Government Responses to Fiscal Austerity:
The Effect of Institutional Fragmentation and Partisanship
Forthcoming in
Comparative Political Studies
Carsten Jensen & Peter B. Mortensen
Aarhus University
Bartholins Alle, building 1331
Contact mail: [email protected]
Abstract
How does the institutional context affect government responses to fiscal austerity? Despite the
‘institutional turn’ in political science, we still possess an incomplete understanding of the
relationship between a core aspect of the institutional setting of countries – namely
institutional fragmentation – and the policy consequences of fiscal pressure. The article
advances research on this question by integrating theories on the blame avoidance effect of
institutional fragmentation with theories on the effect of party constituencies on social
policies. The result is a set of novel hypotheses about the conditional effects of institutional
fragmentation that are tested empirically on quantitative time series data on unemployment
protection from 17 advanced democracies. The analyses show that institutional fragmentation
is an important determinant of government responses to fiscal austerity, but the effect depends
on the partisan composition of the government.
Keywords: Blame avoidance; institutional fragmentation; partisanship; fiscal austerity;
unemployment protection
Acknowledgements: This article has benefitted from the helpful comments and suggestions
of Michael Donnelly, Patrick Emmenegger, Jingjing Huo, Johannes Lindvall, and the
reviewers. It has been presented at the Research Section on Comparative Politics at Aarhus
University, the Department of Political Science at Lund University, MPSA 2011 in Chicago,
and ECPR Joint Sessions in Reykjavik 2011. We thank all participants at these occasions for
their contributions to this work.
2
The impact of fiscal austerity on welfare state retrenchment and how that impact is moderated
by the institutional context in different countries is one of the key issues in comparative
political economy (Pierson, 1994; Huber & Stephens, 2001; Swank, 2002; Cusack, Iversen, &
Rehm, 2006). An important aspect is the role of institutional fragmentation; that is, the effect
of formal decision-making power being scattered across multiple settings. The literature,
however, has failed to reach any firm theoretical and empirical conclusions on the moderating
effect of institutional fragmentation on fiscal shocks.
Institutional fragmentation has been claimed to diffuse blame for unpopular decisions and
events across a number of actors (Weaver & Rockman, 1993; Pierson, 1994; Bonoli, 2001). A
high level of institutional fragmentation has, most notably, been shown to entail a much
weaker propensity for the public to blame the government for poor economic performance
(Powell & Whitten, 1993; Rudolph, 2003; Duch & Stevenson, 2008). Integrating this insight
from the literature on economic voting with recent findings of partisan differences in welfare
state policies (Korpi & Palme, 2003; Allan & Scruggs, 2004; Amable, Gatti, & Schumacher,
2006), we present a new argument on fiscal austerity’s detrimental effect on social protection
and how it is conditioned by the fragmentation of decision-making institutions.
First, challenging conventional wisdom on the impact of fiscal austerity (cf. TaylorGooby, 2002; Ferrera, 2008), we posit that in countries with low institutional fragmentation
and thus easy blame attribution leftwing governments will seek to protect their core
constituency by expanding social protection. Following the logic advocated by Garrett (1998)
and Amable, Gatti, & Schumacher (2006), in countries with low institutional fragmentation
the core constituency of leftwing parties can easily see whether or not their representatives are
actually representing their immediate interests, namely more social protection as the economy
deteriorates. Rightwing governments, on the other hand, do not have to carter to their core
constituency in times of increased fiscal stress since their constituency voters are much less
3
exposed to labor market risks (Cusack, Iversen, & Rehm, 2006). But rightwing governments
are still unwilling to risk offending large middle income groups in systems where policy
responsibility is rather clear and hence their preferred response to fiscal austerity when blame
attribution is easy is to stay at the status quo.
Second, challenging the status quo bias in the ‘new politics’ literature (cf. Starke, 2006),
in systems with high institutional fragmentation and thus diffused policy responsibility we
expect all governments to pursue retrenchment in times of a fiscal shock. Yet, while leftwing
governments will only implement modest retrenchment, rightwing governments will
implement more radical retrenchment since for the latter core constituency preferences and
blame avoidance opportunities will coincide within this institutional context. In other words,
because all governments, regardless of party color, are squeezed between the need for fiscal
responsibility (motivating retrenchment) and the popularity of the welfare state (motivating
blame avoidance), even leftwing governments may be willing to retrench in times of
increased fiscal stress if the political system offers blame diffusion.
These hypotheses not only extend the new politics perspective on government responses
to fiscal austerity; they also refine the traditional understanding of institutional fragmentation
in the political economy literature. From this perspective, a high level of institutional
fragmentation is seen as an impediment to expansion in the decades after the Second World
War (Immergut, 1992; Huber, Ragin, & Stephens, 1993; Huber & Stephens, 2001). As
detailed below, there need not be any fundamental disagreement between the traditional view
and the one advocated here: in the absence of fiscal shocks, like in the Golden Age of welfare
expansion, institutional fragmentation might very well imply stability. In our reading fiscal
austerity, however, represents a shock to equilibrium, monotonously shifting the preferences
of the parties towards a more pro-retrenchment position. In this phase where parties re-
4
position themselves, institutional fragmentation functions mainly as a shelter against public
blame.
As we show below, our expectations are borne out in an empirical test of the effect of
fiscal austerity on unemployment protection in 17 advanced democracies.
Institutional Fragmentation and Government Responses to Fiscal Austerity
Fiscal austerity poses a formidable challenge to modern governments. On the one hand
governments face a need for fiscal responsibility, which at some point may require
retrenchment and major welfare reforms. On the other hand, retrenchment is becoming
continuously harder to implement as the size of the negatively affected group booms
relentlessly. As pointed out by Pierson (1994), retrenchment is generally an exercise in blame
avoidance since cutbacks in social programs raise the risk of electoral retribution. This view
on welfare cutbacks has inspired several studies that privilege the reelection goal of
government leaders and assume that governments’ primary concern is to avoid blame for
unpopular decisions, including retrenchment of the social rights of citizens (Pierson, 1996;
2001; Bonoli, 2001; Cox, 2001; Kitschelt, 2001; Green-Pedersen, 2002; Lindbom, 2007;
Zohlnhöfer, 2007; Vis & Kersbergen, 2007; Vis, 2009).1
Characteristically, this literature emphasizes the major obstacles to welfare reform, in
particular the broad support for core social programs and the rigidity of welfare state
institutions caused by path dependence and powerful actors with the capacity to obstruct
reform. Substantive reforms are possible, but the electoral and institutional resistance can only
be overcome under certain conditions. Some of the conditions suggested by the literature
seem rather dependent on actor constellations that are specific to individual, or at the most, a
few countries (e.g., Kitschelt, 2001; Green-Pedersen, 2002). Yet, one prominent institutional
5
dimension relevant to all western democracies is the institutional fragmentation of the
political system.
As noted by Weaver & Rockman (1993), Pierson (1994), and Bonoli (2001), institutional
fragmentation facilitates blame avoidance because it all else equal makes it more difficult to
unambiguously place responsibility for an unpopular reform on a single actor; an assumption
strongly supported by empirical research on economic voting (see e.g. Powell & Whitten,
1993; Rudolph, 2003; Duch & Stevenson, 2008). Soroka & Wlezien (2009) have even shown
that such fragmentation more broadly depresses public responsiveness to policy changes,
possibly because citizens have a harder time following the complicated processes surrounding
decision-making in these countries. Relating this argument to the issue of government
responses to fiscal austerity, the implication is that reelection-motivated government leaders
will only introduce retrenchment as an answer to mounting fiscal austerity in systems where
responsibility is diffused.
