Download How Large Are The Political Costs of Fiscal Austerity?

How Large Are The Political Costs of Fiscal Austerity?1
Eric Arias2
David Stasavage3
July 2016
1
We would like to thank participants at the International Political Economy Society for comments
on a previous draft.
2
Department of Politics, New York University. Contact: [email protected].
3
Department of Politics, New York University. Contact: [email protected].
Abstract
There are good reasons to think that fiscal austerity can have important costs, and among
these is political instability. We suggest that these political costs may be harder to identify
than one might assume. Using a broad sample of countries from 1870 to 2011 we ask whether
expenditure cuts are associated with increased leader turnover through either regular or irregular means. OLS estimates suggest that there is no effect, but this may be due to bias
whereby leaders only adopt austerity when they can survive it. As an alternative empirical
strategy, we also report instrumental variables estimates in which expenditure cuts are instrumented by exogenous trade and financial shocks, and we continue to observe a null result.
Our results are consistent with three different interpretations of voter behavior. First, voters
may not mind austerity. Second, they may not know it is being implemented. Finally, and
most plausibly, voters may mind austerity but their political allegiances may be too firmly
anchored to vote a government out of office for this reason alone.
1
Introduction
The idea of a link between austerity and instability is firmly anchored in both public and
scholarly imagination. It seems clear that austerity policies in many countries during the
1930s helped fuel instability and extremism. Austerity policies implemented during and
since the recession of 2008 may be doing the same thing. Perhaps because the conventional
wisdom on this point is so strong, there has been relatively little scholarly effort to examine
how large the political costs of austerity actually are. In this paper we take a step in this
direction by examining the association between fiscal austerity and leader turnover. The term
“political instability” can mean many different things, but if austerity is causing instability,
then it is hard to believe that as part of this it wouldn’t produce more frequent turnover of
leaders.
We consider a broad set of 32 countries over a long time period (1870 to the present), and
we ask whether austerity in the form of expenditure cuts has been associated with more frequent leader turnover either by regular (i.e. constitutional) or irregular (extra-constitutional
means). Our leader turnover data comes from the ARCHIGOS data set produced by Goemans et al. (2009), and our expenditure data come from the IMF Historical Public Finances
Dataset (Mauro et al. 2013). We focus on expenditure cuts, rather than revenues or the
overall budget balance, as a measure of austerity because much variation in revenue levels is
driven by the state of the economy whereas this is generally less the case with expenditures.
The key question is whether governments cut expenditures in response to falling revenue.
Our strategy here is in keeping with existing work on this topic.1
We employ two empirical strategies for identifying an effect of expenditure cuts on leader
turnover. The first is a conventional difference in differences style estimation where we use
OLS to regress leader turnover on expenditure change while including country and year fixed
effects and controlling for several observables, most notably real GDP growth. When doing
this we fail to find any evidence that expenditure cuts are associated with increased likelihood
of leader turnover through either regular or irregular means. However, there is a potential
1
See Ponticelli and Voth (2012) for an example.
1
for selection bias with our first approach. If they have some flexibility, leaders may only
choose to implement fiscal austerity if they think they can do so without losing office. This
would bias us against finding that austerity causes political instability, and it may provide
an explanation for our null result with the OLS estimates.
Given the potential for bias in our OLS estimates, we also pursue an instrumental variables
strategy where we use growth shocks to a country’s trading partners interacted with the
cost of capital in international markets. The idea here is that a decline in GDP growth
of trading partners will lead to slower domestic growth, lower revenues, and pressure to
cut expenditures. At the same time, when costs of capital on international markets are
high, then governments will find it costlier to borrow to maintain expenditures in the face
of weaker revenue. It turns out that this instrumental variable is a statistically significant
predictor of expenditure changes, and it passes conventional tests for whether we have a
“weak instrument” problem. For reasons we detail below, it is also likely that our instrument
satisfies the necessary exclusion restriction, particularly when we include real GDP growth
as a control variable in our estimates. In our instrumental variables estimates we continue to
find little evidence that expenditure cuts are associated with increased risk of leader turnover.
One potential question about our instrument is that if politicians are forced into austerity
by exogenous circumstances, then shouldn’t voters recognize this and avoid sanctioning them?
While this argument makes perfect sense, as we discuss below it is also not very well supported
empirically. There is abundant evidence for the countries in our sample that voters reward
or sanction leaders for events, such as changes in world prices for natural resources, that are
clearly beyond government control.
Our null result for austerity and instability also holds when using several alternative
approaches. This included estimating the probability of leader turnover with a probit (or instrumental variables probit) model. It also included estimating probability of leader turnover
using a survival type model that controls for the duration of rule. Finally, our null result also
held when we limited our sample only to democracies, or, alternatively, to only high-income
or middle-income countries.
2
It is important to emphasize that our results have implications for the effect of fiscal
austerity on leader turnover, but we should certainly not therefore conclude that austerity
has no political consequences. It might be the case that fiscal austerity does not increase
the risk of leader turnover, but it does increase other forms of instability, such as strikes
and demonstrations. If so, however, we would need to know why events like strikes and
demonstrations do not themselves feed through into an increased rate of leader turnover. It
might also quite plausibly be the case that austerity prompts voters to opt for more extremist
candidates, yet without altering average rates of leader turnover.
If our results about expenditure cuts and leader turnover are accurate, then what interpretation should we have regarding the relationship between leaders and voters? We will
consider three possibilities below. A first possibility is that voters in many cases actually like
austerity because they think it is necessary. There is some support for this idea, but it is
more anecdotal than empirical. A second possibility is that voters in many instances do not
know that austerity is being implemented. There is some survey evidence to support this
notion. However, it is also the case that governments in many instances make it abundantly
clear to the public that they are implementing austerity policies. A third possibility, and
perhaps the most likely one, is that austerity does not increase leader turnover because in
many instances voters have political allegiances that are too deeply anchored to alter their
support simply because a government has cut expenditures even by a substantial amount.
The remainder of this paper is organized as follows. In the next section we discuss
prevailing views about austerity politics and what existing empirical evidence says. We then
present our data and empirical strategy, following by our OLS and instrumental variables
results. This is followed by a discussion of several robustness tests, a consideration of what
theoretical model would be consistent with our results, and a concluding section.
2
Existing Views About Austerity Politics
It’s fair to say that many people do not think that austerity is very good for political stability.
Mark Blyth has provided a particularly vivid description of this view.
3
In general, the deployment of austerity as economic policy has been as effective
in bringing us peace, prosperity, and crucially, a sustained reduction of debt, as
the Mongol Golden Horde was in furthering the development of Olympic dressage.
It has instead brought us class politics, riots, political instability, more rather than
less debt, assassinations, and war (Blyth, 2013 p.229)
What about the existing empirical evidence on the political impact of fiscal austerity?
