Michael Fertig
email: fertig@rwi-essen.de.

Thomas K Bauer

The Effectiveness of Qualification Measures for Employed Workers -An Evaluation Study for Saxony

Keywords: JEL-Classification: H43, J68 Employment Policy, European Social Fund, Firm-Level Data

This paper investigates whether and to what extent employment policy measures (co-) financed by the European Social Fund in Germany meet their objective. Specifically, it is analyzed whether qualification programs for employed workers in the German state of Saxony were effective in terms of employment protection. To this end, a control function approach is implemented which utilizes a unique firm-level dataset. This model explicitly accounts for unobserved heterogeneity between participating and nonparticipating companies by modeling the participation decision process. Our results suggest a positive effect of program participation. However, this positive treatment effect varies considerably across different sub-groups of the treatment as well as the comparison group.

Introduction

At the Luxembourg Jobs summit in November 1997 the European Commission initiated a set of coordinated policies which have become known as the Luxembourg Process. The Amsterdam Treaty introduced a new Employment Title and thus raised employment issues to the same status as other key goals in the formulation of EU economic policy. This treaty represents a major step in the development of a comprehensive European approach to labor market policy, the European Employment Strategy. Active labor market policies (ALMP) -including job search assistance, subsidized training and direct job provision in the public sector -are an important element of this European Employment Strategy. While such policies have been in use for many years, unemployment still remains persistently high throughout most European countries, raising the question as to the actual effect of employment promotion.

An interesting example in this context is Germany. Every year Germany spends several billions of Euro on active measures of employment promotion with the explicit aim to reduce unemployment (see FERTIG AND [START_REF] Fertig | Discretionary Measures of Active Labor Market Policy: The German Employment Promotion Reform in Perspective[END_REF]). However, unemployment has been a persistent problem throughout the last two decades and until today only insufficient systematic attempts to evaluate the effectiveness and efficiency of these measures have been undertaken. Recently, inspired by a growing body of international evaluation literature (see e.g. the pioneering work of [START_REF] Rubin | Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies[END_REF], ( 1986)), a handful of studies evaluate the labor market impact of some measures of active labor market policy (mainly training measures) implemented in the context of and financed by German labor laws (Sozialgesetzbuch III)

1 . The evidence of these studies concerning the efficacy of active labor market policy interventions is rather mixed. Most of them, as well as the In section 4 the dataset as well as the empirical results are presented and section 5 offers some conclusions regarding the policy implications of the analysis conducted here.

ESF-Funded Qualification Measures for Employed Workers

The European Social Fund (ESF) is the second largest of the four Structural Funds in the European Union3 . The Structural Funds are the main financial instruments of the EU aiming at the reduction of the gap in living standards between regions and to the general promotion of economic and social cohesion within the EU. They are supposed to achieve at least one of several objectives. With the reform of 1999 the number of objectives was reduced from seven to three. For the case at hand, the relevant objective is Objective 1.

This objective mainly aims at promoting regions where GDP per capita is below 75% of the EU average. It is financed by all four structural funds and accounts for around 70% of total Structural Funds spending.

Regarding ESF activities, there are five main areas or policy fields: (i) developing and promoting active labor market policies, (ii) promoting equal opportunities for all in accessing the labor market, (iii) promoting and improving training, education and counseling as part of a life-long learning policy, (iv) promoting a skilled, trained and adaptable workforce, and (v) improving women's access to and participation in the labor market. Irrespective of the activity area, a principal requirement for ESF-funding is the availability of matching funds at the national, regional or community level. other initiatives according to EU guidelines. For instance, in Germany these resources were used to support labor market policy measures which are outside the realm of national labor laws, like qualification measures for employed workers.

In the period from 1994 to 1999, Germany 4 received more than 7,400 Mio. ECU from the ESF for all objectives. Some 57% of these funds were allocated to Objective 1. From this pool, the Objective 1 region of Saxony received around 906 Mio. € which were mainly spent for vocational and further training measures for employed as well as unemployed workers. Together with own financial contributions, total spending in Saxony amounted to more than 1,400 Mio. € during this time period.

In this paper, the effectiveness of qualification measures for employed workers is under investigation. This ESF-(co-)funded program explicitly aims at increasing the competitiveness of companies, secure existing jobs and create new employment opportunities. Funding is provided to institutions offering qualification measures (Maßnahmeträger). This follows the idea that these institutions design their qualification measures in close co-operation with companies interested in training their employees. have to be borne by participating companies.

These measures are originally targeted at small and medium sized enterprises (less than 250 employees). However, there were also larger firms among the participating companies. Furthermore, from the survey among companies -which was conducted for the purpose of evaluation -it became transparent that some of them were not actively involved in the participation of their employees in these qualification measures. Those companies which participated actively did so in several forms. For instance, qualification measures for their employees comprise external and internal courses/seminars, qualification at the workplace (training-on-the-job), participation in workshops or conferences as well as self-controlled learning utilizing new media.

The ESF-funded measures of active labor market policy were implemented in an environment which was characterized by only moderate growth rates of real GDP (see From Table 2 it becomes transparent that unemployment rates were substantial and persistently high during the second half of the 1990s. On average, the number of registered unemployed relative to the civilian labor force amounted to more than 17%.

Furthermore, unemployment was almost constantly rising during these years and the share of registered unemployed with an unemployment spell of more than 12 months (long-term unemployed) was almost one third. In addition to the substantial number of registered unemployed, a considerable share of workers were participating in measures of active labor market policy, like public Since these workers are not registered as unemployed, the genuine number of workers out of employment was substantially higher. On average, more than 190,000 individuals participated in such measures/schemes during 1995-1999.

The program which is under investigation in this paper is somewhat different from these measures. It is targeted at small-and medium-sized companies with the explicit aim to train their existing workforce. In this endeavor, the primary objective was to secure their jobs. Against the background of the rather disillusioning macroeconomic and labor market situation in Saxony during this time, the question, whether the program succeeded as an employment protection measure will be tackled in the next sections.

The Conceptual Framework for Evaluation

To conceptualize ideas, it is helpful to embed our study into the received literature on the [START_REF] Rosenbaum | The central Rile of the Propensity Score in Observational Studies for Causal Effects[END_REF]). This means that conditional on observable characteristics, treatment and comparison groups do not differ in any dimension (relevant for the outcome) other than the treatment itself. In other words, there is no unobserved heterogeneity between both groups. Typically, evaluation studies on the level of e.g. individual workers justify this assumption by controlling for the history of the outcome measure prior to the intervention (pre-treatment outcomes). If both groups differ in unobserved characteristics then this should be reflected in the values of the outcome measure prior to treatment as well. These pre-treatment outcomes can then serve as a proxy for unobserved characteristics provided that these characteristics remain persistent over time and thus repeated measurement of the outcome variable reveals information about them.

In the case at hand, the data set comprises information on the companies participating in qualification measures for their employees since the program was explicitly targeted at small and medium-sized enterprises. Therefore, unobserved heterogeneity might well be a severe problem, since at the level of firms potentially important characteristics like the innovative potential of the management or the motivation of the workforce remain unobserved. If these unobserved characteristics exhibit an impact on the probability to participate in qualification measures and also impinge upon the outcome variable, the estimated treatment effect is biased. An example might be companies in which the management's unobservable innovative capacity is larger. These firms might exhibit a more favorable employment performance and are simultaneously more active with respect to qualifying their workforce. Neglecting this relationship implies that the positive employment effect of the more innovative management of companies in the treatment group will be assigned erroneously to the treatment itself. In other words, the estimated treatment effect is biased upwards. It is straightforward to construct a counter-example in which the treatment effect would be biased downwards, e.g. by neglecting unobserved differences between companies in the treatment and the comparison group with respect to the dependency on a single customer. Hence, any credible evaluation attempt must address the problem of unobserved heterogeneity explicitly.

Unfortunately, our dataset (see also section 4) is limited with respect to the number of observations and does not allow to control for a sufficiently long history of pre-treatment outcomes. Furthermore, companies are in all likelihood much more volatile in their unobserved characteristics than individual workers. Therefore, controlling for pretreatment outcomes alone seems to be no promising approach.

An alternative approach to circumvent the problem of unobserved heterogeneity is to model the participation decision right around the time it is made, i.e. in the cross-section.

Therefore, we implement a control function approach (see [START_REF] Heckman | Sample Selection Bias As A Specification Error[END_REF]) which is similar to an instrumental variable model (see VELLA AND VERBEEK (1999)).

Additionally, this framework enables us to control for the level of the outcome measure at one point in time prior to treatment. The intuitive idea of this control function approach is to model the participation process as an economic decision problem by assuming that companies base their decision process on observable as well as unobservable (to the analyst) characteristics. Those firms participating in the measure although their observable characteristics would suggest the contrary must consequently display unobserved characteristics increasing their propensity to participate. This insight can be exploited to assess the sign and magnitude of a summary measure of unobserved heterogeneity, and to use it as an explanatory factor purging the estimate of interest from bias.

The central identification assumption necessary to proceed in such a way is to assume that unobserved differences between the treatment and the comparison group are fully reflected by an estimated correction term from an auxiliary estimation step (see also below). Furthermore, since the complete approach rests on a linear regression model, it is necessary to assume that there exists a linear relationship between the outcome measure and the explanatory variables and that the latter are strictly exogenous.

Finally, it is important to note that in control function approaches identification is either achieved by parametric assumptions or by employing an exclusion restriction into the two-step model. Such an exclusion restriction is comparable to an instrument in an instrumental variable approach. However, in such a framework the effect of an intervention is identified only if the treatment effect is constant for observation units with the same value of the covariates (see FLORENS ET AL. (2002) and IMBENS AND ANGRIST (1994)).

