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Quantitative Data Analysis and Interpretation Approved Essay Title: Explaining Political Instability in a Quantitative Cross-National Study (QDA) 2007-2008 Word Count: 5202 Page 1 of 29 Abstract: This essay reports statistical correlations and associations between key variables and political instability. The variables selected represent internal and external, as well as economic and political, factors which allows conclusions to be drawn as to their relative importance for the explanation of political instability. This is done through statistical techniques such as regression analysis. This essay finds that economic factors and internal factors seem to have the most relevance for the analysis of the correlates of political instability. The essay concludes by highlighting possible issues with the analysis including the measurement of variables and cause and effect. Part 1 – Introduction The central research question of this essay is concerned with the question of why political instability happens, placing the analysis in a cross-national context. The issue of political instability is important because of the problems, both social and economic, which political instability brings to the people of a country which is being deeply affected by it. Indeed, as Shaw notes, “Prospects for regional development recede as conflicts both escalate and proliferate” (2003, p. 489). An understanding of political instability could lead to developments of policy strategies aimed at preventing these problems when it arises, and aimed at preventing its occurrence. Without an accurate theoretical understanding of why and how instability is generated even the best meaning action may fail. The issue of political instability is also theoretically interesting because of the numerous and disparate variables which have been suggested as explanatory in this field. Just some of the explanations offered for political instability include; economic inequality (Lichbach 1989, passim), poor regional relations (Brown, 2001, p. 16), the impact of crime (Brown, 2001, p. 18) and Page 2 of 29 ethnic fractionalisation (Gurr, 1993, p.161). More research is obviously needed in this area in order to determine what the relationship actually is and which factors are more important. In this essay the relative importance of political and economic factors will be assessed as well as the relative importance of internal and external factors, concluding that economic and internal factors seem most important respectively. The dependent variable of this study is the level of political instability1. The independent variables are; wealth, level of democracy, ethnic fractionalisation and the number of border states with any kind of major conflict. I – Research expectations: Wealth and political instability We may expect, before analysis, a negative relationship between the wealth of the people of a country and political instability. Theoretically there are at least some reasons to suppose that this is the case, mostly relating to what might be termed the opportunity costs of violence. Przeworski and Limongi used income per capita to show that democratic regimes were most vulnerable to collapse when the level of income was low absolutely (1997, p. 161). Taking this as a starting point, the relationship between the overthrowing of democracy and political instability seems intuitively obvious; when democracies are overthrown violence and repression often follows. This gives us a theoretical reason for assuming that wealth and political instability as defined above are related for the same reasons that income per capita and democratic death are related. Here no assumption is made as to the source of the 1 The definition of political instability which is used here will follow Kaufman in his definition of the term “political stability”, although will obviously focus on the negative side. A full explanation of this term can be found in World Bank (2007) and the Univariate analysis section in this work. Page 3 of 29 violence, whether it is initiated from above, by elites, or below, by mass level forces. This theory, though, only explains why violence becomes an option which can be taken; the reason why violence is chosen still needs to be explained. Przeworski and Limongi suggest that the choice is likely to be made by those who seek to enlarge their own share of the income distribution whilst the marginal costs are lowered, and thus so is the risk (1997, p. 166). This is largely in line with Shaw’s observations of conflicts within Africa; increasing conflicts over diminishing resources to acquire (see Shaw, 2003, p. 488). Similarly, Gurr noted that objective conditions like poverty were crucial grievances used by elites to mobilise people for rebellion (1993, p. 189). Whether the effect which Przeworski and Limongi noted still applies strongly is an open question, Bermeo argued that wealth is “not a necessary condition of democratic durability” and as such we may question if wealth still has explanatory force in terms of political instability (2003, p. 169). Level of democracy and political instability It has been observed that when a country becomes a “full” democracy civil wars are an extremely rare, if not completely absent, phenomena (see Hegre et al, 2001, pp. 33-34). This might be because, as Bermeo claimed, that people are more likely to engage in violent competition, rather than peaceful and democratic competition, when the costs of the former are reduced compared to the latter (2003, p. 