The view that fiscal austerity does not affect leader turnover has been supported in two
papers by Alberto Alesina and co-authors. In Alesina, Perotti, and Tavares (1998) evidence
is presented from a broad set of countries showing no correlation between fiscal consolidation
and either the probability of a change in government or the level of popular support for a
government as measured in opinion polls. Alesina, Carloni, and Lecce (2012) present more
recent evidence showing the same finding. However, the conclusions from both of these papers
are based on OLS estimates that may be subject to the selection bias whereby governments
only implement austerity when they can get away with it. Contrary evidence, although
using a different dependent variable, is found by Ponticelli and Voth (2012). Using various
measures of political unrest, rather than leader turnover, they show in a panel of countries
from 1919 to 2008 that fiscal consolidation is positively associated with unrest. Vegh and
Vuletin (2014) show similar results for a set of Latin American countries over recent decades;
fiscal consolidation, in particular during periods of economic recession, is associated with
greater domestic conflict.
3
Data
In order to investigate the political costs of austerity over a long time period we make use of
two existing datasets. The first is the ARCHIGOS dataset of leaders compiled by Goemans
et al. (2009). It is straightforward to use this to measure leader turnover, and this source
also allows us to distinguish between transfers of power through constitutional and extraconstitutional means. We will refer to these as “regular” and “irregular” transfers of power.
The second dataset is the IMF Historical Public Finances Dataset, which provides information
4
on expenditures, revenue, and debt for a broad set of countries over a long time period, in
many cases dating back to 1870 (Mauro et al. 2013). We will use a sample of 32 middle and
high income countries in Europe and the Americas.2
If measuring leader turnover is straightforward, measuring “austerity” is more difficult,
since there is no set definition of the term. To operationalize austerity we will focus on expenditure cuts.3 Table 1 presents a first look at our data in the form of a cross-tabulation
between expenditure changes and leader turnover. The first panel of Table 1 distinguishes
between any increase in real expenditures and any decrease in real expenditures. When looking at regular turnover of leaders we see nearly identical probabilities with either increasing
or decreasing expenditures. Across our sample, on average leaders tend to be replaced about
once every four or five years. When we look at irregular turnover of leaders, which is of
course a considerably rarer event, we do see a higher probability of this event happening
when government expenditures are contracting.
Now consider the bottom panel of Table 1, which distinguishes between very large expenditure cuts (greater than five percent in real terms) and all other cases. Here we continue to
see very close probabilities of regular leader turnover even when placing the bar for what is
considered to be “austerity” much higher. However, we do see again that large expenditure
cuts appear to be associated with a greater probability of irregular leader turnover. Table
A1 in the appendix repeats the above exercise for our expenditure measure based on changes
in the expenditure to GDP ratio. It shows very similar results.
2
The sample includes Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, Colombia, Costa Rica,
Denmark, Dominican Republic, Finland, France, Germany, Honduras, Ireland, Italy, Mexico, Netherlands,
New Zealand, Nicaragua, Norway, Panama, Peru, Portugal, Spain, Sweden, Switzerland, United Kingdom,
United States, Uruguay, Venezuela.
3
In the robustness section we discuss other approaches.
5
Table 1: Leader Turnover and Changes in Government Expenditures
Panel A
Expenditure cuts 0%
Expenditure cuts ¡ 0%
Panel B
Expenditure cuts 5%
Expenditure cuts ¡ 5%
4
Regular
Irregular
.223
.210
.035
.022
.230
.211
.049
.022
Methods
In this section we will present our empirical strategy with results of OLS and instrumental
variables estimates presented in the subsequent two sections. Equation (1) is a standard
difference in differences specification with country and year fixed effects, augmented in some
specifications by control variables that we will discuss below. Our dependent variable is a
dummy variable taking a value of 1 if there is a regular leader transition. We also consider a
second dependent variable that only takes a value of 1 if there is an irregular leader turnover.
In the main portion of this paper we restrict ourselves to estimating linear probability models
of leader turnover. In the appendix we also report models where equation (1) is estimated by
probit, as well as survival type models where the probability of turnover depends on duration
in office. Use of these alternative estimators does not alter our core results.
Turnoverit
α
βExpenditure Growthit
ηi
θt
εit
(1)
Our key independent variable of interest in equation (1) is the percentage change in
government expenditures in real terms. We will measure this, alternatively, using either total
government expenditures or total government expenditures as a fraction of gross domestic
product. In some specifications we will also restrict our attention to cases where there are
large changes in expenditure, based on the notion that it may only be large changes that
influence the likelihood of leader turnover.
If we estimate equation (1) by OLS, then it is difficult to know whether we are capturing
6
a causal effect of expenditure cuts on leader turnover. There may be a selection bias whereby
leaders only cut expenditures if they think that they can do so without getting thrown out of
power. One possibility for dealing with this is to use an instrumental variables strategy. To
do so we need to find a factor that prompts governments to cut expenditures but which has
no direct impact on their survival in office, apart from via this channel of expenditure cuts.
One variable that is sometimes used as an instrument in the literature is external trade
shocks. Following, Jaimovich and Panizza (2007) and Vegh and Vuletin (2015) we first
considered an instrumental variable that is the average of a country’s trading partners GDP
growth, weighted by the importance of trade with each partner country relative to the others.4
The problem we observed with this variable is that while negative trade shocks prompted
some governments to cut spending, it prompted other governments to increase spending,
presumably to offset the shock. In the language of instrumental variables estimation, this
meant that we have both “compliers” and “defiers” in our sample, and so using only trade
shocks as an instrument would provide us with a biased estimate of the effect of expenditure
cuts on political instability because it represents a violation of the monotonicity assumption.
We next considered an instrumental variable that would be more immune to the complier/defier problem. This involved investigating the interactive effect between GDP growth
shocks to trading partners and changes in international costs of capital. The following system
of equations describes our strategy. Equation (2) is the first stage where the change in trading partner GDP (Trade Shock) is interacted with the yield on British government long term
bonds. This yield is taken as a proxy for the cost of capital on international markets. The
logic here is that when costs of external borrowing are high, then a government experiencing
a trade shock is more likely to adopt expenditure cuts because the option of borrowing to
maintain or increase expenditures is simply too costly. The interaction term between these
4
Formally, this was computed as
Trade Shockit
Xi
GDP
i
¸φ
n
ij,t 1 Growthj,t
j 1
where Growth measures the real GDP growth in j at time t, φij,t1 is the fraction of exports from i going to
Xi
measures a country’s i average exports as a share of GDP over the sample period.
j, and GDP
i
7
two variables is the excluded instrument while the Trade Shock variable is included in both
the first stage estimates in equation (2) and the second stage estimates in equation (3). The
yield on British government long term bonds is in effect also included in both the first and
second stage equations because it is absorbed by the year fixed effects.
