Imitation, Contagion, and Exertion Do Colleagues Sickness Absences Influence your Absence Behaviour?

Transcription

1 Imitation, Contagion, and Exertion Do Colleagues Sickness Absences Influence your Absence Behaviour? Harald Dale-Olsen, Kjersti Misje Nilsen, and Pål Schøne Institute for Social Research, Oslo, Norway May 2010 Abstract The main goal of this paper is to analyse social interaction effects in sickness behaviour at the workplace. The main question we ask is: Do co-workers affect each others sickness absence, i.e., is there evidence of group influence in sickness behaviour? This is important for several reasons. The presence of such behaviour is interesting from a behavioural perspective, but even more so from a policy point of view, because if such behaviour is present, this indicates additional social gains since if the absenteeism of one worker is reduced, this influences the absence behaviour of other workers as well. We answer this question by using high quality matched employer-employee data with detailed individual information on sickness absence. We focus on two measures of sickness absence: i) number of sickness absence spells, and ii) duration of sickness absence. To deal with the well known potential problem of endogeneity, we use a 2SLS procedure using peer group characteristics as instruments for peer group outcomes. All main analyses takes into account fixed worker effects, but for robustness checks we even address the importance of fixed job effects. To ensure that we identify social interaction effects we provide separate analyses of contagious illnesses, illnesses related to exertion (muscular-skeletal disorders), mental and vague disorder, as well as we analyse pseudo-peers and attempt to control for bad management and other workplace related causes for absenteeism. Our data is based on a public administrative matched employer-employee register data system on all employees in Norway, comprising information on jobs (e.g., duration of employment, wages, and working hours), workers (e.g., education, sickness absences, income, country of origin), and workplaces (e.g., size, age, industry, region) Our analyses are based on data for the period The final sample used in all the analyses comprises 50% of all male workers years of age, working in plants that have at least 5 employees, and where at least 3 peers exist. As noted above, for all our workers we have information on every physiciancertified sickness absence spell, mainly lasting more than 3 days. For these absences, we also have knowledge about the reason: in the form of ICPC-2 diagnose codes. Thus we can address the importance of contagious illnesses, illnesses related to exertion (muscular-skeletal disorders), mental and vague disorders for how colleagues affect a worker s absence behaviour. The results suggest that social interaction effects in sickness absence at the workplace do exist, and the effects are noticeable in size. Even after controlling for endogeneity issues, our preferred estimate suggests that for every absence spell your colleagues experience, you increase your number of spells by approximately 10 percent. The relationship is even stronger when focussing on the number of lost work days due to sickness absence. This strong relationship is not due to contagious diseases, which although being present, are not the sole reason that the peer group s absence matters for your absence. Index terms: Sickness absence, social interaction, co-workers. JEL codes: H55, J22; Z13 Acknowledgement: This work was financed by the Norwegian Research Council under grant number /S20. Corresponding authors: Harald Dale-Olsen, Institute for Social Research, P.O. Box 3233 Elisenberg, N-0208 OSLO, NORWAY. hdo@socialresearch.no, Pål Schøne, Institute for Social Research, P.O. Box 3233 Elisenberg, N OSLO, NORWAY. psc@socialresearch.no 1

2 1. Introduction The level and development in sickness absence in Norway has in recent years raised concern and spurred the need to better understand determinants of sickness absence. The direct costs of absenteeism are very high 1, and sickness absence becomes even more costly if one takes into account indirect production disruption costs. This paper analyses social interaction effects in sickness behaviour at the workplace. The main question we ask is: Do co-workers affect each others sickness absence, i.e., is there evidence of group influence in sickness behaviour? This is important for several reasons. The presence of such behaviour is interesting from a behavioural perspective, but even more so from a policy point of view, because if such behaviour is present, this indicates additional social gains since if the absenteeism of one worker is reduced, this influences the absence behaviour of other workers as well. A positive correlation between the behaviour of the individual and the behaviour of the co-workers can exist for several reasons. Manski (1993) distinguishes between three types of mechanisms: i) endogenous social interaction effects, arising from the mechanism the behaviour of the individuals in the group directly affects the behaviour of an individual member of the group, ii) contextual interactions, where the behaviour of a person in some way varies with exogenous characteristics of the group members, and iii) correlated effects, where individuals in the same group tend to behave similarly because they have similar individual characteristics or face similar institutional environments. In this paper we aim to present evidence on the first of these mechanisms. We contribute to a growing empirical literature focussing on social interaction effects at the workplace, including studies of fertility, earnings, and productivity (Drago and Garvey 1998, Hamilton, Nickerson, and Owan 2003, Rees et al. 2003, Mas and Moretti 2009, Shvydko 2007, 1 According to Norway s National Budget 2010 publicly paid sick pay constitute 1.5 percent of GDP (37.5 billion Nok) ( 010.pdf), in addition comes the privately paid sick pay. 2

