Potere politico, intervento statale e benessere economico:

WWW.SOCPOL.UNIMI.IT
Dipartimento di Studi Sociali e Politici
Università degli Studi di Milano
Working Paper
Potere politico, intervento statale
e benessere economico:
un'analisi Time-Series Cross-Section
in 13 paesi dell'area Ocse
Federico Podestà
(Università degli Studi di Trento)
WWW.SOCPOL.UNIMI.IT
Dipartimento di Studi Sociali e Politici
Facoltà di Scienze Politiche,
via Conservatorio 7 - 20122
Milano - Italy
Tel.: 02 503 21201
02 503 21220
Fax: 02 503 21240
E-mail: [email protected]
Mercoledì 16 novembre 2011 - ore 14,30
Aula seminari del Dipartimento di Studi sociali e politici
PARTISANSHIP, STATE INTERVENTION AND
ECONOMIC WELL-BEING IN OECD COUNTRIES
Federico Podestà
OPES
c/o University of Trento
[email protected]
Abstract
During last years there has been an explosion of interest in producing measures that go beyond GDP
to represent a broader view of the ways in which societies are progressing and regressing.
Consequently, an impressive number of well-being indicator characterizes the academic and public
debate at international level. Nevertheless, the comparative political economy of developed
countries has often neglected this topics. This was largely due to lack of reliable and comparable
data. In spite of this, Osberg and Sharpe (2009) have recently provided a time series cross-section
dataset of the Index of Economic Well-Being (IEWB) for 14 OECD countries. Accordingly, this
article tries to plug the current gap between the comparative political economy and well-being
literature by presenting an empirical study about the impact of partisanship and state intervention on
IEWB and its four domains (i.e., consumption flows, stocks of wealth, economic equality,
economic security. A main lesson can be drawn from the time series cross-section analysis: left
cabinets and their favourite policies increase stocks of wealth, economic equality and economic
security, rather than consumption flows. This means that leftist governments does not promote the
current prosperity of a typical citizen, but the future and widespread well-being of most population.
Keywords: partisanship; state intervention; economic well-being.
1. Introduction
In the 1930s the US Department of Commerce commissioned Simon Kuznets to develop national
accounts. From that period GDP has progressively become the best recognized indicator of
macroeconomic performance in the world. Currently, it is being used in a variety of political and
financial arenas. GDP serves as a criterion to decide whether a state is eligible to access funds
available from international organizations, such as the EU, IMF and the World Bank, and it is a lead
indicator for forecasts on financial markets and banks. GDP is also used for international
comparison and rankings and plays a crucial role in political debates. Moreover, GDP (per capita)
has been used as the a measure of the well-being of a nation (Canoy and Lerais 2007). Nevertheless,
this trend has been largely brought into question over the last decades. An explosion of interest has
been recorded as regards finding alternative indicators to GDP when it comes to representing the
ways societies progress and regress. Several initiatives have taken place at academic level as well as
major projects have been promoted by a number of national and international organizations,
including, between the most recent and notable,: the OECD Prospect on Measuring the Progress of
Societies, the European Commission Project on Beyond GDP and the Commission on the
Measurement of Economic Performance and Social Progress, established in February 2008 by the
French President Nicholas Sarkozy.
Although many efforts have been made to measure well-being, not a sole indicator is currently
standing out internationally. As a result, an impressive number of indicators have been introduced
in the academic and public debate. In such vein, Afsa et al. (2008) distinguish three approaches to
measure well-being. The first approach that remains in the spirit of national accounts, consists in
producing indexes of “corrected” GDP, purged from elements that do not contribute to well-being
and complemented by monetary evaluations of welfare enhancing items not included in GDP such
as health, life expectancy and leisure. The Measure of Economic Welfare (MEW), developed by
Nordhaus and Tobin (1972), and the Index of Sustainable Economic Welfare (ISEW), put forward
2
by Daly and Cobb (1989), are two major examples of this approach. The second approach consists
in building composite indexes that combine elementary sub-indexes in a more or less arbitrary
fashion. To fill the needs of users for a comprehensive index allowing a synthetic analysis of trends
in social conditions within countries and comparisons between countries, such an approach
proceeds by aggregating several elementary indexes to encompass a broad spectrum of dimensions
affecting what the indicator wants to measure (human development, environmental sustainability,
etc.). This approach does not provide a unified way of measuring heterogeneous dimensions of
well-being. The distinctive features of these indicators relate to the domains covered, the
normalisation methodology, and the procedures of aggregation used. The Human Development
Index (HDI), first introduced by the United Nation Development Programme (UNDP) in 1990, is
the archetype of such indicators. Among the other examples are the Index of Social Health for the
USA, elaborated by Miringoff et al. (1999), the Environmental Sustainability Index (ESI) and the
Environmental Performance Index (EPI) (Estes et al., 2005), and the Index of Economic WellBeing, developed by Osberg and Sharpe (1998; 2002). The last approach to measuring well-being is
based on subjective measures. Here, since individuals are the most interested in their own wellbeing, they are considered as the best judges of their quality of life. Individual well-being is
measured by using subjective questions and defining indicators based on the mean, the median, or
variance of the answers' distribution. The prototypical example of this approach is the InequalityAdjusted Happiness (IAH) produced by Kalmijn and Veenhoven (2005).
