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Political Institutions and
Economic Volatility
Jeroen Klomp and Jakob de Haan
CESifo GmbH
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POLITICAL INSTITUTIONS AND ECONOMIC VOLATILITY
Jeroen Klompa and Jakob de Haana,b
a
University of Groningen, The Netherlands
b
CESifo, Munich, Germany
This version, 25 April 2008
Abstract:
We examine the effect of political institutions on economic growth volatility, using data
95 countries over the period 1960-2005, taking into account various control variables as
suggested in previous studies. Our indicator of volatility is the relative standard deviation of the growth rate of GDP per capita. The results of a heterogenous dynamic panel
model indicate that democracy is negatively related to economic volatility. We also find
that some dimensions of political instability and policy uncertainty increase economic
volatility.
Keywords: Economic volatility, political regime, heterogenous dynamic panel
JEL code: E32, N10, O43
Corresponding author: Jeroen Klomp, Faculty of Economics and Business, University of
Groningen, PO Box 800, 9700 AV Groningen, The Netherlands; email: [email protected]
1
1
Introduction
After the great depression in the 1930s, the view that economic stability is an important
requirement for sustained economic growth became widely accepted. Although various
studies suggest that in OECD countries the volatility in output growth has declined during the last decades, most developing countries still face high levels of volatility (Easterly et al. 2000).
A number studies have tried to explain differences in economic volatility both
over time and across countries (cf. Barrell and Gottschalk, 2004; Blanchard and Simon,
2001; Easterly et al., 2000; McConnell and Perez-Quiros, 2000)1. However, there are
various problems with the existing literature on the determinants of economic volatility.
Firstly, most studies use the standard deviation of the GDP growth rate as an indicator
of economic volatility. However, this indicator does not take differences in growth performance into account and incorporates autocorrelation when it is used in a rolling panel
model. We therefore employ the relative standard deviation of the GDP growth rate that
corrects these problems.
Secondly, most papers focus on the effect of economic variables on economic
volatility, while political factors may be equally, or perhaps even, more important. According to Acemoglu et al. (2003), distortionary policies are more likely to be symptoms of underlying institutional problems rather than the main cause of economic volatility. This hypothesis is confirmed by Mobarak (2005) who finds that there exist a robust link between democracy and development through the volatility channel. Also other dimensions of the political system might be related to economic volatility. However,
there exists a measurement problem as democracy and other dimensions of the political
system are not direct observable. In the empirical literature several indicators have been
suggested to capture (dimensions of) the political system of a country. All these measures are imperfect indicators of latent constructs, like democracy. As suggested by
Wansbeek and Meijer (2000), we therefore apply dynamic factor analysis to construct
measures capturing various dimensions of the political system.
1
Table 1 in the next section gives a overview of the existing literature on economic volatility.
2
Finally, as in most panel studies on economic volatility, pooled estimators (fixed
and random effects, IV or GMM) have been used. However, one may question whether
the data is suitable to be pooled. Pesaran et al. (1998) argue, for example, that the GMM
estimation procedure for dynamic panel models can produce inconsistent and misleading coefficients of the long-run coefficients unless they are truly identical. We therefore
consider an alternative, namely the Pooled Mean Group (PMG) estimator that allows to
control for panel heterogeneity.
Based on data for 95 countries over the period 1960-2005, we examine the effect
of political institutions on economic growth volatility, using a heterogeneous dynamic
panel model in which we include various economic control variables as suggested by
previous studies. We use 3 different (sets of) political system indicators, i.e., the type of
regime, the stability of the regime, and policy uncertainty. On the basis of the specificto-general approach we decide on the specification of our model. We find that democracy is negatively related to economic volatility. We also find that some dimensions of
political instability and policy uncertainty increase economic volatility.
The remainder of the paper is organized as follows. Section 2 discusses determinants of economic volatility as suggested in previous studies. Section 3 explains factor
analysis and summarizes our results using various indicators of political institutions.
Section 4 describes our data and methodology used and section 5 presents our estimates
of the relationship between political institutions and economic volatility, while the final
section offers our conclusions.
2
Determinants of economic volatility
2.1 Socio-economic variables
In this section we discuss which socio-economic variables may affect economic volatility. Table 1 gives a overview of the existing literature on economic volatility and his
determinants. An important driver of volatility is the extent to which a country is hit by
shocks, as well as its abilities to absorb those shocks. Measures of shocks that have
3
been used in empirical studies include the inflation rate, shocks to terms of trade, and
the occurrence of an external war.
When an economy is largely diversified, it has more possibilities to smooth
shocks. This can be done in two ways. First, it can diversify a shock that hit only a limited number of sectors of the economy across the remaining sectors. According to
Mobarak (2005) countries with many high-skilled sectors, such as the service sector, are
more capable to adjust internally to shocks. Also a large population may affect possibilities for internal diversification as the resource base is likely to be broader (Mobarak,
2005). Secondly, a country can externally diversify a shock if it is a member of a trade
or economic union. For example, workers may migrate to a neighboring country if the
home country is hit by a negative shock.
The evidence on the effect of openness on volatility is mixed. Easterly et al.
(2000) find a positive relation between trade and economic volatility, while Blanchard
and Simon (2001) do not find any significant relation between trade and economic volatility. Easterly and Kraay (2000) provide empirical evidence that trade openness can
cause volatility especially for small countries, which is due to larger term of trade
shocks. In general, the effect of openness of trade depends on whether a particular country economic cycle is highly correlated with that of the countries it trades with.
Furthermore, income inequality may lead to more volatility. When income plays
a role in access to credit, a skewed income distribution implies that many people are
financially constrained and therefore unable to smooth consumption. Iyigun and Owen
(2004) argue that in high-income countries greater income inequality is associated with
more volatility in consumption and GDP growth, whereas in low-income countries
greater inequality is associated with less volatility. These authors suggest that financial
development and availability of credit – both of which are generally associated with
higher levels of per capita income – may help to explain why the distribution of income
affects the short-run variability of output differently in high-income countries than in
low-income ones.
Also various other studies have examined the impact of financial development
on economic volatility. Svaleryd and Vlachos (2002) classify financial development
4
into three categories: the size of the financial sector (ratio of liquid liabilities to GDP),
the financial system’s ability to allocate credit (Credit to the private sector to GDP) and
the real interest rate. The effect of financial development on economic growth volatility
is ambiguous. Denizer et al. (2002) argue that when there initial exists market imperfections based on information asymmetry then an increase in financial market development
has a negative effect on economic growth volatility. On the other hand more developed
financial markets may more efficiently match savers and investers and facilitating diversification which both would reduce volatility2.
Easterly et al. (2000) find a non-linear relation between volatility and financial
depth, measured as private sector credit to GDP. They found that while developed financial systems offer opportunities for stabilization, they also may imply higher leverage of firms, which implies more risk and lower stability.
Next, Irvine and Schuh (2005) and Blanchard and Simon (2001) conclude that
improved inventory management is responsible for a decline in the volatility of U.S. real
GDP growth. Changes in inventory behavior can account directly for almost half of the
total reduction in GDP volatility the last decade. Cecchetti et al. (2006) found the same
relation in a sample containing mostly OECD countries.
The Real Business Cycle theory argues that economic fluctuations are primarily
the results of technological shocks. This is empirical confirmed by Karras and Song
(1996) and Simon (2001). They show that a decline in productivity shocks are responsible for decreasing economic volatility.
Furthermore, when a country is in a fixed exchange rate regime, it is less capable
to smooth shocks, because it has to grant the exchange rate. This in turn will lead to
higher economic volatility. Flood and Rose (1995) find that the exchange rate regimes
have no effect on economic volatility. However, Bastourre and Carrera (2004) find that
pegged regimes are systematically associated with higher real volatility if other factors
that affect volatility are taken into account.