3 Hensvik and Nilsson 2009). Still, the literature on workplace social interaction effects focussing on sickness absence is rather scant (Hesselius et al. 2009, Ichino and Maggi 2005 are two exceptions). We contribute to this scant literature in several ways: First, we exploit very rich longitudinal employer-employee register data including detailed information on the whole population. Still, much of the previous research has been conducted on single firms or on firms within a limited regional area. This limits the general applicability of the results. We argue that representative studies drawing on the whole labour market is timely. The longitudinal set up of the data will also enable us to estimate individual fixed effect models, sweeping away time invariant unobserved effects related to the individual. Secondly, we have information on detailed diagnosis, enabling us to conduct diagnosis-specific social interaction effects. Thirdly, we address the severe identification problem associated with the estimation of social interaction effects by employing an instrument variable approach. For social interactions to play a role, agents (workers) must interact thorough their chosen actions. If so, an action chosen by one agent may affect the actions of other agents (Manski 2000). Social interaction effects in sickness absence can come through different mechanisms. To the extent that there are stigmatic effects related to sickness absence at the workplace there are utility costs for the worker related to sickness absence. The stigma effect will tend to decrease with the proportion of your workers that you interact with that is absent due to sickness. In this way the social interaction effect feeds into a social multiplier effect (Brock and Durlauf 2001). Therefore, sickness absence will come with both a direct and indirect effect. A change in a worker s sickness absence will consist of a direct effect, as well as an indirect effect, the last effect operating through the influence that one s absence behaviour has on others. In an international context the Norwegian public sickness benefit system is generous (OECD 2009). If you are employed and have been so for at least four weeks, you are entitled to sick pay from the first day of sickness. The entitlement is limited to maximum one year. The sickness benefit is fully wage compensated, i.e., for most workers the benefit level is set to 100 3

4 per cent of previous earnings. However, for workers with labour income that exceeds 6 times the basic minimum entitlement requirements in the welfare system are not entitled to sickness benefit for the income above the threshold 2, although the majority of employers offer a top up for high income workers as well. The employer disburses sick pay the first 16 days. After this period, the sick pay is publicly disbursed and administered by the Norwegian Labour and welfare administration (NAV). Spells of sickness up till three days is based on self certification by the worker. Sickness absence longer than three days requires certification by a physician. 3 Workers that are not able to return to work after 1 year of sickness absence are offered rehabilitation and benefits to qualify for other types of job. If return is not possible disability benefits are offered. The results in the paper suggest that social interaction effects in sickness absence at the workplace do exist, and the effects are noticeable in size. Even after controlling for endogeneity issues, our preferred estimate suggests that for every absence spell your colleagues experience, you increase your number of spells by 10 percent. The relationship is even stronger when focussing on the number of lost work days due to sickness absence. This strong relationship is not due to contagious diseases, which although being present, are not the sole reason that the peer group s absence matters for your absence. The paper proceeds as follows: Section 2 gives a brief presentation of related literature. Section 3 presents the econometric specifications, while Section 4 presents the data, the sample and the variables. Section 5 presents the main results. Sections 6-8 presents further robustness checks and test other explanations. Section 9 briefly concludes. 2 By May 2009, the basic amount was equal to Norwegian kroner. 6G then equals Norwegian kroner, or approximately Euro. Bonuses and certain fringe benefits depending on being present are likely not being compensated. 3 If the workplace is part of the Integrated working life (IW) -treaty, the workers are entitled to 8 days of sick leave without doctor authorisation. The IW treaty covers approximately half of the labour force. Employers are entitled to allow longer absence periods than 3 days without being certified by a physician. 4

5 2. Related research The empirical economic literature focussing on social interaction effects using the workplace as the analytical unit is growing fast, focussing on a large variety in dependent variables; including productivity (Mas and Moretti 2009), wages (Shvydko 2007), and fertility behaviour (Hensvik and Nilsson 2009). Our paper relates more directly to the scarcer literature focussing on workplace social interaction effects in sickness absence. Hesselius et al. (2009) use Swedish data to analyse how co-workers affect each others work absence. They exploit a large-scale randomized social experiment altering the work absence incentives for half of all employees living in Gothenburg, Sweden. Their results show that employees in workplaces with a high proportion of treated co-workers increase their own absence level significantly. They conclude that their results suggest that social interactions are an important determinant of work absence. However, the authors also recognize that the reduced form analyses they perform, prohibits them from drawing definite conclusions about the exact nature of the underlying causes of the social interaction effect. Ichino and Maggi (2000) analyse shirking behaviour within a large Italian bank. They find that the prevalence of shirking within the bank is characterized by significant regional differentials. They report that absenteeism and misconduct episodes are substantially more prevalent in the south. They put forward a number of potential explanations for this fact: different individual backgrounds, group-interaction effects; sorting of workers across regions; differences in local attributes; different hiring policies; and discrimination against southern workers. Their results suggest that individual backgrounds, group-interaction effects, and sorting effects contribute to explaining the north-south shirking differential, with individual backgrounds being the most important contributor. Lindbeck et al. (2009) use Swedish data and ask whether the average level of sickness absence in a neighbourhood affect individual sickness absence through social interaction on the neighbourhood level. Therefore, contrary to our study, they use neighbourhood as their unit of 5