Although we are generally sympathetic to this argument, we see two problems with it as
it stands. First, it ignores that blame sharing also involves power sharing and hence an
increase in the number of powerful institutions or actors that may impede retrenchment in
times of fiscal austerity. Pierson (1994, p. 33) certainly recognizes this Janus head
characteristic of institutional fragmentation, but instead of specifying the power-versusaccountability relationship theoretically he leaves it as an empirically open question.2 Second,
the argument ignores how the government’s partisan composition may interact with the blame
avoidance opportunities provided by the political system. Though parties are assumed to care
primarily about reelection, they still represent different core constituencies and therefore have
different strategic considerations when it comes to retrenchment policies. Without a
theoretical specification such different effects may not show up in empirical testing either
because they neutralize each other or because they only work in combination. Essentially, this
6
may be why, despite its theoretical prominence, we still lack a large scale cross-national
investigation of how the institutional fragmentation of political systems modifies government
responses to fiscal austerity.
To disentangle the power-versus-accountability effect of institutional fragmentation in
times of fiscal austerity, we draw a distinction between equilibrium and non-equilibrium
situations. In equilibrium, actors have fixed preferences (Tsebelis, 2002). In this situation,
high institutional fragmentation may be expected to imply a status quo bias. The reason is that
– in a situation with fixed preferences – high institutional fragmentation provides actors keen
on protecting the status quo a lot of leverage to do so by vetoing any legislative initiatives.
Fiscal austerity, in our reading, constitutes a shock to the equilibrium, monotonously shifting
the parties’ preferences towards a more pro-retrenchment position. During this disequilibrium
no actors want to use their veto power to block changes. Rather, in the particular phase where
the parties reposition themselves, institutional fragmentation gains a new function as a shelter
against public blame.
Figure 1 brings out the logic visually, albeit in a stylized fashion. The x-axis outlines the
degree of institutional fragmentation, while the y-axis outlines the degree of either stability or
retrenchment. Before the fiscal shock, there is a strong negative relationship between the two:
the more institutional fragmentation, the more stability. In the event of an exogenous shock,
like a failing economy, the line swings outwards and upwards, but disproportionately for
countries with high institutional fragmentation after which a new equilibrium sets in where –
once again – countries ridden with multiple powerful actors will exhibit the most stability. In
other words, in the new equilibrium the relationship continues to be negative. Yet, as evident
on the y-axis, during the shock changes will in fact have been biggest in countries with high
institutional fragmentation compared with countries with a more concentrated responsibility
7
attribution. In country N1 the total amount of retrenchment will be equal to α1; in country N2
the amount of retrenchment will be equal to α2; and α1 < α2.
FIGURE 1 ABOUT HERE
It follows that the typical effect of high institutional fragmentation will be to ensure stability.
This is consistent with the body of literature showing that countries characterized by
institutional features like presidentialism, federalism, and bicameralism have developed
smaller welfare states (Immergut, 1992; Huber, Ragin, & Stephens, 1993; Huber & Stephens,
2001; Ha, 2008; Becher, 2010). We posit that when a country experiences a fiscal shock this
halting effect will be reversed. In these instances, high institutional fragmentation will
facilitate retrenchment until a new equilibrium has set in.
Given this argument, the central question is how partisan differences moderate the effect
of these different blame sharing opportunities provided by the political systems. Recent
developments in the blame avoidance-inspired literature do not expect to find partisan effects
in government responses to fiscal austerity (Vis & Kersbergen, 2007; Vis, 2009). We argue
that on top of the (fundamentally reelection-motivated) institutional effects we should observe
significant partisan differences moderating the relationship between fiscal austerity and the
blame avoidance opportunities provided by the political systems.
The Moderating Effect of Political Parties
Leftwing parties may be seen as representing the interests of low-income individuals, who are
often most vulnerable to economic shocks; rightwing parties are seen as representing highincome individuals in relatively secure positions; in between are middle-income individuals,
who are independents (Iversen & Soskice, 2006; Lupu & Pontusson, 2011). Several authors
8
have investigated how this basic setup influences the response of partisan governments to a
changing economy. Garrett (1998) studies the impact of globalization on left-wing
governments’ social policy. He argues that rising globalization creates economic insecurity
among low-income groups, leading to calls for expansion that leftwing governments cannot
neglect given that low-income groups are their core constituency (for related arguments, see
Kwon & Pontusson, 2010). In a similar vein Korpi & Palme (2003) and Amable, Gatti, &
Schumacher (2006) argue that rightwing governments use a bad economy in general as a
pretext for introducing retrenchment, presumably as a way to reduce inflation and increase the
competitiveness of the private market sectors.
Missing from these accounts is how the effects of partisan differences depend on the
(lack of) blame avoidance opportunities offered by the political system. If it is true that
welfare states are so popular that retrenchment is nearly impossible when blame attribution is
easy, then neither left- nor rightwing governments should dare introduce such policies in
countries with low institutional fragmentation. In the context of a fiscal shock and easy blame
attribution partisan differences should rather be discernable as leftwing expansion. When
blame attribution is easy, the core constituency of leftwing parties can easily see whether or
not their representatives are actually representing their immediate interests, namely more
social protection as the economy deteriorates. If they are not, the response is likely to be
voting abstentions, seriously jeopardizing the prospect of reelection (Arndt, 2011). Rightwing
governments, we hypothesize, will prefer status quo in this situation because their
constituency – medium and high income voters – are less exposed than low-income voters,
the core constituency of leftwing governments. They will not attempt retrenchment due to the
preference among the middle-income voters for a comparably generous welfare state,
especially in a period of mounting fiscal pressure.3
9
In the event of high institutional fragmentation, and hence relatively solid blame
diffusion opportunities, the political dynamic is expected to be different. When retrenchment
becomes feasible without the risk of being blamed, or at least not taking all the blame, both
left- and rightwing governments will try to retrench as a response to fiscal austerity. But given
the generic differences in the preferences of party constituencies, we would expect rightwing
governments to respond much more radically than leftwing governments. The latter will be
motivated to ensure a stable and sound economy now that the electoral costs of doing so have
been minimized, but are still fundamentally in favor of generous social protection. The
former, obviously, also favor a healthy economy, but have a distinct policy agenda to lower
the level of social protection, which is believed to reduce the competitiveness of the private
sector (see also Hibbs, 1977).
TABLE 1 ABOUT HERE
Table 1 summarizes the theoretical expectations. It stresses the dual importance of the blame
avoidance opportunities provided by the institutional context as well as partisan differences
when understanding government responses to fiscal austerity. The utility of these propositions
should be judged based on whether they explain the effect of fiscal austerity better than
models that 1) ignore the blame diffusion effect of institutional fragmentation and/or 2) ignore
the partisan dimension of democratic government.
A Note on the (not so) Permanent Fiscal Austerity
The catch in Pierson’s (1994; 1996) argument on the new politics of welfare states is that
political parties are caught between the need for blame avoidance and fiscal responsibility.
