Evidence from matched data for the US, England and European countries - PowerPoint PPT Presentation

FACT AGE Workshop on Gender Inequalities in Extending Working Lives Health impacts of retiring: Evidence from matched data for the US, England and European countries Ser Sergio o Sali Salis (N (NIESR) 26 th th Sep London, 26 Lon September 2018 2018 This research has been funded under the ESRC/MRC grant ES/L002884/1 National Institute of Economic and Social Research

Outline • Background • Aims • Data • Empirical methodology • Findings • Conclusions National Institute of Economic and Social Research

Background • An ‘extended working lives’ policy agenda promotes working in later life on the basis of pension sustainability and health benefits (OECD, 2015; DWP, 2014; and WHO, 2002) • However, empirical evidence on the health impact of retirement is inconclusive:  Evidence of beneficial effects (Bound and Waidman, 2007; Neuman, 2008; Coe and Lindeboom, 2008; Coe and Zamarro, 2011; De Grip et al., 2012; Eibich, 2015; Insler, 2014)  Retirement as detrimental for health (Behncke, 2012; Siegrist, et al., 2004; Hartlapp and Schmid, 2008; Bonsang et al., 2007; Wu et al. 2016)  No evidence (Hernaes et al., 2013; Coe and Lindeboom, 2008) • These studies are difficult to compare as thy differ in many respects (e.g., econometric strategy, data, country, definition of retirement and health outcomes) National Institute of Economic and Social Research

Aims • Investigating the causal effect (impact) of retirement on older people’s subjective and objective health in a multi-country setting (US, England and 11 European countries) • The endogeneity of the retirement decision potentially biasing impact estimates (health is both a determinant and a consequence of retirement) is addressed using two alternative non-parametric estimation techniques:  Propensity Score Matching (PSM) in tandem with Difference-in-Differences (DID)  Instrumental Variable (IV) • Exploring the heterogeneity of impacts  By retiree’s gender: Women experience a smoother transition to retirement do to established roles and routines in the home (Price and Nesteruk, 2010); loss of an important social role for the men (Coppola and Spizzichino, 2014)  By nature of the job they retire from: Those in physically-demanding jobs typically have less leisure physical activity so less experience from which to build (Berger et al., 2010) National Institute of Economic and Social Research

Data - So Sources • Individual-level data come from three different surveys:  RAND HRS (US)  Harmonised ELSA (England)  Harmonised SHARE (Italy, Germany, Austria, Sweden, Netherlands, Spain, France, Denmark, Switzerland, Belgium and Greece) • Information about health and socio-economic characteristics and circumstances for people aged 50 or over (over 50 for the RAND HRS) and their spouses or partners • Comparability across these three sources National Institute of Economic and Social Research

Data - Ti Time str tructure • Different waves of the three surveys are pooled together to form the waves of a multi-country dataset Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Wave 6 ELSA ✓ ✓ ✓ ✓ ✓ ✓ HRS ✓ ✓ ✓ ✓ ✓ ✓ SHARE ✓ ✓ ✓ ✓ NA NA • Attrition: Sample size progressively reduces for later waves National Institute of Economic and Social Research

Data - Defi finition of f retirees • Two types of individuals are selected:  Retirees (employed at Wave 2 interview and retired at Wave 3 interview)  Non-retirees (employed at both Wave 2 and Wave 3 interviews) Wave 2 interview Wave 3 interview ELSA Wave 2 (Jun 2004-Jul 2005) Wave 3 (May 2006-Aug 2007) Retirement window HRS Wave 7 (Feb 2004-Jan 2005) Wave 8 (Mar 2006-Feb 2007) SHARE Wave 1 (May 2004-Jul 2005) Wave 2 (Jan 2006-Dec 2007) • We observe almost 1,600 retirees and more than 8,000 non-retirees National Institute of Economic and Social Research

Data - Health outcomes • Impact of retirement on two health outcomes:  Self-assessed health (whether the individual reported being in excellent, very good or good health)  Physical health (whether a doctor had ever diagnosed individuals with one or more conditions among heart problem, stroke, cancer, lung problem, arthritis, high blood pressure or diabetes) • Health outcomes are observed immediately after retiring (at Wave 3 interview) and two later time points (Wave 5 and Wave 6 interviews) National Institute of Economic and Social Research

