Perhaps the primary concern of empirical democratic theory is the
relationship between public preferences and public policy. Scholars have
stressed the importance of ‘responsive rule’—the
correspondence between citizens' preferences and government
actions—in democratic governance (see, e.g., Pitkin, 1967; Dahl, 1971;
Saward, 1994). Yet scholars have also questioned whether citizens are
sufficiently informed and/or reactive enough to play a role in
governing the state (Plato's Republic; Mill, 1861; Lippmann,
1925; Schumpeter, 1950; Converse, 1964; Page and
Shapiro, 1992; Dahl, 1998). This work highlights the importance of
the interrelationships between public opinion and policy, both from
opinion to policy and from policy to opinion itself. It implies that those
interested in studying democracy should be concerned with policy
representation—whether and how policy follows public
preferences—and public responsiveness—whether
preferences react to policy as well as to real-world developments.
There already is a large and growing body of work on the
correspondence between public opinion and policy behaviour (e.g., Miller
and Stokes, 1961; Weissberg, 1976; McCrone and Kuklinski, 1979; Monroe, 1979; Bartels, 1991;
Page and Shapiro, 1992; Hartley and Russett, 1992; Erikson et al., 1993; Goggin and Wlezien, 1993; Jacobs, 1993; Stimson et al., 1995; Wlezien 1996a; Wood and Hinton-Anderson, 1998; Hill and
Hurley, 1998; Smith, 1999; Sharpe, 1999; Erikson et al.,
2002; Soroka, 2003; Eichenberg and Stoll, 2003; Wlezien, 2004; Soroka and Wlezien,
N.d.).1
Also see Burstein's
(2003) and Weakliem's (2003) reviews of the literature.
And work on
‘thermostatic’ models of the opinion-policy relationship
suggests not just that policymakers respond to the public, but that the
public adjusts its preferences over time in reaction to policy change
(Wlezien,
1995,
1996a;
Soroka and Wlezien, N.d.). This work is
important. Narrowly conceived, it suggests that policymakers are attentive
to public preferences and that the public is aware of and reactive to
policy change, at least in certain domains. More broadly conceived, it
offers empirical evidence on the nature and quality of representative
democracy.
Unfortunately, longitudinal empirical research on the opinion-policy
link has for the most part been restricted to the United States (US).2
There are some exceptions. See, especially,
Eichenberg and Stoll's (2003) analysis of
defence spending and opinion in different countries.
A wider
application of this line of research could provide valuable insights into
how representation varies across political systems and issue areas,
however. Indeed, work using an opinion-policy
congruence approach
has already flagged some potentially significant differences across
domains and, to a lesser extent, countries (Brooks,
1987,
1990; Monroe,
1979,
1998;
Petry, 1999;
Petry and Mendelsohn,
2004;
Powell, 2000; see also
Miller and Stokes, 1963). Such differences in
representation across countries may be linked to institutional factors.
(So too may differences in public responsiveness, though this has received
even less attention in past work.) There is reason to suspect that
electoral institutions are important, for instance (
Lijphart, 1999;
Powell,
2000). Government institutions may matter, such as the relative
power of the executive versus the legislature. Highlighting differences in
the opinion-policy link and connecting these with institutional
differences may offer crucial information on the comparative effectiveness
of democratic institutions and policymaking structures.
The current paper takes another step towards a comparative study of
the dynamic inter-relationships between public preferences and budgetary
policy. Using a new body of data on public opinion and government
spending, we extend our own previous research on the dynamics of spending
opinion and policy in the US and the UK to Canada. Specifically, we
explore (1) public responsiveness—the degree to which the public
responds ‘thermostatically’ to public expenditures, and (2)
policy representation—the extent to which budgetary decisions
reflect public preferences for spending. In the course of our analyses, we
also consider the nature of responsiveness and representation,
that is, whether the public and policymakers respond to information that
is specific to particular domains, or rely on more ‘global’
information from across the different domestic domains.
The results are interesting, especially in a comparative context. As
in the US and the UK, the Canadian public appears to respond
thermostatically to changes in public spending, and Canadian policymakers
appear to respond to public preferences. The underlying details are not
the same, however, and the pattern of results across the three countries
is suggestive about the structuring role of institutions. Before telling
you where we end up, let us show you how we get there. We begin with our
theoretical model of opinion and policy.
The Thermostatic Model of Opinion and
Policy
The representation of public opinion in policy presupposes that the
public notices and responds to what policymakers do. Without such
responsiveness, policymakers would have little incentive to represent what
the public wants. Indeed, public preferences would contain little
meaningful information. There not only would be a limited basis for
holding politicians accountable; expressed preferences would be of little
use even to those politicians motivated to represent the public for other
reasons.
A responsive public behaves much like a thermostat (Wlezien, 1995).3
This
discussion closely follows Wlezien (1995:
981–983).
That is, the public adjusts its preferences for
‘more’ or ‘less’ policy in response to what
policymakers do. When policy increases (decreases), the preference for
more policy decreases (increases). For expository purposes, the public can
be viewed as a collection of individuals distributed along a dimension of
preference for policy activity, say, spending on defence. This
characterization is not meant to imply that individuals have specific
preferred levels of spending in mind; rather, it is intended to reflect
the fact that some people want more than others. The public preference can
be represented by the median along the dimension, which implies a certain
‘ideal’ level of defence spending.