Pierson talks of the ‘Era of permanent austerity’ commencing around 1980, and later studies
10
have adopted this static understanding of the phenomenon. Indeed, this argument of
permanent fiscal austerity is so well accepted in the literature that it has virtually become a
conventional wisdom: In the Golden Age expansion was feasible; in the ‘Silver Age’ of the
past 30 years it is not (for literature reviews, see Green-Pedersen & Haverland, 2002; TaylorGooby, 2002; Starke, 2006; Ferrera, 2008).
There is no doubt that the economy fared a lot better on average before the late 1970s
than after, but the operative phrase here is ‘on average’. Over the course of the last 30 years
economic fortunes have varied significantly across the Western world. To exemplify, in the
1980s Germany, Japan, and Sweden were all performing well; in the early 1990s all three
countries stumbled; by the late 1990s, only Sweden had recovered with Germany following in
the early 2000s and Japan continuously on its heels. Other countries have followed different
trajectories.
It is important to realize that this is not a mere technicality, but pertains to the very heart
of the literature that emerged in the wake of Pierson’s original work. Fiscal austerity is
viewed as an ‘irresistible force’ in head-on collision with the ‘immovable object’ of the
mature, highly popular welfare state, generating a new form of welfare state politics. Yet, if
the ‘irresistible force’ is only irresistible some of the time, what does this imply for our
thinking and analyses of these processes? As a minimum, it leads us to reconsider the role of
political parties since it is a core premise of the argument that government parties will change
behavior because they are unable to meet public demand for expansion or status quo. But why
should any government risk reelection by cutting back welfare if the economic situation is
reasonably sound? In other words, to properly model modern-day welfare state policies we
need to integrate the most basic premise of the entire literature directly into our theories and
empirical tests. If it is true that austerity is a basic driver of retrenchment, then retrenchment
should all else equal be more pronounced in times of dire economic straits.
11
Research Design, Data, and Model Specification
The dependent variable
We measure our dependent variable as unemployment replacement rates. The labor market is
in general the most conflict-ridden social policy area in modern-day welfare states (Pierson,
1994; Jensen, 2012). If workers are able to secure extensive social protection against labor
market-related income loss, taxes on employers increase, workers are less willing to accept
low wages and poor working conditions, and employers’ ability to generate profit in the first
place deteriorates (Korpi, 1978; 1983; Stephens, 1979). In short, labor market-related social
policy has ramifications for the economy as a whole, which is why this policy area takes
center-stage in both the power resource and varieties of capitalism perspectives. In addition,
income-correlated risk exposure is bigger on the labor market than in policy areas related to
the life-course: low income groups face a considerably higher risk of unemployment, whereas
everybody grows old and risks serious illnesses (Esping-Andersen, 1999; Cusack, Iversen, &
Rehm, 2006). Since our argument assumes class variations in risk exposure as the source of
partisan differences, unemployment protection is the natural social policy scheme to study.
We rely on income replacement rates (i.e., an unemployed’s income as a percentage of
the average production worker’s income) rather than the other standard measure in the
literature, social expenditure as a percentage of the GDP. Replacement rates come closer to
our theoretical understanding of the dependent variable. As Esping-Andersen (1990, p. 21)
famously notes, ‘it is difficult to imagine that anyone fought for spending per se’. Rather,
spending is instrumental to achieving some other objective. When political conflict is
predominantly related to the degree of social protection against income loss, income
replacement rates are exactly what we should be interested in because they measure the
12
income of individuals who have lost their previous source of income.4 Summary statistics on
this and all other variables together with a correlation matrix are reported in the appendix.
Measurement of the core explanatory variables
Fiscal Austerity
Fiscal austerity is caused by an array of socio-economic factors (Pierson, 1998). Ultimately,
however, its political ramifications consist in the constraints it puts on governments. This
constraint (C) may be viewed as a function of the revenue of the government (R) minus the
social rights enjoyed by the citizenry of a country (S) times the actual demand for these rights
at any given point in time (D). That is,
C = R – (S x D)
(eq. 1)
In countries with meager social rights, the effect of rising need in society will have limited
consequences for the government purse. Likewise, if social rights are generous, but need is
low the constraint on governments will be bearable. This is in any event the case as long as
revenue is sufficient to cover the incurred costs. Indeed, this was in some readings what
happened during the Golden Age of welfare state expansion. While social rights and demand
both boomed relentlessly, so did government revenue as productivity increases were enough
for the expansion to be fiscally tolerable (Pierson, 1998).
As defined here, C may best be captured empirically as the so-called primary balance
since this is the margin between revenue (R) and government outlays (S x D); or simply the
net increase in government lending from one year to the next.5 Figure 2 depicts within- and
between-country variation in government lending across the 17 countries analyzed in this
13
paper. Figure 2 documents how volatile government lending actually is over almost 30 years.
The left-hand panel documents the cross-country variation, the right-hand side the temporal
variation. Certainly, we do find an upward trend in the data indicating that the 17 countries
included on average borrow more than they used to (cf. the circles in the right-hand side
panel). In any event, there is much greater variation within a single year or between these 17
countries over the entire period than can be accounted for by the general trends.6
FIGURE 2 ABOUT HERE
In the following analysis government lending is calculated as yearly net government lending
excluding interest payments on consolidated government liabilities, measured as a percentage
of the GDP (a positive coefficient is, thus, equivalent to a poorer fiscal situation). According
to the OECD, this measure is particularly well suited for studying temporal dynamics in the
accumulation of government debt, and is therefore the most widely used measure of a
country’s overall fiscal conditions.7 The great quality is that it captures how much new debt a
government has incurred over the past year. In effect, it measures whether a country
experiences a shock which suddenly increases the economic constraint of the government and
it is exactly the effect of such shocks that our model claims to explain. Data is taken from
OECD (2009).
Institutional fragmentation
The measure of institutional fragmentation has to fulfill two criteria: It must capture the
fragmentation of decision-making power in a country and it must be clearly distinguishable
from the partisan composition of the government at any point in time. We therefore focus on
three features of political systems, namely federalism, bicameralism, and presidentialism.
14
These features are summarized into an additive index following Huber et al. (2004), which
also constitutes our data source.8 Some authors do not include presidentialism when they
measure institutional fragmentation (e.g., Kittel & Obinger, 2003), but we find this odd given
that this institution to a large extent is the very symbol of divided powers. In any event, our
results can be reproduced if we stick to a slim index with just bicameralism and federalism.
We could potentially include the use of popular referenda since this is formally a
decision-making institution too, but its special character suggests that it should be left out.