2
See also Acemoglu and Zilibotti (1997)
5
Next, economic policies may have a stabilizing impact. Blanchard and Simon
(2001) and Romer (1999) find that monetary policy has made a larger contribution to
stabilizing economic downturns than fiscal policy. Monetary authorities actively stabilized shocks, while fiscal policy had a more passive role (largely through the operation
of automatic stabilizers) in moderating economic volatility. In practice, fiscal policy
may not move quickly enough to be counter-cyclical and timing difficulties could actually lead fiscal policies to exacerbate output fluctuations (Kent et al., 2005).
2.2 Political institutions
We identify three dimensions of a political regime that may influence economic volatility: 1) the type of regime, 2) the stability of the regime and 3) policy uncertainty.
Quinn and Woolley (2001) argue that the type of regime influences economic
growth volatility. Democratic governments should take more effort in stabilizing economic growth then autocratic regimes. The reason is that rational voters care about volatility because it indicates to which extent the world is predictable. Voters prefer certainty to uncertainty and punish politicians in election in reaction to volatile economic
growth rates. An alternative explanation is brought by Rodrik (1999). He posits that
democracies better handle economic shocks because of the ways democratic institutions
moderate intense social cleavages. So, we expect to find a negative relation between
economic volatility and democracy. This is hypothesis is empirical confirmed by a
number of studies. Mobarak (2005), Rodrik (1997), Quinn and Woolley (1996, 2001)
all found a strong negative correlation between (prior) levels of democracy and subsequent stability of growth rates.
The second dimension of political institutions that we distinguish is the stability
of the regime in place. Economic growth is more volatile when the political environment is unstable. Political instability increases uncertainty about the political future.
Rodrik (1999) shows in a theoretical model with external conflict shocks that growth
collapses and growth becomes more volatile. On the empirical side, Mobarak (2005)
does not find any significant relationship between economic growth volatility and external war or anti-government demonstrations. Asteriou and Price (2001) conclude on
6
basis of time series evidence that there is a strong positive relationship between political
instability and economic volatility in the United Kingdom. Finally, Campos and Karanasos (2007) find a positive relation between the number of assassinations, strikes and
constitutional changes on one side and economic volatility on the other in Argentina.
Finally, policy uncertainty may play a role in explaining cross-country differences in economic volatility. When the variability of policies increases, it also increases
macroeconomic uncertainty, which will be reflected in economic growth variability.
Aizenman and Marion (1991) argue that investors and entrepreneurs care more about
the stability of economic policies than the stability of the regime itself. Mobarak (2007)
argued that the severity of economic volatility in Latin America, East Asia and Eastern
Europe experienced significant economic crises were caused by profligate governments
adopting inappropriate and distortionary .
3
Factor analysis: methodology and results
3.1 Methodology
Studies that examine the effect of political institutions on economic volatility, usually
choose arbitrary their political indicators. For example Mobarak (2005) takes antigovernmental demonstrations and external war to represent political instability. The
question is if these indicators represent all dimensions of political instability and are
these the right indicators or is perhaps the number of cabinet changes a better indicator.
To come up with a better indicator that includes more information and to determine
whether the various political institutions have a multidimensional character, a so-called
Dynamic Factor Analysis (DFA) is employed.
The objective of an DFA is to identify what different indicators of a latent variable (like political variables) have in common and to separate common factors from specific factors. The major difference with the usual (static) Factor Analysis is that observations are indexed by time. Following Gilbert and Meijer (2004) and Stock and Watson (2002) and Hershberg et al. (1996), the DFA model can be written as:
7
xit = ∆ξit + ε it
(1)
Where xit is a vector containing the M indicators for observation i, i = 1…k (in our case
the various indicators of political institutions) at time t, ∆ is a vector of factor loadings
of order M × k , and ξ is a vector of latent variables with mean zero and positive define
covariance. The random error term ε is assumed to be uncorrelated with the latent variables.3 Under these assumptions, the covariance matrix of xit is:
Ξ = ∆Φ∆ '+ Ωit
(2)
Where Ξ is the parameterised covariance matrix and can be decomposed in the covariance matrix of the factors Φ and the diagonal covariance matrix of error terms Ωit. The
model is estimated with the Maximum Likelihood (ML) method. By assuming that the
factors and the disturbance term are normally distributed, it follows that the indicators
are normally distributed. The log-likelihood function can be written as:
[
ln L = ln Ξ + tr SΞ −1
]
(5)
Where S represents the sample covariance matrix. Minimizing this fit function means
choosing the values for the unknown parameters that lead to the implied covariance
matrix as close as possible to the sample covariance matrix .
The next step is to decide on the number of factors to represent political institutions on the basis of the scree plot, which plots the number of factors against the eigenvalues of the covariance matrix of the indicators. In general, there are two ways of interpreting the graph. First, one can use information criteria such as Akaike’s information criterion or the modification proposed by Bai and Ng (2002). An alternative way is
to look for an ‘elbow’ in the scree plot, i.e., the point after which the remaining factors
3
E(ε) = 0 and E(ζε’) = 0.
8
decline in approximately a linear fashion, and to retain only the factors above the elbow.
Finally, we can use the Kaiser criterion, which indicate that all factors with eigenvalues
below one should be dropped.
After deciding on the number of factors, it is possible that the factors of the
(standardized) solution of the model are difficult to interpret. In that case, we can rotate
the factor loadings, yielding a solution that may be easier to interpret because the matrix
has a simpler structure. Ideally, each indicator is correlated with as few factors as possible. The rotation technique that we use to interpret the factors is the Oblimin rotation,
which allows for correlation among the factors and minimizes the correlation of the
columns of the factor loadings matrix. As a result, a typical indicator will have high
factor loadings on one factor, while it has low loadings on the other factors.
All indicators receive factor scores for the various dimensions (factors) identified. These factor scores are used to come up with the so-called Bartlett predictor, i.e.,
the best linear unbiased predictor of the factor scores:
ξˆi = Φ∆ 'θ −1 xit
(3)
These factor scores can then be used as our indicator of the political institution.
3.2 Results
The results of the factor analysis indicate that just picking (arbitrary) an indicator of for
example political instability or democracy differs from the factors found. This can be
illustrated by the explained and unexplained variance of the individual factors.
Table 2 shows the individual indicators and the factor loadings for the various
dimensions of political institutions on which we apply an Dynamic Factor Analysis. In
our analysis we use the period 1960-2005. The detailed results of the factor analysis are
given in Appendix 1 en 2.
[TABLE 2 ABOUT HERE]
9
The results of the factor analysis on democracy shows that democracy is a highly significant (compared to a satured model) one dimensional construct, which explains more
than sixty percent of the total variance. In the case of political instability we find a four
factor model, which is significant and represents four dimensions of political instability:
political instability due to aggression, political instability due to protest, political instability due to regime instability and political instability due to government instability
(i.e., within regime instability). The four factors in total explain about sixty percent of
the variance. Finally, policy uncertainty is a significant three dimensional construct:
fiscal policy, monetary policy and trade uncertainty. These three dimensions together
explain about sixty-five percent of the total variance.
The results of the factor analysis indicate that just picking an indicator of for example political instability differs from the factors found. This can be seen in the explained
and unexplained variance of the individual factors.
4
Data and methodology
4.1 Dependent variable
Most previous studies on economic volatility use the standard deviation of economic
growth or the output gap as an indicator of volatility4. However there are a number serious problems with this measure of volatility. First, this indicator does not capture all
characteristics of volatility. The standard deviation does not take into account the relative deviation. This can illustrated with the following example, suppose there are two
countries A and B, where country A has the following growth rates: the first year 1 percent, the second year 3 percent and the third year 5 percent. Suppose the growth rates in
country B are: the first year 11 percent, the second year 13 percent and the third year 15
percent. The standard deviation in both countries is 2.08, while the average growth differs, in country A 3.33 and in country B 13.33. This means that the relative impact of
4
See table 1 in the appendix.
10
volatility, which is computed by the standard deviation divided by the average growth,
is much larger in country A then in country B.