Expenditure Growthit
Turnoverit
φ
α
λpTrade Shockit UK Bond Yieldst q
ψTrade Shockit
{
ζi
ϕt
(2)
β Expenditure Growthit
γTrade Shockit
ηi
θt
εit
(3)
One obvious question one might ask about our instrumental variables strategy is that if a
leader is being forced into choosing austerity by external conditions, then shouldn’t citizens
recognize this and refrain from sanctioning them? The latest evidence from a large sample of
Latin American countries from Campello and Zucco (2016) suggests otherwise. Both levels
of presidential popularity and reelection probabilities are correlated with exogenous factors
that are clearly beyond the control of national governments. The same pattern has also been
observed for other groups of countries (or sub-national units) by Achen and Bartels (2004),
Healy et al. (2010), Leigh (2009) and Wolfers (2009). Note, our instrumental variables
strategy does not require that all voters fail to filter out the effects of exogenous shocks; it
only requires that this be true of a significant fraction of voters.
The final critical assumption for our instrumental variable strategy to be a valid one
is that the interaction effect between trade shocks and high international costs of capital
does not have any effect on likelihood of a leader transition apart from through a reduced
government expenditures. The most likely possibility here is that the combination of a trade
shock and higher interest rates could have a negative impact on economic growth, with
negative implications for an incumbent, even if government expenditures remain unchanged.
For this reason we will report specifications where we control for real GDP growth in our
estimates. We will also include several additional control variables in our estimates.
8
it
5
OLS Estimates
Tables 2 and 3 report our OLS estimates where we regress leader turnover on expenditure
cuts. In Table 2 we see that there is a relationship whereby cuts in real expenditures are
associated with increased probability of leader turnover, but this correlation is reversed when
including controls for observables. Most importantly, the correlations are never even close to
being statistically significant, and the maximum implied effect of expenditure cuts on leader
turnover is very small. Based on the estimate in column 1, even a very large expenditure
cut of five percent in real terms would only increase the probability of leader turnover by
.0007. This is a vanishingly small effect, and even when we took the most negative coefficient
within the 95 percent confidence interval, the effect of a five percent cut in expenditures
would still be to increase the probability of leader turnover by only .004. When alternatively
using the change in expenditures relative to GDP as a measure of austerity we continue to
see no evidence of a statistically significant effect on leader turnover.
In Table 3 where we examine irregular turnover we see that the regression coefficients for
expenditure change are consistently negative and somewhat larger in magnitude than in our
Table 2 estimates. The estimated confidence intervals are also somewhat tighter. However,
in no case are the coefficients statistically significant, and once again the implied effects of
expenditure cuts on leader turnover are very small. Based on the estimate in column 1, a five
percent reduction in real expenditures would only increase the probability of leader turnover
by .0015, and even taking the most negative coefficient in the 95 percent confidence interval,
the effect is not much larger.
6
Instrumental Variables Estimates
Tables 4 and 5 report our instrumental variables estimates using two stage least squares.
Changes in expenditure are instrumented using the interaction between GDP shocks to trading partners and international costs of capital as proxied for with UK government borrowing
costs. The first stage estimates (reported in full in the appendix) show quite consistently
9
Table 2: OLS Estimates: Regular Transitions and Government Expenditures
(1)
-0.014
(0.035)
Real Gov. Expenditure Growth
(2)
0.003
(0.037)
Gov. Expenditure Growth (% of GDP)
Democracy
GDP (Ln)
GDP per capita (Ln)
Real GDP growth rate (%)
Gross public debt (% of GDP)
General Government
Country Fixed Effects
Year Fixed Effects
Observations
R2
X
X
3222
0.19
0.167
(0.029)
0.028
(0.033)
-0.089
(0.055)
-0.003
(0.001)
0.000
(0.001)
0.060
(0.045)
X
X
3092
0.21
Clustered standard errors at the country level in parentheses.
p 0.10,
p 0.05, p 0.01
10
(3)
(4)
0.002
(0.039)
0.002
(0.041)
0.167
(0.029)
0.024
(0.034)
-0.085
(0.055)
-0.003
(0.001)
0.000
(0.001)
0.060
(0.045)
X
X
3094
0.21
X
X
3298
0.19
Table 3: OLS Estimates: Irregular Transitions and Government Expenditures
(1)
-0.030
(0.018)
Real Gov. Expenditure Growth
(2)
-0.022
(0.017)
Gov. Expenditure Growth (% of GDP)
Democracy
GDP (Ln)
GDP per capita (Ln)
Real GDP growth rate (%)
Gross public debt (% of GDP)
General Government
Country Fixed Effects
Year Fixed Effects
Observations
R2
X
X
3222
0.08
-0.056
(0.014)
-0.009
(0.014)
0.016
(0.025)
-0.001
(0.001)
-0.000
(0.000)
-0.000
(0.013)
X
X
3092
0.09
Clustered standard errors at the country level in parentheses.
p 0.10,
p 0.05, p 0.01
11
(3)
(4)
-0.013
(0.017)
-0.012
(0.017)
-0.056
(0.014)
-0.008
(0.014)
0.015
(0.026)
-0.001
(0.001)
-0.000
(0.000)
-0.000
(0.013)
X
X
3094
0.09
X
X
3298
0.08
that the interaction between foreign GDP shocks and capital costs is positively correlated
with changes in home country government expenditure. However, we continue to see no
evidence in any of our specifications that changes in government expenditure are associated
with leader turnover of either the regular or irregular variety. In the case of the Table 4
estimates, the coefficients on expenditure changes are now substantially larger than in our
OLS estimates. However, they are never statistically significant. The same holds true for our
Table 5 estimates for irregular turnover.
A key issue that we need to consider is whether our estimates suffer from a “weak instrument” problem. If this is the case then we know that the 2SLS estimate will be biased
towards the OLS estimate, and our null result for austerity would simply be a result of this
bias. Overall, we find evidence to reject the null of underidentification. At the bottom of Tables 4 and 5 we report the Kelibergen-Paap F test, which yields a value of 10 in our preferred
estimations in Columns 2. This satisfies the Staiger and Stock (1997) rule of thumb value,
and it is in between the Stock and Yogo (2005) five percent critical values for ten and fifteen
percent maximum bias (these critical values are 16.38 and 8.96, respectively). As such, the
maximum bias associated with the possible presence of weak instruments can be larger than
10 percent but it is smaller than 15 percent.5
7
Robustness
We also considered a number of different alternative specifications to see whether our null
result for austerity was altered, and in each case it was not.
