The retirement effect on mental health in Europe during 2006-2015: Evidence of Ashenfelter’s dip

The retirement effect on mental health in Europe during 2006-2015: Evidence of Ashenfelter’s dip Thang Vo Duyen Tran University of Economics Ho Chi Minh City Abstract Since ageing raises concerns over the economic efficiency of rising pension age, the impact of retirement on various aspects of life is on the focus of Euro-pean countries’ policies Using panel data from the Survey of Health, Ageing and Retirement in Europe (SHARE), this study investigates the effect of retirement on mental health measured by the EURO-D scale (12 levels of depression) Age above specific-country eligible pension age is used as an instrumental variable for retirement status in the fixed effect model to remedy the potential endogeneity bias This study is the first effort to capture the mental health effect in anticipation of retirement, a phenomenon called ‘Ashenfelter’s dip’ or ‘pre-programme dip’ This study also compares short term effects and long term effects of retirement, which is rarely done before Different impacts of reasons for retirement categorized into three groups are also analyzed in this study The study indicates that retirees feel less depressed than people who remain in the labor force When the age above pension age of individuals is included to pre-dict retirement behavior, the results confirm an analogous effect of retirement on mental health In terms of reasons for retirement, retiring due to positive circum-stances and aspirational motivations reduce depression remarkably, while there is no evidence to confirm that retiring by negative circumstances affect one’s mental health The study finds a similar effect for people who are expected to retire in the next two years, but this is not the case for people who know they will retire in the next four years The potential retirees seem to adjust their lifestyles in response to future retirement Two years after retirement, the effect is reverted, but after four years the results are not conclusive Retirees may adapt to their new life completely and the effect of retirement is no longer important Keywords: SHARE, retirement, mental health, panel data, instrumental variable, Ashenfelter dip Introduction For the last few decades, population ageing has been on the rise to be one of the biggest challenges for politicians and scientists all over the world, especially in Western countries (Butterworth et al., 2006) Particularly in Europe, the rate of elderly accounted for 20.3% in 2000, which was the highest in comparison to other areas’ The projection is that by 2050 there will be over one-third of European population are individuals aged over 60 (see table 1) Furthermore, Eurostat (2015) reports that within the last 50 years, life expectancy at birth in the EU-28 has increased nearly 10 years and European countries are most likely to have the highest life expectancy in the world 501 Since the number of senior citizens is believed to be growing gradually, the popula-tion of retirees is also increasing as a result, which, in turn, raises concern not only for governments but also for work organizations and individuals in general Because, with the number of retirees now enlarging over time, governments have to take into account the sustainability of social welfare, enterprises have to assure the productivity of elderly employees, and people are more likely to feel uncertain about their future when they get retired (Desmette et al., 2015) Therefore, it is essential to understand the correlation between retirement and mental health for developing employment policies (Butterworth et al., 2006; Zhu, 2016) as well as individuals’ life satisfaction Literature review In terms of changes in the life of retirees, previous studies which attempt to investi-gate the correlation between retirement and mental health has shown conflicting results due to different strategies employed (Zhu, 2016) For instance, the studies of Salokan-gas et al (1991), Mein et al (2003), Charles et al (2004) and Mojon-Azzi et al (2007) all find a positive impact of retiring on the mental health of senior citizens to such an extent Similar results are also revealed in Jokela et al (2010)’s research, which takes in to account the age at retirement, reasons for retirement and length of time spent in the retirement through a longitudinal data set In Europe, several studies have found evidence exposing that retiring could possibly impact the mental health of the elderly (Belloni et al., 2016; Borsch-Supan and Schuth, 2014; Heller-Sahlgren, 2017) How-ever, the influence may vary depending upon short- and long-term (Bianchini et al., 2015; HellerSahlgren, 2017), men and women (Belloni et al., 2016), as well as differ-ent geographical areas (Aichberger et al., 2010) Adversely, there are also studies on the topic providing evidence to the contrary Buxton et al (2005) with a total of 1,875 respondents in a cross-sectional analysis shows that early retirees were more likely to have generalised anxiety disorders and depressive disorders The findings support the idea of increasing retirement age in Britain up to Falba et al (2008) employ Poisson regression for 4,241 observations extracted from the Health and Retirement Study (HRS) and suggest that working longer and retiring earlier than expected each may compromise psychological well-being Another study with data from HRS of Calvo et al (2012) shows that early retirements, those occurring prior to traditional and legal retirement age, worsen health This study, however, does not find any disadvantages associated with late retirements Therefore, they suggest that raising the retirement age may decrease the subjective health of retirees because the group of early retirements has been enlarged At the same time, the impact of retirement on health may lead to differential out-comes in one single study One example is the work of Johnston et al (2009), which shows robust evidence that in the shortrun, retirement increases individual’s sense of well being and mental health, but not necessarily their physical health Then the au-thors argue that government expenditure for health would not be significantly affected by increasing the official retirement age Recently, Zhu (2016) employs a panel