Coworker Interactions in Labor Supply∗ Rafael Lalive and Pierpaolo Parrotta†
Abstract This paper investigates the role of interactions in labor supply among pairs of coworkers. Coworkers may affect each other since they exchange information on retirement planning through work and leisure interactions. We study non-employment choices among pairs of differently aged coworkers that commute to work and live in the same municipality in Denmark. Identification is based on the fact that workers aged 60 years or older have access to early retirement benefits. We find that as the older coworker’ crosses the early retirement age threshold, the younger coworker significantly decreases labor supply. Moreover, when we analyze a reform that increases the financial penalty for early retirement, we find that the effect of a coworker’s retirement on own non-employment behavior is substantially reduced. This evidence is consistent with the increase in the financial cost of retiring as the coworker retires. JEL Classification: J26, J14, C40, D10.
Keywords: Coworkers’ joint retirement, friends at the workplace, work/leisure complementarity. ∗ We thank Anna Piil Damm, Tor Eriksson, Jakob Roland Munch, Michael Rosholm, Ott Toomet, Olof Åslund (alphabetical order) and from participants at the EALE 2011 and ESPE 2011 conferences for helpful comments and suggestions. Pierpaolo Parrotta acknowledges the financial support from the Graduate School for Integration, Production and Welfare. The usual disclaimer applies. † Rafael Lalive is affiliated with University of Lausanne, Department of Economics, CH-1015 Lausanne, Switzerland, and CEPR, CESifo, and IZA. E-mail:
[email protected]; Pierpaolo Parrotta is affiliated with Aarhus School of Business and Social Sciences, Aarhus University, Department of Economics, Hermodsvej 22, DK, 8230 Aabyhoj, Denmark, and University of Lausanne, Department of Economics, CH-1015 Lausanne, Switzerland. E-mail:
[email protected].
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Introduction The importance of the employees’ retirement choices has been growing during the last decades, not only in
the economic literature. The reasons behind this increasing interest are mainly associated with the sweeping changes experienced in the labor markets of the OECD countries:1 aging population, drop in fertility rates, increase in female labor supply and retirement rates, and improvement in the general level of living conditions. Each of these factors has contributed to move the sustainability of the social security and pension system to one of the top priorities of the public policies. Thus, the change in the ratio between working and retired people has pushed several national authorities to reform their social security legislation in order to provide incentives aimed at postponing the actual workers’ retirement ages. Whereas in several cases the pension reforms mainly resulted in a raise of the statutory retirement age (especially for women),2 the Danish government sharpened the costs of early retirement in 1999 and lowered the formal retirement age in 2004 (from 67 to 65 for both men and women). The goal of this reform was the shortening of the gap between statutory and actual retirement age. Using the Danish linked employer-employee database (LEED) for the period 1980-2006, this paper analyzes the decisions of joint exit from employment in pairs of coworkers: it investigates whether coworkers’ labor supply decisions are affected by peer behavior in the workplace. Moreover, this study evaluates how the change in the incentive scheme in 1999 affected the phenomenon under analysis. The data set extracted from the LEED is composed of all individuals who have been exclusive pairs of coworkers aged 50-70, being natives and one older than the other, living in the same municipality and commuting to work.3 The implemented estimation strategy is based on a regression discontinuity design (RDD) model and an instrumental variable (IV) approach aimed at identifying the (causal) effect of interest and dealing with the typical endogeneity problem involved in the so-called peer effect analysis. Specifically, the older coworker’s early retirement is directly proxied or instrumented in a two-stage setting by using the typical (early) retirement age recorded in Denmark. The instrumental variable approach allows us to identify the weighted local average treatment effect (LATE) of the older retired coworker on the younger one’s labor supply. Findings from this empirical analysis widely support the presence of joint non-emploment (early retirement) in the selected pairs of coworkers. The older coworker’s retirement increases by about 9.8 percentage points the younger coworker’s probability of exit employment over the entire time period, the effect reaches 17.9 percentage points before the introduction of the pension reform in 1999 and decrases to 5.8 percentage 1 Blöndal
and Scarpetta (1998); OECD (1995, 2005, 2009). et al. (2009). 3 It means that groups of coworkers fulfilling the mentioned conditions but comprising more than 2 components have been keep out of the analysis. 2 Whitehouse
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points in the after-reform period. Thus, such a change in the regulatory system induced a reduction in the attractiveness of coworkers’ joint early retirement. That represents one of the main novelties introduced by the present study: it provides evidence of the reduction in peer effects in coworkers’ retirement caused by an increase in the financial incentives associated with the lengthening of the active participation in the labor market. This outcome implies that the pension system reform may have affected the individuals’ behavior beyond its planned aim. Besides that, to the best of my knowledge, this work represents the first attempt to estimate the coworkers’ joint early retirement behavior. Several studies provide evidence of similarity in coworkers’ retirement decisions: if a worker’s colleagues or friends retire around a given age, it may be that she retires about the same age. However, similarity differs from coordination. Whereas the former can be driven by emulation, peer pressure or social norms, the latter requires differently aged coworkers to retire about the same time as an evidence of spillover effects in leisure, working and commuting time. It is here proposed a precise reduced form approach, well suited for the purpose of this study as it allows for the estimation of a causal effect. Moreover, several checks provide evidence that coworkers’ transitions from work to non-employment (early retirement) are not affected seriously by factors like business cycle effects, firm restructuring or downsizing policies. The structure of the remainder of this paper is as follows. Next section briefly reviews some relevant studies concerning social interactions between coworkers. Section 3 firstly describes the Danish regulatory pension system, secondly informs on the data and variables in use, and finally provides descriptive evidence and statistics on the sample of elderly workers. Section 4 explains the estimation strategy here implemented, focusing on the concenptual framework, identification and computation of the causal effect. Section 5 reports the results and robustness checks. Section 7 concludes.