As a first step, we recoded our expenditure variable so that all changes of less than five
percent (in absolute value terms) take a value of zero. We then repeated our OLS and IV
estimates using this new variable. By focusing only on expenditure changes in excess of five
5
Even though the discussion above suggests that our estimations do not suffer from weak instruments bias,
we nonetheless use the Anderson and Rubin (1949) test to build confidence intervals that are valid in the
presence of weak instruments so to further test the robustness of the result. When we compare the IV 95%
confidence interval with the AR 95% confidence interval, we find that the latter to be wider (r2.95, 7.47s
instead of r2.17, 4.40s for Model 2, Table 4 and r3.21, .89s instead of r2.18, 1.04s for Model 2, Table 5)
thus reinforcing our null result.
12
Table 4: IV Estimates: Regular Transitions and Government Expenditures
(1)
1.030
(3.274)
Real Gov. Expenditure Growth
(2)
0.722
(1.875)
Gov. Expenditure Growth (% of GDP)
Trade Shock
2.565
(10.463)
Democracy
GDP (Ln)
GDP per capita (Ln)
Real GDP growth rate (%)
Gross public debt (% of GDP)
General Government
Country Fixed Effects
Year Fixed Effects
Observations
Kleibergen-Paap F-stat
X
X
2710
5.42
1.301
(6.032)
0.141
(0.031)
0.038
(0.051)
-0.141
(0.061)
-0.007
(0.010)
0.000
(0.001)
0.035
(0.044)
X
X
2623
10.03
(3)
(4)
0.821
(5.294)
1.134
(14.325)
0.988
(2.620)
2.057
(7.915)
0.137
(0.034)
0.028
(0.063)
-0.131
(0.055)
-0.002
(0.003)
0.000
(0.001)
0.036
(0.046)
X
X
2625
6.13
X
X
2786
1.13
Two way clustered standard errors at the country and year level in parentheses
p 0.10,
p 0.05, p 0.01
13
Table 5: IV Estimates: Irregular Transitions and Government Expenditures
(1)
-0.574
(0.815)
Real Gov. Expenditure Growth
(2)
-0.572
(0.824)
Gov. Expenditure Growth (% of GDP)
Trade Shock
-1.791
(2.565)
Democracy
GDP (Ln)
GDP per capita (Ln)
Real GDP growth rate (%)
Gross public debt (% of GDP)
General Government
Country Fixed Effects
Year Fixed Effects
Observations
Kleibergen-Paap F-stat
X
X
2710
5.42
-1.396
(2.371)
-0.066
(0.017)
0.018
(0.028)
0.018
(0.030)
0.002
(0.004)
-0.000
(0.000)
-0.002
(0.015)
X
X
2623
10.03
(3)
(4)
-1.108
(1.626)
-3.000
(4.149)
-0.796
(1.145)
-2.004
(3.154)
-0.063
(0.016)
0.024
(0.038)
0.013
(0.032)
-0.002
(0.002)
-0.000
(0.000)
-0.003
(0.015)
X
X
2625
6.13
X
X
2786
1.13
Two way clustered standard errors at the country and year level in parentheses
p 0.10,
p 0.05, p 0.01
14
percent, we would be considering the large policy moves that should be most likely to have
an effect on leader turnover. When doing this our instrumental variables strategy remained
relatively robust (with Kleibergen-Paap F-test statistics for the excluded instrument in the 6
to 7 range). In spite of only focusing on large policy shifts, we continued to find no evidence of
an association between expenditure change and leader turnover, either regular, or irregular.
The results are reported in appendix tables A4 through A7. Furthermore, we also considered
whether the operationalization of austerity as spending cuts was driven our results. While
we believe that spending changes provide the appropriate measure to capture the potential
impact of austerity on the real welfare of citizens, we also re-estimated our main specifications
using the cyclically adjusted primary balance (CAPB) and still consistently found no effects.
We should acknowledge that when using the CAPB our instrumental variable was no longer
significant.
As a second step, we considered whether the choice to estimate a linear probability model
was producing our null result. We re-estimated our core OLS specifications using a probit
model and saw no change in our results. We re-estimated our 2SLS specifications using an
instrumental variables probit model and again failed to find any evidence of an effect of
expenditure changes on leader turnover. Also, we re-estimated our models including a cubic
polynomial of time since the last leader turnover was observed and once again corroborated
the null result. We should note, however, that our instrumental variable was no longer
significantly correlated with expenditure cuts when analyzing irregular transitions. Results
are reported in annex tables A8 through A11 and A12 through A15, respectively.
As a third step we also restricted our sample in several ways and we considered whether
the effects of austerity on instability depend on the political orientation of the incumbent
government. When restricting our sample exclusively to democracies we continued to fail
to find any evidence that expenditure cuts were associated with an increased probability of
leader turnover. This is important because it is in democracies that citizens should logically
find it easiest to sanction governments for implementing fiscal policies contrary to their liking.
We also failed to find any evidence of austerity’s effect on instability when splitting our sample
15
between middle income countries in Latin American and the high income countries in Europe
and North America, though we should acknowledge that with these reduced sample sizes our
instrumental variable was no longer as significantly correlated with expenditure cuts. Finally,
we also examined whether the effect of austerity on instability was conditional on whether a
left or a right wing government was in power, and found that this was not the case. This is
an important test to consider to the extent one believes that left and right wing voters will
have different preferences with regard to implementing austerity.
8
Interpretation
As we have said above, it is certainly possible that our null results are biased against finding
costs of austerity. But if they are not biased, then we need to think about what interpretation
of voter behavior would be consistent with our results. There are three main possibilities
to consider. The first is that voters do not mind austerity, and they may even see it as
necessary. The second is that voters lack the information to understand when austerity is
being implemented. The third possibility is that voters dislike austerity but their political
allegiances are too firmly anchored to have this sway their voting choices. We will now
consider each of these possibilities in turn.
Might our results be affected by the fact that voters often like austerity? Though it is hotly
contested, some work by economists has suggested that austerity in the form of expenditure
cuts can be have an expansionary effect on the economy even in the short run.6 If this is the
case then it would provide an obvious reason for understanding why fiscal austerity doesn’t
increase the likelihood of leader turnover. However, the expansionary austerity interpretation
is very far from being a consensus view.7 Supporting this, in our sample of countries we see
a very clear correlation between expenditure cuts and slower real GDP growth.8
Another possible interpretation of our results is that voters dislike austerity but they
6
See for example Alesina and Ardagna (2009, 1998), Giavazzi and Pagano (1990) and Alesina et al. (2015).
See Blanchard and Leigh (2013).
8
Here, we regress the Real GDP Growth rate (or the level of logged GDP, controlling for its own lagged
value) on our measure of Real Government Expenditure growth (controlling for unit and time specific fixed
effects as well as covariates). Results are reported in the annex table A16.