Despite the growing interest in producing well-being indicators, the comparative political economy
of developed countries has often neglected this topic. Several empirical works have tried to explain
cross-national variations in macroeconomic outcomes such as growth of GDP, unemployment, and
inflation. A parallel and more recent body of study has focused on understanding differences across
countries in poverty and income inequality. Not even literature has paid much attention to recent
well-being measures. Just a few comparisons have been drawn about subjective satisfaction
(Radcliff 2001; 2005; Pacek and Radcliff 2008), but no real attention has been paid to cross3
country variations of objective well-being. This has been largely due to the lack of good and
comparable data. Although HDI is one of the few indexes that are regularly compiled and widely
disseminated by international organizations to allow systematic cross-country comparisons, it
appears to be useful only for comparisons between developing economies (Afsa et al. 2008), and
not really relevant for capturing differences among developed countries. In spite of this, Osberg and
Sharpe (2009) have recently provided a time series cross-section dataset of the Index of Economic
Well-Being (IEWB) for 14 OECD countries over the last decades. This contribution allows to
investigate the socio-political determinants of the cross-country variation of one of the most
prominent indicators of objective well-being.
This paper illustrates an empirical study about the impact of partisanship and state intervention on
IEWB and its four domains. The plan of the paper is as follows. Section 2 revises the comparative
political economy literature in order to formulate testable hypotheses. Section 3 describes data,
variables and model specification. Section 4 discusses the econometric results, while section 5
sketches some conclusions.
2. Literature and hypotheses
The IEWB derives from the following assumptions. First, if one adopts an average flow like GDP
per capita (or other alternatives, such as the average personal income) as a summative index of
well-being, the analyst is only interested in a typical citizen (representative agent) at the present
time. Hence, a better estimate of the well-being of society should allow analysts to distinguish
between current consumption and the accumulation of productive assets (which determines the
sustainability of current levels of consumption), and thereby enable citizens to apply their differing
values. Secondly, individuals are justifiably concerned about the degree to which they and others
will share in prosperity – there is a long tradition in economics that “social welfare” depends on
4
both average incomes and the degree of inequality and poverty in the distribution of incomes.
Thirdly, if the future is uncertain, and complete insurance is unobtainable (either privately or
through the welfare state), individuals will also care about the degree to which their economic
future is secured for themselves and others (Osberg and Sharpe 2005, 314-15). On the basis of these
arguments, the logic behind IEWB architecture is that it recognizes both trends in average outcomes
and in the diversity of outcomes, both now and in the future. More specifically, IEWB is a
composite index which combines four domains deriving from the intersection between the
following dichotomies: a) representative agent vs. heterogeneity of experiences of all citizens; b)
present vs. future. As a result, the four domains are: 1) consumption flows, relating to the economic
well-being of a "representative agent" at present; 2) stocks of wealth, relating to the economic wellbeing of a "representative agent" in the future, 3) economic equality, relating to the economic wellbeing of heterogeneous citizens at present; 4) economic security, relating to the economic wellbeing of heterogeneous citizens in the future (see Table 1).
Table 1 around here
In order to explain the cross-national variations of the economic well-being index, specific
hypotheses have been formulated for each domain and the overall IEWB. However, before
discussing each hypothesis, some general arguments must be considered. Firstly, although the
comparative political economy of OECD countries has often neglected well-being indicators,
numerous studies have examined the differences across countries with respect to several economic
outcomes which are quite similar to the variables adopted by Osberg and Sharpe (2009) with the
aim of operationalizing the above cited four domains. Accordingly, the hypotheses presented
hereafter have been drawn from specific research lines of comparative political economy. Secondly,
even if studies in comparative political economy are actually composite, a number of them share a
common perspective concerning the role of partisan incumbency and state structure. In particular,
5
most political economists start from the assumption that governments of the Left and Right have
distinct partisan economic policies and objectives that they would prefer to pursue. More precisely,
leftist governments are expected to intervene extensively in the economy to alter market outcomes.
By contrast, rightist governments are expected to pursue less-interventionist strategies (Hibbs 1977,
1987; Lipset 1983; Alvarez et al. 1991).
In line with this perspective several empirical analyses have been carried out in order to estimate the
impact of leftist governments and state intervention on national outcomes. Accordingly, the
following hypotheses for IEWB and its four domains have been formulated by assuming that leftwing cabinets affect economic well-being with respect to their ability to develop big government.
This means that left political influence on economic well-being is not direct but mediated by state
intervention, although the operationalization of some IEWB domains has involved a few exceptions
(see below). In addition, it is to be considered that the ability of governments to act on their partisan
preferences is constrained by several national features. Nevertheless, it is well-established that
prolonged left-wing incumbency has a significant long-term effect on state structure (Huber, Ragin
and Stephens 1993; Huber and Stephens 2001). This means that prolonged left-wing incumbency is
necessary for observing long-term effects on state intervention and, consequently, on economic
well-being.
