Daniel J Van Vuuren

Pierre W C Koning

Disability Insurance and Unemployment Insurance as Substitute Pathways

Keywords: social security, disability, unemployment Firm behaviour, social security, disability, employment determination JEL codes: D21, H55, I12, J2

In this paper, we estimate the degree of substitution between enrolment into Disability Insurance (DI) and Unemployment Insurance (UI) in the Netherlands. Starting in the 1990s many policy measures aimed at reducing DI enrolment, and increase labour force participation. We quantify whether these policy measures have led to a reduction in hidden unemployment in DI. A side effect of the reforms may be increased pressure on UI. Therefore, we simultaneously estimate reverse substitution, that is, hidden disability in UI. To this end, we employ a sample of firms in the Dutch AVO database from the period 1993-2002. Using instrumental variables in a bivariate Tobit specification, we identify the hidden components in both respective schemes. The estimation results indicate that about 3% of all dismissals took place through DI, which implies that about one quarter of the DI enrolments observed in our sample in fact consists of hidden unemployment. We find no evidence for reverse substitution of disabled persons ending up in UI.

Introduction

In this paper, we address substitution between Unemployment Insurance (UI) and Disability Insurance (DI) in the Netherlands. As in other countries, there has been much debate on this issue both by economists and policymakers. By now, it is widely believed that the Dutch DI scheme has contained much hidden unemployment in the past years, although empirical estimates show quite some divergence, with estimates ranging from 10 to 50%. Yet, it is unclear how the current status has developed after the many reforms of DI initiated by the Dutch government in the 1990s. Indeed, enrolment rates have slipped considerably over the years, from over 1.5% of the workforce during the early 1990s until less than 1% in the early 2000s. This suggests that the various policy measures taken have been effective, or, to put it in other words, suggesting that hidden unemployment in DI has fallen considerably. There may however be other factors at stake, such as the business cycle, the population composition, etc. Another possible complication is that persons who are in fact eligible to receiving DI benefits are not receiving any as a result of a wrong decision of the medical examiner (erroneous denials, or type I errors). It may also be the case that disabled persons do not even apply for DI benefits as they do not have any confidence in being admitted to the scheme. In this paper, we will first seek to investigate whether hidden unemployment in DI has indeed decreased over the years, and second, we will investigate whether there has been any reverse substitution of disabled persons into the UI scheme.

In the relevant literature, one of the first contributions giving explicit estimates for the degree of hidden unemployment in the Netherlands' DI scheme was [START_REF] Aarts | Economic Aspects of Disability Behaviour[END_REF] to estimate a share of hidden unemployment within DI enrolment of 33-51%. [START_REF] Westerhout | Hidden unemployment in Dutch disability schemes[END_REF] used aggregate data for the period 1973-1992 and confirms that nearly half of the persons receiving DI benefits are in fact unemployed. The most recent study in this sequence of Hassink, van Ours & Ridder (1997) uses micro data for the period [1988][1989][1990], and finds a percentage of hidden unemployment of 10. Another interesting finding is that a quarter of the employees enrolling into DI are not replaced by new workers, suggesting that these are, in fact, layoffs [START_REF] Hassink | Job destruction through quits or layoffs?[END_REF].

For other countries there exists quite some empirical evidence as well.

For the United States, both [START_REF] Autor | The rise in the disability rolls and the decline in unemployment[END_REF] and [START_REF] Black | The impact of the economic conditions on participation in disability programs: Evidence from the coal boom and bust[END_REF] demonstrate that the U.S. DI scheme contains hidden unemployment. The latter paper uses inter-state variation to show that the recessions of 1991 and 2001 increased pressure on DI. [START_REF] Autor | The rise in the disability rolls and the decline in unemployment[END_REF] find that the rising replacement rates for low skilled workers led to a higher incidence of disability. In Canada, the rise of replacement rates in all provinces except Quebec was found to decrease the labour force participation [START_REF] Gruber | Disability insurance benefits and labour supply[END_REF]. An interesting recent paper by [START_REF] Duggan | Aching to retire? The rise in the full retirement age and its impact on the disability rolls[END_REF] considers the effect of the 1983 Social Security 1 reform on the number of disability recipients in the United States. The authors find that the reduction of Social Security generosity led to increased pressure on Disability Insurance. On the other hand, [START_REF] Riphahn | Disability retirement and unemployment -substitute pathways for labour exit? an empirical test for the case of Germany[END_REF] rejects the hypothesis that DI and UI can be considered as substitutes for German elderly workers.

Our paper adds to this literature in two ways. First, we use a recent administrative data set with employer records on layoffs and disability enrolments. During the 1990s and early 2000s, the Dutch government has almost 1 Social Security is the U.S. public old age pension. continuously reformed the DI scheme, so that the estimates discussed above may be outdated by now. Second, we are able not only to investigate hidden unemployment in DI, but also the 'reverse substitution effect', i.e. hidden disability in UI. Typically, substitution effects are associated with workers entering DI as an alternative to dismissals. Within the context of our model, however, we allow substitution to exist simultaneously in both directions.

As was just mentioned, the reverse substitution effect may have become important, because the various policy measures taken imply an increasing risk of erroneous denials of DI admission.

We will consider a simple model of the firm, which may either dismiss its employees through the UI scheme and/or lose some through the DI scheme, while allowing for substitution between the two schemes (i.e. dismissals through the DI scheme and disabilities through the UI scheme).

The identification of the respective effects hinges on the imposition of exclusion restrictions on bivariate Tobit estimates of enrolment rates in the two schemes. In order to identify the hidden components, we impose two sets of exclusion restrictions. Before discussing these, it should be noted that the exclusion restrictions demand that no direct causal relation exists between the variables and the respective enrolment rates. Given the fact that we control both for observed and unobserved firm and worker characteristics we take account of selection effects that may potentially bias our instrumental variables estimates of the DI enrolment and UI enrolment regressions, respectively. Our first exclusion restriction demands that the wage level should exclusively affect UI enrolment but not DI enrolment. On the one hand, it is intuitive that the wage level affects the layoff decision of firms, as wages directly affect the demand for labour. On the other hand, DI enrolment rates should not depend on such a business decision, as disability is typically the result of health shocks and does not result (directly) from a change in wages. Thus, if we find that the wage level within firms affects DI enrolment rates this indicates the presence of hidden unemployment in DI.

Second, it is assumed that UI enrolment should not be affected by function complexity. Thus, if we find this variable to impact UI enrolment, then we interpret this as hidden disability.

Our empirical findings point out that about 3% of all dismissals are through the DI scheme, which implies that about one quarter of DI enrolments consists of hidden unemployment. This estimate is however not very precise compared to that in [START_REF] Koning | Hidden Unemployment in Disability Insurance in the Netherlands; An Empirical Analysis Based on Employer Data[END_REF], who employ a much larger dataset. For practically the same period, the latter paper however finds nearly the same result with 3% of all dismissals occurring through the DI scheme. In the second place, no evidence is found for reverse substitution between UI and DI. That is, our estimation results point out that the many reforms taken since the 1990s have not led to increased pressure on the UI scheme.

In section 2, we first discuss some background on the Dutch DI scheme.

In section 3 we discuss the data. Section 4 describes the empirical strategy used in this paper. Finally, the estimation results are discussed in section 5.

Disability Insurance in the Netherlands

The public Disability Insurance (DI) scheme in the Netherlands was introduced in 1967, and was designed to insure those workers who do not recover from sickness within a year. Any worker is fully insured from the moment of having a paid job, i.e. it is not required that workers have been insured during a certain period of time prior to the moment of application for DI 5 F o r P e e r R e v i e w benefits (as is the case in most other western countries). A second feature is that any form of disability is insured, whether stemming from professional risk or social risk. A final characteristic worth mentioning is that applicants who lose at least 15% of their earnings capacity qualify for DI benefits. Workers becoming disabled for 80% or more receive full benefits, while partially disabled (15-80%) receive pro rata benefits.2

During the 1970s, enrolment rates doubled from an intial 1% of the labour force to about 2% by the end of the decade (Figure 1). During that time DI was widely used as an early retirement arrangement for workers who were not able to find a job as a consequence of the international recession.

Despite the introduction of 'official' early retirement schemes during the late 1970s / early 1980s, 3 DI enrolment rates remained stable at about 1.5% until the early 1990s. In the mean time, the number of DI recipients had risen from less than 3 to more than 7% of the potential workforce (Figure 1). This development obviously put a huge financial burden on the Dutch working population.

As a consequence of this development, many policy measures were taken.

An important reform started in 1987, when replacement rates were lowered, and admission to DI benefits was no longer linked to the labour market situation. Before that date, labour market conditions were taken into account in the admission decision, thus in fact facilitating hidden unemployment in the DI scheme. However, this reform did not generate substantial effects, and hence the 1990s saw no less than six subsequent reforms. Starting in The latter measure however proved ineffective, as the social partners decided to collectively 'repair' this by raising higher DI premiums. In 1994, financial incentives for employers were introduced to lower the use of sickness benefits, and hence lower the inflow into DI. Those incentives comprised, amongst others, differentiated employer premiums for sickness insurance.

