Discussion Paper No. 615

THE RISE (AND FALL) OF
LABOUR MARKET PROGRAMMES:
THE ROLE OF GLOBAL AND
DOMESTIC FACTORS

Noel Gaston
andGulasekaran Rajaguru

August 2004

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

THE RISE (AND FALL) OF LABOUR MARKET PROGRAMMES:
THE ROLE OF GLOBAL AND DOMESTIC FACTORS

Noel Gaston a,b,* and Gulasekaran Rajaguru baInstitute of Economic and Social Research, Osaka University, Osaka, Japanb

Faculty of Business, Bond University, Gold Coast, Queensland, Australia

Abstract
We study the political economy of labour market policies. First, it is shown that tax andredistributive considerations lead inside workers to prefer spending on active labour market
programmes to passive spending, e.g., on unemployment benefits. We also show that greater
active spending may be a feature of globalising economies. In the empirical work, panel data
for OECD countries are used to examine the relationship between active and passive labour
market spending, various measures of globalisation and controls relevant for analysing thepolitical economy of labour market policies.

Overall, we find that factors other thanglobalisation are more important determinants of labour market expenditures.

Keywords: Active and passive labour market policies; inside workers; panel VAR estimates
JEL classification: J68; H53; C33; P16* Corresponding author. Telephone: +61-7-5595-2220; facsimile: +61-7-5595-1160. E-mail address:
ngaston@bond.edu.au. The authors are grateful to the seminar participants at Griffith University,
the Institute of Economic and Social Research at Osaka University, the Kansai Labour Forum at theKansai Institute for Social and Economic Research, Nanzan University, Otaru University ofCommerce and the Research Institute of Capital Formation at the Development Bank of Japan for
their comments. In addition, conversations with Art Goldsmith, John Hillas and Jeff Kline wereparticularly helpful. Research assistance provided by Nicholas Gaston and David Modol-Flix isalso acknowledged. As usual, all errors, of omission and otherwise, are attributable to the authors
alone.

I.

Introduction
Our focus in this paper is the way in which labour market policies have responded topolitical and globalising pressures. Increased economic insecurity generates strong demands
for remedial government policies from affected groups of workers.

Labour market

intervention takes two forms. Passive labour market programmes (notably, unemployment
benefits) and the increasing use of active labour market programmes (e.g., job search or
relocation assistance and job training), employment policies or ‘workfare’ are intended toaddress the needs of workers in increasingly globalised economies. Thus, labour market
programmes can act as a palliative by providing income support or by increasing theemployability of unemployed workers.
The reliance upon different types of labour market policies and the generosity ofdifferent programmes is thought to be associated with increased economic openness for anumber of reasons. For example, increased economic integration (via trade and investment
liberalisation or via increased immigration) may increase workers’ fear of job dislocation, job
competition, income risk and economic insecurity (Rodrik, 1998). At the macroeconomic
level, there may be a need for adjustment and reform of the traditional microeconomic andlabour market policies. In addition, budgetary considerations may become more important ina more globalised economy. For instance, Blank and Freeman (1994) argue that some
European countries, in the face of increased international competition, tried to reduce the“generosity” of their social programmes.
The demands for certain types of labour market programmes are generally resisted by
the owners of capital. Passive labour market policies, such as unemployment insurance andincome support, as well as regulations on layoffs and union rights have long been a source ofpolitical conflict. The owners of capital tend to view passive labour market policies as
having few productivity benefits, as imposing potentially significant labour market

1distortions and as shifting the burden of taxation onto themselves. Policy-makers tend toview passive income support as weakening work incentives and extending periods of job
search for unemployed workers. In some countries this has motivated a shift in emphasis
away from passive labour market policies; a public policy recommendation outlined inOECD (1995).
The policy shift towards active labour market programmes is often associated with theremoval of labour market rigidities and the increasing flexibility of labour markets. The
goals, of course, are durable reductions in rates of unemployment and improvements in totalfactor productivity. On a practical level, it has been claimed that shifting emphasis awayfrom passive income support for unemployed workers to active labour market programmes
has been successful in Scandinavia, Austria and the Netherlands.1 However, not all countries
have redirected resources from passive to active labour market programmes. In addition, theincreases in active spending that have occurred have generally been quite small (OECD,
2001); while income replacement and ‘benefit dependency’ have been trending sharply
upwards in many countries (OECD, 2003).
An interesting perspective is provided by Saint-Paul (2000), who argues that the most
relevant conflict that lies at the root of modern labour market rigidities and the recent changes
to labour market institutions is that between various types of labour, and not the more
traditional conflict between capital and labour. For example, rather than being driven bysome sense of altruism towards their less fully employed compatriots, Saint-Paul argues thatcertain labour market institutions are supported by a group of employed workers or insiders
because it allows them to increase their own wages and welfare. In the next section wepursue this line of argument by extending Wright’s (1986) median voter model of
1

International Social Security Association (2004); see http://www.issa.int/pdf/iniative/2findings1.pdf. On theother hand, Calmfors et al. (2001) argue that the different types of active labour market policies have hadvarying degrees of success in Sweden. Overall, while active labour market policies may have reduced open
unemployment, some of the specific programmes may also have displaced regular employment.

2

unemployment insurance.

To preview, we show that insiders, with little exposure to

unemployment, will generally prefer a policy mix with a relatively greater emphasis on active
labour market programmes, rather than the customary forms of income support provided to
unemployed workers.
Naturally enough, the growth of active labour market programmes as well as therelationship between passive and active labour market policies depends on a variety of effects.
Whether labour market policy outcomes are driven by insiders or outsiders, the redistributive
consequences of labour market policies are extremely important. More generally, we showthat expenditures on passive and active labour market policies are likely to be positively
correlated. In addition, rising expenditures on active labour market programmes may well bea feature of economies undergoing globalisation. In large measure, this latter feature isdriven by tax and budgetary considerations.
Section II contains the paper’s empirical findings and its main contribution. To date,there has been relatively little empirical work on the determinants of spending on passive andactive labour market policies. The few studies that are related show some sensitivity ofdifferent types of social expenditures to increased international trade (e.g., Burgoon, 2001;
Gaston and Nelson, 2004). We add to these studies by using panel data for OECD countries
to econometrically investigate the effects of various measures of openness and controls
relevant for analysing the labour market policy-globalisation relationship on both passive andactive labour market policies, as well as the relationship between the policies. The lastsection provides a broad overview and contains some concluding observations.
II.

An Insider Model of Active Labour Market Policy
A. Government Spending on Active Labour Market Programmes: In this section we

extend Wright (1986) to illustrate the political determination of active labour market policies.
To begin with we assume a population of n workers consisting of two types, ni + no = n .