With these ideas in mind, it is possible to illustrate the hypotheses for each IEWB domains. The
first domain is consumption flows. It consists of two components: private consumption
expenditures and government expenditures on goods and services consumed either directly or
indirectly by households1 (Osberg and Sharpe 2009). Regarding the second component, it is
important to note that many recent studies have shown how partisanship plays a role in determining
the level of public expenditure. Left parties are generally associated with higher government
1
Three adjustments have been made to these components. Firstly, since economies of scale exist in private household
consumption, private consumer expenditure is adjusted for changes in family size. Secondly, an adjustment is made to
consumption flows to account for the large international differences in growth rates and levels of annual hours worked.
Thirdly, an adjustment for the positive impact of increased life expectancy on well-being is made by adjusting total
consumption flows by the percentage increase in life expectancy (Osberg and Sharpe 2009).
6
spending since they are interested in altering market outcomes via state intervention. On the one
hand, Social democratic parties aim to regulate business cycle by sustaining demand at a high level
over time (Garrett 1998). On the other hand, a vast literature has shown that the strength of leftist
parties and labour organizations is crucial for explaining cross-national variation in welfare
spending levels (Huber e Stephens 2001; Hicks e Swank 1992; Hicks 1999; Korpi e Palme 2003).
In the same way, one may reasonably hypothesize that leftist governments produce a direct and
positive effect on the second component of the first domain of IEWB, public consumption. At first
glance, such a hypothesis may seem to be suitable for predicting the variation of private
consumption. This is because the Keynesian theory assumes that expansionary fiscal policy induces
a short-run stimulus on private spending. However, this mechanism was challenged by the
neoclassical theory. Such a perspective stresses the fact that households internalize the government
budget constraint. Hence, the present value of household disposable income will be negatively
affected by an increase in government expenditures as a consequence of an increase in taxation.
This means that fiscal expansions is associated with lower private consumption growth, while
during fiscal contractions economic activities tend to increase (Giavazzi and Pagano 1990; 1996).
The Neoclassical model appears suitable here because one of its main traits is the fact that a
permanent change in disposable income affects consumption more than a temporary change in
disposable income. In particular, the effect on private consumption will depend on whether the
change in spending is thought to be permanent or transitory. On the one hand, temporary changes in
spending, associated with a temporary increase in taxes, lead to a smaller reduction in private
spending. By contrast, permanent changes, financed by a permanent increase in taxes, will, as a first
approximation, lead to a proportional decrease in private spending (Jonsson 2004; Blanchard 2008).
In line with this perspective, one may hypothesize that a persistent interventionist strategy,
characterized by a high level of public spending and taxation, is associated with low private
consumption. On such basis, the expected signs for the two components of consumption flows
appear to be in contrast: left-wing cabinets affect directly and positively government consumption,
7
while state intervention, built by such political forces, is expected to have a negative long-term
effect on private consumption. However, the relationship would appear to be unpredictable. This
impasse can be overcome by considering the proportions of two components. Indeed, since private
consumption commonly represents the biggest part (generally, from 60 to 80 percent) of the first
domain of IEWB (Osberg and Sharpe 2009), the hypothesized negative effect hypothesized for the
interventionist strategy pursuit by leftist governments may predict the variation of the total
consumption flows.
The second domain of IEWB, stocks of Wealth, consists of four components: the physical capital
stock, the R&D stock, the stock of human capital, and net international investment position. One
adjustment is made to the sum of these components: to account for the social costs of environmental
degradation, the estimated annual cost of greenhouse gas emissions is subtracted (Osberg and
Sharpe 2009). In this case we must take into account that OECD governments have developed
economic strategies that go far beyond demand management policies and welfare state
development. In particular, governments design policies to directly affect the supply-side of
economy, which involves the provision of productive inputs. As Boix (1997, 817-8) observes, like
in the demand-side management, it is possible to sketch two divergent economic strategies. While
right-wing governments rely on private agents to maximize economic growth, left-wing parties
adopt an interventionist supply-side strategy. In a nutshell this latter strategy works as follows.
Marginal tax rates are increased. The new revenues are then primarily devoted to ensure a high
level of public saving. However they are above all allocated in the form of public investments both
to enhance the quality of infrastructure and to upgrade the skills of the population. Thus, left-wing
governments spend heavily in physical and human capital formation in order to increase the
productivity of workers and, more in general, of the economic sector. On this basis, one may
reasonably expect that the first three components of the second domain of IEWB (capital stock,
R&D stock, stock of human capital) become higher where and when left parties are stronger and an
interventionist supply-side strategy is implemented.