In the case of heterogeneous treatment effects, i.e. the impact of the intervention varies with unobserved characteristics over the population, the mean effect of the intervention is identified for the sub-population of the so-called compliers only. That is, In the case hand, one might think of the example of unobservable differences in the innovative capacity of the companies' management discussed above. If e.g. an exogenous change in the exclusion restriction changes the participation behavior of highly innovative companies only, the estimated treatment effect of the model represents the impact of participation in qualification programs for these highly innovative firms only. In other words, in this case we are not able to identify the average effect of treatment on a company randomly selected from the population.

However, since the homogeneity or heterogeneity of treatment effects across the population is unobservable, it is impossible to resolve this problem ultimately. For individual workers it seems plausible to argue that unobserved ability or motivation might be the source of heterogeneous treatment effects. By contrast, at the level of companies it is rather difficult to find convincing arguments for the source and direction of such heterogeneous treatment effects since for firms many potentially important characteristics are unobservable and might unfold an impact into different directions. Again, the dependency from a single customer which might be especially pronounced for the rather small enterprises in our sample might serve as a counter-example to the case of differences in innovative capacity of companies. Hence, in the following we will maintain the hypothesis of a homogenous treatment effect and come back to this issue in the discussion of the estimation results. In more technical terms, we estimate a dummy treatment effect model. This model specifies a fixed treatment effect which captures the impact of the treatment on any observation unit randomly selected from the population. The ultimate aim of such models is to estimate the (K×1)-vector β and the scalar δ of the following multivariate linear regression model for companies indexed by j = 1, .., N,

(1)

j j j j z x y ε δ β + + ′ = .
In this equation y j denotes the outcome measure (in our case employment at the firmlevel) and x j is a (K×1)-vector of observable variables characterizing the company. The strategy pursued here for circumventing this problem is to address the participation decision as an auxiliary first step. The decision to participate is modeled by equation ( 2) as

(2)

j j j u w z + ′ = γ * .
The latent variable * j z denotes the propensity to participate in ESF-funded qualification measures. This propensity is not directly observable. However, it is assumed to depend on Observable to the analyst is only the actual decision of company j whether to participate in ESF-funded qualification measures denoted by z j , where the relationship between * j z and z j is

(3) ⎪ ⎩ ⎪ ⎨ ⎧ ⇔ ⇔ > = . 0 0 1 * ion participat no otherwise ion participat z if z j j
Furthermore, we assume that the error terms ε and u are bivariate normally distributed with expected value zero and covariance matrix Σ, i.e.

(4)

⎥ ⎦ ⎤ ⎢ ⎣ ⎡ = Σ 1 ρ ρ σ
For ρ=0 the covariance of both error terms is zero and both equations are uncorrelated.

In that case, equation (1) can be estimated consistently by OLS. In general, this requirement is not fulfilled. The parameter λ (the so-called inverted Mills-Ratio) with ( 5)

ρσ λ =
captures the effect of self-selection. Following [START_REF] Maddala | Limited-Dependent and Qualitative Variables in Econometrics[END_REF] the parameters β and δ can be estimated by the following two-step estimation procedure. The first step comprises the estimation of a probit model for equation (2), i.e. ( 6)

) ( ) | 1 Pr( γ j j j w w z Φ = =
where Φ denotes the cdf. of the standard normal distribution. This yields an estimate of the so-called hazard h j for each observation unit, i.e. an estimate of the expected value that company j exceeds the threshold for participation in the program.

F o r P e e r R e v i e w 15 (7) ⎪ ⎩ ⎪ ⎨ ⎧ = Φ - - = Φ = 0 )] ( 1 /[ ) ( 1 ) ( / ) ( ˆj j j j j j j z for w w z for w w h γ γ φ γ γ φ
φ denotes the density of a standard normally distributed random variable and γˆ is the estimated value of γ from equation ( 6). With this estimated hazard in hand, one can now extend the model from equation ( 1) and estimate it by OLS. That is, in the second step we estimate the following linear regression model ( 8)

ν λ δ β + + + = j j j j h z x y ˆ.
The parameter δ captures the effect of participation on the outcome measure y and the presence of j h ˆeliminates any correlation between (x j z j ) and ν. A statistically significant estimate for λ suggests that the treatment group is self-selected. The following section contains our empirical application of this approach utilizing a dataset at the firm level for Saxony.

Data and Results

The population of companies for the treatment group comprises firms in Saxony For the estimation of our model from section 3 we utilize the following firm-specific employment variables as outcome measures:

• Outcome measure (i): Number of employees subject to social security payments in 2001.

• Outcome measure (ii): Total number of employees (including employees not subject to social security regulations) in 2001.

For the purpose of evaluating the effectiveness of ESF-funded qualification measures for employed workers, we perform several comparisons to provide answers to the following questions:

a) Did participating firms perform better in terms of employment compared to nonparticipants? Specifically, in a next step we break down the treatment group and compare companies utilizing ESF-funded qualification alone and enterprises combining funded and nonfunded training for their employees separately with all firms in the comparison group providing answers to questions a1) and a2). After that we break down the comparison group into the two sub-groups of companies without qualification and with only nonfunded qualification, respectively. These two sub-groups are then compared in their employment development with the complete treatment group and separately with both sub-groups of the treatment group. This provides answers to questions b), b1), b2), c), c1) and c2). Finally, we compare the performance of both sub-groups of the treatment group

directly as an answer to question d).

In sum, we consequently have the following treatment indicator variables: • Service Sector: Dummy variable taking on the value of 1 if company j belongs to the service sector; 0 otherwise.

• Craftsmen: Dummy variable taking on the value of 1 if company j belongs to the crafts industry; 0 otherwise.

• Self-employed: Dummy variable taking on the value of 1 if company j belongs to the group of self-employed workers; 0 otherwise.

• Independent company: Dummy variable taking on the value of 1 if company j is an independent company, i.e. is not the subsidiary of a holding; 0 otherwise.

• Increased investment in 1998/1999: Dummy variable taking on the value of 1 if company j reported an increase of investment for 1998/1999; 0 otherwise.

• Decreased investment in 1998/1999: Dummy variable taking on the value of 1 if company j reported a decrease of investment for 1998/1999; 0 otherwise.

Finally, although the parameters of the model from section 3 are in principle identified due to the non-linearity in the auxiliary estimation step, many applications demonstrate that robust estimation results require an exclusion restriction (see also VELLA (1998) for a survey). That is, we are searching for a variable that explains the participation decision process but does not impinge upon the outcome measure of equation ( 1). In the case at hand, we argue that this variable is This variable is assumed to have an impact on the decision of companies to participate in qualification measures for their employees but not on current outcomes. The idea behind this is that companies planning to innovate their production and/or organization processes in the near future do not adjust their employment in advance but are more likely to invest into the qualification level of their employees to cope with future challenges.

Clearly, since this is an identification assumption it is not testable and has to hold a priori.

To be a valid exclusion restriction two conditions have to be met. Firstly, the excluded variable must have an impact on the participation decision and secondly, it must not be correlated with the variable(s) responsible for unobserved heterogeneity. In other words, the second condition requires that the excluded variable does not impinge upon the outcome measures in any other way than by the decision to participate in the intervention.

To support the hypothesis that companies which plan innovations in the future are more likely to invest into the human capital of their employees, Table 3 reports the distribution of answers to the question whether a company plans to engage in future qualification activities for both realizations of the exclusion restrictions. These answers suggest that innovative companies are more likely to increase participation in trainings programs for their employees than their counterparts. More than 45% of those companies which plan future innovations also plan to increase qualification activities, compared to only around 28% of non-innovative enterprises. Hence, we can be confident that the first condition is met. However, it is much more difficult to provide evidence that the second criterion is also fulfilled since this is an untestable identification assumption. Fortunately, from a set of OLS regressions of both outcome measures on all explanatory variables plus the exclusion restriction, it becomes transparent that the variable "innovations planned in future" has no significant direct impact on both outcomes 6 . This does, however, not necessarily mean that the exclusion restriction and the unobserved factor(s) are uncorrelated. In the case at hand, the problem is that companies are rather heterogeneous entities for which a variety of potentially important variables are unobservable. Clearly, if companies with a more favorable (in terms of employment development) set of unobserved characteristics are also more likely to implement product, process or organizational innovations, the second condition will be violated and the exclusion restriction is not valid.

However, systematic differences in unobserved firm characteristics imply that observable characteristics in the past should differ between both groups as well. For instance, if a specific group of companies displays a management with a higher innovative capacity and/or less dependency on a single customer, their success in terms of turnover, profits and/or investment should be higher. Fortunately, our data allows us to investigate this in more detail. From this table it becomes transparent that there are only minor differences in reported developments of these three variables. The largest difference is slightly higher than six percentage points. However, variation in answers within in each group is also rather high.

A series of t-tests reveals that there are no statistically significant differences for all variables between both groups of companies at any sensible significance level. Thus, one could be confident that the exclusion restriction "innovations planned in future" is not systematically related with the source of unobserved heterogeneity. The estimation results demonstrate that there are substantial differences in both outcome measures between the different (sub-) groups of companies. Furthermore, it becomes transparent that controlling for observable characteristics of the companies changes the estimated differentials substantially in qualitative as well as quantitative terms. Our results suggest that participating in ESF-funded qualification measures exhibits a strong positive effect on both employment outcomes (see first row of Table 5). Furthermore, this positive treatment is highly significant for both sub-groups of the treatment group (rows

(2) and ( 3)). However, it is larger for companies utilizing ESF-funded training only than for those firms which combined funded and non-funded qualification activities.

Once this comparison is broken down into the two sub-groups forming the comparison group, estimation results reveal that these positive effects stem from different sources.

The fourth row of Table 5 indicates a statistically significant positive effect of funded qualification compared to companies with no qualification activities. This effect is, however, considerably smaller in quantitative terms. Moreover, this small positive effect is driven by enterprises combining funded with non-funded training opportunities for their workforce (see row ( 6)), firms participating in ESF-funded qualification measures only do not perform significantly different from companies without qualification (see row

(5)).

