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Mental Health Effects of Retirement

Abstract

We study the retirement effects on mental health using a fuzzy regression discontinuity design based on the eligibility age to the state pension in the Netherlands. We find that the mental effects are heterogeneous by gender and marital status. Retirement of partnered men positively affects mental health of both themselves and their partners. Partnered female retirement has hardly any effect on their own mental health or the mental health of their partners. Single persons retirement does not seem to have an effect on their mental health status.

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Notes

  1. 1.

    From an overview of the literature, Coile (2015) concludes that in about one-third of working couples partners retire within one year of each other. Bloemen et al. (2019) also find cross-partner retirement effects of retiring. By contrast, we find no indication of coordinated retirement decisions.

  2. 2.

    Some studies focus on the effects of retirement on physical health or health behaviors. See for example Nielsen (2019) and Celidoni and Rebba (2017). There are also studies that investigate the effects of retirement on a composite indicator of health that includes mental health. These are also discussed below.

  3. 3.

    Quite a few studies investigate the relationship between retirement and mortality. These studies are all based on administrative data. The results are all over the place. Hernaes et al. (2013) find that a retirement reform in Norway induced some workers to indeed retire early, but their mortality was not affected. Hallberg et al. (2015) find that a retirement reform for Swedish army personnel increased early retirement and reduced mortality. Bloemen et al. (2017) using a temporary change in the rules for early retirement of older civil servants in the Netherlands finds that early retirement reduces mortality. Fitzpatrick and Moore (2018) on the other hand find that early retirement in the U.S. increased male mortality but for females there is no significant increase in mortality after retirement. Kuhn et al. (2018) use Austrian administrative data finding that retirement increased mortality for men but not for women.

  4. 4.

    For adults, the net monthly minimum wage in the Netherlands in 2018 was €1440.

  5. 5.

    They take value 0 when individual i (or his/her partner) is interviewed in the month in which (s)he becomes eligible to the state pension.

  6. 6.

    Instead of collapsing the information on mental health from ordered categories to dummy indicators, we could have used these categories either in nonlinear ordered response models or, after assigning them a cardinal meaning, in usual linear models estimated by 2SLS. However, Bond and Lang (2019) criticized the use of ordered variables like happiness scores, because it is difficult to compare these variables across individuals. By collapsing them to binary indicators, we avoid this limit.

  7. 7.

    See Knoef and Vos (2009) for an evaluation of the representativeness of the LISS panel and Scherpenzeel (2011, 2010) and Scherpenzeel and Das (2010) for methodological notes on the design of the LISS panel.

  8. 8.

    See https://www.dataarchive.lissdata.nl/study_units/view/1 for the full list of studies of the LISS panel.

  9. 9.

    Hetschko et al. (2013) show that even though unemployed are not actively performing paid work, they do experience an increase in their life satisfaction upon retirement as social norms no longer require them to be employed whereas prior to retirement they were expected to look for a job.

  10. 10.

    Hours of work are defined as usual weekly working hours.

  11. 11.

    See https://www.rand.org/health-care/surveys_tools/mos/36-item-short-form.html.

  12. 12.

    Since not all the respondents to the health survey responded in the same month also to the monthly background variables, we could not match 1101 observations.

  13. 13.

    The Cronbach’s alpha of these five variables suggests relatively high internal consistency. It is equal to 0.836 (0.850) for (wo)men living in a couple and 0.869 (0.857) for (fe)male singles.

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Acknowledgements

We thank CentERdata of Tilburg University for providing us with the LISS (Longitudinal Internet Studies for the Social sciences) panel data on which we based our empirical analysis. The LISS panel data were collected by CentERdata through its MESS project funded by the Netherlands Organization for Scientific Research. We also thank Isabella Giorgetti and participants at the 33rd Italian Association of Labour Economics (AIEL) conference (Ancona, 2018), the 59th Italian Economic Association (SIE) conference (Bologna, 2018) and seminar participants at the University of Bath, Monash University (Melbourne), LISER (Luxembourg), Deakin University (Melbourne), University of Technology Sydney and Erasmus School of Economics (Rotterdam) for helpful comments and suggestions.

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Correspondence to Jan C. van Ours.

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Appendices

Appendix 1: Details on Our Data and Sample

Sample Selection

Between 5072 and 6698 individuals were interviewed each year for the core study on health from 2007 until 2018, resulting in a total of 63,603 records. We matched each record on the basis of the information on the year and month of interview to the corresponding information about the retirement status and age in months coming from the background variables. We were able to match 62,502 records, belonging to 12,958 different individuals.Footnote 12 Given the aim of this paper, we restricted the sample to individuals close to the moment of the state pension eligibility. After defining according to the rules outlined in Sect. 2 a variable that measures the distance in months from the month in which an individual becomes eligible to the state pension, we kept all the observations who were within 84 months away from the month of the state pension eligibility at the moment of the interview. The sample size shrank therefore to 16,551 observations. Since the aim is, not only to unveil the effect of retirement on his/her-own mental health, but also to identify the impact on partner’s outcome variables, we restricted the sample to couples in which both partners answered the questionnaire on health (3497 couples) and to singles (4339 observations).