Empirical methodology - Health ti time tr trends Time trends in self-reported health  Pre-retirement health gap between retirees and non-retirees 89.0 % with good or better health 87.0  Risk to overstate the (negative) 85.0 83.0 impact of retirement Retirees 81.0 Non-retirees 79.0  Need to control for baseline 77.0 health differences between 75.0 Wave 2 Wave 3 Wave 4 Wave 5 Wave 6 retirees and non-retirees Based on 1,588 retirees and 8,219 non-retirees (sample sizes get lower in later waves due to attrition) National Institute of Economic and Social Research

Empirical methodology - Se Selection bia ias Table 1 Baseline compositional differences between retirees and non-retirees Retirees Non-retirees  At baseline, retirees and Age (mean) 64.2*** 57.8 Female (%) 49.0*** 53.1 non-retirees differ in Reaches State Pension age by Wave 3 (%) 50.5*** 12.1 University degree (%) 24.6*** 30.3 several other respects* Married or living with partner (%) 76.8** 79.0 Has children (%) 91.8** 90.2 Family assets (mean of standardised log) 0.116*** -0.001  Need to control for all Earnings (mean of standardised log) -0.228*** 0.100 Job tenure (mean) 17.8*** 15.3 confounders Self-reported health is excellent, very good or good (%) 83.5*** 87.6 Has/has had a chronic condition (%) 65.6*** 51.3 Health limits work (%) 19.3*** 14.4 Sample size varies from 1,590 retirees and 8,227 non-retirees (for variables Age, Female and Reaches State Pension age by Wave 3) to 1,496 retirees and 8,094 non-retirees (for Health limits work); *** and **: difference significant at the 1 and 5% significance level, respectively. * Having reached State Pension age by Wave 3 is highly predictive of retirement for samples defined by Wave 3 outcomes (so a good instrument) National Institute of Economic and Social Research

Empirical methodology - PSM SM & DID ID • Average Treatment Effect on the Treated (ATT) • Conditional Independence Assumption (CIA) or selection on observables • PSM is used in tandem with DID to improve on the estimates (Blundell and Costa Dias, 2000) National Institute of Economic and Social Research

Empirical methodology - IV IV • What if immediately before retiring (and after our baseline time point) people experienced a health shock which cannot be observed? • Local Average Treatment Effect (LATE) • C=1 denotes Compliers, i.e. those who respond to the instrument (State Pension age)  Retire if SPA=1 and stay employed if SPA=0 • LATE conditional on confounders (Frölich, 2007). In our case, the confounders are age, gender and country of residence National Institute of Economic and Social Research

Fin indings - PSM SM & & DID ID esti timates Table 2 I mpact of retirement on retirees' self-assessed and objective health (ATT estimates) Wave 3 Wave 5 Wave 6 Self-reported health Objective health Self-reported health Objective health Self-reported health Objective health • Waves 5 and 6 results not reliable due to Impact on the Impact on the Impact on the Impact on the Impact on the Impact on the covariate balancing issues (sample sizes) proportion of proportion of proportion of proportion of proportion of proportion of retirees reporting retirees having had retirees reporting retirees having had retirees reporting retirees having had • good, very good a health problem good, very good a health problem good, very good a health problem Retirement was found to have reduced the or excellent health diagnosed or excellent health diagnosed or excellent health diagnosed proportion of retirees who experienced 1-to-1 matching (with replacement) -0.057*** 0.028** -0.062 0.024 -0.115*** 0.039 good/better health by over 5ppts in Wave 3 (0.018) (0.012) (0.035) (0.028) (0.042) (0.037) LLR matching -0.051*** 0.020 -0.069** 0.039 -0.091*** 0.065** • Some evidence of a ‘negative’ impact also for (0.015) (0.010) (0.027) (0.021) (0.034) (0.027) objective health (3ppts increase) Number of treated individuals 1,291 [1,279] 1,292 [1,280] 856 [838] 862 [843] 739 [722] 743 [726] Number of untreated individuals 891 [6,946] 899 [6,949] 450 [3,284] 439 [3,288] 318 [2,246] 327 [2,252] Impacts in percentage points are obrained multiplying the estimates in the table by 100; standard errors are reported in round brackets; *** and **: statistically significant at the 1 and 5% significance level, respectively; The numbers of individuals in square brackets refer to LLR matching; Standard errors for impact estimates obtained by means of 1-to-1 matching are based on Abadie and Imbens (2012); Standard errors for impact estimates obtained by means of LLR matching are bootstrapped (1,000 replications). The matching algorithm is chosen based on the observed distribution of the propensity score National Institute of Economic and Social Research