Second, by using the standard deviation as a volatility indicator in a rolling window panel, there is autocorrelation present because of the way the variable is constructed. The Q-Ljung-box test statistic on autocorrelation systematically rejects the null
of no first-order autocorrelation in that case. The p-value for the standard deviation case
ranges from 0.000 to 0.092 for a sample of 120 countries between 1960 and 2005. This
means that when the standard deviation is used, the estimations are not efficient anymore and inconsistent if lagged variables are included in the regression. When we use
instead the relative standard deviation the p-value of the Q-Ljung-box test statistic on
autocorrelation ranges from 0.107 to 0.991.This means that in the relative standard deviation there is no autocorrelation present. Therefore, we define our volatility measure
based on the relative standard deviation as follows
1
σy =
| yit |
∑(y
it
− yit ) 2
n −1
Where yit is the economic growth rate in country i at period t, yit is the average economic growth rate in a five-year rolling window in country i at period t. The standard
deviation and the relative standard deviation differ significantly from one another. The
correlation ranges from 0.31 in developing countries to 0.57 in developed countries; on
average the correlation is about 0.46.
We can also illustrate the difference between the standard deviation and the relative standard deviation in Table 3. The table shows the deviation of a particular time
period compared to the 1970-79 level. Early evidence suggested that economic volatility
decreases since the 70s. If we compare the time dummies, using the standard deviation
as our volatility indicator, we see a significant declining effect overtime. If we use instead the relative standard deviation only the time dummy 1990-99 is significant5. We
5
We do also not find a decline after the 80s.
11
do not find evidence at the global level that volatility decreases after the 1970s. We confirm the earlier conclusions that developing countries are more volatile than developed
countries.
[INSERT TABLE 3 ABOUT HERE]
4.2 Model
By using traditional pooled estimators (fixed and random effects, IV or GMM) the
question remains if the data is suitable to pool into a homogenous panel regression. For
panel data studies with large N and T, assuming homogeneity of the slope coefficients is
quite often rejected. With the so-called Mean Group estimator we can estimate separate
equations for each country and examine the distribution of the estimated coefficients
across groups. This produces consistent estimates of the average of the parameters in
heterogeneous panels provided that group specific parameters are independently distributed and the regressors are exogenous. However, it has also been shown that MG estimates will be inefficient if parameters are the same across groups, i.e., if the long-run
slope homogeneity restriction holds (Pesaran et al., 1998). In this case, Pesaran et al.
(1998) propose a maximum likelihood-based ‘‘pooled mean group’’ (PMG) estimator
which combines pooling and averaging of the individual regression coefficients. This
estimator allows the intercept, short-run coefficients and error variances to differ freely
across groups, but the long-run coefficients are constrained to be the same. Not imposing equality of short-run slope coefficients also allows the dynamic specification, e.g.
the number of lags included to differ across countries. The PMG estimates the error
equation of the autoregressive distributed lag (ARDL) representation. This is given as
follows
σ y = α j + γ jσ y βij X it −l + µij Pit −m + uit
i ,t
it −k
(4)
12
Where σyit is the logarithm of the five-year rolling window of economic growth
volatility in country i at period t. The variable Xit is a vector of socio-economic control
variables in country i at period t, Pit is a vector of political institutions variables in country i at period t. The last term uit is the error term. Note that the intercept term αj may
differ from country to country. For notational convenience we derive the equations here
with one lag in the dependent and explanatory variables for all countries6. In the actual
estimation procedures we allow the lags to differ between series and between the dependent and explanatory variables. The error correction equilibrium representation is
derived as.
∆σ yt = α j + (1 − λ )σ yt−1 + βij X it −1 + θij ∆X it −1 + µij Pit −1 + ϕij ∆Pit −1 + uit
This error correction equation implies the following long run estimation
σy =αj +
t
βij
µij
X it −1 +
Pit −1 + uit
1− λ
1− λ
We start with formulating our baseline model. This is done with the specific-to-general
approach of the PMG model whereby we stepwise add the variables mentioned and motivated in section 2 with the lowest p-value at a ten percent level. Recent work by
Lutkepohl (2007) shows that that the leading approach used for modeling multiple time
series is a specific-to-general approach. It has theoretical and practical advantages in
particular if cointegrated variables are involved. In fact, a bottom-up approach to multiple time series analysis that starts from analyzing individual variables or small groups
of variables and uses the results from that analysis to construct a larger overall model
has a long tradition (see, e.g., Zellner and Palm, 1974; Wallis, 1977; Zellner and Palm
2004).
6
We assume that all of these variables are I(1) and cointegrated. This means uit is an I(0) process for all i
and is independently distributed across t. They are also assumed to be distributed independently of the
regressors.
13
The variables we include in this approach are: herfindahl sector diversification
index, oil producing country dummy, commodity exporter dummy, service as a share of
GDP, agriculture as share of GDP, economic and/or trade union membership dummy,
secondary school enrolment, income inequality, openness of trade, terms of trade
shocks, productivity shocks, inventory changes, total population, fixed exchange rate
dummy, government expenditure, inflation, initial real GDP per capita 1960, M2 as percentage of real GDP, real interest rate, credit domestic credit as percentage of real GDP
(linear and squared).
Additional to variables above we control for foreign shocks by adding economic
volatitility in the rest of the world7. Economic volatility in a country can be determined
by a spill-over effect of the volatility of the rest of the world. Furthermore, Malik and
Temple (2006) argue that geographical location matters for economic volatility. We
include the distance from equator, a dummy for tropical countries, and the distance to
one of three major markets: Europe (Brussels), US and Japan8. Next, it is argued that
volatility is higher in a recession, so we include a dummy in years in which economic
the growth rate is negative. Finally, we add shocks in household consumption and investment, because they contribute for a large part to GDP.
Most data are taken from the World Development Indicators of the World Bank,
World Penn Tables 6.1 by Summers and Heston and the International Financial Statistics of the IMF. The income inequality data is taken from the University of Texas Inequality Project (UTIP). This dataset is derived from the econometric relationship between pay-inequality, other conditioning variables and the Deininger & Squire data set9.
We can use an annual measure of the education level of the population above 15 years
of age, constructed by interpolating the five-year observations from Barro and Lee
(2000) supplemented by data of EDUSTAT (2006).
7
Measured by the population weighted average of economic volatility of all countries minus the domestic
country.
8
Measured from capital to capital (Tokyo, Brussels, Washington).
9
See for all data definitions and sources Appendix 3.
14
5
Empirical results
5.1 Estimation results
In this section we examine the effect of political institutions on economic growth volatility controlling for a number of variables. We use a large cross-country time-series
dataset, comprising 95 developed and developing countries and spanning the years 1960
to 2005.10
In addition to the PMG and MG we also report the dynamic fixed effects (DFE)
to facilitate comparison. Results will vary quite substantially across methodologies given that the MG procedure is the least restrictive, and thus potentially inefficient. The
DFE allows for individual intercepts to vary across countries, and is similar to the
GMM procedure.
We test for long-run homogeneity using a joint Hausman test based on the null
of equivalence between the PMG and MG estimation. If we reject the null (obtain a
probability value of less than 0.05), we reject homogeneity of our cross section’s long
run coefficients (Pesaran et al., 1996). An alternative to the Hausman test is the likelihood ratio test for short-run or long run parameter heterogeneity and has homogeneity as
the null hypothesis.
For all estimations we use a three stage estimation procedure. In the first stage,
we determine the optimal number of lags for each series chosen by the Schwarz Bayesian information Criterium (SBC). All results are robust for alternative selection criteria
like Akaike’s Information Criterium (AIC) and the Hanna-Quinn Information Criteria
(HQ). For the vast majority of the countries, specifications with no lagged dependent
variables are rejected at conventional levels of statistical significance, indicating that
dynamics is important and so that the static fixed effects method is clearly inadequate to
for the task at hand. In the second stage, we search for a suitable initial value of the
10
We only include countries with an average population larger than 200.000.
15
coefficients. In the final stage we use a Newton-Raphson optimization algorithm to
maximize the likelihood function.