Furthermore, we observe no significant difference in both outcome measures between companies in the treatment group and enterprises utilizing non-funded training opportunities only (see row ( 7)). However, this zero effect is the weighted average of a statistically significant positive effect for companies utilizing only ESF-funded training and a zero effect for firms combining it with non-funded activities (see row ( 9)). Finally, the last row of Table 5 suggests that companies combining both forms of qualification opportunities slightly outperform firms which utilize ESF-funded qualification only for outcome measure (i). There is, however, no significant difference between both subgroups of the treatment group for outcome measure (ii).

Estimation results for the first step decision equation suggest that newly established businesses and self-employed individuals are significantly more likely to participate in ESF-sponsored training for their employees, whereas the propensity to engage in qualification is significantly lower in the manufacturing sector and among craftsmen. With respect to the exclusion restriction the first step estimation results suggest that companies which plan innovations in the future are in general more likely to participate in qualification measures for their employees.

However, from Tables A.2-A.11 it becomes transparent that the exclusion restriction is insignificant for five out of ten comparisons. In these cases, identification rests on parametric assumptions and, therefore, on rather shaky grounds. However, the exclusion restriction is significant for the following important comparisons:

• ESF vs. Not-ESF (weakly significant) Additionally, for the identification of the overall program effect as well as for the comparison between companies relying on funded qualification only and firms which abstained form training measures we do not have to rely on parametric assumptions alone. Thus, the exclusion restriction turns out to be significant in the -from a policy perspective -most relevant cases.

Regarding the other covariates, the full set of estimation results reported in Tables A.2 Finally, as already discussed in section 3, it is possible that the quantitatively rather large estimated effect of qualification measures in some comparisons which is identified by invoking the exclusion restriction "Innovations planned in future" might be the effect for a sub-population of companies only. It might reflect the benefits of investing into the human capital of their employees for those firms that are more optimistic with respect to their future prospects or more innovative at all. However, since such an interpretation requires the assumption of heterogeneous treatment effects (with respect to the source of unobserved heterogeneity), it is difficult to assess the extent to which our control function approach identifies the effect for the sub-group of the so-called compliers only. This

Conclusions

This paper analyzed whether qualification programs for employed workers co-funded by the European Social Fund in Saxony and targeted to small and medium sized enterprises were effective. To this end, we performed several comparisons between participating and non-participating companies in a control function approach utilizing a unique firm-level dataset. In sum, the estimation results indicate a positive effect of participation in ESFfunded qualification measures on both employment outcomes and, therefore, suggest that in general the program was effective.

However, against the background of the explicit aim of the program to support firms that otherwise would have abstained from training their employees, our results suggest a more differentiated picture. Companies utilizing only ESF-funded qualification measures do not outperform firms abstaining from training their workforce, whereas enterprises combining ESF-funded with non-funded activities display higher employment levels than those without qualification.

Furthermore, the direct comparison of companies with ESF-funded activities alone and firms with combined training measures reveals that the latter perform better, at least with respect to social security insured employment. An explanation for this finding might be that firms combining both forms of training have to bear higher costs and are therefore more carefully in choosing the specific kind and content of qualification for their employees.

F

  Section 3 explains the conceptual framework of the evaluation study and the identification strategy utilized.

  ESF complements activities of the Member States in these policy fields by supporting the so-called National Action Plans for Employment, set up by the Member States every year as a part of the European Employment Strategy. The principle of joint financing (or co-funding), i.e. the requirement that the financial resources provided by the ESF have to be complemented by resources from the Member States, allows the Member States to supplement their own labor market policy measures by

  long-term training measures, and early retirement schemes.

  whose value of the treatment indicator changes in reaction to an exogenous change in the instrument 5 .

  z j is a binary indicator variable taking the value of 1 if company j decided to participate in ESFfunded qualification measures and zero otherwise. The unknown parameter δ captures the (causal) effect of program participation and the vector β the impact of the confounding factors summarized in x. The random variable ε denotes the error term of the model. Typically, this model cannot be estimated consistently by OLS, since this error term is correlated with the unobservable factors influencing the participation decision, and thus with the program indicator z j .

  observable firm characteristics w. The vector γ captures the effect of these characteristics and u denotes the error term of equation (2).

  participants utilizing ESF-funded measures only perform better than nonparticipants? a2) Did participants combining ESF-funded and non-funded measures perform better than non-participants?These questions concern the effectiveness of ESF-funded qualification measures (alone or together with non-funded activities) if participating companies are compared to all members of the comparison group. Since in the case at hand the comparison group comprises two different sub-groups of companies -those abstaining from qualification and those utilizing only non-funded opportunities -one might then be interested if the answers to question a) differ significantly once the comparison group is broken down into its sub-groups. From a policy point of view, the comparison with companies without any qualification activity is especially interesting since funding is targeted to small and medium sized enterprises with the explicit aim to support them in qualifying their workforce. This program focus is motivated by the idea that without funding these firms would abstain from training their employees. Therefore, the next set of questions comprises: b) Did participants in ESF-funded qualification perform better than companies abstaining from any qualification measure for their workers? b1) Did participants utilizing ESF-funded measures only perform better than companies abstaining from any qualification measure for their workers? participants combining ESF-funded and non-funded measures perform better than companies abstaining from any qualification measure for their workers? c) Did participants in ESF-funded qualification perform better than companies engaging solely in non-funded qualification measures? c1) Did participants utilizing ESF-funded measures only perform better than companies engaging solely in non-funded qualification measures? c2) Did participants combining ESF-funded and non-funded measures perform better than companies engaging solely in non-funded qualification measures? Finally, for the purpose of implementing future funding programs it is interesting to know if there are differences in the performance of companies within the treatment group. That is, whether companies utilizing ESF-funded qualification measures alone perform better or worse than enterprises combining them with non-funded (commercial) training opportunities. Therefore, the final question addressed in this paper is: d) Are there differences in the performance between the two sub-groups of participating companies? Consequently, the set of comparisons conducted in this paper start with the full sample and compare the value of the outcome measure between companies participating in ESFfunded qualification measures (treatment group) and those which did not(comparison group). This provides an answer to question a). Since both treatment and comparison different sub-groups of companies, we then break down this first comparison further by restricting the sample.

  -A.11 indicate that, unsurprisingly, the level of employment in 1999 has a positive impact on the value of outcome measures in 2001. Furthermore, in almost all regressions increased investment activities in 1998/1999 display a positive effect on employment in 2001 and companies in the manufacturing sector experience higher outcomes. Moreover, in some comparisons newly established businesses perform worse than more mature firms.

  the fact that at the level of companies many potentially important determinants of the outcome measures are unobservable.

  Some 80% of the costs of such measures are refunded from public resources (of which 65% are from ESF-funds and 35% are co-funding from the state of Saxony) and 20%

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4 See http://europa.eu.int/comm/employment_social/esf/en/member/ms/germany/gersf.htm.

Table 1 )

 1

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. On average, in the second half of the 1990s the growth rate of real GDP in Saxony was around 2% per annum with stagnation in 1997 and 1998. Furthermore, Table

1

demonstrates that Saxony experienced a small loss of people during this period and relatively constant total employment.

Table 1 : Key Characteristics of Saxony During Funding Period

 1

		Total	Total	GDP in prices	Growth rate
		population	employment	of 1995	of real GDP
		(in 1,000)	(in 1,000)	(in Bn. €)	(in %)
	1995	4,575	1,997	67.0	5.7
	1996	4,556	1,998	68.9	2.9
	1997	4,536	1,971	68.9	-0.1
	1998	4,506	1,970	68.8	-0.1
	1999	4,475	1,984	70.2	2.0
	All years (average):	4,530	1,984	68.8	2.1
	Data source: Statistisches Landesamt Sachsen.

Table 2 : Labor Market Characteristics in Saxony During the Funding Period Unemployment Long-term un- Number of Number of rate employed in % workers in workers in early

 2

		(in %)	of all unemployed	ALMP measures	retirement scheme
	1995	14.4	30.6	156,095	117,607
	1996	15.9	28.0	137,910	68,450
	1997	18.4	27.6	112,985	44,652
	1998	18.8	33.5	125,437	32,007
	1999	18.6	33.7	130,763	29,568
	All years (average):	17.2	30.7	132,638	58,457
	Data source: Statistisches Landesamt Sachsen and Sächsisches Ministerium für Wirtschaft und Arbeit.

Table 3 : Future Qualification Activities of Companies Innovations planned in future? Planned future qualification activities

 3

	No	Yes

Table 4 : Turnover, Profits and Investment of Companies in 1998/1999

 4

			Innovations planned in future?
			No		Yes
		Mean	Standarddeviation	Mean	Standarddeviation
	Turnover 1998/1999:
	Strong increase	0.085	0.278	0.122	0.328
	Modest increase	0.344	0.475	0.408	0.492
	No change	0.366	0.482	0.308	0.462
	Modest decrease	0.157	0.364	0.117	0.322
	Strong decrease	0.048	0.214	0.044	0.205
	Profits 1998/1999:
	Very good	0.064	0.245	0.066	0.248
	Good	0.279	0.449	0.315	0.465
	Satisfactory	0.369	0.483	0.288	0.453
	Enough to stay in market	0.225	0.418	0.249	0.433
	Not sustainable in the long-run	0.046	0.210	0.073	0.261
	Acute risk of bankruptcy	0.017	0.128	0.010	0.098
	Investment 1998/1999:
	Strong increase	0.107	0.310	0.146	0.353
	Modest increase	0.260	0.439	0.294	0.456
	No change	0.537	0.499	0.481	0.500
	Modest decrease	0.062	0.241	0.046	0.210
	Strong decrease	0.034	0.181	0.034	0.181

Table A

 A over time. However, the variation across companies was substantial. Table 5 provides a summary of the estimation results. The full set of estimation results are reported in Tables A.2-A.11 in the appendix. The left panel of Table 5 contains the raw differentials (unconditional group means) of the several comparisons conducted in this paper. The conditional differentials are provided in the right panel. The results reported in this part of Table 5 refer to the two-step estimation procedure if the self-selection coefficient is statistically significant and to the OLS results otherwise.

