Introduction
In many industrialized countries, the population is ageing due to increasing life expectancy and decreasing fertility (Christensen et al.
2009). To reduce the economic burden that the ageing of society places on a welfare state, many countries are encouraging older workers to remain longer in the labour market by increasing the statutory retirement age, reducing generosity of benefits and/or restricting access to early retirement schemes. The actual retirement age, i.e. the age at which workers leave the labour market, however, continues to stay behind the statutory retirement age in most European countries (OECD
2014).
Studies show that there is variation in retirement timing across European countries (e.g. Ebbinghaus and Hofäcker
2013), suggesting that macro-level factors may play a role in retirement timing. To understand cross-country differences in retirement timing, a better understanding of the pension and labour market policies that affect early exit from the labour market is needed.
Macro-level determinants
Decisions on early work exit are influenced by micro-level factors, with health being the most important determinant (Van Rijn et al.
2014), meso-level (workplace) factors (Scharn et al.
2018) and macro-level factors (Scharn et al.
2018). In this study, we focus on macro-level factors as determinants of early work exit. Macro-level factors may affect different routes of early exit from paid work, via public or private early retirement schemes, but also via social insurance programs (unemployment and disability). Determinants of retirement timing can be categorized into pull, push and maintain factors (Ebbinghaus and Hofäcker
2013; Hofäcker et al.
2016). Macro-level factors belonging to each of these groups were included in the present study.
Pull factors provide financially attractive opportunities for older workers to exit the labour market early, e.g. generous unemployment benefits and high implicit taxes on continued work. Previous studies have shown that in countries with more generous pension systems, early work exit is more prevalent (Blöndal and Scarpetta
1999; Duval
2003; Schils
2008). Most studies have examined replacement rates in this regard. A replacement rate is a measure of how generously a pension system provides income after work exit to replace earnings which were the main source of income prior to work exit (Korpi and Palme
2007b). A high unemployment replacement rate acts as a pull factor by providing a financially attractive alternative to continuing work. Higher implicit taxes on continued work also act as pull factors by making early retirement financially more attractive than continuing work (Blöndal and Scarpetta
1999; De Preter et al.
2013; Duval
2003). There is an implicit tax on continued work when the change in pension wealth from working one additional year is less than the value of contributions paid to the pension (Duval
2003). The implicit tax on continued work also captures some of the effects of eligibility ages and generosity of benefits. The higher the replacement rate, the higher is the implicit tax on continued work and the higher the minimum pensionable age, the lower is the implicit tax on continued work before this age (Duval
2003). In addition, expenditure on passive labour market policies (PLMP) (De Preter et al.
2013) may also be associated with early work exit. A high expenditure on PLMP, consisting of expenditure on unemployment benefits and early retirement schemes, may make early work exit more attractive, acting as a pull factor.
Maintain factors, e.g. training older workers, job creation or start-up incentives, promote older workers’ employability. A high expenditure on active labour market policies (ALMP) which comprise policies aimed at improving access to the labour market for the unemployed and disabled, may decrease the rate of early work exit (Ebbinghaus and Hofäcker
2013; Martin
2015; Martin and Grubb
2001) and can therefore be seen as a maintain factor.
Push factors make it more difficult or less attractive to stay in the labour market and therefore ‘push’ workers out of the work force. There is evidence that strictness of employment protection legislation (EPL) is related to early work exit (Bellmann and Florian
2007; Dorn and Sousa-Poza
2007; Ebbinghaus and Radl
2015). A strict EPL may make firms less likely to hire older workers because of expensive future dismissals. This could in turn lead to forcing older workers who can no longer work for their present employer or who were already unemployed into unemployment. Firms ‘pushing’ their older employees into early retirement instead of making them redundant (which is harder under strict EPL) may be another explanation for this positive association (Ebbinghaus and Radl
2015). In this way, a strict EPL can be seen as a push factor. It could also be argued, however, that it acts as a maintain factor, with older workers who are still employed and can still carry out their jobs being better protected and are therefore less at risk of early work exit. Some studies did not find any effect of EPL on early work exit (De Preter et al.