In 1995, part of the 1992 reform was again lifted. The privatisation of sickness insurance in 1996 made employers bear the financial risk of sickness of their personnel. From 1998 on, premiums for DI had to be paid by the employer instead of the employee, and these were differentiated according to DI enrolment history (experience rating). In 1998 it was also made more attractive for employers to hire partially disabled workers.

Finally, since 2002 both employers and employees must prove that they have put enough effort in preventing disability during the preceding period of sickness. If their efforts do not meet requirements, then either the employer may have to pay additional sickness benefits (i.e. the sickness period is extended) or the employee's application for DI benefits may be rejected. This measure is found to be highly effective (de Jong, Lindeboom & van der Klaauw 2006).4

Looking at Figure 2 again, it is observed that the DI enrolment rate shows a downward sloping trend from 1992 on. Moreover, the stock of DI recipients stabilised at a rate slightly lower than 7% of the potential workforce (Figure 1). Enrolment rates were at an all time low during the early 2000s. On the other hand, these were relatively high during the second half of the 1990s. In conclusion, the time pattern since the early 1990s suggests

Data

In this paper, we use the AVO data set of the Netherlands' Labour Inspectorate which covers the period 1993-2002. 5 The data are based on administrative records of firms by means of a stratified two step sampling procedure.

In the first step, a sample of firms is drawn from the Inspectorate's own firm register using a stratified (by industry and firm size) design. The number of strata changes between surveys. Typically, each year's sample contains about 1500-2000 firms drawn from about 100-300 strata. Firms with less than 10 workers are underrepresented, and large firms are overrepresented. This is however not relevant for our analysis, as we aim to describe firm behaviour at the micro-level and the strata selection process can be considered exogenous here. 6 In the second step, a sample of workers is drawn within each of these firms. From small firms (¡20 employees) the entire workforce was sampled, and for larger firms, the fraction of workers who were sampled decreases with firm size. Thus, the second step of the sampling procedure provides us with extensive information on the workforce composition of firms. In particular, we observe age, gender, education, part-time work, income, function type, and function complexity distribution of the workforce. Two important advantages of the AVO data are that we observe both worker and firm characteristics, and that it is based on administrative records so that we have only few missing observations.

The full dataset has 20,958 firm-year observations, of which one third was omitted because the collective labour agreement code was not available.

We will need this last variable in order to take account of unobserved heterogeneity (see section 4). Furthermore, we have omitted a further 3,602 observations (17%) for which the data were incomplete. Thus, the resulting dataset contains 10,437 obervations. Table 1 shows some characteristics of the AVO data set. We see that the average firm has 3.4% of its personnel enrolled into UI, and 0.4% into DI. These figures are expressed on a yearly basis, as each observation in our data set concerns a firm-year observation. It is striking that most firms in the sample either face positive DI enrolment or positive UI enrolment, but that the combination of the two is almost absent. We also calculated correlation coefficients between the two enrolment rates. It is unclear a priori whether substitution between the two schemes should result in a positive or a negative correlation coefficient. If, for example, the economy is in a recession, and firing rates are high, then substitution may imply positive correlation between both enrolment rates.

However, if we condition on the business cycle, then it is more likely that substitution implies negative correlation. The reason is that in case of a fixed state of the business cycle, the desired fraction of workers to be laid off is more or less fixed, and therefore, substitution implies that a rise in the use of one scheme will come together with a decline in the other. Table 1 indeed shows that the correlation coefficient is negative in 7 out of 8 years, suggesting that substitution might play a role. Of course, these are only indications, and a formal proof is yet to be given. Furthermore, Table 1 shows sample statistics concerning the workforce composition, firm size, and year. From these figures it becomes apparent that there exists much variation in exogenous variables among the different firm-year observations, which will obviously be of great help in identifying the key parameters of interest in this paper. This is particularly the case for the variables concerning income and function complexity, as these will be used to identify the shares of hidden unemployment and hidden disability in DI and UI, respectively (in the next two sections we will elaborate further on this crucial point). The observations are evenly distributed over the different years, although no data were availabe for the years 1996 and 1999.

[insert Table 1 about here]

Empirical strategy

The empirical strategy employed in this paper is a generalisation of Hassink et al. (1997) and [START_REF] Koning | Hidden Unemployment in Disability Insurance in the Netherlands; An Empirical Analysis Based on Employer Data[END_REF]. In the first stage we estimate a bivariate Tobit model, and in the second stage the underlying structural parameters are recovered through minimum distance estimation.

While Koning and van Vuuren only focus on hidden unemployment in Disability Insurance (DI), we will also seek to identify the level of hidden disability in Unemployment Insurance (UI). First, we discuss the model, then we translate this into an empirical model in subsection 4.2, and finally we will explain the estimation procedure more precisely in subsection 4.

The model

Firms may experience incentives to dismiss employees through the DI scheme instead of the UI scheme. For instance, making use of the unemployment scheme may involve quite some procedural efforts, while the application for DI benefits may be demanding less time for the employer. Also, financial motives may play a role. While disability is not perfectly verifiable, the DI scheme may thus be an attractive alternative to the UI scheme. On the other hand, the many reforms of the DI scheme (see section 2) may have led to reverse substitution, that is, disabled workers ending up in the UI scheme. A possible reason is that disabled workers are not easily awarded DI benefits, so that the UI scheme may be a convenient alternative.

Let us now formalise substitution between the two schemes in a simple behavioural model for the firm. Firms have a desired dismissal rate f 0 and a 'true' disability rate d 0 . Spillovers between the DI scheme and the UI scheme are characterised by λ (λ ≥ 0) and µ (µ ≥ 0), where the parameter λ represents the fraction of desired layoffs that is directed to the disability scheme, and the parameter µ the fraction of disabled which ends up in the unemployment scheme. Hence, the observed UI and DI enrolment rates

equal f = (1 -λ)f 0 + µd 0 (1a) d = (1 -µ)d 0 + λf 0 (1b)
Clearly, our interest lies in the size of the respective components µd into UI and DI, respectively:

s 1 := µd 0 f = µ * d f -λ * µ(1 + d f ) ≈ µ d f (2a) s 2 := λf 0 d = λ * f d -µ * λ(1 + f d ) ≈ λ f d (2b)
where

λ * := λ 1 -λ -µ and µ * := µ 1 -λ -µ .
Using the terminology of [START_REF] Aarts | Economic Aspects of Disability Behaviour[END_REF] these fractions can also be labelled 'type II errors'. However, these errors may be (partly) canceled out by the opposite effect, namely workers who are wrongfully directed to the other scheme (type I error). If type I error and type II error are of about the same size, then the net amount of substitution between the two schemes will come close to zero. Formally, the net fractions of hidden components in DI and UI write as:

t 1 := 1 - f 0 f = µ * d f -λ * ≈ µ d f -λ (3a) t 2 := 1 - d 0 d = λ * f d -µ * ≈ λ f d -µ (3b)
Comparing these equations with (2), it can be seen that the fractions of type I error in UI and DI approximate λ and µ, respectively. Note from the above expressions that the net substitution (in percentage points) into a given scheme is precisely the negative of that in the other scheme (t 2 d = -t 1 f ), so that either t 1 or t 2 is positive while the other has to be negative.

12 F o r P e e r R e v i e w

Empirical specification

Our starting point is the following empirical specification for the firm's desired layoff rate f 0 and 'true' disability rate d 0 :

f 0 = β 0f x 0 + β f x f + u f (4a) d 0 = β 0d x 0 + β d x d + u d (4b) u =    u f u d    ∼ N (0, Σ) , with Σ =    σ 2 f ρ f d σ f σ d ρ f d σ f σ d σ 2 d    (4c)
It is assumed that the incidence of layoffs and disabilities depends on firm characteristics, worker characteristics, and the state of the economy. However, only the two latter types of variables are contained in the x-vectors; the way to control for firm-specific characteristics is addressed in subsection 4.3.

The vector x 0 contains common covariates, x f is a vector of covariates affecting the desired layoff rate but not the true disability rate, and x d is a vector of covariates affecting the true disability rate but not the desired layoff rate. The slope parameters of our model are organised in the β-vectors with corresponding underscores. Both x f and x d contain a constant term.

We assume normality, and the correlation coefficient ρ f d allows for some 'natural' correlation between f 0 and d 0 . That is, the layoff and disability rates may be correlated even though this needs not be a sign of substitution between the corresponding schemes.

