Master Thesis
MSc International Economics & Business
Effects of Political Institutions on
Economic Volatility
By Chunyiyuan Wang
Author: Chunyiyuan Wang
Student number: 2548348
Place and date: Groningen, 30
thJanuary, 2015
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stsupervisor: A. Samarina
Abstract
This thesis studies the effects of political institutions on economic volatility in developing countries, using data for 67 developing countries over the period 1985-2009. This study focuses on three dimensions of political institutions: democracy, institutional quality, and political instability. The results show that democracy and executive constraints reduce economic volatility, while corruption increases economic volatility. Political instability is associated with higher economic volatility, but only government stability has a significant effect. Additionally, I compare the effects of political institutions on economic volatility across different regions. In Asia, only executive constraints are significantly associated with economic volatility. Compared with Asia and Sub-Saharan Africa, democracy has a greater impact on reducing economic volatility in Latin America. Political instability and corruption have larger effects on economic volatility in Sub-Saharan Africa.
Table of Contents
1. Introduction ... 1
2. Literature Review ... 4
3. Methodology ... 6
4. Data ... 8
4.1 Description of the dataset ... 8
4.1 Differences in economic volatility and political institutions among regions .... 13
5. Estimation results... 15
5.1 Main results ... 15
5.2 Some differences in the results across regions ... 17
1. Introduction
The economic history of the world is by no means a plain sailing, but accompanied with volatility. From industrial depressions of 1870s and 1930s to the Latin American crisis in 1980s, to the debt crisis in African countries, 1997 Asian financial crisis and 2008 financial crisis, economic development replete with recession and depression. Almost every country’s economic development will experience volatility, only in varying degrees and effects.
Recently, there have been numerous publications (Ramey and Ramey, 1995; Flug et al. 1998; Eastly, 2000; Acemoglu et al., 2003; Loayza, 2007; Huang et al. 2012) on economic volatility. These studies find out that a volatile economic environment results in significantly higher welfare costs, undermines educational attainment, harms the income distribution, generates crisis and reduces growth in economic output. Due to the costs and harmful effects of economic volatility, there are thus ample reasons to study determinants of economic volatility.
The high economic volatility is caused by large external shocks, policy uncertainty, economic factors, financial factors and weak institutions. Many theoretical and empirical studies analyzing of economic volatility focus on the aggregate economic and financial determinants. For example, Easterly et al. (2000), Levine (1997, 1998), King and Levine (1993a, 1993b), and Denizer et al. (2002) demonstrate that economies with greater financial development generate less economic volatility. Easterly and Kraay (2000), Razin et al. (2002) and Aghion et al. (2000) argue that trade openness can lead to higher volatility especially in small economies.
Apart from financial and economic factors, political institutions can play an important role as the determinants of macroeconomic volatility. Acemoglu et al. (2003) argue that the major reason of high economic volatility and severe crisis in developing countries is weak institutions.
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First, political institutions in a country with high level of democracy prefer certainty, take more effort in stabilizing economic growth, and better handle economic shocks (Edwards and Thames, 2009; Chatterjee and Shukayev, 2010). Second, the quality of political institution may also influence economic volatility. The effective constraints on politicians and less corruption can decrease policy uncertainty which increases economic volatility (Henisz, 2000; Nooruddin, 2003; Acemoglu et al. 2003). Third, political institutions can reduce economic volatility through improving government stability and reducing internal conflicts (Asteriou and Price, 2001; Klomp and de Haan, 2009).
Much of the literature concentrates on economic volatility among developed countries (e.g., McConnell and Perez-Quiros, 2000; Simon and Blanchard, 2001; Stock and Watson, 2002). But actually there is a lot of evidence showing that developing countries are more volatile. Cable News Network reports a list of the top 10 economically volatile countries in 2013, and all of them are developing countries.1 Developing countries may experience bigger external and domestic shocks. Economic volatility has more serious effects on economy of developing countries, since they have weak institutions that are unable to properly absorb shocks.
Not only are there significant differences in economic volatility across levels of economic development, there are also differences among developing regions. To my knowledge, there are very few theoretical foundations for the research of economic volatility across regions. According to Easterly et al. (2000), Asia has achieved its growth without high economic volatility. Hausman and Gavin (1996) find that the volatility of the economic growth rate in Latin America is more than twice that experienced by the developed countries, while Africa experiences even more economic volatility than does Latin America.