8
A similar argument can be developed about the net international investment position: the incumbent
government’s partisanship may affect foreign investors’ decision to flow into different productive
sectors. Pinto and Pinto (2008) find evidence of the existence of such partisan cycles in the patterns
of direct investment performance across countries and over time at the industry level. In particular,
they observe that in OECD countries that are governed by parties of the left, FDI tends to flow into
industries associated with the production of food, textiles, machinery and vehicles, financial
intermediation, mining and quarrying, and utilities, and out of sectors such as construction and
transportation. Similarly, other studies have shown how large governments may be attractive to
international investors. This is because public goods (i.e., human and physical capital and social and
political stability) provided by big governments are not impediments at all to a more intense capital
inflow (Alesina and Perotti 1996). Consequently, it is reasonable to hypothesize that the fourth
component of the second domain of the IEWB is positively associated with both leftist governments
and state intervention.
Finally, a similar hypothesis can be formulated about the adjustment of this IEWB domain, namely
the estimated annual cost of greenhouse gas emission. Since left-wing parties tend to be more
interventionist in their economic strategies, they may find it easier to install environmental
protection instruments such as taxes in order to limit greenhouse gas emissions. As Neumayer
(2003, 4) argues, given that interventionist policies are aimed to correct market failures on social
grounds, left-wing parties appear inclined to implement interventionist policies on environmental
issues. In conclusion, since the expected effect for all components of the second IEWB domain are
positive, the hypothesized effect of leftist governments and state intervention on stocks of wealth is
positive as well.
Economic equality, the third domain of the IEWB, is composed of income inequality (measured by
the Gini coefficient) and poverty (measured by poverty intensity) (Osberg and Sharpe 2009).
Reviewing the existing literature, Moller et al. (2003) highlight that both left and social democratic
parties, as well as Christian democratic parties, are ideologically committed to poverty reduction.
9
whereas this goal has been less prominent among the secular right and centre parties. Social
democratic parties are also strongly committed to reducing inequality, while Christian democratic
and secular right and center parties are not. This implies that one may plausibly expect a positive
effect of leftist governments on the third IEWB domain. Nevertheless, the impact of left-wing
cabinets on economic equality is essentially due to their engagement in expanding welfare state. As
Brady (2003) argues, left mobilization triggers welfare state expansion, which subsequently reduces
poverty and income inequality. Accordingly, recent studies have focused their attention on the
separate impact of welfare states on distributive outcomes in advanced countries (Scruggs and Allan
2006b; Bradley et al. 2003; Kenworthy, 1999, 2004). Scruggs and Allan (2006b) highlight that this
body of studies has erroneously explained cross-national variations in distributive outcomes since
welfare state effort has been almost exclusively measured by spending indicators, rather than
entitlement generosity measures. Accordingly, they show that the material well-being of the poor
people is improved by the generous entitlements to key social insurance programs (e.g., sickness
and pensions), rather than high levels of welfare spending. Hence, we may expect higher levels of
economic equality where and when welfare state entitlement generosity is more developed.
The last domains of IEWB is, as clarified above, economic security. It consists in four components
called risks to economic well-being facing the population, namely the risk imposed by
unemployment, the financial risk from illness, the risk from single parent poverty, and the risk of
poverty in old age2. In this case, one may hypothesizes that economy security varies positively with
the dominance in government of political forces committed to limit human dependence on the
market, namely left-wing parties. This means that such a IEWB domain should exhibit higher levels
where and when the decommodification accomplished by state policy is more developed. This
hypothesis derives from the fact that economic security is logically interrelated to the concept of
2
The risk imposed by unemployment is determined by two variables: the unemployment rate and the proportion of
earnings that are replaced by unemployment benefits. Each of these measures is scaled and then summed with weights
of 0.8 and 0.2, respectively. The risk imposed by illness corresponds to the private Medical care expenses as a
percentage of disposable income. The risk of single parent poverty consists of three variables: divorce rate, poverty rate
for lone female-headed families and poverty gap for these families. The risk of poverty in old age is proxied by the
poverty intensity experienced by the households headed by a person aged 65 or over.
10
decommodification adopted by Esping-Andersen (1990) in order to identify the three worlds of
welfare capitalism. As a matter of fact, while economic security implies a low exposition to market
risks, decommodification coincides with the degree to which individuals, or families, can uphold a
socially acceptable standard of living independent of market participation (Esping-Andersen 1990,
37). Moreover, these two concepts appear interconnected because the three social programs adopted
by Esping-Andersen (1990) to develop a decommodification index, are appropriate to effectively
prevent three of the four risks, chosen by Osber and Sharpe (2009) to measure economic security. In
particular, pensions, sickness insurance, and unemployment benefits are respectively designed to
contrast the risk of poverty in old age, the financial risk from illness, and the risk imposed by
unemployment. Accordingly, it is not be surprising if economic security is high where the programs
of decommodification are vastly institutionalized.
As a consequence, a last hypothesis is to be formulated regarding the overall economic well-being
index. Since IEWB corresponds to the equally weighted sum of its four domains (Osberg and
Sharpe 2009)3, such a hypothesis necessarily derives from those formulated for each domain.
Specifically, given that in almost all cases (excluding consumption flows) the expected association
is positive (see Table 2), one may reasonably hypothesize that left wing cabinets and, subsequently,
state intervention, produce a positive impact on the overall economic well being Index.