2013; Fischer and Sousa-Poza
2006). Another push factor is a high unemployment rate. Higher unemployment rates have been shown to be associated with early work exit (Gorodnichenko and Song
2013), most likely through discouragement, as unemployment rates reflect the chances of finding a new job (Blöndal and Scarpetta
1999; Fischer and Sousa-Poza
2006). A high statutory pension age may also have a ‘push’ effect. When workers have to work up to higher ages, the risk of early exit due to health reasons may increase (Ebbinghaus and Radl
2015).
Education
Reasons why workers exit the work force early vary by educational level. On the one hand, workers with a low education usually exit the work force earlier than higher educated workers (Denaeghel et al.
2011; Jensen et al.
2012; Visser et al.
2016), which is mostly through disability and unemployment pathways. On the other hand, lower educated workers are more inclined to work longer because of financial necessity (Radl
2013). With more generous pension systems, these financial reasons may become less important, and may therefore increase the probability of early work exit among lower educated workers.
So far, little research has been done on educational differences in macro-level determinants of early work exit. Pension policies may not have the same effect on workers with different educational levels (Buchholz et al.
2013; Schils
2008). Because lower educated workers more often leave the work force through unemployment than higher educated workers (Schuring et al.
2013; Van Zon et al.
2017), we hypothesize that a higher expenditure on PLMP is a stronger predictor of early work exit in lower educated workers, who make more use of the benefits that PLMP offer. For the same reason, we also expect expenditure on ALMP, aimed at improving access to the labour market for the disabled and unemployed, to be a stronger predictor in the lower educated group, but in the opposite direction, with lower expenditure being associated with a higher risk of early work exit. In addition, higher unemployment replacement rates and higher implicit taxes on continued work may also be more strongly associated with early exit in the lower educated, given that this reduces their financial necessity to continue working.
Strictness of EPL may also be more important for lower educated workers, who are more often seen by managers as having trouble with adaptability to changes than higher educated workers (Henkens
2010). With a less strict EPL, these workers can be laid off more easily, with less financial consequences for the employer. It could also be argued, however, that a stricter EPL leads to firms pushing these lower educated workers into involuntary retirement. We hypothesize that a strict EPL acts as a maintain factor in higher educated workers, but as a push factor in lower educated workers.
And finally, a higher statutory pension age may also be more strongly associated with early work exit in lower educated workers, because lower educated people have poorer health compared to the higher educated (see e.g. Mackenbach et al.
2008), and may therefore not be able to work up to higher ages due to health problems.
Sex differences
In many countries, the statutory pension age differed between men and women in the last decades, but differences are generally being phased out (OECD
2011). Previous studies also show that there are sex differences in retirement intentions and behaviours (Albertsen et al.
2007; Dahl et al.
2003) and in the effect of macroeconomic factors on retirement decisions (Axelrad and McNamara
2017; Shuey and O’Rand
2004). Older female workers, for example, may have to work longer to be entitled to the same benefits as men, because they are more likely to have had interruptions in their working careers (Musumeci and Solera
2013). Therefore, we will consider possible sex differences when examining determinants of early work exit.
Present study
So far educational differences have been largely overlooked in research on determinants of early work exit. We contribute to the existing research by combining micro-level and macro-level data and examining cross-level interactions to identify educational differences in the effect of macro-level factors on early work exit. We examined whether (1) there are educational differences in early work exit and (2) which macro-level factors are determinants of early work exit. Our main research question was (3) whether there are educational differences in the effect of these macro-level factors on early work exit.