164). Semi-democracies have been regarded as bringing stabilising characteristics to countries and it has been contended that they can even prevent conflict in societies by allowing demands for at least some popular consultation whilst at the same time allowing the present ruling elites to maintain power which they would be unwilling to Page 4 of 29 give up (see Case, 1996, p. 457). Despite this we should not necessarily expect the relationship to be a simple linear one. It has also been seen that countries which are harshly authoritarian have fewer civil wars than those which are in the intermediate stages of democratisation (Hegre et al, 2001, p. 33). These analyses are also likely to hold true for other forms of violence which fall short of civil war, including ethnic violence (see Snyder, 2000, p. 310). This in itself is surprising if Bermeo was correct. One explanation sees potential conflicts between ethnic groups, or between different communities, as having already existed but laying “dormant” because of the repressive nature of authoritarian regimes (Byman and Van Evera, 1998, p. 33). Another explanation sees national elites who do not wish to abandon their own political power creating conflict and nationalism to allow them to keep their power (Snyder, 2000, p. 36). Indeed, there seems to be considerable amounts of qualitative data to back this claim (see Mansfield and Snyder, 2005, pp. 169-227). The exact expectation is then unclear and will alter depending on whether we accept Case’s analysis or that of Mansfield and Snyder. Near-by conflicts and political instability It is important to note that political instability, as defined by Kaufman, can often be caused by forces external to the country in which the problems are manifest. External factors operating at the mass-level include “spill-over” or “diffusion” of conflict from other states (Brown, 2001, p. 16). These factors can operate regardless of domestic problems, or absence thereof. To focus exclusively on internal factors, then, will lead to an incomplete analysis and may even create misleading results. Spill-over of conflict could be created either by “swarms of refugees” or key radicals Page 5 of 29 moving operations due to conflict (see Brown, 2001, p. 16). Foreign armies can also play a key role in causing political instability including, but not limited to, leading direct military assaults on the government (Shaw, 2003, p. 491). Similarly “diffusion and contagion” of conflicts has been shown in previous research to be a factor in explaining conflict, although not a particularly strong one in all cases (Gurr, 1993, p. 189). Theoretically, then we should expect to see a relationship between the proximity of foreign conflicts or foreign hostile regimes and the amount of political instability which is found within a country. Ethnic fractionalisation and political instability. The degree to which a country is ethnically homogenous has been suggested as a possible explanatory factor in many aspects of political instability. Ted Gurr notes that most countries have seen conflicts relating to the “terms of incorporation for ethnic minorities…” (1993, p. 161). Whilst, as Gurr notes, this does not have to be violent conflict we at least have some reason for expecting to see more conflict in countries which have a higher degree of ethnic heterogeneity (see Gurr, 1993, p. 161). This theory runs counter to Collier and Hoeffler who argue that fractionalisation will increase the costs of recruiting to a rebel force and will therefore reduce instances of civil war, and so political instability (2000, p. 8). The Null Hypothesis Page 6 of 29 As always the null hypothesis is a statement of no difference and can be formulated as; - There is no difference in terms of political instability between countries with differing: levels of wealth, levels of ethnic fractionalisation, number of geographically close states with major conflicts, or levels of democracy. This then means that the hypothesis can be formulated as stating that there is a difference between the levels of political instability in countries, and those countries’: levels of wealth, levels of ethnic fractionalisation, number of geographically close states with major conflicts, or levels of democracy. Showing significance in a relationship between the dependent variable and any one of the independent variables disproves the null hypothesis relating to that variable and shows that there is a difference between countries level of political instability which is related to the independent variable. Importantly significance does not tell us the direction of the relationship, and this must be discovered by an ex post analysis, which will be computed if necessary. The cases are 191 countries from the global indicators data set. These countries represent a population within the field of study. Whilst this means that they are technically not a sample it will still be useful to treat them as a sample of the wider world throughout time. Hopefully this will mean that we can say with greater confidence that the results found are generalisable into the future as well as having current time-period empirical validity. Page 7 of 29 II – Univariate Analysis Concept and measurement of political instability Political instability, the dependent variable, will be measured using Kaufman’s political stability rating. This means that the relationships which are found (positive/negative) will actually be the inverse of the relationship we are concerned with. For example if increasing GDP is found to have a