3

There are ni insiders who are always employed. There are no outsiders. The latter workers
may be either employed, e, unemployed, u, or in active labour market programmes, a.
The transition probabilities between the various states for an outside worker are as
follows. The probability of being laid off is λ . A proportion p of unemployed workers is
randomly placed in an active labour market programme, i.e., (1 − p) remain openly

unemployed. An outside worker finds employment with probability 1 − θ . For a worker inan active labour market programme, the probability of finding employment is 1 − α . We
assume that θ ≥ α , so that participation in a labour market programme may increase theprobability of finding a job.2 To summarise
eot = (1 − λ )eot −1 + (1 − θ )u ot −1 + (1 − α )a ot −1
u ot = λ (1 − p)eot −1 + θu ot −1(1)

a ot = λpeot −1 + αa ot −1 .
Using the fact that eot + u ot + a ot = 1 , the steady state probabilities for an outsider can beshown to beeo* = (1 − θ )(1 − α )∆−1u o* = λ (1 − p)(1 − α )∆−1(2)

ao* = λp(1 − θ )∆−1 ,where ∆ = [(1 − α )(1 + λ − θ ) + λp (α − θ )] . With p = 0, the results are identical to those of
Wright (1986). If θ > α , note that steady state employment increases in p. In this paper, p isthe decision-maker’s policy variable.
Following Saint-Paul (1998), we assume that the compensation for unemployedworkers equals c > 0 , whether they are in active labour market programmes or not. For
2

We abstract from any productivity benefits of active labour market programmes. In this section, intuition
may be aided by thinking of the policy as one which facilitates worker matching or the efficiency of job search(as in Saint-Paul, 1998).

4

simplicity, we also assume that c is fixed, i.e., we treat it as a contractually-determined
entitlement for an unemployed worker. The wage for employed workers is denoted by

w = w( E ) , where E = ni + no eo and wE ≤ 0 . The tax rate on earned incomes is denoted byτ ∈ (0,1) .
The expected utility of an outsider, not conditioned on their current employment
status, is given by

Vo = eoU (w(1 − τ ) ) + (1 − eo )U (c) ,where U(.) is a concave utility function.

(3)
The expected utility of an inside worker is

Vi = U (w(1 − τ ) ) .
In a steady state, the policy-maker’s balanced budget constraint is(ni + no eo )τw = (no (1 − eo ) )c + κp ,(4)

where the cost of administering active labour programmes is κ > 0 . Without loss ofgenerality, we assume that the latter cost does not vary with the number of unemployedworkers placed in active labour market programmes.
Now suppose that labour market institutions are determined by insiders. 3 Hence,labour market policies are chosen to maximise insiders’ post-tax incomes.

That is, pmaximises n (1 − eo ) 
κp
c −
.
w −  o ni + no eo  (ni + no eo )(5)

The advantage of a higher p is that it reduces the total number of unemployed workers.
The disadvantage is that it is a costlier policy. Differentiating equation (5) with respect to p,
setting to zero, rearranging and simplifying yields4
3

A few comments on the restrictiveness of some of model’s assumptions are in order. First, a simple way toendogenise c is to introduce a socially acceptable minimum payment to unemployed workers, c , say. Clearly,
if c is also chosen by insiders, then c* = c , since c does not affect the steady-state level of employment.
Secondly, if the balanced budget condition (equation (4)) does not bind, then the p (and c) chosen by insiders
would be entirely determined by the effect of the policies on wages.

5

p* =

where ω =wπ (nr + Eω ),
κ (1 − π )

(6)

pE p
pno eopwE E≤0, π ==≥ 0 (since eop ≥ 0 ) and c = rw, r ∈ (0,1) .5( ni + n o eo )E
w

B. The Determinants of Active Labour Market Spending: First, p * falls as πapproaches zero. (Note that θ = α and eop = 0 implies p* = 0 .) Hence, the more successful
are active labour market programmes in lowering total public spending on unemployedworkers, the greater the support for spending on active labour market policies. This effectarises due to a tax effect. Next, observe that p * rises in both w and ω . The former effectcaptures the fact that higher wages increase the tax base, affording greater expenditure on
labour market programmes. The latter effect represents an adverse wage formation effect.
Active labour market programmes may raise the supply of better-trained workers. If outside
and inside workers are substitutes, then this would increase job competition for currently
employed workers, so that wages fall in p (see Calmfors, 1995; Calmfors and Lang, 1995).
Thus, an adverse wage effect lowers the demand for active spending.
Ceteris paribus, p* rises in λ , θ and α .

Consequently, higher steady state

unemployment raises the demand for active labour market policies. Also, the more generous
are benefits paid to unemployed workers, the greater the support for active labour market
policies.6 Once again, both of these latter effects are driven by tax considerations. Next, p *Sufficient conditions for a maximum are that wages are concave in employment and that κ is small.
Sufficient for p* > 0 is π ≤ 1 and ω ≥ −r .
45r is the replacement ratio. We substitute c = rw after differentiating equation (5). The economic
interpretation is that private agents regard c as fixed, even though the policy-maker ultimately chooses c so that
it turns out to be a given percentage, r, of w.
6

In this model, the relationship between spending on active and passive labour market policies is affected by
two conflicting pressures. On the one hand, spending on the two policies is negatively correlated due to thebalanced budget constraint (i.e., as p* increases (1 − p*) falls). On the other hand, spending on the two policies
is positively correlated due to the higher benefits paid to all unemployed workers, c (i.e., equation (6)).

6falls in κ . This effect captures the fact that higher costs associated with active labour market
programmes, lower the demand for them.
Finally, consider the effects of globalisation. From the factor price equalisation
theorem, the effects of integration with an unskilled labour abundant country on domestic
labour market policies can be captured by an increase in no, i.e., an immigration of outside
labour.

For sufficiently small values of ω , an increase in no raises p * .

It is alsostraightforward to show that π no ≥ 0 and ω no < 0 (in the latter case, as long as the demand

for labour is not “too” convex). The first effect serves to raise p * , while the second effectserves to lower it. The latter effect implies that increased labour market competition and an
erosion of wages will lower the use of active labour market policies. However, note that if
insider’s wages, as well as their job security, are insulated from an increase in the number ofoutsiders then any adverse wage response, ω , will likely be small. It then follows that thepossibility of higher labour market expenditures on unemployed workers results in a shift
towards policies that lower steady state unemployment. Thus, globalisation, at least as it ismeasured here, would be associated with greater spending on active labour market policies.
C. Overview: Our results are generally unsurprising, in the sense that inside workers
will oppose any labour market policy associated with a higher tax burden. This results fromthe binding balanced budget constraint. For example, higher unemployment benefits imply
higher taxes on earned incomes. Consequently, if the employed are politically influential,
this will lead to political pressure to reduce the generosity of benefits. This has been termed a
redistribution effect or tax effect (Saint Paul, 1996). Additionally, if unemployment benefits
are difficult to reduce, then active labour market policies which lower the steady state
unemployment rate garner more political support. This effect is likely to be offset by an
exposure effect or insurance effect. That is, the more exposed insiders are to unemployed (or7in fact, if they are not the politically pivotal voters), the greater the relative preference forpassive labour market spending.7
The other major finding of this section is that the responsiveness of wages tounemployment is also important for understanding the effects on labour market policies.
When wages are characterised by some degree of insulation to labour market competition,such as in economies with strong unions, both passive and active labour market programmes
will generally be more generous. Albeit, for different reasons.8
Likewise, an increase in the efficiency of active labour market programmes, proxiedby a reduction in κ , say, will lead to an increase in both active and passive labour market
programmes. 9 However, if globalisation places an effective limit on the extent to whichearned incomes can be taxed, then this implies that active and passive types of labour market
policies are substitutes, rather than complements for one another.
Apart from the effects of globalisation, several predominantly domestic factors
influence the direction and level of both passive and active labour market programmes, aswell as the relationship between them. Tax and redistribution effects bear directly on the7

