Compensating Wage Differentials and Seasonal Employment in Austria: Evidence from Administrative Data

Transcription

1 Compensating Wage Differentials and Seasonal Employment in Austria: Evidence from Administrative Data Emilia Del Bono, Andrea Weber First draft February 8, 2005 Abstract In this paper we investigate the existence of compensating wage differentials across seasonal and non seasonal jobs, which arise due to working time restrictions. We build on a theoretical model by Abowd and Ashenfelter (1981) which links the wage differential to anticipated variation in unemployment through fundamental labor market parameters. Since the Austrian labor market is characterized by an unusually high share of seasonal employment it provides the ideal setting in which to empirically test this model. We use the very rich information contained in the Austrian administrative records to derive a flexible definition of seasonal employment which is purely based on observed regularities in employment patterns. We therefore show that while a large part of the wage compensation is paid by the unemployment insurance system, a positive amount is covered by the employer. Keywords: seasonal employment, wage differentials, labor supply elasticity, fixed effects panel estimation JEL classification: J22, J3, C23 We are grateful to David Card for advice and helpful discussion. Financial support for this research was generously provided by the CLE. Andrea Weber acknowledges financial support form the Austrian Science Foundation, Project Nr J2365-G05. All the shortcomings are the authors responsibility. University of Oxford and UC Berkeley, emilia.delbono@economics.oxford.ac.uk; corresponding address: 549 Evans Hall #3880, Berkeley, CA , U.S.A. Institute for Advanced Studies Vienna and UC Berkeley, andrea.weber@ihs.ac.at

2 1 Introduction Our focus on Austria is motivated by the much higher seasonal variability of Austrian employment compared to other European countries. This is only partly accounted for by the relatively larger share of employment in industries characterized by seasonal fluctuations in production, such as construction and tourism. Instead, this phenomenon must be seen in the context of the historical and political developments of the last few decades as well as the institutional framework in which firms and workers operate. In particular, it is well acknowledged that the almost universal coverage of unemployment insurance and the absence of experience rating provide indirect subsidies to industries with high and predictable fluctuations in demand (Card and Levine, 1994). All this implies that while the OECD draws a picture of Austria as a typical continental European country with a highly regulated labor market, restrictive job protection legislation and a generous unemployment insurance system (OECD, 2003), there are many caveats. This was already noted by Fischer and Pichelmann (1991), who investigated the incidence of temporary layoffs. They showed that in Austria about one-third of all unemployment spells per year and almost one-fourth of total unemployment can be ascribed to seasonal fluctuations, similarly to the USA or Canada. Thus, job protection regulations must be flexible enough to explain high seasonal employment fluctuations and frequent job turnover. In this paper we propose to investigate the existence of compensating wage differentials across seasonal and non seasonal jobs. Rather than exploring these compensating differentials at the industry level, we focus on differentials arising because of working time restrictions and higher risk of unemployment in seasonal jobs. This relates to an ongoing discussion in the labor supply literature, which seeks to determine whether all information is incorporated in the equilibrium price (i.e. the competitive wage), or whether individual decisions 1

3 may be influenced by demand-side factors via alternative routes (Card, 1984; Ham, 1986). One situation in which demand-side elements can directly influence individual s labor supply decisions is in the presence of employer-determined working time restrictions. This leads us to refer to the model developed by Abowd and Ashenfelter (1981), in which the compensating wage differential is related to working time restriction through fundamental parameters derived from standard labor supply and risk theory. The Austrian labor market, characterized by a high share of seasonal employment, provides the ideal setting in which to empirically test the model. One of the problems with the empirical implementation of the Abowd and Ashenfelter (1981) model in the past has been the difficulty to distinguish between those workers affected by demand-side restrictions and those unaffected. Very often this distinction can only be established by crude proxies, such as industry affiliation or self-reported definitions (Moretti, 2000; Murphy and Topel, 1987). However, these measures are confounded by the fact that we could be capturing unobserved industry or person specific elements instead. Our approach to this problem is to make use of the very rich information contained in the Austrian social security database. We exploit the pattern of employment and unemployment spells observed for a single individual in order to derive a more flexible definition of seasonality. Specifically, we define a seasonal employment period as being characterized by a sequence of employment spells of approximately the same duration and occurring at the same time during the calendar year. We are then able to relate changes in wage rates across seasonal and non seasonal periods to variations in working time restrictions. We can show that our definition of seasonality is able to capture a large part of the cyclical variation of employment throughout the year. Using the starting month of the job spell as an instrument for the expected effect of working time 2

