Social Interactions at the Workplace: Exploring Sickness Absence Behavior *

Transcription

1 Social Interactions at the Workplace: Exploring Sickness Absence Behavior * Karsten Marshall Elseth Rieck Kjell Vaage Tor Helge Holmås February 2010 WORK IN PROGRESS PLEASE DO NOT QUOTE Abstract We investigate whether a worker s sickness absence is affected by the absence of her colleagues at the workplace. Using a unique matched employer-employee data for Norwegian school teachers for the period 2001 to 2006 with information of different absence types and multiple teacher and school characteristics, we find evidence of significant social interaction effects for the short-term sickness absence. The results are robust to different approaches where we mitigate methodological problems such as the reflection problem and intra-group correlation. JEL codes: C23, I38, J22, J24, J45. Keywords: Social interaction, sickness absence, reflection problem. * Thanks to seminar participants at the UiB/NHH PhD Workshop, 9 December 2009, the Bergen Econometrics Group, 9 November 2009, and the IFAU/Frisch Workshop on absenteeism and social insurance in Uppsala, November 2009, for useful comments and suggestions. Financial support from the Norwegian Research Council (187912) is acknowledged. Department of Economics, University of Bergen. karsten.rieck@econ.uib.no. Department of Economics, University of Bergen. kjell.vaage@econ.uib.no. Stein Rokkan Centre for Social studies, University of Bergen. tor.holmas@econ.uib.no.

2 1. Introduction Social interaction in the form of group effects at the workplace has attained increased attention in resent years. 1 Empirical evidence is, however, relatively scarce and faced with several methodological problems. Nevertheless, some few empirical studies have found credible evidence of social interaction effects in different settings. 2 In this paper, we estimate whether a worker s sickness absence is affected by the absence of her colleagues at the workplace. Sickness absence is a major problem in several European countries, and empirical evidence suggests that the level of absence is correlated with the generosity of the sick-pay system. The sickness insurance in many countries has low co-payment and very lenient ways of controlling the sickness claims with the probability of moral hazard problems being correspondingly high. Group effects, if present, reinforce the alleged moral hazard problems and work as a social multiplier on the propensity of being absent. We can distinguish between two channels through which peers mechanisms can work. First, an individual can be influenced by the peers threshold value for when to report sick. While minor afflictions are sufficient for some individuals to be absent from work, others show up at work even with relatively severe illness. This threshold can be affected by individuals in the same group. Second, peers can influence the tendency to shirk. If social interactions lower the marginal cost of shirking employees can be more prone to dodge work. There are a handful of empirical studies exploring social interactions in relation to sickness absence behavior. Ichino and Maggi (2000) explore movements of employees between branches in a large Italian bank. With the employees as the reference group they find significant interaction effects when the absence is due to illness or misconduct. However, the reported coefficients on misconduct are seemingly exposed to bias from simultaneity. 1 Lazear (1989), Kandel and Lazear (1992), Rotemberg (1994) and Lindbeck, Nyberg and Weibull (1999) have suggested peer group influence in employees behaviour at the workplace. 2 For example Sacerdote (2001), Bandiera, Barankay and Rasul (2005), Guryan, Kroft and Notowidigo (2007), Kato and Shu (2008) and Mas and Moretti (2009). 2