Results
Table
1 shows the micro-level characteristics of the sample. The percentage of men who retired early was smallest in Denmark and largest in Spain. For women, percentage of early work exit was smallest in Estonia and largest in the Czech Republic. The descriptive statistics show that high percentages of early work exit are not more apparent in countries with a higher baseline age or a lower statutory pension age. In Sweden for example, the age at baseline is highest (59.6 years), and the statutory pension age is average (65 years), but the percentage early work exit for men is among the lowest (10.5%). Another example is Slovenia, which has the second highest percentage of early work exit (26.3% for men and 22.2% for women), but has the lowest baseline age (54.7 years for men and 53.6 years for women) and the second lowest statutory pension age (63 years for men and 61 for women). Cross-country differences in the educational distribution were also evident.
Table 1Micro-level characteristics per country
Men |
Austria | 19.8 | 65 | 55.2 (3.5) | 6.2 | 58.0 | 35.8 | 2.5 (0.9) | 419 |
Belgium | 16.5 | 65 | 55.3 (3.5) | 26.3 | 28.6 | 45.1 | 2.6 (0.9) | 479 |
Czech Republic | 16.8 | 62 | 55.6 (3.0) | 43.4 | 36.7 | 19.9 | 2.9 (1.0) | 357 |
Denmark | 9.6 | 65 | 55.8 (3.7) | 7.7 | 46.0 | 46.3 | 2.1 (0.9) | 376 |
England | 18.3 | 65 | 58.4 (3.0) | 20.5 | 51.6 | 27.9 | 2.4 (1.0) | 927 |
Estonia | 14.7 | 63 | 55.5 (3.3) | 13.7 | 62.2 | 24.2 | 3.4 (0.9) | 513 |
France | 19.6 | 65 | 55.0 (3.2) | 17.7 | 52.1 | 30.3 | 2.8 (1.0) | 459 |
Germany | 17.0 | 65 | 58.8 (2.8) | 2.0 | 47.0 | 51.0 | 3.0 (0.9) | 100 |
Italy | 23.9 | 66 | 56.4 (3.5) | 46.6 | 41.0 | 12.4 | 2.6 (0.9) | 234 |
Netherlands | 18.8 | 65 | 56.8 (3.6) | 27.5 | 32.6 | 39.9 | 2.6 (0.9) | 298 |
Slovenia | 26.3 | 63 | 54.7 (3.0) | 10.9 | 64.6 | 24.6 | 2.8 (1.0) | 175 |
Spain | 27.9 | 65 | 56.6 (3.7) | 62.2 | 18.4 | 19.4 | 2.7 (0.9) | 294 |
Sweden | 10.5 | 65 | 59.6 (2.3) | 31.5 | 37.0 | 31.5 | 2.4 (1.0) | 143 |
Switzerland | 10.6 | 65 | 56.5 (3.8) | 6.3 | 69.9 | 23.8 | 2.4 (0.9) | 509 |
Total | 17.4 | M = 64.6 (SD = 1.0) | 56.4 (3.6) | 21.7 | 48.1 | 30.2 | 2.6 (1.0) | 5283 |
Women |
Austria | 19.2 | 60 | 53.8 (2.6) | 19.5 | 43.8 | 36.7 | 2.4 (0.9) | 349 |
Belgium | 13.3 | 65 | 54.7 (3.3) | 21.8 | 33.0 | 45.2 | 2.7 (0.9) | 518 |
Czech Republic | 29.3 | 61 | 54.5 (2.6) | 30.9 | 53.5 | 15.6 | 2.9 (0.9) | 417 |
Denmark | 16.2 | 65 | 55.5 (3.6) | 7.8 | 27.9 | 64.3 | 2.2 (1.0) | 387 |
England | 15.6 | 61 | 55.8 (2.4) | 23.8 | 54.0 | 22.2 | 2.4 (1.0) | 807 |
Estonia | 7.4 | 61 | 54.3 (2.8) | 8.1 | 56.3 | 35.6 | 3.3 (0.9) | 618 |
France | 16.8 | 65 | 54.9 (3.2) | 28.3 | 42.1 | 29.6 | 2.8 (1.0) | 523 |
Germany | 19.5 | 65 | 58.1 (2.6) | 6.8 | 52.0 | 41.2 | 2.9 (0.9) | 148 |