We may now substitute this empirical specification for f 0 and d 0 into (1)

to obtain an empirical specification for the observed enrolment rates into UI 13 and DI. This yields:

f = γ 0f x 0 + γ f f x f + γ df x d + v f (5a) d = γ 0d x 0 + γ f d x f + γ dd x d + v d (5b) v =    v f v d    ∼ N (0, Σ v ) , with Σ v =    τ 2 f ρ v τ f τ d ρ v τ f τ d τ 2 d    (5c)
where

γ 0f = (1 -λ)β 0f + µβ 0d (6a) γ f f = (1 -λ)β f (6b) γ df = µβ d (6c)
γ 0d = (1 -µ)β 0d + λβ 0f (6d)
γ f d = (1 -µ)β d (6e)
γ dd = λβ f (6f) τ 2 f = (1 -λ) 2 σ 2 f + µ 2 σ 2 d + 2µ(1 -λ)ρ f d σ f σ d (6g) τ 2 d = (1 -µ) 2 σ 2 d + λ 2 σ 2 f + 2λ(1 -µ)ρ f d σ f σ d (6h) ρ v τ f τ d = λ(1 -λ)σ 2 f + µ(1 -µ)σ 2 d + (1 -λ -µ + 2λµ)ρ f d σ f σ d (6i)
Here τ 2 f , τ 2 d and ρ v represent the variances, and the correlation between v f and v d , respectively. It can be seen that for λ and µ close to zero, the correlation coefficient ρ v can be approximated by a first order Taylor series expansion:

ρ v ≈ ρ f d + σ f σ d λ + σ d σ f µ.
This expression makes apparent that the correlation coefficient between v f and v d has three components. The first term relates to the correlation between the error terms in the underlying model ('true' effects), whereas the second and third correspond with the substitution parameters λ and µ. Thus, the observed correlation between the We incorporate two refinements into the model specification (5) in order to take account of the fact that smaller firms are likely to witness more variation in their enrolment rates than larger firms. This is simply a consequence of the law of large numbers. The enrolment rate of a firm is identical to the average enrolment rate of the firms' employees, and, while the law of large numbers applies to the latter, the variance of this average decreases at the rate of the size of the firm. Thus, we have a case for heteroscedastic error terms depending on firm size n, which is specified as follows:

ln τ f = α f + η ln n (7a) ln τ d = α d + η ln n (7b)
As is well-known, the law of large numbers predicts that η should be close to -1 2 . However, other factors may impact this parameter as well. For instance, if the (unexplained) variation in enrolment rates is larger for large firms than for small firms, then η will be closer to zero. If indeed η > -1 2 , this may have two explanations. First, it may imply that larger firms are more heterogeneous than smaller firms (between-firm heterogeneity), and second, it may imply that a given large firm has a relatively fanciful course over time of the concerning enrolment rate (within-firm heterogeneity). An analogous reasoning obviously applies for τ d .

The second refinement deals with the large share of zero observations in our data set (see Table 1). To take account of this we will estimate (5) as a Tobit; that is, the error terms v f and v d are (left-) censored at -(γ 0f x 0 +γ f f x f +γ df x d ) and -(γ 0d x 0 +γ f d x f +γ dd x d ), respectively. This will be of particular relevance for smaller firms, which have a higher likelihood of witnessing a zero enrolment rate. Note however that a Tobit version of (5) will improve the model fit for larger firms as well, as these still face a positive probability to observe no enrolment into DI or UI in a given year.

Estimation procedure

Our estimation procedure consists of two steps. In the first step we estimate (5) directly, i.e. without imposing any restrictions on coefficients. This produces maximum likelihood estimates of the parameter vector

θ 0 = (γ 0f , γ f f , γ df , γ 0d , γ f d , γ dd , α f , α d , η, ρ v ).
In the second step we use these reduced form estimates to recover the structural parameters in θ = (β 0f , β f ,

β 0d , β d , σ f , σ d , ρ f d) by Minimum Distance estimation.
In the first step, we will exploit the panel dimension of our data and make an attempt to correct for unobserved heterogeneity through the use of Mundlak time-averages [START_REF] Wooldridge | Econometric Analysis of Cross Section and Panel Data[END_REF]. Because the panel dimension of our data is at the level of collective labour agreements (CLA's) we will only be able to control for unobserved heterogeneity insofar this is at the CLA level. The CLA level is in some instances equivalent to the firm level, but in other instances the CLA concerns more than one firm. However, it is typical that multiple firms being subject to one particular CLA are much alike. A more extensive discussion on this approach to correct for unobserved heterogeneity is found in [START_REF] Koning | Hidden Unemployment in Disability Insurance in the Netherlands; An Empirical Analysis Based on Employer Data[END_REF]

f it = max 0, γ 0f x 0,it + γ f f x f,it + γ df x d,it + c f,i + v f,it (8a)
d it = max 0, γ 0d x 0,it + γ f d x f,it + γ dd x d,it + c d,i + v d,it (8b)
v it =    v f,it v d,it    ∼ N (0, Σ v ) , with Σ v = e 2ηJ(n) •    e 2α f ρ v e α f +α d ρ v e α f +α d e 2α d    (8c)
where

c f,i = δ 0f x0,i + δ f f xf,i + δ df xd,i (9a)
c d,i = δ 0d x0,i + δ f d xf,i + δ dd xd,i (9b)
Here x•,i denotes the time average value of the vector x •,i for a given collective labour agreement i. The log-likelihood equals where

(θ 0 ) = i,t ln φ 2 f it -ω f,it τ f , d it -ω d,it τ d , ρ v I f,it I d,it +   ln Φ   - ω f,it τ f -ρ v d it -ω d,it τ d 1 -ρ 2 v   + ln φ d it -ω d,it τ d -ln τ d   (1-I f,it )I d,it +   ln Φ   - ω d,it τ d -ρ v f it -ω f,it τ f 1 -ρ 2 v   + ln φ f it -ω f,it τ f -ln τ f   I f,it (1-I d,it ) + ln Φ 2 - ω f,it τ f , - ω d,it τ d , ρ v (1-I f,it )(1-I d,it ) , (10) 17
ω f,it := γ 0f x 0,it + γ f f x f,it + γ df x d,it + δ 0f x0,i + δ f f xf,i + δ df xd,i ω d,it := γ 0d x 0,it + γ f d x f,it + γ dd x d,it + δ 0d x0,i + δ f d xf,i + δ dd xd,i τ f := e α f +ηJ(n) τ d := e α d +ηJ(n) I f,it := 1{f it > 0} I d,it := 1{d it > 0}.
Optimisation of this log-likelihood function over

θ 0 = (γ 0f , γ f f , γ df , γ 0d , γ f d , γ dd , α f , α d , η, ρ v )
. produces consistent parameter estimates.

In the second step we make use of exclusion restrictions to identify the parameters λ and µ. Looking at (6), it can be easily seen that β f needs to have at least one element in order to identify λ. The same reasoning holds for β d and µ. Thus, the fact that we are able to make use of variables exclusively affecting one of the two enrolment rates allows us to identify the level of substitution. In the next section we will further discuss the variables on which exclusion restrictions are imposed. For a more extensive discussion of this identification strategy the reader is referred to [START_REF] Koning | Hidden Unemployment in Disability Insurance in the Netherlands; An Empirical Analysis Based on Employer Data[END_REF]. Now let us rewrite the restrictions shown in equation ( 6) as g(θ) = θ 0 .

Note that these equations do not include any restrictions on the heteroscedasticity parameter η, which means that we implicitly assume that the heteroscedastic component in τ f and τ d defined in (7) carries over to the error terms in the original model in (4c

Ψ(θ) = [ θ0 -g(θ)] Σ-1 v [ θ0 -g(θ)] , ( 11
)
where θ0 is the maximum likelihood estimate of the reduced form model based on (10), and Σ-1 v is the associated covariance matrix. The resulting parameter estimates θ are consistent and asymptotically normally distributed with covariance matrix

Ĉ = [ Γ Σ-1 v Γ ] -1
where Γ = [ ∂g(θ) ∂θ ] θ= θ [START_REF] Chamberlain | Panel data[END_REF]. Minimisation of (11) takes place using the same routines as regular Maximimum Likelihood estimation. The likelihood is then defined as -Ψ(θ), and the number of observations and the number of parameters equal dim(θ 0 ) and dim(θ), respectively.

Estimation results

Reduced form parameters

Maximum Likelihood estimation results for the reduced form model ( 8) are shown in Table 2. Apart from firm size, year, and workforce composition variables, we have also included time averaged variables per collective labour agreement in order to control for unobserved heterogeneity. Looking at the coefficient estimates, firm size appears to be an important determinant both of UI and DI enrolment. The concerning dummy variables show a monotonic pattern, with large firms facing higher enrolment rates into both schemes than small firms. As expected, firm size also affects the variability of enrolment rates (η), with small firms facing more variability than large firms. This variability however decreases less quickly for larger firms than would be expected on grounds of the law of large numbers, implying that either large firms have relatively volatile enrolment rates or large firms are more heterogeneous than small firms. Furthermore, it is found that firms with relatively few females in their ranks face higher UI enrolment rates.

On the other hand, the DI enrolment rate does not seem to be affected by the gender composition of the firm's workforce.

Firms with relatively many elderly workers face lower UI and DI enrolment. UI enrolment is lower in firms with relatively many workers over age 50, and for DI enrolment this holds for firms with relatively many workers over age 60. A probable reason for this finding is that official early retirement schemes act as substitutes for UI and DI [START_REF] Woittiez | Labour Force Exit Routes of the Dutch Elderly: A Discrete Choice Model[END_REF][START_REF] Kerkhofs | Retirement, financial incentives and health[END_REF]. Furthermore, a positive effect on DI enrolment is found for firms with relatively many workers in the ages between 30 and 60. We may thus conclude that firms with relatively many young workers (under age 30) and old workers (over age 60) face lower DI enrolment rates than other firms.