data set of the Household, Income and Labour Dynamics in Australia (HILDA) Survey and finds that retirement status has positive and significant effects on women’s self reported health, physical and mental health outcomes A study of Byles et al (2016) confirms that retirement is associated with psychological distress among men in Australia How-ever, this study does not find any association between retirement and psychological distress among women These findings support the provision of flexible employment options for older adults 502 Table 1: Percentage of over-60-year-old population of the world and continents Region 2000 2015 2030 2050 World Africa Asia Europe Latin America and the Caribbean Oceania Northern America 9.9 5.2 8.6 20.3 8.1 13.4 16.2 12.3 5.4 11.6 23.9 11.2 16.5 20.8 16.5 6.3 17.2 29.6 16.8 20.2 26.4 21.5 8.9 24.6 34.2 25.5 23.3 28.3 Source: United Nations (2015) 2.1 Impact mechanism of retirement on mental health Since there are still conflicting results revealed by previous studies on this topic, contrasting mechanisms through which retirement affect the mental health of individu-als have also been discussed in different aspects On the one hand, retirement seems to have a positive impact on the well-being of people by three ways Firstly, Belloni et al (2016), Hessel (2016), Zon et al (2016) and Stenholm et al (2017) argue that once elder employees start to enroll in retirement, the relief from stress caused by working and/or the precarious working environment could improve their mental health to such an ex-tent Secondly, retirees are believed to have more leisure time than people who remain in the labor force, therefore, they are more likely to get engaged in physical activities including exercises, which could advance their health remarkably (Evenson et al., 2002; Zon et al., 2016) At the same time, it is discussed in the study of Myllyntausta et al (2017) that retirees seem to sleep better due to the fact that they have more unoccupied hours after they stop working According to Zon et al (2016), having more time once retired also means that senior citizens are able to have more social engagement, which could be an advantage for their functional health Lastly, social capital and networks of elderly have actually been investigated in many research (e.g Gannon et al (2014), Liu et al (2016), O’Doherty et al (2017)), and is regarded as one of the main impact mechanisms that retirement affect mental health through Because retirees are likely to have more time to find new and voluntary contacts (Heller-Sahlgren, 2017) On the other hand, retirement, which is sometimes refereed as a “stressful event” with major changes for a certain number of people (Ekerdt, 1987; Salokangas et al., 1991), could have a negative influence on the mental health of retirees through different channels To begin with, although the increasing stock of social capital and enlarging network is believed to improve the mental health of most individuals, to other people, stop working could worsen their mental health, because of the decreased bonds with their former colleagues (Stenholm et al., 2017), or the loss of social contacts and par-ticipations related to work (Zon et al., 2016) Moreover, since having occupations are often considered as a basic role of an individual in societies, people who lose that when they get retired are supposed to have less self-respect and feel isolated, which worsens their mental health as a result (Hessel, 2016) In terms of financial issues, retirement particularly leads to a decrease of the regular income In most cases, this affects the financial insecurity of individuals, especially those who have fewer economic resources when they are retired (Heller-Sahlgren, 2017; Zon et al., 2016) Above all, adapting to retirement requires people to have changes in not only the frequency but also the inten-sity of work-related activities (Grundy et al., 1999) As a consequence, the adjustments in their lifestyle are accounted for a worse health outcomes, which includes depression (Dave et al., 2006) 503 2.2 Limitations While studies have investigated this topic on many aspects with different analyzing methods, it is true to say that there are still limitations existing First, in addition to re-search heterogeneity, there are potential measurement problems, including unmeasured variables, level of analysis, and use of inappropriate measures Second, the unavail-ability of data across time and place as well as across either economic or mental health measures highlights a clear need for research that has longitudinal data and larger sam-ple size (Hanisch, 1999) Third, as pointed out in the research of Salokangas et al (1991) and Mein et al (2003), future studies examining this issue should take into ac-count the conditions of retirement, the distinction of people, then clarify the problems in the process of adapting to retirement Because the differences in mental adaptation of each person in their retirement term might not only be caused by the fact that they stop working but also by a combination of earlier life conditions, social-economic sta-tus and retiring motivation Also, in order to analyze changes in the health of retired people that probably caused by retirement, studies need to be prospective and include the control group Without these two factors, many studies dealing with this issue seem to be problematic Above all that, the most important limitation noticed from previous studies is that potential effects of retirement on elderly workers’ well-being before the actual retire-ment might have been underestimated (Hessel, 2016) In other words, since retirement seems to be a predictable event for most people, employees who are approaching retire-ment time could possibly already experience changes in their mental health However, to our knowledge, whether the impact of retirement exists preceding to the retirement still has not been confirmed by any of the former research This “pre-impact” is pretty similar to the “preprogramme - dip” first presented in the paper of Ashenfelter (1985), which is then often refereed as the “Ashenfelter-dip” to generally describe the decline of the outcomes prior to the actual participating of individuals in a particular programme Furthermore, investigating the same topic, Coe and