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Related studies Several theoretical and empirical studies have been of fundamental importance for the understanding of
retirement choices and their consequences on the social security and insurance system. From the literature on retirement we have learnt that a significant fraction of retirement choices can be explained by using individual demographic characteristics, health conditions, wealth status, and labor market experience.4 However, these analyses typically neglect potential social interactions within the workplace: there is no room for cross-worker interactions in individual retirement decision. In fact, although a growing interest in 4 Gustman and Steinmeier (1986); Burtless (1986); Stock and Wise (1990); Rust and Phelan (1997); French (2005) among others.
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phenomena5 involving social interactions has been registered in several fields of economic research, there is very little or no micro-economic evidence on spillovers effects in leisure and work between coworkers. A quite known study close to this topic is Duflo and Saez (2003). Making use of a randomized experiment, they find evidence of social interactions on the employees’ decision to enroll in the employer-sponsored tax deferred account plan of a large university. Their findings support the hypothesis that individuals’ decisions on how much and how to save are affected by the choices of others in the same department. Workers may obtain information from discussions with their colleagues or simply observing others’ plans and retirement choices. Moreover, conformity in consumption and savings behavior may be subject to peer pressure and social norms, too. Evidence of the potential influence of social interactions in the computation of the individual labor supply is provided by Grodner and Kniesner (2006). They show that even the introduction of a relatively low level of social interaction in a worker’s utility function can cause a non-marginal effect on her labor supply and therefore on her consumption plan. Thus, an empirical analysis (based on a structural model) that ignores the influence of social interactions may lead to biased estimates of utility function parameters. Finally, useful insights into coworkers’ retirement decisions can be found in Chalmers et al. (2008). They test if individuals decide, at least in part, when to retire by observing the retirement decisions of their coworkers. Using data recorded over 12 years on the characteristics and retirement decisions of non-federal government employees in the State of Oregon, the authors find support to their hypothesis. Specifically, after controlling for a number of covariates and implementing two different IV strategies, they find evidence that an employee’s retirement is positively affected by her coworkers’ retirement. However, there exist no studies focusing on coworkers’ joint early retirement. This kind of social interaction is quite hard to be identified because it requires (i) a plausible justification for the friendship between coworkers, (ii) an observable timing of coworkers’ retirement decisions, (iii) the inclusion of a large set of control variables that are relevant for the retirement choices, (iv) a proper estimation strategy to instrument the endogenous effect of interest (the effect of the older coworker’s retirement on the younger one’s labor supply), (v) robustness to potential alternative factors causing the joint retirement. 5 School drop-out (Evans et al., 1992); crime (Glaeser et al., 1996); welfare program participation (Bertrand et al., 2000); unemployment (Topa, 2001); family (Gustman and Steinmeier (2000; 2004); educational choices (Sacerdote, 2001; Cipollone and Rosolia, 2007; Lalive and Cattaneo, 2009); labor productivity (Mas and Moretti, 2009); disability behavior (Rege et al., 2009).
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3
Background
3.1
The Danish pension system
This section provides a short overview of the Danish pension system, which was among the first (advanced) retirement regimes introduced more than a century ago (Andersen and Skjodt, 2007). Like most OECD countries, Denmark presents a quite complex regulatory retirement system, composed of three pillars: a universal public pillar, a fully-funded employer-based pillar, and a pillar based on individual savings. This multi-pillar system includes both Beveragean (a flat-rate residence-based national pension) and Bismarckian (pension reflecting the worker’s career path) elements (Guardiancich, 2010). The first pillar is mandatory, universal in coverage and consists of two tiers. The first tier (folkepension/social pension) is unfunded and financed from general tax revenues. Instead, a number of fully-funded supplementary schemes (and a smaller pay-as-you-go one) constitutes the second tier (ATP, the supplementary labor market pension fund).6 These fully-funded components are financed by the employer (2/3) and employee (1/3) through fixed-sum contributions defined by social partners as part of collective agreements. Although the ATP operates as a defined contribution plan, it is classified as a first-pillar scheme because it entails social security features. The second pillar is nearly universal and comprises quasi-mandatory, privately managed and fully-funded occupational pension plans. Their coverage has increased notably during the 1990s, after the formal inclusion of the private sector. Most of these pension schemes have been established by collective labor agreements between employers and labor unions. The third pillar is composed of voluntary personal pension plans managed by banks or insurance companies. It has been created to supplement the “mandatory” pension with sufficient means to ensure an ultimately comfortable retirement. The contribution rate is variable and the amount is deducted from taxable income (the same does not hold true for matured interests or received benefits). The third pillar covers a wide share of the working population. The participation in voluntary supplementary private funds might encourage workers to plan the exit from the labor market more in accordance with their preferences in terms of timing and pension income. In Denmark the statutory (official) retirement age is currently set at 65 for both women and men. It was reduced by 2 years in 2004 (the former one was set at 67). This reform was aimed at shortening the gap between the official retirement age and the actual behavior concerning exit from the labor force. However, there is a possibility of benefiting from deferred retirement until the age of 75 (70 before 2009). Thus, 6 See
Vittas (2008) for more details.
