Fringe Attraction. Compensation Policies, Worker Turnover and

Establishment Performance

by

Harald Dale-Olsen

Institute for Social Research, Oslo

April 1, 2005 Abstract This paper analyses the relationship between establishment compensation policies, fringe benefits and worker turnover among Norwegian establishments. It utilises information from a new questionnaire conducted winter 2003 on new work practices and reward systems, which are matched to linked employer-employee data comprising the period 1995-2003. The results are strongly supportive of the practice of offering fringe benefits to workers. Duration analysis identifies strong worker preferences for fringe benefits, while GMM-analysis reveals a strong causal impact from fringes on the establishments’ quit rates. Finally, system-GMM analysis shows that firms offering relatively more fringes as part of workers’ compensation achieve higher productivity. Keywords: personnel economics, fringe benefits, worker turnover, duration and GMM-analyses. JEL-codes: J32, J41, J63, C23, C41 Acknowledgement: Paper to be presented at the PSI seminar series spring 2005. This paper was written during a period as an academic affiliate at the Department of Economics, University College London. I gratefully thank UCL for its hospitability thus providing me access to a challenging and stimulating environment. This work was financed by the Norwegian Research under grant number 156035/S20. Corresponding author: Harald Dale-Olsen, Institute for Social Research, P.O. Box 3233 Elisenberg, N-0208 OSLO, NORWAY. E-mail: hdo@socialresearch.no

1. Introduction Fringe benefits are becoming increasingly popular as parts of firms’ compensation schemes. For instance, in Norway 1995 29.5 per cent of the Norwegian establishments reported to the tax authorities that fringe benefits were given as part of workers’ compensation. In 2002, more than 57 per cent of the Norwegian establishments offered fringe benefits as part of their compensation schemes. In 2002, certain groups of workers received more than 5 times as many fringe benefits as in 1995. As the prevalence of fringes increases, it becomes more important to understand workers’ preferences for fringes and why firms offer fringes. Which fringes, if any, do workers appreciate? What are the firms’ motivations for introducing fringes into their compensation policies? A slightly elaborate explanation is that firms offer job bundles comprising wages and fringe benefits to retain workers and attract replacement hires. That wages are important instruments for managing the workforce is a well-known empirical fact. Fringes are thought of as similarly important tools, but the empirical evidence is much scarcer. Is it possible to identify a causal impact from fringes on worker turnover? And equally important, is it also possible to identify an impact of fringe benefits policies on productivity? The answers to these questions are exactly the contribution of this paper.

The growth in the use of fringe benefits has not been unnoticed by the economists, and during the recent years, several empirical studies on firm use of fringe benefits has been pub-lished.1 In 2002 fringe benefits even was the topic of a special issue of Journal of Labor Econo-mics. I contribute to this literature by analysing workers’ evaluation of fringe benefits within a dynamic perspective. A dynamic perspective is necessary if one accept the non-wage amenity nature of fringe benefits (see below).2 With few exceptions, most studies of worker turnover and fringes have focused on employer-provided health insurance and pension schemes (Madrian, 1994; Even and Macpherson, 1996; Kapur, 1998; Dey and Flinn, 2000; Gilleskie and Lutz, 2002; Ippolito, 2002), and then particularly on the issue of job-lock. Employer-provided health insu-rance and pension schemes lock workers to their employers. By leaving for another firm, workers loose previous benefits. Very few studies have looked on the impact of other fringe benefits on worker turnover (for an exception, see Dale-Olsen, 2004). Based on an empirical approach that completely controlled for individual heterogeneity, Dale-Olsen (2004) identified strong negative impact of wages and fringe benefits on worker turnover, and the turnover-reducing effect of fringes was actually stronger than the effect of wages. No one has yet addressed the causal impact from fringes on worker turnover, where they take into account the endogenous nature of wages and fringe benefits. Firm motivation for providing fringe benefits is yet unaddressed in the empirical literature to the author’s knowledge, except when discussed as a backdrop.

Why then do firms offer fringe benefits? Fringe benefits have a monetary value. In many countries, fringes should be reported to the tax authorities for tax purposes. Sometimes specific fringes are exempted tax, and this makes it more profitable both for workers and employers to offer/receive fringes instead of money wages (Ehrenberg, 1971). Fringe benefits are originally either bought by the employers or they are manufactured locally. In the former case, an employer may have the necessary market power to get a lower price than that an employee achieves on his own. In the latter case, an employer may save the employee the third-party mark-up.

Fringe benefits, however, share also important traits with non-wage amenities. For the employees, fringe benefits are not readily used as payment for other goods. Fringe benefits are singled out from the basic wage, thus worker evaluation is influenced by endowment and framing 1 See for example Dey and Flinn (2000), Gilleskie and Lutz (2002) Ippolito (2002), Alpert and Woodbury (2000), Olsen (2002), Carrington, McCue and Pierce (2002), Dale-Olsen (2004). 2 When treating fringe benefits as non-wage amenities, fringe benefits may be understood using Rosen’s concept of hedonic prices (Rosen, 1974). Profit maximising employers are matched to utility maximising workers sharing the same evaluation of the relationship between wages and fringe benefits. However, Gronberg and Reed (1994) and Hwang, Mortensen and Reed (1998) point out that the basic hedonic wage approach apply static optimising tools on a dynamic process; workers search continuously for better job offers. An environment where workers change jobs and wages vary though time is not explained by the classical hedonic wage framework.

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effects (Thaler, 1980, Tversky and Kahneman, 1986; Kahneman, Knetsch and Thaler, 1990) and biases of judgement (Rabin, 1998).3 Thus the economic literature has for many years treated fringe benefits as non-wage amenities (Elliot, 1991), and this is also the approach I will follow in this paper. Since workers have preferences for more fringe benefits and higher wages, both can be used as instruments for recruiting and for retaining workers.

The incorporation of fringe benefits into the compensation schemes and the amount of fringe benefits offered are likely to depend on several factors, for instance industry and tradition. But it is also clearly related to size. Larger establishments are more likely to have stronger market power than smaller establishments, and are thus more likely to achieve lower prices. Larger establishments are more focussed on issues related to recruitment and retain, simply because this is necessary to stay large.

This paper’s main contributions are thus four-fold: by utilising a data set comprising questionnaire information (2003) and linked employer-employee data (1995-2003) the paper pro-vides evidence on the relationship between wages, several fringe benefits and establishment size. The fringe benefits range from pension schemes, via health related benefits as private physician and extended own-declaration of sickness absence, to home-related and past-time benefits as cleaning assistance, children care, gym membership, extended holidays and holiday home.

Then the paper presents estimates of workers’ marginal willingness to pay (MWP) for the fringe benefits. These MWP-estimates are made possible by applying the Gronberg and Reed (1994)-approach for evaluating workers’s MWP for work hazards to fringe benefits. Thus the analysis is based on a dynamic perspective, characterised by search frictions and on-the-job-search. By estimating several job duration regression models, I provide MWP-figures for different fringe benefits in private sector. Higher wages significantly reduces the probability of quitting. At the same time I identify strong worker preferences for extended own-declaration of absence, children care and holiday home. Workers’ MWP-estimates for these fringes are significant and have the correct sign.

While the questionnaire data is limited to one period, the linked employer-employee data comprise several years of information. This is crucial for taking into account the endogenous nature of wages and fringe benefits in quit regressions, which are the paper’s second contribution. Although we lack the precise knowledge about the different kinds of fringe benefits, data comprise the tax authorities’ aggregate evaluation of the fringe benefits received by each worker (of course limited to fringes that are reported to the tax authorities). Thus I am able to measure how large part fringes make of total compensation. Applying the GMM-method of Arellano and Bond (1991) to establishment-level quit regressions identifies strong significant negative impact from both fringes and wages on worker turnover.