Firms with a large share of part-time workers working less than 20 hours per week have significantly higher enrolment rates in both schemes. However, we were not able to include time-averages of these variables due to limited variation over time, so that selection effects may play a role (i.e. unhealthy workers select themselves into part-time jobs). The same applies for the educational composition: the higher educated probably prefer less risky jobs in terms of becoming disabled. Thus, the negative signs found for a higher educated workforce with respect to DI enrolment does not necessarily imply a lower risk of this group.

Structural parameters

As was considered in the previous section, the estimation results just discussed can be used to obtain estimates of structural parameters by means of Minimum Distance estimation (MDE). To this end, we have to impose at least two exclusion restrictions. That is, we should have at least one instrumental variable affecting DI enrolment but not UI enrolment, and at least one instrumental variable affecting UI enrolment but not DI enrolment.

(This is easily observed from equation ( 6): if γ f f , γ df , γ f d , and γ dd would be zero, then λ and µ could not be identified.) Our exclusion restrictions therefore demand that no direct causal relation exists between the instrumental variables and the concerning enrolment rates. If there might exist some spurious correlation then this is no problem, because we correct both for observed and unobserved heterogeneity. The latter occurs through the inclusion of time averaged variables in the model (cf. equation ( 9)). Our first exclusion restriction is in line with [START_REF] Koning | Hidden Unemployment in Disability Insurance in the Netherlands; An Empirical Analysis Based on Employer Data[END_REF], and assumes the wage distribution of the firms' employees not to affect their disability risk. The rationale behind this exclusion restriction is that the layoff decision of a firm is influenced by the wage level of its employees, in particular its relation to the productivity level. On the other hand, the 'health shock' leading to disability does not reasonably depend on the wage level of employees. Therefore, the finding in Table 2 that higher wages imply a higher incidence of DI enrolment is an indication of hidden unemployment.

Additionally, we postulate that function complexity exclusively affects disability risk. For instance, it could be expected that workers with relatively simple duties have a higher risk of becoming disabled. On the other hand, there is no reason to expect that the risk of unemployment is modified at different function complexity levels, given that the education level, the wage distribution, and other relevant factors are controlled for.

The results of the MDE routine are shown in Table 3. It is observed that the coefficients other than those used for identifying the structural parameters remain largely unaffected by the MDE routine, so that the discussion of the previous subsection still applies here. The correlation between the 'true' layoff and disability rate (ρ f d ) could not be identified within the estimation routine, and was found using a grid search. As can be read from the table, the optimal value for this variable equals 0, indicating that all correlation found between DI and UI can be explained by substitution effects. That is, no 'natural correlation' between UI and DI is found. Turning to the estimates of the structural parameters we are mostly interested in, we observe that a fraction of 3% of all desired dismissals is through the DI scheme (λ).

On the one hand this may look like a low figure, but then it should be taken into account that enrolment rates in UI are usually higher than in DI. Thus, if we apply equation (2b), it is seen that the figure of 3% translates into a share of hidden unemployment in DI enrolment of 26%. On the contrary, we find no evidence for hidden disability in the unemployment scheme (µ).

Hence, based on our point estimates the conclusion is that about a quarter of DI inflow is in fact hidden unemployment, while there is no evidence for reverse substitution. It is however noted that our point estimate for λ is surrounded by quite a wide confidence interval.

[insert Table 3 In this paper, we presented a model framework which allows us to estimate the amount of substitution between Disability Insurance (DI) and Unemployment Insurance (UI). In this model, a firm may either dismiss its employees through the UI scheme and/or lose some of its workers through the DI scheme, while allowing for substitution between both schemes in both directions. More specifically, a fraction of the desired dismissals may take place through the DI scheme, and a fraction of the disabled workers may end up in the UI scheme. The identification of these two respective fractions follows by the imposition of exclusion restrictions on certain variables, i.e. we assume these variables to affect one enrolment rate but not the other. In this paper we assumed that the firm's wage distribution exclusively affects UI enrolment, as their is no obvious explanation for health shocks to be correlated with wages, given that we correct for both firm and worker (observed and unobserved) heterogeneity. Note that [START_REF] Koning | Hidden Unemployment in Disability Insurance in the Netherlands; An Empirical Analysis Based on Employer Data[END_REF] have checked the robustness of this exclusion restriction by comparing outcomes with other plausible exclusion restrictions, and found no substantial differences between the alternative sets of instrumental variables. Our second exclusion restriction demands that function complexity should not affect the firm's UI enrolment rate given that we control for firm heterogeneity.

Using the approach just outlined, we have estimated the degree of substitution between DI and UI in the Netherlands for the period 1993-2002. To this end, we employed an administrative data set of the Netherlands' Labour Inspectorate. Our estimation results indicate that about 3% of firms' layoffs take place through the DI scheme, which implies that about one quarter 23 (1992) and [START_REF] Westerhout | Hidden unemployment in Dutch disability schemes[END_REF], but somewhat higher than that of Hassink et al. (1997). Anyhow, our point estimates suggest that hidden unemployment in DI was still relevant during the 1990s and early 2000s. Our data have however not allowed us to perform estimations for separate years, so as to uncover any time pattern in this level of hidden unemployment. Using an alternative dataset, [START_REF] Koning | Hidden Unemployment in Disability Insurance in the Netherlands; An Empirical Analysis Based on Employer Data[END_REF] managed to do this, and found that the level of hidden unemployment in DI has substantially decreased over the years.

The large number of policy measures taken during the 1990s were to a large extent meant to reduce erroneous admissions (or type II errors) into DI. However, as is known from statistical theory, the reduction of type II errors implies a greater risk of making type I errors, i.e. erroneous denials.

In this study we did however not find any evidence for hidden disability in UI. Note that with respect to the 2002 reform (see section 2) a similar finding was established in de [START_REF] De Jong | Screening Disability Insurance Applications[END_REF]: the reform proved effective in terms of reducing DI inflow but no additional UI inflow was found.

  scheme was[START_REF] Aarts | Economic Aspects of Disability Behaviour[END_REF], who used cross-section data from 1980

  criteria were tightened, and benefit cuts were introduced.

  policy measures just discussed may have had important (negative) effects on the use of DI.[insert Figure1about here] [insert Figure2about here]

  observe the number of enrolments in both Disability Insurance (DI) and Unemployment Insurance (UI) from the sampled workers.

  0 and λf 0 . Dividing these components by the actual enrolment fractions f and d yields the following expressions for the fractions of erroneous admissions 11

  UI and DI can be decomposed into three terms: the first one relating to some 'natural' correlation between unemployment and disability (ρ f d ), and the second and third relating to substitution between both schemes.

Page

  Introducing time and CLA subscripts t and i, respectively, we thus arrive at the following 16

  20

  enrolments observed in our sample was in fact hidden unemployment. This figure is low compared to the earlier estimates of Aarts & de Jong

Figure 1 :Figure 2 :

 12 Figure 1: Yearly DI and UI enrolment rates (source: UWV, CBS, own calculations)

Table 1 :

 1 Sample statistics AVO-data set, n=10437

			Mean Std.dev. Minimum Maximum
	UI enrolment	3.4%	7.6%	0%	100%
	DI enrolment	0.4%	1.9%	0%	50%
	UI enr. = 0; DI enr. = 0	96.2%
	UI enr. > 0; DI enr. = 0	3.4%
	UI enr. = 0; DI enr. > 0	0.4%
	UI enr. > 0; DI enr. > 0	0.0%
	ρ ( UI enr. , DI enr. )	-0.023
	"	in 1993	-0.011
	"	in 1994	0.180
	"	in 1995	-0.009
	"	in 1997	-0.011
	"	in 1998	-0.039
	"	in 2000	-0.022
	"	in 2001	-0.051
	"	in 2002	-0.038
	Workforce composition
	Age
	15-29		0.35	0.27	0	1
	30-39		0.28	0.19	0	1
	40-49		0.22	0.18	0	1
	50-59		0.13	0.15	0	1
	>=60		0.01	0.05	0	1
	Gender
	Female		0.36	0.33	0	1
	Education
	Primary		0.08	0.18	0	1
	Lower secondary	0.59	0.32	0	1
	Upper secondary	0.25	0.25	0	1
	Tertiary		0.09	0.16	0	1
	Part-time work
	less than 20 hours	0.30	0.25	0	1
	20-32 hours	0.09	0.15	0	1
	32 hours or more	0.61	0.30	0	1
	Income (Ln) a
	First Quartile	6.37	2.53	0	9.22
	Third Quartile	8.01	0.61	0	9.75
	a The logarithmic transformation is used for variables related to income.
			29

Table 1 :

 1 Sample statistics AVO-data set (continued)

		Submitted Manuscript		Page 30 of 34
		Mean Std.dev. Minimum Maximum
	Workforce composition (continued)
	Function type
	Production, technical,
	manual labour	0.40	0.38	0	1
	Administrative	0.11	0.16	0	1
	Automation	0.01	0.04	0	1
	Commercial	0.13	0.27	0	1
	Care, service	0.29	0.37	0	1
	Creative	0.01	0.07	0	1
	Managerial	0.06	0.08	0	1
	Function complexity
	Very basic	0.06	0.16	0	1
	Basic	0.20	0.28	0	1
	Less basic	0.41	0.33	0	1
	Difficult	0.24	0.26	0	1
	Complex	0.08	0.14	0	1
	Lower executive	0.02	0.06	0	1
	Middle executive	0.00	0.01	0	0.36
	Firm size
	1-4 employees	0.21	0.41	0	1
	5-9 employees	0.18	0.38	0	1
	10-19 employees	0.16	0.37	0	1
	20-49 employees	0.15	0.35	0	1
	50-99 employees	0.10	0.30	0	1
	100-199 employees	0.08	0.27	0	1
	200-499 employees	0.08	0.27	0	1
	500 employees or more	0.05	0.22	0	1
	Year Dummies
	1993	0.12	0.33	0	1
	1994	0.13	0.34	0	1
	1995	0.11	0.32	0	1
	1997	0.12	0.32	0	1
	1998	0.12	0.32	0	1
	2000	0.12	0.32	0	1
	2001	0.16	0.36	0	1
	2002	0.12	0.32	0	1
			30