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employees are allowed to postpone their retirement by up to 10 years and obviously be compensated by higher pensions. Although several pathways to early retirement have been made available by the Danish pension system, they can be summarized in three main schemes: anticipatory pension, partial early retirement and voluntary early retirement programs. The eligibility to the first scheme depends on the applicant’s working ability, which needs to be permanently reduced and such that the worker is not able to continue working part-time or in a subsidized job (i.e. flexjob). The second program allows workers to shift from full-time to part-time jobs. It clearly represents a solution to bridge an eventual gap between a full-time job and the exit from the labor market. Eligible people are aged 60-65 and have been employed full-time for at least 10 years during the last 20 years. This scheme is linked with unemployment insurance and pays benefits for people aged at least 60, until they reach the statutory retirement age. Applicants need (i) a membership of an unemployment insurance fund for 25 years over the last 30 years of work and (ii) to have paid voluntary early retirement contributions. Voluntary early retirement pensions (VERP) were more easily accessed before 1999. Since the introduction of a labor market policy program for early retirement in 1979, the transition from ordinary full-time jobs to retirement was facilitated. Workers had the possibility of retiring without having to fulfill formal health requirements (Larsen and Pedersen, 2008). This scheme was adopted during a period of high unemployment to accommodate young workers on the labor market. Before that time, early retirement supported by public transfer income was only possible on health grounds. Thus, the average actual retirement age declined consistently to around 60. In 1999, a major reform of the VERP was introduced and affected all workers born on or later than July 1939. VERP benefits were lowered by roughly 10% for the age group 60-62. Early retirement before age 60 may be possible through the Danish disability or unemployment insurance system. Disability insurance pays out a flat benefit which is quite attractive for workers at the low end of the income distribution but less so for workers who earn more. Pensions are conditional on having lost part of work capacity but until 2003, the loss in work capacity was much less strictly investigated for workers aged 50 or older. Unemployment benefits are paid out for up to 5 years for workers who have cross the age 55 threshold and would be eligible for VERP benefits after (Oorschot and Jensen 2009). Official old age pensions net replacement rates are on the order of 70% of previous income. Replacement rates tend to be lower for the alternative pathways to early retirement.
3.2
Sample and variables
The empirical analysis implemented in this paper is based on a sample extracted from the Danish Integrated Database for Labor Market Research (IDA) and refers to a period of 27 years (1980-2006). IDA is a 6
linked employer-employee database (LEED) containing detailed information on the population of residents in Denmark. It reports the labor market status of each individual, a number of demographic characteristics (age, gender, civic status, nationality/country of provenience), and also valuable information on individual labor income, working hours, education, occupation, firm, industry, place of work and residence. Exploiting the longitudinal dimension of this database, it is possible to construct the labor market history for each resident. There is no precise information on retirement year, however we can observe transitions from employment to non-employment for elderly workers. Apart from deaths and permanent migration, there is no attrition in IDA. The focus of this study is on the coworkers’ interactions in early retirement choices. We therefore put our attention on workers who are likely to interact with each other. We define a coworker dyad to exist for individuals who at some point in time t0 were employed in the same workplace (with less than 150 employees), were living in the same municipality, but workplace is different from the place of residence. It is worth underlyning that we pick out exclusive dyads of coworkers: they are the only two employees (at their workplace) coming from a specific different municipality. It makes more plausible that they know each other and interact with each other (they might travel together, so share the commuting costs). It is possible that an individual is coupled with more than a coworker over her working life if she or her coworker moved at least once from a workplace to another or exited temporarily out of labor market. We constrain the analysis to Danish natives in order to work on a quite homogeneous sample and rule out potential influences on labor demand or supply (i.e. strong family ties and segregation phenomena) specifically related to the status of immigrants. The sample comprises only workers aged 50-70 because they are at risk of retirement, whereas the difference in the age helps me in not confounding the individual age impact with the effect associated with the coworker’s age in the implemented estimation strategy.7 The final data set does not comprise self-employed workers as retiring from own businesses is quite specific and can hardly be affected by the retirement choice of a non self-employed coworker.
3.3
Descriptive statistics
Table 1 and 2 report descriptive statistics (mean, median and standard deviation) for all observations and dyads in the sample, respectively. Specifically, Table 1 provides information on a list of observables that refers to gender, highest level of education achieved (university degree, secondary and lower education), job position (manager, middle manager, blue-collar) and first observed (starting) wage earned by younger and older workers. This table also reports differences in gender, education and job position within dyads and some statistics about levels and changes in employment for the starting firms (where the dyads were formed). 7 See
paragraph 4.1.
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It emerges that higher shares of women characterize younger coworkers. These are slightly more educated than older coworkers, but do not necessarily hold better positions or wages, which may rather reflect work experience and firm tenure. Differences in gender, education and job position within dyads are not marked, so it turns out a high degree of similarity along such dimensions. The share of large and small enterprises prevails over the medium-sized ones. One third of all starting firms included in the sample employs no more than 20 individuals and more than 37% employs workforces larger than 100 people. Thus, no much more than one fourth of all dyads observed over time has been formed in a medium-sized enterprise. Table 2 reports some key descriptives on distinct real and placebo dyads. Whereas we have 326707 observations in the full sample, the number of distinct real dyads is 50915: on average we observe a dyad for 6.4 years. Placebo dyads are built as follows: the older coworker is coupled with a new younger one who either works in the same workplace and lives in a different municipality (placebo1) or lives in the same municipality and works in a different firm (placebo 2). Placebo dyads are 62462 and 89084, respectively. The number of placebo dyads is larger as it is more likely to find exclusive couples of workers fulfilling the above-mentioned characteristics. Mean, median and standard deviation values are shown for the commuting distance8 between starting firm location and place of residence, number of years a dyad worked in the same workplace and lived in the municipality, and gender/education/occupation dyads’ composition (e.g. male-male, tertiary-secondary, manager-middle manager). The median and average commuting distance are a bit more than 18 and 32 kilometers, respectively. Being air-distances, coworkers may spend relatively long time to reach their workplaces. The longer such distances are, the higher might be the increase in the associated commuting costs. Both placebo dyad definitions report similar mean and median values for this distance. On average we observe a dyad working in the same workplace for 6.2 years and living in the same place of residence for 7.7 years.