The third issue in the paper is the employers’ motivations for offering fringe benefits to workers, and how these motivations relate to worker turnover. The questionnaire makes it possible to identify four reasons for offering fringe benefits: employees like fringes, it is important for recruitment and to retain workers, employers achieve a lower price than employees, and employers save pay-roll tax. More than half of the employers use fringes for recruitment purposes and to retain workers. Prime fringes are children care, holiday home and extended vacation. Roughly a quarter of the establishments offer fringe benefits because employees like fringes or because employers achieve a lower price. For the former, cleaning assistance and extended vacation stands out, while for the latter gym membership and holiday home dominates. Only 2 per cent of the employers use fringes to save pay-roll tax. This figure is likely to be down-ward biased due to employers’ desire to improve their image (saving pay-roll tax may interpreted as cheating socially). As expected, larger establishments use fringes more often to retain workers, for recruitment purposes, and since they also achieve lower prices than their workers.

3 The holiday home provided by the employer may have a particular importance for some employees, thus exceeding the equivalence in money. And what are really the true costs of child care or a pension scheme?

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Finally, applying the system-GMM-method of Blundell and Bond (1998) to production function regressions on a restrictive sample of establishments identifies strong positive impact of fringe benefits on total factor productivity. Thus establishments achieve higher productivity by offering fringes instead of wages, and I show that this is not related to saving pay-roll tax.

The structure of the remaining paper is as follows. Section 2 briefly presents the theoretical background and derives expressions of workers’ marginal willingness to pay (MWP) for fringe benefits. Section 3 briefly presents the data. Section 4 provides evidence on the relationship between wages, fringe benefits and establishment size. The results of the individual-level job duration regressions and the derived estimates of workers’ MWP for fringe benefits are presented in Section 5. In Section 6 I turn to the establishment-level regressions using the GMM-framework. Employers’ motivations for fringe benefits and the relation to worker turnover are discussed in Section 7. Section 8 finally analyses the impact of fringe benefits policies on productivity, while Section 9 concludes.

2. Theoretical and empirical background for how to measure workers’ marginal willingness to pay for fringe benefits The theoretical motivation and empirical strategy for identifying workers’ marginal willingness to pay for fringe benefits are found in Hwang, Mortensen and Reed (1997) and Gronberg and Reed (1994). Hwang, Mortensen and Reed (1997) proves the existence of an equilibrium search model incorporating endogenous wage distribution as well as non-wage components. In their model workers search continuously for better job offers from other employers. What is a better job offer? A better job offer is then an offer of a bundle of wage and non-wage components providing workers with higher utility than what they receive from their current job. The utility maximising workers quit whenever a job offer provides higher utility. The profit maximising firms invest in non-wage amenities so the marginal cost equalises the marginal utility workers receive from the non-wage component. The presence of frictions makes firm offer different wages, thus an endogenous wage distribution arises. Gronberg and Reed (1994) describes workers’ behaviour in a search environment, and then derives an empirical method for estimating workers’ MWP for job attributes in such an environment.

In this paper I basically apply the empirical strategy of Gronberg and Reed (1994) to evaluate workers’ MWP for fringe benefits. Assume that workers utility function can be described as U(w,f)=w+bf, where w and f denotes wages and fringe benefits, respectively. Assume further that both wages and fringes affect utility positively, i.e., ∂U(w,f)/∂w > 0, ∂U(w,f)/∂f>0. Workers quit whenever they receive job bundles providing higher utility. This implies that for a given level of wages, more fringes reduce the quit probability, while for a given level of fringes, higher wages reduce the quit probability. More formally, this may be described by introducing a quit function, q(U) where ∂q(U)/∂U < 0. Then it follows that

1) ,0)(<

∂∂

=∂∂

∂∂

=∂

∂Uq

wU

Uq

wUq

2) .0)(<

∂∂

=∂∂

∂∂

=∂

∂ bUq

fU

Uq

fUq

These expressions are easily transformed into an expression for the MWP for fringe benefits:

3) .)(

)(

b

wUqfUq

wUfU

MWP =

∂∂∂

∂

=

∂∂∂∂

=

Gronberg and Reed (1994) estimated workers’ MWP for job hazards using an accelerated failure-time (AFT) duration model based on a generalised gamma distributed error. If one accepts

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simplifications, all quit regressions may be used to provide MWP-figures.4 To avoid the criticism of AFT-models (van den Berg, 2001), I mainly estimate workers’ MWP for fringe benefits using the Weibull model with gamma frailties imposed on individual level, but supplement the analyses by also estimating other job duration models; the Gamma model (AFT-specification) and the Cox proportional hazard model. Furthermore, I also conduct a bootstrap-analysis based on the Weibull model with gamma frailties.

The basic assumption in these duration models is that for each worker, the job duration can be expressed by the random variable T. Depending on the specific choice of model, one then derive a corresponding hazard function, h(t). Observed heterogeneity is incorporated by letting this hazard function depend on covariates, e.g. wages, fringes, and other controls. As the random effects of linear regression models, one can incorporate so-called frailties to take into account unobserved heterogeneity. Furthermore, in my case, since the fringe benefits are reported on the establishment-level, it will be necessary to take into account the clustering on establishment.5 The estimation is conducted straightforwardly using STATA’s streg- and stcox-procedure. 3. Data The analyses are based on two data sets. The first data set comprises a questionnaire, ABU2003, answered early winter 2003 by the daily manager or personnel manager of roughly 2300 Norwegian establishments from both public and private sectors. These establishments are sampled from establishments with more than 10 employees. Furthermore, the sample is constructed so that large establishments are over-sampled (for example, all establishments with more than 300 employees are included in the sample). The ABU2003-establishments employ over 350 000 workers, i.e., nearly a fifth of the Norwegian workforce. The sampling procedure and the questionnaire are described in detail in Holth (2003) and the forthcoming Torp (2005). The questionnaire covers topics such as work practices and organisation issues, wage determination, health and pension issues.6 For my purpose, the questions regarding fringe benefits usage are particularly interesting. The questionnaire includes questions on whether employers’ offer to some or all employees, fringe benefits such as private physician and extended own-declaration of sick-ness absence, cleaning assistance, children care, gym membership, extended holidays and holiday home. Even though it in the questionnaire is not ranked among the fringe benefit-questions, it also asks whether the establishment offer pension schemes to employees. Unfortunately, questions related to fringe benefits are not asked public administration establishments. The second data set, or more precisely, data system, is based on public administrative register data. It comprises all employers and their employees in Norway 1995–2003 (roughly 150000 employers and 1800000 employees each year) employed May 15th each year. This data set is a similar to an integrated register based data system, Current System for Social Data (CSSD), linked by Statistics Norway, comprising information from public administrative registers (except CSSD is not restricted to employment spells active on May 15th). This linked employer-employee data set provide information on workers (gender, education), jobs (for example earnings, daily wage, hourly wage (only 2002-2003) the value of fringes as they are reported to the tax authorities, weekly working hours (intervals, exact hours 2002-2003), seniority) and on establishment-characteristics such as industry (5-digit NACE), sector and municipality. Since these data are very comprehensive, it is possible to link information on the ABU2003-establishments’ local labour market (municipality) and within detailed industry code (3-digit NACE) to the ABU2003-sample. 4 This depends, of course, on how close relationship one desires between the economic and econometric models. For a discussion between the relationship between duration models and economic theory, see van den Berg (2001). Here he points out that “....the AFT model, the hazard does not serve as the focal point of model specification. This has strongly limited the use of these models in social science duration analyses (van den Berg, 2001:3397). 5 The analysis has also been conducted based on the exponential model added gamma frailties. Since this only yields negligible differences, these results are not shown. 6 The questionnaire is quite similar to questionnaires found in many countries, e.g., UK (WIRS, WERS) and US (EQW-NES).