Now, if the level of policy differs from the level the public prefers,
the public favours a corresponding change in policy—basically,
either more or less. If the preferred level is greater than policy itself,
the public favours more spending than currently is being undertaken. If
policymakers respond, and provide more funding—but not too much
more—for defence, then the new policy position would more closely
correspond to the preferred level of spending. And if the public is indeed
responsive to what policymakers do, then the public would now not favour,
as much, more activity on defence. It might still favour more, on balance,
but not as substantially as in the prior period; if policymakers overshoot
the public's preferred level of spending, it would favour less. In
effect, following the thermostatic metaphor, a departure from the favoured
policy temperature, which itself can change, produces a signal to adjust
policy accordingly, and once sufficiently adjusted, the signal stops.4
This conceptualization of public preferences has
deep roots in political science, including Easton's (1965) classic depiction of a political system and
Deutsch's (1963) models of
‘control.’
These expectations can be expressed formally. The public's
preference for ‘more’ policy—its relative preference,
R—represents the difference between the public's
preferred level of policy (P*) and the level it actually gets
(P):
Thus, as the preferred level of policy or policy itself changes, the
relative preference signal changes accordingly. The public is expected to
respond currently to actual policy change when put into effect (at
t). This is straightforward, at least in theory. It is less
straightforward in practice.
Most importantly, we typically do not directly observe P*.
Survey organizations generally do not ask people how much policy they
want. Instead, these organizations ask about relative preferences, whether
we are spending ‘too little,’ whether spending should
‘be increased,’ or whether we should ‘do more.’
This, presumably, is how people think about most policies. (Imagine asking
people how much health or education spending they want.) The
public preference, however defined, also is necessarily relative. This is
actually quite convenient, as we can measure the thermostatic signal the
public sends to policymakers. Because we must rely on proxies for
P*—and also because metrics of each variable in the model
will differ—it is necessary to rewrite the model of
Rt as follows:
where a0 and et
represent the intercept and the error term, respectively, and W
designates the indicators of the public's preferred level of policy.
Notice that levels of relative preferences are expected to be associated
with current (time t) levels of policy; if the thermostatic model
applies, the coefficient (β1) relating the two would be
less than 0.
Now, if policymakers are responsive to these public preferences,
changes in policy (ΔP) will be associated with
lagged levels of the public's relative preference
(R), as follows:
where a1 and μt represent
the intercept and the error term, respectively, and Z represents
the set of other determinants of policy. The coefficient β3
captures responsiveness, where the effect of preferences on policy is
independent of other factors; if the coefficient is greater than 0, policy
‘responds’ to preferences.5
This
does not mean that politicians actually respond to changing public
preferences, for it may be that they and the public both respond to
something else. All we can say for certain is that the coefficient
(β3) captures policy responsiveness in a statistical sense,
that is, whether and the extent to which public preferences directly
influence policy change, other things being equal.
Notice that
the change in expenditure for fiscal year
t is modelled as a
function of net support in year
t − 1. This specification
is not meant to imply that policies do not respond to current opinion;
rather, it is intended to reflect the reality of budgetary decision
making, which largely happens over the course of the previous fiscal year
(see
Wlezien, 1996b;
Wlezien
and Soroka, 2003). Thus, the specification captures responsiveness
to opinion when most budgetary decisions are made.
These expectations are very general ones and we do not expect the
model to apply in all policy domains in all countries. Indeed, public and
policy responsiveness is likely to reflect the political importance (or
‘salience’) of the different domains, if only due to possible
electoral consequences.6
We can explicitly
incorporate salience (S) into our model as follows:
where S ranges between 0 and 1. Here the effect of opinion on
policy depends on the level of salience. This follows Franklin and Wlezien
(1997); also see Jones (1994) and Soroka (2003).
Following Wlezien (
2004), we might expect the pattern of representation
to be symmetric to patterns of public responsiveness: where the public
notices and responds to policy in a particular domain, policymakers will
notice and respond to public preferences themselves; where the public does
not respond to policy, policymakers will not represent public preferences.
This structure fits well in the US, less well in the UK. There, we observe
pervasive and specific public responsiveness and more limited and general
policy responsiveness (
Soroka and Wlezien,
N.d.). Now, let us see how things work in Canada.
The Data
Relative Preferences for Policy
Environics has frequently asked the Canadian public about their
preferences for spending, using a similar question to that employed in the
US and the UK. The question is as follows: “Keeping in mind that
increasing services could increase taxes, do you think the federal
government is spending too much, just the right amount, or should be
spending more on each of the following … [i.e.,
defence]?” Respondents are asked about spending in up to 18
categories in a given survey. Those categories that have been included
consistently for an extended period and for which budgetary data are
readily available are: defence, welfare, education, health care,
environmental protection and transportation. Following previous research,
we create percentage difference measures by subtracting the percentage of
people who think we are spending ‘too much’ from the
percentage of people who think we are spending ‘too little’ in
each domain. The resulting measures of ‘net support’ thus
capture the degree to which the public wants more or less spending over
time—they capture both direction and magnitude. We rely on results
from Environics for all years in which data are available; for fiscal year
(FY) 1996–97, we ‘link’ results from a similar question
asked by Pollara. Complete details are provided in the appendix.
Budgetary Policy
Unlike the US federal government, the Canadian government does not
provide data on appropriations of budget authority at any level of
aggregation. What is readily available are data on actual expenditures.
And although expenditures surely are important, they are not policy
per se. That is, elected politicians have only limited control
over what is actually spent, which may reflect things that they can't
anticipate or manage.7
Using expenditures
thus biases analyses against finding opinion representation and policy
feedback itself. See Wlezien (1996a) and Wlezien
and Soroka (N.d.).