6 analysis. They use several different approaches to disentangle the causal effects of group behaviour on individual behaviour from the effects of individual sorting on neighbourhoods. Their dependent variable is number of sick days. Their results are robust in the sense that regardless of approach and identifying assumptions, they obtain statistically significant estimates indicating group effects. Their IV point estimates lie in the range of 0.67 and This implies that for a person who lives in a neighbourhood with an average that is one day higher than in another neighbourhood will have approximately more sick days. 3. Econometric specification The aim of our analyses is to examine whether individual sickness behaviour is affected by group behaviour. Basically, we want to estimate the following relation: ( 1) Sijt = α1 + α 2Xijt + α3x jt + α4s jt + τt + θi + εijt where S ij is sickness absence for individual i from group j at time t, X ijt is a vector of three types of explanatory variables: i) individual characteristics, ii) characteristics of the individual s workplace, and iii) characteristics of the region where the workplace is located. X jt is a vector with characteristics of the individual s peers, and S jt is a measure of the average sickness absence of i s peers. τt is a time fixed effect, θ i is a time invariant individual effect, and ε ijt is an error term. The main coefficient of interest is α 4. It measures the relationship between the mean absence rate of the co-workers and the individual worker s sickness absence. Estimating sickness absence peer effects from (1) based on OLS raises several methodological questions. The key challenge is to disentangle the peer group effect from the effect that workers with similar unobserved characteristics group together at the same workplace, and the effect due to them being exposed to the same local or regional impacts (contextual 6

7 variables). Running an OLS regression on (1) will tend to produce a biased estimator of α 4 because of the reflection problem (Manski 2000). To reach causal statements on the relationship between individual and group sickness behaviour we use a 2SLS procedure using peer group characteristics as instruments for peer group outcomes (sickness behaviour). Our two instrument variables are the educational attainment of the peers mother and father, measured in number of years, varying from 0 to 12 years after compulsory school. The educational attainment of the parents is measured when the worker is 16 years old. This implies that for the worker this it is a predetermined variable. The identification strategy is that the educational attainments of the peers parents should affect the peers sickness absence, but it should not directly affect the individual s sickness absence. There is a large research literature analysing the effect of parents educational attainment on different child outcomes (Black et al. 2005, Black et al. 2009, Black and Devereux 2010). An important part of this literature is emphasising on the causal mechanisms that underlie this relationship, i.e., it tries to disentangle genetic and environmental effects. In our paper we are not depending on knowing the nature of this relationship. The important point for us is that a relationship exists; genetic or environmental. We argue in the paper that it is reasonable to assume that your parents past behaviour (reflected by their educational attainment) could affect your own sickness absence behaviour today in the labour market, but there is no reason that they should affect your co-workers sickness absence. In the result section we present a battery of specification tests for the instrument variables. Having repeated information on individuals we estimate individual fixed effect versions of the 2SLS procedure. 4. Data and variables The starting point is a public administrative register containing information on all registered jobs in Norway, with information on duration of employment, wages, and working hours. This 7

8 register is linked to other public administrative registers into an integrated data system, all managed by Statistics Norway. The analyses are based on matched employer-employee register data for the period , with a unique individual identifier linking individuals to plants. The data has a panel dimension enabling us to follow individuals over time, and thereby estimate individual fixed effect models. The data set comprises information on all registered workers in the Norwegian labour market. The sample used in all the analyses includes male workers years of age, working in plants that have at least 5 employees. The main focus in our study is on social interaction effects, and thus a crucial dimension is related to the construction of peer groups. We define as a male worker s peer group as similarly educated male colleagues at the workplace, where educational qualification is differentiated by four categories: compulsory schooling only, high school only or vocational schooling, lower university/college-level and finally master- or PhD-level. Our two dependent variables are i) number of sickness absence spells, and ii) duration of sickness absence (number of days). Both variables are measured within each year in the period of observation ( ). Both measures are taken from the public administrative register data base over sickness absences, administered by the Norwegian Labour and Welfare Administration (NAV). The measures of sickness absence cover all periods of sickness absence from work that is certified by a physician. This implies that it covers all absence spells from work due to sickness that lasts longer than 3 days. The data set do also contain individual information on diagnosis (ICPC-2 classification). Based on detailed information on diagnoses we distinguish between four groups of diagnoses: i) Contagious diseases, ii)muscular-skeletal diseases, iii)mental diseases, and iv) Vague disorders. In the Appendix these 4 groups and which ISCP-2-codes that constitute these groups are described more in detail. 8

