4. Quantitative Analyses: The Empty Corner
The implication of the statistical data ... concerning democracy, and the relationships between democracy, economic development, and political legitimacy, is that there are aspects of total social systems which exist, can be stated in theoretical terms, can be compared with similar aspects of other systems, and, at the same time, are derivable from empirical data which can be checked (or questioned) by other researchers.
Seymour Martin Lipset, 1959
Theoretical and qualitative analyses suggest testable hypotheses relating democracy with technological communication capabilities. Whereas anecdotal evidence has borne the weight of claims emanating on both sides of the debate until now, this study seeks to further enhance understanding of the relationship by examining empirical evidence. This section engages quantitative evaluation. The methodology, including epistemological caveats with regard to causality, are discussed first. Then the data are defined and subsequently explored from several different statistical vantage points. In all cases, the results show electronic network interconnectivity to be a substantial and statistically significant predictor of democracy. Various causal suppositions are also tested. The evidence does not support any alternative to the causal link leading from interconnectivity to democracy.
MethodologyRegression analysis has proven to be useful in the social sciences, as well as the physical sciences, as a means to interpret data and gain critical insight into the relationships between key variables. Specifically, starting with Lipset, studies of democracy's correlates have relied extensively on the tools of linear regression. Likewise, motivated by both the inherent utility of the method and by the desire for intellectual continuity and comparative compatibility, this investigation also turns to regression techniques. Democracy ratings for all the countries of the world are regressed against democracy's traditional correlates drawn from historical data comprising a nonrandomized observational study.
Causality Caveats and ClarificationsBefore engaging the statistical analysis, it is important to consider what these tests can and cannot do relative to the objectives of the quantitative section of this research. Negative test results can certainly refute proposed hypotheses, but positive results are rather more ambiguous. Causality, while implicitly central to this dissertation, cannot be proved explicitly in nonrandomized observational studies. Causality cannot be proven conclusively without well controlled experimentation that is rarely, if ever, possible in international affairs. In fact, a prominent statistician claims to have never come across a case in the social sciences in which regression equations succeeded in discovering causal relationships (Freedman, 1994). The objective of the data analysis here is decidedly more modest. The goal is to strengthen, as warranted, a policy argument. Data analyses reveal the extent to which the data can support or deny a given argument to promote certain types of policy actions. The action argument, itself, is developed elsewhere. In a world of uncertainty, an "action argument" can be, and sometimes should be, or even must be, compelling in the policy realm without definitive inferential justification (Hodges, 1995).
Therefore, the fact that causality cannot be rigorously proven does not tarnish the conclusions of this research. However, it does not follow from this statement that the concept of causality is irrelevant to this work. On the contrary, the degree to which the data analyses can bolster the action argument is a function of the degree to which alternative causal explanations are addressed, tested and assigned due credence. Thus, the following references to causality are expressed in a presumptive sense as appropriate to the task at hand rather than in the sense of a formal proof.
When testing the correlates of democracy one can ask either: What causes democracy? or What are the relative effects on democracy of various candidate causes? As suggested above, answering the former question is a formidable endeavor, more than this study is prepared to undertake. To address the latter question, the focal issue is "prima facie causal effect" defined as the algebraic difference in expected values between measured outcomes with and without the specific intervention (Holland, 1986). The prima facie causal effect corresponds with the action argument. While contingent on certain plausible models, it is more tractable to empirical analysis and is more useful to the policymaker when evaluating available policy options.
Logic of the Test SequenceThe various statistical perspectives in this section are chosen specifically to investigate dominant causal premises, especially those that rival the thesis that new communication technologies have an important contributory role in democratic development. Furthermore, the research design employs a variety of models and functional forms for the main exogenous variable, electronic network interconnectivity. In this way, conclusions have the broadest validity and do not rely on any particular model or functional form which would be undoubtedly subject to dispute.