From above discussion, I can point to several limitations of previous studies. First, most of these papers study only the financial and economic determinants of economic volatility. Second, almost all of these papers analyze the relationship between economic volatility and one particular aspect of political institutions. Third, a large literature focuses on economic volatility in developed countries. Seldom do the
existing studies compare differences in economic volatility between developing regions.
This thesis contributes to the literature by analyzing the effects of political institutions on economic volatility in developing countries. I conduct an extensive analysis by focusing on three dimensions of political institutions, namely: democracy, institutional quality and political instability. Moreover, this thesis tries to explain the differences in economic volatility across developing regions.
Political institutions not only have an effect on economic performance, but also affect economic volatility. Pereira and Teles (2011) argue that the right choice of the type of political regime can help develop mechanisms to reduce political and economic players’ opportunistic behaviors. The decreasing risks of opportunistic behaviors contribute to economic stability by increasing effectiveness of political institutions and decreasing policy uncertainty. So the first dimension of political institution I analyze is democracy.
According to North (1991), institutions are the rules of the game which are “the humanly devised constraints that structure economic, political and social interactions”. Politicians and elites play key roles in political institutions and effective constraints on them may determine institutional quality to some extent. Therefore, I examine two variables - executive constraints and corruption - which measure institutional quality. The idea that political instability and economic growth correlate negatively is widely accepted. According to Aisen and Veiga (2011), political instability is viewed as a “malaise” since it has a negative effect on economic outcome and stability. Thus, the third dimension of political institutions I study is political instability.
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government stability significantly affect economic volatility, while corruption and internal conflict are insignificant. Political instability and corruption have higher impacts on economic volatility in Sub-Saharan Africa.
The rest of the paper proceeds as follows. Section 2 gives an overview of the literature and formulates hypotheses. Section 3 describes the methodology. In section 4, I introduce and explore the data. Section 5 presents the empirical findings and compares the results between different regions. Section 6 concludes the paper.
2. Literature review
The existing papers study the relationship between political institutions and economic volatility from different dimensions. This part I discuss the literature which examines the effect of democracy, institutional quality, political instability on economic volatility.
Some papers provide empirical evidence that democratic political institutions generate less volatile growth. The paper written by Rodrik (1997) shows democratic countries are less volatile than nondemocratic countries. This finding is supported by a number of studies. Quinn and Woolley (2001), Mobarak (2005), Klomp and de Haan (2009) and Cavallo and Cavallo (2010) report a strong negative correlation between democracy and economic volatility, emphasizing that democratic institutions can mitigate the negative effects of financial crisis and decrease output volatility. Moreover, the higher level of democracy can lead to higher growth rates as a result of improved political and increasing economic stability (Bruda and Wyplosz, 2009). Democratic institutions may reduce economic volatility in several ways. First, political leaders in democratic countries are risk adverse. In democratic countries, politicians are required to obtain popular support. Since most people prefer a stable economic environment, politicians in democratic institutions always avoid policies with high risk to get the support of citizens (Edwards and Thames, 2009).
“veto players” than authoritarian countries (Henisz, 2000). The decentralization of political power inherent to democratic institutions reduces the uncertainty in policy making thereby reducing economic volatility (Edwards and Thames, 2009). Thus, the first hypothesis to be tested is the following:
HYPOTHESIS I: Democratic countries have lower level of economic volatility.
This paper is closely related to empirical studies that examine the link between institutional quality and economic volatility. It has been shown that the ability of governments to handle economic crisis depends on the quality of institutions (Rodrik, 2000). Cariolle (2014) says that the occurrence and consequences of 2008 worldwide financial crisis can be seen as an illustration of the complex link between institutions’ quality and economic volatility'. He suggests poor transparency and lack of accountability mechanisms in private and public fund management are important reasons leading to this crisis. As important aspects of institution quality, executive constraints’ and corruption’s effects on economic volatility have been discussed in some papers.
Acemoglu et al. (2003) think that the countries with effective constraints on elites and politicians will experience less infighting between various political groups. Less infighting between different political groups decreases political and economic turbulence, and hence, less economic volatility. In addition, Nooruddin (2003) suggests that effective constraints on politicians and elites, for example independence of the executive from the legislature, minority parliamentary government and coalition government, can significantly reduce the economic growth volatility. Therefore, the second hypothesis is the following:
HYPOTHESIS II: Countries which have effective constraints on politicians have lower level of economic volatility.