3 Bradley, Green and Leeves (2006) use a matched teachers-school data set to explore absence reported as illness among Australian school teachers. They also find evidence for interaction effects applying approximately the same empirical model as Ichino and Maggi, but the coefficients are partly insignificant when controlling for simultaneity bias. Hesseslius, Johansson and Nilsson (2009) utilize a reform that extended the self-certified absence for half of all employees in a Swedish municipality in 1988 to identify interaction effects. By viewing the reform as a treatment, they find strong effects for the non-treated employees in workplaces with high proportions of treated co-workers. Other studies use the neighborhood as the reference group. For example, Lindbeck et al. (2009) adopt several different approaches to investigate peer effects with a data set covering the entire Swedish population. Among other things they utilize the mover-model suggested by Ichino and Maggi, immigrants and privatepublic sector differences. Overall, they obtain statistically significant estimates of interaction effects. The present paper makes use of a unique matched employer-employee data for Norwegian teachers in the period 2001 to Different forms of absence (self-certified and certified by physicians, short term and long term) are registered for the majority of primary and secondary school teacher, together with individual information on earnings, education, gender, age, localization, etc. In addition, we have a broad range of school characteristics, including average level of absence, share of female workers, age distribution, etc. Such group variables (unfeasible with ungrouped individual data alone) are important when analyzing the effects of social interaction on sickness absence at the workplace. We believe that the workplace, and not neighborhoods, is the most important arena for group effects to take place as regards sickness absence behavior. 3 Ideally, we want data for 3 Neighborhoods are often used in papers on peer effects, but one might argue that it probably is a less relevant unit, since many of us hardly know our neighbors; not to mention their absence and presence at their different jobs. 3

4 employees from all branches, and the fact that we only have information on teachers absence limits the generality of our results. On the other hand, the schools as workplaces are relatively homogenous regarding organization, job tasks, intercourse with workmates at the common room, etc. Comparing absence behavior and peer effects between schools does therefore make more sense than comparison between workplaces where we know little or nothing about how work is organized, how workmates interrelate, etc. In addition, we have access to selfcertified (short term) and absence certified by physician (short and long term). To our knowledge no other data set offers this possibility of studying social interaction effects on the complete range of absence. 4 It is a challenge, particularly with administrative register data of the present type, to grasp the exact process of social interaction. Peer effects cannot be measured directly; instead we have to rely on indicators. We have argued that interaction effects are transformed from the group to the individual, but what exactly is the reference group in question? It is more likely that female teachers with dependent kids pay attention to their female colleagues with comparable family situation than to the oldest part of their male colleagues, for example. Furthermore, what is the dynamics of the interaction process? For newly employed teachers at a school we would somehow expect that the behavior of the group will have increasingly stronger influence as month and years go by. It is worth noting that the richness of individual and school characteristics and the longitudinal nature of our data allow testing of the relevant peer group as well as the dynamics of the interaction process. Even if we can resolve how the social interaction takes place it is notoriously difficult to empirically separate the peer effects. Manski (1993) distinguishes between three hypotheses that explain why individuals in the same group behave similar: The behavior of an 4 Ichino and Maggi (2000) have access to short and long term absence, but only as number of episodes due to illness. Bradley, Green and Leeves (2006) and Hesselius, Johansson and Nilsson (2009) only use short term absence while Lindbeck, Palme and Persson (2009) make use of long term absence. 4

5 individual can directly influence the outcome of its peers (endogenous interaction), the individual characteristics of an individual can affect the outcome of its peers (contextual interaction) or peers may behave similar because they have similar individual characteristics or because they face the same institutional environments (correlated effects). The challenge is to disentangle the true social interactions (endogenous and contextual) from the correlated effects. In the identification process there are two major complications that are encountered; the simultaneity problem, also known as the reflection problem in the peer literature, and problems of intra-group correlation. Both must be addressed to credible identify peer effects. 5 Our approaches to shed light on social interaction effects are the following two. First, we study teachers that move between schools. We compare sickness absence at the new and the old school and test whether the change absence can be explained (partly) by the change in the level of their co-workers sickness absence. Second, we study individuals who recently have finished their teacher education and are being employed in their first job as teachers. The results suggest significant social interaction effects for the self-certified sickness absence. We find that a teacher appears to increase her sickness absence by up to a half day when her colleagues on average increase their sickness level by one day. We also find that the group effects increase over time and that they appear to be roughly similar whether the absence level at the new schools is higher or lower than the old school. The paper is organized as follows. Section 2 explains the empirical approach, Section 3 explains the data set and institutions, the results are presented in Section 4, while Section 5 brings some concluding remarks. 5 For more details on the methodological problems and identification strategies see Manski (1993, 2000), Brock and Durlauf (2001) and Moffitt (2001). Also, Soetevent (2006) provides a thorough overview. 5