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The job-specific fringe benefit measure is particularly important for my purposes. It consists of an amount given in NoK reflecting the tax authorities’ evaluation of all reported fringes (determined by rates). Among the fringe benefits listed, one finds benefits of lower interest rate from employer provided loans, free or subsidised: telephone/mobile phone, newspaper, work clothing, public holidays, gifts, foods, child care, accident and retirement insurance, gains from buying stocks at lower prices than market value, stock options, housing, membership in private medical service and parking space. The list is thus quite comprehensive. It has changed during the years, as there is a “catch-up race” going on between employers and the tax authorities. Employers introduce new non-reportable fringes, which the tax authorities later declare as reportable.

For research purposes, it is a nice feature of the Norwegian public administrative registers that each individual and each establishment are identified by unique identifying codes (separate number series). In my data, these original numbers are replaced by encrypted numbers. The encrypted number series for the establishments is also used in the questionnaire data set, making linking possible. For those establishments included in the questionnaire and for those workers employed or once employed by these establishments, we have additional register information. Whenever found, these establishments data also comprises information on foreign ownership, value added, factor costs, capital and sales (usually restricted to mining and manufacturing establishments). Similarly, for these workers we have information on absences, country of origin and unemployment histories.

The analyses in this paper are based on information from these two data sets, utilising different samples of the data. Certain analyses thus rest on observations on all establishments in Norway, others analyses rest on more restricted samples. For example, the evaluation of workers’ MWP for the specific fringe benefits will be based on the ABU2003-sample comprising 1374 private sector establishments (public administration excluded) and their 187205 workers. Information on capital is unfortunately restricted to ABU2003-establishments covered by the manufacturing statistics (comprising mining, manufacturing, trade). Thus the productivity analyses rest on 981 observations of 162 establishments only.

4. Establishment size, wages and reported fringe benefits As a brief introduction, Table 1 describes the 2002-relationship between wages, fringe benefits (reported to the tax authorities) and establishment size for different samples of establishments-

[ Table 1 around here ] I have calculated the average daily wage of each establishment, and then grouped the establishments into categories depending on the position in the wage distribution. The column head denotes the six groups: 1-10%, 11-25%, 26-50%, 51-75%, 76%-90%, and 91-100%. The findings of Table 1 are not exactly surprising. As one climbs in the wage distribution, the establishments get larger. Similarly, establishments higher in the wage distribution offer more fringe benefits than establishments lower in the wage distribution. These findings are true across the different samples. Consider the results for all establishments. Establishments in the lowest part of the wage distribution (1-10%) offer on average 1.15 percentage points of their total compensation as fringe benefits. Establishments in the top of the wage distribution (91-100%) offer nearly 5 percentage points of their total compensation as fringe benefits. When we look at the specific fringe benefits in Table 2 (top section) using the information from the questionnaire, the picture is quite similar – high wage establishments offer more fringes. But Table 2 also reveals heterogeneity.

[ Table 2 around here ]

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At the bottom, only 30 percent offer pension schemes, while at the top nearly all offer pension schemes. Cleaning assistance is the least popular fringe benefit, and even in the top of the wage distribution, only 2 percent of the establishments offer this fringe benefit. Although not shown, it is interesting to note that fringes benefits reported to the tax authorities and the fringe benefits pension scheme, gym membership, extended holidays and holiday home are significantly positively correlated. The next issue Table 2 addresses is whether the positive relationship between fringes and establishment wages is caused by fringe offering establishments employing workforces of higher qualifications or if the fringe paying establishments are truly high wage establishments. To answer this, it is necessary to utilise information from the complete panel of establishments and workers 1995-2003 to decompose wages into fixed establishment effects and fixed worker effects. The method and the estimation results are described in Barth, Dale-Olsen, Lalive and Gruetter (2005). Barth, Dale-Olsen, Lalive and Gruetter simplify the method for estimating fixed effects on large unbalanced panels proposed by Abowd, Kramarz and Creecy (2002). I define the establishment’s wage premium as the establishment’s fixed effect, while the workforce qualification is measured as the establishment-specific average of the worker fixed effect. Next I link the questionnaire information on fringe benefits to the establishment measures of wage premiums and workforce qualifications (which of course are slightly inaccurate, since I link winter 2002 information to average measures), and then group the establishments into categories depending on the position in the distribution (similar categories as previously described). The middle section of Table 2 presents the results relating to the wage premiums, while the bottom section presents the results regarding workforce composition. The main impression is that fringes are more extensively used among establishments higher in the wage premium distribution and among the workforces of higher quality. For most fringes one observes a steady increase in the prevalence of fringes as one climbs the distributions. However, there are some important exceptions. Children care is offered primarily to workforces of lower quality. Cleaning assistance is primarily offered by establishments at the very top of the wage premium distribution. 5. Workers’ marginal willingness to pay for fringe benefits 2002-2003 In this section, I turn to the issue of workers’ evaluation of fringe benefits in 2002. 187205 workers are employed May 15th 2002 by 1374 establishments (excluded public administration). During the next year, i.e., until May 15th 2003, 38566 workers quit. Since we know when they departed and when they started, I can follow the strategy described in Section 2 to provide me with the necessary estimates for the MWP-figures. Job spells not ended by May 15th, 2003, are treated as censored.

I start by estimating three different duration models. Model 1 assumes a Weibull duration model including gamma-frailties on the individual level. Model 2 assumes a Gamma distribution (note that this implies an AFT-formulation), while Model 3 assumes a Cox proportional hazards model. In each model I treat each establishment as a cluster. As controls for observable heterogeneity I include: education in years (in excess of compulsory schooling), 8 dummies for field of education, dummies for woman, dummies for working hours (short and long part-time), dummies for industry (2-digit NACE), dummies for counties, mean and variance of log daily wages at the municipality-level, and mean and variance of log daily wages at the industry-level(3-digit NACE). These latter four variables are thought to catch the importance of local labour market variations. In additions to these controls, I include dummies for different kinds of fringe benefits and hourly wages. Models 1-3 of Table 2 present the results from these regressions.

[ Table 3 around here ]

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The impact of the control variables on the hazard is quite robust across the different model specifications. More education or working short part-time implies increased hazards. Rather surprisingly, larger local wage variation or higher local wage reduces the hazards.

Next I turn to the main issue in these regressions - the impact on the hazard of wages and fringe benefits. Higher wages or more fringes clearly reduce the hazards. This is a robust result regardless of model. For extended own-declaration of sickness absence, child care and holiday home I observe strong significant negative impact on the hazards regardless of model. Except for the results regarding private physician, all the signs of the significant results are in accordance with what we would expect given the assumption that workers have positive preferences for fringe benefits.

Model 4’s estimates are from a different set of regressions. Since my data represent a non-random sample of the population of Norwegian establishments (very large establishments – +300 employees – are sampled with a probability of 1, and then the sampling probability diminishes with size), I re-sample 50 data sets from the original data set, using a sampling procedure which make these 50 data sets random samples. Then I re-estimate the hazard model on these data sets assuming a Weibull model with gamma heterogeneity. Model 4 reports the average of the estimates and standard deviations from these 50 regressions. This strategy provides a Bootstrap-analysis and acts as a robustness test. In practice the bootstrap-analysis discards information from the larger establishments and puts emphasis on the information from the small establishments. Since it is reasonable to surmise that small establishments have less developed strategies regarding their wage and fringe benefits policies than large establishments (as Tables 1 and 2 show, they clearly offer less fringe benefits), the bootstrap-analysis should reveal less significant findings (since this emphasises more noisy observations).