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

Table 2 :

 2 Estimation results for whole sample(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002): reduced form parameters of Bivariate Tobit model forUI and DI enrolment (continued)

		UI enrolment		DI enrolment
		Coefficient St.error Coefficient St.error
	Time averages
	Age 30-39	0.089	0.043	0.155	0.033
	Age 40-49	0.036	0.035	0.125	0.033
	Age 50-59	0.083	0.039	0.016	0.041
	Age ≥60	-0.170	0.139	0.194	0.119
	Female	0.018	0.013	0.000	0.013
	First Quartile ln(wage)	0.015	0.002	0.012	0.003
	Third Quartile ln(wage)	0.002	0.010	-0.010	0.012
	Basic	0.010	0.032	0.084	0.031
	Less basic	0.005	0.026	0.024	0.024
	Difficult	-0.028	0.029	0.002	0.027
	Complex	-0.040	0.036	0.017	0.036
	Lower executive	-0.108	0.065	-0.002	0.071
	Middle executive	-0.114	0.268	0.128	0.225
	Other model parameters
	Constant	-0.457	0.083	-0.503	0.091
	τ f	0.204	0.004
	τ d	0.150	0.004
	ρ v	0.015	0.029
	η	-0.171	0.003
		32

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

Table 3 :

 3 Estimation results for whole sample(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002): structural parameters of Bivariate Tobit model for UI and DI enrolment

		UI enrolment		DI enrolment
		Coefficient St.error Coefficient St.error
	Age 30-39	0.010	0.012	0.048	0.014
	Age 40-49	-0.006	0.013	0.050	0.013
	Age 50-59	-0.054	0.014	0.063	0.016
	Age ≥60	-0.191	0.052	-0.034	0.056
	Female	-0.044	0.011	-0.009	0.012
	Lower secondary	0.007	0.011	-0.036	0.010
	Upper secondary	0.002	0.013	-0.040	0.011
	Tertiary	0.025	0.016	-0.046	0.015
	less than 20 hours	0.073	0.016	0.041	0.017
	20-32 hours	-0.015	0.017	0.008	0.018
	First Quartile ln(wage)	-0.016	0.001
	Third Quartile ln(wage)	0.019	0.003
	Administrative	0.016	0.012	-0.029	0.013
	Automation	-0.029	0.047	-0.048	0.041
	Commercial	-0.011	0.009	-0.007	0.009
	Care, service	0.016	0.007	-0.009	0.006
	Creative	-0.035	0.023	0.001	0.026
	Managerial	0.063	0.024	0.073	0.024
	Basic			0.053	0.030
	Less basic			0.047	0.022
	Difficult			0.028	0.025
	Complex			0.019	0.032
	Lower executive			-0.007	0.055
	Middle executive			-0.216	0.231
	5-9 employees	0.041	0.011	0.053	0.012
	10-19 employees	0.080	0.015	0.091	0.016
	20-49 employees	0.114	0.018	0.126	0.020
	50-99 employees	0.145	0.024	0.174	0.025
	100-199 employees	0.158	0.025	0.190	0.027
	200-499 employees	0.173	0.026	0.198	0.028
	≥500 employees	0.152	0.026	0.209	0.028
	Year = 1994	-0.159	0.012	-0.002	0.010
	Year = 1995	-0.160	0.013	-0.015	0.011
	Year = 1997	0.027	0.006	-0.012	0.007
	Year = 1998	0.029	0.007	-0.014	0.006
	Year = 2000	-0.002	0.006	-0.010	0.007
	Year = 2001	0.162	0.010	-0.014	0.010
	Year = 2002	0.146	0.010	-0.016	0.009
		33

Table 3 :

 3 Estimation results for whole sample(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002): structural parameters of Bivariate Tobit model forUI and DI enrolment (continued)

		UI enrolment		DI enrolment
		Coefficient St.error Coefficient St.error
	Time averages
	Age 30-39	0.076	0.047	0.150	0.036
	Age 40-49	0.035	0.038	0.136	0.037
	Age 50-59	0.077	0.041	-0.004	0.041
	Age ≥60	-0.178	0.144	0.235	0.120
	Female	0.019	0.013	0.008	0.013
	First Quartile ln(wage)	0.016	0.002	0.010	0.003
	Third Quartile ln(wage)	-0.002	0.010	0.002	0.012
	Basic	0.053	0.030	0.083	0.032
	Less basic	0.047	0.022	0.023	0.024
	Difficult	0.028	0.025	0.004	0.027
	Complex	0.019	0.032	0.010	0.036
	Lower executive	-0.007	0.055	-0.005	0.070
	Middle executive	-0.216	0.231	0.151	0.223
	Other model parameters
	Constant	-0.441	0.103	-0.431	0.107
	σ f	0.211	0.011
	σ d	0.153	0.012
	ρ f d	0.000	-
	λ	0.030	0.049
	µ	0.000	0.085
		34

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

Note that benefit levels are laid down in collective labour agreements, so that variation exists over different sectors / firms. Also note that, starting in 2006, the minimum disability percentage in order to qualify for DI benefits has has been raised

to 35.3 See, e.g.,[START_REF] Euwals | Early retirement behaviour in the Netherlands. Evidence from a policy reform[END_REF] for a discussion, and evaluation of these early retirement schemes.

Note that new reforms have taken place in 2004 and 2006, but this is beyond the scope of this paper as our data set ends in 2002 (see section 3).

The acronym 'AVO' stands for 'Research on terms of employment' (in Dutch: 'Arbeids Voorwaarden Onderzoek').

Of course, when aggregate predictions are to be made on the basis of our empirical analysis the stratified sampling scheme should be taken into account, e.g. by weighting the observations.

Acknowledgments

The authors thank Rob Euwals, Peter Kooiman, Aico van Vuuren, Adriaan Kalwij, and Peter Rijnsburger for useful comments and discussions, and Ali Aouragh and Janneke Rijn for research assistance.

24

Health, Education, and Welfare, J21 -Labor Force and Employment, Size, and Structure < J2 -Time Allocation, Work Behavior, and Employment Determination/Creation < J -Labor and Demographic


Keywords: employment, employment protection, hiring and firing regulation, unemployment JEL classification: E24, J23, J63, J64, J68 employment, employment protection, hiring and firing regulation, unemployment

We use the results of surveys among senior business executives to measure the strictness of hiring and firing regulations. The survey data are more likely than objective indicators (used in almost all previous studies) to correctly capture the de facto strictness of these regulations and their relevance to the performance of the labor market. Using data from 19 industrial countries for the period 1992 to 2002, we find that more flexible regulations are likely to lower unemployment and to increase employment rates. While the effects on the general population appear to be modest, the effects on female, young and low-skilled workers seem to be substantial.

Introduction

As is well known, the net effects of hiring and firing regulation on unemployment and employment cannot be unambiguously deduced from theory (see, e.g., [START_REF] Bertola | Job Security, Employment and Wages[END_REF]. Given the ambiguity of theoretical models, the direction and magnitude of the impact of hiring and firing regulations have to be resolved empirically. Although many empirical studies have analyzed their effects over the To capture these aspects and thus to shed new light on the unemployment and employment effects of hiring and firing regulations, this paper uses the results of surveys in which high-level managers characterized the strictness of the hiring and firing regulations of their respective economies.

Section 2 describes the data set and the empirical strategy, particularly discussing the strengths and weaknesses of the survey data. Section 3 presents and interprets the regression results. Section 4 concludes.