9 Individual explanatory variables include information on age, duration of employment, seniority, working time, marital status, number of children below 7, and yearly earnings. Age is measured by eight dummy variables, each covering a five year span. Duration of employment is measured in number of working days per year. Seniority is measured in number of years with the present employer. Working time is measured by a dummy variable measuring whether the person works full time (30 hours or more per week), marital status is measured by a dummy variable measuring whether the person is married or not. Yearly earnings are total yearly labour market earnings. All individual level variables are also measured for the peer groups, i.e., for each individual we calculate the peer group average (excluded the individual s values) of all the individual-level variables. Plant level variables include information on number of employees, mean level variables of earnings, mean level variable of workers age, job flow rates in the plant s industry (three digit NACE). Regional variables include information on the unemployment rate in the municipality. Peer level variables include information on the peers age, working time, seniority, number of children below 7, marital status, educational attainment, and yearly earnings. When all variables (peers, regional and industry) are constructed and measured, we discard information on workers facing peer groups with fewer than 3 colleagues, and are forced due to computational constraints to draw a 50 per cent sample of the remaining male workers. These are then followed over time in the period Table A1 in Appendix presents descriptive statistics on the key variables. 5. Main results 5.1 Peer effects related to sickness absence in general We start this section by looking closer on the relationship between the sickness absence levels of the peer groups and the average years of education for the mothers and fathers of the workers in 9

10 the peer group. Figure 5.1 and Figure 5.2 show this relationship for the average number of sickness absence spells and the average number of sickness absence days, respectively. Both figures reveal strongly negative relationships between sickness absence and the peer groups parent s years of education, and the negative relationships are possibly strongest for fathers (compared to mothers). The essence of these figures is anyway that the more highly educated the peer group s parents are, the lower is the peer group s sickness absence rates. This negative relationship will be central to our strategy to overcome the reflection problem. We argue that the education of the peer group s parentsis in principle uncorrelated with a worker s own sickness absence behaviour, thus the educations of the peer group s mothers and fathers are valid as instruments for the peer group s sickness absence in regression of workers sickness absence on average peer group sickness absence. Figure 5.1. The correlation between parent s education and peers sickness absence absence spells Mean Sickness Absence of Peers (spells) Mean Sickness Absence of Peers (spells) Mean Education of Peers' Mothers Mean Education of Peers' Fathers Area of symbol proportional to observations in education group, where education group is defined from years of education 10

11 Figure 5.2. The correlation between parent s education and peers sickness absence absence duration Mean Sickness Absence of Peers (days) Mean Sickness Absence of Peers (days) Mean Education of Peers' Mothers Mean Education of Peers' Fathers Area of symbol proportional to observations in education group, where education group is defined from years of education Note that since the education of mothers and fathers are fixed for each worker constituting a peer group, variation in a peer group s average parental education over time is due to changes in the peer group s workers. In the regressions that follow we apply a fixed effect approach based on withintransformed observations, where we will use variation in the peer groups average years of education among their parents as instruments for the peer groups sickness absences rates. Identification will in this case be ensured by variation over time in the composition of the peer group and variation in peer groups (since a worker may move between workplaces and thus facing different groups). We start in Table 5.1 by presenting the results from several linear regressions (two sets of regressions) where we take into account fixed worker effects. These regressions differ with respect to the dependent left-hand-side variable and peer group absence measure. The first 4 11

12 models focus on the number of sickness absence spells, while the last 4 models focus on the number of sickness absence days. The structure of these regressions, however, is identical. We start in Model 1(5) by running a simple fixed effect linear regression of sickness absence on peers sickness absence and a set of basic controls. These basic controls are an intercept, year dummies (6), age dummies (7, 5-years interval), yearly employment spell in days, dummies for short- and long part-time, proportion of colleagues within the age intervals given by the individual age dummies. Model 2(6) is identical to Model 1(5) except that we instrument the peer groups average sickness absence by the average of their parents years of education. Model 3(6) then introduce a more involved control-vector by adding additional individual and peer level controls. These additional controls are number of children below 7 years of age, married, seniority, log daily earnings, the equivalent average variables for the peer group, peers average years of education. Finally, in Model 4(8) we even add workplace, industry and regional variables to the control vector, or more specifically, we add controls for the number of employees (workplace), workplace mean log daily earnings, workplace age, industry yearly net job growth rate (3-digit SIC), local unemployment rate (municipality). The idea is that these variables should capture the impact of workplace, industry and regional shocks affecting the sickness absence behaviour of workers. The plain fixed effects regressions (Models 1 and 4) reveal strongly and positive relationships between a worker s sickness absence and his peer group s average sickness absence. If the peer group on average increase their absence by 1 spell or 1 day, then the worker increase his absence by 9 or 5 percent, respectively. Next we introduce our instrument variables. Table 5.1 shows that in all the IV-regressions the instruments are highly appropriate, and they correlate as expected negatively with the peer group s parent s years of education. The tests for weak instruments reveal strong instruments, while the over-identification tests clearly reject the idea that our instruments have any statistical power in the second step regressions. 12