We nevertheless are thankful that reliable data are available from
Statistics Canada for expenditures by function back to FY1988–89.
Most importantly for our purposes, these data are functionally consistent
over time—yearly changes reliably reflect changes in functional
spending rather than changes in functional definitions.8
This was not the case in the UK, for example; our analyses in
that country required that we create entirely new estimates of spending by
function.
Another now-discontinued Statistics Canada matrix contains functional
spending data from FY1965–66 to FY1994–95. These data are also
functionally consistent over time, but use slightly different definitions
than the current series.9
There are minor
changes in functional definitions, and also in the definition of total
government expenditures. That said, the differences in spending estimates
are relatively small. For a complete description of the differences
between the old and new CANSIM matrices, see Statistics Canada (2002).
Directly merging the data without any
manipulation would create a strange punctuation in FY1988–89 for
many series. Accordingly, in lieu of a single matrix that covers the
entire period we are studying, we merge the two datasets as follows: the
percentage difference from FY1988–89 to FY1987–1988 is
calculated based on the old series, and this percentage change is then
applied to the new series. This is repeated back to FY1984–85, the
first year for which we have public opinion data. The pre-FY1988–89
data are used only intermittently in forthcoming analyses, due to the
(un)availability of public preferences data; these analyses should be
regarded as preliminary, however, given our estimation of spending series
for this early period.
A second issue in measuring budgetary policy in Canada is whether to
use federal spending or consolidated federal, provincial and local
spending. The survey questions we rely on for public preferences ask about
the federal government; we nevertheless rely on consolidated
spending measures for all domains except defence, where all spending is
federal. Our use of consolidated spending is based on measurement. The
nature of funding in most major policy domains in Canada is such that
federal spending estimates are quite poor measures of federal budgetary
policy. More precisely, federal spending estimates do not include general-
or combined-purpose transfers from the federal to provincial governments.
This is a particular problem in social policy domains, where a vast
majority of federal funding occurs via transfers such as the Canada Health
and Social Transfer (CHST)—transfers that are entirely excluded from
federal spending figures. As a consequence, budgetary policy in these
domains can only be accurately measured using consolidated
spending data. Our data thus reflect a combination of federal and
provincial policy making, which is important to keep in mind in the
analyses that follow.
Consolidated spending is available for all functions and some
subfunctions from FY1988–89 to FY2001–02, and for all
functions before FY1988–89. Some of our spending series are based on
general functional classifications, and so can be estimated using the
procedure outlined above from FY1984–85 to the present. Other series
rely on data derived from a combination of functional and subfunctional
estimates. These data are available only back to FY1988–89. The
exception is the (subfunctional, federal) defence spending series, which
was made available to us by Statistics Canada, and which runs from
FY1965–66 to the present. Complete details of our spending data are
available in the appendix.
A Basic Analysis of Structure
The resulting spending and preferences data are illustrated in Figure
1. In Figure 1A, we can see that spending on
different functions cluster together. A lot more is spent on some
programs—health, welfare and education—than
others—defence, the environment and transportation. The former also
show more variance over time, and the latter change more deliberately.
Spending in all of the domains does tend to trend upward over time,
documenting a well-known pattern of government growth. Most programs do
grow faster in some periods than in others, however; in some periods,
spending actually shrinks, at least in real dollars. Preferences for
spending (in Figure 1B) exhibit a similar
structure. Indeed, preferences for more spending tend to be higher for the
exact same set of programs, namely, health, welfare and education.
Preferences in all of the various domains—including
defence—track together over time. Indeed, preferences appear to be
surprisingly ‘global’ in comparison to the US and the UK.
Figure 1A: Spending, by Function
Figure 1B: Net Preferences, by Function
Factor analyses reported in Table 1 confirm these patterns and provide
additional detail. The analysis of expenditures in Table 1A reveals a guns-butter tradeoff, consistent
with what we find in the US and the UK (Wlezien,
2004; Soroka and Wlezien, forthcoming). That is, defence spending
is negatively related to spending in the domestic domains, which tend to
load (positively) together in the first factor. The one domestic exception
is transportation, for which we have no perfect explanation—it is
not ‘social’ spending, but then again neither is spending on
the ‘environment,’ which loads positively. Regardless, the
pattern does describe the realities of policymaker behaviour during the
period under study, at least in a very general way.
Table 1A: Factor Analysis, Spending
Interestingly, the analysis of preferences in Table
1B shows that the guns-butter tradeoff evident in spending is not
apparent in public preferences. Instead, preferences in all domains are
positively related over time. This may be a function of the time period
for which we have opinion data.10
Because
some of our preferences series do not begin until that year, this factor
analysis only includes data since 1989, and it is entirely possible that
this period—after the Cold War, and during a period of economic
boom—shows a very different relationship between defence and
domestic preferences than earlier periods.
Regardless, the
pattern indicates that preferences for spending in
all domains
have tended to move in the same liberal-conservative direction since 1989.
There thus appears to be a certain ‘global’ movement of
opinion that may drive politicians' behaviour in various policy
domains (
Stimson et al., 1995). The
intercorrelations, even across the purely ‘social’ domains,
are much larger than what we have observed in the US and the UK (
Wlezien, 2004;
Soroka and Wlezien,
N.d.). This is of real consequence for our analysis, as we will
see.