It is interesting to note that if were to denote the insider population ni to be permanently unemployed, rather
than always employed, then p* = 0. This occurs because, for any given τ, a higher p reduces taxable wages andincreases programme costs by κdp. Intuitively, if outside workers were the politically influential voters, thenp* would lie between the p chosen by the permanently employed (i.e., equation (6)) and the permanently
unemployed (i.e., p* = 0). More formally, outsiders maximise U (c) + e o [U (w(1 − τ ) ) − U (c)] . The first ordercondition is e oU ' (w(1 − τ ) )( p o − p*) + e op [U (w(1 − τ ) ) − U (c)] = 0 , where p* is given by equation (6). Sincee op > 0 , then p o < p * , i.e., outsiders would choose a lower p than insiders do.
8

Using a lobbying model, Gaston and Nelson (2004) show that unions and employers pressure policy-makers
to ease tax burdens and cap increases in unemployment benefits when wage bargaining is decentralised. Whenthe risk of unemployment is lower and collective bargaining is more centralised, the demand for publiclyprovided unemployment insurance is higher because workers prefer higher employment risk-higher wage
contracts.
However, this effect is weighted by (θ − α ) . In fact, if active labour market programmes do not appreciably
increase steady state employment, then the tax effect is negligible. Government-funded training programmes in
the United States are commonly found to have negative rates of return (e.g., LaLonde, 1995). Specifically,
participants are no less poor or do not have significantly lower probabilities of unemployment after completionof the programmes. It seems somewhat paradoxical then, that generally speaking, government-sponsoredtraining schemes are politically popular. On the other hand, Fougère et al. (2000) find that the training
component of French active labour market programmes has small productivity and employment effects, but thatthe programmes do lower wage costs for employers.
98generosity of unemployment benefits and spending on active labour market programmes.
There are also wage formation effects that are likely to be more pronounced in moreunionised economies. Finally, the labour market programmes themselves are interrelated.
The connection between the policies is not only determined by the government’s budget
constraint, but also through the way in which the labour market policies influence steady state
unemployment rates and hence, worker welfare. In the next section, we seek to uncover theempirical relationship between the types of labour market policy as well as to investigate theprimary determinants of both active and passive labour market policies.
III.

The Empirical Findings

In this section, we use cross-sectional, time-series data for OECD countries toinvestigate various measures of openness, political variables and other controls relevant toanalysing the determinants of modern labour market policies.10 Before proceeding to theeconometric analysis, we provide a brief overview of the times series behaviour of active andpassive labour market policies in OECD economies. The trends are contrasted with the timeseries behaviour of measures of economic openness. We also provide a selective survey ofrelated empirical studies to further motivate our empirical specification and facilitatecomparison.
A. Data preview: First, figure 1 shows the data for each country for the most recent

year in our dataset for public spending on active and passive labour market measures. In
1998, as a share of GDP, expenditure on passive labour market programmes varied from alow of 0.25 percent (the United States) to a high of 3.37 percent (Denmark). Expenditure on
active labour market programmes varied from a low of 0.18 percent (the United States) to ahigh of 1.96 percent (Switzerland).
10

Our sample consists of the high-income OECD countries for which we could get data for both passive andactive labour market expenditures and other relevant controls: Australia, Austria, Belgium, Canada, Denmark,Finland, France, Germany, Ireland, Japan, Netherlands, Norway, Sweden, Switzerland, the United Kingdom andthe United States. All the data definitions and sources are described in Appendix 1.

9--- Figure 1 here ---At risk of oversimplification, in table 1 we show the broad trends in the data by
splitting our sample into two periods: 1980-89 and 1990-1999. Panel A of Table 1 showsthat, for most countries, spending on labour market programmes grew in the majority ofcountries.

Passive spending fell in 9 of the 16 countries in our sample.

In addition,comparing the two decades, active labour market programmes became more important than
traditional income support programmes in 11 countries.

We also note that average

unemployment rates were higher in 11 of the 16 countries for the 1990s. The latter fact has
been identified as a reason why policy-makers may have been reluctant, or unable, to further
reduce passive labour market spending (OECD, 2001).
Comparing decades again, panel B of table 1 shows that the measures of globalisation
display increases for nearly all countries over the same period. This suggests that increases intrade openness, FDI, portfolio investment and migration may have been associated withincreases in labour market expenditures, and with the rise of active labour market
programmes, in particular.
--- Table 1 here --B. Previous Studies: While there are numerous theoretical papers on the likely

determinants of active and passive labour market policies, there are few empiricalcontributions.

What empirical research has been done has tended to focus, perhaps

understandably, on the determinants of unemployment benefits or unemployment insurance.
To our knowledge, there are almost no studies which examine the determination of bothpassive and active labour market policies.
Saint-Paul (1996) uses panel data for OECD countries to examine the main
determinants of unemployment benefits. In particular, he examines the relative importance ofan exposure or insurance effect, i.e., the exposure of the employed to unemployment, and a10

wage formation effect. Saint-Paul finds stronger evidence for the latter effect, i.e., the moreinelastic is labour demand, the more generous are unemployment benefits. Fiscal effects,
measured by the gross government debt to GDP ratio, are also statistically significant.
Extending Saint-Paul (1996), Gaston and Nelson (2004) use panel data to investigate
whether trade openness and labour market institutions can also help explain within- andbetween-country differences in unemployment benefit entitlements. They find that both leftwing governments and the widespread coverage of workers by union bargaining raise
unemployment benefits.

These results are consistent with those in the large empiricalliterature in comparative political economy which focusses on the link between labour market
institutions, political orientation and welfare state outputs (e.g., Garrett, 1998; Swank, 2002).
Gaston and Nelson also find evidence of a positive relationship between trade openness andunemployment benefits, but a negative relationship between openness interacted with thebudget deficit and unemployment benefits. This suggests that an increase in government
indebtedness lowers the responsiveness of the benefit replacement rate to openness. That is,there is some evidence that globalisation acts as a constraint on the political economy oflabour market outcomes.
The latter result is consistent with Rodrik’s (1997, p.62) somewhat pessimistic
perspective on globalisation. While he finds a significant positive correlation among crosssectional measures of openness and welfare for OECD countries, panel data for the same setof countries suggest the opposite. That is, trade openness is significantly correlated withsizable reductions in social transfers, as is capital openness, albeit less so. Further, theinteraction of trade and capital openness is correlated with even more significant reductions
in welfare spending.
Burgoon (2001) provides an interesting analysis of various components of labour
market and social expenditures. He hypothesises that the nature of politics will be more11

consensual when it comes to the effects of globalisation on the components of social
expenditure which enhance labour productivity. On the other hand, the conflict between
capital and labour over policies which have no obvious productivity benefits, such as
traditional forms of income support, implies that greater openness will have indeterminate
effects. Overall, Burgoon finds that various measures of openness have a small effect onwelfare outcomes and that openness is far from the most important determinant of welfare
efforts in OECD countries. However, he does find evidence that training and relocation
expenditures are “less constrained” by various measures of openness than are social
expenditures, such as retirement benefits, health-care and family benefits.
D. Panel VAR: In this section, we econometrically investigate the determinants ofgovernment spending on active and passive labour market policies using panel data for 16
OECD countries and 20 years, 1980 to 1999.
Unit root tests, described in Appendix 2, suggest that our data are stationary. Hence,
we estimate a panel vector autoregressive (VAR) model to analyse the determinants ofspending on labour market policies.11 The multivariate VAR(q) model with fixed effects
takes the form
( j)
 Activei ,t  q  β AA
 = ∑  ( j)
 Passive 
j =1  β PA
i ,t ( j) Activei ,t − j  q ( j )η   φ   ε 
β AP + ∑ Β X i ,t − j +  A,i  +  A,t  +  A,i ,t  , (7)( j ) η   φ   ε β PP  Passivei ,t − j  j =1
 P ,i   P , t   P , i ,t 

where Active and Passive are, respectively, measures of government spending on active andpassive labour market programmes for country i (=1, …, N) at time t (=1, …, T). X i ,t − j iscomprised of lagged exogenous variables. In particular, we include variables suggested by
the theory developed in the previous section, e.g., government indebtedness which captures
tax effects, as well as measures of globalisation (trade openness, FDI, portfolio capital flows
and migration), unionisation and an indicator of the political orientation of the government.
11

We estimate a panel VAR model in preference to a panel vector error correction model, which requires thatvariables are non-stationary and cointegrated.