13 Table 5.1 The impact of colleagues sickness absence on male worker s sickness absence behaviour Sickness absence spells Sickness absence days FE FE-IV FE-IV FE-IV FE FE-IV FE-IV FE-IV Peers sickness absence ** ** ** ** ** ** ** ** (0.002) (0.031) (0.042) (0.042) (0.002) (0.057) (0.081) (0.077) Additional controls: Basic Yes Yes Yes Yes Yes Yes Yes Yes Individual and peers Yes Yes Yes Yes Workplace, region, industry Yes Yes Instruments and first step Peers mother s education ** ** ** ** ** ** Peers father s education ** ** ** ** ** ** F-test excl. instr Kleibergen-Paap F Hansen J (p-value) Workers Observations Note: Table-elements (first two rows) report the coefficients and SEs on peers sickness absence in linear regressions of a worker s sickness absence on peers sickness absence. FE denotes fixed effect regressions based on the within transformation, while FE-IV denotes fixed effect IV regressions based on the within transformation, where peers sickness absence is considered an endogenous variable and thus instrumented. Peers sickness absence measures the average sickness absence (either spells or duration) among your colleagues, where your colleagues are defined as similarly educated male workers employed at the same workplace. Additional controls; Basic: intercept, year dummies (6), age dummies (7, 5-years interval), yearly employment spell in days, dummies for short- and long part-time, proportion of colleagues within the age intervals given by the individual age dummies; Individual and peers: number of children below 7 years of age, married, seniority, log daily earnings, the equivalent average variables for the peer group, peers average years of education; workplace, region and industry: the number of employees (workplace), workplace mean log daily earnings, workplace age, industry yearly net job growth rate (3-digit SIC), local unemployment rate (municipality). The lower half of the table reports information on the first step parameters associated with the instruments and on tests of the strength/appropriateness of the instruments. Full regression results available from the authors upon request. Robust standard errors are reported in parentheses. ** and * denote 1 and 5 percent level of significance, respectively. By applying the IV-strategy to our regressions we strongly enforce our results, particularly concerning the number of absence days. Thus our results are in accordance with the notion that when one does not take into account endogeneity, the estimates will be biased towards zero. Even after controlling for individual-, peer-, workplace, industry and region-level variation and taking account individual fixed effects, we find that if the peer group on average increase their absence by 1 spell or 1 day, then the worker increase his absence by 10 or 26 percent, respectively. 13

14 5.2 The importance of contagious diseases The obvious question pertaining to our results is whether they are related to or are the result of contagious diseases only? To address this question we conduct two sets of analyses. First, we exclude all absences related to contagious diseases (ending in a physician-certified absence). This absence spells or absence days related to contagious diseases are zeroed out. Then we repeat the regressions of Models 4(8) in Table 5.1. The results are presented in the first two columns of Table 5.2. These regressions reveal that only small and in no way qualitatively changes occur with the relationship between a worker s absence and his peer s absence behaviour. Next, we focus only on those absences that are related to contagious diseases, but otherwise repeat the regressions of Models 4(8) in Table 5.1. These regressions are presented under Models 4(6) in Table 5.2. In Models 3(5) we present the results of the equivalent non-iv regressions. The IV-regressions show that our instruments are inappropriate, i.e., the peer group s parent s years of education have little or no power as instruments for the peer group s absence related to contagious diseases. This is probably not a big surprise, i.e., it is not unexpected that parents educational qualifications are bad predictors for contagious diseases of workers today. However, the ordinary fixed effect analyses (Models 3 and 5) reveal strongly positive relationships between a worker s absence due to contagious diseases and the peer group s absence. If the peer group on average increase their absence due to contagious diseases by 1 spell or 1 day, then the worker increase his absence by 36 or 10 percent, respectively. Our conclusion to this subsection is therefore that while workers become absent due to contagious diseases, and thus one truly can talk about contagion effects, contagious diseases are not the sole reason why we observe peer group effects. 14

