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The Determinants of Political Instability

A cross sectional study of 101 countries.

Submitted to

Dr. Jacqueline Khorassani

ECON 421: Empirical Research

Spring 2010

By

TianQi Xu

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Abstract

This study focuses on the determinants of political instability by using State Fragility Index as the measure of political instability. The study examines the effect of economic variables, social demographic variables and political variables on political instability across 101 countries, using OLS estimation method of estimation. Findings of this study suggest that the population growth rate increases the level of political instability significantly. On the other hand, the effect of the percentage of population living in urban areas, the number of internet users per thousand populations, and the degree of democracy on a nation’s political instability is negative and significant. Moreover, I find that in mid 2000s, African and Latin American nations were more stable than others. This study finds no significant correlation between the level of economic health and development of a nation, or the level of education of its residents, and its political stability.

I) Introduction

Throughout the history of human political development, nations have experienced

various degrees of political instability. Many times, the degree of political instability is so severe

that it eventually becomes a movement that puts an end to an era and starts a new page in history.

As we have seen in Africa, the most unstable continent in the current world, political instability

has eventually led to civil war, ethnic war and even genocide. Even the most powerful state in

the modern world, the United States, is not immune from a certain degree of political instability.

Currently, it seems that the U.S. is experiencing a mild degree of social unrest as the confidence

and trust towards government is not unanimous. In recent years we have also witnessed

authoritarian regimes such as the ones in China and Iran face political riots resulting in the loss

of lives of many innocent individuals. Thus it is important to find the causes of political

instability in order to prevent them from happening in the future.

This study looks at the effect of thirteen different economic, social demographic and

political variables on the degree of political instability. The remainder of this paper is organized

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as follows. In Part II, I review and compare various definitions of political instability and choose

a working definition for this study. A brief review of the literature on political instability is

included in Part III. The empirical model is outlined in Part IV, followed by a brief description of

the observations included in the sample. The results of the multicollinearity and the

heteroskedasticty tests are discussed in Sections V and VI. The discussion of the estimation

results is included in Part VII.

Concluding remarks are summarized in Part IX.

II) Definition and Measurement of Political Instability

In order to evaluate the effect of socio-economic factors on political instability (PI), the

definition of political instability must be clarified. There are numerous definitions of political

instability, each measured differently. Generally, these definitions can be divided into two

categories, one with a focus on government changes, and the other with a focus on social unrest.

In the first category of definitions of political instability, there are some variations. For

example, Lipset (1960) defines political stability as the persistence or continuity of a certain type

of political system. According to Lipset’s definition, a state that has been governed by the same

regime for 25 years or more is considered to be stable. Sander’s definition of political instability

is similar to Lipset’s but he argues that political instability is a relative term. That is, a given

state’s political instability can be only measured in comparison with other states or in

comparison with itself over time (Sanders 1981). Miljkovic (2008) adapts a broader version of

Lipset’s definition. In addition to the changes in political system, Miljkovic also regards a

change in the government itself as a sign of political instability.

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The government-focused definition of political instability has an advantage in that it

results in the collection of a consistent set of data across various nations. The reason is that it is

difficult to hide changes in government. For this reason, researchers such as Siermann (1998)

have used the government-focused definition to measure political instability. The consequence of

employing such measurements is that they may result in underestimation or overestimation of the

true political instability. This approach underestimates the true political instability in that it may

ignore many forces of political instability until they become so tremendous that they cause direct

changes in government. For example, almost all political scientists agree that current

government of Iran is not politically stable. However, because the government of Iran has not

changed yet, it is regarded to be politically stable based on this definition. On the other hand, this

approach may result in an overestimation of political instability as in some cases governmental

changes are not the result of instability but the result of a democratic system. For example the

governments of Italy and Japan may change frequently due to disintegration of political

coalitions. These changes do not necessarily reflect political instability, but they may be regarded

as such based on this definition.

The second category of definitions focuses on the degree of social unrest as a measure of

political instability. For example, Siermann (1998) measures the political instability based on

“the frequency with which certain soico-political events occur” (Siermann 1998 P30). Though he

admits that this approach is difficult to implement, Siermann argues that it results in a more

precise measure of political instability than the approach focusing on governmental changes

only. A similar definition of political instability has been developed by Huntington (1968). This

definition associates the degree of political stability in a nation with the strength of its political

institution. Huntington argues that high levels of social frustrations motivate populations to act

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against their governments, and if the political institution is weak, such a conflict would be

magnified. Gurr (1970) also prefers the more general definition of political instability. He argues

that political instability occurs when a population’s expectations are not met (Gurr 1970). These

expectations could be regarding many different issues. It is not always necessary for populations

to tear down the whole government system or change government leaders to address these issues.

Sometimes, all that is required is for the government to change certain policies. Gurr argues that

the process of changing a policy may be regarded as political instability. A more recent

definition adapted by Dutt and Mitra (2008) simply focuses on the movements between

democratic and dictatorial regimes as a measure of political instability.