6 2. Empirical strategy Our starting point and reference is a model where a teacher s sickness absence is a linear function of the average sickness of the co-workers, controlled for individual and peer group characteristics, school and municipality observables, etc.: S in, t i,t tx 1 in 2 X in,t X in,t S in,t ndn,t 1Wn,t 2Zn,t i n, t (1) n in, t S, S is the sickness absence of individual i and in t is the average sickness absence of all coworkers, excluding individual i, at school n at time t. X i, X it and X in,t are vectors of observable individual characteristics of individual i and the average of the colleagues individual characteristics, excluding individual i, respectively. 6 D jn,t is a dummy equal to one for teachers that work in school n; hence, it controls for unobserved school characteristics. W n,t is a vector of observable school characteristics at school n at time t, and Z n,t is a vector of observable municipality characteristics covering school n at time t. in, t is a i.d.d. error term. The parameters of interest are and β that represents contextual and interaction effects, respectively. The alleged interaction effects from this model will, however, be biased. One of the methodological issues that create this bias is intra-group correlation. If a group-specific component of the error term varies across groups and is correlated with the individual characteristics of the peers it will lead to the standard problem of omitted variables bias. The second methodological problem is the simultaneity problem. Because the sickness absence of the teachers in the peer group affect each other simultaneously, it is difficult to separate out 6 As Ichino and Maggi (2000) we include time-invariant individual characteristics multiplied by time, t. This is to allow for effects of, for example, gender on the change in sickness absence. Implicitly, then, we assume that there is a trend in the effect on the level of sickness absence of the time-invariant variable(s). 6

7 the genuine causal effect that one teacher s absence has on another teacher s absence in the group. This situation, known as the reflection problem after Manski (1993), creates a simultaneity bias Teachers changing workplace The first strategy we pursue in order to isolate the causal effect of peer groups on sickness absence utilizes the variation caused by teachers who change workplace. If teacher i quit her job at school m at time t-1 and starts working at school n at time t the equation of interest is (S in, t S im,t 1 ) X 1 (W 1 i n,t 2 (X W in,t m,t 1 X im,t 1 ) 2 ) (X (Z n,t in,t Z m,t 1 X ) im,t 1 nm ) (S nm (D in,t n,t S D im,t 1 m,t 1 ) ) ( in, t im,t-1 ) (2) where the notation follows equation (1) apart from the observable individual characteristics that are divided into time variant, X in,t and X im, t 1, and time invariant X i vectors. Note that the average level of absence and the vector of school characteristics vary by school and year. This means that school fixed effects, nm, can be identified. However, it turns out that there are too few teachers moving in and out of each school within the time frame of our data to create enough variation. Instead, we are compelled to control for unobserved heterogeneity at the municipal level by including municipality fixed effects. There are 440 municipalities in Norway, so we still expect to pick up a reasonable portion of the local time-invariant characteristics. Model (1) represents all teachers in our sample, while model (2) contains the movers between jobs only, and therefore represents a dramatic drop in number of observations. The procedure has, however, several advantages. First, by taking the first-difference the 7

8 unobserved individual heterogeneity are controlled for. Health is an important example here. Since there is no health information in our data it is reassuring that at least time-invariant health heterogeneity is controlled for. Second, opposite to a standard fixed effect transformation of model (1), model (2) can, at least in principle, handle school fixed effects separately. Furthermore, since the observations in period t relative to period t-1 represent different schools, we expect far more variation in the school-level characteristics than we would typically find in a standard first difference transformation of model (1). Finally, model (2) offers a solution to the reflection problem. A standard solution in fixed effect models is to drop the average sickness absence of the colleagues and replace it with its lagged value. This approach, however, does not eliminate the simultaneity bias if (i) unobserved individual or group characteristics are serial correlated and (ii) the reference group is the same in the two time periods. By utilizing job-changers we circumvent this problem. Because the moving teacher has not yet began working at (the new) school n at time t-1 she does not interact with the employees at school n at time t-1, and hence, she does not influence the peers sickness absence level at that time. Thus, by utilizing the lagged value of the average sickness absence of the peers at school n at time t, we eliminate any potential simultaneity bias. The lagging is performed by instrumenting the average sickness absence of the co-works at time t S in, t with its lagged value S in, t 1. The identifying assumption is that the average sickness absence at the arriving school is not a determinant for the future expected level of absence for the teachers that plan to move. In other word, we assume that there is no self selection going on as regards the average absence level (that is not picked up and integrated out by the individual fixed effect). Note that we can not fully circumvent the problem at the old school m. If we instrument the average sickness absence at time t-1 with its leaded value, t, the average sickness absence at the old school might still be influenced by earlier interaction with the departed teacher. 8