3.4
Descriptive evidence
The figures described in this sections reports descriptive evidence of the phenomenon in object and suggests the estimation strategy implemented in the empirical analysis. Firstly, it seems opportune to understand the probability that coworkers in a dyad are active in the labor market and that they still work together as their dyad gets older (the dyad age is computed as a difference 8 Geographical distance is computed as follows: d = 6378.7 ∗ acos{sin(lat /57.2958) ∗ sin(lat /57.2958) + cos(lat /57.2958) ∗ ij i j i cos(latj /57.2958) ∗ cos(lonj /57.2958 − loni /57.2958)} ..
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between calendar and first year the dyad is formed). Figure 1 shows that such aggregate probabilities decrease over time, considerably in the first years, and their difference typically enlarges with the dyad age as well. For instance, the probability that the two coworkers still work together (are active) is about 29 (63) percentage points after 5 years a dyad is formed and it drops to 0.1 (34) percentage points for the oldest dyad. Figure 2a and 2b reports the average individual transition rate from employment to non-employment against individual’s age for the younger and older coworker, respectively. A discontinuity clearly emerges when the worker reaches the age of 60: the jump is about 9 percentage point for the older coworker and 8 for the younger one. Whether the probability of a transition increases before the discontinuity point, it decreases afterwards. The key evidence is provided in Figure 3 that reports how the younger coworker’s transition to nonemployment varies with the older coworker’s age. Here a discontinuity emerges as well when the older coworker reaches the age of 60. The jump at discontinuity point is about 0.8 percentage points and the trend in average transition rate of younger coworkers looks responsive to the increase in older coworkers‘ age. Though other discontinuities are shown in Figure 3, it seems more opportune to confine our attention to the change observed at 60 because it is one of the largest discontinuity points and other studies confirm that a substantial portion of workers retired around this age. Allowing for a discontinuity at the threshold of the older coworker’s typical early retirement age, this graph superimposes the fit of a quadratic trend in the assignment variable (older coworker’s age) on the two sides of the threshold. The fitted quadratic trends provide an approximation of the average individual probabilities of non-employment. The discontinuities at the threshold in the younger coworker’s average transition rate might suggest that in some cases dyads could decide to exit out of employment at about the same time.
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Estimation strategy This section discusses the conceptual framework, identification, and estimation of coworker interactions.
We discusses the financial and social incentives that affect early retirement.9 To do so, we introduce a very simple framework and use it to discuss the empirical specification. When employed, worker i earns income yi , which is taxed at rate τ , so the net income is yi (1 − τ ). When retired, workers earn a fraction π(a) of income yi . Importantly, π(ai ) is low before the threshold c at age 60 years and increases discontinuously when a worker turns 60 year old due to the early retirement program. Before age 60 workers can retire but the replacement rate is substantially lower than when they retire at age 9
For sake of simplicity, we use the term early retirement when we refer to transitions from employment to non-employment.
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60. Retirees therefore earn an income yπ(ai ). Utility is income evaluated at marginal utility of consumption λ at a given wealth level and we assume marginal utility does not change as workers retire (since wealth does not change). We assume utility is additively separable in income and utility of time. Utility of time for retirees has two components. The individual component αi reflects the mere fact that a retiree has more leisure time when retired. There is an additional utility component that reflects the presence of a coworker. We suppose the utility of being retired in the presence of a retired co-worker is γij rj . The parameter γij reflects both the probability that a worker i knows a co-worker j and how much person i cares about co-worker j being retired. Note that the social component of utility of time might be present for at least three reasons. First, person i might enjoy spending time with person j. Second, the term might reflect learning about retirement. Person i might learn from person j concerning retirement planning. Third, person i might want to be retired more if person j is because he or she imitates person j’s strategy. Workers will retire if the utility of doing so exceeds the cost, i.e. if
λyi π(ai ) + αi + γij rj > λyi (1 − τ )
We allow for heterogeneity in the distribution of utility of leisure α, so the distribution of α conditional on observed characteristics is F (α). The proportion retiring is then
P rob(ri = 1|xi , rj ) = P rob(αi > λyi (1 − τ − π(ai )) − γij rj ) = 1 − F (λyi (1 − τ − π(ai )) − γij rj )
This shows that the probability that individual i retires depends on own age a through the income replacement rate, on income y, and on the presence of a retired co-worker. A convenient and simple parametrization of the probability that worker i retires in period t is
rit = x0it β + γij rjt + f (ait ) + δi I(ait > c) + it
(1)
This equation reflects both financial and social incentives in retiring. The parameter γij measures social interactions among coworker i and j, the function f (ait ) is a non-parametric function of age of person i that reflects to what extent retirement incentives change with age. The parameter δi measures the effect of the change in the replacement rate when individual i crosses the threshold age c of 60 years, and it captures unmeasured characteristics. Note that the effect of retirement incentives on individual retirement can be captured in a Fuzzy Regression Discontinuity Design (Lee and Lemieux 2009). The design is fuzzy since
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people may retire already before they turn 60 years old and some people choose not to retire at age 60. We implement a two sided cubic trend to capture the functional form of f (ait ) and parametrize it as a deviation from the age 60 threshold. We can solve for the reduced form equation by plugging the corresponding equation into (1). Doing so yields the following reduced form specification
(1 + γij γji )rit
= x0it β + f (ait ) + δi I(ait > c) + it + γij (x0jt β + f (ajt ) + δj I(ajt > c) + jt )
(2)
This shows that the financial incentives to retire for co-worker j affect retirement decisions of individual i if social interactions are important. Moreover, note that this reduced form specification contains a double regression discontinuity specification. It features a flexible functional form for the age of person i, f (ait ), along with a flexible function of age of the co-worker, f (ajt ), because the co-worker’s retirement status affects the individual’s retirement status. Moreover, both whether the individual i crosses the threshold and whether the co-worker j crosses the threshold matter for the retirement decision of individual i if individual i is connected to individual j. The parameter γij can be identified from the ratio of the reduced form parameter on the effect of coworker j crossing the threshold, γij δj , and the effect of crossing the threshold on the retirement decision of co-worker j, δj (both scaled by (1 + γij γji )−1 ). The identification strategy is therefore an IV strategy. The instrument for the retirement status of person j is the binary indicator I(ajt > c).