7
16
are unaware that it is being implemented. Certainly, survey evidence from the United States
shows that voters are often unable to accurately state whether the budget deficit has increased
or decreased in recent years, and their responses to this question seem driven by partisan
affiliation as much as anything else.9 However, when there are large expenditure cuts that
result in direct cuts to government transfers voters are certain to be keenly aware of this. It is
also the case that some governments implementing austerity do so by announcing expenditure
cuts as publicly as possible. The Cameron government in the UK provides a good example
of this phenomenon.10
A final possible interpretation, and in the end perhaps the most plausible one, is that
voters dislike austerity but their partisan allegiances are simply too strong for this too have
an impact on their vote choice. This could be the case if vote choice is determined in part by
austerity but is also heavily dependent on incumbent stances on other economic issues or on
social questions. It could also be the case that voters form identities in the manner of Achen
and Bartels (2016) that are resistant to change in the face of policy shifts. In this case the
implications for the ability of citizen preferences to translate into austerity or expansion are
of course very pessimistic.
9
Conclusion
Finding political costs of fiscal austerity is sometimes harder than one might think. Using a
plausible identification strategy we have failed to find evidence that expenditure cuts, large
or small, are associated with more frequent turnover of national leaders. This is true even
when leaders are forced into austerity by external circumstances. We should emphasize that
austerity may still affect other measures of political instability, such as those involving unrest
or a shift to more extreme points of view. However, when we think about the calculus facing
9
For instance, according to the the YouGov/Huffington Post survey of January 28-29, 2014, 54 percent of
Americans thought the budget deficit had increased since Obama took office, while only 19 percent knew it
had decreased. Moreover, 85 percent of Republicans and 57 percent of independents said they believed the
budget deficit had gone up since Obama took office, while only 32 percent of Democrats believed the same.
See http://cdn.yougov.com/cumulus_uploads/document/hbntcynyey/tabs_HP_budget_20140130.pdf.
10
New York Times, “Britain Plans Deepest Cuts to Spending in 60 Years,” October 20, 2010, http://www.
nytimes.com/2010/10/21/world/europe/21britain.html.
17
leaders, for those who believe austerity is detrimental to overall welfare our results pose
a problem. They suggest that on average, leaders have substantial latitude to implement
austerity without being sanctioned.
18
References
[1] Achen, Christopher H. and Larry M. Bartels. 2016. Democracy For Realists: Why Elections Do Not Produce Responsive Government. Princeton: Princeton University Press.
[2] Achen, Christopher H. and Larry M. Bartels. 2004. “Blind Retrospection Electoral Responses to Drought, Flu, and Shark Attacks.” Estudio/Working Paper 2004/199.
[3] Anderson, T. W. and Herman Rubin. 1949. “Estimation of the Parameters of Single
Equation in a Complete System of Stochastic Equations.” Annals of Mathematical Statistics 20:46-63.
[4] Angrist, Joshua D. and Jorn-Steffen Pischke. 2009. Mostly Harmless Econometrics New
York: Princeton University Press.
[5] Alesina, Alberto, Dorian Carloni, and Giampaolo Lecce. 2012. “The Electoral Consequences of Large Fiscal Adjustments.” NBER Working Paper.
[6] Alesina, Alberto, Omar Barbiero, Carlo Favero, Francesco Giavazzi and Matteo Paradisi.
2015. “Austerity in 2009-2013.” NBER Working Paper.
[7] Alesina, Alberto, Robeto Perotti, and José Tavares. 1998. “The Political Economy of
Fiscal Adjustments.” Brookings Papers on Economic Activity. 1:197-266
[8] Alesina, Alberto and Silvia Ardagna. 1998. “Tales of Fiscal Adjustment,” Economic
Policy, 13(27), 487-545.
[9] Alesina, Alberto and Silvia Ardagna. 2009. “Large Changes in Fiscal Policy: Taxes
Versus Spending.” NBER Working Paper.
[10] Blanchard, Olivier and Daniel Leigh. 2013. “Growth Forecast Errors and Fiscal Multipliers.” IMF Working Paper.
[11] Blyth, Mark. 2013. Austerity: The History of a Dangerous Idea New York: Oxford
University Press.
[12] Campello, Daniela and Cesar Zucco Jr. 2016. “Presidential Success and the World Economy.” The Journal of Politics. 78(2): 589-602
[13] Giavazzi, Francesco and Marco Pagano. 1990. “Can Severe Fiscal Contractions Be Expansionary? Tales of Two Small European Countries.” NBER Working Paper.
[14] Goemans, Hein, Kristian Gleditsch and Giacomo Chiozza. 2009. “Introducing Archigos:
A Dataset of Political Leaders.” Journal of Peace Research, 46(2): 269-183.
[15] Healy, Andrew J., Neil Malhotra, and Cecilia Hyunjung Mo. 2010. “Irrelevant events
affect voters evaluations of government performance.” Proceedings of the National
Academy of Sciences, 107(29):12804-9
[16] Hübscher, Evelyne, Achim Kemmerling, and Thomas Sattler. 2015. “Austerity for the
Win? The Effect of Fiscal Consolidation on Political Support for the Government.”
Working paper, University of Geneva.
19
[17] Jaimovich, Dany and Ugo Panizza. 2007. “Procyclicality or Reverse Causality?” RES
Working Papers 4508 Inter-American Development Bank, Research Department.
[18] Leigh, Andrew. 2009. “Does the world economy swing national elections?” Oxford Bulleting of Economics and Statistics, 71(2):163-81
[19] Mauro, Paolo, Rafael Romeu, Ariel Binder, and Asad Zaman. 2013. “A Modern History
of Fiscal Prudence and Profligacy.” IMF Working Paper.
[20] Ponticelli, Jacopo and Hans-Joachim Voth. 2013. “Austerity and Anarchy: Budget Cuts
and Social Unrest in Europe, 1919-2008.” University of Zurich Working Paper.
[21] Staiger, Douglas and James H. Stock. 1997. “Instrumental Variables Regression with
Weak Instruments.” Econometrica 65(3):557-586.
[22] Stock, James H. and Motohiro Yogo. 2005. Testing for Weak Instruments in Linear IV
Regression. In Identification and Inference for Econometric Models: Essays in Honor
of Thomas Rothenberg, ed. Donald W.K. Andrews and James H. Stock. Cambridge
University Press, Ch. 5, pp. 80-108.
[23] Vegh, Carlos A. and Guillermo Vuletin. 2014. “Social Implications of Fiscal Policy Responses During Crises.” NBER Working Paper.
[24] Vegh, Carlos A. and Guillermo Vuletin. 2015. “How Is Tax Policy Conducted over the
Business Cycle?.” American Economic Journal: Economic Policy, 7(3): 327-70.
[25] Wolfers, Justin. 2011. “Are voters rational? Evidence from gubernatorial elections.”
Working Paper.