For the purpose of this study, I have adopted the State Fragility Index as a measure of

political instability. This choice is in line with the social unrest view. Despite the difficulties in

obtaining consistent data, I believe the social unrest view results in a more precise measure of

political instability. The reason is that the change in government is only one expression of

political instability, and it is one that does not often occur. On the other hand, the social unrest

view not only includes government change as an extreme outcome of high social unrest, but it

also includes many smaller forces that change the political environment in a nation.

The State Fragility Index is a composite measure of various political, social and

economic factors that contribute toward political instability. This index is constructed jointly by

the Center for Systematic Peace and the Center for Global Policy. Table 1 in Appendix 1 lists the

variables that are used to build the State Fragility Index. Notice that a variety of political, social

and political factors are incorporated into this index,

Table 1 Summary of Empirical Literature Focus on the Determinates of Political Instability

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III) Literature Review

Many studies have focused on the determinants of political instability. Table 1

summarizes the main aspects of six such studies conducted within the last decade.

Author(year)/title Data Set Estimation method / model

Dependent Variable and Measurement

Independent Variable

Alesina, Alberto Roberto Perotti1996Income Distribution, Political Instability, and Investment

Cross-section and time series data on 71 states in 1960-1985

2SLS 1. Political Instability (PI)

2. Ratio of domestic investment relative to GDP

Economic Variables^Measure of economic output,*Measure of income inequality*Measure of investmentSocial-demographic Variables:*Measure of education#Measure of demographic diversity*Regional dummies

Alesina, Alberto et al 1996(2)Political Instability and Economic Growth

Cross-section and time series data on 113 states in 1950-1982

Amemiya’s GLS

1. Political instability (PI)

2. Contemporaneous growth rate

Economic Variables*Measure of economic growth *Measure of world economic growthSocial-demographic Variables:^Measure of educationPolitical Variables:#Measure of initial government stability #Regional dummies

Gupta, Dipak K et al

1998Democracy, Economic Growth and Political Instability: An integrated Perspective

Cross-section and time series on 120 states in 1965-1986

Instrumental variables

Political Instability (PI)

Economic Variables*Measure of economic output*Measure of economic growth Measure of income inequalityPolitical Variables*Measure of type of government

Table 1 Continued

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Measure of the gap of current democracy and population’s expectation*Measure of political coercion*Measure of initial political violence.

Sierrman, Clements L.J.Politics, Institutions and the Economic Performance of Nations

Cross-section and time series on 2068 observationsIn 1967-1988

Probit Political Instability (PI) Economic Variables:*Measure of economic growth*Measure of economic outputPolitical Variables:*Measure of government change

Rodriguez , Carolyn2000An Empirical test of the Institutionalist View on Income inequality : Economic Growth within United State

Cross section and time series on 50 U.S state governments in 1990-1996

Two step casual model

1. Political Instability (PI)

2. Economic Growth

Economic Variables:*Measure of income inequality *Measure of economic growth *Measure of investment. Social-demographic Variables:*Measure of population growth

MilJkovic, DraganRimal, Arbindra2008The Impact of Socio-Economic Factors on Political Instability: A Cross-Country Analysis

Cross-section and time series on 122 states in 1960-1988

Probit

Negative binomial distribution

Political Instability (PI) Economic Variables:*Measure of economic output*Measure of economic growth

Political Variables:*Measure of government type*Measure of years of independence

* Statistically significant # only significant in PI equation ^ only significant in the second equation

The earliest study reported in Table 1 was conducted in 1996, and the latest was done in

2008. All the projects use panel data on various states during the last five decades. Notice that,

depending on the nature of their studies, a variety of estimation methods are used by various

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scholars. Moreover, the studies outlined in Table 1 are also different in that they are not using a

single definition for political instability (PI). More specifically, the definition of PI could be

separated into two groups: (1) government changes, and (2) social-unrest. Within the two groups,

there is also some variation in measurements.

The studies conducted by Alesina, et al (1996), Siermann (1998) and Miljkovic and

Rimal (2008) utilize the first definition of PI. Siermann, and Miljkovic and Rimal both estimate

the regression model using three different measures of government changes: regular government

changes, irregular government changes, and the total government changes. However, Miljkovic

and Rimal instead of using a quantitative measure of instability use a qualitative measure of

instability that takes a value of 1 if there is a change of government and zero otherwise. Alesina

et al, characterize government change in different ways, the major changes which include all

irregular transfers of power, irregular change by military coups, and the binary variable that

determine if there are any changes regularly or irregularly.

In the social-unrest group of definitions, the measurement of political instability tends to

be more varied. For example, Gupta (1998) uses the frequency of death from internal war as a

measure of PI. Rodriguez (2000) uses the property and violent crime rate as a measure of

political instability. Similarly, Alesina and Perotti (1996) create an index that includes the number

of politically motivated assassinations, the percentage of the population that is killed in domestic

mass violence, the number of successful coups, the number of attempted but unsuccessful coups,

and a dummy variable accounting for the degree of democratization.

As Table 1 indicates, depending on the focus of the study, researchers use a variety of

independent variables in their regression equations. In general, the independent variables can be

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divided into three broad categories: Economic factors, Social Demographic factors and political

factors. In the group of economic variables, there are some variables that are used across many

studies. For example, a measure of the level of economic output (income) or the growth rate of

economic output is consistently used across all of the studies summarized in Table 1.

