This paper investigates changes in health behaviours upon retirement, using data drawn from the Survey of Health Ageing and Retirement in Europe. By exploiting changes in eligibility rules for early and statutory retirement, we identify the causal effect of retiring from work on smoking, alcohol drinking, engagement in physical activity and visits to the general practitioner or specialist. We provide evidence about individual heterogeneous effects related to gender, education, net wealth, early-life conditions and job characteristics. Our main results––obtained using fixed-effect two-stage least squares––show that changes in health behaviours occur upon retirement and may be a key mechanism through which the latter affects health. In particular, the probability of not practicing any physical activity decreases significantly after retirement, and this effect is stronger for individuals with higher education. We also find that different frameworks of European health care systems (i.e. countries with or without a gate-keeping system to regulate the access to specialist services) matter in shaping individuals’ health behaviours after retirement. Our findings provide important information for the design of policies aiming to promote healthy lifestyles in later life, by identifying those who are potential target individuals and which factors may affect their behaviour. Our results also suggest the importance of policies promoting healthy lifestyles well before the end of the working life in order to anticipate the benefits deriving from individuals’ health investments.
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These risk factors, together with unhealthy diet, have a strong impact on the onset of cardiovascular and respiratory diseases, cancers and diabetes, which account for 82 % of chronic diseases .
Higher utilization of medical care after retirement can be the result of more treatment driven by health problems and/or an increased attitude for (or more time devoted to) prevention.
Although a complete literature review of the effect of retirement on health is beyond the scope of this paper, we provide here a brief description of the cited papers. Charles , Neuman  and Insler , looking at US data and accounting for endogeneity, find that retirement is beneficial for health when using subjective indicators. Focussing on the UK, Bound and Waidmann  highlight positive effects of retirement on health only for men, Johnston and Lee’s  estimates point to similar conclusions only for subjective indicators. Coe and Zamarro  analyse European data––the first two waves of SHARE—finding positive effects of retirement on both a self-reported health indicator and a combination of subjective and objective measures of health. Kerkhofs and Lindeboom , using a fixed effect panel data model with Dutch data, find that health deteriorates with employment and labour market history. Dave et al.  estimate a negative effect of retirement on health (mental and physical) in the US using a fixed effect panel data model, whereas Lindeboom et al.  find no effects on mental health for the Netherlands. Behncke  estimates a negative effect of retirement on objective health indicators for the UK based on non-parametric matching and instrumental variable (IV) methods. Celidoni et al. , looking at cognitive decline as outcome, find a negative causal effect of retirement using the first, second and fourth wave of SHARE.
See  for a more detailed theoretical discussion of the interactions between health and retirement.
Another paper  reports an investigation into the effect of retirement on the number of days of inpatient care and mortality but is very specific, since it exploits an early retirement offer to military officers in Sweden.
It must also be observed that the relation between individual behaviour and health is of a simultaneous nature : not only health behaviours can be treated as investments in health, according to the Grossman’s theoretical perspective, but health status itself might constrain health investment options (e.g. disability might prevent physical exercise). We take into account the role of health as a determinant of health behaviour in the robustness analysis by including among the controls several health indicators (limitations in daily activities and chronic diseases) and show that our baseline results do not change.The robustness analysis is reported in Table 6.
Among the eleven countries in the first wave of SHARE, Greece is the only country that has not participated continuously.
Individuals whose age is lower than 50 years are typically spouses of the sampled person, who, according to the survey eligibility rules, is 50 years of age or older. By focusing on individuals whose age is between 45 year and 85 year, we do not include very young spouses and older people, who are typically very selected (this selection drops about the 5 % of observations in the initial sample). Individuals aged 45–49 year considered in the analysis represent the 0.06 % of the whole sample.
The possible responses to this question are: ‘Almost every day’, ‘Five or six days a week’, ‘Three or four days a week’, ‘Once or twice a week’, ‘Once or twice a month’, ‘Less than once a month’, ‘Not at all in the last three months’. Only in waves 2 and 4 were respondents asked how many drinks they consume in a day. This information however does not distinguish precisely the type of drink (the percentage of alcohol by volume varies substantially depending on the type of drinks) and involves a larger measurement error. Even if the indicator we use does not properly capture drinking intensity, nevertheless it could be informative about changes in drinking behaviour. We will discuss this point more extensively in the "Results" section.
We also combined current self-reported retirement status with earnings/self-employment income of the previous year, obtaining summary statistics similar to those reported in Table 1.
ISCED 5–6 (International Standard Classification of Education) identifies individuals with tertiary education.