Including it does not alter the results reported below, however. We also do not include a
measure of coalition governments although authors like Powell & Whitten (1993) and Duch
& Stevenson (2008) do so. The reason is that coalition governments much more frequently
form in PR systems, which are simultaneously well known to produce many more leftwing
governments than majoritarian systems. Iversen & Soskice (2006) show that around three
quarters of all governments in majoritarian systems are rightwing with the reverse being true
in PR systems. If we included coalition government, our measure of institutional
fragmentation would be seriously polluted by the partisan composition of governments. Our
measure, on the other hand, is uncorrelated with government composition (r = .07).9
Partisan composition of the government
Data on cabinet composition is taken from Armingeon et al. (2009) and goes from 0 (no
rightwing participation) to 100 (fully rightwing cabinet). It allows for a relatively narrow
delineation of party families. In our analysis we focus on secular rightwing parties versus all
others. This allows us to skirt around the thorny issues of the Christian democrats, which have
often been demonstrated to have left-leaning preferences in social policy (van Kersbergen,
1995; Esping-Andersen, 1999; Huber & Stephens, 2001). We can do so because we are not
interested in leftwing parties per se, but in parties that are generically in favor of comparably
15
generous welfare, and to categorize the Christian democrats as anything else would be
misleading. Furthermore, it is well established that Christian democrats and more traditional
leftwing parties especially pursue similar policies on areas related to the labor market, i.e., our
field of study, whereas they differ considerably on other social policies like childcare and
elderly care. Here the Christian democrats are often much more hesitant to let the state
intervene since such intervention is viewed as jeopardizing the traditional family structure. In
short, the advantage of studying unemployment protection is that it ensures that all parties we
group as ‘non-right’ actually share roughly similar policy goals.
Control variables
The unemployment rate must be controlled for because it may influence both replacement
rates and government lending. With rising unemployment one may either expect declining
unemployment replacement rates as the room for generous replacement rates shrinks, which is
in line with Huber & Stephens (2001) and Korpi & Palme (2003). On the other hand, one may
also expect that rising unemployment will create more outspoken calls for protection and, in
consequence, higher replacement rates. Unemployment may simultaneously have an impact
on government lending since part of the societal demand (D in equation 1) is caused by the
level of unemployment.
The proportion of the population aged 65 should be controlled for following a similar
logic. Apart from its obvious potential impact on government lending, we might suspect a
large cohort of elderly in society to squeeze unemployment replacement rates as public
resources are shifted to old-age pensions and health care (Lynch, 2006). This is based on the
assumption that public resources are fixed, which evidently they are not, cf. the discussion
above. An alternative expectation is therefore that a large proportion of elderly simply
increases the size of the ‘welfare coalition’ in a country, leading to increasing welfare effort
16
across the board. In that event there should be a positive relationship between the proportion
of the population aged 65 or older and the unemployment replacement rates. Data on
unemployment and proportion of elderly is from OECD (2009).
Economic globalization is often argued to play an important role for social protection,
although it is still unsettled whether its effect is negative or positive (Cameron, 1978;
Katzenstein, 1985; Garrett, 1998; Busemeyer, 2009). Globalization may influence both our
dependent and main independent variables since globalization may change not only
unemployment replacement rates, but also the rate of government debt accumulation. The
reason is that globalization potentially reduces government revenue (R in equation 1) as
companies begin to exit to low-cost countries. In addition, new government lending may itself
become more difficult to obtain in a situation with highly fluid capital markets (Scharpf,
2000). We use Dreher’s (2006) KOF index on economic globalization to capture this
potentially confounding factor.
Statistical model specifications
We test our argument using a series of error correction models. The choice of an error
correction model reflects the theoretical model’s emphasis on fiscal austerity as a shock to the
equilibrium. In error correction models the basic logic is that the changes in an explanatory
variable constitute a shock to the equilibrium relationship between the explanatory and
dependent variables. As a response to the shock, the dependent variable will return to the
equilibrium over time (see Beck, 1991; De Boef and Keele, 2008).10 The first difference in the
explanatory variable measures the short-term response to the shock, while the level of the
explanatory variable measures the long-term response. This distinction is of great value since
it is unrealistic to expect that a change in neither government lending nor government
composition has an immediate effect on unemployment replacement rates. Rather, changes
17
must be expected to have gradual effects as governments realize that the economy has
deteriorated, propose and negotiate new legislation, and finally implements it – processes that
combined may take several years. Therefore, and for reasons of space, our discussion of the
empirical results focuses entirely on the enduring effects (the full models are available upon
request).
The error correction model has the following generic form:
∆yit = α + λyit-1 + ∑ βjXit-1 + ∑ βjΔXit-1 + εit
(eq. 2)
where ∆yit is the change in the dependent variable in country i in year t from one year to the
next. X is a vector of the explanatory variables discussed above with the subscript j referring
to the particular explanatory variable. α is the intercept and ε is the disturbance term. The
long-term effects of the levels (Xit-1) are captured by dividing the coefficient of any particular
variable (βj) by the error correction rate, which is the coefficient of the lagged dependent
variable measured as levels, i.e., βj/λyit-1.
As suggested by Beck & Katz (1995), we use panel-corrected standard errors to correct
for panel heteroskedasticity in the data structure.11 Fixed country effects are included because
the best test of our argument is to see what happens in a country with various levels of
institutional fragmentation and cabinet composition when government lending increases or
decreases (i.e., focusing on temporal dynamics in the event of a shock). Given our argument
this allows us to assess the causality implied most directly.12 The estimations, finally, include
an AR (1) term to control for first-order autoregressive processes (Beck & Katz, 2009).
18
Findings
Table 2 reports the long-term, or enduring, effects of the estimated models. Note first of all,
and across the four models, the negative and highly statistically significant lagged dependent
level variable, which indicates how the error correction model as expected captures
disruptions to the equilibrium. Re-equilibration, in error correction parlance, is not immediate
but occurs over future time periods at a rate dictated by the lagged dependent variable. The
largest part of the movement in unemployment rates will occur in the first year, where
between 21 to 28 percent of the shift will take place, but the rest will only manifest itself
gradually over the following years.
Model I studies the direct effect of all variables included in the models. It is noteworthy
how government lending does not have a direct effect on unemployment rates, which is what
we would expect as governments will only dare venture into retrenchment in countries with a
high level of institutional fragmentation. Rightwing cabinet share has a negative effect, which
is in line with the existing literature (Korpi & Palme, 2003; Allan & Scruggs, 2004) –
although we will show how this effect in fact is moderated by the level of institutional
fragmentation. Institutional fragmentation by itself has no impact, arguably because it is a
moderator of partisan behavior without any direct effect of its own (this mirrors Allan &
Scruggs’ (2004) findings). We also see that a higher proportion of elderly increases
replacement rates, indicating that the elderly seem to be part of a broad-based pro-welfare
coalition. Economic globalization, finally, leads to reduced replacement rates, which fits
Busemeyer’s (2009) observation.
