Quantitative Data Analysis and Interpretation
Approved Essay Title: Explaining Political Instability in a Quantitative Cross-National Study (QDA)
2007-2008
Word Count: 5202
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 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 instability[1]. 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 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 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 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
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.
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 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:
Concept and measurement of wealth
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;