Lindeboom (2008) discuss that the influence of retirement on (mental-)health may not take place right at the time of retire-ment Instead, the well-being of the elderly stands the chance to be improved or worsen even before that (Hessel, 2016) Contribution In this context, fixed-effect models are employed to investigate the impact of retire-ment on mental health and, to control the potential reverse effect, an instrumental vari-able is additionally included in the models The instrument variable used in this study will be discussed specifically in section 3.3 Moreover, since motivations of retirement decision may influence the well-being of retirees when they stop working (Robinson et al., 2010), reasons for retirement are taken into account as explanatory variables The results indicate that retirees seem to feel less depressed than people who remain in the labor force to such an extent Furthermore, when the age above pension age of individuals is included in the models as an instrumental variable to predict retirement behavior, results show an analogous impact of retirement on mental health In terms of reasons for retirement, retiring due to positive circumstances and aspirational moti-vations might reduce depression remarkably, while there is no evidence to confirm that retiring by negative circumstances could affect one’s mental health Last but most importantly, the effect of potential retirement and being retired in the last years on mental health of individuals is also revealed in our research Specifically, employees who are going to retire within the next years seem to have their mental well-being improved already before the retirement 504 However, retiring in the past is likely to dramatically explain the increase of depression level among retirees The rest of the paper is arranged as follows: section describes an overview of SHARE data source and its detail setting particularly in our research Specific explana-tions of regression models and variables are also included in this section Next, section indicates the descriptive statistics both in tables and context with more clarification Results are then provided in section prior to the conclusion in section Background With the population of 738 million people (United Nations, 2015) - which seems to be a minority in comparison with the world population (7.349 billion) and other continents’ such as Asia (4.393 billion) or Africa (1.186 billion) - Europe, however, has always been considered as the area where most of the biggest economies in the world are According to OECD (2017), in 2015, GDP at current prices and PPPs of the Euro area was 13,627.9 billion USD, which was only lower than that of the United States (18,036.6 billion USD) Regarding countries individually, Germany, the UK and France were economies with highest GDP in 2016, following the US, China and Japan (World Bank, 2017) In the period from 2004 to 2015, economies in Europe experienced many changes, including both positive and negative ones These changes could be represented by the fluctuation of GDP growth, the unemployment rate and, most related to our topic, the changes in retirement policies of European countries Firstly, GDP growth of the coun-tries considered in our study is combined from World Bank data and illustrated in graph with time periods divided by SHARE waves99 As can be seen from the bar chart, GDP growth of the countries varied remarkably over waves For most nations, GDP growth seemed to increase from wave to wave 2, before declining sharply in the next waves, where the most significant changes are observed, especially in Spain and Italy - the two countries that experienced notable negative GDP growth Figure 1: GDP growth of European countries over waves Secondly, using the same data combining method, unemployment rate of the coun-tries is indicated in diagram Similarly to GDP growth, there was no certain trends in the unemployment rate of the 99 See Borsch-Supan, Brandt, et al (2013) for more specific information on interview time of each wave 505 countries Instead, the rate was likely to fluctuate from wave to wave The highest proportion could be easily noticed in Spain, with the rate being over 25% in wave 4, while the lowest rate was found in Switzerland As for other economies, Sweden, Italy and France all reached the peak of unemployment rate around 7% to 12% in either wave or Whereas, Austria and Belgium were likely to maintain their unemployment rate over waves, except for a slight decrease in wave Next, Germany seems to be the most standout country in the graph, since it is the only economy that had the unemployment rate slumping gradually during the period Consider now to the unusual events that happened in European countries from 2004 to 2015, it could be said that the European debt crisis since 2009 have made a significant impact on European economies, especially those in European Union Retrospectively, the crisis could be traced back to the global financial crisis from 2007 The Great Crisis from the US is believed to have extended to European countries, where it began with Greece before spreading out to other countries in the Eurozone, mostly Portugal, Ireland, Italy and Spain (usually described as the PIIGS countries) During the crisis, unemployment rate was found to increase in many countries, consequently, tax revenues declined while transfer payment grew sharply Moreover, many governments in the crisis also had to bail out the banking systems, which made the public debt increase even larger (Moro, 2014) Measuring the impact of the crisis on European countries, Eurostat (2017) reports that GDP of the EU-28 plunged by 4.4% in 2009 and 0.5% in 2012 However, the period of years from 2013 witnessed a continue growth in real GDP until it reached 1.9% in 2016 To understand thoroughly the circumstances under which individuals in our sam-ple data were, it is essential to track the retirement policies reformed in Europe at the time Since increasing life expectancy and population aging have been determinated as potential pressure on social welfare of European governments (Desmette et al., 2015), many countries, especially OECD ones, have been following a common path that is to increase normal and early retirement age, as well as tighten the generosity of the pen-sion system (OECD, 