9 2.2. Newly employed teachers The second strategy is to make use of employees that are new in the teacher profession. These individuals have recently finished their education and just started working as teachers in their first full-time job. The advantage of studying them is that they have not yet interacted with peers at the workplace, and are therefore not yet exposed to peer group effects.. If individual i is newly employed at school n at time t the equation of interest is S in, t tx X X S W Z. (3) i,t 1 i 2 in,t in,t in,t 1 n,t 2 n,t int To deal with unobserved heterogeneity we calculate the difference between the first and second year at the school, implying that we restrict the analysis to new teachers that stay for more than one year at their first employer. Note that, opposite to what was the case when we studied moving teachers, we cannot identify school (and municipality) fixed effect because they collapse into a common fixed components of individuals, schools and municipalities. To mitigate potential simultaneity bias we follow the same approach as for the job-changing teachers; the average sickness absence level of the peers at time t is instrumented by its lagged value. A concern in both strategies is endogenous group membership. Newly educated teachers that search for their first full-time teacher job may self-select or unconsciously sort themselves into different schools based on unobservable individual preferences or school characteristics. If such sources of group endogeneity are not dealt with in a satisfactory way empirically identified interaction effects may be spurious. A vital question is whether the selection process is constant across time or not. The individual-, school- and municipalityspecific fixed effects in the model will control for any time-invariant components of the error 9

10 term. However, if the source of selection varies across time the model will fail to absorb the components and the resulting coefficient estimates becomes inconsistent The interaction process: heterogeneity across time and groups The longitudinal nature of our data and the richness of individual and school characteristics allow testing of the dynamics and of the relevant peer group in the interaction process. So far we have only looked at interaction effects at the first year in the new school. There is, however, no reason to believe that the interaction process is constant over time as the new teachers continue their interaction with the existing staff. To explore whether the interaction process evolves over time we follow the teachers into their second and third year at the new school. In the estimation of model (2) we instrument the value of S, with its in t lagged value S in, t 1 in each year. Further, we investigate the heterogeneity by exploring gender effects and whether the interaction effects are evenly distributed across the schools. In the latter approach we separate the sample between teachers who move to a school with higher and lower sickness absence at the new school compared to the old school. 3. Data and institutional settings We use register data for Norwegian school teachers from The Association of Local and Regional Authorities (KS). The data set covers teachers employed in public 7 primary, 7 There are very few private schools in Norway and less then 2 % of the total number of pupils attend. 10

11 lower secondary and upper secondary schools in the majority 8 of the municipalities from 2002 to The information provided is sickness absence records, educational attainment, labor market status, earnings and a set of demographic variables (age, gender). With an employeeemployer identifier the data set is merged with multiple administrative registers to provide detailed information about the peers, schools and municipalities. 9 The information includes school size, school type and social insurance agreements as well as unemployment rates, centrality indexes and school expenditure of the municipalities. Finally, the fact that we can identify all teachers at the respective schools allows us to construct variables indicating the average of peer characteristics. This is a major advantage of using merged register data of the present type. Table 1 shows summary statistics on the variables used in the empirical analysis. Sickness insurance is mandatory in Norway, and covers all workers employed for more than four weeks. The compensation ratio is 100% from day one, for a period of maximum one year. There is an upper compensation limit of approximately EURO, but through negotiations between the employers and the employees this ceiling has been removed in the public sector and in most of the large firms and quite many small firms in the private sector. A worker reporting sick will be financed by his/her employer from day 1 to day 16, after which the National Insurance Administration takes over from day 17 and up to one year, at the maximum. No medical certification is required for sickness spells lasting from one to three days. As of 2001, firms have been encouraged to join a publicly organized campaign called Including Working Life (Inkluderende arbeidsliv, IA), allowing self-certified sickness spells for up to eight days. Workers are allowed to self-report sick up to four times per year. Spells 8 The main exeption is schools in Oslo, which is the Norwegian municipality with the highest number of inhabitants. The municipality it is not a member of KS so the teachers working at these schools are not recorded in the data set. 9 School information is collected from the official informational data register for primary and secondary schools (GSI). Information about the municipalities is from The Municipal-State-Reporting register (KOSTRA) and The Norwegian Social Science Data Service (NSD). The employee identifier is encrypted by Statistics Norway. 11