positive relationship with political stability it has a negative relationship with political instability. The choice for this variable is because Kaufman’s variable captures the important elements of the likelihood of politically motivated violence in a way which no other variable is likely to do. For example, data on the number of politically-motivated deaths in a country may well accord ‘positive’ scores to countries in which non-fatal political violence is often used as a measure of coercion. The year which will be selected to measure political stability will be 1998. Kaufman’s political stability is a largely subjective measure based on ordinal level assessments from experts and respondents based on their assessment of the country they are concerned with regarding political violence (Kaufman et al, 2007, Appendix A). The ordinal sources used to create the political stability variable are weighted according to the reliability which Kaufman et al. believes that they have and then aggregated (World Bank, 2007). The variable measures countries against each other in order to construct a value which can be given to each, such as that the mean is approaching 0 and the standard deviation is approaching 1. Because of the way the variable is constructed it is an inherently imperfect measure of what we are actually trying to measure. Whilst possibilities of error are obviously very important here it is important to note that the variable will Page 8 of 29 still be of use for creating comparisons within the same variable (see Kaufman et al, 2007, p. 15). The variable itself is ostensibly ordinal however because it is continuous and constructed to be used as an interval-ratio level variable it will be used as such. A histogram representing the distribution of the countries which are ranked using the political stability variable is included below (see chart 1 below). As can be seen data is only available for 163 countries in the world. Missing values are not explained by the World Bank nor Kaufman, however they are likely to be caused by lack of available data. It is important to note though that the missing values do not appear to be just limited to the most authoritarian regimes, or even all of the most violent ones, as can be seen by the inclusion of North Korea and the Democratic Republic of Congo respectively. Chart 1: Histogram showing distribution of Kaufman's political stability variable 25 Frequency 20 15 10 5 Mean =-0.0118 Std. Dev. =1.00383 N =163 0 -2.00 0.00 2.00 Kaufman's political stability Concept and measurement of wealth Page 9 of 29 The wealth of the people of a country is a concept which can be measured in many different ways, income per capita being just one. GDP per capita is another, related, way of measuring wealth. Per capita income and GDP per capita are likely to be related in a strong way such as that theoretically one could be operationalized in place of the other, as Bermeo does (2003, p. 169). If either of these are used the comparative rankings of countries are likely to be the same. Neither of these are measures of absolute wealth of countries, this is important because it avoids the issues relating to extremely large but poor countries having a high GDP compared to extremely small yet rich countries. Within the global indicators data set there is data available for GDP per capita for 1997 in 1987 US$. Whilst the year does not perfectly correspond to the year which is used for the dependent variable it is likely that the GDP of countries will not have varied significantly over a one year period. This is of course an untested assumption and could introduce the possibility of error, however in the absence of perfect data it does not seem like a particularly egregious assumption. This histogram below (chart 2) displays the variable distribution graphically; Chart 2: Page 10 of 29 A histogram representing distribution of GDP per capita measured in 1987US$ per country 80 Frequency 60 40 20 Mean =4322.134 Std. Dev. =6940.6476 N =134 0 0.0 5000.0 10000.0 15000.0 20000.0 25000.0 30000.0 GDP per capita in 1997 (measured in 1987US$) As is immediately apparent the values of this variable are not normally distributed. This is because of the few countries which have a comparatively high GDP per capita introducing a positive skew. This is the reason for the extremely high standard deviation. I will however treat this variable as if it was normally distributed for the purpose of this essay, although I am aware of the issues this creates. Data is available for 134 countries. As can be seen from the chart, the standard deviation is comparatively high. Concept and measurement of external conflicts Page 11 of 29 The types of external conflict which impact upon the functioning of a regime and create political instability seem likely to be major conflicts within countries which are geographically close. If this were not the case the “swarms of refugees” problem may not arise (see Brown, 2001, p. 16). Similarly it is only likely to be major conflicts which force insurgents or guerrillas to move operations. In both these cases the countries in which the problems manifest themselves do not have to be directly involved in the conflict. In the case of foreign powers attacking a country geographical proximity is less important. Unfortunately no data for the belligerence of foreign regimes external of geographic proximity is available. This means we are focussing almost exclusively on what Brown referred to as “bad neighbourhoods”, that is to say focussing on mass-level external problems (2001, p. 15). The variable which will be used to measure this concept, then, is the number of border states with any major conflict during the period 1990-19992. The variable will be recoded into a nominal level dummy variable assessing if a country is in a “bad neighbourhood”; this is because the available data makes no attempt to judge neither how serious (beyond an undefined level of “major”) a conflict was nor how long it lasted for. Given this fact, a country which is bordered by six countries which have had “major” yet short lived conflicts which caused little trouble internationally will get a higher score than a country bordered by one country which has had one conflict that destroys much of the underlying infrastructure of that society. Obviously the latter poses the greater danger but could be missed if we simply focussed on the numerical results. To avoid problems like this the variable will be created as having values of “yes” for being in a bad neighbourhood, which is to say has more than one boarder state with a major 2 It is important to note that this variable would almost certainly include countries which go to war against each other but are geographically close. Page 12 of 29 conflict during the period, or “no”, for not having. The underlying source of this variable covers years past 1998 when the political instability was measured, but this may not be a problem. If a major conflict seemed likely to begin within a year it would not be unexpected to see refugees escaping the probable violence, and so some effects might be detected then. Similarly problems can take a long time to be resolved and even a conflict in a bordering state in 1990 might still be having an impact in 1998. The distributions of this variable after recoding are listed below (Table 1); Table 1: Concept and measurement of level of democracy This concept is represented in the global indicators data set as the variable “Democracy-Autocracy, mean (1990-99)”. This is measured by subtracting the commitment to authoritarianism from their commitment to democracy (see CIDCM, 2006). For this variable I shall recode the data into a three category ordinal variable representing how democratic the country is using categories of “democracies”, “semidemocracies” and “autocracies”. This variable is a mean throughout time and as such Page 13 of 29 does not give us exact data for 1998 to compare on equal terms with the political instability variable. The benefit of this, though, is the effect of “averaging out” of “random fluctuations of the variable” (Parvin, 1973, p. 288). This is an advantage for which foregoing some equality of the variables may be worthwhile. The variable is represented in the bar chart below (see chart 3); Chart 3: Bar Chart Representing Distribution of Values of "Autocracy" "Semi-Democracy" and "Democracy" 80 Count 60 40 20 0 Autocracy Semi-democracy Democracy N=157 As can be seen, the largest grouping is “democracy” with autocracy and semidemocracy each having roughly similar distributions. Concept and measurement of fractionalisation Page 14 of 29 Fractionalisation refers to how diverse the character of a country is. There are several important ways of measuring this concept including but not limited to; racial fractionalisation, linguistic fractionalisation and religious fractionalisation. These variables are often used in conjunction. Indeed, it had been common to use “ethnolinguistic fractionalisation” as a variable; however this tends to miss the subtleties between ethnicity and language which make them sometimes congruent and others not (Alesina et al, 2003, p. 159). For example language may be an important part of ethnicity in Africa but in the United States it is unlikely to be taken as such (ibid.). Because of this issue, this essay will use Alesina et al.’s ethnic fractionalisation variable from the global indicators data set. This variable represents both racial and language variables, although the emphasis is on the latter (ibid. pp.159-160). This variable is therefore a composite variable which can range between 0 and 1 to describe the level of ethnic fractionalisation, with higher numbers meaning greater fractionalisation. The level of measurement is ostensibly interval-ratio. The following histogram shows the distribution of this variable (see chart 4); Chart 4: Page 15 of 29 A histogram representing the distribution of ethnic fractionalisation scores 14 12 Frequency 10 8 6 4 2 Mean =0.439376 Std. Dev. =0.2565464 N =188 0 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 Ethnic Fractionalisation The distribution seems bi-modal, showing two fairly distinct peaks. I will however treat the data as the distribution was normal. III – Bivariate analysis Political instability and GDP per capita Chart 5: Page 16 of 29 This scatterplot (chart 5) shows that there is a positive relationship between a country’s GDP per capita and result on the political stability variable. The relationship is significant at the 99.9% level. The regression line has a Y intercept of -0.384, meaning that the regression line crosses the Y axis at a political stability score of 0.384. This means that if a country had 0 GDP per capita in 1997 we could expect it to have a political stability score of -0.384. The gradient of the regression line is 0.0000882. This means that for each unit change in GDP per capita we can expect to see 0.0000882 of a unit change in political instability. Whilst this change appears very small it is due to the large numbers which we deal with GDP per capita in and the ostensible 5 unit