Table 1B: Factor Analysis, Preferences
It also is clear from the factor loadings that the structure of
preferences for spending is not perfectly unidimensional. That is, each of
the preference series exhibits variance over time that is specific to the
particular domains. By inference from the factor loadings, the minimum
proportion of variance that is specific ranges from 14 per cent in the
case of transportation to 82 per cent in the case of the environment. The
median is 22 per cent.11
As the factors are
based on an analysis that includes preferences in each of the domains,
these estimates clearly are conservative ones. Also note that the specific
movement in preferences is not mere sampling error. Given the frequencies
and sample sizes of the actual polls, the amount of observed variance that
is due to sampling error is, on average, 3–4 per cent and no higher
than 6 per cent (welfare). See Heise (1969).
This also is of consequence for the
analysis that follows, as it makes possible specific policy responsiveness
in each of the different spending domains.
The Analysis
Public Responsiveness
Recall that the thermostatic model implies that the public's
relative preference for policy (R) is the difference between the
public's preferred level of policy (P*) and policy
(P) itself. Now, we have available measures of R and
P, at least in six spending domains in Canada. We do not,
however, have measures of P* in these domains, as noted earlier
in the paper. It thus is necessary to rely on proxies. Previous research
is a useful guide.
We know that public preferences for defence spending in the US have
closely followed perceived variation in national security over time.
Specifically, preferences have reflected the ‘likes’ and
‘dislikes’ of the Soviet Union/Russia (Wlezien, 1995, 1996a). There is
reason to expect a similar pattern to hold in Canada. The problem is that
we do not have comparable measures of sentiments toward Russia. However,
if the measure from the US provides a reliable indication of the actual
security threat from the Soviet Union during the period under study, or
broad public perceptions of that threat, then it should work equally well
in the Canadian context.12
Thus, we have relied on the US measure. Our measure
of Net Dislike represents the percentage of Americans who dislike Russia
minus the percentage who like the country. The data are drawn from the
General Social Survey. Other sources of threat may be more relevant during
the period under study, including the threat associated with September 11,
2001, and its aftermath. This is difficult to measure in any general way
but nevertheless easy to control for in the particular years.
Identifying specific variables to indicate underlying preferences in
each of the different domestic domains is much less straightforward. We
nevertheless know that preferences in these areas do exhibit common
movement, and previous research indicates that it follows variation in
economic security over time. It is not clear exactly what effect the
economy has, however, as the findings of different studies differ quite a
lot: economic security is positively related to spending opinion in some
studies (Durr, 1993) and negatively in others
(Erikson et al., 2002). The studies do rely on
different measures: business expectations and unemployment rates,
respectively. Still other research on public opinion relies on the sum of
unemployment and inflation rates (Franklin and Wlezien,
1997). Following our work in the UK, we adopt the latter measure,
commonly known as the ‘misery index.’13
The original decision was based on theoretical
grounds—that the measure provides a good indication of the direction
and impact of economic change—and empirical analyses of the various
measures.
Specifically, the measure used here represents the
average misery index in the current fiscal year.
Following the theoretical model in equation 2, the dependent variables
used in the analyses of preferences represent the levels of net
support for spending. To preserve precious degrees of freedom, we impute
values when opinion data are missing using a straight linear
interpolation. This allows preference time series of up to 17 years each,
and has relatively little consequence for the general pattern of results;
it really only serves to increase the estimated standard errors.14
More powerful techniques for imputing missing
data are not appropriate here—or even possible in some
cases—given the relatively short length of our time series.
The measures are regressed on corresponding levels of spending (in
billions of 1992 dollars) and the proxies for the public's preferred
level of spending.
15 Using a
first-difference specification produces similar results, though such
results are less robust, partly owing to missing data.
A linear
counter variable is also included to account for any trend in the
underlying preferred levels of spending over time, other things being
equal (
Wlezien, 1995). When significant, lagged
net support is also included in the models.
16 That is, lagged net support is included for statistical
reasons only—to capture correlation among the residuals. Where there
is no statistical reason to include this lag, it is dropped. Note also
that Dickey-Fuller tests indicate that the preference series exhibit
stationary tendencies, which is exactly as we would expect, given the
thermostatic model. That is, preferences represent the difference between
what the public wants and what the public gets, both of which are, in
theory, integrated. If this is true, and if policymakers represent
preferences over time, then preferences are stationary by definition,
i.e., the linear combination of two cointegrated series. See Wlezien
(2000).
The results of estimating separate models of spending
preferences for the six domains are shown in
Table
2.
Table 2: Public Responsiveness, by Function
Results in the first column indicate that defence spending preferences
in Canada do closely follow the measures of net dislike for Russia
observed in the US. The coefficient actually is larger than what we
observe in the US and twice the estimated effect found in the UK. It thus
appears that the public's underlying preferred levels of defence
spending in Canada, as in the US and UK, appear to change in
understandable ways.17
Note also that
preferences for more defence spending increased sharply after September
11th.
In direct contrast with what we see in the US
and the UK, however, the coefficient for spending actually is positive but
not significant. This implies that the Canadian public does not notice and
respond to what policymakers do. Part of the problem may be extremely high
levels of collinearity among the independent variables, where the
bivariate correlations with spending are .73 and .83. Excluding the
counter variable—for which the significant positive coefficient
makes little sense without negative feedback—shifts things
substantially.
18 A positive trend without
negative feedback would imply that preferences for more spending
keep increasing over time, which is impossible.