4 restrictions, we then show that while a large part of the compensation is covered by the unemployment insurance, a certain amount of the differential is paid by the employer. The implied estimate for the compensated elasticity of labor supply is 0.18, which is in line with the figures usually found in the empirical literature. We take this as preliminary evidence that the model is not rejected in our empirical setting. The paper is organized as follows. Section 2 presents some descriptive evidence on the seasonal variation in employment in Austria and a brief overview of the relevant institutional factors which explain it. Section 3 and 4 introduce the theoretical model and its empirical implementation. Section 5 describes our data and definition of seasonality. The results are presented in section 6, and the last section concludes. 2 Seasonal employment in Austria Seasonal fluctuations in employment in Austria have historically been of considerable magnitude. As we can see in figure 1, the variation in the percentage of workers employed over the active population is characterized by a pattern which repeats itself regularly during the various phases of the business cycle. Both men and women are affected by this phenomenon, although there are important differences. Apart from the period between the early-70s and the early-80s, the magnitude of the variation in the employment/active ratio for men has always been much higher than what observed for women, and at least 5 percentage points or greater. Moreover, while male employment peaks in the summer and presents a trough in the winter, a second smaller peak appears during the winter from the early 80s onwards in women s employment. This phenomenon is very atypical for a continental European country. In this respect Austria is much more similar to Canada than Germany for example 3

5 (de Raaf et al., 2000). 1 To give some idea of the relative magnitude of the seasonal cycle in Austria, figures 2 and 3 plot monthly seasonal employment and its average deviation from a country-specific trend for Austria, Germany, the USA and Canada between 2001 to The first graph shows the existence of a regular yearly pattern for all countries. The second graph shows that the average amplitude of the seasonal variation experienced in Austria is very similar to that observed in Canada, i.e. about 5 percentage points from peak to trough. The USA and Germany experience much smaller variations, of about 2 percentage points on average, and in Germany the pattern is clearly different from what we can see elsewhere. The industries most exposed to seasonal demand variations are construction and tourism and this is accentuated in Austria due to the climatic and geographical conditions. Due to bad weather, almost all activity in construction is shut down during the winter months - roughly between December and February. Outside the bigger cities, tourism is concentrated in the western, alpine regions of Austria where it is characterized by two yearly seasons. The main season is the skiing season, which occurs during winter and lasts from December to April, the second - shorter season - occurs during the summer. Given that construction and tourism are relatively important industries in the Austrian economy, one can expect that their pronounced seasonal fluctuations also affect other industries. 2 Indeed, if we look at employment by industry in figure 4 we find seasonal patterns throughout the economy. The phenomenon of seasonal employment in Austria cannot be entirely explained by geographic and climatic circumstances, however. Economic policy and the system of industrial relations play an important role. Let s start by examining figure 1 from a historical perspective. The reconstruction period 1 Only Northern European countries such as Sweden and Norway experience similar seasonal upswings, but that is easily explained by their much rougher climate. 2 According to the Labour Force Survey, in 2001 the share of employment in the construction sector was 8.8 in Austria and 7.9 per cent in the EU-15 as a whole, the corresponding shares in the hotel and restaurant sector were 5.4 and

6 after the second world war came to an end in the early 1970s, leading to lower demand in construction and hence to a reduction of seasonal employment (particularly for men). Roughly at the same time the social democrats came into power, becoming the most influential political party in Parliament until the end of the century. The political development during these decades clearly set Austria apart from its neighboring countries. A major economic goal of the socialist government was full employment. So when the state owned steel industry went into a crisis by the end of the 70s and unemployment started to rise, the government subsidized huge construction projects as a means to generate jobs. This lead to an expansion of the construction sector and to a renewed impulse in seasonal cycles. The second industry promoted by the economic policy of the socialist government was tourism. This sector generated big export revenues which were for a long time the main stabilizing factor in the Austrian trade balance. Moreover, since in the alpine regions tourism is often the main activity, subsidies to this sector were considered often a priority. Another important element of the Austrian economy is the highly centralized wage bargaining system. Wages are set at the industry level by collective agreements between employer and employee representatives, unions and government officials. This allows wage and employment policies to be oriented towards the macroeconomic goals of real wage flexibility and employment stability. At the same time it leaves the employers little room to adjust wages at the enterprise level. In the light of these constraints and of the main economic policy objectives of the successive socialist governments, it is perhaps not surprising that other institutions were designed in order to support the seasonal industries. In particular, we refer here to the role of the almost universal unemployment insurance system, the complete absence of experience rating and industry-specific regulations on hiring and firing for blue collar workers. The combination of these elements results in an implicit subsidy to industries which experience periodic 5