As for the effect of the level of output on political instability, the literature is mixed.

Gupta (1998) and Miljkovic and Rimal (2008) find a significant and negative relationship

between economic output and political instability. However, Alesina and Perotti (1996) find the

correlation between the same variables as negative but not significant. Siermann (1998) shows

that the economic output level has a significant and positive effect on political instability only

when the dependent variable measures the regular government changes. Otherwise, Siermann

finds an insignificant correlation between economic output and political instability in the rest of

his models.

As for the effect of economic growth on political instability, most researchers use the

GDP growth rate as the indicator of economic growth. Alesina, et al (1996) and Siermann (1998)

use lagged GDP growth to measure its influence on political instability. On the other hand,

Rodriguez (2000) uses the growth rate in per capita income as the measure of economic growth.

Regardless of the way they measure economic growth, all of the reviewed studies report a

significant negative correlation between political instability and economic growth.

Income inequality is also a common independent variable that is included in the literature

on political instability. Various researchers use different measures of income inequality. Alesina

and Perotti (1996) use the share of the third and fourth quintiles of the population; Gupta (1998)

uses the ratio of income shares of the top twenty percent to the bottom twenty percent of the

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population; Rodriguez (2000) and Miljkovic and Rimal (2008) use the Gini index as a measure

of income inequality. Regardless of how they measure income inequality, all of the above studies

find a positive and significant correlation between income inequality and political instability.

In addition to the economic variables listed above, many researchers have studied the

effect of other variables on political instability. Alesina and Perotti (1996) find a negative and

significant correlation between investment and political instability. On the other hand, Alesina et

al (1996) find a positive and significant correlation between political instability and world

economic growth. Moreover, Rodriguez finds a positive and significant relationship between

investment and political instability.

A variety of social and demographic variables are also commonly used in studies on

political instability. Among these variables is a measure of educational attainment, population

growth, as well as a measure of racial and cultural diversity in the population. As for the effect of

these variables on political instability, Alesina and Perotti (1996) find that a negative correlation

between the percentage of a population that is educated and political instability. In addition, the

same authors find a positive correlation between racial diversity in the population and the level

of political instability. Rodriguez (2000) finds a positive correlation between the population

growth rate and the degree of political instability.

There are two political variables that are included in most studies reviewed for this paper:

a measure of initial political instability and a measure of the type of government. Alesina and

Perotti (1996), Gupta (1998), Siermann (1998), and Miljkovic and Rimal (2008) find that the level

of initial political instability has a positive and significant correlation with current political

instability. Gupta (1998) and Miljkovic and Rimal (2008) have included some measure of

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government type in their models. The overall results of these studies suggest that both the

extreme level of dictatorship and high levels of democracy have a negative and significant effect

on political instability. In addition to the above variables, Gupta also includes a measure of the

gap between the current level of democracy and the level of democracy expected by the

population. He also includes a measure of political coercion. The result of his study reveals that

political coercion has a significant and positive effect on political instability, but the gap in

democracy does not.

In addition to the variables that I have listed above, regional dummies are commonly

included in the studies. Alesina and Perotti (1996) and Alesina et al (1996) have all included

regional dummies in their research. The effect of regional dummies on political instability,

however, has generally been mixed.

IV) Model Specification

This paper uses the OLS estimation method and a sample of 101 countries across the

world1 to estimate Equation 1 below:

Equation 1: PIi= f (Xi) + error termi,

where the dependent variable, PI, is the State Fragility Index in 2009 and the independent

variables, Xi, are listed in Table 2.

1 For more information on countries included in this study, refer to the descriptive statistics section of this paper.

Table 2: The Independent Variables included in Equation 1.

The Dependent Variable is the State Fragility Index

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Independent Variables Abbreviation Measurement Expect signEconomic Variables Lagged GDP squared GDP^2 Square of nation’s GDP in

2006 in 2006 U.S dollar Ambiguous

Lagged GDP GDP Nation’s GDP in 2006 in 2006 U.S dollar

Ambiguous

Lagged GDP growth rate GRO Nation’s GDP growth rate in 2000-2005(% change in GDP)

Negative

Lagged inequality GINI The most recent nation’s Gini index in 1993-2005

Positive

Lagged inflation rate INF Nation’s inflation rate in 2006 PositiveSocial-Demographic VariablesLevel of education EDU Primary school enrollment rate

in 20062. (measured as the ratio of all students who are attending primary schools to the population of the theoretical age group that must attend the primary school)

Ambiguous

Population growth GPOP Percentage change in population 1990-2005

Positive

Urban population rate UPOP Ratio of urban to overall population 2005

Positive

Number of internet users INT Number of Internet users per thousands people in 2005

Ambiguous

Political Variables Degree of democracy squared DEM^2 Square of democracy index

2007Negative

Degree of Democracy DEM Democracy Index 2007 PositiveLatin American states (Dummy) LAT State is in Latin America=1

other=0Positive

Middle East states (Dummy) MID State is in Middle East=1 other=0

Positive

African States (Dummy) AFR State is in Africa=1 other=0 Positive

To be consistent with other studies, the independent variables included in Equation 1 are

placed into three different categories: economic variables, social-demographic variables, and

political variables.