Table 2 around here
3. Data, variables and model specification
3
In the literature, most composite indices assign equal weight to each component. There is no objective sense in which
this weighting scheme is preferable to all others. Nevertheless, since the choice of weights is a value judgment, Osberg
and Sharpe (2009) provide estimates of the Index based on alternative weighting schemes without obtaining significant
effect on the rankings of the countries considered by the Index of Economic Wellbeing.
11
The matrix I have analyzed is a time series cross-section (TSCS) dataset. It is composed of 13
developed countries (Australia, Belgium, Canada, Denmark, Finland, France, Germany, Italy,
Netherlands, Norway, Sweden, United Kingdom, United States) observed over the period 19802000.
The variables included in the regression models estimated are obviously to be divided into
dependent and independent variables. The dependent variables have been drawn from the new data
set developed by Osberg and Sharpe (2009) about the IEWB and its four domains (specified in the
previous section): a) consumption flows; b) stocks of wealth; c) economic equality; d) economic
security; and e) the composite index of economic well-being4.
According to the aforesaid hypotheses, three independent variables have been chosen. The first one
regards partisanship while the second and third one relate to state intervention. Partisanship has
been operativized via a long-term incumbency of left-wing parties indicator. More precisely, since
it is assumed that prolonged left-wing incumbency may produce a substantial long-term effect on
state intervention and consequently on economic well-being (see above), the cumulative left seats in
terms of percentage of seats held by all government parties score from 1946 to the year of the
observation has been considered (Huber et al. 2004).
Government intervention has been first measured by total current receipts for general government
(including central state, and local government) as a percentage of GDP. Such a variable
has been chosen because it is a usual indicator of state intervention (see for example: Garrett 1998;
Huber and Stephens 2001). Moreover, according to the hypothesis (see previous Section), public
revenue may affect both consumption flows and stocks of wealth.
Indeed, the extent to which states intervene in economy is frequently measured by government
spending, as well as by public receipts. Nevertheless, government spending has not been considered
here, for a twofold reason. Firstly, total government expenditure includes government consumption,
namely the first component of the first dependent variable: consumption flows (see above).
4
In order to standardize the ranges of different variables, a linear scaling technique is applied to IEWB and its four
domains (Osberg and Sharpe 2009).
12
Secondly, several works have argued that public spending does not provide a suitable indication of
the state intervention’s effects on individual life chances. Scruggs and Allan (2006a) highlight that
most empirical studies continue to rely heavily on aggregate social expenditure as a measure of
welfare state commitment in spite of the widespread recognition that spending data often give a
misleading picture of such commitment. Increased aggregate spending can coexist with lower
individual entitlements, and programs that decommodify labour may alter behavioural incentives of
nonrecipients of state spending (e.g., via wage demands). Moreover, since I have hypothesized that
policies aimed to limit human dependence on the market have a positive effect on two domains of
IEWB (e.g., economic equality and economic security), the benefit generosity index developed by
Scruggs (2004) has been used in place of spending data. The benefit generosity index is de facto a
refinement of Esping-Andersen’s (1990) de-commodification index, since it is based on more
accurate values of replacement rates, coverage, and qualifying conditions in pension, sickness pay,
and unemployment compensation programs.
Following some recent quantitative studies concerning economic performances and living
conditions of OECD countries, I have also included in the TSCS model regressions the following
control variables: a) real GDP per capita; b) Unemployment Rate; c) deindustrialization, as a unit of
measurement of the percentage of workers in service; and d) economic openness measured by
exports plus imports as percentage of real GDP.
Although some independent variables of key interest appear intuitively more associable to some
IEWB domains than others (consumption flows and stocks of wealth may for instance be more
directly related to public receipts than to welfare state generosity), the five dependent variables have
been regressed against all three independent variables (i.e., left-wing cabinets, public receipts and
welfare state generosity). However, the analysis has been carried out by including one independent
variable at a time. Consequently, three TSCS regressions have been estimated for each dependent
variable: the first regression includes leftist governments, the second one public receipts, and the
third one welfare state generosity. This prevents from the possibility of estimating the effect of
13
leftist governments taking into account the mediation of state intervention (as assumed in section 2),
but collinearity among the three independent variables must be addressed (the correlation between
leftist government and public receipts is .73, between leftist governments and welfare state
generosity is .72, and between public receipts and welfare state generosity is .92). The high
correlation among the three independent variables may depend on the fact that such indicators are
manifestations of specific socio-economic regimes. In particular, long-term incumbency of leftwing parties has contributed to develop a state structure marked by high levels of public receipts
and welfare state generosity, which in turn influence the parliamentary fortunes of social democracy
(Moller et al. 2003; Esping-Andersen 2007).
In this logic, Table 3 includes the average scores computed for the long-term incumbency of left
parties indicator, public receipts and welfare state generosity over the period 1980-2000 and
grouped by three leading welfare regimes: Liberal, Conservative, Social-democratic. Although
Esping-Andersen (1990) did not exactly adopt such variables to identify the three regimes, their
scores appear compatible with such a tripartition. Highest mean scores are shown by the socialdemocratic regime, the Liberal type exhibits the lowest levels for all three variables, while the
conservative regime is in the middle.