As can be seen in
the second column, the coefficient for spending is now negative and
significant, if only modestly so. At the same time, the estimated effect
of net dislike is much lower and no longer significant. The results imply
that the defence spending domain is only of modest importance to the
Canadian public.
19 This is consistent with
some previous research (Nevitte and Gibbins, 1986).
Results for the domestic domains are more supportive of the
thermostatic model. To begin with, note that effects of the misery index
are consistently negative, if only significant for health and environment.
When the misery index increases, the public favours less spending. This is
exactly the opposite relationship of what we found in the UK, where the
public wants governments to spend more during periods of recession. It is
to some extent consistent with what we see in the US, however (Wlezien, 1996a).20
Separate diagnostic analyses reveal that estimating the
separate effects of the two components of misery—unemployment and
inflation—offers little additional information.
Most importantly for our analysis, it appears in Table 2 that the public adjusts its preferences for
more domestic spending in response to spending itself. That is, when
spending goes up (down), preferences for ‘more’ spending go
down (up), other things being equal. This is the crux of the thermostatic
model. The negative coefficient on spending obtains in all domains,
although its size and significance vary substantially. Only for welfare
and environment can we conclude with much confidence that the public
responds to changes in government spending over time (although analyses
below reveal responsiveness in health as well).
The welfare spending coefficient in the third column of Table 2 implies that a one billion-dollar increase in
spending produces a 1.2-point decrease in net support for more welfare
spending. The environment spending coefficient implies a much greater
effect from an identical increase in spending, a 6.3-point drop in
preferences. These differences actually are quite understandable given the
differences in the size and variance of spending in the two domains. For
example, a one-standard deviation (4.6 billion dollars) increase in
welfare spending leads to a 5.7-point decrease in preferences, on average,
roughly the same as a one standard deviation (or 1.1 billion dollars)
increase in environment spending, which produces a 6.7-point drop in
preferences.
In the other domains, the evidence of public responsiveness is more
tenuous. This is in sharp contrast with what we observe in the US and
especially in the UK, where public responsiveness is pervasive across all
domains. It nevertheless is clear that the Canadian public does respond
thermostatically to changes in policy. In a forthcoming section, the
extent and nature of this responsiveness will become clearer still. First,
however, let us examine policy representation.
Policy Representation
Thus far, we have seen that the public responds to spending in Canada,
although the responsiveness varies in significant ways. This is of obvious
importance. However, we also want to know whether and to what extent
politicians represent these preferences in policy itself. For this
analysis, we build directly on recent ‘political’ models of
policy change in the US (Stimson et al., 1995;
Wlezien, 1996a, 2004;
Smith, 1999) and the UK (Soroka and Wlezien, N.d.), which include measures of
(relative) public preferences and party control of government.
Following equation 3, the dependent variables used in the analyses
represent the first differences in spending (in billions of 1992
dollars) for each of the six spending categories. Recall that these
changes in spending are expected to be positively related to the
levels of net support for spending, which capture the
public's relative preferences. Politicians are expected to respond
promptly. In the budgetary context, this means that a change in spending
for fiscal year t follows the level of net support in year
t − 1, when the bulk of spending decisions for fiscal year
t are made.
The party control variable is fairly standard and takes the value
‘1’ under Liberal federal governments and ‘0’
under Conservative federal governments. The measure thus taps the levels
of partisan control, which might appear to be inconsistent with the
(differenced) dependent variables. Given that budgetary policy feeds back
in ‘thermostatic’ fashion on public preferences, however, the
specification is actually theoretically implied (Wlezien, 2004). It is also supported by separate
diagnostic analyses. As mentioned above, relying on consolidated spending
data somewhat complicates the use of party control variables. Party
control of the federal government is likely not an ideal predictor of
party influence on consolidated (federal, provincial and municipal)
expenditures. It is the best we can provide, however, and we include it
here to test the extent to which federal partisanship is related to
combined federal and provincial budgetary policy.21
We tested additional measures of the partisan composition of
provincial governments, including the mean partisanship score for
provincial dummy variables where Liberal, NDP and Bloc governments were
lumped together, and a version where these provincial partisanship dummies
were weighted by the provinces' average yearly shares of total
provincial expenditures. These measures were not statistically significant
and did not have any consistent, discernable effects on the other
estimated parameters. These specifications are all of course far from
perfect, but still about the best we can do without valid and reliable
measures of spending (and opinion) by province.
In addition to preferences and party control variables, the model
includes a measure of net public debt (in billions of 1992 dollars). It is
included based on the expectation that Canadian governments will tend to
reduce spending in reaction to accumulated deficits, following comparative
work by Blais, Blake and Dion (1993, 1996) and our own work in the UK (Soroka and Wlezien, N.d.). That debt is included in
levels rather than changes suggests a particular kind of
reaction—when the national debt remains high, governments are
expected to continue to reduce spending, or at least not increase spending
as much. The specification is supported by diagnostic analysis. In line
with our spending estimates, we use net federal debt for defence and net
consolidated debt otherwise. Including other ‘baseline’
variables such as unemployment and inflation does not meaningfully alter
the results, except for welfare spending, where unemployment is a
significant predictor.22
This is exactly as
we expect given the automatic nature of many welfare entitlements. It also
was not possible to neatly isolate and subtract out entitlement funding,
as it was in the case of the US.
As for our models of
preferences, we include the lagged level of spending when its effect is
statistically significant (see also note 16). Otherwise, the analysis that
follows largely relies on a simple model that includes measures of public
preferences for spending, the party control of government, and the level
of public debt.