As anticipated, Model 4 reveals rather similar, but quite weaker results compared to that of Model 3. The main exception is Child care, which still reduces the hazards. The unfortunate implication from the bootstrap-analysis is that my results are not very robust across different sampling regimes. Sampling from the huge population of small establishments reveals less significant results than focussing on the large establishments.

The interpretation of the estimates related to fringe benefits in models 1-4 faces two potential problems. The information on the fringes that are offered by the employers is on the establishment level. Thus every worker employed by an employer offering a specific fringe benefit, are treated as receiving this. In reality some of the fringe benefits are such that it is highly unlikely that all employees receive these. Furthermore, establishments may offer several other fringe benefits as well, and the impact of these on the quit hazards are also captured by the included dummies for the fringe benefits. Thus in Model 5 I turn to the impact of fringe benefits reported to the tax authorities on the quit hazard. I estimate the hazard model assuming a Weibull model with gamma heterogeneity. The positive gain in this case, is that the total amount of fringes as evaluated by the tax authorities is known on the job level. However, I have no longer information on specific fringe benefits. Since wages and fringes are determined at the esta-blishment level, I still treat the establishment as a cluster. The results of Model 5 show that higher wages or more fringes clearly reduce the hazards, also when fringes are measured at the job level. Using the estimates of Table 3, I then calculate workers’ MWP for fringe benefits (standard errors following the delta method) and present the results in Table 4.

[ Table 4 around here ] Table 4 shows that all significant MWP-figures are correctly signed. The only wrong sided result is related to private physician, which may have been introduced by the establishments due to harsh working condition. Thus it may capture workers dislike for these working conditions.

The significant MWP-figures of models 1-4 are unfortunately rather large. One interpretation is that workers have extremely strong preferences for extended own-declaration of

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sickness absence, child care and holiday home. But it could also follow due to the problems dis-cussed above. Since establishments may offer several other fringe benefits in addition to those in Table 4, this exaggerates the evaluation of the specific fringe benefits in Table 4. However, this problem does not affect the MWP-figure from Model 5. This expresses how much the workers would pay on the margin if the amount of fringe benefits in the total compensation increased by 1 percentage point. As we see, on the average this is measured to 11 NoK. What does this imply? Increasing the amount of fringe benefits by 1 percentage point means to increase fringes by roughly 2 NoK. Thus workers are willing (on average on the margin) to pay 11 NoK of ordinary wages to get 2 NoK in fringe benefits. For the establishments this should seem like a good deal.

6. The dynamics of compensation policies and worker turnover In Dale-Olsen (2004) I showed that wages and fringe benefits were important tools for establishments in achieving the desired labour supply. The empirical evidence rested on regressions of average worker turnover on fixed establishment wage effects and similar fixed fringe benefit effects. This study identified strong negative impact of fringe benefits on worker turnover, but it could be criticised for not taking into account that firms maximise profits choosing optimum wages, fringe benefits and worker turnover simultaneously. In this section I will address this issue by looking closer on compensation policies and worker turnover in a dynamic perspective. More specifically, assume that worker turnover in each establishment f can be characterised by following quit equations: 4) ,ititinctitfitwit uvfwq +++++++= αγγγββ

,1 ititit vv ωη += −

ititu ω, ~ MA(0), |η|<0, where qit is the quit rate of establishment i in year y, wit is the mean wage of establishment i in year t, fit is fringes as share of total compensation, γt expresses a year-specific intercept reflecting aggregate business cycle effects. γc and γn express county- and industry-specific variation.

ititi vu ,,α and itω reflect the error structure. iα is an time-invariant establishment-specific effect. is an establishment-specific turnover shock that may be serially correlated with the previous

period. This reflects that it takes time to implement workforce decisions at the establishment level. and

itv

itu itω are serially uncorrelated error terms. We measure worker turnover on the 15th May each year only when it is reported to the authorities. Accidental variation in the establish-ments’ reporting procedures thus causes measurement errors. The ’s reflect these errors. itu Equation 7) is estimated using two different strategies. Firstly, I estimate Equation 4) directly using an ordinary within-establishment regression. However, to acknowledge that the observations within each establishment are not uncorrelated, I treat each establishment as a cluster. This model is easily estimated using STATA’s areg-procedure, and is rather efficient in that you do not loose observations on many establishments. However, if the ’s are serially correlated, then these estimates are not consistent. In this setting + is serially correlated at all lag lengths (Bond, 2000). To address this issue, I turn to the second strategy – to estimate Equation 7) using the GMM-method of Arellano and Bond (1991)(incorporated in STATA’s xtabond2-procedure). To successfully apply the method, I first need the dynamic representation of Equation 7):

itv

itv itu

5) 111 −−− −+−+−+= ttitfitfitwitwitit ffwwqq ργγρββρββρ

+ .)1())(1( 1−−++−++− itititinc uu ρωαργγρ If no measurement errors exist, then itω is serially uncorrelated. If transient measurement errors do exist, then itω follows a MA (1)-process. In either case applying the GMM-method on Equation 8) will yield consistent estimates.

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The estimation results regarding Equation 4) and Equation 5) are presented in Table 5. Models 1-4 present the results from the within-establishments regressions, while models 5 present the results from the GMM-estimation.

[ Table 5 around here ] To simplify the regressions as much as possible, I only include time dummies as additional controls in the within-establishment regressions. In the GMM-regressions I also add industry (3-digit) and county dummies to control for fixed industry and county variation. In Model 1 I use all the observations from all establishments in Norway 1995-2002 (inclusive public administration). The impact of wages and fringes on the quit rate is as expected. Increasing wages by 1 NoK significantly reduces the quit rate by 0.019 percentage point. Since the average quit rate is around 20 percentage points and the average daily wage is close to 1000NoK, increasing the wage by 20 percent reduces the turnover by 10 percent. Increasing the amount of fringes as part of the total compensation by 1 percentage point, reduces turnover by 0.06 percentage point. Models 2 and 3 focus on the private sector establishments, but in addition Model 3 discards observations of establishments with less than 11 employees. This is done to make comparisons to the ABU2003-sample possible (which is a sample of establishments with more than 10 employees). The results of Model 2 are quite comparable to Model 1’s results. It has, however, major impact on the parameter estimates to focus on large establishments. Both wages and fringes still affect the quit rate negatively, but the quit rate is now less sensitive to wages and more sensitive to fringes. For example, increasing the amount of fringes as part of the total compensation by 1 percentage point, reduces turnover by 0.35 percentage points. Model 4 focuses on the ABU2003-sample. The results are quite comparable to the results of Model 3. Next I turn to the GMM-estimation of Model 5 and this raises the issue of how to treat the explanatory variables – endogenous or simply predetermined? I treat all explanatory variables with the exception of the industry, county and year dummies as endogenous variables. This raises the need for instruments. Model 5 presents the results when following the standard treatment for endogenous variables in the diff-GMM: in first difference equations, lagged levels dated t-2 are used as instruments (lagged levels for wages: t-3). To acknowledge possible heteroskedasticity I present robust standard errors. I intended to estimate the model using observations of all large establishments (with more than 10 employees) in Norway, but due to computational limitations I have been forced to draw a 25 percent random sample. Still, the regression results rest on 34100 observations of more than 8100 establishments. The results of Model 5 clearly indicate that quits are weakly serially correlated, with an estimate of ρ =0.036. The impact of fringes this period on the quit rate is five as strong as indicated by Model 3, while the similar impact of wages is eight times as strong. The lagged values of fringes and wages are unfortunately not significant. I present three measures to check whether the estimation is consistent. Firstly, the Arellano-Bond test for AR(1) and AR(2) indicates that the residuals follow an AR(1)-process (as expected), but not an AR(2)(which implies bad instruments). Thus from this perspective, the regression results are satisfying. The appropriateness of the instruments is expressed by the Hansen J-statistic, which are robust to heteroskedasticity (which the Sargan-statistic is not). The Hansen-test does not reject the instruments. I have estimated several other GMM-specifications than these reported in Table 5, using different sets of observations, different wage and fringe benefit-measures (e.g., average fringe benefit level measured in NoK), different controls and different GMM-methods (e.g., system-GMM). While the impacts of wages and fringes vary only slightly, the Arellano-Bond and Hansen tests are very sensitive to variations. For example, I have not been able to get satisfying test-results using the system-GMM-method.