7 and predictable seasonal fluctuations in demand. The phenomenon is large and difficult to quantify, but it was estimated that in 1993 the direct costs (unemployment insurance and unemployment benefits) amounted to about 250m Euros, while taking into account also social security contributions and payroll taxes not paid brings the total to 290m Euros, almost 0.2 percent of GDP (Brandel et al., 1994). 3 Despite repeated attempts to reform the tax and social security system, the basic structure of Austrian social policy - as it has been outlined above - remains unaltered. Moreover, since our main period of investigation covers the years 1989 to 2001 and only smaller and narrowly targeted interventions took place during these years, we will not address here any issue related to these reforms. This unfortunately means that it is not possible for us to exploit changes in the system in order to provide alternative tests of the robustness of our findings. On the other hand, as the seasonal pattern in figure 1 shows, we have a fairly long period of time with a regular seasonal pattern to exploit in our analysis. 3 A theoretical model of compensating wage differentials The importance of the construction and tourism sectors in Austria and the magnitude and regularity of the seasonal cycles clearly suggest that the institutional setting in Austria allows certain employers to react flexibly to demand conditions. This flexibility is not achieved through wage adjustments, since this is not allowed by the centralized wage bargaining process, rather by means of evenly spaced lay offs and rehires. It is therefore possible to think of the Austrian labor market as being characterized by two types of implicit contractual agreements between employers and workers. In the first scenario, a workers is 3 Details on the unemployment insurance and hiring and firing regulations can be found in the Appendix. 6

8 employed throughout the entire year, while in the second case the worker is offered a seasonal job and is temporarily laid off during the off-season to be rehired at a later point in time. Given the absence of experience rating, this system of lay offs and rehires is clearly an optimal solution for the employer, but what does this entail from an employee s point of view? To understand a worker s decision to work either in a permanent or a seasonal job it is useful to go back to a model of the labor market where unemployment is analyzed in terms of demand-side restrictions as opposed to be the outcome of an individual s choice between market and non market time. A simple framework is that proposed by Abowd and Ashenfelter (1981). Their model shows that the determination of wage rates in the presence of anticipated working time constraints is systematically related to the determination of labor supply in the absence of such constraints. This allows us to analyze the worker s labor supply decisions in the presence of employer-determined working time constraints in terms of the familiar concepts of compensated labor supply elasticity and relative risk aversion. Abowd and Ashenfelter (1981) consider an economy characterized by two contracts. In the unconstrained contract the worker can choose the optimal amount of working time h 0 to supply at a fixed wage rate w. In the constrained contract the worker accepts a contract which sets the working time at h < h 0 and the wage at w. The model is static, there is no substitution over time. If workers are identical in all respects and there are no costs to moving, the worker s utility must be the same in the two contracts. This equilibrium condition implies that in the constrained contract a compensating wage differential must be paid to the worker in order to give her the same utility level she would achieve in the unconstrained contract. The model also formalizes the existence of a relationship between the compensating wage differential between the constrained and unconstrained contracts 7

9 and the working hours restrictions imposed by the employer in the constrained contract. In particular, it can be shown that in the presence of working hours constraints the competitive wage incorporates a compensating differential which is proportional to the squared unemployment rate, while the coefficient of proportionality can be expressed as half the inverse of the compensated labor supply elasticity. That is: w w w 1 (h 0 h) 2 2e hh 0 (1) where e is the compensated labor supply elasticity. 4 Now assume that the working time restriction in the constrained contract is not a priori fixed, but there is some uncertainty attached to it. In this case a risk averse employee asks for an extra compensation for the risk, and gets a wage w. The actual time worked can be modelled as a random variable h with E( h) = h and V ( h) = σ 2. In this case the additional compensating wage differential can be shown to be proportional to the variance of expected unemployment. Formally, we can write: w w w 1 2 r σ2 h 2 (2) where the factor of proportionality is half the coefficient of relative risk aversion r. Next, we can consider the effect of unemployment insurance on the wage differential. Suppose that under the unemployment insurance scheme benefits cover the wage for a fraction γ of the lost working time h 0 h and total labor income amounts to w [ h + γ(h 0 h)]. The wage differential without uncertainty can be expressed as: w w w γ(h0 h) h + γ(h 0 h) + 1 (h 0 h) 2 2e h 0 [ h + γ(h 0 h)] (3) 4 For the exact derivations see Abowd and Ashenfelter (1981). 8