2 Due to data availability, the observations on Albania, Brazil, Netherlands, Trinidad Yemen are from 2004

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As Table 2 indicates, five economic variables are included in Equation 1. Huntington

(1968) argues that the relationship between political instability and economic development is

nonlinear. The reason is that a poor population does not have the means to organize or participate

in political movements. As the level of economic development increases, however, the

population starts focusing on the “grand transformation of society” (Huntington 1968), resulting

in an increase in political instability. Once economic development reaches an optimal level, the

population is satisfied. Beyond that point, as the level of economic development increases, the

political instability declines. In general, the best measure of economic development is per capita

income. However, due to the fact that per capita income is a measure that is included in the

fragility index, I choose to not include per capita income in the set of my independent variables.

Instead, I include GDP in Equation 1. GDP is the primary measurement of the effect of the size

of the economic output on political instability. To capture a possible nonlinear relationship

between GDP and political instability, Equation 1 includes both GDP and squared GDP as two of

its independent variables. Given that GDP is not the best measure of economic development, the

expected effect of GDP and GDP squared on political instability is ambiguous.

The GDP growth rate captures the effect of growth in economic output on political

instability. Based on previous empirical results in the literature and the relative deprivation

theory developed by Gurr (1970), the expected effect of the GDP growth rate on political

instability is negative. Gurr’s relative deprivation theory argues that the gap between value

expectations and value capabilities diminishes as economic growth increases. Hence, a

population is more satisfied and less likely to engage in movements resulting in political

instability.

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The inequality of income is another commonly used economic variable from the

literature. This paper uses the Gini index as a measure of income inequality. Gurr’s relative

deprivation theory suggests that an individual’s satisfaction which regards to the political

environment in part depends on the gap between his economic welfare and that of others. Thus,

the expected sign on the coefficient of the Gini index variable is positive.

As Table 2 indicates, one of the economic variables included in Equation 1 is the

inflation rate. Although none of the reviewed literature examines the effect of inflation on

political instability, I argue for the inclusion of this variable in the set of independent variables of

Equation 1. Inflation is one important economic factor that measures the economic performance

of a nation. Inflation, not only affects a nation’s economic performance adversely, but it also

may create fear among a country’s population which may eventually create distrust toward

government and its ability to maintain economic stability. Such distrust is the source of political

instability. Also, maintaining a low rate of inflation is a sign of government effectiveness. For

this reason, the expected sign of the coefficient on inflation is positive.

Four social-demographic variables are included among the independent variables of

Equation 1. The first social-demographic variable measures the level of education in the

population. Specifically, the enrollment rate measures the fraction of students that are attending

primary schools relative to the eligible population. Alesina and Perroti (1996) find a negative

and significant correlation between the level of education and political instability. This finding

make sense because all else equal, a more educated population tends to be more satisfied with

government. On the other hand, an unsatisfied educated population has a better chance of

communicating with others in order to form a movement against the government. This argument

suggests that the effect of gross primary enrollment on political instability may also be positive.

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Based on the above analysis, I argue that education has an ambiguous correlation with political

instability.

This study includes two population specific variables in its empirical model. These

variables are the population growth rate as well as the percentage of the population that lives in

urban areas. As mentioned before, Rodriguez (2000) finds a significant positive correlation

between the population growth rate and the degree of political instability. This makes sense

because holding the GDP growth rate constant, a higher population growth rate results in a lower

growth rate in per capita income which decreases the level of population satisfaction. As Table 2

indicates, expected sign of the coefficient of the variable measuring the percentage of the

population that lives in an urban area is ambiguous. The reason is that the degree of urbanization

may have a positive or negative effect on political instability. The positive effect is based on the

fact that income inequality is more visible in urban areas; hence increasing the level of

dissatisfaction among poor. Furthermore, it is easier for the population that lives in urban areas

to exchange information and organized a protest against government. The negative effect of the

degree of urbanization on political instability is based on the fact that the complex lifestyle of the

people who live in urban areas does not allow them to participate in political protest. All else

equal, due to economies of scale, urban areas can provide their population with a higher standard

of living, resulting in a higher level of satisfaction.

The number of internet users per thousand people is a variable that this study includes in

its empirical equation. The effect of this variable on political instability is ambiguous. The reason

is that the internet may both increase and decrease the degree of political instability. To the

extent that the internet allows individuals to entertain themselves at a low marginal cost, it

increases individual satisfaction, hence diminishing the level of political instability. On the other

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hand, the internet may increase political instability because it allows dissatisfied individuals to

learn about others’ lack of satisfaction and it makes it easier to form a movement against the

government. A good example of the internet’s influence on political instability is the recent

Iranian protest against their government based on their dissatisfaction with the results of the

presidential election.