12

(Variable construction and the data sources are described in Appendix 1.) η i is a countryspecific fixed effect, φt is a time effect and ε i,t is a multivariate normally distributed random
disturbance. A fixed effects model, rather than a random effects model is estimated, as theη i ’s are likely to represent omitted country-specific characteristics which are correlated withother explanatory variables.12
The system of equations described by equation (7) assumes that the random error
terms are orthogonal to the country fixed effects and the time effects, as well as the lagged
values of the endogenous variables. Further, the errors are assumed to have positive varianceand to be uncorrelated across cross-sectional units and time. However, due to the likely
correlation between the lagged endogenous variables and the fixed effects in equation (7), theleast squares dummy variable estimator produces biassed parameter estimates. Accordingly,
we remove the fixed effects by differencing, i.e., equation (7) is rewritten as( j) ∆Activei ,t  q  β AA
 = ∑  ( j) ∆Passive 
j =1  β PA
i ,t ( j) ∆Activei ,t − j  q ( j ) ∆φ   ∆ ε 
β AP + ∑ Β ∆X i , t − j +  A , t  +  A , i , t  ,( j )  ∆φ   ∆ ε β PP  ∆Passivei ,t − j  j =1
 P ,t   P ,i ,t 

(8)where ∆ is the first difference operator, e.g., ∆X i ,t = X i ,t − X i ,t −1 .
Since the transformed lagged endogenous variables and the transformed error terms
in equation (8) may be correlated in panels with a limited time dimension (see Nickell, 1981;
Kiviet, 1995), we estimate the coefficients in equation (8) by the generalised methods ofmoments (GMM) technique proposed by Arellano and Bond (1991). This technique uses thepre-determined lags of the system variables as instruments to exploit a potentially large set ofover-identifying restrictions and provides consistent coefficient estimates (see Bond, 2002).
The errors in equation (8) satisfy the following orthogonality conditions
E ( Activei ,s ∆ε i ,t ) = E ( Passivei , s ∆ε i ,t ) = E ( X i , s ∆ε i ,t ) = 0, ∀s < t − 1 .
12(9)Hsiao (1986) shows that the generalised least squares estimator for the random effects model, under anassumption of independence between the fixed effects and the explanatory variables, is biassed.

13

Assuming serially uncorrelated errors, the orthogonality conditions imply that the vector of
instruments available to identify the parameters of equation (8) has the form
Z i ,t = [ Activei ,t − 2 ,..., Activei ,1 ; Passivei ,t − 2 ,..., Passivei ,1 ; X i ,t −2 ,..., X i ,1 ] .

(10)Letting Z i* be a block diagonal matrix whose tth block is given by equation (10), fort = 1,..., T − 2 , the matrix of instruments for each equation of the VAR is Z = ( Z 1* ,..., Z N* )′ .
The one-step GMM estimator for the k × 1 coefficient vector for each equation of the
VAR in equation (8) is given by13
~ ′

−1

~

~ ′

βˆ =  X * ZAN Z ′X *  X * ZAN Z ′Y ,


(11)



−1


1 N ′
where Y is a N (T − q − 1) ×1 vector of stacked dependent variables, AN =  ∑ Z i* HZ i*  , N i =1

with H a T − 2 square matrix with 2’s on the main diagonal, -1’s on the first sub-diagonal~
and 0’s elsewhere, and X * is an N (T − q − 1) × k design matrix stacked by cross-sectional
units with typical row~
X i*,t = [∆Activei ,t −1 ,..., ∆Activei ,t − q , ∆Passivei ,t −1 ,..., ∆Passivei ,t − q , ∆X i ,t −1 ,..., ∆X i ,t − q , ∆φt ] . (12)Finally, the asymptotic variance-covariance matrix of the GMM coefficient vector isgiven by

()

−1

−1

~′
~
~ ′
~ ~ ′
~
a var βˆ = N  X * ZAN Z ′X *  X * ZAN V N AN Z ′X *  X * ZAN Z ′X *  ,





(15)

N
′
′
where V N = N −1 ∑ Z i ∆ε i ∆ε i Z i and the ∆ε i ’s are the GMM residuals.
i13

Bond (2002) notes that most applied work using GMM employs the one-step estimator rather than the twostep estimator. Arellano and Bond (1991) use simulations to show that only modest efficiency gains result fromusing the two-step procedure, even in the presence of heteroscedasticity.

14

C. The results

The optimal lag length q is determined by nested likelihood ratio tests.14 In all cases,
we find that q = 1 is optimal. The GMM estimates for the panel VAR(1) are reported intables 2 and 3. For T > 3 the model is over-identified and the validity of the assumptions
used to estimate equation (8) can be tested using the standard GMM test of over-identifying
restrictions or a Sargan test. From the Sargan test statistics and the p-values reported intables 2 and 3, the null hypothesis that the moment conditions are valid (i.e., equation (9)) isunable to be rejected. In this context, the key identifying assumption that there is no serial
correlation in the ε it disturbances can be tested by testing for no second-order serial
correlation in the first-differenced residuals (Arellano and Bond, 1991). 15 The results
generally show the absence of serial correlation and that the estimated models satisfy thestandard assumptions.16--- Tables 2 and 3 here ---In table 2, we present estimates for two measures of spending on active and passive
labour market policies.

They represent alternative ways of normalising labour market

expenditures by the size of an economy. In the second and third columns, we report theresults for nominal labour market expenditures divided by nominal GDP. In the fourth andfifth columns, we report the results for real labour market expenditures divided by the size ofthe labour force. As can be quickly gathered from the sign patterns of the significant results,
14

The correct lag length is critical for the panel VAR since excessively short lags may fail to capture thesystem’s dynamics, lead to omitted variables, bias the remaining coefficients and be likely to produce seriallycorrelated errors. On the other hand, too many lags lead to a rapid loss of degrees of freedom and to overparameterisation. See Holtz-Eakin et al. (1988) for more detail about the nested likelihood test for lag length
selection.
Negative first-order serial correlation is expected in the first-differenced residuals if ε it is serially
uncorrelated.
15

16

In addition, note that the asymptotic standard errors reported in tables 3 and 4 are robust to
heteroscedasticity (see Arellano and Bond, 1991). However, as a check we regress the squared residuals on the
independent variables for each of the estimated models. Wald tests indicate homoscedastic errors. For the sakeof brevity, these test results are not presented here. (The results are available from the authors upon request.)