TABLE 2 ABOUT HERE
19
Model II studies the interaction effect between government lending and the level of
institutional fragmentation and Figure 3 reports the marginal effects of the interaction term
(cf. Brambor, Clark, & Golder, 2006). It is immediately evident that our main hypothesis
receives support. In countries with low institutional fragmentation, mounting government debt
does not cause retrenchment, but in countries with high institutional fragmentation it does.13
In countries with maximum institutional fragmentation the long-term coefficient is
approximately -.014 (-.003/.211). This entails that if government lending rises with a
percentage point from one year to the next, a 1.3 percent decrease in replacement rates will
eventually follow. A two standard deviation rise in government lending – that is, a significant,
but not highly unlikely increase – sums up to a fairly sizeable 8.4 percent cutback.14
FIGURE 3 ABOUT HERE
The U.S. is a country with a very high level of institutional fragmentation and interestingly
replacement rates in the U.S. fell from an all-time high of .703 to .548 – clearly contrary to
arguments equating fragmentation with stability (for a similar conclusion using different data,
see Hacker, 2006). Figure 4 outlines the bivariate relationship between government lending
and replacement rates in the U.S. and compares it with the U.K., which is characterized by
low levels of institutional fragmentation (Lijphart, 1999), but which resembles the U.S. in
terms of a range of cultural background factors and welfare state type. Both belong to the
Anglo-Saxon family of nations and base their core social programs on residual principles
(Esping-Andersen 1990). Though the graph is strictly illustrative, it is nevertheless rather
telling as there is a strong negative relationship in the U.S.: the more the government borrows
(x-axis), the smaller the replacement rates become (y-axis). The bivariate correlation is -.57
and is highly significant with a p-value of .006. As discussed by Hacker & Pierson (2006) and
20
Jones & Williams (2008), government debt has generally been rising in the U.S. the past
decades as a natural consequence of massive under-financed tax cuts (reducing R in equation
1). Our results seem to indicate that a consequence of this increased indebtedness has been a
drop in the replacement rates of the unemployed. Compare this with the positive, but
insignificant correlation in the U.K. where fiscal austerity evidently does not by itself lead to
retrenchment of labor market protection. Note, of course, that this is not equivalent to saying
that no cutbacks have materialized here – cutbacks are well documented, especially under
Thatcher (Korpi & Palme, 2003; Allan & Scruggs, 2004) – only that they have not been
caused by a deteriorating economy. Certainly, since the U.S. and U.K. share so many cultural
and institutional features, it is difficult to see what might have caused these different
responses apart from the degree of institutional fragmentation.
FIGURE 4 ABOUT HERE
Next, in order to investigate these findings more thoroughly, we break the sample into two
country groups with ‘low’ and ‘high’ institutional fragmentation, respectively. What counts as
low and high is to some extent arbitrary. We opt for the pragmatic solution and choose the
mean as our cut-off point, entailing that we have eight countries in the low fragmentation
group and nine in the high fragmentation group.
Model III reports the findings for countries with low institutional fragmentation, i.e.,
where we argue retrenchment will cause serious blame attribution. The first coefficient of
interest is the long-term effect of government lending, which captures the effect of this
variable when the value of the rightwing cabinet variable is zero. In other words, what is the
effect of government lending when rightwing parties have no influence on the government in
a country? As expected the effect is positive, which indicates that center-left governments
21
expand unemployment replacement rates as a response to fiscal austerity. Indeed, if
government lending expands by a percentage point, replacement rates will increase by
approximately 1.1 percent, entailing that a two standard deviation rise will lead to a 7.1
percent increase. The left-hand side of Figure 5 reports the marginal effects, allowing us to
see what happens as rightwing parties start to file into the cabinet. Note that even though the
positive effect disappears as soon as there are just about any rightwing cabinet members in
government, the positive and statistically significant effect is of substantial importance given
that in roughly 35 percent of our observations the rightwing cabinet share of the government
is zero.
FIGURE 5 ABOUT HERE
Model IV and the right-hand side of Figure 5 report the findings for the countries with high
institutional fragmentation, and once again our expectations are corroborated. We find that
leftwing governments are induced by government lending to pass retrenchment, but we also
see how the negative effect of government lending is bigger when the government is
dominated by rightwing parties. The effect of a percentage point increase in government debt
for pure center-left governments is a .5 percent decrease of replacement rates; the effect for
pure rightwing governments is a decline of 1.2 percent. A two standard deviation rise in
government lending sums up to a 2.3 and 7.8 percent decrease, respectively. 15 Gauging Figure
5, then, gives us the elaborate picture of how fiscal austerity confronts the need for blame
avoidance and the generic preferences of different parties – in short, the new partisan politics
of welfare states.16
22
Conclusion
While previous studies list fiscal austerity, cabinet composition, and blame avoidance as
important behavioral motives for governments in mature welfare states, this article offers an
integrated model of the combined importance of all three drivers of welfare retrenchment.
Basically, we posit that the blame sharing opportunities offered by the institutional context do
affect government responses to increased fiscal austerity, but the effect is shaped by partisan
differences.
The large N empirical analysis on the impact of fiscal austerity on social policy across 17
advanced democracies provided strong empirical support for the core expectations derived
from the model. First, in accord with the argument that governments only enact unpopular
policies in response to increased fiscal austerity when they have minimized the risk of being
blamed, the analysis shows that fiscal austerity has no discernible unconditional effect on
retrenchment. Second, and contrary to a large literature equating institutional fragmentation
with stability (Huber, Ragin, & Stephens, 1993; Huber & Stephens, 2001; Tsebelis, 2002; Ha,
2008; Becher, 2010), the analysis reveals that only in fragmented systems do governments
respond to fiscal austerity by retrenching social protection. Third, consistent with the
assumption that left- and rightwing governments still entertain distinct policy agendas,
partisan politics matter, but the response to increased fiscal austerity is strongly affected by
the political system’s blame diffusion opportunities.
This study has three important implications for future research on the impact of the
institutional context on government responses to fiscal austerity. First, it specifies the
accountability-versus-power effect of institutional fragmentation by showing how the
accountability dimension dominates in times of increasing fiscal austerity. It has been argued
that the accountability effect also varies with the degree of competition in the political system
(Scharpf, 1997; Bonoli, 2001). Hence, this relationship may be further specified in future
23
studies by extending our basic model with indicators of for instance election cycle dynamics
and various measures of the political system’s degree of competitiveness.
Second, we have demonstrated the importance of government partisan composition when
understanding the effect of institutional fragmentation on government responses to fiscal
austerity. Both the blame avoidance literature focusing on reelection as well as the power
resource tradition focusing on partisan goals has offered significant insights on government
responses to fiscal austerity, but no one (including Pierson) has taken seriously Pierson’s
(1994, p. 17) original emphasis of the dual preferences of government leaders. According to
Pierson, government leaders want to advance their policy agendas and they want to get
reelected. We believe the findings presented in this article are very difficult to explain without
an integrated model that takes both objectives into account.
Finally, the analyses have questioned the usefulness of conceptualizing economic
conditions from around 1980 and onwards as an era of permanent fiscal austerity. Data used
in this article are all from after 1980 and they clearly display much meaningful variation that
would have been ignored by treating fiscal austerity as a constant across time and countries.
With the on-set of the Credit Crisis in 2007 this basic point is underscored even further.
Appendix
TABLE A1 AND A2 ABOUT HERE
24
References
Allan, J. P., & Scruggs, L. (2004). Political Partisanship and Welfare State Reform in
Advanced Industrial Societies. American Journal of Political Science, 48, 496–512.
Amable, B., Gatti, D., & Schumacher, J. (2006). Welfare-state Retrenchment: The Partisan
Effect Revisited. Oxford Review of Economic Policy, 22, 426-444.
Armingeon, K., Engler, S., Gerber, M., & Leimgruber, P. (2009). Comparative Political Data
Set 1960–2007. Institute of Political Science, University of Berne.
Arndt, C. (2011). The Electoral Consequences of Third Way Welfare State Reforms. Social
Democracy’s Transformation and Its Political Costs. Aarhus: Politica.
Becher, M. (2010). Constraining Ministerial Power: The Impact of Veto Players on Labor
Market Reforms in Industrial Democracies, 1973–2000. Comparative Political Studies
43, 33-60.
Beck, N. (1991). Comparing Dynamic Specifications: The Case of Presidential Approval.
Political Analysis 3, 51-87.