In order to provide some preliminary comments about the association between dependent and
independent variables, in Table 3 are also included average scores of IEWB and its for domains
over the period 1980-2000. As expected, countries with low levels of left-wing cabinets, public
receipts and welfare state generosity tend to exhibit high scores of consumption flows and low
levels of stock of wealth, economic equality and economic security. By contrast, countries with
high levels of three independent variables tend to display the opposite pattern.
Table 3 around here
Turning back to the model specification issue, it is to be noted that fixed effects for countries are
not included in TSCS regressions in order to avoid additional problems of collinearity. In fact, unit
14
fixed effects are highly correlated with relevant variables (left-wing cabinets, public receipts, and
welfare state generosity) that are nearly constant over the time (for an analogous strategy of
analysis, see Huber et al. 1993: 733)5. The high persistence does not only regard the independent
variables of key interest, but all the variables herewith analyzed. Indeed, they are strongly autoregressive. Specifically, the auto-regressive coefficients, estimated for the above described
variables, vary from 0.92 to 1.02. Accordingly, one may have tested for unit root in a formal way
and, eventually, tested for co-integration. Nevertheless, Beck and Katz (forthcoming) have recently
asserted that the impressive apparatus built over the last two decades to estimate models with I(1)
series does not provide the tools needed for many, if not most, political economy TSCS datasets.
Making a similar argument to that of the authors, if these series had unit roots, there would be
tendency for them to return to their means and the variance of the observations would grow larger
and larger over time. However, public receipts and the left-wing cabinet are by definition between
zero and one hundred per cent. If either series were I(1), then we would be equally likely to see an
increase or decrease in either variable regardless of its present value; do we really believe that there
is no tendency for public receipts to rise when it is low and to fall when high, or similarly for leftwing cabinets strength? So, even if these series are very persistent, they simply cannot be I(1). In
line with this hypothesis, I have avoided any test for unit root and co-integration and estimated all
models in levels in order to capture long-term relationships6. Clearly, this does not prevent from the
risk of taking spurious regressions for granted. However, it must be noted that the results obtained
via TSCS regressions (see next section) are confirmed by repeated cross-section analyses 7. This
alleviates the problem since the spurious correlation deriving from high temporal persistence does
not arise in cross-section analysis, even if the underlying TSCS variables are strongly autoregressive (Pesaran and Smith 1995: 93).
5
The fixed effect specification is also avoided because it allows to capture the effects with respect to the intra-unit
variation only. This is because country dummies inclusion replaces the dependent and independent variables with their
unit centered deviations, removing any of the average unit to unit variation from the analysis (Greene 2003).
6
The regressions are Prais Winsten estimates—panel-corrected standard errors and corrections for first-order
autoregressiveness.
7
The repeated cross-section analyses are not reported here. They are available upon request.
15
4. Results
TSCS regression estimates are reported in Table 4. They are grouped according to the abovedescribed five dependent variables. In particular, column 1, 2 and 3 include the results for
consumption flows; columns 4, 5 and 6 show the results for stocks of wealth; columns 7, 8 and 9
report the results for economic equality; columns 10, 11 and 12 exhibit the results for economic
security. Finally, columns 13, 14 and 15 include the results for IEWB. The three columns associated
to each dependent variable derive from the fact that leftists governments, public receipts and
welfare state generosity have been included in the model regression one by one (see previous
section).
Table 4 around here
Before considering the results obtained for the independent variables of key interest, a few remarks
must be made to the estimated parameters for control variables. GDP per-capita is negatively
associated with economic equality and economic security, and positively associated with
consumption flows and stock of wealth. This means that the "traditional" indicator of national
welfare does not increase each domain of economic well-being. Nevertheless these contradictory
results are contrasted by the positive impact of this aggregate on the composite IEWB (see column
13, 14 and 15 in Table 4). This suggests that the productive activity does not inhibit economic wellbeing.
Contradictory effects are also estimated for the percentage of workers in service. Such an indicator
is positively associated with consumption flows and stocks of wealth, but negatively associated
with the remaining three dependent variables. This means that, after controlling the other variables,
16
deindustrialization decreases economic equality, economic security and overall economic wellbeing index. These results are not surprising. Iversen and Cusack (2000) argued that increasing
risks, produced by deindustrialization, involve massive demands for compensation.
Moreover, as one may reasonably expect, unemployment tends to decrease several aspects of
economic well-being. Apart from the positive association between such a control variable and stock
of wealth, high levels of unemployment are coupled with low scores of consumption flows,
economic equality, economic security and IEWB. By contrast, trade openness tends to improve all
IEWB domains and the corresponding composite index. In fact, all estimated signs for this control
variable are positive. This means that the recent globalization of markets does not obstacle wellbeing in developed countries.
Looking at the parameters estimated for the independent variables of main interest, all coefficients
appear correctly signed and supported by the confidence intervals in almost all cases (zero is
included in 95% confidence intervals for two coefficients only, see Table 4).