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the equity market volatility. They find low corruption level can reduce equity market volatility by decreasing uncertainty and increasing investors’ confidence. Bilgin et al. (2013) and Zhang (2012) both find evidence of the negative effect of corruption on financial market stability. The third hypothesis is the following:
HYPOTHESIS III: Countries with serious corruption have higher level of economic volatility.
Another dimension of political institutions that some research analyzes is the instability of the political regime. Rodrik (1999) shows external conflicts make economic growth more volatile. Asteriou and Price (2001) conclude that there is a positive relationship between political instability, measured by various political violence indicators, and economic volatility. Klomp and de Haan (2009) use a four-factor model which includes “aggression”, “protest”, “regime instability” and “government instability” to measure the political instability. They find all four factors of political instability are positively related to economic volatility.
There are several reasons why political instability may affect economic volatility. Violent changes may increase volatility because they damage or destroy physical capital generating harmful effect and uncertainty on economically productive activities (Jong-A-Pin, 2009). Countries with political instability are often sensitive to political shocks, leading to greater economic volatility (Arisen and Veiga, 2006). This leads to the last hypothesis:
HYPOTHESIS IV: Political instability increases economic volatility in developing countries.
3. Methodology
This chapter presents the methodology used in this study. First, I describe the model used in this paper. Then, I discuss the econometric problems of this model and the application of system-GMM estimation.
each country, while time fixed effects capture all variation in the data specific to some period. The model specification is the following:
Vit=α+β1Vit-1 +β2PIit+β3Xit+μi+θt+εit (1)
Where Vit is the dependent variable measuring economic volatility in country i in year
t, Vit-1 is the lagged economic volatility, PIit are the various dimensions of the political
institutions, Xit is a vector of control variables, α is a constant term, μi denotes the
country fixed effect of country i, θt is time fixed effect and the final term εit is an error
term. The error term has a mean zero and variance σ2
.
Several econometric problems may arise from estimating the model (1). First, the inclusion of the lagged dependent variable Vit-1 may lead to autocorrelation. Second,
country specific effects and omitted characteristics in error term, such as geography and culture, may be correlated with explanatory variables. If country specific effects correlate with political institutions and economic volatility, the model would be estimated inconsistently. Third, endogeneity may lead to reverse causality, for example, economic volatility itself may affect the political institutions. To solve these problems, I use the GMM estimation developed by Arellano and Bond (1991), Arelano and Bover (1995), and Blundell and Bond (1998).
In the GMM estimation, it solves the endogenetiy problem by using the lagged values of endogenous variables as instruments. This solution makes the endogenous variable pre-determined and, therefore, not correlated with the error term. There are two kinds of GMM estimations -“difference GMM” and “system GMM”. The difference-GMM model uses the first-differences to transform the model (1) into:
∆Vit=β1∆Vit-1 +β2∆PIit+β3∆Xit+∆θt+∆εit (2)
This transformation has removed the fixed country-specific effect, but the lagged dependent variable is still endogenous. Vit-1 in ∆Vit-1=Vit-1-Vit-2 and predetermined
variables in PIit may correlate with the εit-1 in ∆εit =εit -εit-1. Another disadvantage of
difference-GMM is that it broadens gaps in unbalanced panels (Roodman, 2006). For instance, if some Vit are missing, then both ∆Vit and ∆Vit+1 are missing in the
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The system-GMM model combines the level equation (1) with the first-difference equation (2). Compare with difference-GMM, system GMM uses more instruments and assumes that first-differenced instruments are uncorrelated with the error term. Therefore, a number of studies (Roodman, 2006; Chen, 2010) argue that system-GMM estimation usually increases efficiency. Due to these advantages, I use the system-GMM estimation in this paper2.
In the empirical analysis, I apply the Hansen test to check the validity of instruments and the Arellano-Bond test to check the second-order autocorrelation of the residuals. The GMM estimation results are also affected by the number of lags instrumenting the endogenous regressor. In this model, the results are similar by using the different number of lags as instruments.
4. Data
4.1 Description of the dataset
My dataset includes annual data of economic, political, and institutional variables from 1985 to 2009 for 67 developing countries. I use averages of the underlying annual data in consecutive, non-overlapping, five-year periods. The data is collected from the following sources: World Bank, International Country Risk Guide database, Global Financial Development database and Polity IV. Appendix A shows the list of countries included in the sample.