Less well educated people generally show a lower probability of contacting a specialist at all ages; this is probably due to their reduced access to this type of health care, owing to a lack of information or economic resources.
We also performed a Hausman test in order to ascertain the inconsistency of random effects (RE) estimates. The results obtained, not shown here but available on request, support the inconsistency of RE.
See, for instance, Bingley and Martinello , who argue the relevance of education not only as a determinant of health in later life but also as an appropriate control when using retirement ages as an instrument for the retirement decision: differences in retirement ages across countries are associated positively with multi-country differences in average educational levels.
For pensioners eligibility rules refer to the reported retirement year, for employed individuals eligibility is defined according to the interview year.
Similarly to , in Appendix 2, we show in Figs. 7 and 8 the histograms of retirement age by country for males and females, highlighting in dark gray/black the range of early/statutory retirement eligibility ages. Figures 7 and 8 show that there is significant variability across countries and gender in eligibility criteria, and that we are able to predict important peaks in the retirement age. This evidence supports our identification strategy.
Of these, 5.10 % of transitions occurred in Austria, 9.20 % in Germany, 17.36 % in Sweden, 10.66 % in the Netherlands, 4.85 % in Spain, 8.75 % in Italy, 13.76 % in France, 11.71 % in Denmark, 6.15 % in Switzerland and 12.46 % in Belgium. The heterogeneity in the number of transitions observed across countries can be the result of several factors––institutional factors related to eligibility criteria, gender specific labour market participation, sampling or response behaviour.
Even if critical values do not refer to cases when standard errors are clustered, according to Baum et al. , they can nevertheless be used to reveal weak identification issues.
According to Angrist and Pischke [42, p. 198], regardless of whether the outcome variable is binary, non-negative or continuously distributed, IV-2SLS captures the local average treatment effects we are interested in.
Net wealth quartiles are based on imputed data. See http://www.share-project.org for detailed documentation about the imputation procedure. Results do not change whether we use equivalent household net wealth quartiles, or equivalent household net income quartiles with the square root of the household size as equivalence scale (results are available upon request).
This indicator has been used also by Brunello et al. , who highlight the importance of early-life interventions to capture lower returns to college for individuals who grew up in disadvantaged households.
This has to be taken into account when interpreting our results, since we are combining at the same time long exposure to particular job characteristics and more recent effects of the last job. Short-term exposure is for those who changed job characteristics at the end of their work career.
According to , the two questions are related to the dimensions of physical and psychosocial work quality.
Based on the job description provided, we use the following classification: high skilled white collar (legislator, senior official, manager, professional, technician or associate professional); low skilled white collar (clerk, service worker, shop and market sales worker, armed forces); high skilled blue collar (skilled agricultural or fishery worker, craft and related trade workers, plant and machine operator or assembler); low skilled blue collar (elementary occupation).
Even if not influenced by reporting heterogeneity, these second job categorisations have been criticised for being too coarse and unable to capture the multi-dimensional burden of a job . Detailed ISCO coding could be used to construct a physical or a psycho-social job burden index, as proposed by Kroll , but unfortunately this information is available only in wave 1 for the last/current job.
The reported F-statistic is the Kleibergen-Paap rk Wald F-statistic, which deals with clustered standard errors and corresponds to the standard F-statistic on the excluded instruments when there is a single endogenous variable.
It may be argued that intensity of physical activity is not well captured by our two indicators: especially for those in physically demanding occupations, it may be that, although transiting into retirement leads to a higher probability of exercising, this does not translate into an increased burning of calories . But, as we will see later, this behavioural change is attributable to white collar workers who usually have more sedentary jobs.
It can be seen that 2SLS point estimates are larger than OLS. One possible explanation is that we capture the effect of retirement for those individuals who are driven into retirement by the pension eligibility rules we use as instruments, leading to a Local Average Treatment Effect interpretation . Additionally, fixed-effects estimates are also susceptible to attenuation bias if the retirement variable is affected by a measurement error . In fact, some respondents may self-report being retired simply because they left their main job, even though they are still working full- or part-time , or they may misreport the retirement year . Moreover, as suggested by Angrist and Pischke [42, p. 167], with multiple instruments, one can run overidentification tests as formal tests of treatment effect homogeneity. For all outcomes considered in Table 2, the Sargan-Hansen test of over-identifying restriction does not reject the null of the J test; results are available upon request.
We tried including also depression and self reported health among controls but results––available upon request––do not change.
We also run FE-2SLS estimates separately by country—these are available upon request.