Data

To measure the strictness of hiring and firing regulation, this paper uses results from the World Economic Forum's Executive Opinion Surveys (EOS), which are carried out annually in a large number of countries to determine the international competitiveness of the relevant economies. The respondents are a company's CEO or a member of its senior management. In each country approximately 60 to 70 executives are interviewed. The industry structure of the companies questioned corresponds largely to the industry structure of the relevant economy (excluding the agricultural sector). Also, care is taken to question companies of various size categories and types (e.g., private and state-owned, domestically oriented and internationally active enterprises). The typical EOS question asks participants to indicate on a numerical scale which of the two statements specified in each case they favor. After the questioning, arithmetic means for each question are calculated from the answers for each country. The Box contains the questions on hiring and firing regulation used in the following analysis. The World Economic Forum used different scales in the years before 1997. For the purpose of our analysis, we converted all pre-1997 answers to the 1-to-7 scale used in the more recent surveys. The period covered by our empirical analysis is 1992 to 2002. In the years prior to 1992, the respective questions were phrased very differently. For example, in 1991 the EOS statement read: "Flexibility of management to adjust employment levels during difficult periods: 0 = low, 100 = high" (World Economic Forum and Institute for Management Development 1991). Thus the question did not explicitly refer to hiring and firing regulations. In 1990 the EOS statement read: "Flexibility of enterprises to adjust employment and compensation levels to economic realities: 0 = not at all, to 100 = a great deal" (World Economic Forum and Institute for Management Development 1990). Similarly, in the years 1984 to 1987 and in 1989 (there was no survey in 1988), the EOS statement read: "Flexibility of enterprises to adjust job security and compensation standards to economic realities: 0 = none at all, to 100 = a great deal" (EMF Foundation -The World Economic Forum 1984[START_REF]Report on International Competitiveness[END_REF][START_REF]World Competitiveness Report 1986[END_REF]World Economic Forum 1987;World Economic Forum and Institute for Management Development 1989). Thus before 1991, the questions did not exclusively refer to adjustments in employment or job security (and thus at least implicitly to hiring and firing regulations) but also to wage adjustments. Because of these substantial differences, we excluded the answers to the pre-1992 questions from our data set. The questions from the Executive Opinion Surveys 1992 to 2002 explicitly refer to hiring and firing regulations and are all phrased in a similar way (see Box). There are some slight variations but these are only refinements of style to make the questions more precise. Thus the answers to all 11 questions can be used simultaneously. -First, the selection of respondents is largely representative and the respondents have comprehensive knowledge of and practical experience with the hiring and firing regulations of their countries.

-Second, the questions are phrased objectively and, at the same time, permit a better coverage of the various facets of hiring and firing regulations than hard data. For example, a country's dismissal regulations comprise a large number of legal standards, administrative orders and court decisions. If only the duration of the statutory notice period and the amount of the severance payment for a worker with ten years' length of service are used as indicators, as was the case with [START_REF] Lazear | Job Security Provisions and Employment[END_REF], the degree of strictness of hiring and firing regulations is not completely and correctly measured. The OECD's (1999OECD's ( , 2004a) ) employment protection legislation (EPL) indicator, which has been used in almost all recent studies, is much more sophisticated than [START_REF] Lazear | Job Security Provisions and Employment[END_REF] indicator. Still, although the OECD's EPL indicator does an excellent job in measuring the de jure strictness of hiring and firing regulations, it is unable to capture their de facto strictness. As already pointed out, the de facto strictness also depends on the degree of enforcement of the law and on informal norms. For instance, in some countries, like France, advance notice before dismissal given orally is more important than the length of the notice period stated in the law. Informal norms and the degree of enforcement of the law cannot be captured at all by hard data. By contrast, the answers to the EOS questions appear to be well suited to capture the de facto strictness of hiring and firing regulations.

-Third, as the respondents decide on the recruitment and dismissal of workers, their answers are likely to reflect the relevance of the strictness of hiring and firing regulations to the performance of the labor market. In fact, the answers are likely to reflect this relevance better than objective For instance, when deciding on whether to hire additional workers on a fixed-term basis, the decisive aspect is neither the allowed maximum number of successive fixed-term contracts nor their allowed maximum cumulated duration, as stated by the law (aspects measured by the OECD's EPL indicator). What is decisive is whether the regulations on fixed-term contracts are flexible enough in the opinion of those who decide on recruitment and dismissal. As pointed out in section 1, employer's opinions are not only determined by domestic rules, formal and informal, and the manner the rules are implemented in practice. They are also influenced by (changes to) the international competitiveness of these rules. For example, if other economies have recently relaxed their hiring and firing regulations, employers may conclude that the domestic regulations have become relatively more burdensome, even if these regulations have not changed. They may thus relocate jobs abroad, leading to lower employment and higher unemployment in the domestic economy. Indeed, empirical studies have found that countries with more flexible hiring and firing regulations are able to attract more foreign direct investment [START_REF] Cooke | The Influence of Industrial Relations Factors on U.S. Foreign Direct Investment Abroad[END_REF][START_REF] Görg | Fancy a Stay at the 'Hotel California'? The Role of Easy Entry and Exit for FDI[END_REF]).

Of course, potential drawbacks also have to be considered in connection with the use of the Executive Opinion Surveys. One potential drawback is that the answers may be distorted by the state of the business cycle prevailing at the time of the questioning. For example, the managers of a country might judge the dismissal protection regulations favorably during a boom when they do not have to lay off many workers. In a subsequent recession, when they do have to substantially reduce staff, they might feel restricted by those regulations. Thus, they might then judge them less favorably, although the regulations have not been altered in the meantime. However, a closer look at the data suggests that the business cycle does not affect the EOS scores. A first indication is that the correlation coefficient between the variable output gap and the EOS-based variable hiring and firing regulation, at -0.21, is low and negative, not positive as could be expected if the EOS scores would be affected by the business cycle in the way described previously. Furthermore, presents four different regressions with the output gap as an independent variable and flexible hiring and firing regulation as the dependent variable. Regressions (1) and (2) use the fixed effects method to control for unobserved country effects. In regression (1), the coefficient on output gap is statistically significant at the 1% level, but it is negative as well. If we add log income per capita to control for the impact of the level of economic development, the coefficient on output gap becomes statistically insignificant [regression (2)]. It remains statistically insignificant if we apply the random effects instead of the fixed effects method [regression (3)]. 4 If we do not control for unobserved country effects but just for the level of economic development, the coefficient on output gap is statistically significant at the 1% level [regression (4)]. However, once again, it has a negative algebraic sign, contradicting the hypothesis that booms (recessions) lead to higher (lower) EOS scores. All in all, we can safely reject this hypothesis.

A second potential drawback of the EOS data is that each respondent could use his own yardstick when answering the questions. For example, on the 1-to-7 scale, an item marked 7 by one person may only be marked 5 by another. This is a concern particularly because only 60 to 70 managers were interviewed in each country. However, in the planning, implementation and analysis of the surveys, care was taken to ensure the use of a uniform yardstick. For one, the respondents were provided with a written explanation of the answering scale. Also, the answers were examined for robustness and consistency using various methods. In one of these checks, half of the responses in each country were randomly dropped from the sample. As the national EOS scores remained stable in the process, they have obviously not been distorted by individual peculiarities in responding (Cornelius and Warner 2000, p. 94;Cornelius and McArthur 2002, pp. 169-173;Blanke et al. 2003, pp. 372-375). Another concern is that there may be a systematic bias among respondents at the national level. For example, respondents in a country might have a similar biased assessment of the strictness of hiring and firing regulation if this topic has recently been discussed extensively and with a certain flavor in the press. Also, the questions may be interpreted differently in different countries. For these reasons, the survey results may not accurately reflect differences in national hiring and firing regulation. The authors of the Executive Opinion Surveys tried to avoid this problem by providing all respondents with a written explanation of the answering scale and by asking them to think in world terms rather than in national terms.

In order to check whether a "perception bias" exists, we compare the national EOS scores with related hard data. As an objective indicator, we use the OECD's (2004a) employment protection legislation (EPL) indicator. This indicator measures the strictness of protection against individual dismissals for workers with regular contracts and the strictness of regulation of temporary employment (fixed-term contracts, temporary work agency employment). 5 As mentioned previously, it solely uses objective data. For example, to measure the strictness of protection against individual dismissal, it takes into account, inter alia, the length of the notice period and the amount of severance pay at different lengths of tenure. To measure the strictness of regulation of temporary employment, it takes into account, inter alia, the maximum number and the maximum cumulated duration of successive fixed-term contracts as well as the maximum cumulated duration of temporary work agency contracts. The OECD converted these measures into cardinal scores that were normalized to range from 0 to 6, with higher scores representing stricter regulation. In As hiring and firing regulations are usually reformed only rarely, most of the variation results from differences across countries rather than from changes through time. Thus we first calculate country averages for the EPL indicator and the EOS scores. Figure 1 plots the EPL indicator on the horizontal axis and the survey results on the vertical axis. The graph shows a strong relationship between the two variables. Indeed, the correlation coefficient for these country averages is -0.84.

For example, the United States and the United Kingdom had both the lowest EPL scores and some of the highest EOS scores on average between 1992 and 2002. At the other end of the spectrum are Italy and Portugal, for example. On average between 1992 and 2002, these countries had both very high EPL scores and some of the lowest EOS scores. Thus in countries with strict (flexible) hiring and firing regulations, as measured by the EPL indicator, few (many) respondents thought that hiring and firing practices were flexibly determined by employers.

Figure 2 presents the corresponding correlation for the annual data. Unsurprisingly, the overall fit is somewhat weaker than for the country averages. Still, Figure 2 also indicates that higher (lower) levels of employment protection strictness, as measured by the OECD's indicator, are strongly associated with lower (higher) EOS scores. The correlation coefficient for the panel data is -0.68. Furthermore, Figure 2 illustrates that changes in hiring and firing regulation that have occurred in some countries during our period of investigation are reflected not only in the EPL scores but in the EOS scores as well. For example, in 1994 Spain relaxed procedural requirements for dismissals for economic reasons, shortened notice periods, permitted temporary work agencies and tightened rules governing renewals of fixed-term contracts. As a result, not only did its EPL score drop from 3.8 in 1993 to 3.1 in 1994, in addition Spain's EOS score increased from 2.32 in 1993 to 2.56 in 1994. In 1995, its EOS score increased further to 2.76, which is plausible because the Executive Opinion Belgium is a similar case in point. In 1997 it reduced restrictions on temporary work agencies and made fixed-term contracts renewable. As a result, Belgium's EPL score fell from 3.2 in 1996 to 2.2 in 1997. Concurrently, its EOS score increased from 2.63 in 1996 to 3.00 in 1997 and 3.26 in 1998.