2015e) Specially, in 2011 the reform of raising retirement age was performed in Italy to frame the equalization between the two genders (OECD, 2015d) While in Denmark, from 2014, pension age was planned to be increased from 65 to 67 years by 2022 for people who were born after July 1955 (OECD, 2015b) Tax rates on the pension are particularly set higher for early retirees in Belgium: rather than 16.5% as previously, it is now 20% for retirement at age 60 and 18% at age 62 OECD (2015a) In 2014, pension income tax was also increased in Sweden, while pension benefits for Spain elderly was decided to be adjusted in the future depending on the contributions of individuals Furthermore, in Austria, the 506 penalty for early retiring was raised from 4.2% to 5.1%, whereas pensions below 1,200 EUR were “frozen” in France from April 2014 Lastly, in 2015, the insurance for old-age, survivors and disability was reduced to approximately 9% in Germany Figure 3: Depression level of European countries over waves Data and Methods 3.1 Data source This study uses data from the Survey of Health, Ageing and Retirement in Eu-rope (SHARE)100 (see Borsch-Supan, Brandt, et al (2013) for Data Resource Profile) SHARE is a longitudinal, multidisciplinary and cross-national survey, which aims to collect data of health, socio-economic status along with social and family networks of non-institutionalized people aged over 50 in 21 European countries and Israel 101 Those countries have such divergent institutional conditions that the sampling has to be de-signed differently for each of them, it ranges from simple random selection of house-holds to complicated multistage Households are selected for the survey if it includes at least one member who was born before 1955, is a native speaker of the country and not living abroad at the time The interviews in SHARE are conducted with Computer Assisted Personal Inter-view (CAPI) and a paper-and-pencil questionnaire CAPI questionnaires contain 20 modules, which covers different aspects of an individual such as demographics, social networks, children, physical and mental health, behavioral risks, cognitive function, health care, employment and pensions, grip strength, walking speed, peek flow, social support, financial transfers, housing, household income, assets, activities, expectations, social and physical activities, and consumption Information that is more sensitive, like social and psychological wellbeing, religiosity and political affiliation, is collected by paper-and-pencil questionnaire The SHARE data collection has been primarily funded by the European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812) and FP7 (SHARE-PREP: No.211909, SHARE-LEAP: No.227822, SHARE M4: No.261982) Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the U.S National Institute on Aging (U01 AG09740-13S2, P01 AG005842, P01 AG08291, P30 AG12815, R21 AG025169, Y1-AG-4553-01, IAG BSR06-11, OGHA 04-064, HHSN271201300071C) and from various national funding sources is gratefully acknowledged 101 Wave started with 11 European countries and Israel, other countries (Czech Republic, Poland, Ireland, Luxembourg, Hungary, Portugal, Slovenia, Estonia, and Croatia) were added in later waves 100 507 3.2 Model specification In other to investigate the correlation between retiring and mental health among senior population, firstly, fixed-effect models were employed as in equation 1: MHit = α + β1Retireit + β2𝑋⃗it + ui + єit (1) (1) MHit and Retireit respectively denote mental health measure and retirement status of individual i at time t 𝑋⃗it is a combination of control variables which represent demographic Where background (age, marital status, number of children and years of education) as well as health background (numbers of chronic diseases, visiting hospital and BMI) Furthermore, factors that could possibly affect the well-being of individuals such as limitations with daily activities and the frequency of playing sports are also included in 𝑋⃗it Next, ui is unobserved heterogeneity by time invariance with individual fixed effect, and єit represents distinctive error term Table 2: Country-specific pension age Country Male Female Country Male Female Germany Sweden Spain France Denmark Belgium 65 65 65 66.2 65 65 65 65 65 66.2 65 65 Switzerland Austria Italy 65 65 66.3 64 60 63.8 Source: OECD (2015c), OECD (2015d), and OECD (2015e) However, the coefficient in FE models is estimated with the assumption that it does not correlate with Retireit (retirement decision) This, in turn, is believed to be violated easily for different reasons (Zhu, 2016) In addition, Eibich (2015) points out that it is crucial to concern the endogeneity of retirement, which could be caused by bias in omitted variables and justification, as well as reverse causality Therefore, following Belloni et al (2016), Coe and Zamarro (2011), Heller-Sahlgren (2017), and Zhu (2016), Fix Effects Instrumental Variable (FE-IV) estimation is then applied in this study to control unobservable factors by time variance and reverse causal impact Retireit = θInstrumentit + λ𝑋⃗it + ui + ɛit (2) Equation is the first stage of the FE-IV models, in which, Instrumentit is the instrument for Retireit, defined as Instrumentit=I(Ageit ≥ Agept ) Where I is the indicator function, Ageit is the age of individual i at time t and Agept is the country-specific pension age by OECD (2015e) as in table I takes the value of Ageit −Aget if the condition is true, and otherwise The second stage in the FE-IV estimation could be described as in equation 3, which is similar to equation in which Retireit is the predict value of retirement status from the first stage function (2) ̂ it + β2𝑋⃗it + ui + єit (3) MHit = α + β1𝑅𝑒𝑡𝑖𝑟𝑒 The coefficient in the Equation represents the average impact of retirement on mental health in the year of retirement This average impact may includes impact from the current retirement and past retirement Therefore, we seperate the impact of past retirement from the impact of current retirement by 508 ̂ past) indicating whether individuals have already retired adding to the Equation an dummy (β’1𝑅𝑒𝑡𝑖𝑟𝑒 in previous waves So β’1 is the impact of past retirement ̂ it + β’1𝑅𝑒𝑡𝑖𝑟𝑒 ̂ past + β2𝑋⃗it + ui + єit MHit = α + β1𝑅𝑒𝑡𝑖𝑟𝑒 (4) In addtion, the retirement event is predictable, and