15 Table 5.2 Are contagious diseases the reason why colleagues sickness absences matter for your absence behaviour? Excluded contagious diseases Contagious diseases only Spells Days Spells Spells Days Days Method: FE-IV FE-IV FE FE-IV FE FE-IV Peers sickness absence ** ** ** ** (0.038) (0.077) (0.002) (0.529) (0.003) (0.529) Additional controls: Basic Yes Yes Yes Yes Yes Yes Individual and peers Yes Yes Yes Yes Yes Yes Workplace, region, industry Yes Yes Yes Yes Yes Yes Instruments F-test excl. instr Kleibergen-Paap F Hansen J (p-value) Workers Observations Note: Table-elements (first two rows) report the coefficients and SEs on peers sickness absence in linear regressions of a worker s sickness absence on peers sickness absence. Absences (for both worker and peers) are measured only related to the illnesses expressed by column head. See text for definitions on contagious diseases. FE denotes fixed effect regressions based on the within transformation, while FE-IV denotes fixed effect IV regressions based on the within transformation, where peers sickness absence is considered an endogenous variable and thus instrumented. Peers sickness absence measures the average sickness absence (either spells or duration) among your colleagues, where your colleagues are defined as similarly educated male workers employed at the same workplace. Additional controls; Basic: intercept, year dummies (6), age dummies (7, 5-years interval), yearly employment spell in days, dummies for short- and long part-time, proportion of colleagues within the age intervals given by the individual age dummies; Individual and peers: number of children below 7 years of age, married, seniority, log daily earnings, the equivalent average variables for the peer group, peers average years of education; workplace, region and industry: the number of employees (workplace), workplace mean log daily earnings, workplace age, industry yearly net job growth rate (3-digit SIC), local unemployment rate (municipality). The lower half of the table reports information on tests of the strength/appropriateness of the instruments. Full regression results available from the authors upon request. Robust standard errors are reported in parentheses. ** and * denote 1 and 5 percent level of significance, respectively. 5.3 The importance of strenuous work and bad working conditions The next question is whether the peer group effects follow from strenuous work and bad working conditions affecting the worker and his peers equally, i. e., that detrimental health effects affect all workers belonging to a peer group jointly. This is a difficult question empirically, since the classical illnesses related to heavy and strenuous work are also illnesses that are more subjective. Consider for example certain muscular-skeletal diseases. Absences following muscleskeletal disorder such as back pain may clearly follow from repetitive and physically strenuous work (heavy lifting), but back pain can also be hard to define just from physical evidence, and so such absences are more to the discretion of the worker than serious injuries and other illnesses. 15

16 Our strategy is basically to conduct similar regressions as when we focused on contagious diseases. First, we estimate within-regressions (IV and not-iv) of absences where we have excluded all muscular-skeletal diseases. As reflected by our discussion above, we admit that this strategy may actually also exclude instances of interactions. The results from these regressions are presented in the first 4 regressions of Table 5.3. Then we repeat the analyses for muscular-skeletal diseases only. The last 4 regressions of Table 5.3 present the results from these analyses. Our analyses reveal that muscular-skeletal diseases are clearly important for our peer effect results. By excluding muscular-skeletal diseases, we reduce the positive peer effect. Table 5.3 Do colleagues sickness absences matter only due to strenuous work Excluded muscular-skeletal Muscular-skeletal Spells Spells Days Days Spells Spells Days Days Method: FE FE-IV FE FE-IV FE FE-IV FE FE-IV Peers sickness absence ** x ** ** ** ** ** (0.003) (0.069) (0.003) (0.177) (0.002) (0.048) (0.003) (0.079) Additional controls: Basic Yes Yes Yes Yes Yes Yes Yes Yes Individual and peers Yes Yes Yes Yes Yes Yes Yes Yes Workplace,region,industry Yes Yes Yes Yes Yes Yes Yes Yes Instruments F-test excl. instr Kleibergen-Paap F Hansen J (p-value) Workers Observations Note: Table-elements (first two rows) report the coefficients and SEs on peers sickness absence in linear regressions of a worker s sickness absence on peers sickness absence. Absences (for both worker and peers) are measured only related to the illnesses expressed by column head. See text for definitions on the illnesses. FE denotes fixed effect regressions based on the within transformation, while FE-IV denotes fixed effect IV regressions based on the within transformation, where peers sickness absence is considered an endogenous variable and thus instrumented. Peers sickness absence measures the average sickness absence (either spells or duration) among your colleagues, where your colleagues are defined as similarly educated male workers employed at the same workplace. Additional controls; Basic: intercept, year dummies (6), age dummies (7, 5-years interval), yearly employment spell in days, dummies for short- and long part-time, proportion of colleagues within the age intervals given by the individual age dummies; Individual and peers: number of children below 7 years of age, married, seniority, log daily earnings, the equivalent average variables for the peer group, peers average years of education; workplace, region and industry: the number of employees (workplace), workplace mean log daily earnings, workplace age, industry yearly net job growth rate (3- digit SIC), local unemployment rate (municipality). The lower half of the table reports information on tests of the strength/appropriateness of the instruments. Full regression results available from the authors upon request. Robust standard errors are reported in parentheses. **, * and x denote 1, 5 and 10 percent level of significance, respectively. 16