Starting from consumption flows, one may note that the coefficient estimated for left-wing cabinets,
public receipts and welfare state generosity denote negative signs. Although such results are not
systematically confirmed by confidence intervals (zero is not included in 95% confidence intervals
for left-wing cabinets only, see column 1 in Table 4), one may realistically assert that the long-term
incumbency of left-wing cabinets tends to decrease the current prosperity of the representative
agent expressed by the first IEWB domain. Such an effect may depend on the fact that households
internalize the government budget constraint, deriving from a persistent intervention strategy, and
consequently, reduce consumption flows.
By contrast, stocks of wealth are positively influenced by the indicators of partisanship and the two
measures of state intervention (see column 4, 5 and 6 in Table 4). As expected, the estimated
coefficients for such variables show positive signs. Therefore, it appears confirmed that the
interventionist supply-side strategy adopted by left-wing parties tends to increase the accumulation
of productive assets.
17
Similar results have been obtained for economic equality and economic security. Firstly, both
variables appear positively associated with left-wing cabinets, public receipts and welfare state
generosity (see column 7, 8, 9, 10, 11 and 12 in Table 4). In other words, the stronger the Left and
the larger state intervention, the lower income inequality, poverty and risks to economic well-being
for the population will be.
Finally, the overall economic well being Index results positively related to public receipts, welfare
state generosity and a long-term incumbency of left-wing parties. Such results derive from the fact
that in almost all cases the three independent variables positively covary with the four domains of
IEWB (see above). The positive effect of left wing cabinets and, subsequently, state intervention on
IEWB may be thus interpreted as a consequence of how such an index is computed.
5. Conclusions
The aim of this article is to partially bridge the existing gap in comparative political economy about
the cross-national variation of objective well-being. On the basis of the new dataset for selected
OECD countries (Osberg and Sharpe 2009), an empirical study about the impact of partisanship and
state intervention on economic well being and its four domains has been illustrated. In particular,
since governments of the Left and Right have distinct partisan policies about the balance between
state and market, some hypotheses about the impact of long-term incumbency of left parties and,
subsequently, state intervention on consumption flows, stocks of wealth, economic equality and
economic security have been tested. A main conclusion derives from the TSCS analysis: the
interventionism supported by left-wing parties tends to increase overall IEWB. However, such a
result is based on a more complex dynamics. More precisely, leftist governments and their favourite
policies do not promote the current prosperity of the typical citizen, but the future and widespread
well-being of most citizens. This is because they increase stocks of wealth, economic equality and
18
economic security, rather than consumption flows. This means that state intervention is developed
as redistributive and protective instrument, rather than a source of well-being for a representative
agent at the present time. As a matter of fact, it appears able to contribute to the sustainability of
current levels of well-being and protect citizens from poverty, income inequality, and other risks of
the market.
19
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22
Table 1 - Domains of Economic Well-being
Time Period
Citizenship
Present
Future
"Typical citizen" or "representative agent"
Consumption flows
Stocks of wealth
Heterogeneous citizens
Economic equality
Economic security
Table 2 - Hypothesized effects of left power and state intervention on IEWB and its four domains.
Left power and state
intervention
Consumption
flows
Stocks of
wealth
Economic
equality
Economic
security
IEWB
-
+
+
+
+
Notes: plus (+) and minus (-) signs indicate positive and negative associations respectively.
Table 3 - Left cabinet, public receipts, state generosity, and IEWB and its four domains by welfare
regime (average scores over the period 1980-2000)
Left
cabinets
Public
receipts
State
generosity
Consumption
flows
Stocks of
wealth
Economic
equality
Economic
security
IEWB
LIBERAL
Australia
Canada
United Kingdom
United States
Mean
14.3
0.0
16.6
0.0
7.7
34.1
42.0
40.7
33.1
37.5
19.8
24.4
20.2
19.1
20.9
0.43
0.41
0.36
0.54
0.44
0.27
0.32
0.29
0.43
0.33
0.45
0.52
0.46
0.20
0.41
0.65
0.67
0.49
0.38
0.55
0.47
0.49
0.41
0.41
0.45
CONSERVATIVE
Belgium
France
Germany
Italy
Mean
14.5
9.9
12.4
4.9
10.4
47.6
48.4
44.4
41.7
45.5
32.3
30.5
28.5
23.1
28.6
0.52
0.39
0.35
0.40
0.41
0.35
0.32
0.42
0.32
0.35
0.80
0.65
0.70
0.50
0.66
0.75
0.72
0.71
0.71
0.72
0.63
0.53
0.53
0.5
0.55
0.69
0.85
0.62
0.76
0.68
0.72
0.78
0.76
0.73
0.79
0.82
0.78
0.54
0.52
0.57
0.55
0.54
0.54
SOCIAL-DEMOCRATIC
Denmark
26.7
54.5
36.9
0.33
0.38
Finland
18.6
52.0
32.7
0.21
0.34
Netherlands
10.1
50.1
35.6
0.45
0.46
Norway
33.9
53.7
40.5
0.26
0.53
Sweden
36.8
59.9
41.8
0.32
0.33
Mean
25.2
54.1
37.5
0.31
0.41
Sources: calculus drawn from Huber et al. (2004), Scruggs (2004) and Osberg e sharpe (2009).