therefore the potential impact of retirement decision on mental health might not coincide with the exact timing of retire-ment (Coe and Linderboom, 2008) and (Philipp 2015) Mental health might improve or worsen in anticipation of retirement, close to the so called ‘Ashenfelter’s dip’ or ‘pre-programme dip’ (Ashenfelter and Card, 1985) and (Heckman 1999)102 The current study attempts to capture this potential effect by checking whether the level of mental health changes among non-retired and will-retired in two years and in four years before the actual retirement The model in this case is as follows: ̂ it + β’’1𝑅𝑒𝑡𝑖𝑟𝑒 ̂ future + β2𝑋⃗it + ui + єit (5) MHit = α + β1𝑅𝑒𝑡𝑖𝑟𝑒 Where Retiref uture denotes whether an individual will retire in the next two years (or in the next four years for the longer period) or not So β’’1 is the impact of ‘Ashenfelter’s dip’ The control variables (𝑋⃗it) are chosen in the same year with mental health indexes Next, to test the consistence of results from the fixed-effect models, we employ the propensity score matching (PSM) approach formalized by Rosenbaum et al (1983) This method, additionally, is believed to control the impact of cofounding covariates effectively (Brenna et al., 2015) In our study, PSM was used to identify control and treatment groups based on potential characteristics that could possibly affect mental health of an individual Similarly to the FE models, the observables include gender, country, marital status, number of children, years of education, numbers of chronic diseases, BMI and daily activities Once control and treatment groups are already chosen, the changes in mental health of retirees is estimated as in equation Similar functions are also engaged in the research of Aranda (2015) and Marcus (2013) E[Y 𝐸[𝑌1𝑖(𝑡+𝑠) − 𝑌0𝑖(𝑡+𝑠) |𝐷𝑖𝑡 = 1] = 𝐸[𝑌1𝑖(𝑡+𝑠) |𝐷𝑖𝑡 = 1] − 𝐸[𝑌0𝑖(𝑡+𝑠) |𝐷𝑖𝑡 = 1] (6) 𝐷𝑖𝑡 denotes the change in retirement status, individuals who are considered as re-tirees are categorized as treated and take 𝐷𝑖𝑡 = On the contrary, people who are untreated take 𝐷𝑖𝑡 = Furthermore, 𝑌1𝑖(𝑡+𝑠) is the depression level of individual i at time t + s (s ≥ 0), considered as after enrolling in retirement Meanwhile, 𝑌0𝑖(𝑡+𝑠) is likely to present the mental state of the person if there had not been a change in retirement status Apparently, E[𝑌0𝑖(𝑡+𝑠) |𝐷𝑖𝑡 = 1] is the expected outcome estimation of treated population would have had if they did not retire, or before the retirement term of treated group Because of the potential Ashenfelter’s dip, the difference-in-difference (DID) ap-proach is not useful in the case of retirement (***) But the PSM estimation itself can reveal if the Ashenfelter’s dip is present or not We firstly use PSM to choose the control and treatment groups at the year of retirement Then we trace back to get information of mental health of these two groups in previous waves The difference in mental health after controlling for other variables are attributed to the impact of anticipating retire-ment Ashenfelter (1978) noted a potentially serious limitation of this procedure when he observed that the mean earnings of participants in government training programmes decline in the period prior to pro-gramme entry Subsequent research finds this regularity, sometimes called ‘Ashenfelter’s dip’ or the ‘pre-programme dip’, for participants in many other training and adult education programmes (see Ashenfelter and Card, 1985, Bassi, 1983, 1984, and the comprehensive survey by Heckman et al., 1999) 102 509 3.3 Variables 3.3.1 Mental health Mental health outcome in this study is represented by variable EURO-D, which was developed by the Concerted Action Programme - EURODEP (Brenna et al., 2015) EURO-D is built on a 12-item scale to measure the range of being depressed The inter-views that got EURO-D values were in the local language and included questions about depression: whether the respondent has been sad or depressed in the last month; pes-simism: refers to the respondents hopes for the future; suicidality: whether they have felt that they would rather be dead in the last month; guilt: whether they feel like blam-ing themselves or feel guilty about anything; sleep: whether they have trouble with sleeping; interest: changes in the general interest; feel irritable; changes in appetite, fatigue: whether they had too little energy to the things they wanted to in the pre-vious month, concentration: has difficulties when it comes to concentrating; enjoyment and tearfulness: whether they have cried at all in the last month If the answer for one of those questions is “yes”, it is coded as “1”, vice versa, “no” is coded as “0” The scores were summed ultimately based on the answers of each person The EURO-D score ranges from 0, which represents not depressed, to 12 very depressed 3.3.2 Retirement status The central explanatory variable used in this paper is retirement status, which takes the value “1” for retirees and “0” for other people However, according to Heller-Sahlgren (2017), there are three different ways to define retirement status First, the “other people” could be employed, unemployed people, homemakers as well as those who are permanently ill or disabled The second retirement definition, meanwhile, takes homemakers, permanently ill or disabled persons into retirees, as long as they report not doing any paid work for the last month Thirdly, the sample simply includes people who admit being retired or employed Our study follows Butterworth et al (2006) and recodes people who were employed/self-employed or unemployed as “not retired” This way of defining retiring is also argued to be used internationally (Butterworth et al., 2006) 3.3.3 Reasons of retirement There are ten reasons of retirement in SHARE data The interviewees were asked whether they got retired to (1) became eligible for public pension, (2) became eligible for private occupational pension, (3) became eligible for a private pension, or (4) be-cause of an early retirement offer with special incentives, (5) a redundancy, (6) own ill health, (7) ill health of relative(s)/friend(s), (8) to retire at same time as spouse or part-ner, (9) to spend more time with family or finally, (10) to enjoy life However, we did not exploit all of the reasons individually, because there seem to be similar motivations in some