15

both measures paint essentially the same picture. In table 3, we focus our attention on theshare of active labour market spending as a proportion of all labour market spending. The
results in table 3 are particularly useful for discerning the factors which have leadgovernments to prefer one type of labour market programme over another. (Also, the model
specification used for table 3 is more directly relevant to the theory developed in section II.)First, labour market expenditures on both active and passive measures are pathdependent. This possibly reflects the persistence of spending on entitlement programmes andthe quasi-contractual nature of some types of social policies. Passive expenditures andtraditional forms of income support for unemployed workers spring to mind in this regard.
More interesting, is the fact that higher active expenditures tend to result in lower passive
expenditures. This seems consistent with the OECD’s policy recommendation that active
spending replace passive spending. More difficult to explain is the finding that laggedchanges in passive spending are unrelated to subsequent active spending. Once again, thismay point to the general difficulty of reducing passive spending prior to introducing newactive labour market policies. In addition, the quid pro quo for achieving reductions inpassive spending seems to be increased active spending.

Tax/Budget effects: These effects are crucial elements of political economic theories
of government spending on various policies. They are central to the papers surveyed in subsection B above, as well as the simple theory exposited in section II. Each country’s budget
acts as a hard constraint on labour market spending on active and passive programmes.
Higher Debt lowers both active and passive expenditures.

Table 3 reveals that higher

government indebtedness is particularly constraining for passive labour market expenditures.
However, we find that the unemployment rate, UR, is insignificant in both tables 2
and 3. This was expected. As Wright (1986) points out, the generosity of unemployment
benefits is influenced by two conflicting motives. The demand for insurance and income

16support rises with any factor which increases the risk of unemployment for inside workers.
In contrast, higher unemployment benefits imply higher taxes on earned incomes.
Consequently, if insiders are politically influential, this will lead to political pressure to
reduce the generosity of benefits. This is the redistribution effect or tax effect, as termed bySaint-Paul (1996). That is, economies with higher steady state unemployment rates cannot
afford to have generous unemployment benefits.
The findings for the Dependency ratio are also very interesting. This variable ismeasured as the percentage of the population younger than fifteen years or older than sixtyfour years. In the political economy literature (Burgoon, 2001, e.g.), a higher Dependency

ratio is thought to be positively correlated with spending on policies directed at the elderly or
the young (such as retirement benefits or family benefits, respectively). The results indicatethat a greater Dependency ratio is associated with higher passive spending and lower active
spending. For OECD countries, since the higher values of the Dependency ratio are likely to
be positively correlated with older workforces, we speculate that our findings reflect the factthat active spending has lower potential returns for economies with demographically older
workforces. That is, in such economies support for unemployed workers may be more
optimally directed at traditional income support rather than retraining and job search
assistance.

Exposure/Insider and Wage effects: Obviously central to our theory is that labour
policy outcomes are largely driven by the preferences of insiders. Saint-Paul (1996, p.310)
notes that one typically expects unemployment benefits to decrease with the unemployment
level, but to increase with the change in unemployment, DUR. As mentioned, the former
effect captures a tax effect. If the level of unemployment is controlled for, Saint-Paul argues
that the latter effect captures the exposure of currently employed workers to the risk of
unemployment. The findings in table 2 are consistent with this line of argument, in the sense17that greater insider exposure stimulates demand for greater expenditures on all programmes
devoted to the unemployed, i.e., both active and passive measures. Table 3 further indicates ashift in workers’ preferences towards relatively greater insurance and passive spending, themore exposed employed workers become to the risk of unemployment (a result consistent
with both Wright, 1986 and Saint-Paul, 1996).
The effects on labour market policies of the percentage of the workforce that isunionised can be thought to capture both insider and wage effects. To the extent that Union
is positively correlated with insulating workers from the rigours of competitive labour
markets, the Union effect should boost both passive and active spending. As in Gaston and
Nelson (2004), Union boosts passive spending because higher unemployment benefits
increase the value of the union worker’s threat point in a bargaining game with employers,
increasing the negotiated wage. Additionally, the theory in section II suggests that Union
will insulate the wages of inside workers from the greater labour market competition that
results from the workers graduating from active labour market programmes. Table 3 reveals
that these effects may be largely compensatory when it comes to the share of active spending
in total spending.

Globalisation effects: Our results for the four variables which are used to capture theeffects of globalisation are uniform in the sense that they have no statistically significant
effect on labour market expenditures. At most, the effects of Migration may have resulted ina slight and marginally significant shift towards spending on passive programmes. In line,with the section II theory, the overall absence of globalisation effects may reflect theoffsetting wage and employment effects of active labour market policies. However, it seems
safer to conclude that the various measurable elements of globalisation have had no
indiscernible effects on the formation of labour market policies. To the extent that we have18

been able to identify factors which have driven changes in labour market policies, they are
not the factors directly related to the internationalisation of economies and labour markets.17

Politics and partisan effects: Finally, we found that our political variable had no
statistically significant effect on labour market policy formation. In some sense, this is one ofour more unexpected results because Left is normally associated with policies which are morefavourable to labour. There are also anecdotes which seem to indicate distinctly different
political preferences for labour market policies of governments of varying persuasion.18 On
the other hand, the fact that the reform of labour market policies seems to have been
occurring independently of partisan politics may reflect the ultimate importance of budgetaryand fiscal considerations.

Sensitivity analysis: In this sub-section, we discuss the results of tests that were
performed to check the robustness of our results. To see how sensitive our results are topooling the OECD countries, we first delete each country one at a time and compare theresulting model with the results reported in tables 2 and 3. Overall, this procedure did notaffect any of the signs of the coefficients reported in the tables.
Unsurprisingly, however, due to the smaller sample sizes, the regression results are
somewhat sensitive to country exclusions. Depending on the country, the coefficients ofsome variables became statistically significant, while others became statistically insignificant.
(It is important to reiterate that there were no sign reversals.) These results are summarisedin Appendix table 2.

17

We chose not to tabulate the results which show that the general pattern of insignificant results for the
globalisation variables pertains when the openness measure is replaced by import penetration and the exportoutput ratio, and the measures for direct and portfolio investment flows are disaggregated into inflow andoutflow measures.
18

For example, prior to the change of government in 1996, Australia's share of active labour market measuresin total labour market expenditures was similar to that for many other OECD countries. Following a change ofgovernment in 1996, expenditures on labour market programmes were halved and the operation of labour
market programmes was privatised. See http://www.reformmonitor.org/index.php3?mode=status.

19

To illustrate how to interpret Appendix table 2, consider deleting the Netherlands(NL) from the sample. Doing so makes the coefficient for the Dependency ratio statisticallyinsignificant in the Active spending to GDP ratio equation. In addition, the coefficient for UR
becomes statistically significant in the Passive spending to GDP ratio equation. Hence, one
could conclude that the dependency ratio is a particularly important determinant of active
spending for the Netherlands.