The literature on political instability also refers to the degree of democracy in a country

as a major determinant of its stability [see Dutt (2008) for example]. As Table 1 reveals,

studies such as Alesina, et al (1996) and Miljkovic and Dragan (2008) use a dummy variable to

capture the effect of a democratic government on political instability. As for the effect of this

dummy variable on political instability, there is no consistency among the results of previous

studies. Instead of a dummy, this study uses the democracy index developed by The Economist

as a measure of the degree of democracy in a nation. The democracy index is a composite

measure based on the results of a survey measuring the population’s perception on factors such

as the degree of competitiveness of the political election, the level of civil liberties, and the

degree of government effectiveness. This index is preferred to a dummy variable as it can take

values other than just zero or one. In addition to the degree of democracy, this study also

includes the square of the degree of democracy among its independent variables. The reason for

this nonlinear formulation is that, according to Dutt (2008), the relationship between the degree

of democracy and political instability is nonlinear. Specifically, Dott (2008) argues that both the

extremely democratic and the extremely dictatorial governments are very stable. However, as a

country starts the process of transitioning from a dictatorship to democracy, its political

instability increases at first. Once a certain level of democracy is achieved, however, the

political instability starts to fall as the degree of democracy continues to rise. Thus, the expected

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sign of the coefficient of squared democracy is negative and the sign of the coefficient of

democracy is positive.

Based on the previous literature review, the geographic position of one state is an

important determinant of its political instability. The reason is that certain areas in the world

have historically been more unstable than others. Regional dummies could catch the effect of

geographic location. Thus, the inclusion of regional dummies in Equation 1 is necessary. As

Table 2 indicates, there are three regional dummies (African states, Latin American states,

Middle East states) in Equation 1. As for the effect of the regional dummies on political

instability, depending on the nature of previous studies and the time frame under their

consideration, their findings vary. Given that the data utilized in this study is collected from the

mid to late 2000s, I expect to find a positive coefficient on the regional dummy variables.

V) Descriptive Statistics:

As mentioned in the previous section, this study uses a sample of 101 countries from the

mid to late 2000s to examine the determinants of political instability. The sample consists of 34

European states, 28 African states, 12 Latin American states, 3 North American states, and 24

Asian states. Table 3 includes the list of all nations included in this study. To be consistent with

the definition of the dummies that are included in Equation 1, Table 3 lists the countries in four

categories, Latin American countries, African countries, Middle East countries and other

countries. Due to the unavailability of data, only 6 Middle Eastern countries are included in the

sample. This may result in an unreliable estimation on the coefficient of the regional dummy

variable, MID, for the Middle Eastern states. On the other hand, the representation of African

states and Latin American states in the sample is much better. This can be easily seen in Figure

1

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Table 3: Countries Included in the Sample

African Countries

Latin America Countries

Middle East Countries

Other Countries

Other Countries

Colombia Brazil Egypt Belgium CambodiaCameroon Argentina Iran Austria ChinaCentral African Chile Israel Finland AustraliaAlgeria Peru Turkey France India Niger Panama Yemen Poland Indonesia Nigeria Paraguay Jordan Russia JapanRwanda Trinidad Slovakia KazakhstanSenegal Venezuela Slovenia LaosMozambique El Salvador Netherlands Kyrgyzstan Namibia Costa Rica Norway PhilippinesSouth Africa Honduras Portugal NepalTanzania Bolivia Romania New ZealandTunisia Spain PakistanUganda Sweden TajikistanSwaziland Switzerland UzbekistanMadagascar Turkey MalaysiaMalawi Albania MongoliaMali Latvia BangladeshMauritania Lithuania CanadaMorocco Macedonia United StatesDominican Republic

Moldova Mexico

Ethiopia Czech Republic

United Kingdom

Gambia DenmarkGuinea EstoniaBenin GeorgiaBotswana GreeceBurkina Faso ItalyBurundi Ireland

HungaryArmeniaAzerbaijanBelarusBulgaria

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Figure 1: Countries Included in the Sample

African CountriesLatin American CountriesMiddle East Contries Other Contries

In terms of the dependent variable, as mentioned earlier, this study uses the State Fragility Index

as the measure of political instability.3

Summary of descriptive statistics on the variables included in Equation 1 is reported in Table 4.

Table 4: The Descriptive Statistics on the Variables Included in Equation 1

Dependent Variable Max Min MedianState Fragility Index 20(Nigeria) 0(Sweden) 8(Indonesia)Independent Variable Max Min MedianLagged GDP(billions) $13262.7(U.S.) 0.506(Albania) 34.204(Belarus)Lagged GDP growth rate 12.7(Azerbaijan) -1.4 (Central Africa) 4.2(Costa Rica)Lagged Gini index(1 to 100=maximum inequality )

74.3(Namibia) 24.7(Denmark) 38.6(Guinea)

Lagged inflation rate 34.7(Guinea) 0.054(Niger) 4.5(Mongolia)Level of education 147.42(Rwanda ) 50.35(Niger) 102.34 (Austria )Population growth 3.7(Yemen) -1.3(Georgia) 1.4(Indonesia)Urban population rate 97(Belgium) 10(Burundi) 59(Greece)Number of internet users 764(Sweden) 1(Tajikistan) 94(Tunisia)Democracy index (1 to 10=complete democracy)

9.88(Sweden) 1.61(Central African Rep.)