Beck, N., & Katz, J. N. (1995). What to Do (and Not to Do) With Time-series Cross-section
Data. American Political Science Review 89: 634–47.
Beck, N., & Katz, J. N. (2009). Modeling Dynamics in Time-series-cross-section Political
Economy Data. California Institute of Technology: Social Science Working Paper 1304.
Blekesaune, M., & Quadagno, J. (2003). Public Attitudes Towards Welfare State Policies. A
Comparative Analysis of 24 Nations. European Sociological Review 19: 415-427.
Bonoli, G. (2001). Political Institutions, Veto Points, and the Processes of Welfare State
Adaptation. In P. Pierson (Ed.), The New Politics of the Welfare State (pp. 238-265).
Oxford: Oxford University Press.
Brambor, T., Clark, R. W., & Golder, M. (2006). Understanding interaction models:
Improving empirical analysis. Political Analysis, 14, 63-82.
25
Breunig, C., & Busemeyer, M. R. (Forthcoming). Fiscal austerity and the trade-off between
public investment and social spending. Journal of European Public Policy.
Busemeyer, M. R. (2009). From Myth to Reality: Globalization and Public Spending in
OECD Countries Revisited. European Journal of Political Research, 48, 455-482.
Cameron, D. R. (1978). The Expansion of the Public Economy: A Comparative Analysis.
American Political Science Review, 72, 1243-1261.
Clasen, J., & Siegel, N. A. (Eds.) (2007). Investigating Welfare State Change. The
‘Dependent Variable Problem’ in Comparative Analysis. Cheltenham and Northampton:
Edward Elgar.
Cox, R. H. (2001). The Social Construction of an Imperative: Why Welfare Reform
Happened in Denmark and the Netherlands but not in Germany. World Politics, 53, 463498
Cusack, T., Iversen, T., & Rehm, P. (2006). Risks at Work: The Demand and Supply Sides of
Government Redistribution. Oxford Review of Economic Policy, 22, 365-389.
De Boef, S., & Keele, L. (2008). Taking time seriously. American Journal of Political
Science, 52, 184-200.
Dreher, A. (2006). Does Globalization Affect Growth? Evidence from a New Index of
Globalization. Applied Economics, 38, 1091-1110.
Duch, R. M., & Stevenson, R. T. (2008). The Economic Vote: How Political and Economy
Institutions Condition Election Results. Cambridge: Cambridge University Press.
Esping-Andersen, G. (1990). The Three Worlds of Welfare Capitalism. Cambridge: Polity
Press.
Esping-Andersen, G. (1999). Social Foundations of Postindustrial Economies. Oxford:
Oxford University Press.
26
Ferrera, M. (2008). The European Welfare State: Golden Achievements, Silver Prospects.
West European Politics, 31, 82-107.
Franzese, R. J. (2002). Macroeconomic Policies in Developed Democracies. New York:
Cambridge University Press.
Garrett, G. (1998). Partisan Politics in the Global Economy. Cambridge: Cambridge
University Press.
Giger, N. (2010). Do voters punish the government for welfare state retrenchment? A
comparative study of electoral costs associated with social policy. Comparative
European Politics, 8, 4, 415-443.
Giger, N., & Nelson, M. (2011). The Electoral Consequences of Welfare State Reform. Blame
Avoidance or Credit Claiming in the Era of Permanent Austerity? European Journal of
Political Research, 50, 1, 1-23.
Green-Pedersen, C. (2002). The Politics of Justification. Party Competition and Welfare-State
Retrenchment in Denmark and the Netherlands from 1982 to 1998. Amsterdam:
Amsterdam University Press.
Green-Pedersen, C., & Haverland, M. (2002). The New Politics and Scholarship of the
Welfare State: A Review Essay. Journal of European Social Policy, 12, 43–51.
Ha, E. (2008). Globalization, Veto Players, and Welfare Spending. Comparative Political
Studies, 41, 783-813.
Hacker, J. (2006). The Great Risk Shift. New York: Basic Books.
Hacker, J., & Pierson, P. (2006). Off Center: The Republican Revolution and the Erosion of
American Democracy. New Haven: Yale University Press.
Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica, 46, 1251-1271.
Hibbs, D. A. (1977). Political Parties and Macroeconomic Policy. American Political Science
Review, 71, 1467-1487.
27
Hicks, A. (1999). Social Democracy and Welfare Capitalism. A Century of Income Security
Politics. Ithaca and London: Cornell University Press.
Huber, E., Ragin, C., & Stephens, J. D. (1993). Social Democracy, Christian Democracy,
Constitutional Structure, and the Welfare State. American Journal of Sociology, 99, 711749.
Huber, E., Ragin, C., Stephens, J. D., Brady, D., & Beckfield, J. (2004). Comparative Welfare
States Data Set. Northwestern University, University of North Carolina, Duke University
and Indiana University.
Huber, E., & Stephens, J. D. (2001). Development and Crisis of the Welfare State. Parties and
Politics in Global Markets. Chicago and London: The University of Chicago Press.
Huo, J., Nelson, M., & Stephens, J. D. (2008). Decommodification and Activation in Social
Democratic Policy: Resolving the Paradox. Journal of European Social Policy, 18, 6-20.
Immergut, E. M. (1992). The Rules of the Game: The Logic of Health Policy-making in
France, Switzerland, and Sweden. In S. Steinmo, K. Thelen, & F. Longstreth (Eds.),
Structuring Politics: Historical Institutionalism in Comparative Analysis. Cambridge:
Cambridge University Press.
Iversen, T., & Soskice, D. (2006). Electoral Institutions and the Politics of Coalitions: Why
Some Democracies Redistribute More Than Others. American Political Science Review,
100, 165-181.
Jensen, C. (2012). Labor Market- versus Life Course-related Social Policies. Understanding
Cross-program Differences. Journal of European Public Policy, 19, 275-291.
Jones, B. D., & Williams, W. (2008). The Politics of Bad Ideas: The Great Tax Cut Delusion
and the Decline of Good Government in America. New York, NY: Pearson Longman.
Katzenstein, P. J. (1985). Small States in World Markets. Industrial Policy in Europe. Ithaca
and London: Cornell University Press.
28
Kitschelt, H. (2001). Partisan Competition and Welfare State Retrenchment: When Do
Politicians Choose Unpopular Policies? In P. Pierson (Ed.), The New Politics of the
Welfare State. Oxford: Oxford University Press.
Kittel, B., & Obinger, H. (2003). Political Parties, Institutions, and the Dynamics of Social
Expenditure in Times of Austerity. Journal of European Public Policy, 10, 20-45.
Kiviet, J. F. (1995). On Bias, Inconsistency, and Efficiency of Various Estimators in Dynamic
Panel Data Models. Journal of Econometrics, 68, 53-78.
Korpi, W. (1978). The Working Class in Welfare Capitalism. Work Unions and Politics in
Sweden. London: Routledge & Kegan Paul Ltd.
Korpi, W. (1983). The Democratic Class Struggle. London: Routledge & Kegan Paul.
Korpi, W., & Palme, J. (2003). New Politics and Class Politics in the Context of Austerity and
Globalization: Welfare State Regress in 18 Countries, 1975-1995. American Political
Science Review, 97, 425-446.
Kwon, H. Y., & Pontusson, J. (2010). Globalization, Labor Power and Government
Partisanship in OECD Countries, 1971-2002. Working paper. Princeton University.