The case of New Zealand also illustrates that substantial changes in the strictness of hiring and firing regulations are reflected in changes in EOS scores. Until 1999, New Zealand's hiring and firing regulations were relatively flexible. In 2000, the employment relations act tightened legislation on dismissals, fixed-term contracts and temporary work agencies. This reform is not only reflected in an increase in New Zealand's EPL score from 0.9 in 1999 to 1.5 in 2000 but also in a drop of its EOS score from 4.46 in 1999 to 3.00 in 2000 and 2.80 in 2001. The examples of these three countries illustrate that significant changes in the strictness of hiring and firing regulations are reflected in corresponding changes in EOS scores.

Overall, Figures 1 and2 indicate a close connection between perceived hiring and firing regulation and employment protection legislation, as measured by the OECD's indicator. Thus the subjective data from the Executive Opinion Surveys appear to be well suited for analyzing the impact of hiring and firing regulations on labor market performance. To be sure, as pointed out previously, they may have certain limitations. However, objective measures have shortcomings of their own and, on balance, may be less suitable. For instance, they measure only de jure strictness, whereas the EOS scores measure de facto strictness, which certainly is more relevant. Thus the judgments of highlevel business executives, as recorded in the Executive Opinion Surveys, provide an alternative characterization that may shed useful new light on the effects of hiring and firing regulations. To measure the performance of the labor market, we use not only the overall rates of unemployment and employment but also unemployment and employment rates relating to women, youths and the low skilled. Thus we analyze not only whether the strictness of hiring and firing regulation affects the general situation on the labor market but also whether it affects three demographic groups that usually have above-average unemployment rates.

We control for the impact of most other labor market institutions that have been considered in the recent literature. As previous empirical studies have shown, certain labor market institutions appear to have a considerable impact on the performance of the labor market (see footnote 1). By using adequate controls, we try to make sure as far as possible that the coefficients on flexible hiring and firing regulation are not biased due to omitted variables. To date, the OECD has undertaken the most extensive effort to quantify labor market institutions and has developed the best indicators.

Therefore, we use these data, although they are available for only 19 industrial countries (see Appendix A). The data cover the following labor market institutions (for variables definitions and sources, see Appendix B):

-trade union density, -collective bargaining coverage, -wage bargaining at industry level6 , -wage bargaining coordination, -tax burden on labor ('tax wedge'), -unemployment benefits replacement rates, -expenditure on active labor market policies per unemployed person. We use the output gap to control for the state of the business cycle. Additionally, we use log income per capita to account for the effects of differences in the level of economic development, e.g., with respect to the labor force participation of women. In the regressions to explain the youth employment rate, we also employ the tertiary enrollment rate. This variable is meant to account for varying academic education frequencies, which are likely to affect youth employment.

Finally, we control for unobserved country effects by using country-specific fixed effects. The fixed effects regressions, presented in the upper parts of Tables 2 and3, are the baselines for our empirical estimates. 7 To check whether other methodologies yield similar results, the lower parts of both tables present the coefficients on our variable of interest from FGLS regressions with countryspecific random effects and from pooled OLS regressions, respectively. In both cases, the control variables are the same as the ones used for the fixed effects regressions presented in the upper part of the tables. 8 We prefer the fixed effects model to the random effects model because in 5 out of 8 regressions, the Hausman test indicates that the random effects estimates may be biased (Tables 2 and3). We prefer the fixed effects model to the pooled OLS model because, first, the estimates from the latter model are likely to be biased as well in these five cases, and, second, because the 7 While controlling for the effects of most other major labor market institutions, the business cycle, the level of economic development and unobserved country effects goes a long way to avoid omitted variables bias, endogeneity still may be a problem for estimation because changes in unemployment and employment rates may lead to changes in the (perceived) strictness of hiring and firing regulation. Unfortunately, due to a lack of instruments we are unable to directly address this reverse causality problem. (This is in line with the previous literature, which does not use instruments either.) However, given the results presented in Table 1, it is unlikely that reverse causality is relevant in our case. (Unemployment and employment rates are strongly correlated with the output gap.) 8 To save space, the estimates for the controls from the random effects FGLS and the pooled OLS regressions are not presented in Tables 2 and3.

Results

Tables 2 and 3 present our multivariate regressions to explain unemployment and employment rates, respectively. Our results for the control variables largely accord with those obtained in many earlier studies. For example, in line with most previous studies we find that an increase in the tax wedge is likely to both raise unemployment and lower the employment level. 10 Furthermore, we find that generous unemployment benefit schemes appear to increase unemployment. This is also in line with most recent studies. 11 Additionally, while some of our estimates corroborate [START_REF] Calmfors | Bargaining Structure, Corporatism and Macroeconomic Performance[END_REF] hump hypothesis, overall the evidence in favor of this hypothesis is weak. This, too, accords with the previous literature [START_REF] Aidt | Unions and Collective Bargaining: Economic Effects in a Global Environment[END_REF].

We now turn to the results for our variable of interest. Indeed, the key finding of our empirical investigation is that hiring and firing regulation appears to have a statistically significant and robust effect on labor market performance. The coefficient on flexible hiring and firing regulation is statistically significant in all fixed effects regressions, in seven out of eight random effects regressions and in four out of eight pooled OLS regressions. According to our estimates, more flexible hiring and firing regulation is associated with lower unemployment among the total labor force as well as among female, young and low-skilled workers (Table 2). Similarly, it is associated 9 We also checked the robustness of our results by dropping the control variables from our baseline specifications one at a time. In these checks (results not reported here), the coefficient on our variable of interest remained very similar.

10 Important studies on the effects of labor taxes include, e.g., [START_REF] Daveri | Unemployment, Growth and Taxation in Industrial Countries[END_REF] and Prescott (2004). 11 See, e.g., [START_REF] Jenkins | The Relationship between Unemployment Benefits and Re-employment Probabilities: Evidence from Spain[END_REF], [START_REF] Lalive | Benefit Entitlement and Unemployment Duration: The Role of Policy Endogeneity[END_REF] and [START_REF] Nickell | Unemployment in the OECD since the 1960s. What Do We Know?[END_REF]. 14 with a higher level of employment among the overall working-age population as well as among women, youths and the low-skilled (Table 3). 12 Our estimates suggest that liberalizing hiring and firing regulation would have a notable, though generally modest, pay-off in terms of lower unemployment and higher employment. For example, according to the Executive Opinion Surveys, France had one of the strictest hiring and firing regulation in our sample of 19 industrial countries. During the period under review, its EOS score averaged 2.84. By contrast, Switzerland had one of the most flexible hiring and firing regulation. Its EOS score averaged 5.31. Switzerland also had substantially lower unemployment rates as well as substantially higher employment rates, both among the total population and among each of the three demographic groups. According to our fixed effects estimates, if hiring and firing regulation in France had been as flexible as in Switzerland, the French unemployment rate would have been 1.1 percentage points lower among the total labor force, 1.6 percentage points lower among women, 2.1 percentage points lower among young people and 1.7 percentage points lower among the low skilled, ceteris paribus. Additionally, France's employment rate would have been 1.1 percentage points higher both among the total working-age population and among women, 1.5 percentage points higher among young people and 2.1 percentage points higher among low-skilled workers, ceteris paribus. Of course, these figures should be interpreted with some caution. However, they illustrate the magnitude of the effects.

According to our results, flexible hiring and firing regulations are favorable for workers, particularly for female, young and low-skilled workers. If the regulations relating to fixed-term 12 As mentioned in section 1, the evidence from previous empirical studies, which almost exclusively use objective indicators, so far is mixed. By contrast, our results are less ambiguous. The main reason for this difference may be that our survey-based indicator is more likely than objective indicators to correctly capture the de facto strictness of hiring and firing regulations and their relevance to the performance of the labor market (see section 2). 15 contracts and temporary work agencies are flexible, these workers seem more often to have an opportunity to find a job via temporary employment contracts and thus to establish themselves in working life. If dismissal regulations are not too restrictive, employers seem to be more inclined to hire women, young people and low-skilled workers for an indefinite period, as they are in a position to rapidly dismiss them if the profitability of their employment proves to be inadequate or if the business situation deteriorates. In the end, these workers benefit from this planning scope of employers.

Our results corroborate those theoretical models according to which high costs of hiring and firing impair the employment situation. For example, extending [START_REF] Bertola | Job Security, Employment and Wages[END_REF] well-know model, [START_REF] Risager | On the Effects of Firing Costs When Investment is Endogenous: An Extension of a Model by Bertola[END_REF] have shown that an increase in hiring and firing costs ceteris paribus reduces the return on capital, lowering investment, labor demand and employment. As our results indicate that strict hiring and firing rules are likely to have an especially adverse impact on women, young people and low-skilled workers, they particularly corroborate [START_REF] Lindbeck | The Insider-Outsider Theory of Employment and Unemployment[END_REF] insider-outsider theory, according to which these kind of rules deteriorate the employment opportunities of these groups in particular.

