-0,2-0,1 0 0,1 0,2 0,3 0,4 # Interview s = 1 # Interview s = 2-4 Int.Mode : Self-completion Interview Mode : CAPI Age of Head < 25 years Age of Head > 64 years # of Adults in HH = 2 # of Adults in HH = 3+ Kids in HH (<17 years) Head: Female Head: Foreign citizenship Head: Uni./FHS-Degree Head: No voc.training -0,3-0,2-0,1 0 0,1 0,2 0,3 0,4 Head: 1-11 months FT Head: 1-11 months unempl. Head: 12 months unempl. Head: 1-11 months PT Head: 12 months PT Head: 1-11 pensioner Head: 12 months pensioner East Germany Community Size < Income in Top Decile Income in Bottom Decile Ow ner occupier Head: Low life satisf. 8 Source: SOEP.
9 8 Principles of Imputation Using longitudinal information in the Imputation process from previous & future interviews [t 0,, t -1, t +1,, t m ] for imputation of INR in t EU-financed CHINTEX-Project (Change from input to ex-post harmonization) ECHP: existing imputation mainly based on x-sectional methods Spiess & Göbel (2003): longitudinal imputation improves data quality significantly (multiple imputation techniques) 9
10 9 Principles of Imputation Difference between Imputed values and register data Annual Gross Labor Income, Finland 2000 (Spiess & Göbel 2003) Current ECHP-imputation CHINTEX-imputation 10
11 10 Does imputation help to draw the right picture? Equivalent Post-Government income (Equiv. scale = SQRT HH-Size) Income Distribution, Inequality, Poverty Income Mobility Comparing results from analysis based on Observed cases only vs. All cases (observed + imputed) 11
12 11 Does imputation help to draw the right picture? Equivalent Post-Gov t Income, Inequality and Poverty 2001 Mean Median MLD Gini SCV 90:10 90:50 50:10 Poverty Rate Imputation Status All cases Observed Imputed cases cases [35 977; ] [35 385; ] [37 234; ] [33 175; ] [0.1266; ] [0.2616; ] [0.2580; ] 3.44 [3.29; 3.66] 1.77 [1.69; 1.85] 1.94 [1.90; 2.01] 14.3 [13.1; 15.4] [32 203; ] [0.1169; ] [0.2552; ] [0.2440; ] 3.32 [3.16; 3.54] 1.75 [1.70; 1.85] 1.89 [1.85; 1.95] 14.1 [12.3; 15,5] [33 912; ] [0.1444; ] [0.2674; ] [0.2379; ] 3.89 [3.58; 4.05] 1.79 [1.69; 1.88] 2.18 [2.04; 2.41] 15.2 [14.9; 15,9] Deviation: "All" vs. "Observed" (%) +1,7 +1,6 +5,2 +2,2 +0,5 +3,6 +1,1 +2,6 +1,4 N (x-section 2001) ,6 Values in brackets give a 93% confidence interval based on variance estimation according to the Random Group Approach. 12 Shaded cells indicate statistically significant deviations
13 12 Does imputation help to draw the right picture? Income Mobility and Imputation Index All cases Imputation Status Observed cases Imputed cases Deviation "All" vs. "Observed" (%) Quintile Matrix Mobility Average jump [0.452; 0.483] [0.394; 0.437] [0.541; 0.618] Quintile Matrix Mobility Normalized average jump [0.181; 0193] [0.157; 0.175] [0.216; 0.247] Fields & Ok (1996) Percentage income mobility [17.23; 19.43] [15.34; 16.78] [21.52; 23.78] Fields & Ok (1999) Non-directional [0.200; 0.220] [0.167; 0.203] [0.244; 0.271] Shorrocks (1987) using Theil Coefficient [0.0709; 0.095] [0.0629; ] [0.0814; ] N (balanced panel) Values in brackets give a 93% confidence interval based on variance estimation according to the Random Group Approach. 13
14 13 Summary Item-non-response is highly selective Case-wise deletion yields to biased results Imputation provides effective means to cope with INR Empirical results suggest to make use of longitudinal data Relevance of INR (and imputation) varies significantly across income distribution Panel Research: INR is positively correlated with any type of non-response in subsequent waves Consideration of multiple Imputation, to cope with the uncertainty embedded in the imputation process, however data structure is more complex Harmonization of Imputation technique across countries matters (Frick & Grabka 2010) 14