12 lasting more than three/eight days require medical certificate, and an even more detailed one after eight weeks. It should be noted, though, that Norwegian GPs are considered being very liberal gatekeepers. Moral hazard, which is always a problem with this type of social insurance is, accordingly, very much an issue. In our data the absence records are divided into self-certified absence and absence certified by physicians. Both are recorded as cumulative number of days per quarter. In the paper we will explore peer effects separately for the two categories of absence because of their different structure and time frame. Self-certified absence spells are always short, while absence certified by a physician can be short or long. Records with zero absence are included in the sample. To our knowledge, we are the first to study peer effects for both short and long term absence. The main strategy in the paper involves studying the absence behavior of moving teachers. In the sample, about 7 % of the teachers have changed workplace from one school to another between 2002 and The movers are on average five years younger then the nonmovers and have accordingly lower seniority and earnings. Because of limitations in the data set, i.e. unbalanced panel with observational gaps, we are not able to identify the exact year of the job-change for the entire sample of movers. This inaccuracy in the identification process may create some noise in estimations of the empirical model. In addition, because the main data set is organized according to the calendar year while the registers providing school characteristics are structured according to the school year, we must limit the observation of teachers to the fall semester to ensure that we pinpoint the job-changing teachers to the correct schools at the correct point in time. To make a reliable measure of the sickness absence before and after the job change we identify and measure the teachers sickness absence in the third quarter every year. This procedure also removes seasonality from the data. The strategy 12

13 implies that time t -1 and time t in equation (2) is the third quarter in the old and the new school, respectively. Regarding the study of newly educated teachers, about 3.3 % of the sample is teachers who are new in the profession. Since we do not have a direct measure of their transition from education to work we have identified them indirectly. We observe teachers less than 30 years that are reported with zero years of seniority. Lack of seniority implies that the individuals have not been employed in any of the Norwegian municipality, counties or in the government. 10 We can therefore with high degree of certainty observe teachers that are newly educated and that have not been employed as teachers prior to the observed employment. There is a possibility that the individuals may have had full time jobs in the private sector. However, we assume that this is a negligible fraction because we limited the new teachers to individuals less than 30 years of age. Table 2 presents summary statistics for the different subgroups. 4. Results Table 3 displays the results of the estimations for teacher changing jobs. In both specifications we use lagged values of the group average absence before the arrival of the new/moving teacher as instrument for the contemporary group average at the new school. Starting with the ones that changes jobs our results suggest that there are significant interaction effects present for the self-reported absence. For doctor certified absence the coefficient is also positive, but statistically insignificant at standard levels. This suggests that when teachers change their workplace they tend to adjust the short term sickness absence 10 Note that the public schools in Norway are owned by the municipality, counties or the government. If you change jobs within the public sector the seniority is added to the new job. 13