of those Instead, we follow Robinson et al (2010), who uses three subscales to measure reasons of retirement, and divides the ten reasons into three groups Reasons 8, and 10 are categorized into “Aspirational Motivations”, while reasons to are in the group of “Positive Circumstances” The last group includes the rest of the reasons and is under the name of “Negative Circumstances” 3.3.4 Instrument variables According to Zhu (2016), instrument variables should meet two conditions, which is being related to the explanatory variable, and orthogonal to exogeneity condition Heller-Sahlgren (2017), whose research similarly investigates the impact of retirement on mental health, also shares this point of view on instrument variables In other words, retirement is the only “channel” that the instrument variable chosen in this paper could alter the outcomes To be more specific, we follow previous studies that employed comparable models, such as those of Belloni et al (2016), Coe and Zamarro (2011), 510 Table 3: Summary of retirement transition pattern Table 4: Summary Statistics of main variables Descriptive statistics Table shows the transition of retirement through waves For further purpose of analyzing, which is to balance the time spent in retirement to such an extent, retirees in wave was dropped out of the sample, since it contains people who had already retired long before the interview As can be seen from the table, the number of retirees have been putting up after waves For example, from wave to wave 2, there were only 274 individuals got retired, then senior citizens in the sample of SHARE kept “joining the team” until the number of retirees in wave was 1,222 people It is also noteworthy that the total figure is balance in all the waves with 1,715 individuals Next, the summary statistics of main variables divided by retirement status are illus-trated in table It can be described that the average depression level, which is indicated by EUROD, is slightly higher in the group of non-retirees by 10% However, the av-erage chronic diseases of retired people is reported to be more than that of people who remained in the labor force The Body Mass Index of the two groups are 26.1 and 26.6, which could be categorized as “normal” according to Alavinia et al (2008) Addition-ally, nonretirees seem to spend more time in schools (12.9 years vs 12.5 years), and retirees is likely to have more limitations in daily activities Other than that, further descriptive statistics for country, hospital, BMI status and the frequency of vigorous activities divided by the two groups are reported in table in proportion 512 Table 5: Demographic characteristics by proportion Retirement Status Not Retired No % Retired No Total % No % Country identifier Austria Germany Sweden Spain Italy France Denmark Switzerland Belgium 174 498 1,104 299 378 539 920 488 896 3.3% 9.4% 20.8% 5.6% 7.1% 10.2% 17.4% 9.2% 16.9% 176 292 671 181 342 396 410 222 589 5.4% 8.9% 20.5% 5.5% 10.4% 12.1% 12.5% 6.8% 18.0% 350 790 1,775 480 720 935 1,330 710 1,485 4.1% 9.2% 20.7% 5.6% 8.4% 10.9% 15.5% 8.3% 17.3% Total 5,296 100.0% 3,279 100.0% 8,575 100.0% Marital status Not married Married 1,377 3,919 26.0% 74.0% 831 2,448 25.3% 74.7% 2,208 6,367 25.7% 74.3% Total 5,296 100.0% 3,279 100.0% 8,575 100.0% In hospital last 12 months No Yes 4,846 450 91.5% 8.5% 2,906 373 88.6% 11.4% 7,752 823 90.4% 9.6% Total 5,296 100.0% 3,279 100.0% 8,575 100.0% BMI Status Normal BMI Over BMI Obesity BMI 2,293 2,188 815 43.3% 41.3% 15.4% 1,288 1,399 592 39.3% 42.7% 18.1% 3,581 3,587 1,407 41.8% 41.8% 16.4% Total 5,296 100.0% 3,279 100.0% 8,575 100.0% Vigorous activities More than once a week Once a week One to three times a month Hardly ever, or never 2,680 850 463 1,294 50.7% 16.1% 8.8% 24.5% 1,402 585 281 1,008 42.8% 17.9% 8.6% 30.8% 4,082 1,435 744 2,302 47.7% 16.8% 8.7% 26.9% Total 5,287 100.0% 3,276 100.0% 8,563 100.0% Results The results of regression models are presented in table ?? As can be seen, the first two columns indicate results of fixed-effect models with retirement status and dummies of retired reasons as explanatory variables respectively Firstly, it is shown from the ta-ble that retirement seems to make people feel less depressed by 0.12 point The similar result has also been found in several studies, including ones that exploit the same data source (Belloni et al., 2016) and others (Charles et al., 2004; Mein et al., 2003; Mojon-Azzi et al., 2007) When reasons for retirement are considered, more specific results are interpreted in the table Firstly, people who retire by aspirational motivations (e.g to enjoy life, to retire as the same time with spouse, or to spend more 513 time with family) are likely to have better mental health in comparison with those who remain in the labor force Secondly, the retirees who have stopped working due to positive circumstances, for example, they became eligible to receive pension or were offered an early retire-ment, might also experience less depressed Furthermore, the coefficients of these two groups of reasons are -0.283 and -0.123 respectively, which could mean that retiring because of aspirational motivations influences mental health slightly better than that of positive circumstances Thirdly, there is no evidence indicating changes in the mental health of individuals who retire by negative circumstances In the research of Robin-son et al (2010), which considers aspirational motivations, positive circumstances and negative circumstances as instrument variables, similar findings are also approved It is revealed that, the first two groups of reasons have a remarkable positive impact on life satisfaction of retirees, while negative circumstances seem not to make any significant effect Table 6: Regression results with EURO-D as dependent variable (1) FE Retirement Age Age Age Marital status Number of children In hospital last 12 months Chronic diseases BMI Years of education ISCED 1997 ADLs iADLs Vigorous activities Moderate energy activities GDP growth Unemployment rate (2) FE -0.115 (0.053) -0.155 (0.067) 0.00116 (0.00041) -0.582 (0.14) -0.0480 (0.034) 0.222 (0.056) 0.148 (0.021) -0.0263 (0.011) -0.0178 (0.010) -0.129 (0.11) 0.0855 (0.072) 0.141 (0.066) -0.0347 (0.015) -0.102 (0.026) -0.0102 (0.020) -0.0303 (0.0080) -0.137 (0.049) 0.00111 (0.00042) -0.577 (0.14) -0.0482 (0.034) 0.221 (0.056) 0.149 (0.021) -0.0270 (0.011) -0.0197 (0.010) -0.128 (0.11) 0.0877 (0.072) 0.141 (0.066) -0.0324 (0.015) -0.103 (0.026) -0.0100 (0.015) -0.0271 (0.0079) 514 (3) FE-IV(I) (4) FE-IV(II) -0.552 (0.020) 0.00486 (0.00015) -0.0172 (0.030) 0.00458 (0.0075) 0.0192 (0.012) 0.0101 (0.0047) 0.000704 (0.0024) -0.00211 (0.0023) -0.0169 (0.024) -0.00182 (0.016) -0.00837 (0.015) -0.000382 (0.0034) 0.0205 (0.0058) -0.0109 (0.0044) -0.00629 (0.0018) -0.494 (0.23) -0.261 (0.093) 0.00208 (0.00069) -0.588 (0.14) -0.0459 (0.034) 0.228 (0.057) 0.152 (0.022) -0.0257 (0.011) -0.0180 (0.011) -0.135 (0.11) 0.0866 (0.073) 0.138 (0.066) -0.0348 (0.016) -0.0928 (0.027) -0.0148 (0.020) -0.0326 (0.0082) Retired due to AMs -0.283 (0.14) -0.123 (0.060) -0.0775 (0.065) Retired due to PCs Retired due to NCs Years over pension age Constant r2 N F p 9.029 8.222 -0.0917 (0.0047) 15.66 (2.92) (1.52) (0.75) 0.0267 8490 9.305 0.0258 8490 9.955 0.554 8490 421.3 15 12.08 (3.45) 8490 Standard errors in parentheses p < 0:05, p < 0:01, p < 0:001 Moving now to the fixed-effect models instrument variable taken into account, the first stage’s result shows that, the age over pension age has a significant negative in-fluence on retired decision making ( = 0:0917, p < 0:001) As the instrument variable takes the values of years over pension age for individuals who are older than then pension age, and zero otherwise, whether they are retired or not This could also be reasonable for people who choose to keep working while being over retirement age Next, the most important findings of this paper are shown in the second stage of the FE-IV model It is indicated that retiring is likely to have a positive impact on mental health of senior citizens by 0.494 point This, on the other hand, is analogous with the result found in the first FE model Additionally, a variety of tests to detect weak instru-ment variables are also performed by xtivreg2 command in Stata The result15 interprets that the instrument variable used in our study is suitable Next, we test the consistency of the models by applying them with other sets of data, which have fewer waves involved Similar results are presented in Tables 9, 10, and 11 provided in the Appendix It could be easily noticed that the observations are greater as the numbers of waves are lessen in these tables However, the population of retirees in each set of data are simultaneously reduced This leads to the fact that the ratio of retirees/observations dwindles with less waves involved As a result of the law of large numbers, the outcomes in table 11 and some of these in table 10 has no statistical significance Interestingly, results in the data of wave 4, and (Table 10) depict evidence that retiring by negative circumstances could negatively affect the mental health of people ( = 0:127, p < 0:05) Furthermore, when potential retirement and past retirement are respectively em-ployed in the models, similar results are shown in table and Firstly, table presents results with potential retirement added in the models As can be seen in column (2), among people with similar potential retired decision as well as other demographic char-acteristics, retirees seem to feel less depressed by 1.504 point (p < 0:01) While results in column (4) indicates that, individuals who are going to retire within the next years seem to have better mental health than those who are not ( = 1:537; p < 0:01) Secondly, results with ‘retiring in the last wave’ considered as control and explanatory variable are illustrated in table Similarly to former results, retirement is likely to Weak identification test: Cragg-Donald Wald F statistic = 14.95; Stock-Yogo weak ID test critical values for 10%, 15%, 20% and 25% maximal IV size: 16.38, 8.96, 6.66, 5.53 Weak-instrument-robust inference: Anderson-Rubin Wald test F (1; 6771) = 5:24; P = 0:0222; Anderson-Rubin Wald test: = 5:25; P = 0:0219; Stock-Wright LM S statistic: = 5:25; P = 0:0220 515 have a positive impact on mental health of the elderly remarkably However, being retired in the last wave seem to make people feel more depressed by 2.692 point (p < 0:01) Considering other previous research that apply comparable models, a variety of conclusions has been exposed For instance, Coe and Zamarro (2011) examine the influence of retirement on different aspects of health, including mental well-being, and find that there is no significant correlation between retired status and depression in their FE-IV models Furthermore, there is also no evidence found in the study of Belloni et al (2016), who investigate the same data source as Coe and Zamarro (2011) (SHARE) and attempt to distinguish the similar interaction, divided by genders It is noteworthy that the instrument variable employed in these two papers is slightly different from ours, it takes value if the age of the considering individual is greater or equal the pension age, and otherwise However, when Zhu (2016) uses the same instrument in his FE-IV models with data from the Household, Income and Labor Dynamics in Australia (HILDA) Survey, retire-ment status seems to have an essential impact on women’s mental health This result, however, is opposite with the one disclosed from the FE models in the same research Zhu (2016) argues that, in all likelihood, it might be the reverse causality of health on retirement decision in the FE models that makes the results contrary Moreover, with data from SHARE, Heller-Sahlgren (2017) discovers a negative influence of retirement upon mental health of elderly in long term Short-time effect, on the other hand, is not confirmed by any statistic significance As for control variables, findings from all of the models share common trends Firstly, age and age square clearly have conflicting outcomes on mental health While age seems to reduce stress for retirees, the latter is likely to make an adverse “reaction” This means the relationship between age and depression is diminish, which leads to the fact that age could probably decrease depression initially, yet the impact gets negligi-ble for greater age Next, being married appears to help individuals have better mental health, as Brenna et al (2015) discuss in their study Considering another variable that indicates background of a person, years of