17 For absence spells, we see that the within-estimates drop from 0.09 to 0.07, but the IV-estimate indicates only minor changes. For the sickness absence duration the peer effect is reduced to 0.02, but the IV-estimate indicate quite sizeable point estimate (0.19). When we focus on muscular-skeletal diseases only, we find strong positive relationships between a worker s sickness absence due to muscular-skeletal diseases and his peer group s sickness absence due to the same diagnosis. This is true both for the ordinary within-estimates and for the IV-estimates. Our conclusion to this sub-section is that muscular-skeletal diseases are clearly important for our observed peer effects, but not the sole reason. Thus we interpret our results as supportive of the notion that part of the peer group effect is due to strenuous and detrimental working conditions affecting workers and peers equally. However, even after taking this into account, we are left with considerable variation due to what we interpret as social interaction. 5.4 Other reasons? In this final sub-section we focus on two other sets of illnesses. The first group of illnesses are mental disorders, and these constitute a well-defined set of diseases (ICPC code P). The other group, Vague disorders, which constitute a heterogeneous mix of unspecified diagnoses, vague muscular-related illness, different fears and more socially related problems. Consider first the mental disorders. Un-instrumented the within-regressions indicate weak positive peer effects, but when we apply IV-regressions these effects disappears. Thus we find really no strong evidence that peer effects related to mental disorders matter for the sickness absence of workers. Next, consider the vague disorders. In this case the un-instrumented regressions reveal strong and sizeable peer effects. If the peer s increase their absence by 1 spells on average, then a worker increases his sickness absence spells by 15 percent. Similarly, if the peer s increase their average number of sickness days by 1 day, then a worker increases his total absence duration by 6 17

18 percent. Unfortunately, the IV-test results clearly indicate inferior instruments and thus this make the IV-regression results unreliable. Table 5.4 Other reasons why colleagues sickness absences matter for your absence behaviour? Mental Vague disorders Spells Spells Days Days Spells Spells Days Days Method: FE FE-IV FE FE-IV FE FE-IV FE FE-IV Peers sickness absence ** ** ** ** (0.002) (0.164) (0.003) (0.392) (0.003) (0.256) (0.003) (0.823) Additional controls: Basic Yes Yes Yes Yes Yes Yes Yes Yes Individual and peers Yes Yes Yes Yes Yes Yes Yes Yes Workplace,region, industry Yes Yes Yes Yes Yes Yes Yes Yes Instruments F-test excl. instr Kleibergen-Paap F Hansen J (p-value) Workers Observations Note: Table-elements (first two rows) report the coefficients and SEs on peers sickness absence in linear regressions of a worker s sickness absence on peers sickness absence. Absences (for both worker and peers) are measured only related to the illnesses expressed by column head. See text for definitions on the illnesses. FE denotes fixed effect regressions based on the within transformation, while FE-IV denotes fixed effect IV regressions based on the within transformation, where peers sickness absence is considered an endogenous variable and thus instrumented. Peers sickness absence measures the average sickness absence (either spells or duration) among your colleagues, where your colleagues are defined as similarly educated male workers employed at the same workplace. Additional controls; Basic: intercept, year dummies (6), age dummies (7, 5-years interval), yearly employment spell in days, dummies for short- and long part-time, proportion of colleagues within the age intervals given by the individual age dummies; Individual and peers: number of children below 7 years of age, married, seniority, log daily earnings, the equivalent average variables for the peer group, peers average years of education; workplace, region and industry: the number of employees (workplace), workplace mean log daily earnings, workplace age, industry yearly net job growth rate (3- digit SIC), local unemployment rate (municipality). The lower half of the table reports information on tests of the strength/appropriateness of the instruments. Full regression results available from the authors upon request. Robust standard errors are reported in parentheses. **, * and x denote 1, 5 and 10 percent level of significance, respectively. 6. Truly social interaction, other workplace related causes or exertion? In this section we conduct several robustness checks to ensure that our results reflect social interaction effects. Our main test of whether the peer effect is really related to social interaction is to create pseudo-peers, i.e., by drawing random samples of similarly educated peer groups but from other workplaces than the original. Thus, if our results are strongly related to occupational or educational characteristics but not related to social interaction, our previous results will be 18