20
Appendix
Table A1: Leader Turnover and Changes in Government Expenditures (% of GDP)
Panel A
Expenditure cuts 0%
Expenditure cuts ¡ 0%
Panel B
Expenditure cuts 5%
Expenditure cuts ¡ 5%
21
Regular
Irregular
.210
.213
.027
.025
.208
.214
.034
.024
Table A2: First Stage Estimates: Regular Transitions and Government Expenditures
Trade Shock
Trade Shock
UK Bond Yield
(1)
-0.042
(0.016)
0.002
(0.001)
Democracy
GDP (Ln)
GDP per capita (Ln)
Real GDP growth rate (%)
Gross public debt (% of GDP)
General Government
Country Fixed Effects
Year Fixed Effects
Observations
R2
X
X
2710
0.14
(2)
-0.044
(0.014)
0.003
(0.001)
0.003
(0.010)
0.023
(0.011)
0.009
(0.016)
0.005
(0.002)
-0.000
(0.000)
-0.005
(0.008)
X
X
2623
0.16
Clustered standard errors at the country level in parentheses.
p 0.10,
p 0.05, p 0.01
22
(3)
-0.033
(0.011)
0.001
(0.001)
X
X
2786
0.14
(4)
-0.039
(0.012)
0.002
(0.001)
0.005
(0.009)
0.023
(0.011)
0.002
(0.014)
-0.001
(0.001)
-0.000
(0.000)
-0.005
(0.007)
X
X
2625
0.15
Table A3: First Stage Estimates: Irregular Transitions and Government Expenditures
Trade Shock
Trade Shock
UK Bond Yield
(1)
-0.042
(0.016)
0.002
(0.001)
Democracy
GDP (Ln)
GDP per capita (Ln)
Real GDP growth rate (%)
Gross public debt (% of GDP)
General Government
Country Fixed Effects
Year Fixed Effects
Observations
R2
X
X
2710
0.14
(2)
-0.044
(0.014)
0.003
(0.001)
0.003
(0.010)
0.023
(0.011)
0.009
(0.016)
0.005
(0.002)
-0.000
(0.000)
-0.005
(0.008)
X
X
2623
0.16
Clustered standard errors at the country level in parentheses.
p 0.10,
p 0.05, p 0.01
23
(3)
-0.033
(0.011)
0.001
(0.001)
X
X
2786
0.14
(4)
-0.039
(0.012)
0.002
(0.001)
0.005
(0.009)
0.023
(0.011)
0.002
(0.014)
-0.001
(0.001)
-0.000
(0.000)
-0.005
(0.007)
X
X
2625
0.15
Table A4: Robustness - OLS Estimates: Regular Transitions and Government
Expenditures
(1)
-0.014
(0.035)
Real Gov. Expenditure Growth
Gov. Expenditure Growth (% of GDP)
Democracy
GDP (Ln)
GDP per capita (Ln)
Real GDP growth rate (%)
Gross public debt (% of GDP)
General Government
Country Fixed Effects
Year Fixed Effects
Observations
R2
Clustered standard errors in parentheses.
(2)
0.002
(0.036)
(3)
(4)
-0.002
(0.040)
-0.000
(0.043)
0.167
(0.029)
0.024
(0.034)
-0.085
(0.055)
-0.003
(0.001)
0.000
(0.001)
0.060
(0.045)
X
X
3094
0.21
0.167
(0.029)
0.028
(0.033)
-0.089
(0.055)
-0.003
(0.001)
0.000
(0.001)
0.060
(0.045)
X
X
X
X
X
X
3222
3092
3298
0.19
0.21
0.19
p 0.10, p 0.05,
p 0.01
Expenditure variables are recoded so that all changes of less than five percent take a value of zero.
24
Table A5: Robustness - OLS Estimates: Irregular Transitions and Government
Expenditures
(1)
-0.032
(0.018)
Real Gov. Expenditure Growth
Gov. Expenditure Growth (% of GDP)
Democracy
GDP (Ln)
GDP per capita (Ln)
Real GDP growth rate (%)
Gross public debt (% of GDP)
General Government
Country Fixed Effects
Year Fixed Effects
Observations
R2
Clustered standard errors in parentheses.
(2)
-0.024
(0.017)
(3)
(4)
-0.010
(0.017)
-0.009
(0.017)
-0.056
(0.014)
-0.008
(0.014)
0.015
(0.026)
-0.001
(0.001)
-0.000
(0.000)
-0.000
(0.013)
X
X
3094
0.09
-0.056
(0.014)
-0.009
(0.014)
0.016
(0.025)
-0.001
(0.001)
-0.000
(0.000)
-0.001
(0.013)
X
X
X
X
X
X
3222
3092
3298
0.08
0.09
0.08
p 0.10, p 0.05,
p 0.01
Expenditure variables are recoded so that all changes of less than five percent take a value of zero.
25
Table A6: Robustness - IV Estimates: Regular Transitions and Government Expenditures
Real Gov. Expenditure Growth
(1)
1.108
(3.630)
(2)
0.728
(1.922)
Gov. Expenditure Growth (% of GDP)
Democracy
GDP (Ln)
GDP per capita (Ln)
Real GDP growth rate (%)
Gross public debt (% of GDP)
General Government
Country Fixed Effects
Year Fixed Effects
Observations
Kleibergen-Paap F-stat
X
X
2710
3.04
0.139
(0.032)
0.037
(0.053)
-0.135
(0.054)
-0.007
(0.010)
0.000
(0.001)
0.040
(0.051)
X
X
2623
6.80
(3)
(4)
0.866
(5.593)
1.078
(2.855)
0.138
(0.034)
0.027
(0.063)
-0.134
(0.058)
-0.002
(0.003)
0.000
(0.001)
0.036
(0.047)
X
X
2625
6.50
X
X
2786
1.19
Two way clustered standard errors at the country and year level in parentheses.
p 0.10,
p 0.05, p 0.01
Expenditure variables are recoded so that all changes of less than five percent take a value of zero.
26
Table A7: Robustness - IV Estimates: Irregular Transitions and Government
Expenditures
Real Gov. Expenditure Growth
(1)
-0.618
(0.896)
(2)
-0.577
(0.830)
Gov. Expenditure Growth (% of GDP)
Democracy
GDP (Ln)
GDP per capita (Ln)
Real GDP growth rate (%)
Gross public debt (% of GDP)
General Government
Country Fixed Effects
Year Fixed Effects
Observations
Kleibergen-Paap F-stat
X
X
2710
3.04
-0.064
(0.016)
0.019
(0.030)
0.013
(0.029)
0.002
(0.004)
-0.000
(0.000)
-0.006
(0.016)
X
X
2623
6.80
(3)
(4)
-1.169
(1.676)
-0.868
(1.244)
-0.063
(0.017)
0.024
(0.039)
0.015
(0.032)
-0.002
(0.002)
-0.000
(0.000)
-0.003
(0.016)
X
X
2625
6.50
X
X
2786
1.19
Two way clustered standard errors at the country and year level in parentheses.
p 0.10,
p 0.05, p 0.01
Expenditure variables are recoded so that all changes of less than five percent take a value of zero.