education is found to decrease depression among retirees marginally Similarly, Crespo et al (2014), who intensely explored the relationship between education and mental health of European, discovered that depression level would highly decline by 6.5% with an extra year spent in school Regarding the health-related group of control variables, respondents who are for-merly suffered from illness stand more chances to experience worse mental health in their retirement term Specifically, having been hospitalized increases 0.222 point2of depression level (p < 0:001), while the numbers of chronic diseases could possibly raise that by 0.148 point (p < 0:001) Besides, issues relating to activities also appear to affect mental well-being of the elderly remarkably For example, people who strug-gle with instrument activities seem to have higher depression level Other than that, the less frequently people get involve in sport activities, the more chances they have to be depressed Table 7: FE and FE-IV regression results, Wave 4-5-6 (1) FE-IV(I) Retirement Years over pension age Potential retirement -0.0519 (0.0029) -0.671 (0.0047) (2) FE-IV(II) (3) FE-IV(I) (4) FE-IV(II) -1.504 (0.46) -0.853 (0.0060) -0.0496 (0.0033) -1.348 (0.42) -0.992 (0.31) First FE model 516 -1.537 (0.49) Age Age Age Marital status Number of children In hospital last 12 months Chronic diseases BMI Years of education ISCED 1997 ADLs iADLs Vigorous activities Moderate energy activities GDP growth Unemployment rate r2 N F p -0.391 (0.011) 0.00336 (0.000087) 0.0122 (0.013) 0.00301 (0.0030) 0.00517 (0.0047) 0.00189 (0.0018) -0.000176 (0.00073) -0.000385 (0.0019) 0.0273 (0.015) -0.00841 (0.0058) -0.00169 (0.0049) -0.00409 (0.0014) 0.00384 (0.0021) -0.000352 (0.00096) 0.00255 -0.519 (0.14) 0.00389 (0.0012) -0.723 (0.11) 0.0130 (0.024) 0.264 (0.039) 0.180 (0.015) -0.00703 (0.0060) -0.00324 (0.015) 0.00131 (0.13) 0.200 (0.048) 0.244 (0.040) -0.0317 (0.012) -0.0174 (0.018) -0.00239 (0.0079) 0.0196 -0.219 (0.013) 0.00185 (0.00010) -0.00614 (0.015) 0.00409 (0.0033) 0.00300 (0.0053) 0.000989 (0.0020) -0.000421 (0.00082) -0.000462 (0.0021) 0.00314 (0.017) -0.00916 (0.0066) 0.00478 (0.0055) -0.000281 (0.0016) 0.00137 (0.0024) -0.00252 (0.0011) 0.000704 -0.284 (0.085) 0.00182 (0.00064) -0.750 (0.11) 0.0148 (0.025) 0.261 (0.039) 0.179 (0.015) -0.00742 (0.0060) -0.00338 (0.016) -0.0335 (0.13) 0.198 (0.048) 0.254 (0.040) -0.0262 (0.011) -0.0209 (0.018) -0.00571 (0.0079) 0.0169 (0.00085) (0.0072) (0.00096) (0.0071) 0.700 22719 1857.7 22719 0.619 22719 1291.4 22719 Standard errors in parentheses (2) Instrumented variables: Retirement; IV: Years over penstion age Instrumented variables: Potential Retirement; IV: Years over penstion age p < 0:05, p < 0:01, p < 0:001 Table 8: FE and FE-IV regression results, Wave 4-5-6 (1) FE-IV(I) Retirement Years over pension age -0.0461 517 (2) FE-IV(II) (3) FE-IV(I) (4) FE-IV(II) -1.703 (0.54) 0.140 (0.0089) 0.0283 -0.414 (0.14) Retired in last wave Age Age Age Marital status Number of children In hospital last 12 months Chronic diseases BMI Years of education ISCED 1997 ADLs iADLs Vigorous activities Moderate energy activities GDP growth Unemployment rate Constant r2 N F p (0.0044) 0.116 (0.0073) -0.540 (0.016) 0.00470 (0.00013) 0.0377 (0.020) 0.000413 (0.0045) 0.00840 (0.0071) 0.00304 (0.0027) 0.0000777 (0.0011) -0.000181 (0.0029) 0.0552 (0.023) -0.00677 (0.0088) -0.0105 (0.0074) -0.00912 (0.0021) 0.00749 (0.0032) 0.00294 (0.0015) 0.00421 (0.0013) 15.20 (0.0049) 0.165 (0.073) -0.854 (0.24) 0.00686 (0.0021) -0.678 (0.11) 0.00929 (0.025) 0.270 (0.040) 0.182 (0.015) -0.00660 (0.0062) -0.00297 (0.016) 0.0545 (0.13) 0.201 (0.049) 0.229 (0.042) -0.0409 (0.013) -0.0107 (0.019) 0.00311 (0.0084) 0.0230 (0.0077) 28.51 -0.188 (0.019) 0.00149 (0.00015) -0.000824 (0.022) 0.00180 (0.0049) -0.00989 (0.0078) -0.00188 (0.0030) 0.00144 (0.0012) 0.0000829 (0.0031) 0.0228 (0.026) 0.0135 (0.0097) -0.00666 (0.0081) 0.00130 (0.0023) -0.00660 (0.0036) 0.00124 (0.0016) 0.00490 (0.0014) 5.655 2.692 (0.96) 0.557 (0.26) -0.00502 (0.0021) -0.739 (0.12) 0.00370 (0.028) 0.283 (0.044) 0.182 (0.017) -0.0107 (0.0069) -0.00290 (0.017) -0.0997 (0.14) 0.176 (0.056) 0.264 (0.046) -0.0292 (0.013) -0.00523 (0.021) -0.00518 (0.0089) 0.00266 (0.0091) -12.25 (0.57) (7.10) (0.64) (7.89) 0.310 22719 358.1 22719 Standard errors in parentheses (2) Instrumented variables: Retirement; IV: Years over penstion age (4) Instrumented variables: Past Retirement; IV: Years over penstion age p < 0:05, p < 0:01, p < 0:001 Conclusion 518 0.123 22719 112.1 22719 The central idea of this study is to analyze the correlation between retirement and mental health of European elderly population, if there is any, with different statistic models applied In addition, the causal effect of this relation ship is also carefully tested, since there is much likelihood that mental health has an adverse influence on retirement of individuals (Coe and Zamarro, 2011; Zhu, 2016) Next, different motivations of retirement are taken into account through reasons for retirement divided into three groups By way of doing that, the paper explores how different reasons for retirement could affect one’s mental health and to what extent To sum up, we discover that retirement has a positive impact on the well-being of individuals in terms of mentality A similar result is also revealed when instrument vari-able is included in the model, in order to control the causal influence Finally, the results indicate that retirement due to aspirational motivations and positive circumstances have a greater beneficial effect on mental health, in comparison with retirement in general However, retirement by negative circumstances seems not to make any significant con-sequence in most cases Analyzing the aging issues, including retirement matter and well-being of the el-derly, future research should first discover whether positive changes in health could lead to improvements in the quality of life (Coe and Zamarro, 2011) Secondly, the duration of time spent in retirement should be cautiously included in the models, since previous studies have found not short, but the long-term effect of retirement on health (HellerSahlgren, 2017) Finally, characteristics of the career path from which people retire 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