Table 4 shows our baseline results. of the three estimators11. The result of the
PMG estimator appears substantially different from those using the DFE estimators regarding the size of coefficients. However, the PMG result is fairly similar with that using the MG. This is encouraging because the two estimators use different methods to
estimate the model while the coefficients from both estimations, unlike the DFE, are
based on long-run effects. Compared to the results using the MG estimator, the PMG
results improve the precision of estimations. This outcome is expected given that the
MG estimators are known to be inefficient due to a low degree of freedom. Comparison
of the MG and PMG results indicates that imposing long-run homogeneity reduces the
standard errors of the long-run coefficients, but changes little the estimates. The Hausman test and the likelikhood ratio statistic, which suggests sample countries can be
pooled to provide common long-term coefficients, confirms this.
The PMG results in column (2) indicate that initial GDP per capita has a negative effect on economic growth volatility. This means that there is a kind of wealth effect on economic volatility. The hypothesis that countries that are in a recession are
more volatile then countries that have positive growth rates is confirmed. Next, the economic sector diversification of a country is an important instrument to smooth the impact of a shock. This is shown by the significant negative effect of the herfindahl diversification index on volatility. Countries that have a high educational level are also less
vulnerable to economic volatility.
Our result on the cyclical government spending contradicts the argument that fiscal
policy is ineffective. We find a significant negative effect of government spending on
economic volatility. This means that the government is capable of smoothing shocks in
economic growth. Next, we find support for the Real Business Theory that productivity
shocks is one of the main contributors to economic volatility. We estimated productivity
shock by taking the relative standard deviation of the Solow residual estimated in a
11
The short-run parameter estimation along with the individual country MG results are available from the
authors on request.
16
Random Coefficient Model. Finally we find shocks in the terms of trade have an effect
on the economic growth volatility.
[INSERT TABLE 4 ABOUT HERE]
In the regression shown Table 5, we add our first political regime indicator12.
The results show that democracy has a significant negative effect on economic volatility
in the PMG regression. This regression is preferred above the MG regression. The PMG
result confirms the conclusion of Mobarak (2005) and Rodrik (1999). Because the dependent and independent variables are here also formulated in logarithms, we can interpret the coefficients as elasticities. If democracy increases by one percent then economic growth volatility declines by 1.8 percent.
Next, in the second part of Table 5 we add the four dimensions of political instability to the baseline regression. The Hausman test indicates that the PMG regression is
the appropriate model. The results on political instability are mixed. Although all dimensions have a positive effect on growth volatility, only regime instability and government instability have a significant effect. Also are the effects small compared to the
democracy effect. This partially confirms the conclusions of Rodrik (1999) and Mobarak (2005). The latter also did also not find a significant result on protest.
Finally in the last part of table 5 we add our three policy uncertainty measures.
The results indicate that fiscal and monetary policy uncertainty have a positive effect on
economic volatility. The magnitude of these two variables is higher compared with the
other political variables. Trade uncertainty is insignificant.
[INSERT TABLE 5 ABOUT HERE]
Mobarak (2005, 2007) argues that there is an interaction effect between democracy and political instability on one hand and policy uncertainty on the other. When
12
We tested with a modified Holtz-Eakin model if the causality runs from political institutions to economic volatility. The test confirms our hypothesis and the results are available upon request.
17
political actors unilaterally set policies, the variance of policies and outcomes will generally be higher than if policies are chosen through consensus. Policy choices are also
significantly more stable over time in democracies.
To test this interaction we add an interaction effect into the regression13. We estimate an interaction effect between policy uncertainty and democracy or political instability14. In general we find the same pattern on the control variables as before. On the
results of the political variable, we find that the interaction between uncertainty and the
other political institutions is significant.
To interpret the result on the interaction variables (between two continuous latent variables) we use the percentiles of the uncertainty measure and the political institution to show what the total effect is. Table 6 shows the different impact of the interaction variables between the three interquantile ranges. The results indicate that a high
degree of democracy and a low policy uncertainty level has a negative effect on the volatility of the economic growth rates. On the side of political instability we see that a
low level of instability and uncertainty results in a lower degree of volatility.
[INSERT TABLE 6 ABOUT HERE]
Problem till now is that although the joint hausman test indicates that the PMG
is the appropriate estimation technique, the individual Hausman test on homogeneity of
the political variables indicate that they vary across countries. Therefore we estimate the
model with only a lagged dependent variable and the political variable. Now the mean
group (MG) model is the appropriate model. The former joint Hausman test is probably
dominated by the impact of the economic variables and the lack of degrees of freedom
due to inclusion of many control variables. The results in Table 7 still indicate that political institutions matter and give rise to the idea that the impact of political institutions
differ across countries. It supports a popular view among political scientists that politi13
We have centered the political indicators around their mean, the reason is that we can then use them
later in an interaction term between two continuous variables.
14
We only show the results of the interaction effect of regime and government instability, because the
interaction and the level variables of protest and aggression are insignificant.
18
cal variables are more heterogenous across countries then economic variables. This
combined with the PMG results above indicates that there, at least in the short-run, is
heterogeneity present. This means that the DFE/GMM is not the right method to estimate this kind of models. The magnitude is higher, but this is probably cause by an
omitted variable bias. This is also the reason why trade policy uncertainty is now weakly significant.
[INSERT TABEL 7 ABOUT HERE]
5.2 Robustness of the evidence
It could be argued that one individual country could significantly affect the estimated
parameters, even when the Hausman tests and/or the likelihood test on homogeneity do
not reject the hypothesis of common long-run coefficients. A sensitivity analysis was
thus performed on our preferred specification in order to assess the robustness of the
results to variation of country coverage. We re-estimated the PMG regressions including the political institutions with the bootstrap method. In this method, we replicate the
regressions 1,000 times by estimating it with a changing sample of countries of 40 percent of the total sample. The purpose of this procedure is to examine the stability of the
regressions by checking on sample sensitivity. Table 8 shows the bootstrap results for
the political variables of the regressions. The results show the same pattern than the
basic regressions, meaning that the relation between political institutions and economic
volatility, although there is heterogeneity on the short run, relative stable across country.
If we have a closer look at developing and developed countries we find that democracy
and political instability has a higher impact in developing countries, while policy uncertainty has a larger impact in developed countries. These results are shown in Table 9.
[INSERT TABLE 8 AND 9 ABOUT HERE]
Next, because there could be an measurement error in the factor analysis on the
political institutions, we also re-estimated the regressions with common used indicators
19
for democracy, political instability and policy uncertainty. The results in Table 11 show
the same pattern as before. The democracy variable taken from the Polity IV is significant at a five percent level of significance. The number of coupes taken from the Bank’s
international database is also significant with the expected sign, while antigovernmental demonstrations is not significant. This confirms the conclusion of Mobarak (2005). Next, we add the relative standard deviation of the government expenditure
as an alternative for our policy uncertainty variable. Also here we find a significant result. So, to sum up we find in all alternative cases the same results as with our factor
analysis based indicators, although the significance is somewhat lower.
[INSERT TABLE 10 ABOUT HERE]
Finally we included step-wise, after we include the political institutions, the variables that were deleted in the specific-to-general method to check if these variables
become significant after including the political variables. This was not the case15.
15
The results are available upon request.
20
6
Concluding remarks
In this study we examine the relationship between political institutions and economic
growth volatility. We used the relative standard deviation of the growth rate to compute
our economic volatility variable. We subsequently use this measure to examine the impact of political factors on economic volatility in a heterogenous dynamic panel model
with various economic variables. We use 3 different (sets of) political system indicators,
i.e., the type of regime, the political stability and policy uncertainty We take a long list
of potential control variables into account, as suggested by previous studies. On the basis of the specific-to-general approach we decide on the specification of our model.
First, we find that there exists a negative relation between democracy and economic
volatility. Second, some dimensions on political instability and policy uncertainty have
a positive effect on economic volatility. Third, we find a interaction effect between policy uncertainty and democracy or political instability. Finally, the results are in general
robust to sample and variable selection.