27
Table A8: Robustness - Probit Estimates: Regular Transitions and Government
Expenditures
(1)
-0.109
(0.124)
Real Gov. Expenditure Growth
Gov. Expenditure Growth (% of GDP)
(2)
-0.039
(0.139)
(3)
-0.014
(0.127)
(4)
-0.028
(0.151)
Democracy
0.650
0.649
(0.134)
(0.133)
GDP (Ln)
0.036
0.035
(0.077)
(0.077)
GDP per capita (Ln)
0.118
0.124
(0.289)
(0.287)
Real GDP growth rate (%)
-0.016
-0.017
(0.005)
(0.005)
Gross public debt (% of GDP)
0.000
0.000
(0.002)
(0.002)
General Government
0.303
0.303
(0.142)
(0.142)
Region Fixed Effects
X
X
X
X
Year Fixed Effects
X
X
X
X
Observations
3192
3063
3267
3065
Log-Likelihood
-1652.79 -1519.53 -1689.35 -1521.72
Clustered standard errors at the country level in parentheses. p 0.10, p 0.05, p 0.01
28
Table A9: Robustness - Probit Estimates: Irregular Transitions and Government
Expenditures
Real Gov. Expenditure Growth
(1)
-0.596
(0.362)
Gov. Expenditure Growth (% of GDP)
(2)
-0.503
(0.337)
(3)
-0.200
(0.305)
(4)
-0.213
(0.292)
Democracy
-0.895
-0.883
(0.178)
(0.178)
GDP (Ln)
-0.060
-0.061
(0.071)
(0.071)
GDP per capita (Ln)
0.035
0.024
(0.183)
(0.185)
Real GDP growth rate (%)
-0.009
-0.011
(0.015)
(0.015)
Gross public debt (% of GDP)
0.001
0.001
(0.002)
(0.002)
General Government
-0.381
-0.385
(0.290)
(0.293)
Region Fixed Effects
X
X
X
X
Year Fixed Effects
X
X
X
X
Observations
1428
1300
1502
1300
Log-Likelihood
-291.95 -246.38 -302.52
-247.20
Clustered standard errors at the country level in parentheses. p 0.10, p 0.05, p 0.01
29
Table A10: Robustness - IV Probit Estimates: Regular Transitions and Government Expenditures
Real Gov. Expenditure Growth
(1)
1.944
(5.583)
Gov. Expenditure Growth (% of GDP)
(2)
1.970
(4.436)
(3)
1.727
(7.782)
0.072
(0.124)
(4)
2.473
(5.377)
Trade Shock
0.088
0.067
0.071
(0.112) (0.085)
(0.093)
Democracy
0.573
0.550
(0.239)
(0.295)
GDP (Ln)
0.020
0.020
(0.069)
(0.067)
GDP per capita (Ln)
0.139
0.140
(0.260)
(0.256)
Real GDP growth rate (%)
-0.029
-0.016
(0.019)
(0.014)
Gross public debt (% of GDP)
-0.000
-0.000
(0.002)
(0.002)
General Government
0.202
0.199
(0.133)
(0.133)
Real Gov.
Gov. Exp.
Exp. Growth
Growth (% of GDP)
Trade Shock
-0.036 -0.037 -0.028
-0.032
(0.014) (0.013)
(0.010)
(0.012)
Trade Shock UK Bond Yield
0.004 0.004
0.003
0.003
(0.001) (0.001)
(0.001)
(0.001)
Democracy
0.006
0.007
(0.009)
(0.008)
GDP (Ln)
-0.001
-0.002
(0.003)
(0.003)
GDP per capita (Ln)
-0.001
-0.001
(0.009)
(0.008)
Real GDP growth rate (%)
0.005
-0.001
(0.002)
(0.001)
Gross public debt (% of GDP)
-0.000
-0.000
(0.000)
(0.000)
General Government
-0.009
-0.008
(0.006)
(0.005)
Region Fixed Effects
X
X
X
X
Year Fixed Effects
X
X
X
X
Observations
2663
2571
2738
2573
Log-Likelihood
-354.26 -265.29
-186.73
-115.62
Clustered standard errors at the country level in parentheses. p 0.10, p 0.05, p 0.01
30
Table A11: Robustness - IV Probit Estimates: Irregular Transitions and Government Expenditures
Real Gov. Expenditure Growth
(1)
-1.992
(14.021)
(2)
-5.513
(3.326)
(3)
(4)
-5.967
(2.519)
Trade Shock
-0.090
-0.006
-0.017
(0.098)
(0.112)
(0.100)
Democracy
-0.617
-0.479
(0.716)
(0.656)
GDP (Ln)
0.004
-0.003
(0.078)
(0.065)
GDP per capita (Ln)
0.021
0.033
(0.104)
(0.102)
Real GDP growth rate (%)
0.031
-0.006
(0.032)
(0.008)
Gross public debt (% of GDP)
0.000
0.000
(0.004)
(0.003)
General Government
-0.349
-0.325
(0.340)
(0.300)
Real Gov.
Gov. Exp.
