Lifetime Income Inequality: quantile treatment effect of retirement on the distribution of lifetime income.
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1 Lifetime Income Inequality: quantile treatment effect of retirement on the distribution of lifetime income. Małgorzata Karolina Kozłowska University of Rome "Tor Vergata" February 6, 26 Małgorzata Karolina Kozłowska ("Tor Vergata") QTE of retirement on lifetime income. February 6, 26 /
2 Overview Overview Life cycle, social security and inequality Human Capital Earnings Function (HCEF) Fanning out of earnings profiles across education groups as the cohort ages. Permanent Income Hypothesis (PIH) Within cohort inequality evolves over life-cycle, reaching the highest levels in the old age. Disparities in earnings between groups with different schooling levels grow in retirement age. Reducing the share of income that is transferred through Social Security Systems increases life cycle inequality. Social Welfare Theory (SW) By Pigou-Dalton Principal of Transfers any (mean-preserving) progressive transfer decreases inequality. Małgorzata Karolina Kozłowska ("Tor Vergata") QTE of retirement on lifetime income. February 6, 26 2 /
3 Overview Overview What this study proposes Evaluate the effect of postponing retirement on income inequality through estimating the causal effect of staying longer in labor force on lifetime income distribution. Małgorzata Karolina Kozłowska ("Tor Vergata") QTE of retirement on lifetime income. February 6, 26 3 /
4 Overview Overview Research questions What is the impact of staying longer in labor force on distribution of lifetime income? What is the impact of postponing retirement age on lifetime income inequality? Małgorzata Karolina Kozłowska ("Tor Vergata") QTE of retirement on lifetime income. February 6, 26 4 /
5 Data and sample Data and sample Survey of Health Aging and Retirement Multidisciplinary cross-country longitudinal survey that collects micro data on individuals aged 5+. The data used in this study consists of two regular panel waves followed by the life history questionnaire of which the data were collected accordingly in 24-25, and This study covers European countries: Austria, Germany, Sweden, The Netherlands, Spain, Italy, France, Denmark, Switzerland, Belgium. Main advantages of using SHARE Its crossnational and multidisciplinary dimension. Inclusion of retrospective statements about socio-economic status spanning lifetime, allowing deriving lifetime income measure. Małgorzata Karolina Kozłowska ("Tor Vergata") QTE of retirement on lifetime income. February 6, 26 5 /
6 Data and sample Data and sample Lifetime income Lifetime income for individuals who are retired at the moment of the interview is represented by the formula: R i Y i = ω j W ij + s j+ ω j P ij j= R i + where: Y i - total lifetime income W ij - lifetime earnings from work at age j P ij - lifetime retirement pension at age j R i - retirement age s j+ - probability of surviving to age j+, predicted based on Lee-Carter model ω j - discount rate ( 2% to age 5) Małgorzata Karolina Kozłowska ("Tor Vergata") QTE of retirement on lifetime income. February 6, 26 6 /
7 Descriptive statistics Descriptive statistics Kernel density estimates Distribution of Log of lifetime income Austria Germany (BRD) Sweden Netherlands Spain Italy France Denmark Switzerland.5 Belgium Total sample Histogram Kernel density Median q5 q95 Małgorzata Karolina Kozłowska ("Tor Vergata") QTE of retirement on lifetime income. February 6, 26 7 /
8 Descriptive statistics Descriptive statistics Kernel density estimates by retirement age Distribution of lifetime income by retirement age category Austria Germany (BRD) Sweden Netherlands Spain Italy France Denmark Switzerland Belgium Total sample _6_62 63_64_65 q5 q95 6_6_62 q5 q95 63_64_65 Małgorzata Karolina Kozłowska ("Tor Vergata") QTE of retirement on lifetime income. February 6, 26 8 /
9 Cross country setting Estimation framework Model specification Specification of the model in a cross-country setting: Y i = α + α R i + β X + ε i Q τ (Y i ) = α τ + α τ R i + β τ X i where: Y i - personal lifetime income R i - binary indicator taking value if an individual retired exactly at age 63 or later, and if an individual retired by age 62 X - set of country dummies. Małgorzata Karolina Kozłowska ("Tor Vergata") QTE of retirement on lifetime income. February 6, 26 9 /