21
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24
APPENDIX 1: Factor analysis political institutions1617
Type of the regime
We included 145 countries18 in the factor analysis on the type of political regime in
place. Figure A1 shows the scree plot. According to the Kaiser rule, two factors should
be retained. If instead the elbow criterion is used, democracy can be represented as a
one dimensional construct. We test both models, but the one-factor model is the most
appropriate. The goodness-of-fit test statistic is 22097 which is χ2(48) distributed and is
significant at the five percent level. The factor loadings of the different indicators and
the variance explained is shown in Table A1. The one factor model can almost explain
all variance “Competition of participation”, while it only explains about fifteen percent
of the type of regime of the Banks’ International Dataset. Overall, the one factor model
explains about sixty percent of the total variance.
16
In all following factor analyses we have less then 5 percent missing data. In order not to lose valuable
information, we applied the EM algorithm to compute the missing observations. The EM algorithm was
suggested by Dempster et al. (1977) to solve maximum likelihood problems with missing data. It is an
iterative method, the expectation step involves forming a log-likelihood function for the latent data as if
they were observed and taking its expectation, while in the maximization step the resulting expected loglikelihood is maximized.
17
All factor loadings presented in this section are an avarage over time, because there is little change over
time in all factor analysis.
18
We only include countries with a population larger than 200.000
25
Figure A1. Scree plot of the eigenvalue and factors of democracy
Table A1. Factor matrix democracy
Type of regime19:
Factor
Variance explained
Regulations of Chief Executive recruitment
0.763
0.58
Competition of Chief Executive selection
0.882
0.78
Openness of Chief Executive
0.531
0.28
Decision rules
0.940
0.88
Competition of participation
0.990
0.98
Executive competition
0.793
0.63
Executive legitimacy
0.818
0.67
Type of regime
0.390
0.15
Parliamentary responsibility
0.523
0.27
Legislator selection
0.435
0.19
Political instability
For the analysis of indicators of political instability we follow the same procedure as
Jong-A-Pin (2006), using the same 145 countries as above. Figure A2 shows the scree
plot. According to the Kaiser rule, six factors should be extracted, but this is probably a
19
Data from the Polity IV and the Banks’ database on political institutions, see appendix for further
source information.
26
Heywood case20. Following the ‘elbow criterion’, we can identify four factors in the
scree plot, hence we decided to use the four factor model. The goodness-off-fit-statistic
is 17421, which is χ2(98) distributed and is significant at a five percent level. The factor
loadings of the rotated factors is shown in Table A2. Overall, the four factor model explains about sixty percent of the variance.
Since the oblimin rotation minimizes the correlation between columns of the factor loadings matrix, the general pattern that arises is that most indicators have a high
loading on one factor. On the basis of these results we can therefore interpret the factors
identified. The first factor is highly correlated with guerrilla, revolutions, and assassinations and therefore we call this factor “aggression”. The second factor is highly correlated with strikes, riots, and anti-governmental demonstrations and therefore we call this
factor “protest”. The third factor is highly correlated with number of coupes, regime
durability, and constitutional changes and therefore we call this factor “regime instability”. The final factor is highly correlated with government fractionalization and cabinet
changes therefore we call this factor “within regime instability”. The correlation matrix
of these four dimensions of political instability, as shown in Table A3, indicates that
each factor measures a different dimension of political instability, because the correlations are very low.
20
Heywood solutions are solutions in which some of the unique variances of the indicators are estimated
smaller than zero. In general a Heywood case (Heywood, 1931) is an indication of a poorly specified
model
27
Figure A2. Scree plot of the eigenvalue and factors of political instability
Table A2. Factor matrix political instability
Political instability21:
Regime durability
Government fractionalisation
Assassinations
Strikes
Guerrilla
Government crises
Purges
Riots
Revolutions
Anti-government demonstrations
Coalitions
Number of coupes
Number of legislative elections
Number of executive elections
Constitutional changes
Cabinet changes
Aggression
Protest
Regime
Government
-0.131
0.015
0.557
0.122
0.898
0.245
0.183
0.180
0.740
0.075
0.108
0.126
-0.085
0.110
0.029
0.084
-0.030
-0.070
0.169
0.628
0.159
0.451
0.179
0.830
0.067
0.872
0.066
0.000
0.164
0.211
0.461
0.302
-0.354
0.231
-0.063
-0.101
-0.234
0.291
-0.245
-0.053
0.488
0.041
0.420
0.749
0.526
0.778
0.244
0.357
0.104
0.520
0.226
0.332
-0.042
0.565
-0.119
-0.028
-0.123
0.073
0.358
-0.031
0.283
-0.073
0.365
0.596
21
Variance
explained
0.15
0.33
0.39
0.53
0.89
0.67
0.14
0.72
0.81
0.77
0.32
0.58
0.39
0.67
0.41
0.58
Data from the Polity IV and the Banks’ database on political institutions, see appendix for further
source information.
28
Table A3. Correlation matrix factors
Aggression
Regime insta-
Within regime
Aggression
Protest
bility
instability
1.00
0.17
0.22
0.11
1.00
0.13
0.15
1.00
0.18
Protest
Regime instability
Within regime instability
1.00
Policy uncertainty
The final political dimension to which we apply EFA is policy uncertainty. We computed the uncertainty of a variable by using the relative standard deviation of the error
term of a AR(1). We computed this for 145 individual countries. On the basis of the
Kaiser rule and the elbow criteria we decided to retain one factor. The goodness-of-fit
test statistic is 9342, which is χ2(32) distributed and is highly significant at a five percent significance level. The factor loadings of the individual indicators and the explained variance of the individual indicators are shown in Table A4. It explains about
sixty-five percent of the total variance.
Because we are also using here the oblimin rotation, we can interpret the factors.
The first factor is highly correlated with fiscal variables, this can be labeled as fiscal
policy uncertainty, The second factor is highly correlated with monetary variables,
which we can label monetary policy uncertainty. Finally, the last factor is high correlated with international trade. This factor we can label as trade uncertainty.
Figure A3. Eigenvalues and factors of policy uncertainty
29
Table A4. Factor matrix policy uncertainty
Indicator22
Government expenditure as % of GDP
Factor 1:
Factor 2:
Factor 3:
Variance
Fiscal policy
Monetary policy
Trade policy
explained
0.516
0.265
0.304
0.43
Tax revenue as % of GDP
0.715
0.210
0.483
0.79
Government debt as % of GDP
0.800
0.156
0.292
0.75
Budget deficit as % of GDP
0.893
0.299
0.302
0.98
Growth of M2 as % of GDP
0.248
0.469
0.159
0.31
Inflation rate
0.298
0.753
0.251
0.72
Short term interest rate
0.175
0.671
0.195
0.52
Real exchange rate
0.166
0.240
0.715
0.60
Trade as % of GDP
0.277
0.242
0.629
0.53
Current account as % of GDP
0.340
0.174
0.471
0.37
22
The individual indicators are measured with the relative standard deviation of the error term of a AR(1)
process. Data from the International Financial Statistics, IMF (2006), Worldbank (2006) and Przeworski
et al. (2000).
30
APPENDIX 2: Factor analysis variables used
TVariable
Source
Regulations of Chief Executive recruitment
Polity
Competition of Chief Executive selection
Polity
Openness of Chief Executive
Polity
Decision rules
Polity
Competition of participation
Polity
Executive competition
Banks’ database on political institutions
Executive legitimacy
Banks’ database on political institutions
Type of regime
Banks’ database on political institutions
Parliamentary responsibility
Banks’ database on political institutions
Legislator selection
Banks’ database on political institutions
Political instability:
Regime durability
Polity
Assassinations
Banks’ database on political institutions
Strikes
Banks’ database on political institutions
Guerrilla
Banks’ database on political institutions
Government crises
Banks’ database on political institutions
Purges
Banks’ database on political institutions
Riots
Banks’ database on political institutions
Revolutions
Banks’ database on political institutions
Anti-government demonstrations
Banks’ database on political institutions
Coalitions
Banks’ database on political institutions
Number of coupes
Banks’ database on political institutions
Number of legislative elections
Banks’ database on political institutions
Number of executive elections
Banks’ database on political institutions
Constitutional changes
Banks’ database on political institutions
Cabinet changes
Banks’ database on political institutions
Policy uncertainty23
Government expenditure
Worldbank (2006), Przeworski et al. (2001), IMF (2006)
Government revenue
Worldbank (2006), Przeworski et al. (2001), IMF (2006)
Government debt as % of GDP
IMF (2006), Przeworski et al. (2001)
Budget deficit as % of GDP
IMF (2006), Przeworski et al. (2001)
Growth rate of domestic credit
Worldbank (2006), IMF (2006)
Growth of M2 as % of GDP
Worldbank (2006), IMF (2006)
23
The individual indicators are measured with a GARCH(1,1).