14 relative to the corresponding absence of their new colleagues, while there is no sizable effect on the longer, certified absence. Moving to newly employed teachers we find that the colleagues self reported but not the doctor-certified absence is significantly different from zero. See table 4. While the interaction coefficient is smaller than in the case of job-changers, this finding strengthens the notion that it is only the short term absence that is affected by the colleagues. Table 5 displays the dynamics of the group effect, in that it reports the contemporary coefficient together with estimates one and two years after arrival at the new school, respectively. Due to the restricted period of observation we loose observation for each lag, and ends with only 733 teachers that can be observed two years after she has moved. This lack of observations is also the reason why we report the dynamic pattern only for jobchanging teachers and not for the newly educated. For the self reported absence, we see a clear positive trend, i.e. the social interaction effects appears to increase over time, which is in accordance with our a priori assumptions. Also, there is no significant interaction effect for the doctor certified absence. Finally, we explore the heterogeneity across groups. Table 6 and 7 displays the results for the men and women. For the self reported absence, it appears that it is the women that drive the results for the job-changing teachers. While the interaction coefficient is insignificant for men, it is large and highly significant for women. In exploring whether there is symmetrical effect of moving different absence rates, the results in table 8 indicate that teachers that move to a school with a higher absence level than the old school do not adjust their sickness absence differently from the others, i.e. those moving to a school with a lower absence average. [More TBA in Section 4!] 14

15 5. Conclusion In this study we have investigated whether there are social interaction effects present in the context of sickness absence. Our results are significant and robust when it comes to the self-reported sickness absence. We have adopted different approaches, looking at both jobchangers and newly employed teachers, and used different specifications of the empirical model. In all cases the results suggest that there are social interaction effects present. In addition, the effects appear to increase after one and two years after the teachers have changed workplace, and they do not seem to differ between high- and low-absence schools. The magnitude of the coefficients indicate that an individual increase her absence by up to a half a day when her colleagues increase their absence by one day on average. 15

16 References Bandiera, O., I. Barankay and I. Rasul (2005). "Social Preferences and the Response to Incentives: Evidence from Personnel Data*." The Quarterly Journal of Economics, 120(3): 917. Baum, C. F., M. E. Schaffer and S. Stillman (2002). "IVREG2: Stata module for extended instrumental variables/2sls and GMM estimation." Statistical Software Components, Boston College Department of Economics, S Bradley, S., C. Green and G. Leeves (2007). "Worker absence and shirking: Evidence from matched teacher-school data." Labour economics, 14(3): 319. Brock, W. and S. Durlauf (2001). "Interactions-Based Models". J. a. E. L. Heckman, Elsevier Science B. V. 5: Guryan, J., K. Kroft and M. Notowidigo (2007). "Peer effects in the workplace: Evidence from random groupings in professional golf tournaments NBER working paper series." Hesselius, P., P. Johansson and P. Nilsson (2009). "Sick of Your Collegues Absence?" Journal of the European Economic Association, 7(2-3): Ichino, A. and G. Maggi (2000). "Work Environment and Individual Background: Explaining Regional Shirking Differentials in a Large Italian Firm." The Quarterly Journal of Economics, 115(3): Kandel, E. and E. Lazear (1992). "Peer pressure and partnerships." The Journal of Political Economy: 801. Kato, T. and P. Shu (2008). "Performance Spillovers and Social Network in the Workplace: Evidence from Rural and Urban Weavers in a Chinese Textile Firm." IZA Discussion paper, No Lazear, E. P. (1989). "Pay Equality and Industrial-Politics." Journal of Political Economy, 97(3): Lindbeck, A., S. Nyberg and J. Weibull (1999). "Social Norms and Economic Incentives in The Welfare State*." The Quarterly Journal of Economics, 114(1): 1. Lindbeck, A., M. Palme and M. Persson (2009). "Social Interaction and Sickness Absence." CESIFO Working Paper, Manski, C. (1993). "Identification of Endogenous Social Effects: The Reflection Problem." The Review of Economic Studies, 60(3): 531. Manski, C. (2000). "Economic Analysis of Social Interactions." The Journal of Economic Perspectives, 14(3): Mas, A. and E. Moretti (2009). "Peers at work." The American Economic Review, 99(1). 16