Italy | 12.3 | 62 | 54.7 (2.8) | 36.3 | 46.2 | 17.5 | 2.8 (1.0) | 171 |
Netherlands | 12.9 | 65 | 55.9 (3.5) | 29.5 | 31.6 | 38.9 | 2.6 (1.0) | 288 |
Slovenia | 22.2 | 61 | 53.6 (2.3) | 10.8 | 50.0 | 39.2 | 2.8 (0.9) | 158 |
Spain | 14.8 | 65 | 55.5 (3.7) | 53.8 | 24.3 | 21.9 | 2.8 (0.9) | 210 |
Sweden | 17.6 | 65 | 59.1 (2.8) | 22.3 | 35.1 | 42.6 | 2.4 (1.1) | 188 |
Switzerland | 10.2 | 64 | 55.7 (3.5) | 13.7 | 65.9 | 20.4 | 2.4 (0.9) | 519 |
Total | 15.5 | M = 63.0 (SD = 2.0) | 55.2 (3.2) | 20.9 | 46.2 | 32.9 | 2.7 (1.0) | 5301 |
Table
2 shows the macro-level characteristics per country. With regard to unemployment, England (0.12) had the least generous benefits whereas Slovenia (0.79) had the most generous benefits. There were also large cross-country differences in ALMP expenditure, ranging from 0.22 (Estonia) to 1.6 (Sweden), and in PLMP expenditure, ranging from 0.31 (England) to 2.82 (Spain). Belgium (3.13) had the strictest and England (1.76) the least strict EPL. Unemployment rates were highest in Spain (21.4) and lowest in Switzerland (4.4). The implicit tax on continued work in old age pension systems were highest in Slovenia (78.77% of average earnings) and lowest in Denmark (− 0.15% of average earnings) and in early retirement they were highest in Spain (64.38% of average earnings) and lowest in the Netherlands (2.81% of average earnings).
Table 2Macro-level characteristics per country
Austria | 44.5 | 0.45 | 0.73 | 1.25 | 2.44 | 4.6 | 5.92 | 58.59 |
Belgium | 41.5 | 0.76 | 0.84 | 2.04 | 3.13 | 7.1 | 32.71 | 28.50 |
Czech Republic | 28.8 | 0.60 | 0.26 | 0.27 | 2.75 | 6.7 | 17.39 | 17.96 |
Denmark | 44.4 | 0.58 | 2.02 | 1.60 | 2.32 | 7.6 | − 0.15 | 3.92 |
England | 36.6 | 0.12 | 0.23 | 0.31 | 1.76 | 8.0 | 13.85 | 22.14 |
Estonia | 24.5 | 0.47 | 0.22 | 0.48 | 2.07 | 12.3 | 23.53 | 12.53 |
France | 37.5 | 0.69 | 0.93 | 1.80 | 2.82 | 8.8 | 9.93 | 32.73 |
Germany | 42.7 | 0.62 | 0.77 | 0.99 | 2.84 | 5.8 | 15.45 | 13.59 |
Italy | 39.9 | 0.69 | 0.41 | 1.24 | 3.03 | 8.4 | 15.56 | 4.24 |
Netherlands | 46.1 | 0.66 | 1.03 | 1.36 | 2.88 | 5.0 | 1.64 | 2.81 |
Slovenia | 28.8 | 0.79 | 0.35 | 0.91 | 2.67 | 8.2 | 78.77 | 60.33 |
Spain | 32.1 | 0.63 | 0.87 | 2.82 | 2.56 | 21.4 | 26.50 | 64.38 |
Sweden | 43.8 | 0.54 | 1.16 | 0.60 | 2.52 | 7.8 | 25.13 | 18.57 |
Switzerland | 56.2 | 0.71 | 0.56 | 0.51 | 2.10 | 4.4 | 18.84 | 17.47 |
Cross-country differences in early work exit
The intercept-only (null) model showed statistically significant cross-country differences in the odds of early work exit (Table
3, model 1). The MOR of 1.38 and 1.40 for men and women, respectively, means that a person from a country with a high risk of early work exit has 1.38 (men) or 1.40 (women) times the odds of exiting the work force early compared to a person from a country with a low risk of early work exit.