Finally, we dropped from the samples 128 couples for which at least one partner is interviewed in the month in which the eligibility to the state pension was attained, or in the previous or in the subsequent month. Similarly, we eliminated 79 singles. This refinement is due to a kind of heaping problem (Barreca et al. 2016) or rounding error (Dong 2015). From 1 January 2012, the state pension eligibility is indeed received from the day in which one satisfies the age requirement. Since we do not have the day of birth, but only the month in which an individual becomes eligible to the state pension, we cannot be sure, for those interviewed in the month in which they become eligible to the state pension, whether they are already eligible at the moment of the interview or they will be soon eligible to the state pension. Although this kind of error is likely to be randomly distributed across those observations interviewed in the month of state pension eligibility, it is present only above the cutoff (Lee and Card 2008). Given the small number of such observations, omitting them from the sample is the easiest way of facing the problem and getting unbiased estimates of the treatment effect for all the others (Barreca et al. 2016). The remaining sample has 3369 records of couples and 4260 singles.

Along the paper, we use different subsamples, depending on the chosen bandwidth. For example, when the chosen bandwidth is 42 (for couples satisfied by both partners), the sample is made up of 1250 couples and 2120 singles (861 men and 1259 women).

Definition of Variables and Descriptive Statistics

Table 5 clarifies the discrete nature of the outcome variables and the meaning attached to the numeric values. Information on happiness, calmness, depression, anxiety and feeling down in the last month is collected by asking individuals whether in the last month they felt, respectively, “happy”, “calm and peaceful”, “depressed and gloomy”, “very anxious” and “so down that nothing could cheer me up”. They could choose one the following six: (1) never, (2) seldom, (3) sometimes, (4) often, (5) mostly and (6) continuously. Since the top or the bottom categories were sometimes indicated rarely by respondents, we had to group them as detailed in the last column of Table 5. These five variables measuring mental health, with their original scores from 1 to 6, are the ones used to build the MHI-5 scale.Footnote 13 We summed up the scores of the five mental health variables after reversing depression, anxiety and feeling down, subtracted 5 and multiplied by 4. By doing so, the resulting MHI-5 scale spans from 100, for those reporting all 6s, to 0, for those reporting all 1 s. Larger numbers represent therefore better mental health conditions.

Table 5 Outcome indicators

Table 6 provides descriptive statistics of the outcome variables. Table 7 reports summary statistics of the retirement indicator, the number of months from the age of state pension eligibility and the other control variables used in the econometric analysis. Finally, Fig. 4 shows the distribution of the age difference between husbands and wives in the sample used for our baseline estimates. The age difference is predominantly positive: males are on average older than their female partners. In almost 77% of the couples the male is older than the female. For 30% of the couples the male is at least two years older.

Table 6 Descriptive statistics of the outcome variables
Table 7 Descriptive statistics of the covariates
Fig. 4
figure4

Distribution of the age difference between partners are within the bandwidth of 42 months (age man minus age woman). Note: The sample is limited to 1250 couples with both partners within the bandwidth of 42 months

Appendix 2: Validity and Falsification Tests

As suggested by McCrary (2008), a jump in the density of the running variable at the threshold would be direct evidence of the failure of the local randomization assumption. Figure 5 displays the local polynomial density estimate of the running variable described in Cattaneo et al. (2018). The graphs show that there is no evidence of discontinuity in the population density at the cutoff, for both genders and whether living in a couple or not.

Fig. 5
figure5

Graphical density test of the running variable, Notes: The solid line is the the local polynomial density estimate of the running variable described in Cattaneo et al. (2018). The local polynomial is of order 3. The robust bias-corrected test proposed in Cattaneo et al. (2018) cannot reject the null hypothesis of the absence of discontinuity: p value equal to 0.711 (0.293) for men (women) for couples; p value equal to 0.161 (0.872) for men (women) for singles

If the retirement probability is locally randomized near the cutoff, then the treatment should not have an effect on the pre-treatment covariates, i.e. the treated units should be similar to control units in terms of observed characteristics. We follow Lee and Lemieux (2010) and test if the discontinuity influences our predetermined variables, by estimating a seemingly unrelated regression (SUR) with one equation for each of the predetermined variables, with the same bandwidth (42 months) and local polynomial regression as in the baseline estimates. After the estimation of the SUR model, we performed joint and individual tests of the significance of the discontinuities. They are reported in Table 8. The single tests do not show systematic jumps at the cutoff: only 2 discontinuities out of 39 are significantly different from 0 at the usual 5% confidence level. All the joint tests do not reject the null hypothesis. Since we are testing the presence of discontinuities for many covariates, the joint tests suggest that the two significant discontinuities are so by random chance (Lee and Lemieux 2010).

Table 8 Falsification test: discontinuity of the predetermined variables at the cutoff for couples (SUR estimation, bandwidth equal to 42)

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Picchio, M., Ours, J.C. Mental Health Effects of Retirement. De Economist 168, 419–452 (2020). https://doi.org/10.1007/s10645-020-09369-8

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Keywords

  • Retirement
  • Health
  • Well-being
  • Happiness
  • Regression discontinuity design

JEL Classification

  • H55
  • J14
  • J26