17 Moffitt, R. (2001). "Policy Interventions, Low-Level Equilibria, and Socail Interactions". S. N. Durlauf and H. P. Young. Cambridge, Massachusetts, MIT Press. pp Rotemberg, J. (1994). "Human relations in the workplace." The Journal of Political Economy: 684. Sacerdote, B. (2001). "Peer Effects with Random Assignment: Results for Dartmouth Roommates*." The Quarterly Journal of Economics, 116(2): 681. Soetevent, A. R. (2006). "Empirics of the Identification of Social Interactions; An Evaluation of the Approaches and Their Results." Journal of Economic Surveys, 20(2): (Ichino and Maggi, 2000) (Lazear, 1989) (Kandel and Lazear, 1992) (Rotemberg, 1994) (Lindbeck et al., 1999) (Bradley et al., 2007) (Hesselius et al., 2009) (Lindbeck et al., 2009) (Bandiera et al., 2005) (Guryan et al., 2007) (Kato and Shu, 2008) (Mas and Moretti, 2009) (Sacerdote, 2001) (Manski, 1993) (Manski, 2000) (Brock and Durlauf, 2001) (Moffitt, 2001) (Soetevent, 2006) (Baum et al., 2002) 17

18 Tables Table 1. Summary statistics. Mean SD Individual characteristics Self reported sickness absence Doctor certified sickness absence Female (%) Age Seniority (years) Montly earnings (NOK) 25, , Working hours (%) Adjunkt (%) Lektor (%) Teacher (%) School/peer characteristics Number of teachers Share of females Primary schools Lower secondary schools Combined primary and low sec. schools Special primary and/or low sec. schools Upper secondary schools WI-workplace Avg. age Avg. seniority Avg. earnings 25, , Avg. working hours Avg. adjunkt Avg. lektor Avg. teacher Municipality characteristics Density index (0-9) Urbanity index (1-7) Unemployment rate Sickness absence rate Human Development Index (1-10) Expenditure to teaching per pupil Expenditure to school maintenance per pupil Expenditure to after-school programme per pupil Number of observations (spells) 718,851 Number of teachers 57,217 Number of schools 2,026 Number of municipalities

19 Table 2. Summary statistics, detailed. Jobchangers Non jobchangers Employed Newly employed Self reported sickness absence (1.12) (1.15) (1.12) (1.16) Doctor certified sickness absence (13.40) (12.46) (13.40) (8.29) Female (%) (0.47) (0.47) (0.47) (0.45) Age (10.55) (10.21) (10.45) (4.63) Seniority (years) (10.32) (9.57) (10.17) (1.32) Montly earnings (NOK) 25, , , , (5,524.66) (5,213.51) (5,506.28) (4,844.09) Working hours (%) (0.17) (0.15) (0.16) (0.18) Teacher with bachelor degree ( adjunkt, %) (0.44) (0.40) (0.44) (0.40) Teacher with master degree ( lektor", %) (0.31) (0.30) (0.31) (0.33) Teacher with formal teacher educ. ( teacher, %) (0.36) (0.30) (0.35) (0.27) Number of observations 667,612 51, ,887 12,964 Number of teachers 52,918 4,299 55,314 1,912 19

20 Table 3. Results. Job-changing teachers. Self reported Doctor certificated Avg. sickness absence *** (0.149) (0.123) Individual characteristics Female (0.038) *** (0.457) Age ** (0.029) (0.372) Seniority * (0.019) * (0.182) Teacher with bachelor degree (adjunkt) (0.077) (1.039) Teacher with master degree (lektor) (0.179) (2.676) School characteristics Number of teachers, lower middle quartile (0.037) (0.521) Number of teachers, upper middle quartile (0.040) (0.529) Number of teachers, upper quartile (0.050) (0.622) Lower secondary school (0.049) (0.601) Combined prim. and low. sec. school (0.044) (0.569) Special school (0.205) * (2.073) Upper secondary school (0.141) (1.577) Share of females (0.120) (1.715) Avg. age (0.013) (0.175) Avg. seniority (0.012) (0.177) Avg. working hours (0.354) (4.658) Share of teachers with bachelor degree (adjunkt) (0.140) (1.915) Share of teachers with master degree (lektor) (0.166) (2.490) Municipality characteristics Unemployment rate (0.030) (0.511) Sickness absence rate (0.033) (0.466) Human Development Index (0.036) ** (0.555) Exp. teaching per pupil (0.008) (0.113) Exp. maintenance (0.004) (0.044) Exp. after-school program (0.003) (0.113) Housing density * (0.078) *** (1.109) Urban location *** (0.176) *** (2.496) Yearly dummies (0.042) (0.599) (0.082) * (1.266) (0.058) (0.802) (0.044) (0.620) Constant (0.051) (0.553) N 3,245 3,245 R Note: * p < 0.10, ** p < 0.05, *** p < The estimations for the absence types are run separately. In all regressions municipality fixed effects are included. 20