Table 3Multilevel models with associations between education and macro-level factors and early work exit
Model 1: Null model |
Between-country variance (σu02) | .115 | .136 |
MOR | 1.382 | 1.402 |
Model 2a: Education (crude)a |
Low education | 1.914 (.262)** | 1.679 (.225)** |
Intermediate education | 1.253 (.129)* | 1.244 (.129)* |
Model 2b: Education (adjusted)b |
Low education | 1.980 (.317)** | 1.620 (.257)** |
Intermediate education | 1.371 (.158)** | 1.285 (.149)* |
Model 3: Macro-level factorsc |
Unemployment RR | 6.654 (4.692)** | 1.334 (1.095) |
ALMP expenditured | .927 (.345) | 1.282 (.435) |
PLMP expenditured | 1.994 (.327)** | 1.546 (.209)* |
EPL | 2.036 (.577)* | 1.268 (.437) |
Unemployment rate | .993 (.039) | .947 (.036) |
SPA | 1.072 (.181) | .876 (.063) |
Implicit tax old age pension | 1.011 (.008) | 1.004 (.008) |
Implicit tax early retirement | 1.015 (.006)* | 1.013 (.006)* |
Educational differences in early work exit
Men (OR = 1.980) and women (OR = 1.620) with a low education had higher odds of early work exit than their higher educated peers (Table
3, models 2). ORs for intermediate education were smaller than those for low education, but also suggested higher odds of early work exit in the intermediate groups compared to the highest educated groups. By including an interaction term between sex and education, we examined whether the sex differences in ORs were statistically significant. There was no statistically significant sex difference in the association between education and early work exit.
Macro-level determinants of early work exit
There were four macro-level factors that were significantly associated with early work exit (Table
3, model 3) and we found statistically significant sex differences in some of these determinants. A higher unemployment RR and a stricter EPL were associated with a higher risk of early work exit, in men only. A higher expenditure on PLMP and a higher implicit tax in the early retirement route were associated with early work exit in both men and women.
Educational differences in macro-level determinants of early work exit
Interaction effects between education and the macro-level factors are shown in Table
4. These interactions show whether there are educational differences in macro-level determinants of early work exit, i.e. whether the associations between the macro-level factors and early work exit differ across educational groups.