31
Inflation rate
IMF (2006)
Short term intrest rate
IMF (2006)
Real exchange rate
IMF (2006)
Trade as % of GDP
Worldbank (2006), IMF (2006)
Current account as % of GDP
IMF (2006)
32
APPENDIX 3: Regression variables used
Variable
Definition
Source
Sector diversification
Herfindahl index between agriculture, industry and service value
added as a share of GDP
World Development Indicators
(2005)
Oil producing country
Dummy variable taking the value 1 if export consists of more than
50% of oil
World Development Indicators
(2005)
Commodity exporter
Dummy variable taking the value 1 if export consists of more than
50% of commodities
World Development Indicators
(2005)
Service as a share of GDP
Value added of the service sector as a share of GDP
Agriculture as a share of
GDP,
Union membership
Value added of the agriculture sector as a share of GDP
World Development Indicators
(2005)
World Development Indicators
(2005)
Wikipedia
Secondary school enrolment
Secondary school attianment rate of the population above 15
Barro and Lee (2000) and
EDUSTAT (2006)
Income inequality
Estimated Household Income Inequality
University of Texas Inequality
Project
Openness of trade
Import plus export as % of Real GDP (Constant $ in year 2000)
Population
All residents regardless of legal status or citizenship - except for
refugees not permanently settled in the country of asylum
Summers Heston World Penn
Tables 6.1
World Development Indicators
(2005)
Fixed exchange rate
Fixed exchange rate dummy
Rogoff …..
Government expenditure
Government spending as % of real GDP (Constant $ in year 2000)
Summers Heston World Penn
Tables 6.1
Inflation
Consumer Price Index
World Development Indicators
(2005)
Initial real GDP per capita
Real GDP per capita in 1960
M2 as a share of GDP
Money and quasi money comprise the sum of currency outside
banks, demand deposits other than those of the central government,
and the time, savings, and foreign currency deposits of resident
sectors other than the central government as a share of GDP.
World Development Indicators
(2005)
Real interest rate
Volatility in the lending interest rate (geef line uit IFS) adjusted for
inflation as measured by the GDP deflator.
International Financial Statistics IMF (2005)
World Development Indicators
(2005)
World Development Indicators
(2005)
own calculations
Dummy variable taking the value 1 if a country is a member of a
trade or economic union
Domestic credit
Terms of trade shocks
Relative standard deviation of terms of trade
Productivity shocks
Relative standard deviation of the Total Factor Production, which is
computed for each individual country using the Solow model
33
Inventory changes
Inventories are stocks of goods held by firms to meet temporary or
unexpected fluctuations in production or sales, and "work in
progress", measured as a share of real GDP
World Development Indicators
(2005)
Investment shock
Relative standard deviation of investment as a share of Real GDP
Latitude
Distance to the equator
Summers Heston World Penn
Tables 6.1
CIA fact book
Capital city distance
Nearest distance to Brussels, Tokio or Washington, measured from
the capital city
CIA fact book
Temperature
Average temperature
CIA fact book
World economic volatility
Volatility of world economy
own calculations
Household consumption
shock
Relative standard deviation of final household consumption as a
share of Real GDP
World Development Indicators
(2005)
34
Table 1. Literature survey:
Author
Dependent variable
Indepedent economic variables
Bleany and Fielding (1999)
sd real GDP growth rate
Exchange rate, terms of trade shock, agriculture, income
and region dummies
Bekaert et al. (2006)
High-low range and sd of the GDP growth, consumption growth, investment governreal GDP growth rate
ment consumption, secondary school enrolment, population growth life expectancy trade, inflation, black market
premium, private credit market-capital ratio, liberalization
indicators
sd real GDP growth rate
Trade, diversified exports, population, population density,
sector variables
Giovanni and Levchenko (2006)
Mobarak (2005)
Political variables
Countries
Time period
Method
Developing
1965-1989
Cross-country
mixed
1980-2000
Panel
mixed
1970-1999
Panel/Crosscountry
(OLS, IV)
mixed
1960-2000
Cross-country
mixed
1980-1999
Cross-country
1960-1999
Cross-country
OECD
1960-1990
Cross-country
mixed
1960-1999
panel
Democracy, political openness, political competitivenss, anti-governmental
demonstrations, Civil liberties, political constraints
Koskela and Viren (2003)
High-low range and sd of the Trade, sector fraxctionalization, services sector, diversireal GDP growth rate
fied exports, population, fuel export, education, initial
GDP per capita, black market premium, inflation Credit
to private sector, Gini coefficient of income distribution
investment, muslim majority, settler morality, independence.
sd real GDP growth rate
Government size, GDP per capita, GDP growth
Malik and Temple (2006)
sd real GDP growth rate
Governance indicators, political con- Developing
straint, executive constraint, competitivens of political partivipation, government type,
Karras and Song (1996)
sd real GDP growth rate
Kose et al. (2003)
sd real GDP growth rate
Cecchetti et al. (2005)
sd error term AR(1)
Private credit, trade, Central bank turnover rate, inflation
targeting, inflation volatility.
mixed
1980-2003
panel
Easterly et al (2000)
sd real GDP growth rate
Developing country dummy, OECD dummy, stock market value, long-term private debt, private and public bond
market, GDP per capita growth\, trade sd change log real
wage index, sd money supply growth, private capital
flows, sd private capital flow, credit to the private sector,
mixed
1960-1997
panel
various trade variables, various governance measures,
settler mortality, sd real exchange rate, sd inflation, sd
fiscal surplus, private capital flow, credit to the privat
sector, ethnic fractionalization, religious fractionalization,
war dummy, population
sd solow residual, sd money supply , openness, exchange
rate, price flexibility industrial structure, government
expenditure
Current account restriction, trade openness, capital account restrictions, financial openness, relative income
terms of trade volatility, Money supply, volatility of
money supply, inflation, fiscal policy volatility
35
Denizer, Iyigun and owen (2000)
Irvine and Schuh (2005)
Financial development, investment growth, income
growth, consumption growth, inflation, inflation shock,
openness of trade, government expenditure, government
expenditure shock, exchange rate shock, money supply,
private credit, bank liquidity
Inventory investment
Political regime
mixed
1956-1998
Cross-country
United
states
United
states
OECD
1959-2003
timeseries
1959-2003
timeseries
1960-2000
panel
Ahmed et al. (2004)
Variance real GDP growth
rate
sd real GDP growth rate
Buch et al. (2005)
sd band filtered real GDP
Capital controls, financial openness, interest rate, government expenditure, oil price shock, terms of trade
Barell and Gottschalk (2004)
sd GDP gap
inflation, inflation volatility, openness,financial wealth
G7
1970-1999
panel/timeseries
Kent at al. (2006)
sd real GDP growth rate
product market regulations, monetary policy regime,
labour disputes, openness, finanical liberalisation, inflation volatility, fiscal policy volatility, oil price volatility
Developed
1978-2003
panel
Bejan (2006)
sd real GDP growth rate
openness, total GDP, GDP per capita, population, human
capital, FDI inflow, investment, government expenditure,
export index, sector diversification index, terms of trade
shock, inflation, black market premium, liquid liabilities,
interest, credit to the private sector, foreign debt
mixed
1950-2000
Cross-country
Breen and Garcia-Penalosa (1999)
sd real GDP growth rate
mixed
1984-1995
Cross-country
Anbarci et al. (2005)
sd GDP
sd government expenditure, secondary school enrolment socio-political instability index
rate.