21 Table 4. Results. Newly employed teachers. Self reported Doctor certificated Avg. sickness absence ** (0.088) (0.050) Individual characteristics (0.359) Female ** (0.060) (1.084) Age (0.143) (0.453) Seniority ** (0.110) (1.005) Teacher with bachelor degree (adjunkt) *** (0.274) (8.492) Teacher with master degree (lektor) *** (0.382) School characteristics Number of teachers, lower middle quartile (0.116) (0.865) Number of teachers, upper middle quartile (0.147) (0.927) Number of teachers, upper quartile (0.210) (1.442) Lower secondary school *** (0.501) *** (2.786) Combined prim. and low. sec. school ** (0.285) (1.473) Share of females (0.616) * (3.131) Avg. age (0.051) (0.345) Avg. seniority (0.051) (0.290) Avg. working hours * (0.950) * (9.148) Share of teachers with bachelor degree (adjunkt) (0.637) (5.813) Share of teachers with master degree (lektor) (0.892) (7.734) Municipality characteristics Unemployment rate (0.083) (0.510) Sickness absence rate (0.057) (0.412) Human Development Index ** (0.085) ** (0.611) Exp. teaching per pupil (0.016) ** (0.096) Exp. maintenance (0.006) * (0.038) Exp. after-school program (0.007) (0.070) Yearly dummies ** (0.187) (3.581) * (0.220) (2.486) (0.150) (1.818) (0.104) (0.949) Constant ** (0.104) (0.477) N 1,328 1,328 Note: * p < 0.10, ** p < 0.05, *** p < The estimations are run separately for the absence types. In all regressions municipality fixed effects are included. 21

22 Table 5. Results: Effects over time for job-changing teachers. Initial 1 year after 2 years after Self-reported Avg. sickness absence ** ** * N 1,394 1, Physician certified Avg. sickness absence N 1,394 1, Note: * p < 0.10, ** p < 0.05, *** p < The estimations for the absence types are run separately. In all regressions individual, peer/school and municipality characteristics are included. In addition, yearly dummies and municipality fixed effects are included. Table 6. Results: Separating between men and women. Self reported absence Men Women Avg. sickness absence (0.251) *** (0.157) N 1,084 2,252 Note: * p < 0.10, ** p < 0.05, *** p < The estimations are run separately for men and women.. In all regressions individual, peer/school and municipality characteristics are included. In addition, yearly dummies and municipality fixed effects are included. Table 7. Results: Separating between men and women. Doctor certificated absence Men Women Avg. sickness absence (0.185) 0,249 (0.151) N 1,084 2,252 Note: * p < 0.10, ** p < 0.05, *** p < The estimations are run separately for men and women.. In all regressions individual, peer/school and municipality characteristics are included. In addition, yearly dummies and municipality fixed effects are included. Table 8. Results: Distribution across schools for job-changing teachers. Self reported Doctor certificated Lower avg. absence at new school Higher avg. absence at new school Lower avg. absence at new school Higher avg. absence at new school Avg. sickness absence N 1,590 1,746 1,437 1,899 Note: * p < 0.10, ** p < 0.05, *** p < The estimations are run separately for all regressions. In all regressions individual, peer/school and municipality characteristics are included. In addition, yearly dummies and municipality fixed effects are included. 22