range of Kaufman’s political stability variable. This allows us to predict values for political stability by using the formula “Y’=-0.384+0.0000882 X” where X is any value of GDP per capita and Y’ is the predicted political stability value. However because we are primarily concerned with political instability the Page 17 of 29 result of this analysis is that increasing GDP per capita has a negative effect on political instability. For this model both t and F report a significant relationship. The F value and t values reach significance levels of 99.9% which tells us our regression model is significantly better at predication than using the mean value, as well as having a slope which is different from 0 (Field, 2005, pp. 154-156). This allows us to reject our nullhypothesis and conclude that there is a difference in terms of political instability. The R of the relationship represents the correlation between the two variables and was 0.637. The R² of this relationship was 0.406. This means that we can account for 40.6% of the variation in political instability by knowing the country’s GDP per capita. Following Healey, we can interpret these two figures as showing a strong positive relationship (Healey 2005, p. 404). Political instability and ethnic fractionalisation Chart 6: A Scatterplot showing political stability against ethnic fractionalisation Kaufman's political stability 2.00 0.00 -2.00 R Sq Linear = 0.156 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 Ethnic fractionalisation Page 18 of 29 This scatterplot (chart 6) shows a negative relationship between political stability and ethnic fractionalisation. That is to say as countries get more ethnically fractured they get less stable or, of more concern for the present research question, they get more unstable. Here the Y intercept, the value which we would expect on the political stability variable if ethnic fractionalisation was 0, is 0.719. The gradient is 1.562, meaning that for each unit change in ethnic fractionalisation we would expect to see a fall of 1.562 units on the political stability variable. It is important to note, though, that ethnic fractionalisation runs completely from 0 to 1 so in fact the slope isn’t as steep as it might at first seem from that number. Similarly to the analysis of GDP per capita and political stability above, both F and t scores report a significant relationship at the 99.9% level, allowing us to reject the null hypothesis. As such we can reject our null hypothesis and say that there is a difference between a country’s rating on the political stability variable based on differing values of ethnic fractionalisation. The R was 3.95 which, following Healey, we can interpret as a weak to moderate relationship (Healey, 2005, p. 404). The R² was 0.156; this means that 15.6% of the variance of the political stability variable was explained by the amount of ethnic fractionalisation. Political instability and bad neighbours For the following two bivariate analyses the dependent variable has been collapsed because in it’s former state as an ostensibly interval-ratio level variable it contained too many possible values or “scores”. Assessing this nominal level variable Page 19 of 29 we can see a moderately strong3 relationship between the variables, but one which is significant at the 99% level. This significance allows us to reject the null hypothesis and state that political instability and being in a bad neighbourhood is related. The results from the Cramer’s V analysis are presented below (table 2); Table 2: Looking at a cross-tabulation table we can see that the relationship is such that if countries are in a bad neighbourhood the likelihood of them being stable themselves will be reduced; and so the likelihood of them being unstable increased. Political instability and semi-democracy The results shown below (table 3) demonstrate that there is a relationship between semi-democracy and political instability. The relationship is significant at the 99% level which is enough for us to be confident in rejecting the null hypothesis. Following Healey we can say a Gamma of 0.455 represents a moderate relationship (2005, p. 368). Obviously Cramer’s V does not have an exact interpretation in terms of the strength of the relationship, however here I am following Healey in his interpretation (see 2005, p. 342) 3 Page 20 of 29 Table 3: The gamma also shows direction, showing increasing democracy levels are positively related to political stability or negatively to political instability. Treating the variables in this way however assumes linearity, which we should assume not to be the case from our theoretical assumptions. Taking the original continuous data from the data set and measuring against the non-compacted political stability variable we can see a curvilinear relationship which we expected in theory, with an R² of 0.337, meaning 33.7% of the variance is explained by this variable (see chart 7); Page 21 of 29 Chart 7: Expanded political stability against expanded autocracy/semidemocracy/democracy Kaufman's political stability 2.00 0.00 -2.00 R Sq Quadratic =0.337 -9.0 -6.0 -3.0 0.0 3.0 6.0 9.0 Autocracy/semi-democracy/democracy IV – Multivariate Analysis Multivariate analysis will create a model which includes all of the variables which have been used in this essay to construct a more complete model. Here all of the variables are treated as if they were interval-ratio level. This will allow us to assess the relative importance of each variable when they are being controlled for the effects of each