Economic volatility is most commonly measured by the standard deviation of the GDP growth rate (see Ramey and Ramey, 1995; Serven, 1997; Acemoglu et al., 2003; Raddatz, 2007; van der Ploeg and Poelheeke, 2009). However, this measure has some drawbacks. First, growth and its volatility may be determined together because both of them are in income process. Mobarak (2005) argue that the study should analyze growth and economic volatility together, while the standard deviation only considers
2 I use the xtabond2 command in Stata to do the regression; I include V
it-1 in
the economic volatility. He also points out that the standard deviation can be easily affected by outliers. Second, Klomp and de Haan (2009) suggest that the standard deviation treats growth outcomes equally and does not consider the distinctions of economic performance between different countries.
Due to these disadvantages of the standard deviation discussed above, Klomp and de Haan (2009) use the relative standard deviation of the GDP growth rate to measure the economic volatility. The relative standard deviation is defined as the standard deviation divided by the absolute mean growth rate. Compared with standard deviation, relative standard deviation does not exhibit the first-order autocorrelation. In line with Klomp and de Haan (2009), I measure economic volatility by the relative standard deviation of the GDP growth rate defined as the standard deviation divided by the absolute mean growth rate, with a five-year rolling window. The data of annual growth rate of GDP per capita in constant 2005 U.S. dollars is collected from World Bank.
Vit-1 is the lagged economic volatility. Edwards et al. (2006) and Ahmed et al. (2005)
find that past volatility can explain current levels of volatility because volatility also has a business-cycle dynamic similarly to economic growth.
PIit in equation (1) identifies variables of three dimensions of political institution that
may influence economic volatility: 1) democracy, 2) institutional quality, 3) political instability. Data on democracy, corruption, government stability and internal conflict is collected from International Country Risk Guide database, provided by the Political Risk Service group. Following the ICRG methodology, the democracy is defined as how responsive government is to its people, with a score from 0 to 6. In general, the highest number of points represents democracy, while the lowest number of points represents autocracy.
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score means the more effective constitutional and social limits on politicians’ and elites’ power.
To measure political instability, I use two factors – government stability and internal conflict. The score of government stability is the sum of three subcomponents: government unity, legislative strength and popular support. The maximum score of 12 points means very low risk and a minimum score of 0 point means very high risk. Internal conflict including civil war/coup threat, terrorism/political violence, civil disorder is an assessment of political violence in the country. The scale of this factor is the same as the government stability.
Figure 1 shows the bivariate relationship between democracy, executive constraints, corruption, government stability, internal conflict and economic volatility. Countries with high level democracy, effective constraints on politicians and stable government have lower economic volatility, while countries with high corruption are more volatile. Surprisingly, less internal conflict is associated with higher economic volatility.
Figure 1
The vector X contains a set of control variables suggested in previous studies and I summarized them into three groups: economic-initial GDP per capita and trade openness, finance-financial depth, economic policy-government expenditure and CPI inflation rate.
Referring to the relationship between trade openness and economic volatility, economists have different opinions. Simon and Blanchard (2001) do not find any relationship between trade openness and economic volatility. Haddad et al. (2012) suggest trade openness can decrease economic volatility if countries are well diversified. Giovanni and Levchenko (2008) find that higher trade openness is associated with higher volatility and this situation is more serious in developing countries than in developed countries. I expect trade openness increases economic volatility, because trade sectors in developing countries are less diversified. The import plus export as a share of GDP is used to measure trade openness.
Acemoglu et al. (2003) find lower economic volatility in countries with higher initial GDP per capita. They argue that the inclusion of initial GDP per capita take the fact that poor countries are affected more by economic volatility into account. Another reason of including initial GDP per capita is to control for economic size. Dabla-Norris and Srivisal (2013) argue that smaller countries are more likely to focus on particular industries and are less diversified, leading to higher economic volatility. Therefore, countries with higher initial GDP per capita are expected to experience less economic volatility. I measure initial GDP per capita by including log of real GDP per capita in constant 2005 USD, at the beginning of each 5-year periods.
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al. (2011) find deeper financial development can increase economic volatility by reducing incidence and cost of crisis. Thus, I expect greater financial depth is associated with lower economic volatility. The financial depth is measured by the aggregate private credit provided by deposit money banks and other financial institutions as a share of GDP.