Table 4Cross-level interactions between education and macro-level factors (adjusted for age, SRH and GDP per capita, and including a random slope for education and age)
Unemployment RR * low education | 1.045 (.841) | .523 (.517) | 7.824 (4.039)** | 2.698 (2.091) |
Unemployment RR * intermediate education | | | 3.583 (1.549)** | 2.479 (1.378) |
ALMP expenditure * low education | .818 (.358) | 1.215 (.486) | 1.056 (.387) | 1.042 (.357) |
ALMP expenditure * intermediate education | | | 1.192 (.291) | 1.074 (.244) |
PLMP expenditure * low education | 1.664 (.379)* | 1.495 (.402) | 1.159 (.227) | 1.037 (.220) |
PLMP expenditure * intermediate education | | | 1.242 (.196) | 1.034 (.175) |
EPL * low education | 1.214 (.444) | 1.017 (.450) | 1.951 (.623)* | 1.265 (.472) |
EPL * intermediate education | | | 1.413 (.334) | 1.252 (.333) |
Unemployment rate * low education | .965 (.049) | .924 (.048) | 1.027 (.037) | 1.032 (.044) |
Unemployment rate * intermediate education | | | 1.046 (.033) | 1.012 (.040) |
SPA * low education | 1.341 (.274) | .952 (.087) | .762 (.106)# | .892 (.069) |
SPA * intermediate education | | .859 (.103) | .942 (.053) | |
Implicit tax old age pension * low education | .996 (.010) | .997 (.010) | 1.022 (.010)* | 1.002 (.010) |
Implicit tax old age pension * intermediate education | | 1.012 (.007)# | 1.011 (.007) | |
Implicit tax early retirement * low education | 1.007 (.008) | 1.009 (.008) | 1.005 (.008) | 1.005 (.008) |
Implicit tax early retirement * intermediate education | | 1.010 (.006)# | 1.004 (.006) | |
The OR for macro-level factor (first two columns) represents the effect of the macro-level factor on early work exit for the high education group. ORs for the low and intermediate education groups can be calculated by multiplying the OR for macro-level factor by the OR for the interaction macro-level factor * education (next two columns). In men, the effect of unemployment RR on early work exit differed by educational level. The effect was much stronger in low (OR = 1.045 * 7.824 = 8.176, p = .011) and intermediately (OR = 1.045 * 3.583 = 3.744, p = .087) educated men, compared to men with a high educational level (OR = 1.045, p = .956). In low-educated men, a higher unemployment RR was statistically significantly associated with higher odds of early work exit, whereas in higher educated men unemployment RR did not have a significant effect on timing of work exit.
In men, stricter EPL was associated with higher odds of early work exit in the low-educated workers (OR = 2.369, p = .007), and to a lesser extent in the intermediately educated workers (OR = 1.716, p = .078), but not in those with a high education (OR = 1.214, p = .596).
We also found an interaction between SPA and education in men, but the association between SPA and early work exit was not statistically significant in any of the educational groups (low: OR = 1.022, p = .901; intermediate: OR = .897, p = .158; high: OR = 1.151, p = .428).
In men, the association between implicit tax on continued work and early work exit also differed statistically significantly across educational groups in both the regular old age pension route (low: OR = 1.019, p = .056; intermediate: OR = 1.008, p = .309; high: OR = .996. p = .706) and early retirement route (low: OR = 1.013, p = .048; intermediate: OR = 1.018, p = .005; high: OR = 1.007, p = .354), with stronger associations in the lower educated.
Sensitivity analyses
Because we included only 14 countries, we performed sensitivity analyses to check the robustness of our findings. To make sure our results were not affected by outliers, we additionally conducted the analyses with dichotomized macro-level factors. These analyses yielded similar results compared to the results obtained with the continuous macro-level factors (results available upon request).
Discussion
So far, research on macro-level determinants of early work exit has neglected possible educational differences. Our aim was to examine (1) whether there are educational differences in early work exit, (2) which macro-level factors are determinants of early work exit, and (3) whether there are educational differences in the effect of these macro-level factors on early work exit.
Consistent with earlier findings (e.g. Ebbinghaus and Hofäcker
2013), we found cross-country differences in early work exit. Results from our multilevel analyses further show that those with a low education generally have a higher risk of exiting the work force early than those with a high education. This finding is consistent with the literature (Denaeghel et al.
2011; Jensen et al.
2012; Visser et al.
2016).
Previous studies have shown associations between early work exit and generosity of benefits (Blöndal and Scarpetta
1999; Duval
2003; Schils
2008), expenditure on active labour market policies (ALMP) and passive labour market policies (PLMP) (De Preter et al.