FE-2SLS estimates regarding inactivity (i.e. exercise requiring either a moderate or a substantial level of energy) are −0.0294 (SE 0.0134) for Denmark, the Netherlands and Sweden and −0.137 (SE 0.0777) for Mediterranean countries.
Excluding the Netherlands––which is a private mandatory health insurance system evolving from a previous social health insurance––the other countries with gate-keeping (Denmark, Italy, Spain and Sweden) are all National Health Services, financed mainly by taxes and providing universal coverage (Beveridgean systems). If we consider only Beveridgean systems, the results of Table 3 still hold (the estimated coefficient for exercise requiring either a moderate or a substantial level of energy is −0.0789 (SE 0.0222) and significant at 1 %, whereas for exercise requiring a substantial level of energy the coefficient is −0.165 (SE 0.0409) and significant at 1 %. This may be interpreted as a result of more systematic interventions in these countries––through community care and counselling––to promote physical exercise, involving a number of actors even outside the health care sector. According to a report on policy development in the area of nutrition, physical activity and the prevention of obesity , Denmark, Italy, Spain and Sweden stand out among the other countries since they implemented specific actions involving multiple settings (schools, workplaces, health care services), and various sectors of government (environment, agriculture, sport, research and housing) at all levels (national, regional and local).
No income or wealth effect are considered in our discussion, since we include in our specifications net wealth quartile dummies that should control for those effects.
For individuals with physically demanding jobs in particular, transiting into retirement does not affect significantly the probability of practising sports and vigorous activities. This is in line with the estimated effect of early retirement on body mass index .
We use work experience to define eligibility.
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We thank Daniel Avdic, Marco Bertoni, Eric Bonsang, Giorgio Brunello, Emilia Del Bono, Mariacristina De Nardi, Peter Eibich, Fabrizio Mazzonna, Omar Paccagnella, Luca Salmasi, Elisabetta Trevisan, Guglielmo Weber, two anonymous referees, the participants at the 5th SHARE User Conference-Luxembourg (Luxembourg, 12–13 November 2015), the 11th iHEA World Congress in Health Economics (Milan, Italy, 12–15 July 2015), the Essen Health Conference (Essen, Germany, 29–31 May 2015) and the XIX AIES Conference (Venice, Italy, 27–28 October 2014). Funding from the University of Padua and Farmafactoring Foundation is gratefully acknowledged. This paper uses data from SHARE wave 4 release 1.1.1, as of 28 March 2013 (doi:10.6103/SHARE.w4.111) or SHARE wave 1 and 2 release 2.6.0, as of 29 November 2013 (doi:10.6103/SHARE.w1.260 and 10.6103/SHARE.w2.260) or SHARELIFE release 1, as of 24 November 2010 (doi:10.6103/SHARE.w3.100). The SHARE data collection was funded primarily by the European Commission through the 5th Framework Programme (project QLK6-CT-2001-00360 in the thematic programme Quality of Life), through the 6th Framework Programme (projects SHARE-I3, RII-CT-2006-062193, COMPARE, CIT5- CT-2005-028857, and SHARELIFE, CIT4-CT-2006-028812) and through the 7th Framework Programme (SHARE-PREP, N° 211909, SHARE-LEAP, N° 227822 and SHARE M4, N° 261982). Additional funding from the US National Institute on Aging (U01 AG09740-13S2, P01 AG005842, P01 AG08291, P30 AG12815, R21 AG025169, Y1-AG-4553-01, IAG BSR06-11 and OGHA 04-064), and the German Ministry of Education and Research, as well as from various national sources is gratefully acknowledged (see http://www.share-project.org for a full list of funding institutions).
This study was funded by the University of Padua (POPA_EHR-Population aging-economics, health, retirement and the welfare state); Martina Celidoni received research grants from Fondazione Farmafactoring (Bando 2014).
Conflict of interest
The two authors are involved in a scientific capacity in the design and running of the Survey on Health, Ageing and Retirement in Europe (SHARE), which is the main data source used in the paper. They declare that they have no conflict of interest and no relevant or material financial interests that relate to the research described in this paper.
The initial sources of information about eligibility criteria are Gruber and Wise (1999, 2010) and Wise (2012). Other country-specific auxiliary data sources are given below. ER early retirement. SR statutory (normal) retirement.
Austria (see Staubli and Zweimüller 2011)
ER: 60 for men and 55 for women until 2001. From 2001 until 2004, early retirement depends on year of birth. For men it is 61 until 1942 and 62 from 1943 onwards. For women it is 56 for those born in 1947, 57 for those born between 1948 and 1951, 58 for those born from 1952 onwards. From 2005 onwards, it is 62.