Appropriate government policies may have a stabilizing effect on economy. Deburn et al. (2008) argue that the stable monetary policy decreases economic volatility in OECD countries. Chatterjee and Shukayev (2010) argue that institutional factors affect volatility through a variety of fiscal policies. Acemoglu et al. (2003) have compared many different measures of economic policies and find that government size and average inflation rate work best. Therefore, I add general government final consumption expenditure as a share of GDP and CPI inflation rate as control variables to measure government policies.
The empirical evidence of the impact government consumption on economic volatility is mixed. Some scholars (Evrensel, 2010; Fatas and Mihov, 2013) argue that increasing government spending reduces economic volatility. Dabla-Norris and Srivisal (2013) suggest that large government consumption can be regarded as an indication that government uses the tax collected from people in a way reducing economic volatility. Acemoglu et al. (2003) suggest that reducing the government size can help developing countries stabilize economy. According to Bekaert et al. (2004), large government consumption in developing countries indicates more waste, because developing countries always have profligate governments. Therefore, countries with larger government sectors are expected to experience more economic volatility. Inflation may lead to more economic volatility. Acemoglu et al. (2003) suggest that high inflation increases uncertainty, discourages investment and may result in crisis. A numerous studies have confirmed the conclusion that high inflation increases economic volatility (Bekaert et al., 2004; Mobarak, 2005; Klomp and de Haan, 2009; Carboni and Ellison, 2009; Evrensel, 2010). Definition and sources for all variables are given in Appendix B.
economic volatility and democracy, executive constraints and government stability. Corruption and internal conflict are associated with higher economic volatility. Table 1
Descriptive statistics, 1985-2009 (5-year periods)
Variable Obs Mean Std.Dev. Min Max
Economic volatility 335 3.473 2.664 0.255 18.102 Democracy 335 3.508 1.379 0.008 6.750 Executive constraints 335 4.579 1.839 0.800 7.000 Corruption 335 2.879 1.319 0.000 6.000 Government stability 335 7.397 1.920 2.517 11.083 Internal conflict 335 8.237 2.147 0.733 12.000 Trade openness 335 70.046 39.417 12.000 319.000
Initial GDP per capita 335 8.237 2.147 0.733 12.000
Financial depth 335 28.891 25.489 1.435 145.292
Government expenditure 335 14.541 5.762 4.080 48.062
Inflation rate 335 42.969 196.168 -3.016 2414.346
Table 2
Correlation matrix
Note: This table reports pairwise correlation coefficients.
4.2 Differences in economic volatility and political institutions among regions
Developing countries have experienced greater economic volatility than developed countries. Over 1961-2000, economic volatility in developed countries was always lower than in developing countries. Figure 2 shows the economic volatility of middle-income and low-income developing countries fell in 1990s, but it still was much higher than that for developed countries.
Figure 2
Economic volatiility, medians by income group
1 2 3 4 5 6 7 8 9 10 11 Economic volatility 1 1.00 Democracy 2 -0.160 1.00 Executive constraints 3 -0.170 0.57 1.00 Corruption 4 0.03 0.24 0.2 1.00 Government stability 5 -0.26 0.13 0.02 -0.08 1.00 Internal conflict 6 0.14 0.31 0.21 0.2 0.50 1.00 Trade openness 7 0.002 0.16 0.1 -0.04 0.18 0.34 1.00
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Source: World Bank World Development Indicators.
There are not only significant differences in economic volatility across levels of economic development, but also significant differences across regions. Figure 3 shows the economic volatility between 1985 and 2009 in different developing regions. The economic volatility in Sub-Saharan is higher than in full sample and other developing regions. Asia experienced highest economic volatility during the 1995-1999 period. One important reason of the great economic volatility in this period is the 1997 Asian financial crisis. In Latin America, the economic volatility is lower than that in full sample.
Figure 3
Economic volatility across regions
Source: Author’s calculations based on World Bank statistics.
Sub-Saharan Africa. Inflation rate in Latin America is almost five times larger than in other regions. Among these regions, Sub-Saharan has the highest corruption level and lowest level of government stability.