6.22(El Salvador)

Regional dummies - - -

A glance at Table 4 reveals that Nigeria (an African country) is the most politically

unstable country in my sample. It is worth noting that the next two most unstable countries

3 The exact definition is discussed in the first part as well as the appendix of this paper,

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(Rwanda and Burundi) are also in Africa. On the other hand, the top three stable countries

(Sweden, United Kingdom, and Slovenia) are all located in Europe.

There are a few interesting findings in Table 4. In terms of the economic independent

variables, the highest GDP belongs to the U.S. and the lowest GDP belongs to Albania. Note that

the U.S GDP is 26210 times Albanian’s GDP. The lowest economic growth belongs to the

Central African Republic. On the other hand, Azerbaijan’s economic growth is the highest.

In terms of social demographic variables, surprisingly, the highest gross primary

enrollment rate belongs to the country of Rwanda, which is the most unstable state in the sample.

This is consistent with the argument that education could have either a positive or a negative

effect on political instability. As I mentioned before, Burundi is the third most politically

unstable nation in my sample. Notice that only 10% of population of Burundi lives in urban

areas, which indicates a potential of negative correlation between the urban population rate and

the political instability. Another interesting finding is that Sweden has the highest number of

internet users per thousand population in my sample. Since Sweden is also the most stable

country in 2007, this finding might indicate a negative correlation between the number of

internet users in a nation and its political instability.

In terms of political variables, the highest degree of democracy index belongs to Sweden

(the most stable nation in the sample) which has a score of 9.88 (out of maximum of 10). This

observation indicates that there might be a negative correlation between democracy and nation’s

political instability.

VI) Test for Multicollinearity

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Because the OLS method of estimation is used in this study, a test for multicollinearity

must be conducted before estimating the equation. If untreated, a strong multicollinearity among

the independent variables in Equation 1 would increase the standard errors of the estimated

coefficients. Consequently, the value of the t-statistics would decrease, resulting in an unreliable

test of significance of the coefficients.

There are numerous ways to test for a multicollinearity problem. This study examines the

correlation coefficient between each pair of independent variables included in Equation 1.

Generally, the correlation coefficient with an absolute value of 0.8 or higher is an indication of a

strong multicollinearity problem.

Table 5 below reports the correlation coefficients among the independent variables

included in Equation 1. As expected, the correlation between GDP squared and GDP on one

hand and Democracy Squared and Democracy on the other hand are high. None of the other

correlation coefficients exceed the 0.8 threshold. Thus, multicollinearity is not problem.

Table 5 Correlation Coefficients among the Independent Variables included in Equation 1

GDP^2

GDP GRO GINI

INF EDU INT GPOP UPOP DEM^2 DEM LAT MID AFR

GDP^2 1GDP 0.95 1GRO 0.75 0.13 1GINI 0.01 0.02 0.13 1INF 0.06 0.14 0.20 0.10 1EDU 0.03 0.01 0.01 0.23 0.10 1INT 0.21 0.2 0.36 0.42 0.42 0.50 1GPOP 0.03 0.17 0.17 0.48 0.2 0.1 0.48 1UPOP 0.12 0.29 0.28 0.07 0.3 0.08 0.65 0.44 1DEM^2 0.16 0.39 0.39 0.23 0.41 0.10 0.79 0.43 0.61 1DEM 0.11 0.35 0.35 0.19 0.41 0.14 0.73 0.43 0.60 0.98 1AFR 0.07 0.10 0.10 0.30 0.13 0.08 0.38 0.45 0.43 0.37 0.36 1LAT 0.04 0.12 0.13 0.47 0.06 0.06 0.14 0.14 0.18 0.04 0.09 0.22 1MID 0.02 0.03 0.04 0.03 0.09 0.09 0.19 0.18 0.03 0.09 0.08 0.03 0.08 1

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VII) Heteroskedasticity Test

Another test that has to be conducted before estimating Equation 1 is the

heteroskedasticity test. For the Ordinary Least Squares procedure to result in unbiased and

efficient estimates, the errors in Equation 1 must have constant variances across the observations

in the data set. If this condition does not hold, the errors in Equation 1 have a heteroskedasticity

problem. If this problem remains untreated, the OLS procedure may yield incorrect estimates of

the variances of the coefficients, hence making the results of the t-test of significance unreliable.

There are different ways to check for the existence of the heteroskedasticity problem. The

White Test is the one that this study chooses to use. To conduct the White test, we must first

estimate Equation 1 using the OLS method. Next, the residuals of the estimated equation are

used as the dependent variable in a subsequent regression equation that includes all of the

independent variables of Equation 1 and their squares in the set of its independent variables.

Then the number of observations (n, 101) is multiplied by this Equation’s R-squared (nR2). The

null hypothesis (H0) is the absence of heteroskedasticity. The alternative hypothesis is

heteroskedasticity. If nR2 is higher than the critical chi–square, then we reject H0 and conclude

that a heteroskedasticity problem exists. Otherwise we fail to reject H0, concluding that

heteroskedasticity is not a problem. The nR2 of this subsequent equation is 26, the critical chi–

square at 23 degrees of freedom (two times the number of independent variables in Equation 1

minus the number of the dummy variables minus squared GDP) at 5% level of significance is

35.17. Therefore, we conclude that Equation 1 does not have a heteroskedasticity problem.

23

VIII) Estimation Results

The estimation results of two specifications of Equation 1 are shown in Table 6 below. As Table

6 indicates, there are two version of Equation 1. The second version of Equation 1 drops all the

squared variables to estimate the result of see if a linear function is a better fit.