Using a similar argument, the unemployment rate is an

insignificant factor driving changes in passive spending for the Netherlands. Overall, what is
immediately obvious from Appendix table 2 is that our most robust results are for thosereported in table 3. That is, the conclusions that were drawn about the determinants of therelative importance of active spending in total labour market spending are the most defensible.
Appendix table 3 contains estimates for the EU country and non-EU country subsamples. (For comparison, the pooled sample results from table 3 are repeated in column 2.)
Not only does this allow further investigation of the effects of pooling the data, but doing so
also enables us to examine whether countries in the EU behave significantly differently inpolitical terms than their non-EU counterparts. An obvious difference in the estimates for thetwo samples, that require a caveat to be placed on the conclusions drawn above, is that

Openness does appear to have played some role in the use of active labour market policies in
EU countries. Also, conservative governments in non-EU countries apparently favour the useof active rather than passive labour market spending.
IV.

Discussion and concluding comments

This paper has been concerned with passive and active labour policies, theirdeterminants and the relationship between them. In particular, we have focussed on theimpact that different elements of globalisation may have had on policies to help theunemployed. There are two competing perspectives on the relationship between the welfare
state and globalisation. The first is that globalisation places considerable stress on the20

welfare state, so that some social and labour market policies will display tendencies of a “raceto the bottom”. The effect of globalisation on certain types of workers, particularly organised
labour, is also generally taken to be negative.

Consequently, this may have negative

consequences for the welfare state. For instance, Tanzi (1995) argues that increased mobilityof capital not only erodes the tax base, reducing the welfare state’s ability to fund its
programmes, but by shifting taxes onto labour, the capacity of the State to redistribute is
reduced. In a similar fashion, Garrett (1998) has argued that, by forcing welfare states to turnincreasingly to borrowing to fund programmes, the international capital market ends up
imposing an increasing premium on large welfare states. In ways that are harder to quantify,
but seem prima facie plausible, the decreasing cost of the exit option increases the relative
power of business in policy-making (Huber and Stephens, 1998). Finally, it has been argued
that globalisation increases the general credibility of orthodox (i.e., market-oriented) policy
advice, thus reducing the plausibility of arguments supporting welfare state expansion andenhancing the credibility of arguments in favour of welfare state retrenchment (Krugman,1999).
The opposing view is that social policies, including labour market policies, respond inways such as to minimise any adverse consequences of globalisation for vulnerable workers.
A similar argument is that the classic, large welfare states developed in the context ofconsiderably more open economies than did the smaller, market conforming welfare states(Huber and Stephens, 1998). A plausible story advanced by some authors is that moregenerous unemployment benefits and changes to cash transfer and income tax systems havearisen to ensure acquiescence by the potential losers from globalisation and microeconomic
reforms, such as trade liberalisation (e.g., Rodrik, 1998). That is, greater “progressive”
redistribution may be “the price to pay” for political or social compliance with the labour
market and microeconomic reforms necessitated by globalisation.

21

Interestingly, Bordo et al. (1999) carry this argument further, suggesting that thepresence of sizable welfare states, and Keynesian macroeconomic policy, may have played an
important role in providing sufficient indifference to globalisation, that policies like support
for the GATT/WTO system and the Bretton Woods institutions continued even in the face ofrecessions that might have had system closing consequences in earlier eras. In addition, it
has been widely argued that heterogeneity of domestic political, as well as labour market,
institutions support heterogeneity of responses to globalisation (Calmfors and Driffill, 1988;
Garrett, 1998; Swank, 2002).
In this paper, we provided a simple theoretical model that shows that the growth ofactive labour market programmes as well as the relationship between passive and active
labour market policies depends on a variety of effects. All workers have a view to theiremployment status and the probability of future spells of unemployment, their earned
incomes, the wage formation effects of the government’s policies and the tax burdens
necessary to fund new and existing labour market programmes. In particular, when labour
market policy outcomes are driven by insiders, the redistributive consequences of spendingon labour market policies become extremely important. Tax effects bear directly on thegenerosity of unemployment benefits and the extent of active labour market programmes.
We also showed that, once again due to tax and budgetary considerations, the rise in active
labour market programmes may well be a feature of economies undergoing globalisation.
Finally, it was shown that passive and active labour market programmes areinterrelated. The relationship between the spending policies is not only determined by thegovernment’s budget constraint, but also through the way in which the labour market policies
influence steady state unemployment rates and hence, worker welfare.
In the empirical section of the paper, we used data for 16 OECD countries to estimatea panel VAR model to investigate the relationship between both passive and active labour

22market policies, various measures of openness and controls relevant for analysing the labour
market policy-globalisation relationship. We found a number of factors to be important. For
example, a determinant of the reliance upon active rather than passive spending policies, anda feature of economies that was devoted insufficient attention in this paper, seems to be thedemographic structure of each country’s labour force.
More importantly, for the purpose of this paper at least, our findings provide support
to the view that globalisation has had at best only a very modest effect on the formation of,and relationship between, passive and active labour market policies. Other effects appear to
be more important. For example, the extent of government indebtedness and the exposure of
employed workers to the risk of unemployment seem particularly important. The statisticalsignificance of these two determinants are indicative of the central role played by the burdenof taxation and the redistributive consequences associated with different types of labour
market policies.

23References

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26

Figure 1. Expenditures on Labour Market Policies
4

3.5

3

2.5
ActivePassive

% 2
1.5

1

0.5

0
AL

AU

BE

CA

DK

FN

FR

GE

IR

JA

NL

NW

SW

SZ

UK

Country

Note: Data are for active and passive labour market programmes as percentages of GDP. Data are for 1998, except for AL, CA and US
where the data are for 1999.

27

US

Table 1
A. Trends in Labour Market Programmes and Unemployment Rates (averages)

Active
AL
AU
BE
CA
DK
FN
FR
GE
IR
JA
NL
NW
SW
SZ
UK
US

Active ÷ Passive

Passive

UE rate1980-89

1990-99

1980-89

1990-99

1980-89

1990-99

1980-89

1990-99

0.33
0.27
1.33
0.44
0.91
0.97
0.75
0.84
1.46
0.16
0.88
0.60
1.65
0.14
0.69
0.18

0.55
0.36
1.29
0.55
1.59
1.51
1.17
1.34
1.41
0.18
1.16
1.08
2.39
0.46
0.48
0.20

1.07
0.83
3.17
1.77
4.55
1.09
2.14
0.81
3.13
0.39
3.05
0.61
0.74
0.20
1.40
0.57

1.39
1.06
2.73
1.54
4.41
3.28
1.88
1.46
2.51
0.33
2.85
1.08
2.09
0.95
0.83
0.40

0.31
0.33
0.43
0.25
0.21
0.97
0.35
1.05
0.47
0.59
0.30
0.90
2.32
0.85
0.55
0.34

0.40
0.35
0.47
0.39
0.37
0.51
0.63
0.95
0.57
0.52
0.41
1.09
1.20
0.64
0.63
0.53

7.23
3.30
11.12
9.31
8.08
4.82
9.05
6.06
14.00
2.50
9.79
2.75
2.72
0.61
9.67
7.17

8.60
3.82
11.35
9.49
7.61
11.74
11.17
7.78
12.10
3.04
6.11
4.81
7.52
3.14
8.08
5.72

28

B. Trends in Globalisation (averages)

Openness
AL
AU
BE
CA
DK
FN
FR
GE
IR
JA
NL
NW
SW
SZ
UK
US

FDI

Portfolio investment

Migration

1980-89

1990-99

1980-89

1990-99

1980-89

1990-99

1980-89

1990-99

33.04
74.16
133.15
52.48
67.72
56.70
43.85
54.97
106.8623.39
107.02
75.20
64.10
71.54
52.33
18.77