10 This means that in the presence of unemployment insurance the wage differential can actually be negative. The compensating differential in the case of uncertainty is given by: w w w 1 2 r σ 2 h[ h + γ(h 0 h)] (4) If we combine equations (4) and (3), the total compensating wage differential amounts to w w w γ(h0 h) h + γ(h 0 h) + 1 (h 0 h) 2 2e h 0 [ h + γ(h 0 h)] + w w 1 2 r σ 2 h[ h + γ(h 0 h)] (5) Expressed otherwise, and omitting cross product terms (or approximating w w 1) we get the equation estimated by Abowd and Ashenfelter (1981): w w w γ(h 0 h) h 0 + (γ 1)(h 0 h) (h 0 h) 2 2 e h 0 [h 0 + (γ 1)(h 0 h)] r σ 2 h[h 0 + (γ 1)(h 0 h)] (6) We apply this model with a slight simplification in order to avoid the nonlinearity in the parameter γ. Specifically, we assume h 0 + (γ 1)(h 0 h) = h 0. 5 This allows us to express the compensated wage differential as: w w w ( h 0 γ h ) h e ( h 0 h h 0 ) r σ2 hh 0 (7) 5 We know that the net replacement ratio is 0.55 and that the gross replacement ratio is about 0.45, but this figure does not take family allowances into account, so that a value of γ between 0.50 and 0.75 could still be reasonable. On the other hand, our data shows that (h 0 h)/h for seasonal workers. Therefore, our approximation error is likely to be rather small. 9

11 and proceed with an empirical strategy in order to subject it to adequate testing. 4 Model application and estimation We apply the model of working time restriction and compensated wage differentials to a labor market with permanent jobs and seasonal job. Workers in permanent jobs are employed for the full year whereas workers in seasonal jobs are restricted to work only for part of the year. The measure of working time is the number of days at work during the year. We assume that a worker i in the permanent job with unrestricted working time works h 0 equal to 365 days a year. The seasonal worker is affected by restrictions and works a number of days h it in year t. The wage variable w it refers to gross monthly wage. We estimate two versions of the model. First we look at a model without uncertainty for which we estimate the following fixed effects specification ( h 0 ln w it = X it β γ h ) it h ( h 0 h ) 2 it 2e h 0 + u i + ɛ it (8) X it is a set of time varying individual characteristics determining the wage and the coefficient vector β measures their influence. The second and third term on the right hand side are different from zero only for seasonal workers. The estimated parameters are γ, the replacement ratio from unemployment insurance, and e, the compensated labor supply elasticity. In this specification we use individual actual unemployment experience in terms of percentage of time unemployed while in seasonal employment as a proxy for h0 h it. We decompose h 0 the error term into an individual specific component u i reflecting time invariant differences in taste for consumption and leisure and a time varying component ɛ it. In the model with uncertainty we include the risk term like in equation (7) and 10

12 estimate ( h 0 ln w it = X it β γ h ) it h e ( h 0 h it h 0 ) r σ2 it (h 0 ) 2 + u i + ɛ it (9) Since only the predictable component of unemployment now affects the wage, we use a two stage estimation strategy. In a first stage regression we estimate a model for the percentage of time unemployed. As an instrument to pick out movements in unemployment that truly represent changes in working time constraints we use the start month of the current employment spell. The regular seasonal cycles make the amount of time employed and unemployed highly predictable for seasonal workers. For example, if someone starts a job early in the season she can expect a longer employment duration than someone starting late. In the second stage equation we use the predicted values for percentage of time unemployed for seasonal workers as a proxy for h0 h it, and the variance h 0 of the predictions as proxy for coefficient of relative risk aversion r. σ 2 it (h 0 ) 2. The additional parameter estimated is the 5 Data We use longitudinal information on a random sample of male workers drawn from the Austrian social security records during the years The social security authority collects detailed information on all workers in Austria, with the exception of self-employed, civil servants and marginal workers. The sample we use for our analysis consists of new entrants into the labor market. An individual is defined as an entrant if she was not observed in employment, unemployment, apprenticeship, or maternity leave during the first two years Thereafter we follow her employment career up to the year The data contains information on the individual s labor market status in employment, unemployment and various other qualifications on a daily basis. For individuals in employment we can track the employer by an employer identifier. 11

13 We define a job as an uninterrupted employment spell with the same employer. The full line in figure 5 plots weekly employment over active population for the sample of males. We find the same regular pattern as in the aggregate figure 1. Because we have a sample with relatively young workers, the seasonal pattern is even more pronounced with a variation in employment of about 10 percentage points over the year. Figure 7 presents employment by worker type. The graph makes clear that seasonality affects employment of blue collar workers most. Similarly to figure 4, we find seasonal employment fluctuations in all industries in the sample. Therefore our analysis will not be restricted to a specific group of industries. 5.1 Definition of seasonality from spell data The precise timing information and the longitudinal nature of the data allow us to identify patterns of seasonal employment. The approach we use defines seasonality purely by the pattern of employment and nonemployment during the calender year. In this way our definition of seasonality is similar to de Raaf et al. (2003). Specifically, we define a worker in seasonal employment if she ends a job (with a duration of at least 2 months) within the same three month window in two consecutive years. Note that this definition allows other jobs to lie in between the two jobs which define the seasonality. We call time stretches with a seasonal employment pattern a period of seasonal employment. The period consists of a series of spells in employment, unemployment or out of labor force. In this way we avoid restricting the definition of seasonal employment to specific industries, times of the year, or employer recalls. In contrast to seasonal employment we define permanent employment as a job with a minimum duration of 10 months. Sequences of permanent jobs with only short interruptions together define a permanent employment period. The residual group of jobs, that do not qualify as either long term employment or fall into a period with 12