Cross-sectional univariate analyses confirm a strong correlation between levels of democracy and of network interconnectivity, both in an absolute sense and relative to democracy's other traditional correlates. Since the strong correlation could conceivably be a result of excluded variable biases, the inclusion of additional correlates in multivariate regressions analyses tests this hypotheses. Alternatively, regional effects could underlie the strong correlation. Categorical analyses control for potential regional influences. Time has a unique role in the consideration of causality. Longitudinal analyses introduce the time dimension to ensure the cross-sectional results are not a circumstantial artifact, but rather an outcome consistent with systematic change. The correlation of interest is strong not only on absolute levels. Relative changes in the level of democracy over a ten year period also strongly correlate with changes in the level of network interconnectivity.
Correlations are silent on questions related to causal direction. There are significant reasons to believe that democracy influences interconnectivity, in addition to the reverse. The strong correlation could result from either as the driving force. Systems of simultaneous equations involving more than one endogenous variable can be helpful to compare the relative effects. These analyses additionally include economic development as a third endogenous variable since it is a leading candidate for a compounding factor that affects both democracy and electronic communication networks simultaneously. Finally, the potential effects of collinearities between economic development and communication technologies are further tested by including other telecommunications media in the analyses. The single causal arrow that consistently withstands hypothesis testing is that which points from interconnectivity toward democracy.
DemocracyDemocracy is an abstract concept difficult to quantify. Assigning each country a single number to represent the relative level of democracy is rife with pitfalls. First, democracy is a subjective quality and therefore liable to interpretive biases. Second, countries are complex systems. A nominally democratic country may have decidedly non-democratic organizations and practices and vice versa. Third, democracy is multi-dimensional. Flattening and compressing democracy into a single scalar inexorably oversimplifies and truncates important descriptive information. The inherent problems associated with developing a useful and robust measure for democracy have come to command substantial attention in academic circles.
Despite the theoretical obstacles, a practical consensus has been achieved in several key aspects of a viable democracy metric. Face validity of operational definitions of democracy consistently call for the inclusion of two or three interdependent elements. (i) Political Rights. These refer to the institutions and procedures by which citizens are assured the capability to freely participate in selecting policymakers and influencing policy decisions. (ii) Civil Liberties. These refer the guarantees of freedom for acts of political participation and in daily life. The former guarantees safeguard rights such as free speech that may conflict with official state positions and the latter protect people from the likes of slavery and torture. (iii) Institutionalized Checks and Balances. These refer primarily to constraints on the exercise of executive power. Citizens of a democracy not only select their leaders but also retain the capacity to institutionally limit the leaders' power (Jaggers and Gurr, 1995).
Relative democracy rankings by independent authorities based on differing criteria agree quite broadly. Concurrence suggests that, although the concept of democracy may be difficult to describe explicitly, it is nonetheless well understood intuitively (at least by Western analysts).
[D]emocracy is a distinctive and highly coherent syndrome of characteristics such that anyone measuring only a few of the salient characteristics will classify nations in much the same way as will another analyst who also measured only a few qualities but uses a different set of characteristics, so long as both have selected their indicators from the same larger pool of valid measures. Far from being like the elephant confronting the blind sages, democracy is more like a ball of wax. (Inkeles, 1990: 5).General conformity in ordinal rankings among different rating schemes confers validity on these constructs for democracy.
One of the leading published sources of democracy measurements is Freedom House's Comparative Survey of Freedom. Freedom House scores are often employed in scholarly research to evaluate the correlates to democracy. In accordance with this increasingly common academic practice, the metric for democracy in the following statistical analyses is derived from Freedom House data, specifically from the 1993/1994 report. A brief overview of the Freedom House survey and methodology is provided below.
Freedom House conducts a pair of annual world-wide surveys on political rights and civil liberties. Scores are assessed relative to checklists of questions. The checklist for political rights includes nine questions such as: "Is the head of state and/or head of government or other chief authority elected through free and fair election?" and "Are the voters able to endow their freely elected representatives with real power?" The checklist for civil rights includes thirteen questions such as: "Are there free and independent media, literature and other cultural expressions?" and "Are there free businesses or cooperatives?" and "Is there freedom from extreme government indifference and corruption?" With both objective and subjective assessments, each checklist item per country is assigned from 0 to 4 raw points relative to the degree the question can be answered affirmatively. The maximum raw score for political rights is 36 and for civil liberties is 52. In each survey, the range of possible scores is divided into seven nearly equal categories. At the end of each year, Freedom House reports a rating from 1 to 7 for every country, from the greatest freedom to the least, respectively. Since both political rights and civil liberties are recognized as fundamental elements of democracy, and since not surprisingly both are highly correlated, the single dependent democracy variable in this study is the average between these two measures. This scale is then inverted and normalized to 100 for intuitive convenience. The result of these cosmetic conversions is a metric with 13 discrete values, the maximum democracy rating is 100 (instead of 1) and the minimum is 0 (instead of 7).