Lijphart, A. (1999). Patterns of Democracy. New Haven: Yale University Press.
Lindbom, A. (2007). Obfuscating Retrenchment: Swedish Welfare Policy in the 1990s.
Journal of Public Policy, 27, 129-150.
Lupu, N., & Pontusson, J. (2011). The Structure of Inequality and the Politics of
Redistribution. American Political Science Review, 105, 316-336.
Lynch, J. (2006). Age in the Welfare State: The Origins of Social Spending on Pensioners,
Workers, and Children. Cambridge: Cambridge University Press.
OECD (2009). OECD.StatExtracts. Paris: OECD Publishing. Downloaded from
www.sourceoecd.org.
29
Pierson, P. (1994). Dismantling the Welfare State. Reagan, Thatcher, and the Politics of
Retrenchment. Cambridge: Cambridge University Press.
Pierson, P. (1996). The New Politics of the Welfare State. World Politics, 48, 143-179.
Pierson, P. (1998). Irresistible Forces, Immovable Objects: Post-Industrial Welfare States
Confront Permanent Austerity. Journal of European Public Policy, 5, 539-560.
Pierson, P. (2001). Coping with Permanent Austerity: Welfare State Restructuring in Affluent
Democracies. In P. Pierson (Ed.), The New Politics of the Welfare State. Oxford: Oxford
University Press.
Powell, G. B., & Whitten, G. D. (1993). A Cross-national Analysis of Economic Voting:
Taking Account of the Political Context. American Journal of Political Science, 37, 391414.
Rudolph, T. J. (2003). Who’s Responsible for the Economy? The Formation and
Consequences of Responsibility Attributions. American Journal of Political Research,
47, 698-713.
Scharpf, F. (1997). Games Real Actors Play. Actor-Centered Institutionalism in Policy
Research. Boulder: Westview Press
Scharpf, F. (2000). Economic Changes, Vulnerabilities, and Institutional Capabilities. In F.
W. Scharpf & V. A. Schmidt (Eds.), Welfare and Work in the Open Economy. Vol.1:
From Vulnerability to Competitiveness (pp. 21-124). Oxford: Oxford University Press.
Soroka, S., & Wlezien, C. (2009). Degrees of Democracy. Cambridge: Cambridge University
Press.
Starke, P. (2006). The Politics of Welfare State Retrenchment: A Literature Review. Social
Policy and Administration, 40, 104-120.
Stephens, J. D. (1979). The Transition from Capitalism to Socialism. Urbana and Chicago:
University of Illinois Press.
30
Swank, D. (2002). Global Capital, Political Institutions, and Policy Change in Developed
Welfare States. Cambridge: Cambridge University Press.
Taylor-Gooby, P. (2002). The Silver Age of the Welfare State: Perspectives on Resilience.
Journal of Social Policy, 31, 597-622.
Tsebelis, G. (2002). Veto Players. How Political Institutions Work. Princeton: Princeton
University Press.
van Kersbergen, K. (1995). Social Capitalism. A Study of Christian Democracy and the
Welfare State. London and New York: Routledge.
Vis, B. (2009). Governments and Unpopular Social Policy Reform: Biting the Bullet or
Steering Clear? European Journal of Political Research, 48, 31-57.
Vis, B., & Kersbergen, K. v. (2007). Why and How Do Political Actors Pursue Risky
Reforms? Journal of Theoretical Politics, 19, 53-172.
Wagschall, U., & Wenzelburger, G. (2008). Roads to success: Budget consolidations in
OECD countries. Journal of Public Policy, 28, 309-339.
Weaver, R. K., & Rockman, B. A. (1993). Do Institutions Matter? Government Capabilities
in the United States and Abroad. Washington, DC: The Brookings Institution.
Zohlnhöfer, R. (2007). The Politics of Budget Consolidation in Britain and Germany: The
Impact of Blame Avoidance Opportunities. West European Politics, 30, 1120-1138.
Notes
1
New research has questioned whether governments are in fact blamed for welfare state
retrenchment (see e.g. Giger, 2010; Giger & Nelson, 2011). However, as also recognized by
the authors of this research (see e.g. Giger & Nelson, 2011, pp. 20-21), these studies of voter
reactions do not control for blame avoidance strategies, which means that insignificant results
can indicate either that voters wanted to punish retrenchment but were somehow deterred
31
from doing so, or that they never had any intention of punishing retrenchment in the first
place. Consequently, of more importance to our argument are the more qualitative oriented
studies that show how governments in general tend to view retrenchment as a discipline of
blame avoidance rather than credit claiming (see, e.g., Pierson, 1994; Green-Pedersen, 2002;
Lindbom, 2007).
2
The same is true of Bonoli (2001, p. 239).
3
Blekesaune & Quadagno (2003) show that support for unemployment protection increases
during times of time unemployment, a finding that lends considerable credibility to our
argument.
4
Many authors have relied on this kind of data, including Hicks, 1999; Korpi & Palme, 2003;
Allan & Scruggs, 2004; Amable, Gatti, & Schumacher, 2006; Huo, Nelson, & Stephens,
2008; Becher, 2010; and Jensen, 2012. For thorough discussions on how to most
appropriately measure the dependent variable, which support our choice, see EspingAndersen (1990), Korpi & Palme (2003), and Clasen & Siegel (2007). The biggest drawback
of the data is that it ends in 2002, which limits our analysis to 1980-2002. We find this a
reasonable price to pay to get the most valid data.
5
For a more thorough appraisal of the net lending measure, see Wagschal & Wenzelburger
(2008). These authors also use the debt ratio in their qualitative case studies, but we need to
stick to a single measure in our quantitative analysis, and net lending clearly appears the most
appropriate given our definition of government constraint. See also Breunig & Busemeyer
(forthcoming). One thing that our measure cannot control for is those constraints stemming
from European integration with it’s formally rather strict budgetary rules. We note, however,
that these rules clearly have not been such hefty constraints that they stopped countries from
indebting themselves as the current debt crisis clearly shows.
32
6
In principle, one may suspect some circularity in equation 1 since governments at time t-1
may influence S, which in turn leads to a higher C at time t. This is less of a problem in real
life, however. First, both D and (to a high extent) R are outside the scope of government
manipulation so C will never be a mere function of previous levels of S. Second, empirically
there is no positive association between S and C; estimating the effect of unemployment
replacement rates on government lending using the same setup and controls as those reported
below yields no statistically significant results.
7
For a detailed discussion of this and other measures, see
http://www.oecd.org/document/40/0,3343,en_2649_34573_1850792_1_1_1_1,00.html.
8
The index formula is: federalism (none, weak, strong), presidentialism (absent, present), and
bicameralism (absent, weak, strong). The index thus runs from 0 to 5.
9
Since this question has been subject to some controversy we also estimated all the analyses
presented below including the electoral system in the measure of institutional fragmentation.
This modification did not change substantially any of the results presented below.
10
Traditionally, error correction models (ECM) have been used as a solution to potential unit
root problems (Beck, 1991; Franzese, 2002) and co-integration, but as shown by De Boef &
Keele (2008) ECM is a class of general models that may as well be applied on stationary time
series data. Using Maddala and Wu’s Fisher type unit root test for panel data yields no
evidence of non-stationarity in our dependent variable (chi2 = 237; p-value < .000).