15 Thank you for your attention. DIW Berlin Deutsches Institut für Wirtschaftsforschung e.v. Mohrenstraße 58, Berlin Editor Dr. Markus M. Grabka
16 2 Forschung Harmonization of imputation techniques Germany: Socio-Economic Panel Study, SOEP [ ] Australia: Household, Income and Labour Dynamics in Australia, HILDA [ ] UK: British Household Panel Survey, BHPS [ ] Variable of Interest: Individual Annual Labor Earnings 16
17 2 Forschung Imputation strategy: SOEP & HILDA 1. Step: row-and-column-imputation (Little & Su 1989): + nearest neighbor matching including an error term SOEP: entire population HILDA: within several age groups 2. Step (in case of lacking longitudinal information) SOEP: Hot deck + randomly chosen error term HILDA: nearest neighbor regression method Data Quality Check (simulation studies): Starick (2005): L&S in case of HILDA yields more reliable results than hot deck Frick&Grabka (2005): similar finding for SOEP longitudinal imputation superior to purely cross-sectional imputation 17
18 2 Forschung Imputation strategy: BHPS regression based predictive mean matching (PMM), Little (1988) = Hot Deck shifting 3-year windows 14 different regression models Advantage: a truly observed value is used as basis for imputation plus error term Quality : R 2 in the first 3 waves varies between 0.78 and
19 2 Forschung Does the choice of the imputation technique affect substantive research results and thus crossnational comparability? Robustness check by implementing the row-and-column imputation for BHPS Comparison of HILDA SOEP Row-and-column imputation Row-and-column imputation BHPS (1) Original imputation BHPS (2) Row-and-column imputation 19
20 2 Forschung Imputation and labor income inequality ( using only observed cases understates inequality) Germany (SOEP) Australia (HILDA) UK (BHPS) UK (BHPS)-L+S All cases Deviation: "All" vs. "Obs." Deviation: All "All" vs. cases "Obs." All cases Deviation: "All" vs. "Obs." All cases Deviation: "All" vs. "Obs." Mean , , , ,2 Median , , , ,1 Income inequality Theil 0 (MLD) 0, ,0 0, ,5 0, ,6 0, ,4 Gini 0, ,0 0, ,9 0, ,7 0, ,4 Half-SCV 0, ,5 0, ,5 0, ,9 0, ,6 Decile ratio 90:10 13,7 +0,4 14,9 +5,2 12,7 +6,1 12,4 +3,9 Decile ratio 50:10 6,5-0,6 6,8 +2,7 5,4 +4,2 5,4 +3,2 Average N per cross-section , , , ,1 Source: SOEP survey years ; HILDA survey years ; BHPS survey years Shaded cells indicate statistically significant deviations
21 2 Results from fixed-effects wage regression Germany (S OEP) Australia (HILDA) UK (BHPS) UK (BHPS) L&S observed all cases observed all cases observed all cases observed all cases Age Age squared Female with kid(s)* Male with kid(s) * Disability Status * Married * Metrop. area * Remote area* Intermed. education* Upper education* Highest educ. level* East Germany* n.a. n.a. n.a. n.a. n.a. n.a. Self employed* Became retired* Left education * Unempl. (last year) * Months FT (last year) Months PT (last year) n.a. n.a. n.a. n.a. n.a. n.a. Imputed Labor Y* 0.064*** 0,052** -0,042** 0,047** Constant Observations N (Persons) R-squared 0,49 0,45 0,22 0,17 0,52 0,44 0,52 0,37 21 Population: working age: (Germany), (Australia and UK). (*) indicates dummy variables. Note: Time effects controlled, but not reported. Significance level: +++/--- sig. at 1%; ++/-- sig. at 5%; +/- sig. at 10%. Source: SOEP survey years ; HILDA survey years ; BHPS survey years