Table 3
Some differences among developing regions
Asia Latin America Sub-Saharan Africa Others Democracy 3.671 3.938 3.045 3.495 Executive constraints 5.040 5.755 3.495 4.085 Corruption 2.612 2.612 3.128 2.955 Government stability 7.417 7.000 6.617 7.963 Internal conflict 7.913 8.271 7.986 9.255 Trade openness 76.458 75.267 61.299 74.837 Financial depth 49.645 28.703 16.466 36.231 Government expenditure 12.073 13.648 14.304 17.842 Inflation rate 13.377 103.802 23.212 25.61
Note: This table reports the average value of explanatory variables of different regions during the
1985-2009 period. 5. Estimation results 5.1 Main results
One goal of this section is to emphasize the importance of considering an appropriate procedure to estimate a model. In order to see the advantage of using system GMM method to estimate a dynamic panel data model, I compare the results with fixed effect estimation and system GMM estimation. Table 3 shows the results of fixed effect estimation without time effect, fixed effect estimation, system GMM estimation without time effect and system GMM estimation, respectively.
Based on the results shown in Column 3 and Column 5 of Table 3, the coefficients associated with the lag of economic volatility, initial GDP per capita and inflation rate are not significant under fixed effect estimates, while they are significant under system GMM approach. For the coefficients of democracy and government stability, they are significant at 5% level under fixed effect estimation, while they are significant at 1% level under system GMM approach. Therefore, using an inappropriate method might result in misleading conclusions.
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variation or special events which may affect economic volatility. After the including of time fixed effect, the coefficients associated with the lag of economic volatility, corruption, inflation rate become significant. The coefficients of democracy, initial GDP per capita and government expenditure become more significant under the system GMM estimation with time fixed effect.
Table 3
Economic volatility and Political institutions
Fixed effect (without time effect)
Fixed effect System GMM (without time effect)
System GMM
Lag of economic volatility -0.127(0.398) 0.063(0.058) 0.026(0.091) 0.107(0.045)** Democracy -0.184(0.104)* -0.439(0.185)** -0.380(0.191)* -0.532(0.165)*** Executive constraints -0.569(0.167)** -0.779(0.144)*** -0.444(0.128)*** -0.535(0.137)*** Corruption 0.486(0.222)** 0.632(0.258)** 0.340(0.319) 0.739 (0.364)** Government stability -0.162(0.104) -0.415(0.169)** -0.364(0.086)*** -0.659 (0.149)*** Internal conflict -0.077(0.124) -0.044(0.095) 0.049(0.102) 0.028 (0.103) Trade openness 0.003(0.011) 0.001(0.003) 0.002(0.005) 0.003 (0.008)
Initial GDP per capita 0.282(0.364) 0.149(0.107) -0.351(0.182)* -0.339 (0.154)**
Financial depth 0.026(0.017) 0.004(0.005) 0.002(0.008) -0.002(0.013)
Government expenditure -0.006(0.032) 0.124(0.046)*** 0.066(0.038)* 0.114 (0.034)*** Inflation rate 0.0008(0.001) 0.001(0.0009) 0.0012(0.0008) 0.0013(0.0007)* Hansen test p-value
0.285 0.383
AR(2) test p-value 0.294 0.884
Notes: The table reports coefficients estimate with robust standard errors in parentheses. ***, **, and *
represent significance on 1%, 5%, and 10% levels, respectively. The Hansen test presents the Hansen over-identification statistics. AB (2) is the Arellano-Bond test to check the second-order autocorrelation of the residuals.
To keep the validity of GMM estimators, the instruments have to be exogenous and the model should be adequately identified. Therefore, the Hansen test and Arellano-Bond test are applied to check the validity of instruments and the autocorrelation of the residuals. The statistics of these two tests are presented in the Table 3 which show the validity of instruments cannot be rejected.
will be easier for politicians and elites to get greater income through bribe. Corruption may lead to redistribution and inequality which will increase economic volatility. Next, I discuss the effect of political instability represented by government stability and internal conflict. The results show that government stability is negatively related to economic volatility and internal conflict is positively related to economic volatility. But, internal conflict does not seem to be statistically significantly associated with economic volatility. This result is partially different from the conclusion of Klomp and de Haan (2009).
In terms of control variables, trade openness and financial depth do not have significant effects on economic volatility. Initial GDP per capita, government expenditure and inflation rate appear as significant variables that affect economic volatility. Initial GDP per capita is negatively associated with economic volatility, while government expenditure is positively related to economic volatility. The effect of inflation rate is positive and small.