19 largely unchanged. If social interaction is a major force behind our results, the analyses based on pseudo-peers will provide little support for the notion that peer s sickness absence is important for a worker s absence behaviour. The results from these regressions are presented in Models 1 and 2 of Table 6.1. Neither for the number of absence spells nor for the number of absence days do we find evidence of any significant interaction effects. Thus, when we compare workers and randomly drawn but similarly educated peers, we find no interaction effects a result which is in accordance with the notion that our main results are clearly driven by the interaction between workers and their peers or at least that these workers share something common that affect their absence behaviour. Our second test then tries to disentangle the social interaction effect from the importance of bad management or bad work environment. The main motivation for our test is based on the notion that bad management or bad work environment is likely to affect all workers within a workplace, not just specifically one peer group (but admittedly, the latter cannot be ruled out). To capture this, we estimate similar regressions as before, but add as controls measures of the absence rates for the remaining workforce in the workplace (outside the worker and his peer group). As seen in Models 3 and 4 of Table 6.1, adding these controls strongly enforce our results. Thus controlling for other groups of workers absence behaviour makes the relationship between a worker and his peer group even stronger, the opposite of what we would have expected if our notion of bad management and bad work environment where the driving force behind our previous results. A third issue is related to selection and sorting effects and how these are related to our results concerning social interaction. To shed light on this issue, first we focus on those male workers that are active in the labour market during the whole observation period. The idea is that these workers constitute the core workers, where we can expect to find pure interaction effects, while workers where absences follow form strenuous and taxing jobs are more likely to be forced out of the labour market. 19

20 Table 6.1 Social interaction, other workplace related causes or exertion? FE-IV. Pseudo-peers All Working all years All Spells Days Spells Days Spells Days Spells Days Peers sickness absence ** ** ** * (0.019) (0.029) (0.047) (0.085) (0.071) (0.139) (0.080) (0.167) Additional controls: Basic Yes Yes Yes Yes Yes Yes Yes Yes Individual and peers Yes Yes Yes Yes Yes Yes Yes Yes Workplace, region, industry Yes Yes Yes Yes Yes Yes Yes Yes Absences of other groups Yes Yes Fixed job effects Yes Yes Instruments F-test excl. instr Kleibergen-Paap F Hansen J (p-value) Workers Observations Note: Table-elements (first two rows) report the coefficients and SEs on peers sickness absence in linear regressions of a worker s sickness absence on peers sickness absence. Except models 7 and 8 all models report the results from fixed worker effects IV regressions based on the within transformation, where peers sickness absence is considered an endogenous variable and thus instrumented. In models 7 and 8 we replace the fixed worker effect by fixed job effects. See previous tables for details on variables, instruments and controls. Models 1 and 2 report regressions on observations where the real peer groups have replaced by similarly educated pseudo-peers, i.e., randomly drawn peer groups. Models 3 and 4 report observations adding controls for the average number of absence spells and duration of the remaining workers within the workplace (i.e., besides the worker and his peer group). The lower half of the table reports information on the first step parameters associated with the instruments and on tests of the strength/appropriateness of the instruments. Full regression results available from the authors upon request. Robust standard errors are reported in parentheses. ** and * denote 1 and 5 percent level of significance, respectively. Models 5 and 6 of Table 6.1 reveal that while no interaction effect is found regarding spells, the relationship between the number of absence days for workers and their peers is much stronger for these core workers (0.356) than what is found on average (0.261). Thus for these core workers frequent peer absence does not matter for their behaviour, but if the number of absent days starts to increase, then they will respond by increasing theirs. This indicates support for the notions that both social interaction and work exertion matter. Next, we analyse the interaction effects utilising only the variation within jobs, i.e., by taking account of fixed job effects we in practice focus on interaction effects among stayers. The figures of models 7 and 8 in Table 6.1 indicate once again that we find little evidence of interaction effects concerning spells, but strong positive interaction effects when it concerns duration. Given our fixed worker effects results, the former implies a sorting of jobs between 20

21 workplaces; some workplaces are characterised by few absence spells (both for workers and their peers) while other workplaces are characterised by many absence spells (both for workers and their peers). The latter result reveals that even stayers respond to increased absence duration for peers by increasing their own absence duration. There are several reasons why the group effect should be stronger for absence duration than for absence spells. The duration is much more visible than spells, duration has stronger implications for the work load of non-absent workers, and the duration is more at the discretion of workers than the spells. We argue that our results in this sub-section have shown that a worker s colleagues clearly influence the worker s absence behaviour, and that this is less likely to be caused by bad management. Furthermore, since the effects are at least as strong for core workers (for duration) and when we take into account workers distribution over workplaces (for duration), we find it hard to believe that our results are purely the results of selection and exertion. 7. Do peers influence each other equally? The final issue in our study is related to the age and seniority structure of the peer group compared to the worker. If the age or seniority discrepancy between our worker and the main group of the workers within a peer group is large this could signify different roles. Furthermore, since we worry that additional strenuous work following the absence of co-workers may increase the absence of a worker, one could assume that this effect would be stronger for older than younger workers, quite simply since younger workers are more healthy than older workers and thus better able to cope with the extra strain. To shed light on this issue we have split our sample of workers into four quartiles depending on the difference in age between our worker and the average worker age within his peer group (due to attrition problems, we have excluded the top age group (55-60 years) in these analyses). The first quartile thus comprises the observations of younger workers and mainly older peers (8 years age discrepancy), while the fourth quartile 21