23
Table 4 - Prais-Winsten estimates of determinants of IEWB and its four domains
VARIABLES
Left cabinet
1
-0.002
(-0.004 - -0.001)
Public receipts
Consumption flows
2
Unemployment
Deindustrialization
Trade Openness
Constant
Observations
4
0.001
(-0.001 - 0.003)
6
-0.001
(-0.002 - 0.001)
0.003
(0.001 - 0.005)
0.004
(0.003 - 0.005)
-0.001
(-0.003 - 0.000)
0.007
(0.005 - 0.009)
0.001
(0.000 - 0.001)
0.004
(0.003 - 0.005)
-0.001
(-0.002 - 0.001)
0.006
(0.004 - 0.008)
0.000
(0.000 - 0.001)
-0.000
(-0.002 - 0.001)
0.004
0.003
0.003
(0.003 - 0.005) (0.002 - 0.004) (0.002 - 0.004)
-0.001
0.004
0.004
(-0.003 - 0.001) (0.001 - 0.007) (0.001 - 0.007)
0.006
0.001
0.001
(0.005 - 0.008) (-0.002 - 0.004) (-0.002 - 0.004)
0.000
0.001
0.001
(-0.000 - 0.001) (0.000 - 0.001) (0.000 - 0.001)
-0.448
(-0.564 - -0.331)
-0.409
(-0.529 - -0.290)
-0.428
-0.122
-0.239
-0.226
(-0.547 - -0.309) (-0.274 - 0.031) (-0.419 - -0.059) (-0.412 - -0.040)
State generosity
Per-capita GDP
3
Stock of wealth
5
7
0.005
(0.003 - 0.008)
Economic equality
8
9
0.003
(-0.000 - 0.007)
0.004
(0.001 - 0.006)
0.003
-0.003
(0.002 - 0.005) (-0.004 - -0.001)
0.005
-0.001
(0.002 - 0.007) (-0.006 - 0.003)
0.001
-0.004
(-0.002 - 0.005) (-0.010 - 0.003)
0.001
0.002
(0.000 - 0.001) (0.001 - 0.003)
0.914
(0.601 - 1.226)
-0.003
(-0.004 - -0.001)
-0.002
(-0.006 - 0.002)
-0.003
(-0.009 - 0.003)
0.002
(0.001 - 0.003)
0.005
(0.000 - 0.009)
-0.003
(-0.004 - -0.001)
-0.002
(-0.006 - 0.003)
-0.004
(-0.010 - 0.001)
0.003
(0.002 - 0.004)
0.793
(0.458 - 1.127)
0.867
(0.556 - 1.179)
272
272
272
272
272
272
272
272
272
Ρ
0.976
0.965
0.978
0.907
0.902
0.923
0.947
0.940
0.918
R-squared
0.527
0.561
0.507
0.267
0.291
0.262
0.604
0.617
0.665
Notes: 95% confidence intervals in parentheses
24
(Table 4 continued from previous page)
VARIABLES
Left cabinet
Economic security
11
12
10
0.004
(0.003 - 0.005)
Public receipts
0.005
(0.004 - 0.007)
State generosity
Per-capita GDP
Unemployment
Deindustrialization
Trade openness
Constant
Observations
13
0.001
(0.000 - 0.002)
IEWB
14
15
0.002
(0.001 - 0.003)
-0.001
(-0.002 - -0.000)
-0.006
(-0.008 - -0.004)
-0.007
(-0.009 - -0.004)
0.002
(0.001 - 0.002)
-0.001
(-0.001 - -0.000)
-0.007
(-0.010 - -0.005)
-0.006
(-0.008 - -0.004)
0.002
(0.001 - 0.002)
0.006
(0.004 - 0.008)
-0.000
(-0.001 - 0.000)
-0.006
(-0.009 - -0.004)
-0.006
(-0.009 - -0.004)
0.002
(0.001 - 0.002)
1.091
(0.996 - 1.187)
0.879
(0.776 - 0.982)
0.941
(0.853 - 1.030)
0.001
(0.000 - 0.002)
-0.001
(-0.003 - 0.000)
-0.001
(-0.003 - 0.001)
0.002
(0.001 - 0.002)
0.001
(0.001 - 0.002)
-0.002
(-0.003 - -0.000)
-0.000
(-0.002 - 0.001)
0.001
(0.001 - 0.002)
0.002
(0.001 - 0.003)
0.001
(0.001 - 0.002)
-0.001
(-0.003 - 0.000)
-0.001
(-0.003 - 0.001)
0.002
(0.001 - 0.002)
0.357
(0.262 - 0.452)
0.285
(0.184 - 0.385)
0.324
(0.229 - 0.418)
272
272
272
272
272
272
Ρ
0.893
0.886
0.867
0.900
0.904
0.887
R-squared
0.901
0.904
0.903
0.887
0.889
0.893
Notes: 95% confidence intervals in parentheses
25