Results of estimating the model for the six budgetary domains are
shown in Table 3. The control variables act
about as we predicted. The debt coefficient is negative in all domestic
domains, although it is statistically significant only twice—for
welfare and education. Liberal control of (federal) government is much
less consistent in its effect, as we might expect given the use of
consolidated spending figures. The party control of government appears to
have made little difference, other things being equal.
Table 3: Policy Representation, by Function
Our central interest is in the public preference variables. In the
first column notice that the link between defence spending preferences and
actual spending is modest: the coefficient is positive but just
misses conventional levels (p = .10) of significance. There thus
is a strong hint of representation in the Canadian defence spending
domain.23
Note that even minimal adjustment
for sampling error boosts significance across the threshold.
Although this is in clear contrast with the US and the UK, where
responsiveness is highly reliable, it fits nicely with the analysis of
Canadian public preferences, which shows only weak responsiveness to
spending. Indeed, the result is exactly as we might expect.
For the other domains, the preference coefficient is positive in every
case except transportation, and statistically significant twice, for
welfare and health. A one-point increase in net preferences is associated
with an average increase in consolidated welfare spending of about 348
million (1992) dollars and to a smaller 80 million (1992) dollar increase
in consolidated health spending. Given that levels of spending in the two
domains are about equal on balance, the differences are quite
meaningful.24
For instance, the differences
in standardized effects for the two domains are only slightly less
pronounced: for welfare, a one-standard deviation (or 9.1-point) increase
in preferences leads to a 3.2 billion dollar increase in welfare spending;
for health, a one-standard deviation (17-point) increase leads to a 1.4
billion dollar increase in spending.
But why welfare and health? What about the other four domains? One
possibility is that welfare and health are the more salient domains. This
clearly seems the case for welfare, where we have seen clear evidence of
public responsiveness. In this domain, therefore, responsiveness and
representation are symmetrical. It is less clear for health care, where we
have not yet observed public responsiveness, but see the analyses to come.
The environment domain is more troublesome. Here we see striking public
responsiveness that is unmatched by policy representation.
On the Nature of Responsiveness and Representation in
Different Domains
Thus far, we have assumed that representation (and public
responsiveness) is specific to domains. That is, politicians are expected
to respond to public opinion within particular areas. This reflects the
traditional characterization of representation (see, e.g., Monroe, 1979; Bartels, 1991;
Page and Shapiro, 1992; Hartley and Russett, 1992; Geer, 1996; Sharpe, 1999). It may be, however, that representation
is more general (see Stimson et al., 1995; Wlezien, 2004). We have already seen that preferences
for spending and actual spending in the different domestic programs move
together over time. Perhaps policymakers notice this common or
‘global’ movement and not the specific movement unique to the
different domains. Perhaps the public likewise notices the changes in
spending for the different programs taken together and not the changes
that are particular to each domain. It is important to consider these
possibilities.
One way to address these issues is to separate out the specific
component of spending preferences (and spending itself) in the different
areas from the common component that is shared across these domains. For
expository purposes, let us focus on spending. The most direct measure of
the common variance of spending comes from factor analysis of the five
domestic domains. This gives us the domestic (‘global’)
component. The specific component is simply the variance that is unique to
each domain. To create a common metric, the domestic component of spending
represents the predicted values from the pooled regression of by-domain
spending on the underlying factor score. The domain-specific component
consists of the residuals from this regression, i.e., the actual value of
spending minus the domestic component.
By substituting the ‘domestic’ and ‘specific’
components of spending into the models of preferences, we can directly
assess the focus of public responsiveness (Wlezien,
2004). If the public is responding to spending in particular
domains, preferences will be about equally related to both components of
spending; if the public is responding to domestic spending alone,
preferences will only be related to the common component. An identical set
of expectations applies on the spending side, of course. That is, we can
substitute the ‘domestic’ and ‘specific’
components of preferences into the models of spending and directly assess
the focus of policy responsiveness. The results of the analysis of
preferences and spending are described in Tables 4 and 5, respectively.
Table 4: Assessing the Focus of Public Responsiveness, by
Function
Table 5: Assessing The Focus of Policy Representation, by
Function
Consider first the results of the analysis of public preferences in
Table 4. Here we can see that the coefficients
for the ‘global’ component are negative and statistically
significant in all domains but transport. The public thus is much more
responsive than analysis using by-domain measures of spending would lead
us to believe. That is, there is evident responsiveness for health and
education, where we did not see responsiveness in the preceding analysis.
The results help make more understandable the fairly strict parallelism of
preferences over time. The coefficients do differ, however, especially in
the case of the environment. This is because the metrics relating spending
and preferences differ. For the environment, the variance (and mean) of
the global spending measure is much smaller than for the other domains,
approximately one-ninth as much on average. Thus, as noted earlier, a
much larger change in ‘welfare’ spending is required
to generate the same public response.
The coefficient for the specific component is significant only for
welfare, however. Since the global and specific coefficients in this
domain are also not distinguishable (F1,9 = .11, p =
.75), we can conclude that the Canadian public notices welfare spending,
which is exactly the result which we see in the US. In the health and
education domains, the coefficients for the specific components are
positive. In the environment domain, the coefficient is appropriately
negative but highly unreliable. With the important exception of welfare,
the public in Canada appears to notice only very general patterns of
social spending. Let us see how this fits with the spending side.