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Table 6: The Estimation Results of Two Variations of Equation 1 (The Dependent Variable is the Political Instability Index –Taking a Value of 0 to 25)

Independent Variables

Coefficient( t-stats.)

Expect Sign of coefficient

Equation 1A Equation 1BEconomic Variables Lagged GDP Square 1.97E-08（0.330882)

Ambiguous

Lagged GDP -0.00017（-0.222706)7.35E-05(0.338822)

Ambiguous

Lagged GDP Growth rate

0.099988(0.677876)

0.10541(0.742897)

Negative

Lagged Inequality of Economy

-0.02752(-0.641858)

-0.02812(-0.66366)

Positive

Lagged Inflation rate 0.064053(0.89907)

0.066731(0.96368)

Positive

Social-Demographic VariablesLevel of education 0.003187

(0.141427)0.002013(0.091943)

Ambiguous

Population growth 1.868422(3.638125)*

1.464701(4.178451)*

Positive

Urban Population rate

-0.03722（-1.829064)*-0.03799(-1.90537)*

Ambiguous

Number of internet users

-0.0064（-2.070642)***-0.0064(-2.3969)***

Ambiguous

Political Variables Degree of democracy Square

0.00082(0.009544)

Negative

Degree of Democracy

-0.99043(-1.071906)

-0.98049(-4.36153)***

Positive (Negative for linear function)

Latin American -1.64097(-1.367132)*

-1.57849(-1.35567)*

Positive

Middle East 2.462235(1.738909)*

2.476333(1.805417)*

Positive

African 1.878305(2.148957)**

1.921463(2.263968)**

Positive

Adjusted R2 0.789659 0.794176Sample 101 101

*** Significant at 1%, ** Significant at 5%, *Significant at 10%

Adjusted R2 is a measure of how well the independent variables included in Equation 1

explain the variations in political instability around mean political instability. The higher the

25

adjusted R2, the better the independent variables explain the movements of the dependent

variable around its mean. As shown in Table 6, the adjusted R2 in both variations of Equation 1 is

around 0.79, which means that 79% of the variation in political instability around its mean is

explained by the variables included in Equation 1.

Despite the high level of the adjusted R2, there are many variables that have insignificant

coefficients. This result may be caused by highly correlated independent variables or the

heteroskedasticity problem. As Table 5 indicates, though all of the correlation coefficients are

below 0.8, there are some variables that are highly correlated with others. Also, although

Equation 1 passed the heteroskedasticity test, it may still have some degree of heteroskedasticity

problem.

As Table 6 indicates, the coefficients of some independent variables are statistically

significant in both versions of Equation 1. These coefficients belong to the variables measuring

the population growth, the percentage of population living in urban areas, the number of internet

users per 1000 population, and the geographic location of the countries. Moreover, the

coefficient of the variable measuring the degree of democracy is significant in Equation 1B.

Among the significant coefficients in both Equations (1A & 1B), the signs of the

coefficient on population growth as well as the dummy variables for Middle Eastern and African

nations are consistent with my expectations. Specifically, the results of my estimations reveal

that on average as the population of a nation increases by 1 percent, its political instability index

goes up by 1.7. This finding is consistent with previous empirical literature. Another finding that

meets my expectation is that, all else equal, the Middle Eastern as well as the African countries

are more politically unstable than countries located elsewhere in the world. Specifically, I find

26

that, all else equal, countries that are located in these two regions of the world have a fragility

index that is about 2 points higher than that of other nations.

A glance at Table 6 reveals that although neither the democracy index nor the squared

democracy index seem to have a significant effect on the level of political instability, once the

squared democracy index is dropped from the equation, the effect of the level of democracy on

political instability becomes significant. This result may, in part, be due to the fact that these two

variables are highly correlated with each other. My estimation results suggest that, under a linear

function, the level of democracy has a negative effect on political instability, at 1% significance

level. Notice that the estimated coefficient on this variable is around 1. This means that, all else

equal, a one point increase in the democracy index results in a 1 point decrease in the political

instability index, making the nation more stable.

As Table 6 shows, the coefficient on the dummy variable that takes a value of 1 for Latin

American nations is significant at the 10 percent level in an unexpected direction. This result

indicates that, all else equal, in 2006 the Latin American countries generally had a fragility index

that was 1.6 less than other nations in the sample. This finding is not supported by previous

empirical research. However, this can be explained by the fact that various studies use different

measurements of political instability and cover different time frames.

In terms of the independent variables that are expected to have an ambiguous effect on

political instability, the urban population rate is found to have a negative effect on political

instability. Specifically, for a one percent increase in the urban population rate, the fragility

index is found to decrease by 0.03, which is consistent with the idea that the population living in

27

the urban areas has better access to basic resources and, hence, is happier than the population

living in the rural areas.