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Finally, in the bottom section of Table 5 I have estimated the corresponding figures for workers’ MWP for fringe benefits. They show the figures are highly sensitive to size-cut-off (e.g., only considering large establishments) and endogeneity issues. Including small establishments dramatically lower the estimates of workers’ MWP for fringe benefits. Similarly, taking account of the endogeneity issue using GMM more than halves the workers’ MWP-figures (Model 3 vs. Model 5). In this latter case, though, I still find that workers are willing (on the margin) to pay nearly 18 NoK for increasing fringes share of total compensation by 1 percentage point. The major lesson to be drawn from this section is that fringe benefits have a very strong impact on the worker turnover. The results are robust across a wide range of specifications, some of which even take into account simultaneity issues regarding wages, fringe benefits and worker turnover. It even appears that the establishment quit rate reacts stronger to changes in the level of fringe benefits than to changes in the wage level. This does not imply that workers do not have positive preferences regarding wages. I have adopted a fixed effect approach, so any per-manent part of the turnover is controlled away. The wage level of an establishment – being high wage or low wage – may very well be much of a permanent characteristic of this establishment. 7. Employer motivation, fringe benefits and worker turnover In this section I look closer on what motivations the employers have for the use of fringe benefits, and how these motivations relate to worker turnover. The questionnaire presents four alternatives and asks the employers which he or she finds suitable. The employer may choose one or several or may find no suitable answer. Table 6 describes the relation between the employers’ motivation and what kind(s) of fringe benefit are offered. Over 60 percent of the employers’ answer that fringe benefits are important for recruitment purposes and to retain workers. Nearly a quarter of the employers replies that employees like fringes or that fringes are offered since the establishments can achieve lower prices than the employees. Extremely few employers reply that they offer fringe benefits to save pay-roll tax. This may of course reflect that the employers deem this answer for socially unacceptable. Table 5 also shows that the motivation vary with establishment size. The larger the establishment, the more likely is the employer to motivate fringe benefits as important for recruitment. A similar size-effect is found regarding the price motivation. This may not be surprising, since larger establishments clearly have more market power than small establishments. Finally, what kind of fringe benefits do they offer? For those who argue that employees like fringes, only cleaning assistance and extended vacation really stands out. Pension schemes, child care, holiday home and extended vacation are clearly more popular than other fringes among employers that consider fringes important for recruitment purposes. Pension schemes, gym membership and holiday home are the most common fringes among employers that (think they) achieve lower prices than their employees. Next I am to relate these motivations and the fringe benefits to the establishments’ worker turnover. To do so, I return to the individual-level Weibull duration regressions of Section 5, and include dummies for the motivations in the models. Models 6 and 7 of Table 4 present the results. One would expect that establishments offering fringes to recruit and retain workers to have lower quit hazards than other establishments. Model 6 shows that this is indeed the case. Model 7 shows that 20 percent of this lower quit hazard can be contributed to the fringe benefits. This only reflects that establishments offering fringes to recruit and retain workers, quite likely is in general preoccupied with retaining workers. It is also interesting to note that, although not significant, the point estimates indicates that establishments offering fringes for other reasons than retain and recruit, experience higher turnover than establishments not offering fringes. 8. The impact of fringe benefits policy on productivity The final issue I address in this paper is whether or not the fringe benefit policy of establishments is reflected in some productivity gains. To address this issue I choose a rather simple strategy. In

10

the literature on dynamic panel estimation (e.g., Arellano and Bond (1991), Blundell and Bond (1998)) the empirical examples are often based on the estimation of production functions. Furthermore, Griffiths (1999) shows how a simple Cobb-Douglas production function may be augmented to take into account efficiency wage strategies of establishments. Thus it is quite natural to examine the productivity impact of fringe benefits policies by utilising an augmented but simple Cobb-Douglas production function. It contains value added (Y), labour (L), capital (K), and index of fringes relative to total wages (eθF). F expresses the fringe as share of total compensation. The index is 1 when no fringe benefit is offered. The parameter θ is anticipated to be positive for two reasons. First, offering fringe benefits instead of money wages may save pay-roll tax, thus saves labour costs, which again should increase productivity. Second, offering fringes instead of money wages may motivate workers to put forward more effort than the same amount in money wages. For establishment f at time t the production function is expressed by Equation 6): 6) ,lnlnln ftftnlctftftKftLft uvFKLY +++++++= γγγθββ

,1 ftftft vv ωη += − ftftv ω, ~ MA(0), |η|<0, where I also have included γt (a year-specific intercept) to reflect aggregate business cycle effects, γlc and γn to control for large city- and industry-specific variation. and ftft vu , ftω reflect the error structure. is an establishment-specific productivity shock that may be serially correlated with the previous period. and

ftv

ftu ftω are serially uncorrelated error terms. Accidental variation in the establishments’ reporting procedures thus causes measurement errors. The ’s reflect these errors. I do not enforce constant returns to scale. Equation 6) is the baseline equation for this section. However, I will also allow observable labour heterogeneity in that I will disaggregate labour into high (college and university educated) and low skilled labour. Unfortunately, although my data are very comprehensive along certain dimensions, information on value added and capital are restricted to very few establishments (981 observations of 162 establishments). As in Section 6) I base the system GMM-regressions on the dynamic representation of Equation 6):

itu

7) 111 lnlnlnln −−− −−+++= ftKftLftftftKftLft KLYFKLY ρβρβρθββ .))(1( 11 −− −+++−+− itititnlctf uuF ρωγγρρθ

I start the analyses by estimating Equation 6) by simple OLS. The results are presented in Model 1 of Table 7. The elasticity of labour is found close to unity, while the elasticity of capital is 0.17. The main result is related to the impact of fringes on value added. Increasing the share of fringes by 1 percentage points implies 5.7 percent higher productivity. Model 2 presents the results from a simple within-establishment regression of Equation 6). The elasticities of labour and capital drop, but remain strongly significant. Fringes have no longer any significant impact.

Model 3 presents the results when following the standard treatment for endogenous variables in the system GMM: in first difference equations, lagged levels dated t-2 are used as instruments, while in level equations, lagged first-differences dated t-1 are used as instruments. To acknowledge possible heteroskedasticity I present robust standard errors. Furthermore, since my analyses rest on rather few observations I have corrected for the finite sample bias of the variance using Windmeijer’s technique (Windmeijer, 2004). As in Section 6) I present the Arellano-Bond test for AR(1) and AR(2) and the Hansen J-statistic to show that the estimation is consistent and the instruments are appropriate. All three measures and tests indicate satisfying regression results.