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.1 in the appendix provides some descriptive statistics for our sample. From these figures it becomes transparent that both outcome measures did not change substantially

Table 5 : Summary of Results

 5

			RAW DIFFERENTIALS		C ONDITIONAL DIFFERENTIALS
		Outcome	Outcome	Outcome	Outcome
		Measure (i)	Measure (ii)	Measure (i)	Measure (ii)
		Coeff.	t-value	Coeff.	t-value	Coeff.	t-value	Coeff.	t-value
				a) Comparison of participants with non-participants:
	(1) ESF vs. Not-ESF	6.97	1.56	6.47	1.41	35.67 (*)	2.35	35.30 (*)	2.40
				a1) Participants in funded measures only
	(2) Only ESF-funded	-11.12	-3.90	-12.99	-4.69	39.70	(*)	4.42	48.99 (*)	4.68
	qualification vs. Not-ESF
				a2) Participants in funded and non-funded measures
	(3) Combination ESF/comm..	23.88	4.17	26.49	4.40	25.24 (*)	3.68	28.45 (*)	3.94
	vs. Not-ESF
		b

) Comparison of participants with non-participants abstaining from qualification:

	(4) ESF vs. no qualification	15.65	2.58	16.36	2.64	1.84	2.72	1.96	2.72
				b1) Participants in funded measures only
	(5) Only ESF-funded qualify.	-2.44	-1.43	-3.10	-1.87	1.43	2.17	0.79	1.26
	vs. no qualification
				b2) Participants in funded and non-funded measures
	(6) Combination ESF/comm..	31.38	4.01	35.05	4.30	2.37	2.68	2.94	3.06
	vs. no qualification
		c)

Comparison of participants with non-participants engaging in non-funded qualification:

	(7) ESF vs. commercial qual.	-0.56	-0.09	-2.46	-0.39	0.47		0.70	0.44	0.57
				c1) Participants in funded measures only
	(8) Only ESF-funded qual.	-18.64	-5.20	-21.92	-6.25	33.01	(*)	4.01	44.30 (*)	4.26
	vs. commercial qual.
				c2) Participants in funded and non-funded measures
	(9) Combination ESF/comm..	16.91	2.15	18.19	2.18	1.25		1.44	1.91	1.91
	vs. only commercial qual.
	(10) Only ESF-funded qual.	-33.81	-3.13	-38.12	-3.61	-11.33	(*)	-1.98	-1.20	-1.09
	vs. combination ESF/comm.

d) Comparison of both participant sub-groups:

Notes: (*) Results of two-step estimation procedure since coefficient of self-selection is significant; OLS results otherwise.

  Hence, especially the positive effect of participation for those companies which combined ESF-funded and non-funded measures is identified by the exclusion restriction.

	F o
	r
	P e
	e
	r
	R e
	v i
	e w
	• Combination ESF/commercial vs. Not-ESF
	• ESF vs. No qualification
	• Only ESF vs. no qualification
	• Combination ESF/commercial vs. no qualification
	28

Table A .2: Complete Results -ESF-funded Qualification vs. Not-ESF

 A With respect to the implementation of future funding programs, our results suggest that training workers wile they are employed is a promising way to secure existing jobs. This implies that the program should be retained although -due to lacking data -we are not able to evaluate its cost-efficiency. However, the specific funding system of the ESF seems to be in need of reform. Specifically, it seems advisable for the design of future funding initiatives to provide incentives for participating companies to interact more intensively with the providers of qualification measures to ensure a more careful and problem-directed selection of the form and contents of training activities for their employees.

			Outcome Measure (i)		Outcome Measure (ii)
			OLS	Two-step	OLS	Two-step
					procedure			procedure
			Coeff. t-value Coeff. t-value Coeff. t-value Coeff. t-value
	OUTCOME EQUATION:
					Treatment Indicators:
	ESF-funded qualification		1.05	1.87	35.67	2.35	1.10	1.79	35.30	2.40
					Control Variables:
	Newly established business	-0.09	-0.12	-6.74	-2.08	-0.35	-0.42	-7.15	-2.21
	Manufacturing sector		4.11	5.13	11.95	3.21	4.29	4.84	12.31	3.29
	Service sector Craftsmen Self-employed Independent company	o r F o r F o r F o r F o r 0.31 -1.11 0.09 -0.55 F o r	0.49 -1.77 0.10 -0.52	4.50 1.21 -6.95 0.71	2.08 0.80 -2.00 0.35	0.86 -1.27 -0.23 -2.57	1.25 -1.84 -0.25 -2.19	5.12 0.74 -9.17 -1.44	2.37 0.52 -2.21 -0.72
	Increased investment in 1998/1999	3.14	5.62	1.42	1.13	3.10	5.07	1.68	1.43
	P e e P e e P e e P e e P e e Decreased investment in 1998/1999 -3.45 -3.70 Employment 1999 0.97 325.70 Constant -0.91 -0.76 P e e	-3.39 0.96 -16.44	-2.01 145.07 -2.31	-3.67 0.97 1.16	-3.58 321.26 0.87	-3.30 0.96 -14.41	-1.95 155.36 -2.05
				r r r r r r
				R e R e R e R e R e R e
					v i v i v i v i v i v i
						e w e w e w e w e w e w
				30 34

Table A .3: Complete Results -Only ESF-funded Qualification vs. Not-ESF

 A

			Outcome Measure (i)			Outcome Measure (ii)
			OLS			Two-step		OLS	Two-step
						procedure			procedure
		Coeff. t-value Coeff. t-value Coeff. t-value Coeff. t-value
	OUTCOME EQUATION:
						Treatment Indicators:
	Only ESF-funded qualification	1.38	2.09		39.70	4.42	0.63	0.90	48.99	4.68
						Control Variables:
	Newly established business	0.11	0.14		-6.26	-2.97	0.09	0.11	-8.62	-3.32
	Manufacturing sector	3.42	4.40		9.45	4.67	3.29	3.86	11.16	4.53
	Service sector	1.00	1.59		4.33	3.06	1.37	2.02	5.26	3.19
	Craftsmen	-0.87	-1.44		0.24	0.20	-1.02	-1.55	0.33	0.23
	Self-employed	0.77	0.82		-4.77	-2.19	0.60	0.61	-10.09	-3.29
	Independent company Increased investment in 1998/1999 Decreased investment in 1998/1999 Employment 1999 F o -0.53 2.34 -2.91 1.01 Constant -1.99 r	-0.48 4.29 -3.26 146.99 -1.57	-1.13 2.38 -2.26 1.04 -11.47	-0.54 2.32 -1.34 70.25 -3.54	-0.84 2.09 -2.95 1.01 -1.34	-0.69 3.52 -3.05 142.82 -0.97	-2.70 2.71 -1.90 1.05 -12.91	-1.05 2.17 -0.93 62.17 -3.39
	DECISION EQUATION: Innovations planned in future Newly established business Manufacturing sector Service sector Craftsmen Self-employed	---P e -------e --r	0.12 0.51 -0.67 -0.32 -0.11 0.43	1.20 4.39 -4.80 -3.21 -1.07 3.09		------	------	0.11 0.54 -0.67 -0.28 -0.10 0.63	1.17 4.91 -5.02 -3.04 -1.02 5.07
	Independent company Increased investment in 1998/1999 Decreased investment in 1998/1999 Employment 1999 Constant Selection coefficient Number of observations:	------	------	0.09 0.00 -0.06 -0.01 R e -0.67 -21.98 v 0.45 0.05 -0.40 -3.44 -3.07 -4.32 i 1,319 e w	------	------	0.19 -0.03 -0.08 -0.01 -0.74 -28.04 1,423	0.98 -0.40 -0.54 -3.48 -3.46 -4.67

Table A .4: Complete Results -Combination ESF/Commercial vs. Not-ESF

 A

			Outcome Measure (i)			Outcome Measure (ii)
			OLS			Two-step		OLS	Two-step
						procedure			procedure
		Coeff. t-value Coeff. t-value Coeff. t-value Coeff. t-value
	OUTCOME EQUATION:
						Treatment Indicators:
	Combination with commercial qualifi. 1.79	2.51		25.24	3.68	2.46	3.11	28.45	3.94
						Control Variables:
	Newly established business	-0.51	-0.52		-3.69	-2.33	-0.79	-0.74	-4.64	-2.63
	Manufacturing sector	4.34	4.75		7.75	4.95	4.88	4.76	8.92	5.11
	Service sector	0.40	0.53		2.60	2.18	1.21	1.45	4.03	3.00
	Craftsmen	-0.99	-1.35		1.22	1.05	-1.04	-1.28	1.16	0.94
	Self-employed	-0.22	-0.20		-4.58	-2.37	-0.77	-0.65	-6.84	-2.99
	Independent company Increased investment in 1998/1999 Decreased investment in 1998/1999 Employment 1999 F o -0.32 3.32 -4.08 0.97 Constant -1.37 r	-0.27 5.06 -3.78 301.34 -0.99	2.02 1.15 -5.20 0.96 -10.35	1.16 1.06 -3.53 183.66 -3.25	-2.34 3.40 -4.42 0.97 0.39	-1.76 4.66 -3.67 295.44 0.26	0.45 1.12 -5.33 0.96 -9.90	0.24 0.98 -3.33 181.21 -2.85
	DECISION EQUATION: Innovations planned in future Newly established business Manufacturing sector Service sector Craftsmen Self-employed	---P e -------e --r	0.21 0.41 -0.57 -0.30 -0.29 0.56	2.42 3.72 -4.93 -3.26 -3.17 4.53		------	------	0.22 0.45 -0.59 -0.34 -0.25 0.70	2.54 4.18 -5.29 -3.91 -2.83 5.94
	Independent company Increased investment in 1998/1999 Decreased investment in 1998/1999 Employment 1999 Constant Selection coefficient Number of observations:	------	------	-0.15 0.27 0.15 0.00 R e -0.52 -14.05 v -1.02 3.42 1.18 4.55 -3.13 -3.47 i 1,397 e w	------	------	-0.19 0.26 0.11 0.00 -0.47 -15.59 1,478	-1.36 3.32 0.87 4.90 -2.95 -3.65