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Results The following paragraphs present findings from the Fuzzy Regression Discontinuity design (FRDD) im-
plemented and just described above. Specifically, Tables 3, 4, and 5 report main results, robustness checks, and placebo results, respectively.
5.1
Main results
Table 3 shows estimated parameters on coworker’s (i.e. old coworker’s) transition, own (i.e. younger coworker) and coworker’s threshold ages, linear deviations from them, and interactions between the latter and threshold ages. For each specification we report whether we include year dummies or other controls (3 digits industry dummies, education, job position and demographic characteristics), number of dyads,
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observations, and R-squared. The first four columns inform on the sensitivity of estimates to the choice of functional form design. The parameter on own threshold age is stable across the functional forms, whether the coefficients on coworker’s threshold age and linear deviations seem to be more precisely estimated in the quadratic approximation as suggested in the descriptive evidence. The choice of the second order polynomial in the two-side deviation from the own and coworker’s threshold ages is also grounded on the fact that there is no significant improvement in the R-squared when third degree terms are added. In the fifth column we check whether the chosen design works properly when we restrict the analysis to the ±5 years from the threshold ages. The parameter of interest (coworker’s threshold age) and on the own threshold age does not vary significantly. The number of observations, which is fixed for the other specifications, decreases by about 30 percentage points. The explanatory power of our model doubles after the inclusion of the other controls in the baseline specification. The parameter of interest is about 0.7 percentage points. Not surprisingly, it is not affected by other controls as the latter are well balanced before and after the coworker’s threshold age. Differently, the own threshold age is now associated with a slightly lower coefficient, 7.54 rather than 8 percentage points. Parameters on linear deviations show signs and dimensions in line with the coordination hypothesis: the larger (smaller) the difference between own (coworker’s) actual and threshold age is, the (less) more likely is the decision to exit our of employment. However, the latter parameters may be biased due to a potential dynamic selection in non-employment occurring after crossing own or coworkers’ threshold ages. Obviously, same arguments apply to the interaction terms. Estimates from first and second stages of the IV regression are reported in the last two columns. These findings are in line with the reduced form (baseline) model: the proxied older coworker’s transition from employment to non-employment increases a similar younger coworker’s transition by 9.8% and multiplying this effect by her probability to exit employment when crossed the age of 60 we obtain the exact coefficient of interest shown in the baseline specification. Findings from both the IV and reduced form models support the hypothesis of joint exit from employment and therefore provide evidence of coworkers’ interactions in labor supply.
5.2
Robustness checks and placebos
Although Table 3 provides evidence of interactions in labor supply behavior between pairs of coworkers, it reports findings potentially affected by several other factors. It might be the case that the workplace environment or the employer’s preferences for given workforces could affect the coworkers’ transitions simultaneously. Thus, firm specific effects may induce such transitions. 12
For instance, workplace may become less pleasant for elderly workers: they could front problems in understanding or implementing efficiently new procedures or they could feel less integrated in newly defined team works. In addition, employers may induce the early retirement of elderly workers to change the compositions of the workforce (e.g. as a strategy for firm restructuring) or to avoid firings. Besides the specific workplace characteristics, size of residence place, commuting distance, time spent in the same municipality of residence, and before- or after-reform period may influence transitions to nonemployment. In fact, we expect that interaction between coworkers is stronger for smaller places of residence and higher number of years lived in such places: it is more likely that two coworkers know well each other and that they may present complementarities in leasure. Moreover, within dyads interactions might more often occur when commuting distance are longer as coworkers may be more willing to travel together and then save a relatively larger amount of money. In addition, the cost of traveling alone represents a financial incentive to retire at about the same time. As financial incentives matter, younger coworker’s age and beforeor after-reform period may affect the effect of interest as well. Given the existence of these possibilities, we decided to perform opportune robustness checks, which are reported in Table 4. We find evidence that the effect of interest is similar between smaller and larger workforces but significant only for the latter ones. As described in paragraph 3.3, workforces larger than 100 employees are the most represented in our sample. Interactions in labor supply are stronger and significant for expanded workforce, meaning that most of the phenomenon we observe is not related to firm restructuring or downsizing. Further, those interactions are not affected by the average age at the workplace. Conversely, the effect of interest is stronger and significant for coworkers who spent more than 5 years in the same place of residence. Municipality size affects the significance of the parameter of interest rather than its dimension. We find almost the same effect for less or more aged coworkers, however it is significant only for the former category. Results confirm our assumption on commuting distances and before-reform period. It seems that the pension reform in 1999 decreased the incentives to early retire drastically: the utility gain related to the cross-coworker complementarities in work and leasure seems to be lower than early retirement costs. Whereas robustness checks may reinforce the reliability of main results, they do not provide any information on the proper identification of the “right” coworker dyads. As mentioned in paragraph 3.2, we focus on coworker dyads that have lived in the same place of residence and worked together in a workplace located in a different municipality. A dyad needs to be exclusive: there is only a dyad composed of workers commuting from a given municipality. However, the just cited requirements are tested, too. Placebo dyads are formed by taking either coworkers from same workplace but residence place or commuting from/to the same municipality but employed in different workplaces. Placebo tests support the assumptions behind the construction of the dyads in the main analysis. As expected, neither significant nor sizeable effects of interest are found 13
for both placebos.