Model 3 shows that the labour elasticity increases somewhat compared to Model 2. The capital elasticity drops further and unfortunately turns insignificant. On the other hand, the impact of fringe benefits is once again significantly positive, and close to the impact revealed by

11

Model 1. Increasing fringes 1 percentage today yields a 4.6 percent increase in total factor productivity. Model 3 also shows that value added are serially correlated, with an estimate of ρ =0.39. However, the other lagged variables have no significant impact. In Model 4 I differentiate between college- and university educated labour (high educated) and less educated workers (low educated). This reveals that only the elasticity of low educated workers is significantly positive and around 0.44. The elasticity of high educated labour is insignificant and just one sixth of the elasticity of low educated workers. The elasticity of capital remains insignificant and small. The impact of fringe benefits remains relatively unchanged and is still positive and significant. Compared to the results of Model 3, it also brings only small changes to the other estimates. In the final regression, Model 5, I augment the regression of Model 4 with log pay-roll tax rate. For each of the establishments in the data I know the amount of pay-roll tax they pay to the government. This tax rate is determined by the government and varies depending on geographical location (more in centralised areas, less in the northern district). However, each establishment has a certain degree of discretion to determine the tax rate, since they can offer workers renumeration packages comprising job attributes that are not taxable. Model 5 shows that the pay-roll tax rate in it self is not significant. The inclusion of this variable brings forward two obvious changes. Firstly, the impact on value added of the current value of fringe benefits is no longer significant. Secondly, the lagged value of fringe benefits now affects value added significantly and positive. Also, if one take into account the estimate of ρ =0.39, this implies an impact on the productivity of 10.9 percent (which is significant on a 5 percent level of signifi-cance). This indicates that there exists an impact on productivity from offering fringe benefits in excess of what can be explained by saved pay-roll taxes. Furthermore, any eventual impact of fringe benefits caused by saved pay-roll tax basically works on the current period productivity. 9. Conclusion This paper has analysed employers’ use of fringe benefits as part of their compensation policies from a dynamic perspective. By that I mean that the main focus has been on how fringe benefits affect establishments’ worker turnover. But I have also studied the productivity impact of fringe benefits. My results must be interpreted as strongly supportive of the practice of offering fringe benefits to workers. Workers evaluate fringe benefits higher than the equivalence in money wages, and fringe benefits-establishments experience at the same time a productivity gain.

In 2002, it appears that workers particularly appreciate holiday homes, extended own-declaration of sickness absence and child care. And yes, I find a strong impact of fringes as part of the total compensation on worker turnover. My study also shows that the establishments’ worker turnover is related to employer motivation for introducing fringes. Employers introducing fringes to retain workers and for recruitment purposes experience lower worker turnover in excess of the reduction caused by the fringe benefits policy. It appears that introducing fringe benefits for reasons not associated with recruitment or to retain workers implies higher turnover, although this finding is not significant. Finally, I have shown that an active fringe policy actually works – at least for this sample of establishments. Establishments offering higher valued fringe benefits relative to the total compensation achieve higher productivity than establishments offering little fringe benefits. And since this productivity gain is achieved not by saving pay-roll tax, it reflects efficiency gains.

In this study I have addressed fringe benefits issues using a wide range of statistical methods on different establishment populations and different periods of observations. As expected, the results divert somewhat, but not much. However, it remains to be seen if the pro-ductivity impact holds up for larger samples of establishments, since it is an unfortunate weak-ness that the productivity analysis could be conducted for a small sample of establishments only.

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Tables Table 1 Establishment size, wages and reported fringe benefits. Interval of the distribution over average establishment wages 0 – 10% 11 – 25% 26 – 50% 51 – 75% 76 – 90% 91 – 100% All establishments and their workers 1995-2002 Number of employees 5.82 11.73 16.55 18.22 18.59 15.84 Average daily wage 254.27 519.60 689.33 856.49 1063.22 1597.11 Fringe benefits (%-points) 1.15 1.13 1.68 2.81 4.08 4.97 Establishments with more than 10 employees and their workers 1995-2002 Number of employees 29.71 34.86 45.48 56.30 60.44 69.90 Average daily wage 324.70 527.69 692.38 859.40 1068.13 1556.33 Fringe benefits (%-points) 0.60 0.59 1.18 2.34 3.23 4.45 ABU-sample of establishments with more than 10 employees and their workers 2002 Number of employees 32.90 36.33 41.37 49.07 59.36 89.27 Average daily wage 334.18 530.45 701.67 857.54 1076.93 1565.49 Fringe benefits (%-points) 0.57 0.62 0.88 2.05 3.80 4.28 Average daily wage expresses establishment-specific average of daily wages less the reported values of fringes. Fringe benefits are reported as share of total compensation (in percentage points). Total compensation comprises of the reported values of fringes and wages (reported to the tax authorities). Table 2 Variation in wage and fringe benefits policies. 2002 Interval of the distribution over average establishment wages 0 – 10% 11 – 25% 26 – 50% 51 – 75% 76 – 90% 91 – 100% Pension (%) 28.36 59.38 45.99 59.20 77.73 97.59 Extended sickness absence(%) 12.34 29.69 28.54 31.09 27.06 30.25 Private physician (%) 8.72 10.37 19.15 18.07 18.50 17.53 Gym (%) 30.05 25.85 27.35 38.16 53.85 41.79 Children care (%) 3.54 4.28 1.48 1.38 5.90 2.82 Cleaning assistance (%) 0 0.17 0 0.66 2.02 0.67 Holiday home (%) 3.89 11.80 13.11 21.76 19.78 37.58 Extended vacation (%) 25.97 17.81 18.83 19.28 26.20 42.84 Interval of the distribution over establishments’ wage premium 0 – 10% 11 – 25% 26 – 50% 51 – 75% 76 – 90% 91 – 100% Pension (%) 31.17 32.72 52.85 58.38 72.07 77.86 Extended sickness absence(%) 13.54 19.75 28.54 28.51 38.96 24.84 Private physician (%) 10.92 12.86 27.20 18.48 20.59 10.10 Gym (%) 34.00 21.14 14.51 29.39 53.61 44.37 Children care (%) 1.76 1.11 2.31 1.42 8.44 3.94 Cleaning assistance (%) 0 0.22 0.17 0 0.80 2.20 Holiday home (%) 7.35 7.68 11.34 17.24 25.10 21.84 Extended vacation (%) 28.95 15.12 20.84 20.03 21.70 30.85 Interval of the distribution over workforces’ qualifications 0 – 10% 11 – 25% 26 – 50% 51 – 75% 76 – 90% 91 – 100% Pension (%) 41.21 55.59 52.62 52.22 61.23 65.32 Extended sickness absence(%) 16.84 24.78 27.67 29.46 27.08 27.87 Private physician (%) 4.17 16.10 18.25 12.21 22.69 15.19 Gym (%) 28.77 33.95 35.89 32.70 38.52 23.73 Children care (%) 9.12 1.53 3.20 2.69 1.33 0.50 Cleaning assistance (%) 0.93 0 0.32 0.65 0.50 0 Holiday home (%) 4.68 12.48 16.85 15.36 15.16 22.98 Extended vacation (%) 33.19 18.84 19.42 18.98 22.10 26.22 Note: Population: 1374 establishments, but figures are weighted so they are representative. Extended sickness absence implies that sickness absences not physician certified are not limited to three days (which are the limitation provided by law). Establishment wage premium is defined by the estimated establishment fixed effects arising when decomposing log wages into time-varying job-specific variables, fixed establishment and fixed individual effects. Average workforce productivity is defined as the establishment-specific average of the estimated workers’ fixed effects, arising when decomposing log wages into time-varying job-specific variables, fixed establishment and fixed individual effects (see text).

