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The European Journal of Health Economics

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26.09.2016 | Original Paper

Healthier lifestyles after retirement in Europe? Evidence from SHARE

verfasst von: Martina Celidoni, Vincenzo Rebba

Erschienen in: The European Journal of Health Economics | Ausgabe 7/2017

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Abstract

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 [12].

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 [13], Neuman [15] and Insler [17], 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 [14] highlight positive effects of retirement on health only for men, Johnston and Lee’s [18] estimates point to similar conclusions only for subjective indicators. Coe and Zamarro [16] 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 [19], using a fixed effect panel data model with Dutch data, find that health deteriorates with employment and labour market history. Dave et al. [20] 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. [21] find no effects on mental health for the Netherlands. Behncke [22] 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. [23], looking at cognitive decline as outcome, find a negative causal effect of retirement using the first, second and fourth wave of SHARE.

See [24] for a more detailed theoretical discussion of the interactions between health and retirement.

Another paper [33] 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 [34]: 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 [36], 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 [38], 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. [41], 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. [45], 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 [47], 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 [50]. Detailed ISCO coding could be used to construct a physical or a psycho-social job burden index, as proposed by Kroll [51], 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 [53]. 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 [54]. Additionally, fixed-effects estimates are also susceptible to attenuation bias if the retirement variable is affected by a measurement error [55]. 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 [16], or they may misreport the retirement year [56]. 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 [57], 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 [60].

We use work experience to define eligibility.

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Titel: Healthier lifestyles after retirement in Europe? Evidence from SHARE
verfasst von: Martina Celidoni
Vincenzo Rebba
Publikationsdatum: 26.09.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: The European Journal of Health Economics / Ausgabe 7/2017
Print ISSN: 1618-7598
Elektronische ISSN: 1618-7601
DOI: https://doi.org/10.1007/s10198-016-0828-8

Live-Webinar: Aktuelle Leitlinien bei Herz-Kreislauf-Erkrankungen

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Healthier lifestyles after retirement in Europe? Evidence from SHARE

Abstract

Live-Webinar: Aktuelle Leitlinien bei Herz-Kreislauf-Erkrankungen

Springer Medizin

Abstract

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