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Conclusions The present work provides evidence of coordination in early retirement for pairs of differently aged cowork-
ers. Using data from the Danish employer-employees database for the period 1980-2006, we investigate whether the transition from employment to non-employment (early retirement) of one worker induces the other to exit out of employment approximately about the same period. In order to evaluate the presence and economic relevance of this influence, we perform an empirical analysis based on a FRDD. That allows for the identification of the effect of the older coworker’s retirement on the other’s labor market participation. Specifically, combining the advantages of RDD models with those characterizing the IV estimation approach, it is possible to compute the weighed local average treatment effect: the effect of approaching (reaching) the threshold represented by the typical early retirement age on the younger coworker’ s probability to exit out of employment as well. The empirical analysis accounts (for each coworker) for the typical early retirement and current age; linear and quadratic (two-sided) differences between these ages; a large set of socio-demographic characteristics including gender, education and job position among others. Our results shed light on the importance of interactions in labor supply involving potential friends employed at the same workplace: a portion of them seems to coordinate their participation choices. Findings are quite robust, several specifications support the presence of coworker interactions. We find that older coworker’s early retirement increases by 9.8% the younger coworker’s transition from employment to nonemployment. The effect of the older coworker’s early retirement is larger before-reform than afterwards. That may represent a strong point of this paper as findings suggest that the change in the pension regulatory system produced a reduction in the value attributed to the coworkers’ shared leisure and work. Thus, the increase in the financial incentives associated with the lengthening of the active participation in the labor market seems to dominate the utility gain related to the cross-coworker complementarity in leisure..
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[14] Glaeser, E, Sacredote, B. and Scheinkman, J. A. (1996). ”Crime and social interactions.” The Quarterly Journal of Economics, 111: 507-548. [15] Grodner, A., and Kniesner, T. J. (2006). ”Social interactions in labor supply.” Journal of the European Economic Association, 7: 1226-1248. [16] Guardiancich, I. (2010). ”Denmark. Current pension system: first assessment of reform outcomes and output.” European Social Observatory. [17] Gustman, A. L. and Steinmeier, T. L. (1986), “A structural retirement model.” Econometrica, 54(3): 555-584. [18] Gustman, A. L. and Steinmeier, T. L. (2000a). “Retirement in dual career families.” Journal of Labor Economics 18(3): 503-545. [19] Gustman, A. and Steinmeier, T. L. (2004). “Social security, pensions and retirement behavior within the family.“ Journal of Applied Econometrics, 19(6): 723-737. [20] Hahn, J., Todd, P. and Van Der Klaauw, W. (2001). “Identification and estimation of treatment effects with a regression discontinuity design.” Econometrica 69, 201–209. [21] Imbens, G.W. (2009). “Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua.” Harvard University, Massachusetts, Boston. [22] Imbens, G.W. and Lemieux, T. (2008). “Regression discontinuity design: A guide to practice.“ Journal of Econometrics, 142(2): 615-635. [23] Lalive, R. (2008). “How do Extended Benefits affect Unemployment Duration? A Regression Discontinuity Approach.” Journal of Econometrics, 142(2), 785-806. [24] Lalive, R., and Cattaneo, A. (2009). “Social interactions and schooling decisions.” The Review of Economics and Statistics, 91: 457-477. [25] Larsen, M. and Pedersen, P.J. (2008). “Pathways to early retirement in Denmark, 1984-2000.” International Journal of Manpower 29(5): 384-409. [26] Lee, D.S. and Lemieux, T. (2009). “Regression Discontinuity Designs in Economics.” NBER Working Paper 14723. [27] Mas, A. and Moretti, E. (2009). “Peers at Work.” American Economic Review, 99(1), pp. 112-145.
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[28] McPherson, M. and Smith-Lovin, L. and Cook, J. M. (2001). “BIRDS OF A FEATHER: Homophily in Social Networks.” Annual Review of Sociology 27: 415-44. [29] OECD (1995). “The transition from work to retirement.“ Social Policy Studies No.16. OECD, Paris. [30] OECD (2005). “Ageing and Employment Policies.” OECD, Paris. [31] OECD (2009). “Pensions at a Glance: Retirement Income Systems in OECD Countries.” OECD, Paris. [32] Rege, M., Telle, K. and Votruba, M. (2009). ”The effect of plant downsizing on disability pension Utilization.” Journal of the European Economic Association, 7: 754-785. [33] Oorschot, Wim van and Jensen, Per H. (2009), “Early retirement differences between Denmark and The Netherlands: A cross-national comparison of push and pull factors in two small European welfare states”, Journal of Aging Studies, 23(4): 267-278. [34] Rust, J. and Phelan, C. (1997). “How Social security and medicare affect retirement behavior in a world of incomplete markets.” Econometrica, 65(4): 781-831. [35] Sacerdote, B. (2001). ”Peer effects with random assignment: results for Dartmouth roommates.” The Quarterly Journal of Economics, 116: 681-704. [36] Stock, J. H. and Wise, D. A. (1990). “Pensions, the option value of work, and retirement.” Econometrica, 58(5): 1151-80. [37] Topa, G. (2001). ”Social interactions, local spillovers and unemployment.“ Review of Economic Studies, 68: 261-295. [38] Vittas, D. (2008). “A Short Note on the ATP Fund of Denmark.” WPS 4505, The World Bank. [39] White, H. (1980). “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity.” Econometrica 48, 817–838. [40] Whitehouse, E., D’Addio A., Chomik, R. and Reilly, A. (2009). “Two Decades of Pension Reform: What has been Achieved and What Remains to be Done?” The Geneva Papers on Risk and Insurance - Issues and Practice 34, 515-535..