14 seasonal employment are classified as frequent change jobs. Taken together they define periods of frequent employment change. The dashed line in Figure 5 plots the ratio of employed over active population for all but the seasonal workers. Excluding seasonality according to our definition reduces the yearly employment variation by half. This means that our definition takes a conservative point of view but it clearly captures the phenomenon. Some of the seasonal demand variation is still reflected in the periods with frequent changes. This is even more convincingly shown in figure 6, which gives the share of employed over active population in the three employment categories by week of the year. The variation over the year is largest in seasonal employment, where employment peaks during the summer months and is lowest in February. We see, however, lower employment ratios during winter also in the frequent change group and even a small drop in employment at the beginning of the year for permanent workers. 5.2 Employment and wage panel In our analysis of wage differentials we focus on male workers who are either in seasonal or in permanent employment. We restrict the sample to blue collar workers entering the labor market between the ages of 15 and 35. We select this group of workers, because among them the share of seasonal employment is especially high. In addition, they are not affected two problems which typically complicate studies based on administrative data: the share of part time work in this group is thought to be very low, and almost all wages are below the top coding threshold. 6 From the spell data with identified seasonal and permanent employment periods 6 In our sample we observe a share of 0.05% top-censored wages. Moreover, since the upper ceiling for unemployment benefits depends on the upper earnings limit for social security contributions, we can ignore both these problems. As for workers who earn below the minimum level of contribution, i.e. the so-called marginal workers, we only know that a very large group of them (about 72 percent) are women and therefore we do not think this will affect our results. 13

15 we generate a panel with yearly frequency. This panel will be the basis for the estimations. We do this the following way. First, we define the yearly employment as seasonal or permanent on the basis of the period which occupied the larger part of the year. Next, we select a representative employment spell from which we get information about wage, industry, and other characteristics. For permanent employment we select the spell with the longest duration during the current year. For seasonal employment we select the spell with the longest employer tenure. Working time restrictions for seasonal workers are defined by the percentage of unemployment (or non employment) during the whole period of seasonal employment. Workers in permanent employment are by definition employed throughout the period. To avoid irregularities we only consider employment in years were the individual worked for more than 90 days. Further, we focus on years in which the individual was either in seasonal or permanent employment using only unbroken sequences of observations. If there is more than one sequence per individual we select the longest one. Using these rules, we get an unbalanced panel of 3,004 workers for whom we have a total of 24,038 yearly observations. According to table 1, which reports summary statistics at the individual level, 32% of the workers are observed in seasonal employment at least once. We also observe a high number of transitions between seasonal and permanent employment. This share of about 26% of workers is important for the identification of wage differences between seasonal and permanent jobs. Transitions occur in both directions, from seasonal to permanent jobs and the other way round, with about the same frequency. At least it is not clear that there is career advancement form seasonal to permanent jobs for young workers. Table 2 reports summary statistics on all person-year observations. These include 3,405 (14%) occurrences of seasonal jobs. The table is divided in three sub-panels reporting observations by industry, region and starting month of 14

16 the spell. The industries with highest employment are manufacturing and construction. It is obvious that most of the seasonal jobs are concentrated in construction and tourism, which together account for about half of the seasonal observations. But we find seasonality in every industry. Seasonality also varies with the region. Especially in the Alpine parts (Salzburg, Tirol, Carinthia) of the country, which rely heavily on the tourism industry, we observe a high share of seasonal employment. The two different seasonal cycles are clearly marked by the starting month of job spells. Seasonal jobs typically start in the spring (March - May) or in December when the winter season takes off. For permanent jobs, on the other hand, the distribution of the starting month of the spell is fairly even throughout the year, small peaks occurring only in January, March, and September. Summary statistics on mean wage by industry, region, and starting month are given in table 3. In our sample permanent jobs pay on average more than seasonal jobs, with an average raw differential of about 9 percentage points. We find negative differentials for all industries, except for tourism, where the average wage is the lowest among all industries, however. Figure 8 plots the mean wage differentials over time for the different industries. We find high negative differentials in agriculture and manufacturing, and more or less negative ones in the other industries. The differential is almost zero in construction, and is positive in the hotel industry in all years. The negative wage differentials between seasonal and nonseasonal jobs indicate that it is necessary to account for the existence of an unemployment insurance system in the model. As we saw in the in the previous section theory would predict a positive wage differential in the absence of unemployment insurance. 15