Table A .5: Complete Results -ESF vs. No Qualification

 A

			Outcome Measure (i)		Outcome Measure (ii)
			OLS	Two-step	OLS	Two-step
					procedure			procedure
			Coeff. t-value Coeff. t-value Coeff. t-value Coeff. t-value
	OUTCOME EQUATION:
					Treatment Indicators:
	ESF-funded qualification		1.84	2.72	1.11	0.31	1.96	2.72	0.92	0.24
					Control Variables:
	Newly established business	-0.39	-0.43	-0.27	-0.26	-0.63	-0.66	-0.45	-0.39
	Manufacturing sector		3.81	3.84	3.63	2.76	4.11	3.85	3.85	2.72
	Service sector Craftsmen Self-employed Independent company	-0.47 -1.34 -0.26 3.06 o F r	-0.59 -1.69 -0.25 2.25	-0.53 -1.35 -0.11 3.00	-0.63 -1.71 -0.09 2.15	0.00 -1.34 -0.46 -0.32	0.00 -1.60 -0.45 -0.22	-0.10 -1.35 -0.20 -0.42	-0.11 -1.62 -0.14 -0.28
	Increased investment in 1998/1999	3.16	4.52	3.24	4.03	3.20	4.34	3.31	3.95
	Decreased investment in 1998/1999 -3.95 Employment 1999 0.96 Constant -4.23 P -3.48 304.09 -2.76 e e	-3.95 0.96 -3.77	-3.50 281.55 -1.40	-4.23 0.96 -1.03	-3.52 309.11 -0.63	-4.25 0.96 -0.36	-3.54 284.46 -0.12
				r
				R e
					v i
						e w

Table A .6: Complete Results -Only ESF-funded Qualification vs. No Qualification

 A

			Outcome Measure (i)		Outcome Measure (ii)
			OLS	Two-step	OLS	Two-step
				procedure			procedure
		Coeff. t-value Coeff. t-value Coeff. t-value Coeff. t-value
	OUTCOME EQUATION:
				Treatment Indicators:
	Only ESF-funded qualification	1.43	2.17	-3.19	-0.55	0.79	1.26	-5.78	-0.96
				Control Variables:
	Newly established business	-0.61	-0.69	0.26	0.18	-0.51	-0.60	0.85	0.55
	Manufacturing sector	3.38	3.62	2.33	1.43	3.40	3.69	1.83	1.06
	Service sector	-0.01	-0.01	-0.35	-0.39	0.09	0.12	-0.39	-0.45
	Craftsmen	-0.99	-1.33	-0.95	-1.24	-0.84	-1.17	-0.77	-1.01
	Self-employed	0.25	0.24	1.23	0.75	0.24	0.25	2.09	1.06
	Independent company Increased investment in 1998/1999 Decreased investment in 1998/1999 Employment 1999 o 2.57 2.13 -2.69 0.93 F o 64.34 F 1.47 2.28 1.53 1.27 0.88 1.68 1.07 3.86 2.78 3.80 2.48 3.84 2.69 3.80 -2.60 -2.85 -2.64 -2.74 -2.74 -3.01 -2.78 63.36 0.92 59.71 0.91 68.51 0.91 r r
		P e P e
			e e
			r r
			R e R e
				v i v i
				e w e w
			40

Table A .8: Complete Results -ESF vs. Only Commercial Qualification

 A

		Outcome Measure (i)	Outcome Measure (ii)
		OLS	Two-step	OLS	Two-step
			procedure		procedure
		Coeff. t-value Coeff. t-value Coeff. t-value Coeff. t-value
	OUTCOME EQUATION:
			Treatment Indicators:
	ESF-funded qualification	0.47 -18.97 F 0.70 -28.62 -1.13 0.44 0.57 o F o F o
		r r r
		P e P e P e
		e e e
		r r r
		R e R e R e
			v i v i v i
			e w e w e w

The other three structural funds are the European Regional Development Fund (ERDF), the Financial Instrument of Fisheries Guidance (FIFG) and the European Agricultural Guidance and Guarantee Fund (EAGGF).

This is also known as the concept of local average treatment effects (LATE; see IMBENS AND[START_REF] Imbens | Identification and Estimation of Local Average Treatment Effects[END_REF]).

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UKSubmitted Manuscript

Project Evaluation|Social Discount Rate < H4 -Publicly Provided Goods < H -Public Economics, J68 -Public Policy < J6 -Mobility, Unemployment, and Vacancies < J -Labor and

Appendix


Kristien Werck
email: kristien.werck@vub.ac.be

Mona Grinwis

Plaat Stultjes

Bruno Heyndels

JEL Code: L31 -Nonprofit Institutions < L3 -Nonprofit Organizations and Public Enterprise < L -Industrial Organization

Keywords: theatre, subsidies, programmatic choices programmatic choices, theatre, subsidies

HAL is

Introduction

Like any other firm, a theatre needs money. Artistic productions can be presented to the audience only when sufficient budgetary means are available. Broadly speaking, theatres rely on both the market (through ticket sales, sponsoring, …) and subsidizing governments to soften this constraint. But popular or corporate support may increase or decrease, subsidies may be augmented or cut back. For the theatre directors this means that the budgetary constraint loosens or tightens and action has to be undertaken. Programmatic choices adjusting the theatrical output are a prime instrument for the theatre management. It can adapt the number of performances or productions to the new budgetary situation or it can change the nature of the theatre's output. These types of reactions are the focus of the present analysis. More specifically, we examine how budgetary constraints affect the programmatic choices made by Flemish theatres.

The program of a performing arts organisation can be seen as the result of an economic optimisation process [START_REF] Frey | A Comparative Institutional Analysis in the Arts: the Theatre[END_REF]. The decision-maker, making programmatic choices, pursues certain objectives. A discussion on the objectives of managers in the non-profit performing arts is in Section 2. Full realisation of these objectives is confined by budgetary constraints. Section 3 reviews the literature on the impact of budgetary changes on artistic programming. Section 4 uses a panel of Flemish theatres to analyse how budgetary changes affect programmatic choices. We account explicitly for the heterogeneity of theatre output: programming corresponds to selecting a Lancasterian set of characteristics. A simultaneous equation approach is used also incorporating the behaviour of consumers and of the subsidizing government. A conclusion is in Section 5. A theatre manager making programmatic choices will -in most general termsmaximise his/her utility under a set of constraints. Most economic models of nonprofit performing arts organisations assume that theatre managers are biased towards the maximization of artistic quality [START_REF] Krebs | Politico-Economic Interactions of German Public Performing Arts Institutions[END_REF]. The pursuit of quality can take many forms. A theatre director could opt for quality by programming innovative, less popular works that appeal only to the most 'refined' tastes. Quality could also be achieved by making the production of any work as impressive as possible, for example by a virtuoso performance, by lavish stage sets or costumes [START_REF] Hansmann | Nonprofit Enterprise in the Performing Arts[END_REF].

Although the quality objective dominates in the literature, alternatives have been suggested. First, a theatre director could maximise audience size e.g. to spread culture to a broad segment of the population [START_REF] Caves | Creative Industries: Contracts between Art and Commerce[END_REF]. There may be a trade-off: a 'highbrow' (high quality) program may decrease audience sizes [START_REF] Krebs | Politico-Economic Interactions of German Public Performing Arts Institutions[END_REF]. Other alternatives to quality maximization are budget maximization (Hansmann, 1981, 347) 1 , acquiring legitimacy of the organisation [START_REF] Dimaggio | Nonprofit Organisations in the Production and Distribution of Culture[END_REF] or maximisation of the theatre manager's standing among his peers [START_REF] Krebs | Politico-Economic Interactions of German Public Performing Arts Institutions[END_REF]. The latter objective may reflect the existence of agency problems [START_REF] Bois | Agency Problems and unrelated business income of non-profit organizations: an empirical analysis[END_REF].

1 His approach is in line with Niskanen's description of the budget maximising bureaucracy (Niskanen, Several empirical studies examine the effect of government subsidy on one particular aspect of theatrical output, namely the extent to which the repertoire is diverse or innovative. The general assumption is that government support -as opposed to box office revenues or private donations -enhances artistic freedom and the possibility to experiment. [START_REF] Martorella | The Structure of the Market and Musical Style. The Economics of Opera Production and Repertoire: An Exploration[END_REF] gives the example of the Metropolitan and Lyric Opera of Chicago. These depend heavily on box office revenues. Their programming turns out to be 'conservative'. In contrast, the New York City Opera that has been subsidized by the National Council of the Arts and the Ford Foundation, has been able to commission new works and present avant-garde or rarely performed operas. [START_REF] Heilbrun | Empirical Evidence of a Decline in Repertory Diversity among American Opera Companies 1991/92 to 1997/98[END_REF] suggests that the increasing diversity in the Canadian opera repertoire (as compared to decreasing diversity in the U.S.) is explained by the higher levels of public support. [START_REF] Pierce | Programmatic Risk-Taking by American Opera Companies[END_REF] examines how variations in funding sources affect 'program conventionality' of U.S. opera companies. 'Conventionality' refers to the extent to which the program of an organisation is similar to the programs of other organisations. Local government funding significantly increases conventionality. A probable reason is that local politicians need to support programs that are popular. The opposite effect was found for support from the National Endowment for the Arts: it decreases the conventionality of opera programming.