17
0
dyads working, dyads still together .2 .4 .6 .8 1
Figure 1
1 5 10 15 dyads’age 20 25
.05
individual retirement rate .1
.15
Figure 2a
50
55
60 individual’s age
65
70
.05
coworker’s retirement rate .1 .15
Figure 2b
50 55 60 coworker’s age 65 70
.04
individual’s retirement rate .05 .06 .07 .08
Figure 3
50 55 60 coworker’s age 65 70
Table 1 -‐ Descrip2ve sta2s2cs on full sample Variable
mean younger coworker
median
st.dev.
0.4454 0.5546
0.0000 1.0000
0.4970 0.4970
ter7ary voca7onal other
0.0606 0.1904 0.7391
0.0000 0.0000 1.0000
0.2386 0.3936 0.4407
job characteris)cs manager middle manager other
0.0372 0.3384 0.6244
0.0000 0.0000 1.0000
0.1893 0.4732 0.4843
214.1220
141.3721
0.3443 0.6557
0.0000 1.0000
0.4751 0.4751
ter7ary voca7onal other
0.0597 0.1833 0.7470
0.0000 0.0000 1.0000
0.2370 0.3869 0.4408
job characteris)cs manager middle manager other
0.0417 0.2465 0.7118
0.0000 0.0000 1.0000
0.1999 0.4310 0.4529
232.3086 dyad
215.1690
156.9896
0.0000 0.0000 0.0000
0.4818 0.4348 0.4630
0.0000 0.0000 0.0000 0.0000 326707
0.4610 0.3828 0.3173 0.4846
gender female male educa)on
star7ng wage
229.8922 older cowoker gender
female male educa)on
star7ng wage differences in gender educa7on job posi7on firm size less than 20 employees firm size between 20 and 49 firm size between 50 and 99 firm size larger than 100 Observa7ons
0.3662 0.2532 0.3112 star7ng firm 0.3163 0.1783 0.1276 0.3778
firm change in employment 29.5272 1.0000 768.2991 firm size less than 20 employees 0.3314 0.0000 0.4707 firm size between 20 and 49 0.1783 0.0000 0.3828 firm size between 50 and 99 0.1136 0.0000 0.3173 firm size larger than 100 0.3767 0.0000 0.4856 Observa7ons 326707 Note: Star7ng wage and star7ng firm refer to the first wage and firm observed in the sample period.
Table 2 -‐ Descip.ve sta.s.cs on dyads Variable distance commu2ng distance years worked same workplace lived in same place worked same municipality gender composi0on man-‐man man-‐woman woman-‐man woman-‐woman educa0on composi0on ter2ary-‐ter2ary ter2ary-‐secondary ter2ary-‐other secondary-‐ter2ary secondary-‐secondary secondary-‐other other-‐ter2ary other-‐secondary other-‐other occupa0on composi0on manager-‐manager manager-‐middle manager manager-‐other middle manager-‐manager middle manager-‐middle manager middle manager-‐other other-‐manager other-‐middle manager other-‐other dyads
mean
median real dyad
st.dev.
mean
median placebo 1
st.dev.
mean
median placebo 2
st.dev.
32.1348
18.5574
36.5490
30.1398
18.0271
34.7669
33.7528
19.5750
38.0288
4.1631 6.1597 7.6673
3.0000 6.0000 7.0000
4.1749 3.7484 3.7411
2.3875 -‐ 8.9992
2.0000 -‐ 8.0000
2.4825 -‐ 4.7130
-‐ 6.3262 8.2145
-‐ 6.0000 8.0000
-‐ 5.3426 4.9165
0.4275 0.2251 0.1339 0.2136
0.0000 0.0000 0.0000 0.0000
0.4947 0.4176 0.3405 0.4098
0.3693 0.1506 0.1928 0.2873
0.0000 0.0000 0.0000 0.0000
0.4826 0.3577 0.3945 0.4525
0.3734 0.2474 0.2158 0.1634
0.0000 0.0000 0.0000 0.0000
0.4837 0.4315 0.4114 0.3698
0.0240 0.0167 0.0237 0.0161 0.0924 0.0853 0.0274 0.0938 0.6206
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000
0.1529 0.1281 0.1521 0.1259 0.2896 0.2794 0.1633 0.2916 0.4852
0.0140 0.0087 0.0153 0.0112 0.0786 0.0712 0.0342 0.1322 0.6345
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000
0.1175 0.0929 0.1228 0.1050 0.2692 0.2572 0.1819 0.3387 0.4816
0.0094 0.0161 0.0443 0.0148 0.0397 0.1336 0.0450 0.1485 0.5487
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000
0.0965 0.1257 0.2058 0.1206 0.1953 0.3402 0.2072 0.3556 1.0000
0.0043 0.0136 0.0159 0.0086 0.1389 0.0630 0.0184 0.1387 0.5986
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 50915
0.0653 0.1158 0.1253 0.0924 0.3458 0.2430 0.1343 0.3457 0.4902
0.0031 0.0169 0.0166 0.0148 0.2572 0.1623 0.0161 0.1581 0.3549
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 62462
0.0558 0.1287 0.1278 0.1209 0.4371 0.3687 0.1259 0.3648 0.4785
0.0017 0.0137 0.0166 0.0130 0.1658 0.1601 0.0210 0.2089 0.3993
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 89084
0.0411 0.1164 0.1276 0.1134 0.3719 0.3667 0.1432 0.4065 0.4898
Table 3 -‐ Main results Variable Non employment_older
linear
linear
quadra,c
cubic
loclinear
baseline
first