Exp. Growth
Growth (% of GDP)
Trade Shock
-0.013
-0.007
-0.008
-0.007
(0.012)
(0.012)
(0.013)
(0.013)
Trade Shock UK Bond Yield
0.001
0.002
0.000
0.001
(0.002)
(0.002)
(0.002)
(0.002)
Democracy
0.000
0.003
(0.014)
(0.013)
GDP (Ln)
0.008
0.005
(0.003)
(0.004)
GDP per capita (Ln)
0.001
0.004
(0.012)
(0.011)
Real GDP growth rate (%)
0.007
-0.000
(0.002)
(0.001)
Gross public debt (% of GDP)
-0.000
-0.000
(0.000)
(0.000)
General Government
-0.015
-0.017
(0.010)
(0.009)
Region Fixed Effects
X
X
X
X
Year Fixed Effects
X
X
X
X
Observations
1182
1095
1252
1095
Log-Likelihood
242.28
261.51
343.47
303.49
Clustered standard errors at the country level in parentheses. p 0.10, p 0.05, p 0.01
Gov. Expenditure Growth (% of GDP)
-4.647
(17.394)
-0.088
(0.122)
31
Table A12: Robustness - Probit Estimates: Regular Transitions and Government
Expenditures
(1)
-0.123
(0.136)
Real Gov. Expenditure Growth
Gov. Expenditure Growth (% of GDP)
(2)
-0.046
(0.150)
(3)
-0.029
(0.140)
(4)
-0.031
(0.160)
Democracy
0.547
0.546
(0.130)
(0.129)
GDP (Ln)
0.019
0.018
(0.071)
(0.071)
GDP per capita (Ln)
0.096
0.101
(0.268)
(0.267)
Real GDP growth rate (%)
-0.015
-0.016
(0.006)
(0.006)
Gross public debt (% of GDP)
0.001
0.001
(0.001)
(0.001)
General Government
0.264
0.264
(0.118)
(0.118)
Region Fixed Effects
X
X
X
X
Year Fixed Effects
X
X
X
X
Observations
3192
3063
3267
3065
Log-Likelihood
-1594.22 -1495.43 -1631.10 -1497.58
Clustered standard errors at the country level in parentheses. p 0.10, p 0.05, p 0.01
32
Table A13: Robustness - Probit Estimates: Irregular Transitions and Government
Expenditures
Real Gov. Expenditure Growth
(1)
-0.550
(0.342)
Gov. Expenditure Growth (% of GDP)
(2)
-0.502
(0.325)
(3)
-0.160
(0.279)
(4)
-0.210
(0.281)
Democracy
-0.880
-0.868
(0.172)
(0.172)
GDP (Ln)
-0.056
-0.057
(0.066)
(0.066)
GDP per capita (Ln)
0.005
-0.005
(0.182)
(0.182)
Real GDP growth rate (%)
-0.008
-0.010
(0.015)
(0.015)
Gross public debt (% of GDP)
0.001
0.001
(0.002)
(0.002)
General Government
-0.380
-0.381
(0.267)
(0.267)
Region Fixed Effects
X
X
X
X
Year Fixed Effects
X
X
X
X
Observations
1428
1300
1502
1300
Log-Likelihood
-286.89 -245.29 -297.36
-246.11
Clustered standard errors at the country level in parentheses. p 0.10, p 0.05, p 0.01
33
Table A14: Robustness - IV Probit Estimates: Regular Transitions and Government Expenditures
Real Gov. Expenditure Growth
(1)
2.983
(4.024)
Gov. Expenditure Growth (% of GDP)
(2)
2.384
(4.096)
(3)
3.666
(4.776)
0.073
(0.083)
(4)
2.974
(4.830)
Trade Shock
0.072
0.053
0.059
(0.086) (0.079)
(0.086)
Democracy
0.493
0.463
(0.263)
(0.316)
GDP (Ln)
0.010
0.010
(0.062)
(0.060)
GDP per capita (Ln)
0.110
0.110
(0.243)
(0.237)
Real GDP growth rate (%)
-0.029
-0.013
(0.018)
(0.014)
Gross public debt (% of GDP)
0.000
0.000
(0.002)
(0.002)
General Government
0.184
0.179
(0.118)
(0.121)
Real Gov.
Gov. Exp.
Exp. Growth
Growth (% of GDP)
Trade Shock
-0.036 -0.036 -0.028
-0.031
(0.014) (0.013)
(0.010)
(0.012)
Trade Shock UK Bond Yield
0.004 0.004
0.003
0.003
(0.001) (0.001)
(0.001)
(0.001)
Democracy
0.009
0.011
(0.009)
(0.008)
GDP (Ln)
-0.001
-0.002
(0.003)
(0.003)
GDP per capita (Ln)
-0.002
-0.001
(0.009)
(0.008)
Real GDP growth rate (%)
0.005
-0.001
(0.002)
(0.001)
Gross public debt (% of GDP)
-0.000
-0.000
(0.000)
(0.000)
General Government
-0.008
-0.006
(0.006)
(0.005)
Region Fixed Effects
X
X
X
X
Year Fixed Effects
X
X
X
X
Observations
2663
2571
2738
2573
Log-Likelihood
-322.63 -253.12
-155.25
-103.89
Clustered standard errors at the country level in parentheses. p 0.10, p 0.05, p 0.01
34
Table A15: Robustness - IV Probit Estimates: Irregular Transitions and Government Expenditures
Real Gov. Expenditure Growth
(1)
1.050
(17.647)
(2)
-5.873
(2.200)
(3)
(4)
-6.273
(1.596)
Trade Shock
-0.074
-0.005
-0.017
(0.159)
(0.101)
(0.091)
Democracy
-0.542
-0.400
(0.624)
(0.546)
GDP (Ln)
0.017
0.008
(0.072)
(0.062)
GDP per capita (Ln)
-0.030
-0.018
(0.119)
(0.115)
Real GDP growth rate (%)
0.035
-0.005
(0.024)
(0.007)
Gross public debt (% of GDP)
-0.001
-0.000
(0.003)
(0.003)
General Government
-0.349
-0.320
(0.289)
(0.241)
Real Gov.
Gov. Exp.
Exp. Growth
Growth (% of GDP)
Trade Shock
-0.012
-0.006
-0.007
-0.006
(0.012)
(0.013)
(0.015)
(0.014)
Trade Shock UK Bond Yield
0.001
0.001
0.000
0.001
(0.002)
(0.002)
(0.002)
(0.002)
Democracy
-0.004
-0.002
(0.013)
(0.012)
GDP (Ln)
0.008
0.005
(0.004)
(0.001)
GDP per capita (Ln)
-0.006
-0.003
(0.015)
(0.014)
Real GDP growth rate (%)
0.007
-0.000
(0.002)
(0.001)
Gross public debt (% of GDP)
-0.000
-0.000
(0.000)
(0.000)
General Government
-0.020
-0.023
(0.010)
(0.009)
Region Fixed Effects
X
X
X
X
Year Fixed Effects
X
X
X
X
Observations
1182
1095
1252
1095
Log-Likelihood
246.99
263.75
349.79
305.89
Clustered standard errors at the country level in parentheses. p 0.10, p 0.05, p 0.01
Gov. Expenditure Growth (% of GDP)
5.886
(36.363)
0.003
(1.080)
35
Table A16: OLS Estimates: GDP Growth and Government Expenditures
Real Gov. Expenditure Growth
(1)
4.065
(0.707)
Democracyt1
GDPt1 (Ln)
GDP per capitat1 (Ln)
Gross public debtt1 (% of GDP)
General Government
Country Fixed Effects
Year Fixed Effects
Observations
R2
X
X
3463
0.22
(2)
3.968
(0.766)
0.078
(0.244)
-1.162
(0.658)
-0.653
(0.712)
-0.004
(0.004)
0.269
(0.407)
X
X
3314
0.24
(3)
0.053
(0.008)
0.981
(0.005)
X
X
3473
1.00
Clustered standard errors at the country level in parentheses.
p 0.10,
p 0.05, p 0.01
DV in columns 1 and 2 is Real GDP Growth rate (%)
while in columns 3 and 4 is Log(GDP) controling for its own lagged.
36
(4)
0.052
(0.008)
-0.002
(0.003)
0.987
(0.007)
-0.015
(0.007)
-0.000
(0.000)
0.000
(0.003)
X
X
3324
1.00