Theatre studies largely support the hypothesis that public support increases diversity. [START_REF]On the Impact of Revenue Subsidies on Repertory Theatre Policy[END_REF] finds that subsidy encourages the presentation of minority interest plays in English provincial repertory theatres. In their study of American resident theatres, [START_REF] Dimaggio | Why Do Some Theatres Innovate More than Others? An Empirical Analysis[END_REF] show how a higher dependence on the market is associated with a greater conformity of repertoire. In a study on English theatres, O'Hagan and Neligan (2005) find a negative relationship between subsidy as a proportion of total income and the conventionality of theatre programs. Baumol and Baumol (1985b) analyse the effect of ever-increasing costs on the output of performing arts organisations. Their concept of the 'suppressed cost disease' is the less well-known brother of Baumol's cost disease. The intuition behind the latter is simple: (a) the labour intensive performing arts sector does not benefit from technological progress as much as other sectors and (b) the tendency for wages in the arts sector to follow wages elsewhere leads to ever-increasing costs [START_REF] Baumol | Performing Arts: The Economic Dilemma. A Study of Problems Common to Theater, Opera, Music and Dance. The Twentieth Century Fund[END_REF]. When performing arts organisations make qualitative changes in their output due to cost pressures this is referred to as the suppressed form of the cost disease. As a general rule, we expect increasing labour costs to induce a shift within the artistic sector from highly labour intensive productions to less labour intensive productions. The cost disease leads then to a situation where performing arts institutions "debase the product" (Baumol and Baumol, 1985b, 222). They may cut the number of rehearsals, perform in large capacity playhouses, use simpler sets … in the economically feasible range of (artistic) options. An obvious strategy is to reduce labour costs, since these typically make up for the largest part of total costs.

Assuming that wages are more or less fixed, this requires reducing labour input. One way to do this is to produce plays with smaller casts. Still, it might be difficult to produce a certain play with fewer actors. In the words of Alan Peacock: "Removing Judge Bouck from the cast of Hedda Gabler would certainly reduce labour input to Ibsen's masterpiece, but it would also destroy the product" (Peacock, 1996, 219).

But, of course, a change in the overall composition of the repertoire does make it possible to reduce labour input. Evidence by Baumol and Baumol (1985a;1985b) shows that this is exactly what happened on Broadway: the average cast size of plays fell from 15,8 to 8,1 between 1946 and 1977. Another cost-saving option that affects the repertoire mix is to choose fewer modern works for which copyright fees have to be paid [START_REF] Throsby | Economic Circumstances of the Performing Artist: Baumol and Bowen Thirty Years On[END_REF].

Flemish theatres: 1980-2000

The Flemish professional theatre sector has gone through remarkable changes in the past decades. As will be shown, the plays that appear on stage today are clearly different from those that were programmed 20 years ago. Our analysis is restricted to those companies that have received 'structural' subsidies from the Flemish community. Altogether these represent the (rather) well-established and professional Flemish theatre sector. In Section 4.1 we describe the output of the Flemish theatres in the period 1980-2000. 2 Section 4.2 gives the evolution of the theatres' budget.

Section 4.3 discusses several hypotheses concerning programmatic choices and the impact of budgetary restrictions. The empirical analysis is in Section 4.4.

2 We consider the seasons 1980-1981 until 2000-2001. In the remainder of this study, theatre seasons will be referred to by the first year mentioned (for example, the season 1993-1994 will be referred to by the year 1993). To examine the changes in the Flemish theatre output, we describe output at the level of the individual theatres. Rather than focusing on the level of theatre output only, we also account for the heterogeneity of output. Theatre productions differ considerably from one another in many dimensions. Analysing decision-making in the context of the performing arts should explicitly account for such differences [START_REF] Throsby | Perception of quality in demand for the theatre[END_REF]).

Lancaster's characteristics approach then suggests itself as a tool of analysis [START_REF] Lancaster | A New Approach to Consumer Theory[END_REF]. Following this approach, a theatre production consists of a unique set of N characteristics z i (i = 1, …, N) like the language of the play, the period in which the play was written, the number of actors on stage, their identity, and so on:

Z = Z { z 1 , z 2 , z 3 , …, z N }
From the theatre manager's perspective, a programmatic choice on what plays to put on stage corresponds with a choice of specific values for all z i . More generally, programmatic choices involve not only the choice of a given play to perform but rather the choice of a set of plays (sets of characteristics) that are performed during a given season along with a decision on how often each given play will be performed,

…

The Lancasterian characteristics approach has been incorporated into the empirical literature on performing arts attendance. In a demand study of 3 Australian theatre companies, [START_REF] Throsby | Perception of quality in demand for the theatre[END_REF] 8 repertoire into classic, modern, contemporary and atypical plays based on the date the plays were written and on whether the playwright is a living author or not. Analysing demand for French theatres, [START_REF] Urrutiaguer | Quality judgements and demand for French public theatre[END_REF] divides the repertoire into four categories according to the date of composition, the date of birth and the mother tongue of the playwright. Dummy variables express whether the share of performances programmed by a theatre and belonging to a certain category is 10 % higher than the overall average or not. [START_REF] Corning | Demand for Live Theater with Market Segmentation and Seasonality[END_REF] examine demand for performances of the Pacific Conservatory of the Performing Arts. They include indicators for comedies, musicals, and dramas as well as a specific variable for the musical 'Tommy'.

As these studies show, the choice of relevant characteristics to describe output is not straightforward. We identify several characteristics to describe qualitative aspects of individual theatre productions. The following 5 characteristics are considered 3 :

• z 1 : the original language of the play • z 2 : the age of the playwright • z 3 : the fact whether the play is an adaptation 4 or not

• z 4 : the number of actors

• z 5 : the fact whether the production is new or a retake 3 The selection of the characteristics was driven by the literature and the availability of data. Data on programmatic choices come from the 'Vlaamse Theaterjaarboeken' over the period 1980-1998, the database of the 'Vlaams Theater Instituut' and the Flemish Ministry of Culture. As these characteristics describe individual theatre productions, the relevant description of the program of a theatre for a whole season -a set of productions -can be seen in terms of an aggregation of these characteristics. To describe programmatic choices involving the quantity of output we look at:

• the number of productions

• the number of performances per production. [START_REF] Werck | Programmatic Choices and the Demand for Theatre: The Case of Flemish Theatres[END_REF] show how the choice of repertoire by Flemish theatres changed. Over the period 1980-2000 the share of plays by non-Dutch-speaking authors fell dramatically (from 79 to 26 % of all plays), there was a tendency towards more recent plays and more adaptations. A notable evolution concerns the cast size.

Whereas 5,77 actors appeared on stage in the average production in 1980, there are only 4,38 actors per production in 2000. Finally, more productions are being retaken.

The output of the Flemish theatres has also undergone changes in terms of quantity.

The average number of productions per theatre increased (from 5,22 in 1980 to 8,04 in 2000) while at the same time the number of performances per production fell (from almost 40 to 20).

The evolution of the budgetary situation

The budget of the Flemish theatres consists of two main sources: structural subsidies of the Flemish Community and own (box-office) income. 5 The former typically cover 1975 and the 'Podiumkunstendecreet' (Performing Arts Decree) from 1993 shaped the currently existing system. A crucial innovation in the latter Decree is the replacement of the yearly allocation of subsidies by a system of 'structural' subsidies assigned for 4 year-periods. This institutional change was welcomed by the theatres as it facilitated them to plan activities for a number of years without the (financial) uncertainty that was current practice before 1993 [START_REF] Brouwers | Het Podiumkunstendecreet: Veel geblaat, weinig wol?[END_REF].

The budgetary means of the Flemish theatres fluctuated over the years. After an initial decline in the early 80s the total available budget (subsidies and box office) for all subsidized theatres increased again to a maximum of €23,6 million in 1999 (in prices of 1996). The average theatre budget fluctuated between € 650.000 in the early 80s to almost € 946.000 in 1999. Naturally, the evolution in subsidies was largely responsible for these fluctuations: total subsidies fell from over €17,4 million in the early 80s to €13,4 million in 1986, after which they started to rise again. Average theatre subsidies were around € 572.000 in 1980 and increased to € 650.000 for the period 1997-2000.

Budgetary changes and Flemish theatre output

When making programmatic choices, the theatre manager has to take into account the constraints imposed by the public, by the subsidizing bodies and by its own technology. Depending on the objective(s) of the organisation, different hypotheses on the effect of budgetary changes can be made. A quality-maximising manager could be biased towards a theatre program that is artistically diverse (e.g. with a 11 maximum number of different productions). Audience-maximising managers may prefer larger numbers of performances. They may be biased towards more 'popular' plays. 6 Depending on which objective dominates, there exists a trade-off between the number of productions and the number of performances per production.

By retaking old productions, certain fixed production costs (such as scenery and costumes) can be avoided, and the number of rehearsals can possibly be limited.

However, it is a programmatic choice that quality-maximising theatre managers might be reluctant to make, as it does not enhance artistic diversity [START_REF] Grinwis | Vernieuwing in de podiumkunsten. Een verkennend onderzoek[END_REF].

Therefore, we expect such theatre managers to decrease the number of retakes when more budgetary means are available.

It is not straightforward to make a priori assumptions on the evolution of output characteristics under changing budgetary restrictions. The choice of repertoire -and the actors performing it -is one of the most important decisions to be made.

Reducing cast size seems an obvious way to reduce labour costs. Hence, a theatre manager may decrease (increase) cast size when confronted with a tightening (expanding) budget. Of course, when a quality-maximising manager regards large productions to be important, he will be reluctant to reduce average cast size.