38.47
79.11
136.61
67.48
67.44
60.79
44.64
51.61
134.4218.15
108.16
71.97
68.43
70.63
53.40
22.47

2.77
0.58
2.90
2.24
0.89
1.25
0.001
0.001
0.001
4.78
1.50
2.58
3.50
0.005
0.001

2.65
1.98
18.98
3.89
5.04
5.48
0.005
0.003
7.04
0.001
10.48
3.70
9.28
7.31
0.007
0.002

2.66
3.12
6.49
3.44
1.46
2.25
0.002
0.003
4.52
0.004
3.34
2.02
0.57
9.33
0.006
0.001

4.29
8.48
44.00
4.91
5.29
7.22
0.006
0.007
30.88
0.003
12.23
3.91
5.32
9.86
0.009
0.004

6.80
1.33
0.09
4.49
0.69
0.69
0.92
3.40
-5.67
-0.06
1.77
1.41
1.72
3.25
0.42
2.76

4.65
5.72
1.52
6.40
2.50
1.10
1.03
5.33
1.93
-0.04
3.16
2.21
2.41
3.75
1.41
3.31

Note: Abbreviations: Australia (AL), Austria (AU), Belgium (BE), Canada (CA), Denmark (DK), Finland (FN), France (FR), Germany (GE), Ireland(IR), Japan (JA), Netherlands (NL), Norway (NW), Sweden (SW), Switzerland (SZ), the United Kingdom (UK) and the United States (US).
Data sources and all variable definitions are described in the Appendix 1.

29

Table 2. Determinants of Active and Passive Labour Market Expenditures(Panel VAR(1) estimates of equation (8) a))
Expenditures/GDP
ActiveConstant
Active (-1)
Passive (-1)
Tax/Budget effects
UR (-1)
Debt (-1)
Dependency ratio (-1)
Exposure/Insider & Wage effects
DUR (-1)
Union (-1)
Globalisation
Openness (-1)
FDI (-1)
Portfolio investment (-1)
Migration (-1)
Politics
Left (-1)
Diagnostic tests b)
Sargan statistic
Sargan p-value
AR(1)
AR(2)

Passive

Real Expenditures per capita
ActivePassive0.025**

0.03***

0.09***

0.18***(0.009)(0.01)(0.03)(0.04)

0.50***-0.19**

0.54***-0.21***(0.13)(0.09)(0.16)(0.07)

0.06

0.73***

0.13

0.79***(0.04)(0.05)(0.09)(0.07)-0.004

-0.04

-0.04

-0.06

(0.01)(0.02)(0.06)(0.08)-0.004*-0.01***-0.01*-0.04***(0.0025)(0.003)(0.007)(0.01)-0.06*

0.08***-0.24**

0.19*(0.03)(0.03)(0.11)(0.12)

0.01*

0.12***

0.03*

0.41***(0.006)(0.03)(0.016)(0.08)

0.01***

0.03***

0.04***

0.14***(0.003)(0.01)(0.01)(0.04)-0.003-0.0003-0.006-0.006(0.002)(0.003)(0.006)(0.01)-0.063

-2.49

-2.54

-9.42

(0.74)(2.98)(2.17)(8.73)

-0.05

-0.02

-0.05

0.26

(0.06)(0.33)(0.34)(1.39)

0.003

0.01**-0.0008

0.03(0.005)(0.005)(0.02)(0.03)-0.03-0.006

-0.07

0.07

(0.02)(0.009)(0.09)(0.06)

173.1
1.00
-1.56
-1.34

176.1
1.00
-1.61
-1.28

164.6
1.00
-1.77*
-0.95

173.1
1.00
-2.07**-1.18

Notes: a) Asymptotic standard errors are in parentheses. ***, ** and * denote rejection of null of zero
restriction at 1%, 5% and 10% levels of significance, respectively;b) Sargan statistic tests over-identifying restrictions (asymptotically chi-squared distributed). AR(1)and AR(2) are tests for first-order and second order serial correlation (asymptotically standard
normally distributed).

30

Table 3. Determinants of Share of Active Spending(Panel VAR(1) estimates of equation (8) a))
Active/LMP Expenditures
Constant-0.001(0.001)

Active/LMP (-1)

0.76***(0.04)

Tax/Budget effects
UR (-1)0.00003(0.003)

Debt (-1)

0.002***(0.0006)

Dependency ratio (-1)-0.02***(0.007)

Exposure/Insider & Wage effects
DUR (-1)-0.01***(0.003)

Union (-1)-0.0003(0.002)Globalisation
Openness (-1)

0.0002(0.0006)

FDI (-1)-0.28(0.23)

Portfolio investment (-1)

0.09
(0.07)

Migration (-1)-0.005*(0.003)

Politics
Left (-1)

0.0001(0.003)

Diagnostic tests b)
Sargan statistic (p-value)
AR(1)
AR(2)

177.9 (1.00)-2.41*-0.54

Notes:a) Asymptotic standard errors are in parentheses. ***, ** and * denote
rejection of null of zero restriction at 1%, 5% and 10% levels ofsignificance, respectively;b) Sargan statistic tests over-identifying restrictions (asymptoticallychi-squared distributed). AR(1) and AR(2) are tests for first-orderand second order serial correlation (asymptotically standard
normally distributed).

31

Appendix 1. Variable description and data sources
Dependent variables: The data for spending on labour market programmes are from theOECD’s Social Expenditure Database.

Active is spending on active labour market

programmes. This category includes spending on: labour market training, youth measures,subsidised employment, employment measures for disabled and employment service andadministration. Passive is primarily unemployment compensation. We use three measures ofActive and Passive. The first is expenditure on active or passive labour market programmes
expressed as a percentage of GDP (SNA93). The second is real expenditure on active or
passive labour market programmes (at constant 1995 prices and PPPs in U.S. dollars) divided
by the labour force. The third is expenditure on active labour market programmes expressed
as a percentage of total labour market expenditures. The size of the labour force is calculated
by multiplying each country’s population (taken from the IMF’s International Financial

Statistics (IFS)) by its labour force participation rate (obtained from the OECD’s Structure orcomposition of certain economic aggregates).
Independent variables: Tax and Budget variables: Debt is measured as consolidated central

government gross debt as a fraction of GDP. The source is Robert J. Franzese, Jr., "ThePolitical Economy of Public Debt: An Empirical Examination of the OECD Postwar
Experience" paper for the Wallis Conference on Political Economy, Northwestern University,
November 1998. The Dependency ratio is measured as (1 – the percentage of the population
aged from 15 to 64 years of age). The latter data are from OECD’s Labour Force Statistics,

1980-2000. UR is unemployment as a percentage of total labour force (from the OECD).
Insider effects are proxied by the change in the unemployment rate (see Saint-Paul, 1996).
Globalisation variables: Openness is measured as (Total exports of goods and services +total imports of goods and services)/GDP. FDI is from IFS and is measured as (Direct
Investment Abroad + Dir. Invest. in Rep. Econ.)/GDP. Portfolio investment is from IFS andis measured as (Portfolio Investment Assets + Portfolio Investment Liab.)/GDP. GDP data
are from OECD’s, Annual National Accounts - Comparative tables based on exchange rates

and PPPs. Migration data are from OECD’s Labour Force Statistics, 1980-2000.
Wage effects are captured by Union which is measured by the total union membership (lessself-employed) weighted by the total dependent workforce. Data for European countries arefrom the CD-Rom accompanying Bernhard Ebbinghaus and Jelle Visser, eds. (2000), Trade

Unions in Western Europe since 1945, London: Macmillan. Data for Australia, Canada,32

Japan and the United States are from Miriam Golden, Michael Wallerstein and Peter Lange(1998), “Union Centralization among Advanced Industrial Societies”, National Science
Foundation.