Democracy, executive openness,
investment, GDP per capita, GDP per capita growth,
government expenditure, inflation exchange rate, credit competitive participation
by banks, secondary education, population size, population growth rate, feritility rate, mortality rate.
mixed
1973-2000
Cross-country
Hakura (2007)
sd real GDP growth rate
Fiscal procyclicality, Financial sector development, trade
openennes, relative income, terms of trade shock, exchange rate flexibility, capital account restrictions.
mixed
1970-2003
Cross-country
(OLS/IV)
Hnatkovska and Loayza (2003)
sd output gap
mixed
1960-2000
Cross-country
Bastourre and Carrera (2005)
sd industrial production
secondary enrollment rate, credit to the private sector,
trade openness, index of institutional development, government consumption, fiscal policy procyclicality, inflation volatility, real exchange rate, systematic banking
crises, terms of trade shocks
GDP per capita, GDP growth, trade openness, terms of
trade shock, investment shock
mixed
1974-2000
Panel(FE,RE,GMM)
Bugamelli and Paterno (2006)
sd real GDP growth rate
mixed
1980-2003
Cross-country
Dutt and Mitra (2007)
sd real GDP growth rate
Trade openness, financial openness, terms of trade shock,
financial shock, credit to the private sector, money
supply, fiscal shocks, monetary shocks, migrants remittances
Fiscal policy shock, trade policy shock, inflation, terms of
trade shock, exchange rate overvaluation, government
expenditure, openness of trade,
mixed
1960-2000
Cross-country
Inventory investment
36
Tang et al. (2008)
sd real GDP growth rate
Total factor production growth, region dummies, initial
income, mortality rate
Tang et al. (2005)
sd real GDP growth rate
Easterly and Kraay (2000)
sd real GDP growth rate
mixed
1965-2000
Cross-country
Total factor production growth,population region dummies, time dummies,
mixed
1960-2000
panel
Commodity exporter, oil exporter, terms of trade shock
mixed
1960-1995
Cross-country
sd = standard deviation
37
Expropriate risk, executive constraint
Table 2. Indicators of political institutions and their sources
Type of regime24:
Factor
Regulations of Chief Executive recruitment
0.763
Competition of Chief Executive selection
0.882
Openness of Chief Executive
0.531
Decision rules
0.940
Competition of participation
0.990
Executive competition
0.793
Executive legitimacy
0.818
Type of regime
0.390
Parliamentary responsibility
0.523
Legislator selection
0.435
Political instability25:
Aggression
Protest
Regime
Government
-0.131
0.015
0.557
0.122
0.898
0.245
0.183
0.180
0.740
0.075
0.108
0.126
-0.085
0.110
0.029
0.084
-0.030
-0.070
0.169
0.628
0.159
0.451
0.179
0.830
0.067
0.872
0.066
0.000
0.164
0.211
0.461
0.302
-0.354
0.231
-0.063
-0.101
-0.234
0.291
-0.245
-0.053
0.488
0.041
0.420
0.749
0.526
0.778
0.144
0.457
0.104
0.520
0.226
0.332
-0.042
0.565
-0.119
-0.028
-0.123
0.073
0.358
-0.031
0.283
-0.073
0.665
0.396
Fiscal
Monetary
Trade
Policy
Policy
policy
Regime durability
Government fractionalisation
Assassinations
Strikes
Guerrilla
Government crises
Purges
Riots
Revolutions
Anti-government demonstrations
Coalitions
Number of coupes
Number of legislative elections
Number of executive elections
Constitutional changes
Cabinet changes
Policy uncertainty
26
24
Data from the Polity IV and the Banks’database on political institutions, see appendix for further source
information.
25
Data from the Polity IV and the Banks’database on political institutions, see appendix for further source
information.
26
The individual indicators are measured with the relative standard deviation of the error term of a AR(1)
process. Data from the International Financial Statistics, IMF (2006), Worldbank (2006) and Przeworski,
Alvarez, Cheibub and Limongi. Democracy and Development: Political Institutions and Material WellBeing in the World, 1950-1990. Cambridge: Cambridge University Press.
38
Government expenditure as % of GDP
0.516
0.265
0.304
0.43
Tax revenue as % of GDP
0.715
0.210
0.483
0.79
Government debt as % of GDP
0.800
0.156
0.292
0.75
Budget deficit as % of GDP
0.893
0.299
0.302
0.98
Growth of M2 as % of GDP
0.248
0.469
0.159
0.31
Inflation rate
0.298
0.753
0.251
0.72
Short term intrest rate
0.175
0.671
0.195
0.52
Real exchange rate
0.166
0.240
0.715
0.60
Trade as % of GDP
0.277
0.242
0.629
Current account as % of GDP
0.340
0.174
0.471
39
0.53
0.37
Table 3: Volatility through the world and time
Standard deviation
World
Europe
Central en North Asia
South-East Asia
Australia and Oceania
North Africa
South and Central Africa
Middle East
North America
Central America and the Caribbean
Latin America
High income countries
Middle income countries
Low income countries
Relative standard deviation
World
Europe
Central en North Asia
South-East Asia
Australia and Oceania
North Africa
South and Central Africa
Middle East
North America
Central America and the Caribbean
Latin America
High income countries
Middle income countries
Low income countries
Year 1980-89
Year 1990-99
Year 2000-05
**
-0.097
-0.374**
0.043
-0.167**
-0.474**
-0.035
-0.060
-0.463**
0.047
0.086
0.386**
-0.299**
0.074**
0.082
**
-0.179
-0.318**
0.054
-0.267**
-0.510**
-0.315**
-0.076*
-0.533**
-0.409**
-0.132**
-0.068
-0.467**
-0.045*
0.421**
**
-0.414
-0.827**
-0.205**
-0.183
-0.897**
-0.797**
-0.178**
-0.807**
-0.541**
-0.392**
0.073
-0.548**
-0.343*
0.101
Year 1980-89
Year 1990-99
Year 2000-05
**
0.044
-0.018
-0.069
0.260
-0.043
0.512**
-0.155**
-0.194
0.072
0.419**
0.502**
-0.179**
0.281**
-0.713**
-0.117
-0.458**
-0.325**
0.677**
-0.747**
-0.644*
-0.345**
0.371*
-0.067
0.324**
0.909**
-0.015
-0.092
0.279
0.160
-0.028
-0.131
-0.121
0.407*
0.347
0.080
-0.245
0.609**
0.719
0.942**
-0.042
0.348**
-0.009
Average Countries Observations
4.964
3.362
4.915
2.951
4.879
3.982
6.194
8.637
3.627
3.993
4.154
3.738
5.338
7.758
4931
990
578
284
126
99
1469
384
99
473