10 Cross country setting Estimation framework Estimation and identification Issues : Endogenity Identification: Quantile Instrumental Variables techniques Abadie, Angrist & Imbens (22) Chernozukov & Hansen (25) Instruments: legislated early and normal retirement ages differenced with age: Z=, if A i leg ER Z=, if A i leg ER < where: A i is the actual age of an individual in the year of the interview, leg ER is the earliest legal retirement age that an individual is eligible for. Małgorzata Karolina Kozłowska ("Tor Vergata") QTE of retirement on lifetime income. February 6, 26 /
11 Cross country setting Results Ordinary Least Squares Estimates vs. Ordinary Quantile Regression 2 OLS vs QREG 5 Coefficient 5-5 Q Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Quantile OLS QREG 95% QREG CI Małgorzata Karolina Kozłowska ("Tor Vergata") QTE of retirement on lifetime income. February 6, 26 /
12 Cross country setting Results Quantile Regression vs. Quantile Treatment Effect (AAI) 3 QR vs. QTE-AAI 2 Coefficient - -2 Q Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Quantile QREG 95% QREG CI QTE-AAI 95% QTE-AAI CI Małgorzata Karolina Kozłowska ("Tor Vergata") QTE of retirement on lifetime income. February 6, 26 2 /
13 Cross country setting Results Quantile Treatment Effect by the two estimators QTE-AAI and QIV-ChH 4 QTE-AAI vs. QIV-ChH 2 Treatment effect -2-4 Q Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Quantile QTE-AAI QTE-ChH 95% QTE-AAI CI 95% QTE-ChH CI Małgorzata Karolina Kozłowska ("Tor Vergata") QTE of retirement on lifetime income. February 6, 26 3 /
14 Country specific setting Estimation framework Model specification Specification of the model in a country specific setting: Y i = α + α D i + β X + ε i where: Y i - personal lifetime income D i - binary indicator taking value if an individual retired beyond the relevant cut-off treshold, and if an individual retired before X - set of cohort dummies. Małgorzata Karolina Kozłowska ("Tor Vergata") QTE of retirement on lifetime income. February 6, 26 4 /
15 Country specific setting Estimation framework Estimation and identification Issues : Endogenity Identification: Quantile Instrumental Variables in Regression Discontinuity Design Frandsen, Foelich & Melly (22) Instruments: Z i = (B i b ) B i - running variable (year of birth) Małgorzata Karolina Kozłowska ("Tor Vergata") QTE of retirement on lifetime income. February 6, 26 5 /
16 Country specific setting Results Quantile Treatment effect of retirement on lifetime income by country in RD design Regression discontinuity design by country Spain Italy Treatment Effect Q Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Denmark -45 Q Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Switzerland Q Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 QTE-AAI RDDQTE Quantiles Małgorzata Karolina Kozłowska ("Tor Vergata") QTE of retirement on lifetime income. February 6, 26 6 /
17 Country specific setting Results Quantile Treatment effect of retirement on lifetime income by country in RD design Regression discontinuity design by country Spain Italy Treatment Effect - 5 Q Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Denmark -8 5 Q Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Switzerland Q Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 QTE-AAI RDDQTE Quantiles Małgorzata Karolina Kozłowska ("Tor Vergata") QTE of retirement on lifetime income. February 6, 26 7 /
18 Conclusions Conclusions Conclusions Clearly heterogenous, redistributive effect of postponing retirement to later ages across the quantiles of lifetime income in the overall sample. Similarity of results of the two estimators (QTE-AAI and QTE-ChH) suggest that assumptions underlying both estimators are plausible. Subpopulation of compliers is a fair representation of the overall population. Małgorzata Karolina Kozłowska ("Tor Vergata") QTE of retirement on lifetime income. February 6, 26 8 /
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