5.2 Some differences in the results across regions
This part I compare the results across different developing regions. Table 4 shows the regressions results for the sample of Asia, Sub-Saharan Africa, Latin America and other countries.
Table 4
Economic volatility and political institutions across regions
Asia Latin America Sub-Saharan Africa
Others
Lag of economic volatility 0.304(0.093)** 0.352(0.101)*** 0.223(0.122)* 0.309(0.115)**
Democracy -0.336(0.409) -0.480(0.256)* -0.585(0.397) 0.108(0.250) Executive constraints -0.491(0.144)*** -0.399(0.087)*** -0.247(0.261) -1.702(0.827)** Corruption 0.818(1.082) 0.306(0.451) 0.501(0.267)* 0.773(1.188) Government stability -0.472(0.399) -0.566(0.185)*** -0.758(0.308)** -0.256(1.262) Internal conflict -0.172(0.435) 0.038(0.301) 0.431(0.241)* -0.805(0.505) Trade openness 0.029(0.435) 0.057(0.036) 0.026(0.013)** 0.013(0.027)
Initial GDP per capita -2.787(2.736) -1.921(0.817)** -0.917(0.498)* 2.294(1.582)
Financial depth 0.045(0.015)** -0.096(0.041)** -0.011(0.015) -0.082(0.046)* Government expenditure -0.571(0.296)* -0.106(0.199) -0.012(0.063) 0.295(0.137)** Inflation rate 0.001(0.0004)** 0.077(0.033)** 0.004(0.001)*** 0.005(0.009)
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A-B AR(2) test p-value 0.216 0.557 0.259 0.840
Notes: The table reports coefficients estimate with robust standard errors in parentheses. ***, **, and *
represent significance on 1%, 5%, and 10% levels, respectively. Time fixed effects are included in the estimations but not reported. The Hansen test presents the Hansen over-identification statistics. AB (2) is the Arellano-Bond test to check the second-order autocorrelation of the residuals.
In Asia, executive constraints are the only statistically significant political institutions variable that explains economic volatility. The results indicate that credible executive constraints decrease economic volatility.
Compared with other regions, the coefficient of financial depth in Asia shows an opposite sign. The negative sign of financial depth means that greater financial depth will increase economic volatility. When the financial system becomes larger, it generates more risk and leverage which reduce the economic stability. This also confirms the conclusion of Easterly et al. (2000) that Asia develops financial depth at the expense of high volatility in financial sector.
The results indicate the impact of democracy is significantly stronger in Latin America than in Asia and Sub-Saharan Africa. In Latin America, executive constraints and government stability are significantly associated with economic volatility. The increase of executive constraints and government stability can effectively reduce economic volatility. The negative effect of inflation on economic stability is more serious in Latin America than in other regions. In Table 4, the coefficient of inflation rate of Latin America is much bigger than that of other regions.
Table 4 shows corruption leads to greater economic volatility in Sub-Saharan Africa, while it does not have a significant effect in Asia and Latin America. According to Corruption Perceptions Index in 2014, an average score of CPI in Sub-Saharan Africa is lowest, which means Sub-Saharan Africa has the highest level of corruption compared with other regions. The finding suggests political instability has higher impact on economic volatility in Sub-Saharan Africa.
In this part I report evidence from robustness tests to show that the results presented in the Table 3 offers solid evidence that political institutions play an indispensable role in explaining economic volatility.
I re-estimate the regressions by using different time horizons. When longer time periods are considered (7 years), I still find negative and significant relation between democracy, executive constraints, government stability and economic volatility. Corruption and internal conflict do not have significant effects on economic volatility. Estimating the model for a shorter 3 year period, the coefficients of democracy, corruption and government stability are significant and show the expected sign. Executive constraints and internal conflict are insignificant.
Table 5
Economic volatility and political institutions over different time horizons
Three-Year Seven-Year
Lag of economic volatility 0.144(0.058)** 0.447(0.104)***
Democracy -0.366(0.168)** -0.715(0.252)*** Executive constraints -0.204(0.059)*** -0.172(0.147) Corruption 0.155(0.120) 1.019(0.547)* Government stability -0.167(0.093)* -0.805(0.350)** Internal conflict 0.078(0.066) -0.506(0.293) Trade openness 0.039(0.025) 1.359(1.930)
Initial GDP per capita -0.040(0.001)*** -0.403(0.341)
Financial depth -0.008(0.006) -0.026(0.028)
Government expenditure 0.069(0.024)*** -0.069(0.089)
Inflation rate 0.0003(0.0003) 0.002(0.003)
Hansen test 0.660 0.890
A-B AR(2) test p-value 0.519 0.725
Note: The table reports coefficients estimate with robust standard errors in parentheses. ***, **, and *
represent significance on 1%, 5%, and 10% levels, respectively. Time fixed effects are included in the estimations but not reported. The Hansen test presents the Hansen over-identification statistics. AB (2) is the Arellano-Bond test to check the second-order autocorrelation of the residuals.