The results of estimating the parallel models of spending change are
displayed in Table 5. What we see here is only
partly consistent with what we saw in the preceding table. There is clear,
specific responsiveness in the welfare domain. The effects of both global
and specific preferences are positive and significant; indeed, the
coefficients are virtually identical. This fits perfectly with the
analysis of public preferences. There is also clear global responsiveness
in the health domain. The effect of the global preference is positive and
significant and the effect of the specific preference is tiny and
irretrievable. This is also consistent with the analysis of public
preferences. There is no evidence of representation in the other domains,
however. The result is in contrast with our analysis of public preferences
for education and environmental spending, which revealed strong global
public responsiveness. Canadian policymakers thus appear
dependent—at least reliant—on public opinion in some domains
and largely independent in others.
The Domestic Domains Taken Together
Although the analysis shows that public responsiveness and policy
representation vary across domains, it is useful to consider what happens
when we aggregate across the set of social programs. It has, after all,
become quite commonplace to lump programs together in analyses, especially
of representation (e.g., Erikson et al., 1993; Stimson
et al., 1995; Wood and Hinton Andersson, 1998), and doing so may
have consequences for our analyses. Such aggregation may actually reveal
additional feedback and/or representation. That is, it may be that the
different policy domains are substitutable to the public (policymakers),
so that responsiveness (representation) is collective. Were this the case,
responsiveness and representation would be more pronounced when the
domains are taken together than when taken separately (Wlezien, 2004).
To consider the effects of aggregating, the basic model is estimated
using total domestic spending and average net support in the five domestic
domains. The results of this analysis are shown in Table 6. The first column contains results from an
aggregate model of domestic spending preferences—our model of public
responsiveness. These results essentially summarize the results of the
by-domain analyses in Table 2. The
‘domestic’ spending preference moves alongside economic
conditions: when the misery index increases, the Canadian public favours
less spending in these domains. The domestic preference also reflects
spending itself. A one-billion (1992) dollar increase in spending across
the seven domestic domains produces a .3 point decrease in average net
preferences for spending in these domains.25
This is a slightly smaller level of responsiveness than we
would expect based solely on the analyses in Table
2, though this is not entirely clear from the by-domain results.
The total spending figures conceal important differences in the levels
and, more importantly, the annual changes in spending differ across
domains. A one-billion dollar change in our measure of domestic spending
does not come equally from the five domains, but instead is more likely to
be found in welfare and health and education. In order to draw strict
comparisons with by-domain analyses, we must weight the coefficients in
Table 2. Doing so, which is not straightforward,
indicates that estimated responsiveness to aggregate domestic spending is
slightly less than we would expect based on evident by-domain
responsiveness. Also see note 24.
Table 6: Responsiveness and Representation, Domestic Functions
Combined
The second column of Table 6 shows the
results of the aggregate spending model—our model of policy
representation. These results also summarize the by-domain analyses, from
Table 3. A one-point increase in net preferences
leads to an average increase in aggregate domestic spending of 282 million
(1992) dollars. This is less than we would expect based on the by-domain
coefficients. The sum of the coefficients for the five domains in Table 3 is .437, which implies that a one-point change
in preferences in each domain would produce a 400- to 500-million (1992)
dollar increase in spending. Thus, aggregating does not clearly add
anything to what we already knew from Table 3;
if anything, it would appear to subtract.26
This is to some extent deceiving, as the results of the
aggregate model are not directly comparable to the results of the
by-domain analyses. That is, aggregate domestic spending preferences
reflect the different variances of preferences in the particular domains.
Thus, a one-unit change in aggregate preferences does not represent a
one-unit change in all of the particular domains. Taking the variances
into account predictably reduces the difference in estimated
representation. Also see note 25.
The results for the other variables are less straightforward. The
estimated effect of party control is appropriately positive and highly
unreliable, which is expected given the mixed effects from the results in
Table 3. The effect of debt is appropriately
negative, consistent with the by-domain analyses, but nevertheless
insignificant. This is somewhat surprising. It is also deceiving, as high
collinearity (r = .89) between government partisanship and debt inflates
the standard errors.27
The collinearity is
understandable given that the move from Conservative to Liberal rule
coincided with a move towards balanced budgets.
Including
partisanship and debt in the model individually provides some clarity. In
column 3, where the debt variable is excluded from the model, we see that
partisanship has a significant effect on government spending. The negative
coefficient for Liberal governments is not what one would expect,
however—all other things being equal, Liberal governments should
spend more than Conservative governments. In column 4, where the party
variable is excluded, we can see that the public debt coefficient is both
significant and appropriately negatively signed. The adjusted
R-squareds also indicate that this model outperforms the others.
It appears that it was looming debt, not government partisanship, that
influenced domestic spending during this period.
28 Recall that the weakness of the partisanship variable here is
not surprising, given that our dependent variable is consolidated (federal
and provincial) spending.
Of course, as we have seen, public
opinion did too.
Discussion and Conclusions
What we have observed in Canada both comports and conflicts with what
we observe in other countries. To begin, consider the fundamental
similarity: as in the US and the UK, the thermostatic model works in
Canada. We observe that the public adjusts its relative preferences for
spending in response to spending itself—there is negative feedback.
We also observe that spending itself follows changes in preferences over
time—there is representation. In effect, democracy works, at least
in a very general sense.
There are important differences in the details, however. First, public
responsiveness is less pronounced in Canada than in the US and the UK.