Similarly, an audience-maximising manager will have a preference for large productions if they attract more theatregoers. 6 An audience-maximising manager is indifferent between programming n different plays with each one performance, or performing 1 production n times. Still, as programming a new production has sizeable fixed costs he will perform as few plays as possible (the constraint being that the play has to be popular enough to fill the theatre). The choice of a play that is contemporary, an adaptation of a classic play or of a play that is written by a Dutch-speaking playwright has less clear-cut budgetary implications. Still, cost-saving measures are possible, e.g. by programming fewer modern works for which copyright fees have to be paid [START_REF] Throsby | Economic Circumstances of the Performing Artist: Baumol and Bowen Thirty Years On[END_REF]Baumol and Baumol, 1985b). Again, a quality-maximising theatre manager who regards innovation as indispensable will be less willing to take such a measure.

It is clear that programmatic choices are taken simultaneously. Moreover, decisionmaking is intrinsically interdependent if only because subsidizing bodies take into account (past) programmatic choices as well [START_REF] Jenkins | Interdependent Decision-Making in Non-Profit Industries. A Simultaneous Equation Analysis of English Provincial Theatre[END_REF][START_REF] Luksetich | A Simultaneous Model of Nonprofit Symphony Orchestra Behavior[END_REF]. Politicians who decide on the level of subsidization face a re-election constraint. Therefore they may want to try and avoid drops in popular support. Theatre managers take this into account and will be careful not to provoke politicians, for example by falling short of box office income or by attracting negative publicity [START_REF] Krebs | Politico-Economic Interactions of German Public Performing Arts Institutions[END_REF]. Demand will also be affected by programmatic choices. [START_REF] Werck | Programmatic Choices and the Demand for Theatre: The Case of Flemish Theatres[END_REF] found that Flemish theatre attendance is positively affected by large cast sizes and plays written by Dutchspeaking playwrights. Expectations about consumers' reactions are likely to feed back into the programmatic decision-making, as attendance has a budgetary impact through the box-office.

Empirical analysis

Responses to budgetary changes are examined using data on all theatres that were subsidized during the period 1980-2000. Our data set consists of an unbalanced panel of 59 theatres. The set of theatres varies over time as some theatres cease to exist, To model all aspects of theatre behaviour and to incorporate the simultaneity of decision-making, we estimate a system of simultaneous equations. Quantitative output measures, output characteristics, subsidies, attendance and ticket price are determined simultaneously. Quantitative output measures for theatre i in year t are the number of productions (PROD i,t ) and the number of performances per productions (RUN i,t ). Five output characteristics are taken as dependent variables. CAST i,t stands for the average cast size of all productions of theatre i in year t. INNO i,t is a measure for artistic innovation. It corresponds with the proportion of new productions to total productions. ADAPT i,t gives the share of adaptations relative to the number of productions of the company. The dependent variable AGE i,t , is a proxy for the age of the plays. It corresponds with the difference between t and the average birth year of the playwrights of all productions of the company. Finally, DUTCH i,t gives the proportion of plays (originally) written in Dutch. These main choice variables of the individual theatres are determined taking into account possible effects on subsidies (SUB i,t ), attendance (Q i,t ) and on the average ticket price charged (P i,t ). Table 1 gives a summary of the system of simultaneous equations.

[Insert Table 1 about here] The explanatory variables comprise theatre-specific and market specific determinants.

For the latter, a distinction is made between touring and non-touring theatres through a dummy variable, PLAYH i,t , which equals 1 when a company is residing in its own 7 The number of subsidized theatres in any given year varies between 24 and 35. 14 playhouse and 0 when it is a touring company with no fixed location. The difference between the two types is important since they serve different types of markets. In principle, touring companies can perform in the whole Flemish and Brussels Region.

Their 'relevant market' therefore corresponds with the complete set of (308) Flemish and ( 19) Brussels municipalities. Non-touring companies perform in a playhouse in a particular municipality. Hence, their 'relevant market' is more limited in size. This difference between the two types is accounted for in the definitions of market specific explanatory variables: population size, consumer income and the prices of substitutes: cinema and other theatres' productions.

Population size (POP i,t ) is used as a proxy for the size of the 'relevant market' of theatre company i. For touring companies this corresponds with the total population in all Flemish and Brussels municipalities. For non-touring companies the market is geographically limited. It is natural to consider the municipality in which the playhouse resides as part of its 'relevant market'. Still, it is most likely that the market extends beyond the borders of this municipality. Just how far this market extends is an empirical issue. [START_REF] Werck | Programmatic Choices and the Demand for Theatre: The Case of Flemish Theatres[END_REF], analyzing demand for Flemish theatres, compare a number of possibilities. They define a 327 x 327 spatial weighting matrix W [START_REF] Revelli | Spatial patterns in local taxation: tax mimicking or error mimicking?[END_REF][START_REF] Revelli | Reaction or Interaction? Spatial Process Identification in Multi-tiered Government Structures[END_REF]. Elements of the matrix, w jk , equal 1 if municipality k is part of the 'relevant market' for theatre i (located in j) and 0 otherwise. They find a preferred specification in which w jk =1 in the municipality where the theatre is located (j=k) as well as in its border sharing municipalities. We use this result for our own analysis. In practical terms, this means that POP i,t for a non-touring theatre i corresponds to the sum of the population in the municipality where i is located and in its neighbouring municipalities. 16 Ticket price, subsidy and theatre attendance are determined simultaneously with the programmatic decision variables. Price, P i,t , is estimated as a function of attendance in the previous season, Q i,t-1 , as well as the output variables and consumer income Y i,t (equation 8). Equation 9 examines whether subsidy, SUB i,t , is dependent on a theatre's achievements in the previous season. Finally, equation 10 estimates the demand for theatre as a function of ticket price, consumer income, population, the price of cinema and the average price charged by other theatre companies as well as the output characteristics (see [START_REF] Werck | Programmatic Choices and the Demand for Theatre: The Case of Flemish Theatres[END_REF].

The equation system summarized in Table 1 is an over-identified system of simultaneous equations. We use 3-stage least squares as the simultaneous equation method, which yields more efficient results than single equation estimations when the disturbances are correlated across equations. 12 [Insert Table 2 about here] round'. So TUS t = 0 in all years before 1993 as well as in 1996 and 2000 (in the latter years decisions are taken on the subsidies for the following 4 years); TUS t = 1 in 1995 and 1999, 2 in 1994 and 1998 and 3 in 1993 and 1997. We could not find evidence of a 'budget cycle'.

12 A Hausman specification test examines the correct system specification, and determines whether 2SLS or 3SLS is the appropriate estimation technique. The results show that a simultaneous equations model is a better estimation technique (2SLS or 3SLS) than a single equation system (OLS), and that the full-information method (3SLS) provides better estimates than a limited-information method (2SLS). Table 2 summarises the results. Before turning to the coefficients of central interest (those on BUDGET variables) we briefly discuss the other findings. We restrict ourselves to the results in columns (1) to (7) as these are the main focus of our article.

A first observation is that, overall, the lagged variables of the output variables are highly significant, demonstrating that programmatic choices are strongly 'determined' by choices from the past. This reflects that individual theatres have their own specific 'identity' as a result of which artistic choices show stability over time.

A second general finding is the apparent distinction between touring and non-touring (resident) companies. The latter program larger numbers of different productions.

This reflects the larger 'relevant' market of the touring companies. Since most theatregoers are not likely to attend the same production more than once, non-touring theatre companies are obliged to offer a 'diverse' program in order to attract potential theatregoers coming from within a certain distance from the playhouse. 13 As equation 4 shows, the playhouse dummy has a significant and positive effect on innovation: non-touring theatre companies on average program more new productions. This might be explained by their more limited potential audience (smaller 'relevant market'). The proportion of 'Dutch' plays in the program is significantly lower for resident companies. A possible explanation is -again -that they are 'obliged' to present a more diverse program (as their 'relevant market' is smaller) in combination with the fact that the Dutch-language repertoire is limited relative to the overall theatre repertoire. Turning to the impact of budgetary changes which are the focus of the current analysis two main conclusions can be drawn. First, we find clear evidence that the theatre's budget has an immediate impact on both the quantity and the nature of the output. Second, the introduction of the 4-year subsidy regime in 1993 has dramatically changed this relationship.

In the pre-1993 period (when subsidies where allocated on a yearly basis) budgetary changes translate into changes in the number of productions and the cast size (equations ( 1) and (3) respectively). Higher budgets are associated with more artistic diversity as reflected by the increased number of different productions. On the other hand, there is a negative but insignificant relationship between the budget and the average number of performances per production (equation 2). These results suggest that -given sufficient means -theatre managers prefer programming different productions rather than extending the average run of a production. As there is an obvious cost difference between staging a new production and programming an extra performance of an existing production, this result may reflect that Flemish theatre managers maximise quality (defined in terms of programmatic diversity) rather than quantity. Only when forced by the budgetary pressure, the number of productions will be lowered. Negative changes in the budget lead the theatre to lower the number of productions while keeping the average run constant.

Before 1993 the theatre's budgetary situation not only affects the artistic diversity in terms of the quantity of plays. Budgets also have systematic effects on the nature of these plays. Column (3) reveals a highly significant and positive relationship between a theatre's budget and the average cast size of the productions in a given season.

Increased budgets are used to expand cast sizes, probably by staging larger productions. 14 As such the evolution on Flemish theatre scenes towards plays with smaller cast sizes (as sketched in section 4.1) can be attributed -at least in part -to budgetary pressures.

This structural decline in average cast size in Flemish subsidized theatres is to our knowledge the first confirmation of Baumol and Baumol's (1985a;1985b) findings on the so-called suppressed cost disease.

Under the yearly subsidy regime though, we find no significant budgetary influence on the proportion of new productions, the choice for contemporary plays, adaptations or plays written by a Dutch-speaking playwright.