396
1650
3631
276
Average Countries Observations
5.873
3.768
2.860
2.769
5.027
4.129
5.705
7.752
3.026
8.356
8.349
4.719
6.161
7.185
*significant at a 10 percent level, **significant at a 5 percent level
40
170
39
24
9
4
3
46
13
3
16
12
54
116
9
170
39
24
9
4
3
46
13
3
16
12
54
116
9
4931
990
578
284
126
99
1469
384
99
473
396
1650
3631
276
Table 4: Regression I – Baseline model
Explanatory Variables
Economic variables
Economic growth volatility lagged
Initial GDP per capita
-
Recession dummy
+
Diversification index
-
Secondary enrolment rate
Government Expenditure as % of GDP
-/+
-
Productivity shock
Terms of trade shock
+
MG
(1)
PMG
(2)
DFE
(3)
0.615
[1.54]
-0.366
[3.58]
0.771
[2.17]
-0.636
[-2.16]
-0.316
[-3.54]
-1.525
[-1.88]
1.517
[2.38]
2.044
[2.97]
0.704
[2.12]
-0.505
[4.27]
0.904
[2.44]
-0.800
[-2.71]
-0.336
[-3.96]
-1.607
[-2.30]
2.113
[2.92]
2.101
[3.88]
0.474
[3.15]
-1.201
[4.83]
1.225
[2.80]
-0.214
[-3.72]
-0.214
[-3.51]
-1.724
[-1.87]
0.844
[1.51]
1.866
[5.73]
Number of countries
Number of observations
Hausman test p-value
Likelihood ratio test p-value
94
3459
0.230
0.210
t-values between brackets
41
94
3459
na
na
Table 5: Regression II – Democracy
Democracy
Explanatory Variables
Economic variables
Economic growth volatility lagged
Initial GDP per capita
-
Recession dummy
+
Diversification index
-
Secondary enrolment rate
Government Expenditure as % of GDP
-/+
-
Productivity shock
Terms of trade shock
+
PMG
(2)
PMG
(2)
PMG
(8)
PMG
(5)
PMG
(5)
PMG
(8)
0.679
[2.01]
-0.535
[4.10]
0.824
[2.23]
-0.890
[-2.50]
-0.371
[-3.81]
-1.573
[-2.29]
1.730
[2.91]
2.350
[3.58]
0.866
[2.00]
-0.399
[4.03]
0.762
[2.22]
-0.668
[-2.45]
-0.303
[-3.76]
-1.147
[-2.29]
2.008
[2.82]
1.570
[3.81]
0.706
[1.96]
-0.410
[4.26]
1.092
[2.42]
-0.783
[-2.70]
-0.288
[-3.71]
-1.528
[-2.18]
1.724
[2.68]
1.482
[3.81]
0.821
[1.53]
-0.405
[4.69]
1.015
[1.57]
-0.747
[-1.86]
-0.337
[-3.54]
-1.807
[-2.98]
1.768
[2.47]
2.080
[3.41]
0.990
[1.78]
-0.205
[2.82]
0.694
[2.83]
-0.776
[-2.07]
-0.313
[-3.28]
-1.175
[-1.93]
1.947
[2.03]
0.753
[3.14]
0.591
[2.17]
-0.402
[3.55]
0.632
[1.94]
-0.766
[-2.26]
-0.363
[-4.05]
-1.131
[-1.99]
0.691
[3.12]
0.447
[4.67]
3.504
[3.16]
4.742
[4.28]
2.686
[3.19]
3.530
[4.41]
Political variables
Democracy
-1.751
[3.85]
Fiscal policy uncertainty
+
Monetary policy uncertainty
+
Trade policy uncertainty
+
-1.519
[3.31]
2.424
[2.97]
2.715
[4.12]
1.615
[1.52]
Democracy x fiscal policy uncertainty
4.274
[2.94]
4.631
[4.14]
0.109
[3.84]
0.209
[3.01]
Democracy x monetary policy uncertainty
Political instability: aggression
+
Political instability: protest
+
Political instability: government instab.
+
Political instability: regime instab.
+
0.098
[1.01]
0.084
[1.12]
0.104
[2.75]
0.127
[2.98]
Political institution x fiscal policy uncertainty
0.085
[2.44]
0.073
[3.84]
0.075
Political institution x monetary policy uncertainty
42
0.094
[2.82]
0.060
[3.84]
0.072
[3.01]
[3.01]
Number of countries
Number of observations
Hausman test p-value
85
3128
0.21
78
2583
0.23
92
3201
0.11
85
3128
0.22
78
2583
0.19
78
2583
0.18
Likelihood ratio test
0.16
0.21
0.14
0.19
0.22
0.21
t-values between brackets
43
Table 6: Interaction effect
Democracy
τ = 0.25
Fiscal policy
τ = 0.75
Fiscal policy
Fiscal policy
τ = 0.25
τ = 0.50
τ = 0.75
0.886
1.617
2.398
0.122
0.192
0.293
-1.250
-0.457
-0.718
τ = 0.50
1.573
2.581
3.159
0.219
0.323
0.401
-0.453
-0.785
-0.943
τ = 0.75
2.434
3.269
4.000
0.276
0.400
0.500
-0.716
-0.977
-0.219
Monetary τ = 0.25
policy
τ = 0.50
τ = 0.25 τ = 0.50 τ = 0.75 τ = 0.25 τ = 0.50 τ = 0.75
Regime instability
τ = 0.25
Fiscal policy
τ = 0.25
Monetary
τ = 0.50
policy
τ = 0.75
τ = 0.50
τ = 0.75
Fiscal policy
Fiscal policy
τ = 0.25
τ = 0.50
τ = 0.75
τ = 0.25 τ = 0.50 τ = 0.75 τ = 0.25 τ = 0.50 τ = 0.75
-0.751
-0.279
-0.456
0.078
0.208
0.318
0.266
0.506
0.725
-0.326
-0.460
-0.592
0.183
0.291
0.393
0.543
0.715
0.910
-0.438
-0.624
-0.144
0.301
0.404
0.500
0.783
0.983
1.210
Government instability
τ = 0.25
Fiscal policy
τ = 0.25
Monetary
τ = 0.50
policy
τ = 0.75
τ = 0.50
τ = 0.75
Fiscal policy
Fiscal policy
τ = 0.25
τ = 0.50
τ = 0.75
τ = 0.25 τ = 0.50 τ = 0.75 τ = 0.25 τ = 0.50 τ = 0.75
-0.912
-0.355
-0.508
0.097
0.246
0.407
0.355
0.624
0.979
-0.377
-0.557
-0.685
0.276
0.392
0.543
0.631
0.934
1.236
-0.524
-0.704
-0.163
0.389
0.507
0.648
0.973
1.237
1.512
44
Table 7: Regression results V
MG
Coefficient t-value
-2.626
[4.52]
Democracy
PMG
Joint hausman
Coefficient
t-value
test
-2.844
[4.59]
0.02
Aggression
0.091
[1.08]
0.113
[1.26]
0.03
Protest
0.121
[1.16]
0.129
[1.22]
0.03
Regime instability
0.127
[2.82]
0.134
[2.96]
0.03
Government instability
0.159
[3.46]
0.207
[4.23]
0.03
Fiscal policy uncertainty
3.729
[3.71]
3.877
[4.06]
0.01
Monetary policy uncertainty
4.313
[5.16]
4.440
[5.30]
0.01
Trade policy uncertainty
1.892
[1.68]
2.242
[1.77]
0.01
Table 8: Regression results VI – Bootstrap
PMG
Democracy
-1.751
[4.09]
0.098
[1.15]
2.424
[1.04]
2.715
[2.77]
Aggression
Protest
Regime instability
Government instability
1.615
Fiscal policy uncertainty
[2.85]
2.424
[2.12]
Monetary policy uncertainty
2.715
Trade policy uncertainty
[4.90]
1.615
[1.29]
45
Table 9: Regression results VII
Democracy
Aggression
Protest
Regime instability
Government instability
Fiscal policy uncertainty
Monetary policy uncertainty
Trade policy uncertainty
Developing countries
PMG
Hausman
-2.755
0.21
[5.54]
0.102
0.31
[1.33]
0.100
0.34
[0.94]
0.192
0.33
[2.74]
0.123
0.32
[2.67]
2.018
0.27
[1.49]
2.109
0.23
[0.96]
1.311
0.25
[0.89]
Developed countries
PMG
Hausman
-0.685
0.13
[0.92]
0.039
0.14
[0.49]
0.042
0.14
[0.49]
0.082
0.14
[1.29]
0.066
0.14
[0.94]
4.289
0.14
[3.91]
4.799
0.13
[5.06]
2.814
0.12
[1.93]
Table 10: Regression results VIII
Policy IV
Coupes
Anti-governmental demonstrations
Government expenditure uncertainty
46
PMG
Hausman
-1.407
[3.51]
0.079
[2.82]
0.057
[1.08]
1.712
[2.45]
0.17
0.21
0.34
0.19