Responsiveness is spottier and also more general, particularly in
comparison with the UK. We have suggested elsewhere that the particularly
pronounced feedback in the UK may partly be a product of that
country's unitary system, which makes the locus of responsibility for
policy comparatively clear and reliable (Soroka and
Wlezien, N.d.). The Canadian findings are perfectly consistent with
this conjecture: that is, given the country's very high level of
federalism.29
For a very useful fiscal
perspective, see Joumard and Kongsrud (2003).
The vertical division of powers
increases the number of different governments making policy, and in theory
makes less clear what ‘government’ is doing (see, e.g.,
Downs, 1999). As effective public responsiveness
depends on an accurate signal of what government is doing, it may be that
the relationship between spending and opinion is mitigated in policy
domains for which multiple governments have responsibility. This may make
the apparently only very global responsiveness in health and education in
Canada more understandable.
Second, policy representation in Canada is less pronounced than in the
US and more like what we observe in the UK. We have suggested that the
differences between the US and the UK may reflect the horizontal
separation of powers (Soroka and Wlezien, N.d.).
That is, we might expect that policymakers are less representative in
parliamentary systems than in presidential systems, reflecting the
relative imbalance of power between the executive and legislature.30
See Bagehot (1867)
and Jennings (1959) for traditional statements
and Laver and Shepsle (1996) for a more modern
one, but especially see Tsebelis (2002).
In parliamentary systems, the cabinet
exercises substantial control over policy decisions in all domains. First,
the legislature does not propose. Second, it cannot effectively amend what
the cabinet does propose. By implication, the cabinet is able to exercise
considerable discretion.
31 It can choose not
to approve government policies. This obviously is—or can be—a
very big check on government if it takes the form of a no-confidence vote.
Undertaking such a vote, after all, requires that governing party
legislators are willing to face election.
We thus might expect
governments in parliamentary systems to be less
reliably
responsive to the public. This is what we observe in the UK and in
Canada.
Of course, we cannot be sure about these explanations. After all, what
we have provided here is a third case. Only when our analyses encompass a
larger and more wide-ranging set of countries can we really sort out how
and why opinion-policy linkages vary. We do now know quite a lot, however.
Most importantly, we know that the thermostatic model works in at least
three different countries.
Appendix. Data Sources
Government Expenditures
Complete descriptions of functions and subfunctions are available from
Statistics Canada, Financial Management System
(FMS) 2002. Series were transformed into constant 1992
Canadian dollars using the deflator in CANSIM series V735319. Brief
descriptions of the series follow.
Defence: Subfunction 12.01, National Defence. A series that
is functionally consistent from FY1965–66 was made available by
Statistics Canada. All spending is by the federal government.
Welfare: Subfunction 15.01, Social Assistance. Includes
income maintenance, social security, family allowances and miscellaneous
social services. Consolidated expenditures available from FY1988–99;
federal spending accounts for between 75 and 90 per cent of consolidated
spending.
Education: Function 16, Education. Includes elementary and
secondary education, and retraining services. Consolidated expenditures
estimated from 1980–81; from FY1988–89, federal spending
accounts for between 6 and 12 per cent of consolidated spending.
Health: Function 14, Health. Includes hospital care, medical
care, preventative care and other health services. Consolidated
expenditures estimated from 1980–81; from FY1988–89 to
1995–96, federal spending accounts for between 15 and 20 per cent of
total spending; following the establishment of CHST (FY1996–97),
federal spending accounts for about 2 per cent of consolidated
spending.
Environmental Protection: Function 18, Environment. Includes
water purification and supply, pollution control and other environmental
services. Consolidated expenditures estimated from 1980–81; from
FY1988–89 to 1992–93, federal spending accounts for about 10
per cent of consolidated spending; following the Green Plan
(FY1993–94), federal spending accounts for about 16 per cent of
consolidated spending.
Transportation/Infrastructure: Function 13,
Transportation and Communication minus federal expenditures on subfunction
13.07, Telecommunications (provincial expenditures on subfunction 13.07
are unavailable). Includes air transport, road transport, public transit,
rail transit, water transit, etc. Consolidated expenditures available from
FY1988–99; federal spending declines steadily from 25 to 10 per cent
of total expenditures.
Public Preferences
Series on net public preferences for spending are drawn from
Environics' Focus Canada omnibus surveys, usually in the
3rd quarter of the current fiscal year. Environics did not ask
these questions in FY1996–97, so results from Pollara are used to
substitute for this missing data. The Pollara question is not preceded
with the caveat “Keeping in mind that increasing services could mean
increasing taxes ….” As a consequence, Pollara net
preferences are consistently higher than Environics net preferences.
Predictably, the two series move together longitudinally (see Figure A1), so we use the Pollara data to estimate the
missing value in the Environics series, at least in those series where
Pollara has equivalent spending domains (defence, welfare, health care,
environmental protection and transportation).
Figure A1: Environics and Pollara Net Preferences Data on
Welfare
Acknowledgments
A previous version of this paper was presented at the Annual Meeting
of the Canadian Political Science Association, Halifax, May 29–June
1, 2003. Portions also were presented at the Annual Meeting of the
American Political Science Association, Philadelphia, August 28–31,
2003, and at the Juan March Institute, Madrid. We are grateful to
Environics and Pollara for making their public opinion data available, and
to Terry Moore and Graham Lyttle at Statistics Canada for their help with
Canadian budgetary data. We also thank Keith Banting, Jose Maria Maravall,
Juan Ramon Montero, James Stimson and, especially, Robert Shapiro for
their comments and advice. We also thank the two excellent anonymous
Journal reviewers.