0.0058 0.0049 0.0073 0.0074 0.0065 0.0072 0.0719 (0.0015) (0.0015) (0.0023) (0.0036) (0.0019) (0.0023) (0.0027) linear devia,on_older -‐0.0004 -‐0.0005 -‐0.0020 -‐0.0021 -‐0.0011 -‐0.0017 0.0034 (0.0003) (0.0003) (0.0010) (0.0029) (0.0005) (0.0010) (0.0010) interac,on_older -‐0.0001 -‐0.0001 0.0008 0.0013 0.0001 0.0007 -‐0.0199 (0.0003) (0.0003) (0.0012) (0.0032) (0.0006) (0.0012) (0.0014) age threshold_younger 0.0789 0.0783 0.0800 0.0842 0.0835 0.0754 0.0143 (0.0025) (0.0025) (0.0036) (0.0048) (0.0035) (0.0035) (0.0036) linear devia,on_younger 0.0025 0.0024 0.0046 0.0027 0.0026 0.0028 -‐0.0022 (0.0002) (0.0002) (0.0008) (0.0023) (0.0002) (0.0008) (0.0010) interac,on_younger -‐0.0130 -‐0.0129 -‐0.0236 -‐0.0283 -‐0.0178 -‐0.0270 0.0020 (0.0007) (0.0007) (0.0022) (0.0049) (0.0018) (0.0022) (0.0021) year dummies no yes yes yes yes yes yes other controls no no no no no yes yes age brackets_younger 50-‐69 50-‐69 50-‐69 50-‐69 50-‐69 50-‐69 50-‐69 age brackets_older 51-‐70 51-‐70 51-‐70 51-‐70 55-‐65 51-‐70 51-‐70 Dyads 50915 50915 50915 50915 50915 50915 50915 Observa,ons 326707 326707 326707 326707 229018 326707 326707 R-‐squared 0.0161 0.0200 0.0201 0.0201 0.0206 0.0456 0.0455 Note: coefficients in bold, bold&italics and italics are sta,s,cally significant at the 1%, 5% and 10% level, respec,vely. Standard errors are in parenthesis. The dependent variable is the individual transi,on to non-‐employment.
iv 0.0975 (0.0314)
age threshold_older
-‐0.0020 (0.0011) 0.0027 (0.0015) 0.0740 (0.0035) 0.0030 (0.0008) -‐0.0272 (0.0022) yes yes 50-‐69 51-‐70 50915 326707 0.0491
Table 4 -‐ Robustness checks !me period Non employment_older
1981-‐1989 1990-‐1998 1999-‐2006 0.1757 0.1786 0.0575 (0.0591) (0.0625) (0.0375) age threshold_younger 0.0571 0.0604 0.0813 (0.0065) (0.0076) (0.0042) Dyads 22411 21286 44668 ObservaEons 112405 90704 211381 R-‐squared 0.0467 0.0405 0.0478 firm employment change contracted stable expanded Non employment_older 0.0722 0.0862 0.1294 (0.0589) (0.0593) (0.0431) age threshold_younger 0.0834 0.0597 0.0936 (0.0072) (0.0050) (0.0069) Dyads 30138 34273 21863 ObservaEons 89155 153113 81518 R-‐squared 0.0630 0.0518 0.0253 firm size =100 Non employment_older 0.0960 0.0977 (0.0528) (0.0347) age threshold_younger 0.0623 0.0956 (0.0044) (0.0059) Dyads 32882 21136 ObservaEons 201415 122371 R-‐squared 0.0581 0.0343 municipality size =50000 Non employment_older 0.0986 0.0904 (0.0339) (0.0829) age threshold_younger 0.0757 0.0614 (0.0037) (0.0101) Dyads 46211 11935 ObservaEons 288083 35703 R-‐squared 0.0503 0.0394 firm age composi!on =p(50) Non employment_older 0.0945 0.0980 (0.0339) (0.0351) age threshold_younger 0.0677 0.0810 (0.0047) (0.0052) Dyads 31657 33685 ObservaEons 161893 161893 R-‐squared 0.0496 0.0496 commu!ng distance =16 Non employment_older 0.0780 0.1184 (0.0488) (0.0410) age threshold_younger 0.0845 0.0678 (0.0058) (0.0044) Dyads 18901 32014 ObservaEons 121620 202166 R-‐squared 0.0475 0.0518 years lived in same municipality =6 Non employment_older 0.0602 0.1022 (0.0861) (0.0340) age threshold_younger 0.0715 0.0740 (0.0106) (0.0037) Dyads 22490 47404 ObservaEons 30125 293661 R-‐squared 0.0447 0.0503 younger coworker's age 50-‐54 55-‐59 Non employment_older 0.1085 0.1024 (0.0453) (0.0675) age threshold_younger . . . . Dyads 45367 32909 ObservaEons 143570 131395 R-‐squared 0.0307 0.0409 Note: coefficients in bold, bold&italics and italics are staEsEcally significant at the 1%, 5% and 10% level, respecEvely. Standard errors are in parenthesis. The dependent variable is the individual transiEon to non-‐employment.
Table 5 -‐ Placebo results Variable Non employment_older age threshold_older
reduced
first placebo1
iv 0.0203 (0.0278)
reduced
first placebo2
iv -‐0.0468 (0.0344)
0.0017 0.0859 -‐0.0040 0.0852 (0.0024) (0.0028) (0.0050) (0.0070) age threshold_younger 0.0906 0.0007 0.0906 0.0885 0.0009 0.0815 (0.0039) (0.0036) (0.0039) (0.0093) (0.0013) (0.0063) Dyads 63052 63052 63052 89084 89084 89084 ObservaIons 351335 351335 351335 1572658 1572658 1572658 R-‐squared 0.0181 0.0243 0.0185 0.0383 0.0461 0.0347 Note: coefficients in bold, bold&italics and italics are staIsIcally significant at the 1%, 5% and 10% level, respecIvely. Standard errors are in parenthesis. The dependent variable is the individual transiIon to non-‐employment.