11
A modified Wald statistic indicates problems with group-wise heteroskedasticity (chi2 =
1,996; p-value < .000).
12
Kiviet (1995) warns that including unit-specific fixed effects with a lagged dependent
variable on the right-hand side of the equation may yield an inconsistent estimator. However,
since this problem is most likely to occur when the number of cross-sectional units (N) is
large and time series (T) very short, it is unlikely in the present case where N=17 while T=23
33
(see also Kwon & Pontusson, 2010). Furthermore, in post estimations the null hypothesis
underlying the Hausman (1978) test was clearly rejected, corroborating the appropriateness of
estimating a fixed effects model rather than a random effects model (chi2 = 34; p-value <
.000). A final issue relates to our use of a more or less time-invariant measure of institutional
fragmentation. Using fixed country effects means that we risk boosting the level of
multicollinarity in the model, thereby increasing the standard errors. Essentially, of course,
this means that we are biasing the test against finding support for our argument.
13
We have re-estimated the model using a measure of total government debt, i.e., the
cumulated fiscal pressure. This is done in order to see if our emphasis on shocks is warranted.
It turns out that there is no significant interaction term. To us this indicates that our argument
is validated and that fiscally-induced policy change indeed mostly will follow more or less
radical change in socio-economic conditions, possibly because this effectively functions as
focusing events in the political system.
14
Since the mean value of the institutional fragmentation variable is around 1.5 it could be
argued that a simple linear modeling of its effect is inappropriate because so much of the
variation is squeezed in at the low end of the scale. We have therefore also estimated a model
with a log transformed variable. This yields similar results with the interesting difference that
government lending in countries with very limited institutional fragmentation leads to
expanding unemployment replacement rates. This is in line with our argument since it may be
explained by the high degree of visibility in these countries. Below we split the sample at the
mean value of the fragmentation index meaning that we no longer have to worry about this
particular issue as the variable is equally distributed in the two subsamples.
15
These effects are smaller than those calculated from Model II due to the bigger error
correction rate in Model III and IV. Given the higher R2 in Model III and IV, we presume that
these are the better models and that the smaller effects are the most accurate.
34
16
One concern might be that the results have been driven by extreme cases such as the U.S.,
which may be said to have an exceptionally high share of right-wing parties in government,
an exceptionally high level of institutional fragmentation as well as exceptionally high levels
of retrenchment. However, re-estimating the main analysis without the U.S. observations does
not change any of the substantial conclusions. Furthermore, we have conducted a jackknife
analysis where we leave out observations one at a time to assess the robustness of the
findings. The result of this robustness checks are also reassuring since none of the findings
change substantially.
35
Figure 1. Institutional fragmentation in equilibrium and under shock
Retrenchment
α1
Equilibrium
after shock
α2
Equilibrium
before shock
Stability
Low institutional
fragmentation
N1
N2
36
High institutional
fragmentation
Table 1. Hypothesized effects of fiscal austerity on social protection
Institutional
fragmentation
High
Low
Government party
Left
Right
Modest retrenchment
Radical retrenchment
Expansion
Stability
37
Figure 2. Government lending as percentage of GDP (minimum, mean, and maximum), 1980–
2007
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Australia
Austria
Belgium
Canada
Denmark
Finland
France
Germany
Ireland
Italy
Netherlands
New Zealand
Norway
Sweden
Switzerland
United Kingdom
United States
-10
-5
0
5
-10
10
-5
0
5
10
Note: Triangles indicate minimum values, circles indicate mean values, and squares indicate maximum values.
Source: OECD (2009).
38
Table 2. Long-term determinants of unemployment replacement rates
Lagged DPV
Government lending
Inst. fragmentation
Right cabinet
Lending x inst. frag.
Model I
(all countries)
-.2114 ***
(.0246)
.0002
(.0005)
.0057
(.0048)
-.0001 **
(.0000)
Model II
(all countries)
-.2115 ***
(.0251)
.0009
(.0008)
.0033
(.0050)
-.0001 **
(.0000)
-.0008 **
(.0003)
Lending x rightwing
Unemployment
Model III
(low inst. frag.)
-.2626 ***
(.0346)
.0028 **
(.0013)
Model IV
(high inst. frag.)
-.2818 ***
(.0247)
-.0014 **
(.0005)
.0000
(.0000)
-.0002 ***
(.0000)
-.0001 **
-.0001 *
(.0000)
(.0000)
-.0008
-.0011
-.0011
-.0008
(.0007)
(.0008)
(.0009)
(.0007)
Aged 65+
.0116 ***
.0124 ***
.0222 ***
.0028
(.0027)
(.0029)
(.0048)
(.0026)
Globalization
-.0005 **
-.0005 *
-.0012 ***
.0002
(.0002)
(.0003)
(.0003)
(.0002)
R2
.22
.23
.31
.24
Common rho
.07
.07
.12
.03
No. of countries
17
17
8
9
No. of observations
329
329
158
171
Note: Panel corrected standard errors in brackets. ***= p-value < .01; ** = p-value < .05; * = p-value < .10.
Two-tailed t-tests. All independent variables are lagged one year. All estimated models include an AR(1) term
and fixed unit effects. The full estimations are available upon request.
39
-.006
-.004
-.002
0
.002
Figure 3. The marginal effect of government lending
0
1
2
3
4
5
Institutional fragmentation
Note: The point estimate of the marginal effects are represented by the full lines; the 95% confidence intervals
by the dashed lines.
40
.7
.7
Figure 4. Government lending and replacement rates in the U.S. and U.K.
r = .34
.3
.55
.4
.6
.5
.65
.6
r = -.57 ***
-4
-2
0
2
4
-5
US
0
5
UK
Note: Unemployment replacement rates on the y-axis; government lending on the x-axis.
41
10
Figure 5. The marginal effect of government lending
High institutional fragmentation countries
-.005
-.005
-.004
-.003
0
-.002
-.001
.005
0
Low institutional fragmentation countries
0
20
40
60
80
0
100
20
40
60
80
100
Rightwing cabinet share
Rightwing cabinet share
Note: The point estimate of the marginal effects are represented by the full lines; the 95% confidence interval by
the dashed lines.
42
Table A1. Summary statistics
Unemployment
replacement rates
Government lending
Min.
Max.
Mean
Std.dev.
.10
.93
.66
.14
-11.75
.23
9.52
3.14
0
5
1.57
1.39
Institutional
fragmentation
Rightwing cabinet
share
Unemployment
0
100
37.99
38.30
.19
18.43
7.06
3.36
Aged 65+
9.10
20.00
14.11
12.28
Globalization
34.27
96.04
74.37
12.28
43
Table A2. Correlation matrix
Unemployment
replace. rate
1.000
Government
lending
-.0241
1.000
-.0726
-.0492
1.000
-.0792
.0073
.0728
1.000
-.2649
-.2352
-.0967
.1558
1.000
Aged 65+
.0895
.1439
-.1457
-.2296
-.2885
1.000
Globalization
.2754
.3108
-.2263
.0612
.0960
.2393
Unemployment
replace. rates
Government
lending
Institutional
fragmentation
Rightwing
cabinet share
Unemployment
Institutional
fragmentation
44
Rightwing
cabinet share
Unemployment
Aged 65+
Globalization
1.000