other. Cases were excluded “pair wise” to maintain a higher number of cases for analysis, despite noting criticisms of this method by Field (2005, p. 183). The total model summary is shown in table 4. Page 22 of 29 Table 4: Here we can see that the R², which is to say the amount of variance on the dependent variable which is explained by the independent variables, is 0.482 or 48.2%. The adjusted R² value tells us how much variance would be explained by our model if the model had been derived from the population which the sample was drawn from. As was stated above the global indicators data is being treated as representing a timesample to aid in generalisations, even though strictly speaking it is a population in itself. The Durbin-Watson score is one of the assumptions which must be met for a sample regression to be applicable to the population; here testing for independent errors (Field, 2005, p. 170). A value of between 1 and 3 is good enough, although 2 is the best result for this test (ibid.). Because the value is close to 2 we can assume that at least this requirement has been met4. The R for the model is .694 which, following Healey, shows a strong relationship between the four variables and the dependent variable (Healey, 2005, p. 404). The ANOVA results show that the model is significant, meaning that the model predicts the results significantly better than using the mean values (see Field, 2005, p. 189). 4 Obviously there are more assumptions which must be met in order for us to be completely satisfied that we can generalise, a full list of which can be found in Field, 2005, pp. 169-170. Some of the assumptions, such as linearity, have already been questioned in the above research however this will probably offer a reduction in our model’s predictive capacities so at worse serves to make us more cautious than we might be otherwise. Other assumptions will not be tested, but will be assumed to have been met. Page 23 of 29 Table 5: The above table (table 5) allows us to analyse which variables have the biggest impact upon our ability to predict the outcome of the dependent variable and to state the least squares regression line. The regression line can be stated as; Y’ = -0.456 + (-0.455)X1 + (0.0000704)X2 + (0.311)X3 + (-0.113)X4 Where Y’ is the predicted value on the political stability variable, X 1 is ethnic fractionalisation, X2 is 1997 GDP per capita, X3 is whether a country is autocratic/semi-democratic or democratic5, and X4 is whether the country has bad neighbours. The direction of the relationship shows ethnic fractionalisation and whether or not the country was in a bad neighbourhood had a negative relationship to political stability. This means that if the countries were in a bad neighbourhood or were ethnically fractured political stability would decrease, and so political instability would increase. As the level of democracy and GDP per capita increases political 5 Because for this multivariate analysis linearity has to be assumed, it will be. This may weaken the effect of this variable in the final analysis. The compacted form of the data described in the univariate analysis is used here. Page 24 of 29 stability increases, which means increasing democracy and GDP per capita results in political instability decreasing. The standardised beta values represent the strength of the relationships between each independent variable and the dependent variable in a way which makes them directly comparable. The unit used is standard deviations and tells us how many standard deviations the dependent variable will change if there is a one standard deviation change in the independent variable (Field, 2005, p. 193). As can be seen the country’s level of GDP has the biggest impact upon the political stability variable, and so on political instability. The autocracy/semi-democracy/democracy has the next biggest. Ethnic fractionalisation represented a lower effect. Whether or not the country in question was in a “bad neighbourhood” represented the lowest impact. Not all variables when measured as part of a model were still significant at the 99% level, although GDP and autocracy/semi-democracy/democracy were. The significance of increasing ethnic fractionalisation was under the 90% level, which is too low for us to accept that it is making a significant effect. Whether or not the country was in a “bad neighbourhood” only resulted in significance at just over the 50% level, which is far too low for us to say with confidence that this variable is making a significant contribution to the model. V – Discussion The results, and especially the importance of per capita GDP, seem to suggest that “bad domestic problems” are the key factor within the explanation; here this refers to mass-level internal issues (see Brown, 2001, p. 15). Similarly, other domestic issues are no doubt of importance (how democratic the country is, for example). The Page 25 of 29 only external issue that was analysed was the issue of bad neighbours, but this pointed to a far weaker relationship. Obviously it is too early to say that domestic issues are far more important for political instability than external issues but the results are suggestive of this. To test this proposition more fully it would be useful to create variables to deal with the magnitude of geographically close conflicts and variables to deal with conflicts against the country in question which arose from a non-bordering state. The low importance of ethnic fractionalisation was surprising, although the direction seems to lend support to Gurr’s analysis (1993, p. 161), contra Collier and Hoeffler (2000, p. 8). The results also seem to be suggestive of the interpretation that economic factors are better predictors of political instability than political factors. However there is a concern that the full relationship is actually; “political instability reduced development and investment reduced wealth”. An analysis which is consistent with the findings of Shaw (2003, p. 489), Collier and Hoeffler (2000, p. 2) and Alesina and Perotti (1996, p. 1203), although contrary to Przeworski and Limongi (1997, p172). Choosing 1997 for the year to measure per capita GDP and 1998 for political stability, and thus introducing a small time lag, does not fully avoid this problem because of the possibility of unstable countries staying so for significant amounts of time. This could perhaps be resolved by a smalln qualitative study, attempting to discover which change precedes which. Page 26 of 29 There were several other concerns with this research which if more time and resources were available would need to be addressed. Firstly Kaufman’s political stability variable is far from a perfect measure because it is based on perceptions of the likelihood of violence rather than on actual violence; so the USA’s score dropped sharply following the attacks on the 11th of September 2001, even though the actual likelihood of politically motivated violence stayed largely constant (World Bank, 2007). Whilst the data in question did not cover this time period similar effects could potentially occur during the time period under study in different countries. Secondly, most of the data used represented a single year within each given country. Whilst this isn’t necessarily a problem, Parvin rightly noted that “Representing the average value of several years as one point has the benefit of averaging out a large portion of the transient or random fluctuations of the variable” (1973, p. 288). It may be advisable in future research, then, to attempt to achieve this averaging. Thirdly no attempt was made to deal with the relationship between income inequality and political instability. This was partly because of issues relating to how to measure and evaluate the institutions which society has to manage inequality and partly because of the poor quality of the data available (see Weede, 1981, p. 641 and Cramer, 2003, p. 397). Finally to fit the assumptions of the multivariate analysis linearity had to be assumed even though we should not expect this to be the case. Indeed, chart 7 seemed to provide some persuasive evidence to suggest that this certainly isn’t the case. In a more complete analysis the curvilinear assumptions should be built into the multivariate analysis in order to see the variable’s true weight. Page 27 of 29 Bibliography Web sources CIDCM (2006), http://www.cidcm.umd.edu/polity/data/variables.asp, accessed on 21/1/08 World Bank (2007), http://info.worldbank.org/governance/wgi2007/faq.htm, accessed on 10/1/08 Journal Articles Alesina, A. at al (2003) “Fractionalization” Journal of Economic Growth 8. pp. 155194. Alesina, A. and Perotti, R. (1996) “Income distribution, Political Instability and Investment” European Economic Review 40. pp. 1203-1228 Bermeo, N. (2003) “What the Democratisation Literature Says – Or Doesn’t Say – About Postwar Democratisation” Global Governance 9. pp. 159-177 Byman, D and Van Evera, S (1998) “Why they fight: Hypotheses on the causes of contemporary deadly conflict”, Security Studies 7, pp. 1 - 50 Case, W. (1996) “Can the "Halfway House" Stand? Semidemocracy and Elite Theory in Three Southeast Asian Countries” Comparative Politics, 28. pp. 437-464. Collier, P. and Hoeffler, A. (2000) “Greed and Grievance in Civil War” World Bank Policy Research Working Paper 2355. pp. 2-44. Cramer, C. (2003) “Does inequality Cause Conflict?” Journal of International Development 15, pp. 397–412. Gurr, T (1993) “Why Minorities Rebel: A Global Analysis of Communal Mobilization and Conflict Since 1945”. International Political Science Review, 14. pp.161-201 Hegre, H. et al. (2001) “Towards a Democratic Civil Peace? Democracy, Political Change and Civil War 1816-1992” American Political Science Review 95. pp.33-48 Kaufman, D. et al. (2007) “Governance Matters VI: Aggregate and Individual Governance Indicators 1996-2006” World Bank Policy Research Working Paper 4280 Lichbach, M. (1989) “An Evaluation of "Does Economic Inequality Breed Political Conflict?" Studies” World Politics, 41. pp. 431-470 Parvin, M. (1973) “Economic Determinants of Political Unrest: An Econometric Approach”, Journal of Conflict Resolution 17, pp. 271-296 Page 28 of 29 Przeworski, A. and Limongi, F. (1997) “Modernization: Theories and Facts” World Politics 49. pp.155-183 Shaw, T. (2003) “Regional Dimensions of conflict and peace building in contemporary Africa” Journal of International Development 15, pp. 487–498 Weede, E. (1981) “Income Inequality, Average Income, and Domestic Violence” The Journal of Conflict Resolution, 25. pp. 639-654. Books and Edited Volumes Brown, M. (2001) “The causes of Internal Conflict” in Brown et al (eds.) Nationalism and Ethnic Conflict. London: MIT press. pp. 3-25 Field, A (2005) Discovering Statistics Using SPSS (second edition). London: Sage. Healey, J (2005) Statistics: a tool for social research (seventh edition). London: Thompson Mansfield, E. and Snyder, J. (2004) Electing to Fight: Why Emerging Democracies go to War. London: MIT Press Snyder, J. (2000) From Voting to Violence: Democratisation and Nationalist Conflict. London: Norton. Page 29 of 29