Table 3 Wages, fringes and job durations. Establishment-clustering. Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Duration model: WG Gamma CoxPH WG/B WG WG WG Hourly wage -0.0027*** 0.0020*** -0.0018*** -0.0021*** -0.0028*** -0.0028*** -0.0026***

(0.0004) (0.0003) (0.0002) (0.0006) (0.0004) (0.0004) (0.0004) Fringe benefits (%-points) -0.0302*** (0.0077) Pension scheme -0.0566 0.0474 -0.0353 -0.0373 -0.0511 (0.0830) (0.0692) (0.0718) (0.0841) (0.0804)

Extended sickness absence -0.2297*** 0.1911*** -0.1815*** -0.1303 -0.2219***

(0.0817) (0.0681) (0.0695) (0.0834) (0.0804) Private physician 0.0803 -0.0685 0.0624 0.1488 0.0749 (0.0862) (0.0716) (0.0734) (0.1026) (0.0883) Gym membership -0.0480 0.0335 -0.0326 -0.0293 -0.0473 (0.0758) (0.0628) (0.0644) (0.0766) (0.0796) Child care -0. 5100*** 0.4139*** -0.4635*** -0.3825** -0. 4953**

(0.1906) (0.1612) (0.1585) (0.1767) (0.1940) Cleaning assistance -0.1624 0.1460 -0.1324 -0.2831 -0.1758 (0.1981) (0.1581) (0. 1645) (0.2869) (0.1752) Holiday home -0.1712** 0.1446** -0.1435** -0.1004 -0.1326*

(0.0717) (0.0596) (0.0622) (0.0941) (0.0748) Extended vacation 0.0727 -0.0599 0.0707 -0.0207 0.0733 (0.0921) (0.0768) (0.0783) (0.0911) (0.0957) Saves pay-roll tax 0.0683 0.1585 (0.2008) (0.2049) Achieve lower prices 0.0827 0.1304 (0.0851) (0.0841) Employees like fringes 0.1039 0.0803 (0.1101) (0.1088) Recruitment and retain -0.2652*** -0.2052***

(0.0759) (0.0779) Union member -0.6920*** 0.5784*** -0.5463*** 0.1271 -0.7349*** -0.7204*** -0.7048***

(0.0458) (0.0368) (0.0351) (0.1085) (0.0481) (0.0474) (0.0463) Woman 0.0764*** -0.0701*** 0.0959*** 0.0367 0.0482* 0.0673** 0.0721***

(0.0289) (0.0232) (0.0237) (0.0464) (0.0293) (0.0295) (0.0291) Years of education 0.0968*** -0.0471*** 0.0581*** -0.0462*** -0.0520*** -0.0537*** -0.0476***

(0.0093) (0.0084) (0.0019) (0.0132) (0.0109) (0.0108) (0.0103) Years of experience -0.0743*** 0.0560*** -0.0508*** -0.0743*** -0.0668*** -0.0680*** -0.0669***

(0.0043) (0.0016) (0.0086) (0.0043) (0.0021) (0.0021) (0.0022) Short part-time (4-19 weekly working hours)

0.7382***

(0.0576) -0.6171***

(0.0473) 0.5829***

(0.0480) 0.9224***

(0.0727) 0.7246***

(0.0581) 0.7475***

(0.0594) 0.7422***

(0.0578) Long part-time (20-29 weekly work. hours)

0.1841***

(0.0559) -0.1516***

(0.0462) 0.1593***

(0.0468) 0.3848***

(0.0919) 0.1704***

(0.0566) 0.1848***

(0.0584) 0.1943***

(0.0560) Municipality mean log daily wage

0.6815*

(0.4012) 0.5317

(0.6016) 0.5012

(0.3423) 0.5245

(0.4240) 0.5770

(0.4369) 0.5977

(0.4400) 0.7094*

(0.3973) Municipality variance log daily wage

-0.5549 (0.7222)

0.5025 (0.9376)

-0.3914 (0.6158)

0.5213 (0.7703)

-0.2673 (0.7745)

-0.1280 (0. 7614)

-0.7682 (0.2523)

Industry (3-digit) mean log daily wage

-0.4417*

(0.2378) 0.3619*

(0.1947) -0.2808 (0.1872)

-0.4079*

(0.2410) -0.4173*

(0.2452) -0.4708*

(0.2418) -0.4316*

(0.2348) Industry (3-digit) variance log daily wage

-0.8151***

(0.2570) 0.6744***

(0.2198) -0.6597**

(0.2193) -0.6946***

(0.2893) -0.8323***

(0.2559) -0.8818***

(0.2616) -0.7682***

(0.2348) Sigma 1.2401 (0.0622) Dummies: industry/county/ worker’s field of education

YES YES YES YES YES YES YES

Log pseudo-likelihood -117400.0 -117294.5 -426018.1 -112577.8 -117685.3 -116261.1 -115818.4 N(quits) 38566 38566 38566 38566 38566 38566 38566 N 187205 187205 187205 187205 187205 187205 187205 Note: Column heading denotes duration model: WG=Weibull model with gamma frailties; WG/B=Bootstrap-analysis based on Weibull modell with gamma frailties; G=Gamma model; CoxPH=Cox proportional hazard model.

Models 1, 2, 3, 5, 6 and 7: estimates show impact on log relative hazard rate. Model 2: estimates shows impact on the negative of the log relative hazard rate divided by sigma. This estimation is conducted using the accelerated-failure-time form of duration models. Models 1, 2, 3, 5, 6 and 7: Observations of 187205 workers employed by 1374 establishments (public administration is excluded) on May 15, 2002, which then are followed until May 15, 2003. Model 4 – WG/B: Bootstrap-analysis: Average estimates and standard deviations from 50 separate regressions on 50 re-sampled data sets (see text). Industry is expressed by 2-digit NACE-codes. All regressions also include an intercept. The different fringe benefits are reported as establishment-level variables, thus each establishment is treated as a cluster. The variable Fringe benefits (%-points) expresses for each worker the total value of fringe benefits as share of total compensation (in percentage points) as it is reported to the tax authorities. Robust standard errors in parentheses. ***, **, and * denote 1, 5, and 10 percent level of significance, respectively. Table 4 Marginal willingness to pay for fringes. In hourly wages. Model 1 Model 2 Model 3 Model 4 Model 5 Duration model: W–G Gamma CoxPH W–G/B W–G Pension scheme 20.9 23.9 18.8 18.1 (31.1) (35.5) (38.3) (41.4) Extended sickness absence 84.7** 96.6** 96.4** 63.0 (33.4) (37.8) (38.8) (41.9) Private physician -29.6 -34.6 -33.2 -72.1 (32.4) (36.9) (39.3) (54.2) Gym membership 17.7 16.9 17.3 14.3 (28.1) (31.9) (34.2) (37.8) Child care 188.0** 209.3** 246.2*** 185.2* (78.3) (89.2) (90.3) (103.8) Cleaning assistance 59.9 73.8 70.3 137.2 (74.3) (81.4) (87.9) (148.6) Holiday home 63.1** 73.1** 76.2** 48.5 (29.1) (32.8) (34.6) (49.0) Extended vacation -26.8 -30.3 -37.5 10.0 (33.9) (38.7) (41.6) (44.2) Fringe benefits (%-points) 11.0***

(3.6) Note: Calculated from the estimates presented in Table 2. Model 4 is calculated from the bootstrap-analysis: Average estimates and standard deviations from 50 separate regressions on 50 re-sampled data sets where I have taken into account the non-random sampling of the original data set. ***, **, and * denote 1, 5, and 10 percent level of significance, respectively. Mean and standard deviation (in parentheses) of hourly wage across all sectors (public administration excluded) are 200.4 (115.3).