2013; Martin and Grubb
2001), strictness of employment protection legislation (EPL) (Bellmann and Florian
2007; Dorn and Sousa-Poza
2007; Ebbinghaus and Radl
2015), and unemployment rates (Blöndal and Scarpetta
1999; Fischer and Sousa-Poza
2006). We extended these findings by showing that a higher expenditure on PLMP and a higher implicit tax on continued work in early retirement schemes were associated with a higher risk of early work exit in both men and women. In men, a higher unemployment replacement rate and a stricter EPL were also associated with a higher risk of early work exit. We did not find an association between unemployment rates and early work exit. Our selection of a sample of employed persons could explain this lack of effect: those already employed may not be as affected by unemployment rates compared to the unemployed trying to find a job.
Addressing our third research question, we found that the macro-level pull factors, which make early retirement financially more attractive, seem to have an effect only on the lower educated workers. The educational differences in the effects of these pull factors, i.e. unemployment replacement rates, expenditure on PLMP, and implicit taxes on continued work, support our hypothesis that low-educated workers’ financial reasons to continue working become more important when facing less generous benefits. We hypothesized that strictness of EPL would be a push factor for the lower educated and a maintain factor for the higher educated workers. Indeed, we found evidence to support the first part of this hypothesis. So although a strict EPL may be seen as a protective measure, this protectiveness is not applicable to workers with a low educational level. These lower educated workers, who generally have more physically demanding jobs and who are seen as less adaptable by their employers (Henkens
2010), seem to be ‘pushed’ out of the labour market through alternative routes instead of being made redundant when facing strict EPL (Ebbinghaus and Radl
2015). We did not find evidence that a strict EPL was a maintain factor for highly educated workers.
We did not find an association between ALMP expenditure and early work exit, regardless of educational level. This may be due to the characteristics of our sample: we selected respondents who had paid work at baseline and follow-up was only 2 years later. The expenditure on these policies, aimed at getting unemployed and disabled people back into the work force, may therefore be less applicable to our specific sample of older workers. It could also be argued that this measure of ALMP spending may not be ideal for this specific group of older workers, since it reflects spending on the entire population and may be biased towards other age groups (Hofäcker and Unt
2013).
We also did not find an association between the statutory pension age and early work exit. This does not necessarily mean that SPA does not affect early work exit. This lack of effect may be due to the small variation in SPA, especially in men.
It is important to keep in mind that these macro-level factors not only have a negative effect, i.e. increase the risk of early exit from work, but also contribute positively to older workers and retirees. Higher replacement rates and a higher social expenditure are also associated with better population health (Bergqvist et al.
2013). Some workers can no longer work due to health problems and these policies give them the opportunity to exit the work force early. Work place interventions and health promotion are important in enabling this group of workers to continue working (De Boer et al.
2004; Oakman et al.
2018).
Strengths of this study are the combination of micro-level and macro-level data and the inclusion of cross-level interactions to identify educational differences. Our study also has some limitations. We could include only 14 countries in our multilevel analyses, which resulted in low statistical power. Because of this power issue, we could not fit full models to examine the effects of multiple macro-level determinants on early work exit. Future research including more countries, should therefore replicate our findings. In addition, while in the current study we only examined macro-level factors, further research should also investigate how these macro-level factors interact with factors on the meso-level. Also, the OECD EPL index we used as a measure of strictness of EPL has its disadvantages. Like any synthetic index it does not take into account the extent of the enforcement of laws (Myant
2016). Therefore, any effects of EPL should be interpreted with caution. We operationalized early work exit as having exited the work force before reaching the statutory pension age at 2 year follow-up. A limitation of this operationalization is that someone who does not work at follow-up may continue working later on and someone who is still working at follow-up could still exit work early, especially given the relatively low age at follow-up. Ideally one would want to follow each respondent up to the statutory pension age, but unfortunately this was not possible, because in SHARE not all countries participated in each wave.
To conclude, our study identified several macro-level factors that are associated with early work exit. It is clear, however, that educational differences as well as sex differences should be taken into account when examining the effects of these factors. Low-educated men seem to be especially responsive to the effects of pull factors that make early retirement financially more attractive.
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