22 2 Results from fixed-effects wage regression Germany (S OEP) Australia (HILDA) UK (BHPS) UK (BHPS) L&S observed all cases observed all cases observed all cases observed all cases Age Age squared Female with kid(s)* Male with kid(s) * Disability Status * Married * Metrop. area * Remote area* Intermed. education* Upper education* Highest educ. level* East Germany* n.a. n.a. n.a. n.a. n.a. n.a. Self employed* Became retired* Left education * Unempl. (last year) * Months FT (last year) Months PT (last year) n.a. n.a. n.a. n.a. n.a. n.a. Imputed Labor Y* 0.064*** 0,052** -0,042** 0,047** Constant Observations N (Persons) R-squared 0,49 0,45 0,22 0,17 0,52 0,44 0,52 0,37 22 Population: working age: (Germany), (Australia and UK). (*) indicates dummy variables. Note: Time effects controlled, but not reported. Significance level: +++/--- sig. at 1%; ++/-- sig. at 5%; +/- sig. at 10%. Source: SOEP survey years ; HILDA survey years ; BHPS survey years
23 2 Results from fixed-effects wage regression Germany (S OEP) Australia (HILDA) UK (BHPS) UK (BHPS) L&S observed all cases observed all cases observed all cases observed all cases Age Age squared Female with kid(s)* Male with kid(s) * Disability Status * Married * Metrop. area * Remote area* Intermed. education* Upper education* Highest educ. level* East Germany* n.a. n.a. n.a. n.a. n.a. n.a. Self employed* Became retired* Left education * Unempl. (last year) * Months FT (last year) Months PT (last year) n.a. n.a. n.a. n.a. n.a. n.a. Imputed Labor Y* 0.064*** 0,052** -0,042** 0,047** Constant Observations N (Persons) R-squared 0,49 0,45 0,22 0,17 0,52 0,44 0,52 0,37 23 Population: working age: (Germany), (Australia and UK). (*) indicates dummy variables. Note: Time effects controlled, but not reported. Significance level: +++/--- sig. at 1%; ++/-- sig. at 5%; +/- sig. at 10%. Source: SOEP survey years ; HILDA survey years ; BHPS survey years
24 2 Results from fixed-effects wage regression Germany (S OEP) Australia (HILDA) UK (BHPS) UK (BHPS) L&S observed all cases observed all cases observed all cases observed all cases Age Age squared Female with kid(s)* Male with kid(s) * Disability Status * Married * Metrop. area * Remote area* Intermed. education* Upper education* Highest educ. level* East Germany* n.a. n.a. n.a. n.a. n.a. n.a. Self employed* Became retired* Left education * Unempl. (last year) * Months FT (last year) Months PT (last year) n.a. n.a. n.a. n.a. n.a. n.a. Imputed Labor Y* 0.064*** 0,052** -0,042** 0,047** Constant Observations N (Persons) R-squared 0,49 0,45 0,22 0,17 0,52 0,44 0,52 0,37 24 Population: working age: (Germany), (Australia and UK). (*) indicates dummy variables. Note: Time effects controlled, but not reported. Significance level: +++/--- sig. at 1%; ++/-- sig. at 5%; +/- sig. at 10%. Source: SOEP survey years ; HILDA survey years ; BHPS survey years
25 2 Quantile wage regressions: Coefficients for "imputation flag p25 p50 p75 Germany (SOEP) -0,027** 0,004 +0,046** Australia (HILDA) ** ** UK (BHPS) ** ** ** UK (BHPS - "L+S") ** ** Population: working age: (Germany), (Australia and UK). Note: Controls include age, sex, kids in HH, marital status, health status, region, education, (change in) employment status, unemployment experience, time effects. +++/--- sig. at 1%; ++/-- sig. at 5%; +/- sig. at 10%. Source: SOEP survey years ; HILDA survey years ; BHPS survey years
26 2 Quantile wage regressions: Coefficients for "imputation flag p25 p50 p75 Germany (SOEP) -0,027** 0,004 +0,046** Australia (HILDA) ** ** UK (BHPS) ** ** ** UK (BHPS - "L+S") ** ** Population: working age: (Germany), (Australia and UK). Note: Controls include age, sex, kids in HH, marital status, health status, region, education, (change in) employment status, unemployment experience, time effects. +++/--- sig. at 1%; ++/-- sig. at 5%; +/- sig. at 10%. Source: SOEP survey years ; HILDA survey years ; BHPS survey years
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