17 6 Empirical results We start by estimating a simple wage regression using all the available time varying information about an individual s jobs and characteristics. This is presented in table 8 using two specifications, one of which includes a seasonal dummy and its interactions with the industry. Since we use a fixed effect estimator, the identification of the seasonal dummy is due to the presence of individuals who experience at least one transition between permanent and seasonal periods of employment. Because our sample consists mainly of young people, the proportion of workers who experience at least one such transition is about 26 per cent and this makes the identification of this parameter quite robust. As the table shows, being in a seasonal job results in a wage which is 4.7% lower than that perceived while in permanent employment. Beside this overall effect, there is also substantial variation by industry. As we mentioned in the descriptive analysis, the average compensating wage differential between seasonal and non seasonal jobs is usually negative, with the exception of seasonal workers operating in the hotel sector who earn about 2.1% more than similar workers in permanent positions. Indeed, a large portion of the seasonal effect is captured by the difference between the hotel and the other industries, so that when we move to a specification which does not take into account seasonality effects the most important difference is seen in the change in the hotel industry dummy. All the other parameters remain very stable and no significant reduction in the explanatory power of the model occurs. Apart from taking into account the effect of seasonality, our model is based on a rather conventional specification and therefore all the other results are in line with our expectations. For example, we find a concave-shaped relationship in age, experience and tenure. In particular, we see that the effect of tenure is rather small and this is consistent with the fact that we have a rather young 16

18 sample of workers. As well as these effects, the specification takes into account regional variation and business cycle factors, here proxied by the growth rate of regional GDP. All these estimated parameters are significant at the 1% level and robust to alternative specifications of the wage equation, so that in what follows we will always take these effects into account although we will not present them in the tables or comment any further. The previous analysis was important in order to choose a specification of the wage equation that would capture the main features of our data. Using this as a background, we estimate equation (8) in section 4 to take into account the effect of the working time restrictions on the compensating wage differentials. So, table 5 shows the effect of introducing a linear and a quadratic term in the individual experience of unemployment, expressed in terms of percentage of time unemployed while in seasonal employment. The same specification is presented with respect to non employment, as the theory does not distinguish between unemployment and non participation and either of the two measures could be a proxy of restrictions in desired working hours. Note, however, that we use the model controlling for the effects of an unemployment insurance system. In this respect focussing on the percentage of time unemployed makes more sense. Hence we will we give first priority to the specification with unemployment in the interpretation of results. We implement two specifications of the model. First we allow for the possibility of an overall seasonal effect by introducing a seasonal dummy (and its interactions with industry), then we restrict the model such that the entire effect of seasonality must be accounted for by differences in the unemployment or non employment terms. 7 As we can see, the results we obtain are not in line with the theoretical predictions. While in the first specification all our estimated pa- 7 We drop the interactions between seasonality and industries as well in the second specification. Testing for the joint significance of these interaction terms results in a significant p-value in all specifications. This is so because of the different sign of the wage differential in the hotel industry. 17

19 rameters are significant, the signs are opposite with respect to what the theory would predict and we obtain an overall negative relationship between the wage and the unemployment or non employment terms. When we do not consider the season dummy, on the other hand, most of our parameters become insignificant while the signs still generally contradict the theoretical predictions. These results indicate that, if anything, the wage of seasonal workers decreases in the actual amount of unemployment or non employment experienced by the individual over the seasonal period. Does this imply a rejection of the compensating wage differentials model? There are at least two reasons why it should not be so. The first has to do with the fact that, as stated clearly by Abowd and Ashenfelter (1981), the individual s realized unemployment rate is not what is relevant. What matters in this context is its predictable component, therefore the expected unemployment or non employment percentage is what should affect the compensating differential. The second point is that, from a purely empirical perspective, the percentage of the period spent in unemployment or in non employment may be endogenous and therefore using it may lead to inconsistent estimates of the parameters. If unobservable factors, which are not captured by time-invariant characteristics of the individual, and which are negatively related to the wage and positively related to the length of the period out of employment come into play, we might expect to observe a negative relationship between our measures of hours restrictions and the compensating wage differentials. Taking into account this potential endogeneity, and estimating a predicted measure of unemployment or non employment is therefore our next step. As was already discussed in section 4 we obtain exogenous variation in the endogenous variable by exploiting variation across seasonal jobs due to differences in the month in which the job started. To get an idea how this instrument might affect out results we order seasonal workers mean percentage of time spent un- 18