Table 5 The dynamics of compensation policy and worker turnover 1995-2002. Model 1 Model 2 Model 3 Model 4 Model 5 Population: All Private Private 10+ Private ABU 25% Private 10+ Dependent variable: Qft Qft Qft Qft Qft

Daily wageft -0.019*** -0.019** -0.008*** -0.008** -0.066*** (0.001) (0.001) (0.002) (0.004) (0.022) Fringe benefits (%-points)ft -0.058** -0.058** -0.346*** -0.295* -1.172**

(0.023) (0.023) (0.076) (0.179) (0.591) Qft-1 0.036**

(0.018) Daily wageft-1 0.006 (0.022) Fringe benefits (%-points)ft-1 -0.330 (0.334) Absorb and cluster: establishment YES YES YES YES Dummies year YES YES YES YES YES Dummies industry (3-digit) and county YES Lags for instruments 2 (wage: 3) Hansen test for over-id., p-value 0.114 Arellano-Bond test AR(1) in first diff., p-value 0.000 Arellano-Bond test AR(2) in first diff., p-value 0.509 R2–adj. 0.342 0.344 0.339 0.327 N 238541 232610 54884 1368 8108 NxT 1136976 1094886 253525 9483 34100

Implied marginal willingness to pay for increasing fringe benefits share of total compensation by 1 percentage point 2.991** 2.991** 41.051** 38.728 17.736*

(1.170) (1.181) (12.076) (32.624) (10.556) Note: Populations: All=all Norwegian establishments 1995-2002; Private=all Norwegian establishments 1995-2002 excluded public administration; Private 10+=all Norwegian establishments 1995-2002 excluded public administration with at least 10 employees; 25% Private 10+=25 percent random sample of all Norwegian establishments 1995-2002 excluded public administration with at least 10 employees; Private ABU= Norwegian establishments 1995-2002 excluded public administration in the ABU-sample. Dependent variables: Qft denotes the quit rate of establishment f at time t. Qft is measured in percentage points. Fringe benefits are expressed as the establishment-specific average of the share fringe benefits comprise of total worker compensation (in percentage points). t-1 denotes variables lagged one period. Robust standard errors in parentheses. ***, **, and * denote 1, 5, and 10 percent level of significance, respectively. Table 6 Why do establishments use fringe benefits? Motivation for using fringe benefits as part of total compensation Employees like

fringes Important for

recruitment and retain

Achieve lower prices than

workers

Saves pay-roll tax

Mean (%) 25.5 55.0 24.1 2.0 Pearson correlation coefficients (Fisher’s z-transformation). Number of employees -0.015 0.080** 0.071** -0.015 Pension scheme Extended sickness absence 0.027 0.062* 0.026 0.080**

Private physician 0.100*** 0.069* 0.033 0.128***

Gym membership 0.016 0.089** 0.161*** 0.030 Child care 0.000 0.046 -0.037 0.085**

Cleaning assistance 0.070* 0.033 -0.013 0.157***

Holiday home -0.084** 0.090** 0.130*** -0.038 Extended vacation 0.241*** 0.129*** -0.088** -0.100***

Note: Weighted. Figures based on the response from 789 private sector establishments offering at least one fringe benefit (not counting pension scheme). Note that the categories of motivation are not mutually exclusive. ***, **, and * denote 1, 5, and 10 percent level of significance, respectively.

Table 7 The impact of fringe benefits policies on productivity. Model 1 Model 2 Model 3 Model 4 Model 5 Log number of employeesft 0.914*** 0.414*** 0.591*** (0.060) (0.120) (0.186) Log number of low educated employeesft 0.437** 0.426**

(0.171) (0.176) Log number of high educated employeesft 0.067 0.045 (0.126) (0.132) Log capitalft 0.171*** 0.087*** 0.077 0.027 0.042 (0.035) (0.026) (0.076) (0.058) (0.055) Fringe benefits (%-points)ft 0.057*** -0.019 0.046*** 0.033** 0.024 (0.012) (0.014) (0.015) (0.016) (0.017) Log pay-roll taxft 0.039 (0.110) Log value addedft-1 0.392*** 0.330*** 0.387***

(0.082) (0.072) (0.073) Log number of employeesft-1 -0.007 (0.091) Log number of low educated employeesft-1 -0.165 -0.202 (0.134) (0.102) Log number of high educated employeesft-1 0.153 0.179 (0.126) (0.122) Log capitalft-1 -0.048 -0.034 0.052 (0.064) (0.056) (0.050) Fringe benefits (%-points)ft-1 -0.010 -0.039** -0.042**

(0.016) (0.020) (0.019) Log pay-roll taxft-1 0.094 (0.101) Absorb establishment YES Dummies year YES YES YES YES YES Dummies industry (10) YES YES YES YES Dummies Oslo and Bergen YES YES

Lags for instruments 2 2 2 Hansen test for over-id., p-value 0.341 0.447 0.907 Arellano-Bond test AR(1) in first diff., p-value 0.000 0.000 0.000 Arellano-Bond test AR(2) in first diff., p-value 0.916 0.870 0.942 R2–adj. 0.785 0.921 N 162 162 162 162 162 NxT 981 981 981 981 981 Note: Population: 981 observations of 162 establishments in the ABU-sample with non-missing information on key variables. Dependent variable: Log value added of establishment f at time t. t-1 denotes variables lagged one period. Robust standard errors in parentheses. ***, **, and * denote 1, 5, and 10 percent level of significance, respectively.

Table A.1 Descriptive statistics. ABU-sample (all establishments

with more than 10 employees, public administration excluded)

Individual level – duration regressions Mean Standard deviation Hourly total compensation 200.10 115.19 Hourly wage 194.75 107.23 Hourly amount of fringe benefits 5.35 25.42 Pension scheme 0.86 0.35 Extended own-declaration of sickness absence 0.54 0.50 Private physician 0.17 0.37 Gym membership 0.56 0.49 Child care 0.06 0.23 Cleaning assistance 0.02 0.14 Holiday home 0.52 0.50 Extended holiday 0.28 0.45 Years of education(excess comp.school) 3.39 2.45 Woman 0.32 0.46 Short part-time (4-19hweek) 0.10 0.29 Long part-time (20-29hweek) 0.04 0.19 Job duration in years 7.59 7.18 Industry mean log compensation 6.64 0.41 Industry variance log compensation 0.38 0.25 Municipality mean log compensation 6.48 0.21 Municipality variance log compensation 0.57 0.11 N (workers) 187205 All establishments

(public administration excluded)All establishments with more

than 10 employees (public administration excluded)

Establishment level – quit regressions Mean Standard deviation Mean Standard deviation Quit rate (%) 29.94 47.76 27.17 28.71 Number of employees 12.77 57.70 43.55 114.56 Average daily wage 508.26 302.39 589.41 255.53 Fringe benefits (%-points) 1.66 6.09 1.15 2.14 N (establishments) 232610 54884 NXT 1094886 253525 ABU-SAMPLE Quit rate (%) 22.03 20.61 Number of employees 153.61 248.33 Average daily wage 697.11 281.30 Fringe benefits (%-points) 1.48 2.15 N (establishments) 1368 NXT 9483

ABU-sample with capital information

Establishment level – productivity regressions Mean Standard deviation Log value added 11.22 1.34 Log capital 11.97 1.58 Log number of employees 5.00 1.06 Log number of low educated workers 4.74 1.03 Log number of high educated workers 3.04 1.56 Fringe benefits (%-points) 1.51 1.85 Log pay-roll tax 2.50 0.42 N (establishments) 162 NXT 981