After 1993, this situation changes dramatically with the introduction of the 4-yearly subsidies. While the responsiveness of the number of productions to budgetary changes is not significantly affected -also in the post 1993 period higher budgets lead to higher numbers of productions -for all other output variables we find a significant difference with the pre-1993 period. Indeed, the coefficient on the interaction term is significant in equations ( 2) to (7). First, BUDGET*DS has a negatively significant effect on the number of performances per production (RUN). During the 4-year regime higher budgets translate into less performances per production and (as in the pre-1993 period) into larger numbers of productions. This change in behavior is consistent with the observation that theatre managers work in a financially less risky environment. As they are assured of a sizeable part of their budgets they can 'afford' to take the more costly and (in terms of ticket sales) risky decision to choose for new productions instead of longer runs.

Second, a similar change towards more risky programmatic choices may be reflected in the fact that, after 1993, looser budgetary constraints lead theatres to stage more recent plays (equation ( 6)) and more adaptations (equation ( 5)). Tighter budgets on the other hand induce (less risky) choices for older and well-established plays that are then performed in their 'original' version.

However, the tendency towards using the budget in order to present an artistically more risky program since the introduction of the 4-yearly subsidy regime is possibly contradicted by the observation in column (4). There we find that larger budgets in the post-1992 period lead to less innovation defined in terms of the share of new productions to retakes. 15Finally, column (3) reveals a striking shift in theatres' responses to budgetary changes. Whereas, as discussed, in the pre-1993 period higher budgets translate readily into larger casts, this relation is no longer apparent since the introduction of the four-year regime. While there remains a positive effect (the sum of the coefficients on BUDGET and BUDGET*DS is still significantly positive), it is now much lower. Larger (lower) budgets no longer lead to strong increases (decreases) in the average cast size. A possible explanation is that theatres use additional budgetary means to conform their programmatic choices to the subsidizer's preferences. Before 1993 theatre managers enjoyed relatively less freedom. The subsidizing government made explicit reference to the priority that should be given to (artistic) employment. Theatres received subsidies to spend on wages and general operations. They were obliged to spend minimally 90 % of their personnel expenses on actors and directors that were binded to the theatre by contract. Since 1993 this obligation is no longer present and, given that subsidies are allocated for 4-year periods, this seems to have changed the nature of the theatres' dependence on government subsidies.

Conclusions

Programmatic choices by theatre managers are not taken in isolation. Decisions on which plays to perform, what actors to attract, how often to perform a given play, … reflect the objectives pursued by theatre managers and the constraints they face. We analyse how programmatic choices by Flemish subsidized theatres depend on their budgetary constraints. As these (non-profit) theatres depend heavily on government grants, the budgetary constraint not only reflects a dependency on the market but also on the government. Empirically analysing programmatic choices is therefore only possible through estimation of a simultaneous equation system that accounts for this interdependency between theatre managers, the public and the government. We estimate such a model on an (unbalanced) panel of theatres that have received structural subsidies from the Flemish Regional government (over the period .

We find that theatres' budgets have an impact not only on the number of plays they perform but also on their nature. While indirect, our results offer to our knowledge the first systematic empirical evidence of Baumol's suppressed cost disease. This notion refers to a qualitative change in the artistic output in the performing arts due to relative price changes. A crucial observation is that the budgetary impact on programmatic choices depends on the subsidy regime. In the period under consideration, the Flemish government changed the institutional context: until 1992 subsidies were assigned on a yearly basis, since 1993 a 4-yearly subsidy regime came in place. Interestingly, this change in subsidy regime is reflected in the way that theatres respond to budgetary changes.

Under the yearly regime changes in a theatre's budget only affect the number of productions (artistic diversity) and the average cast size. Under the 4-yearly regime, budgetary changes have a much more 'general' impact on the nature of the theatrical output: theatres still respond to positive budgetary changes by increasing the number of productions and by increasing cast size (though much less than in the pre-1993 period). At the same time, however, they choose for shorter runs, less innovation, more adaptations and more recent plays and plays written in Dutch. All but the responses in terms of innovation (the share of new productions relative to retakes is actually lowered when theatres are given more budgetary leeway) reflect how theatres in a 4-yearly subsidy regime use additional revenue to make artistically more risky choices. Artistic risk -a choice variable of the individual theatres -substitutes the financial risk which was imposed by the yearly subsidy regime.

F

  -making in the performing artsA theatre manager faces constraints imposed by the 'artistic technology' and the budget. The latter is typically composed of three parts: the public's contribution in the form of ticket sales, corporate sponsoring and public subsidies. Changes in the technology, prices of inputs and/or the budget can be expected to lead to other programmatic choices. The cultural economics literature has focused on two issues: the impact of subsidy on output and the effect of increasing costs (input prices) on the performing arts in general.

  description of Flemish theatre output

  includes a variable for repertoire classification. Using dummy variables, plays are classified into four groups according to the date of composition and the familiarity of the playwright. Abbé-Decarroux (1994) estimates the demand for 64 productions of a Swiss theatre company. He classifies the

4

  An adaptation is an existing story or play that serves as the source of inspiration for a Flemish author who transforms it in an innovative way

  52 % of all expenses. Different legislations have regulated the allocation of subsidies. Before 1975, only the large 'repertoire' theatres and a few 'chamber' theatres received subsidies. The 'Theaterdecreet' (Theatre Decree) from

  exist but no longer receive structural subsidies, and new theatres are created and subsidized.7

19 of 28 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

  14 Alternatively cast size expansions can reflect the situation where individual actors do no longer play multiple roles within one and the same play. While such a situation is not inexistent in Flemish theatres, it can by no means explain the empirical effect found in table 2.

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Table 1 : Simultaneous equations model Quantity of output supplied

 1 (1) Productions PROD i,t = f (PROD i,t-1 , BUDGET i,t , BUDGET i,t *DS t , PLAYHi ,t ) (2) Performances per production RUN i,t = f (RUN i,t-1 , BUDGET i,t , BUDGET i,t *DS t , PLAYH i,t ) Output characteristics (3) Average cast size CAST i,t = f (CAST i,t-1 , BUDGET i,t , BUDGET i,t *DS t , PLAYH i,t ) (4) Innovation (proportion of new productions) INNO i,t = f (INNO i,t-1 , BUDGET i,t , BUDGET i,t *DS t , PLAYH i,t ) (5) Proportion of adaptations ADAPT i,t = f (ADAPT i,t-1 , BUDGET i,t , BUDGET i,t *DS t , PLAYH i,t ) (6) Average age of the playwrights AGE i,t = f (AGE i,t-1 , BUDGET i,t , BUDGET i,t *DS t , PLAYH i,t ) (7) Proportion of Dutch language plays DUTCH i,t = f (DUTCH i,t-1 , BUDGET i,t , BUDGET i,t *DS t , PLAYH i,t ) Price (8) Price P i,t = f (Q i,t-1 , PROD i,t , RUN i,t , CAST i,t , INNO i,t , ADAPT i,t , AGE i,t , DUTCH i,t , Y i,t , PLAYH i,t ) Government subsidy (9) Subsidy SUB i,t = f (Q i,t-1 , PROD i,t-1 , RUN i,t-1 , CAST i,t-1 , INNO i,t-1 , ADAPT i,t-1 , AGE i,t-1 , DUTCH i,t-1 , PLAYH i,t ) Consumer demand (10) Demand Q i,t = f (P i,t , Y i,t , POP i,t , PSUBTHEA i,t , PCINEMA i,t , CAST i,t , INNO i,t , ADAPT i,t , AGE i,t , DUTCH i,t , PLAYH i,t )

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Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Local governments -municipalities and provinces -also subsidize theatres. However, due to the large number of missing data, we did not take these subsidies into account.

This result seems to be in line with the finding by[START_REF] Dimaggio | Why Do Some Theatres Innovate More than Others? An Empirical Analysis[END_REF] for U.S. resident theatres. They find that resident theatres with a larger market have a less 'conformist' repertoire. Of course, diversity as measured by the number of different productions is not the same as 'nonconformity' as measured by DiMaggio and Stenberg's Conformity Index.

We have no straightforward explanation for this unless, maybe, that the main incentive to choose for new productions is to convince the subsidizing government to grant (more) subsidies. Indeed, as can be seen from column (9) theatres that are very innovative in these terms systematically receive higher subsidies. This incentive (to convince the subsidizing body) is likely to be smaller under the 4-year subsidy regime.

appropriation or grant" (Niskanen, 1971).

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Consumer income (Y i,t ) and the prices of substitutes -cinema ticket price (PCINEMA i,t ) and the ticket price of other theatre companies (PSUBTHEA i,t ) -are also defined in terms of the 'relevant market'. For touring companies this means that Y i,t , PCINEMA i,t and PSUBTHEA i,t correspond to the average per capita income (weighted by population), the average movie ticket price and the average ticket price of all other theatres in the Flemish and Brussels Region. For non-touring companies, the averages are again calculated for the theatre's own and neighbouring municipalities. 8,9 Equations (1) to ( 7) are of central interest. The first two equations have the quantitative output variables as dependent variables. Equations (3) to (7) treat the output characteristics. Each output variable is estimated as a function of its own lag and the theatre's budget (BUDGET i,t ). 10 The budget consists of own (box-office) income and government subsidy. As mentioned, two regimes can be distinguished.

Until 1992 subsidies were allocated on a yearly basis. Since then, a 4-year allocation is in use. Responses to budgetary pressures may have changed. To take this into account we interact BUDGET i,t with a dummy DS t that identifies the subsidy regime. DS t =1 in the years from 1993 onwards (4-year allocation regime) and 0 in the preceding period. 11 8 The relevant market's income is an average weighted by population. 9 In practice, movie theatres are only located in (relatively) large municipalities and not in their neighbouring municipalities. Hence, using or not using the spatial weighting matrix makes no difference. Formally, however, the 'relevant market' spreads over to neighbouring municipalities even if in these municipalities no substitute (cinema) is present. 10 A specification with the lagged value of the budget was estimated as well. The results were similar to those presented in the main text.

11 Following the current practice in empirical work on political budget cycles, we experimented with alternatives for DS. We defined a variable TUS t which gives the 'time until the following subsidy