Conclusion

This paper provides new evidence of the effects of hiring and firing regulations on unemployment and employment rates. In contrast to the previous literature, which almost exclusively relies on hard data, we use the results of surveys among senior business executives to measure the strictness of these kind of regulations. The survey data are more likely than objective indicators to correctly capture their de facto strictness and their relevance to the performance of the labor market.

Controlling for the impact of important labor market institutions, the business cycle, the level of (1997, 1998, 1999, 2005a). Flexible hiring & firing regulation: Survey responses from the World Economic Forum's annual Executive Opinion Surveys. In each country approximately 60 to 70 senior business executives were interviewed (see text for a more detailed description of the surveys). The answer scale ranges from 1 to 7, with higher marks on the scale meaning more flexible regulation (see Box for phrasing of questions). Source: World Economic Forum (1996,1997,1998,1999,2000,2002,2003), World Economic Forum, Institute for Management Development (1992Development ( , 1993Development ( , 1994Development ( , 1995)).

Log income per capita: The natural logarithm of gross national income divided by midyear population, converted into current international dollars using purchasing power parity rates. Source: World Economic Forum (1996,1997,1998,1999,2000,2002,2003), World Economic Forum and Institute for Management Development (1992Development ( , 1993Development ( , 1994Development ( , 1995)). Pooled least squares estimates with country-specific fixed effects. The lower parts of the table present estimates from FGLS regressions with country-specific random effects and from pooled OLS regressions, respectively. In both cases, the control variables are the same as the ones used for the fixed effects regressions presented in the upper part of the table. Data for 19 industrial countries from the years 1992 to 2002. Heteroskedasticity-consistent t-statistics in parentheses (White method), except for the pooled OLS regressions, where the figures in parentheses are heteroskedasticity-and autocorrelation-consistent tstatistics (Newey-West method). ***(**/*) denotes statistically significant at the 1%(5%/10%) level. All regressions also contain a constant term. See Appendix A for a list of countries. See Appendix B for variables descriptions and sources. Pooled least squares estimates with country-specific fixed effects. The lower parts of the table present estimates from FGLS regressions with country-specific random effects and from pooled OLS regressions, respectively. In both cases, the control variables are the same as the ones used for the fixed effects regressions presented in the upper part of the table. Data for 19 industrial countries from the years 1992 to 2002. Heteroskedasticity-consistent t-statistics in parentheses (White method), except for the pooled OLS regressions, where the figures in parentheses are heteroskedasticity-and autocorrelation-consistent tstatistics (Newey-West method). ***(**/*) denotes statistically significant at the 1%(5%/10%) level. All regressions also contain a constant term. See Appendix A for a list of countries. See Appendix B for variables descriptions and sources.

  2

  3

  number of reasons to assume that the answers of the Executive Opinion Surveys correctly reflect the strictness of hiring and firing regulations and thus may be superior to objective indicators:

  indicator of EPL strictness, it assigned the same weight to the regulation covering regular and temporary contracts.

  conducted in the early months of each year. Obviously, in the early months of 1995 the scope of the reform was more clearly visible than in the early months of 1994.

  Therefore, this paper complements previous studies of other researchers who have almost exclusively used objective measures.

  not control for unobserved country effects. Instead, it treats successive observations for each country as independent. 9

  unobserved country effects, we find that more flexible hiring and firing regulations are likely to lower unemployment and to increase the employment level. While the effects on the general population appear to be modest, the effects on women, young people and lowskilled workers seem to be substantial.

  low-skilled workers: Employed with less than upper secondary education as a percentage of the population with the same educational attainment. Persons aged 25 to 64 years. Source: OECD

Female

  employment rate: Employed women aged 15 to 64 years as a percentage of the female population in the same age bracket. Source: International Labour Office (2003), OECD (2005a; 2005b). Female unemployment rate: Unemployed women aged 15 to 64 years as a percentage of the female labor force in the same age bracket. Source: International Labour Office (2003), OECD (2005a).

  Source: World Bank(2005).Output gap: Deviations of actual GDP from potential GDP as a per cent of potential GDP. Source: OECD (2005c).Tax wedge: Income tax plus employee's and employer's social security contributions less cash benefits as a percentage of labor costs; one-earner family with two children; average production worker. Source: OECD (2004b).

  and Institute for Management Development (1991), The World Competitiveness Report 1991, Geneva: World Economic Forum, Lausanne: Institute for Management Development. World Economic Forum and Institute for Management Development (1992), The World Competitiveness Report 1992, Geneva: World Economic Forum, Lausanne: Institute for Management Development. World Economic Forum and Institute for Management Development (1993), The World Competitiveness Report 1993, Geneva: World Economic Forum, Lausanne: Institute for Management Development. World Economic Forum and Institute for Management Development (1994), The World Competitiveness Report 1994, Geneva: World Economic Forum, Lausanne: Institute for Management Development. World Economic Forum and Institute for Management Development (1995), The World Competitiveness Report 1995, Geneva: World Economic Forum, Lausanne: Institute for Management Development.

  : "Hiring and firing practices are: (0 = too restricted by government, 100 = flexible enough)" -EOS 1993, 1994 & 1995: "Hiring and firing practices are: (0 = too restricted by government, 10 = flexible enough)" -EOS 1996: "Hiring and firing practices are flexible enough (1 = strongly disagree; 6 = strongly agree)" -EOS 1997, 1998 & 1999: "Hiring and firing practices are flexibly determined by employers (1 = strongly disagree; 7 = strongly agree)" -EOS 2000: "Hiring and firing practices by companies are determined by employers. (1 = strongly disagree; 7 = strongly agree)" -EOS 2001 & 2002: "Hiring and firing of workers is: (1 = impeded by regulations, 7 = flexibly determined by employers)"

  Figure 1. Hard versus survey data on hiring & firing regulation -Country averages a)

Table 1

 1

	F o r
	P
	e
	e r
	R
	e
	v i e
	w

Table 2 . Fixed effects regressions to explain unemployment rates

 2

	a)

a)

Table 3 . Fixed effects regressions to explain employment rates

 3

	Page 31 of 32	Submitted Manuscript
	b) Swamy-Arora method.

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK a)

Page 33 of 32 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

	b) Swamy-Arora method.

As higher marks on the EOS scale indicate more flexible regulation, we label our variable of interest 'flexible hiring and firing regulation'.

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

A second version of the EPL indicator additionally includes specific requirements for collective dismissals. However, data for this broader indicator are only available since the late 1990s. This does not pose a major problem, though, as specific requirements for collective dismissals do not play a major role. Indeed, as the OECD (2004a, p. 72) has demonstrated, taking account of these specific requirements in the overall measure of EPL strictness does not affect cross-country comparisons much.

This variable is meant to test[START_REF] Calmfors | Bargaining Structure, Corporatism and Macroeconomic Performance[END_REF] hump hypothesis, according to which unemployment (employment) will be comparatively high (low) if wages are negotiated at the industry level.

Appendix A. List of countries

Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, United States.

Appendix B. Definitions and sources of variables

Active labor market policies: Expenditure on active labor market programs per unemployed person, divided by 1,000. Source: OECD (2004a).

Collective bargaining coverage: Percentage of salaried workers subject to union-negotiated terms and conditions of employment. Source: OECD (2004a).

Employment protection legislation: Indicator for strictness of protection against individual dismissals and for strictness of regulation of temporary employment (fixed-term contracts, temporary work agency employment). The indicator ranges from 0 to 6, with higher values representing stricter regulation. Source: OECD (2004a).

Employment rate: Employed aged 15 to 64 years as a percentage of the population in the same age bracket. Source: International Labour Office (2003), OECD (2005aOECD ( , 2005b)).

Tertiary enrollment rate: Students enrolled in tertiary education, regardless of age, as a percentage of the population of the age group that officially corresponds to this level of education.

Source : World Bank (2004).

Trade union density: Percentage of employees in trade unions. Source: OECD (2004a). Unemployment benefits replacement rates: Gross unemployment benefits as a percentage of previous gross wage earnings. Averages across two earnings levels, three family types, and three unemployment duration categories. Source: OECD (2004c).

Unemployment rate: Unemployed as a percentage of the civilian labor force (standardized rates).

Source: OECD (2005c).

Unemployment rate among low-skilled workers: Unemployed with less than upper secondary education as a percentage of the labor force with the same educational attainment. Persons aged 25 to 64 years. Source: OECD (1997, 1998, 1999, 2005a).

Wage bargaining at industry level: Industry level predominant in wage bargaining, dummy variable.

Source: OECD (2004a).

Wage bargaining coordination: Degree of coordination in wage bargaining. The indicator ranges from 1 to 5, with higher values representing a higher degree of coordination. Source: OECD (2004a).

Youth employment rate: Employed aged 15 to 24 years as a percentage of the population in the same age bracket. Source: OECD (2005aOECD ( , 2005b)).

Youth unemployment rate: Unemployed aged 15 to 24 years as a percentage of the labor force in the same age bracket. Source: International Labour Office (2003), OECD (2005a).