Political or partisan effects are proxied by Left. Left = 1 if there is right-wing domination inboth government and parliament; = 2 if right-wing or centre parties make up between 33.3%
and 66.6% of government; = 3 if centre parties make up 50% or more of government; = 4 if
left-wing or centre parties make up between 33.3% and 66.6% of government; and = 5 if leftwing parties dominate the government. These data are from Jaap Woldendorp, Hans Kemanand Ian Budge (1998), “Party Government in 20 democracies: an update (1990-1995)”,

European Journal of Political Research 33, pp.125-64.

33

Appendix 2. Panel unit root tests

As with pure time series regression analysis, the asymptotic distributions of the estimators inpanel regressions are likely to be affected by the presence of unit roots. This is especiallylikely in datasets with relatively long time series and short cross-sectional dimensions. Hence,as precursor to our econometric analysis, we examine the stationarity of our data. The
variables we use for our econometric analysis are described in detail in Appendix 1.
We test for unit-roots using the test proposed by Im et al. (2003) (henceforth, IPS).19
Using obvious notation, the heterogeneous panel data model is given byqi∆y it = µ i + β i y it −1 + ∑ ϕ j ∆yit − j + γ i t + ε it , i = 1,2,..., N ; t = 1,2,..., T .

(A.1)

j =1

The null hypothesis to test for unit roots is given by H 0 : β i = 0 , for all i, H 1 : β i < 0 , forsome i. Based on equation (A.1), each individual component of the panel is estimatedseparately by OLS and then the test statistics are obtained as studentised averages of the test
statistics for each equation. The number of lags, qi , in the model is determined by the
Akaike Information Criteria.20
The t -statistic proposed by IPS is defined as the average of the individual ADF τ statistics, i.e.,
t =

βˆi
1 N
τ
=
.
τ
,
where
∑ i
i
N i =1
σˆ βi

(A.2)

The critical values for the t -statistic are obtained by stochastic simulation using 100,000
replications.
The IPS panel unit root test results are reported in Appendix table 1. The statistics
suggest that the data are stationary. Hence, we estimate a panel vector autoregressive (VAR)
model to analyse the determinants of labour market policies in preference to a panel vector
error correction model (which requires that variables are non-stationary and cointegrated).21

19

Unlike the augmented Dickey-Fuller (ADF) test, the power of the IPS panel unit root test increases with thenumber of panels.
20

The general results do not change when we use other criteria, such as Schwartz Criteria.

21

Binder et al. (2003) show that GMM estimation of panel vector autoregressions based on orthogonality
conditions breakdown if the underlying time series contain unit roots.

34

Appendix table 1: IPS Panel Unit Root Test Results
Variablet -statistic

Active expenditures/GDP-2.97***(0.0001)

Passive expenditures/GDP-2.79***(0.0001)

Real active expenditures per capita-2.78***(0.0001)

Real passive expenditures per capita-2.88***(0.0001)

Active/total LMP expenditures-2.03**(0.014)UR-3.79***(0.0001)

Debt-2.36***(0.0003)

Dependency ratio-2.65***(0.0001)

Union-2.87***(0.0001)

Openness-2.03**(0.014)FDI-2.64***(0.0001)

Portfolio investment-2.69***(0.0001)

Migration-2.34***(0.0003)Note:a) ****, ** and * represent rejection of the unit root hypothesis at the
1%, 5% and 10% significance level, respectively;b) All variables are de-trended prior to the unit root test for consistency,
i.e., the IPS panel unit root test is based on the model with anintercept;c) Values in the parentheses are p-values and are obtained fromstochastic simulations with 100,000 replications.

35

Appendix table 2. Sensitivity Analysis
Expenditures/GDP
ActiveDependency ratio (-1)

ActivePassive

Active%DK #FN, FR, GE, IR, JA,
NL, NW, SW, SZ #BE, CA, DK, FN, IR,
SW *IR, NL, NW *Exposure/Insider & Wage effects
DK,FN, FR, IR, SW *DUR (-1)
Union (-1)
Globalisation
Openness (-1)
FDI (-1)
Portfolio investment (-1)
Migration (-1)
Politics
Left (-1)

Passive

Active/LMP Expenditures

DK, FN, SW *Active (-1)
Passive (-1)
Active/LMP (-1)
Tax/Budget effects
UR (-1)
Debt (-1)

Real Expenditures per capita

CA, DK, SW *DK, FR *DK *AL, AU, BE, CA, FN, FR,
GE, IR, JA, SW, SZ, US *DK, IR, NL, NW,
SW, UK *SZ #FN *FR, GE, IR #
UK #

DK *

FR #
SW #

UK #

SW #

FN #
SW #
DK #

BE, SW #Note: # denotes that coefficient became statistically significant when one of the countries listed was deleted from the sample; * denotes that coefficient became
statistically insignificant when one of the countries listed was deleted from the sample.

36

Appendix table 3. Determinants of Share of Active Spending
Stratified by EU countries and Non-EU countries
All countries
Constant
Active/LMP (-1)
Tax/Budget effects
UR (-1)
Debt (-1)
Dependency ratio (-1)
Exposure/Insider & Wage effects
DUR (-1)
Union (-1)
Globalisation
Openness (-1)
FDI (-1)
Portfolio investment (-1)
Migration (-1)
Politics
Left (-1)
Diagnostic tests b)
Sargan statistic (p-value)
AR(1)
AR(2)

EU countries

Non-EU countries-0.0010.0002-0.007*(0.001)(0.0008)(0.004)0.76***

0.78***

0.75***(0.04)(0.08)(0.09)

0.00003

0.008***-0.0007(0.003)(0.002)(0.003)0.002***

0.001***

0.005***(0.0006)(0.002)(0.0008)-0.02***-0.02***-0.02(0.007)(0.004)(0.03)-0.01***-0.005*-0.02***(0.003)(0.002)(0.008)-0.0003-0.0005

0.01(0.002)(0.0009)(0.003)0.00020.0008***-0.003(0.0006)(0.0002)(0.002)

-0.28

0.11

-0.01

(0.23)(0.13)(0.25)

0.09

0.007

0.29

(0.07)(0.025)(0.37)-0.005*-0.0007-0.005(0.003)(0.0008)(0.005)0.00010.0001-0.009**(0.003)(0.0002)(0.004)177.9 (1.00)-2.41*-0.54

60.24 (1.00)
-1.44
-0.34

69.40 (1.000)-2.19**-0.37

Notes:a) Asymptotic standard errors are in parentheses. ***, ** and * denote rejection of null of zero restriction
at 1%, 5% and 10% levels of significance, respectively;b) Sargan statistic tests over-identifying restrictions (asymptotically chi-squared distributed). AR(1) andAR(2) are tests for first-order and second order serial correlation (asymptotically standard normally
distributed).

37