6. Conclusion
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constraints on politicians and stable government can attenuate economic volatility. Furthermore, it turns out that corruption and internal conflict increase economic volatility, but the effect of internal conflict is not significant. All the hypotheses are confirmed.
I recognize there are significant differences in economic volatility across developing regions. Asia experienced highest economic volatility during the 1995-1999 period. In 2000s, economic volatility seems to increase in Asia. Surpringly, Latin America has lower economic volatility than that of all developing countries in the sample. The economic volatility in Sub-Saharan is higher than in full sample and other developing regions.
Then I compare the impact of political institutions on economic volatility between different regions. In Asia, only executive constraints have significant effects on economic volatility. In Latin America, democracy, executive constraints and government stability can reduce economic volatility significantly. Compared with other regions, the increase of corruption will generate more economic volatility in Sub-Saharan Africa. Moreover, political instability has a higher impact on economic volatility in Sub-Saharan Africa.
Although this paper finds the relationship between political institutions and economic volatility, there are still some limitations. This study not only employs many political institutions variables, but also takes into account various control variables derived from previous studies. Many countries miss a lot of data of these variables in some years. Therefore, the sample excludes these countries and comprises 67 developing countries. This does not matter the regression of full sample. When I examine the effect of political institutions on economic volatility across regions, the results of some regions are limited. It will be difficult to find significant relationships from the data if the sample size is too small. In Asia, I only use the data from 10 countries to analyze the relationship between political institutions and economic volatility. The results indicate that most political institution indicators do not have significant effects on economic volatility.
between political institutions and economic volatility in different regions, this relationship does not seem to be significant in some regions due to the small sample size. Therefore, further empirical research can study the economic volatility in different regions by including more countries and controlling some regional factors (e.g., regional policy, economic diversification).
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Appendix A
List of countries included in the sample
Asia Latin America Sub-Saharan Africa Others
Bangladesh Argentina Suriname Bostwana Senegal Bahrain
China Bolivia Trinidad&Tobago Burkina Faso South Africa Bulgaria
India Brazil Uruguay Cameroon Sudan Egypt
Indonesia Colombia Congo Tanzania Hungary
Malaysia Costa Rica Ethiopia Togo Jordan
Mongolia Dominican Republic Gabon Uganda Kuwait
Pakistan Ecuador Gambia,The Zambia Morocco
Philippines Guatemala Ghana Zimbabwe Papua New Guinea
Sri Landa Guyana Guinea-Bissau Poland
Thailand Honduras Kenya Romania
Jamaica Madagascar Saudi Arabia
Mexico Malawi Syria
Panama Mali Tunisia
Paraguay Niger Turkey
Peru Nigeria Uruguay
Appendix B
Variables definitions and sources
Variable Description of variable Data sources
Economic volatility 5-year rolling relative standard deviation of annual GDP growth rates
World Bank
Democracy A measure of how responsive government is to its people, with a value from 0 (autocracy) to 6 (democracy)
ICRG database
Executive constraints A seven-point scale, from 1 to 7, with a higher score meaning more constraints
Polity IV dataset
Corruption Institutional quality indicator, values from 0 to 6 ICRG dataset Government stability Government’s ability to carry out its declared
program, and its ability to stay in office, values from 0 (very low risk) to 12 (very high risk)
ICRG dataset
Internal conflict Political violence in the country and its actual or potential impact on governance, values from 0 (very low risk) to 12 (very high risk)
ICRG database
Trade openness The total value of import plus export as a share of GDP
World Bank Initial GDP per capita Log of real GDP per capita in constant 2005
USD, at the beginning of each 5-year periods
World Bank Financial depth Private credit (of banks and other financial
institutions)/GDP
Development database Government expenditure Government consumption as a share of GDP World Bank