20 employed by the starting month of the job. We get a ranking of months from the starting month with the lowest mean percentage of unemployment to the starting month with highest percentage of unemployment. Using the same order of starting months we plot the mean seasonal wage by each starting month. The idea is that if the instrument is able to pick out the positive correlation between the percentage unemployed and the wage differential we should find a higher mean seasonal wage for starting months with a higher percentage of unemployment. Looking at figure 9 the graph indeed shows an upward sloping line in mean wage for most months. If we repeat the same exercise for the percentage non employed and plot seasonal mean wage against the order of starting month generated by ranking according to percentage of time in non-employment we get a negative relationship, however (see figure 10). Thus looking at the raw sample means we can infer that using predicted unemployment might give us estimates for the model parameters that correspond to theory. For predicted non-employment there is no hope for such an effect. The first stage regressions of the percentage of the period spent in unemployment or in non employment on a set of endogenous regressors are presented in table 6. As we can see from the table, the interactions between the seasonal dummy and the starting month of the job are very significant, both individually and jointly. Jobs starting in December or in the spring period (from April to June) are those for which the predicted share of unemployment is higher. In contrast, a job starting in January or in the fall is indicative of a different pattern and clearly predicts lower overall unemployment. We also find some evidence that the percentage of unemployment increases as the season progresses. The estimates increase from January to June and also from October to December. It would have been interesting to interpret the effects of seasonal cycles separately for all industries. So, ideally we could have included a complete set of month dummies for all industries, but the data do not provide enough variation in terms of individuals switching between seasonal and non- 19

21 seasonal employment, industries, and starting months to identify all the effects separately. Our estimates should pick up the main seasonal pattern, however. While we are confident that we are able to pick up the main seasonal pattern in the relationship between the starting month of the job and the percentage of the period spent in unemployment, it becomes more complicated when we analyze non employment. The main difference is that in this case the months which are most strongly related to a longer period of non employment are the summer months up to (and including) September, plus December. This seems to indicate that we are perhaps capturing the movements of people who are less attached to the labor force, but who might simply have a very fractured working experience and who therefore stay out of employment on average longer. In other words, the non employment variable might be too noisy to capture the effect we would like to represent in our model. Next, we move to the second stage and estimate equation (9) in section 4 in order to include expected unemployment or non employment in the model. The results in table 7 confirm our speculations about the effect of the instrument on the correlation between predicted unemployment or non employment on the wage differential. In particular, the specification in which we use expected non employment gives us results which are similar to what we find using actual non employment percentages. Although the point estimates are higher in the specification with expected non employment, they imply the same functional form relationship. It is more interesting to see what happens in the specification with expected unemployment, however. The first column in table 7 shows the specification which includes a seasonal dummy, as well as the linear and quadratic expected unemployment and the variance of the prediction. In this case all parameter estimates are insignificant. Dropping the seasonality dummy, like in the second column and estimating the model corresponding to equation (9), does not de- 20

22 crease the fit of the equation. However, the parameter estimates on the linear and quadratic expected unemployment terms become significant. The functional form relationship implied is upward sloping quadratic, like the one indicated in figure 9. Discussing the parameter estimates the implied parameter γ is about 0.68, which is in line with the replacement ratio in the unemployment insurance system. For the elasticity of substitution of labor supply we get an estimate of about The only coefficient which is undetermined in this specification is the coefficient of relative risk aversion. The estimate for this parameter is negative and also statistically insignificant. 7 Conclusions In this paper we bring to the attention an unexplored phenomenon in the Austrian labor market. Unlike similar continental European countries, Austria experiences huge seasonal fluctuations in employment, which make it comparable to Canada. However, whereas in Canada there are few regulations governing employment and firms are taxed in proportion of their turnover, in Austria the institutional setting is less flexible but there is a quite generous unemployment insurance system and no experience rating. This results in potentially large indirect subsidies to industries which operate under seasonal demand fluctuations. We examine wage differentials between workers in seasonal and non seasonal employment in the context of a theoretical model which relates these differentials to employer-determined working time restrictions. Given that our definition of seasonality is directly derived by observed regular features of employment patterns in the data, we can control for industry and individual specific effects in our estimation procedure. Moreover we can use exogenous variation in the starting month of the employment spell in order to derive a measure of anticipated working time restrictions. 21

23 Our results imply that the there is a relationship between the magnitude of the compensating differential and the amount of expected unemployment of seasonal workers as predicted by the theory. The implied estimated parameters indicate that a large part of the compensation is absorbed by the unemployment insurance system. In particular, we find that for the average level of unemployment experienced by a worker in a seasonal job the differential is still negative. This implies that the employer receives a net subsidy of about 2% of the wage. 22