The number of internet users per thousand people is also found to have a negative and

significant correlation with political instability. Specifically, I find that as the number of internet

users per thousand people increases by 1, the fragility index goes down by 0.006. This finding

suggests that the entertainment effect of the internet exceeds its effect as a means of organizing a

political movement.

As Table 6 reveals, there are a few variables that do not significantly affect political

instability. These variables are lagged GDP square and lagged GDP, lagged Gini index, lagged

GDP growth rate, lagged inflation rate, and the primary school enrollment rate.. As indicated

above, these findings may be due to a mild multicollinearity and heteroeskedastcity problems.

Moreover, the fact that the dependent variable is a composite measure of political instability built

in part based on a few economic variables, may be another reason for the insignificant

coefficients of some of these variables. However, based on the estimation results of Equation 1,

one may argue that political instability is not affected by the economic factors. Neither is it

affected by the level of education of the population in a nation.

IX) Conclusion

This paper empirically examines the determinants of political instability by analyzing a

cross sectional data set of 101 nations in the year 2006 using the OLS method of estimation.

According to the estimation results of this paper, all else equal, countries that have higher

population growth or are located in Africa or in the Middle East, have less political stability than

others. Moreover, this study finds that, all else equal, those nations that have a higher percentage

28

of their population living in urban areas, have a higher number of internet users per thousand

population, have a higher degree of democracy, or are located in Latin America, are more

politically stable. On the other hand, this study finds no significant correlation between the level

of economic health and development of a nation, or the level of education of its residents, and

the nation’s political instability.

As indicated above, the dependent variable used in this study is a composite measure of

the political instability which is constructed based many variables (including a nation’s per

capita GDP). This may be a reason why the variation in some of the independent variables

included in Equation 1 (specifically the economic variables) do not result in a variation in the

dependent variable. For this reason, I suggest that the future studies look for a measure of the

political instability that is not a composite blend of many factors. Also, instead of estimating an

equation that models political instability as a function of economic instability, future studies may

build a system of two equations that simultaneously examine the determinants of the political as

well as the economic instability. As it is true with any empirical study, the results must be

replicated many times, using various samples and various methods of estimation, before they can

be fully trusted.

Table 7: Variables included in the State Fragility Index

29

Appendix I

The State Fragility Index is a complex measure of political instability. It is constructed based on

several indices. Table 7 lists the components of the State Fragility Index. The detail information

on the method of calculation is available in Global Report 2009: State, Governance and State

Fragility.

Variables Measured based onSecurity Effectiveness general security and vulnerability to political violenceSecurity legitimacy State repression: political terror scale Arm Conflict Indicator the country’s most recent experience with major armed

conflict, including wars of independence, communal wars, ethnic wars, revolutionary wars, and inter-state wars.

Political Effectiveness Regime/Governance StabilityPolitical legitimacy Regime/Governance InclusionRegime type general indicator of the country’s regime Economic Effectiveness Gross Domestic Product per Capita Economic Legitimacy Share of Export Trade in Manufactured Goods Net oil Production or Consumption

information on a country’spetroleum energy profile

Social Effectiveness Human Capital Development IndexSocial legitimacy Human Capital Care indexRegional Effect information to identify two important “neighborhood”

clusters of countries

Data Source

30

State Fragility Index is found in Global Report Conflict, Governance, and State Fragilityhttp://www.humansecuritygateway.com/documents/CSP_GlobalReport2009_ConflictGovernanceStateFragility.pdf

Data on GDP is found in The World Economic Outlook Database from IMFhttp://www.imf.org/external/pubs/ft/weo/2009/02/weodata/index.aspx

Data on GDP growth is found in Growth of output from World Bankhttp://siteresources.worldbank.org/DATASTATISTICS/Resources/table4_1.pdf

Data for Education is found in Education statistic from World Bank http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTEDUCATION/EXTDATASTATISTICS/EXTEDSTATS/0,,menuPK:3232818~pagePK:64168427~piPK:64168435~theSitePK:3232764,00.html

Data for Gini index is found in Distribution of income or consumption from World Bank. http://siteresources.worldbank.org/DATASTATISTICS/Resources/table2_7.pdf

Data for population growth is found in World Population dynamics from World Bankhttp://siteresources.worldbank.org/DATASTATISTICS/Resources/table2_1.pdf

Data for Inflation rate is found in The World Economic Outlook Database from IMFhttp://www.imf.org/external/pubs/ft/weo/2009/02/weodata/index.aspx

Data for Urban Population rate is found in World Development Indicator from World Bankhttp://web.worldbank.org/WBSITE/EXTERNAL/DATASTATISTICS/0,,contentMDK:20398986~menuPK:64133163~pagePK:64133150~piPK:64133175~theSitePK:239419,00.html

Data for Internet user per thousands population is found in The information age from World Bankhttp://siteresources.worldbank.org/DATASTATISTICS/Resources/table5_11.pdf

Data for degree of democracy is found in Democracy index 2007 from Economist Intelligence Unithttp://www.economist.com/media/pdf/Democracy_Index_2007_v3.pdf

Reference

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