SR: 65 for men and 60 for women.
Belgium (see Jousten et al. 2010)
ER: No early retirement until 1966, 60 afterwards for men, for women 55 until 1986 and 60 from 1987.
SR: 65 for men, for women 60 until 1996, 61 from 1997 to 1999, 62 from 2000 to 2002, 63 from 2003 to 2005, 64 from 2006 to 2008, 65 from 2009.
Denmark (see Bingley et al. 2010)
ER: 60 for both men and women consistently, except from 1992 to 1993, when the ER was lowered to 55, and from 1994 to 1995, when it was 50.
SR: 67 until 2003, 65 from 2004, for both men and women.
France (see Hamblin 2013)
ER: No early retirement until 1963. 60 from 1963 to 1980, 55 from 1981 onwards.
SR: 65 until 1982 and 60 from 1983 to 2010; from 2011 60 for those born up to 1952, 61 for those born between 1953 and 1954, and 62 for those born since 1955.
Germany (see Berkel and Börsch-Supan 2004, and Mazzonna and Peracchi 2014, DRV 2015)
ER: For men, no early retirement until 1972, 60 from 1973 until 2003, 63 from 2004 onwards. For women, no early retirement in 1961, 60 from 1962.
SR: 65 for all.
Italy (see Angelini et al. 2009; Mazzonna and Peracchi 2014)
ER: from 1965 to 1995, early retirement was possible at any age with 35 years of contributionsFootnote 35 (25 in the public sector) for both men and women; from 1996 it was increased stepwise up to 57 for both the private and public sector (58 for self-employed).
SR: The statutory retirement age was 60 (65 in the public sector) for men and 55 (60 in the public sector) for women from 1961 to 1993. Several consecutive reforms (1992, 1995 and 1998) increased the statutory retirement age to 65 for men and 60 for women with step-wise increments from 1994.
Netherlands (see Euwals et al. 2010)
ER: No early retirement until 1974. 60 from 1975 onwards, for both men and women.
SR: 65 for both men and women.
Spain (see Blanco 2000; Mazzonna and Peracchi 2014)
ER: 64 until 1982, 60 from 1983 to 1993, 61 from 1994 onwards, for both men and women.
SR: 65 for both men and women.
Sweden (see Mazzonna and Peracchi 2014)
ER: No early retirement until 1962, 60 from 1963 to 1997, 61 from 1998 onwards.
SR: 67 for both men and women until 1994, 65 from 1995 onwards.
Switzerland (see Dorn and Sousa-Poza 2003; Mazzonna and Peracchi 2014)
ER: No early retirement until 1996 for men and until 2000 for women. Then, 64 for men from 1997 until 2000 and 63 from 2001, for women 62 from 2001.
SR: 65 for men, for women 63 until 1963, 62 from 1964 until 2000, 63 from 2001 to 2004, 64 from 2005.
Additional references for retirement ages
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Note: The graphs report retirement age histograms by country and gender, highlighting in dark gray early retirement ages in black statutory (normal) retirement ages––that have changed over time for the cohorts considered (see Appendix 1). Within each bin, we show the proportion of individuals declaring why they retired.
See Table 6.
Following Jones et al. (2013) and Verbeek and Nijman (1992), an initial test for non-response bias is to include in our 2SLS specification two variables describing the pattern of survey response: nextwave and allwaves. The former indicates whether the individual participated in the next wave, the latter identifies individuals who participated in all three waves. In the FE-2SLS, only nextwave is included, since allwaves is a time-invariant characteristic. As Jones et al. (2013) suggested, there should be no intrinsic reason why the survey response should have an effect on individuals’ health behaviours, but, in the presence of selection bias there will be a statistical association between survey response variables and our outcome measures. Table 7 shows that there is a statistical association between survey response variables and our outcome measures, but generally not for our FE-2SLS specifications. One possible strategy to see whether attrition might be problematic for our results is to compare estimates between balanced and unbalanced panel sample (see Jones et al. 2013, and Cheng and Trivedi 2015). In the absence of non-response bias, these estimates should be comparable, as may be seen in Table 8.
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Celidoni, M., Rebba, V. Healthier lifestyles after retirement in Europe? Evidence from SHARE. Eur J Health Econ 18, 805–830 (2017). https://doi.org/10.1007/s10198-016-0828-8
- Health behaviour
- Fixed effects
- Instrumental variables