24 References Abowd, J. M., Ashenfelter, O., Anticipated unemployment, temporary layoffs and compensating wage differntials. In: Rosen, S. (Ed.), Studies in Labor Markets. University of Chicago Press, Chicago, pp Brandel, F., Hofer, H., Pichelmann, K., Saisonale Muster von Beschäftigung und Arbeitslosigkeit in Österreich, Materialen zu Wirtschaft und Gelleschaft. Kammer für Arbeiter und Angestellte n. 54. Card, D., Supply and demand in the labor market. Working Paper, University of Chicago. Card, D., Levine, P. B., Unemployment insurance taxes and the cyclical and seasonal properties of unemployment. Journal of Public Economics 53, de Raaf, S., Kapsalis, C., Vincent, C., Seasonal employment and reliance on employment insurance: evidence from the SLID. SDRC Working Paper Series. de Raaf, S., Motte, A., Vincent, C., Applied research branch, strategic policy, human resources developent canada. SDRC Working Paper Series. Fischer, G., Pichelmann, K., Temporary layoff unemployment in austria: empirical evidence from adiministrative data. Applied Economics 23, Ham, J., Testing whether unemployment represents intertemporal labor supply behavior. Review of Economic Studies 53, Lalive, R., van Ours, J. C., Zweimüller, J., How changes in financial incentives affect the duration of unemployment. IZA Discussion Paper n

25 Moretti, E., Do wages compensate for risk of unemployment? parametric and semiparametric evidence from seasonal jobs. Journal of Risk and Uncertainty 20, Murphy, K., Topel, R., Unemployment risk and earnings: testing for equalizing wage differentials in tha labor market. In: Lang, Leonard, J. (Eds.), Unemployment and the structure of the labor market. Blackwell, New York. OECD, OECD Economic Surveys Austria Paris. OECD, OECD Economic Surveys Austria 2003 Paris. 24

26 Appendix The Austrian unemployment insurance system. The system of unemployment insurance in Austria is almost universal, that is to say compulsory for all except the self-employed. It is articulated in the administration of unemployment benefits (Arbeitslosengeld) and, after these expire, unemployment assistance (Notstandshilfe). In order to qualify for unemployment benefits a worker has to have been employed and insured under the scheme for at least 52 weeks in the past two years. This requirement is lowered to only 26 weeks within the past year for young people below 25 and for those repeatedly unemployed. The duration of the period of unemployment benefits can be up to 30 weeks, depending on the duration of the employment period preceding the spell of unemployment. The replacement ratio is about 55 per cent of net income, which is low by European standards, but becomes substantially higher once family allowances are taken into account. According to OECD figures for 1994, for example, the net replacement ratio for a single-earner household earning two-thirds of the average wage of blue collar workers was between 58 and 74 per cent, depending on the presence of children (OECD, 1997). After unemployment benefits are exhausted, the worker can apply to receive unemployment assistance. The duration of this programme is potentially indefinite and under this scheme the worker receives up to 92 per cent of the amount of the previous unemployment benefits. The main difference with the previous scheme consists in the fact that unemployment assistance is means tested and therefore depends on the presence and the economic condition of the partner. To give an example of the incidence of means testing Lalive et al. (2004) estimate that in 1990 the unemployment assistance payment was about 70 per cent of the median unemployment benefit check. 25

27 Regulations for termination of an employment contract. Examining employment laws we find that no regulations apply to layoffs in jobs with a duration less than 6 months. For longer jobs a period of advanced notice is required during which the employee gets time off to look for a new job. The period of notice for blue collar workers is regulated separately by industry in collective bargained contracts. These are the same contracts which also include the wage agreements. Typically it is no more than 2 weeks. For example, the hotel industry requires 2 weeks of notice during which the employee is allowed to take off 2 half days for job search. The employer also has to give a reasons for the contract termination. The argument must be either related to employee behavior or to the economic interests of the firm, otherwise the layoff can be appealed in court. Severance payment regulations apply only for job durations above 3 years. 26

28 Figure 1: Total monthly employment over active population in Austria employment as a % of the active pop Jan Jan Jan Jan Jan Jan 00 men women Source: Statistics Austria. 27

29 Figure 2: Seasonal variation in total monthly employment by country monthly employment, base year = USA Germany Canada Austria Notes: Total monthly employment normalized at Series for Austria and Germany exclude self-employed. Source: OECD Main Economic Indicators, Statistics Austria, Statistics Germany. Figure 3: Amplitude of seasonal variation in total monthly employment by country 6 5 deviation from trend in % Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec USA Germany Canada Austria Notes: Average deviation of total monthly employment from 1-year moving average. Series for Austria and Germany exclude self-employed. Source: OECD Main Economic Indicators, Statistics Austria, Statistics Germany. 28

30 Figure 4: Total monthly employment in Austria by industry AGRICULTURE MANUFACTURING CONSTRUCTION CARSALE WHOLESALE RETAIL HOTEL TRANSPORT SERVICES Source: Statistics Austria. 29