A recent study correlated eight leading indicators of democracy including both scores from both Freedom House surveys. All concurred to a great extent. The correlation coefficient between the Freedom House dataset and the others ranged from 0.79 to 0.93, thus supporting its validity as a construct for democracy (Jaggers and Gurr, 1995). It is worth mentioning, however, that generally agreeing measurements for absolute levels of democracy do not necessarily mitigate difficulties in measuring relative changes in democracy.
InterconnectivityThe main exogenous variable of interest in this study, the prevalence of information revolution technologies, might seem to be easier to quantify than the endogenous democracy variable because the latter involves keeping track of tangible equipment. Yet, construct validity with this variable is no less problematic. Some difficulties are definitional, others practical. As communication technologies increasingly overlap, recalling Ithiel de Sola Pool's "convergence of modes" (1983b: 23), the question of what ought to be included as an information revolution technology becomes non-trivial to answer. Computers can send faxes, audio and video; radio waves and television cables can transmit e-mail messages.
This study focuses specifically on electronic mail because it is the technology that enables people to discourse across borders in ways that have never been possible. The term given to the metric to be used here is "interconnectivity." Of the numerous e-mail networks, four are globally dominant: Internet, BITNET, UUCP and FidoNet. Record keeping has not been consistent, regular or accurate across the networks. The best available and most comprehensive data are for the numbers of nodes, which therefore constitute the basic unit of measure for interconnectivity in this report. Nodes themselves, however, are not equal, even within the same network. A node may consist of a single computer and user or an entire organization with many of both. The Matrix Information Directory Service (MIDS) tracks and maintains historic data on the size of these networks aggregated by country. The first year for which comprehensive data are available is 1993. These data were compiled by MIDS in October 1993. By this time, there was already wide self-selected variation in the levels of connectivity.
The numerical value of a country's interconnectivity variable derived from these aggregated data are calculated in three different ways throughout this study. Each is described below in the reverse order to which they will be encountered in this report. All three formulations are per capita measures that give equivalent weight to each of the four networks. For comparison among telecommunications media, since televisions, telephones and computer network nodes all have similarly large positively skewed distributions, the interconnectivity metric in this case is the logarithmic transformation of the algebraic sum of the network nodes. The discontinuity at zero of the log transformation of the interconnectivity variable is problematic because the node count in 1993 was identically zero in approximately a third of the countries. Therefore an additional binary variable is paired with the log transformation of the interconnectivity variable, to indicate the existence, or not, of electronic mail networks in each country.
The second transformation is the square root of the sum of the nodes on all four networks. This transformation preserves some of the magnitude effects without the discontinuity at the origin. Thus, every country's level of interconnectivity can be described with just one variable. This transformation is useful to describe the change in connectivity over time in the longitudinal analysis. An actual physical significance of the square root transformation is difficult to conceptualize, however.
The first form of the interconnectivity variable to be encountered is a bit more computationally intensive than the other two. The data are transformed to yield a linear metric rating of countries according to their relative extent of interconnectivity. By network, countries are ranked and scored with a number from 0 to 4. The 0 is assigned to all countries with no nodes in a particular network. The numbers 1 through 4 are assigned by quartile. The lowest quartile countries with one or more nodes in a particular network receive a score of 1. The highest quartile countries receive a score of 4. The sum of the four scores determines the level of interconnectivity on a scale from 0 to 16. The resulting scale is a useful metric to evaluate the correlation between electronic mail network interconnectivity and democracy.
In each of the three functional forms, the equal weights are justifiable because the ability to exchange e-mail is relatively generic capability. However, assumed equivalence does introduce some theoretic difficulties. Despite the universal similarity in supporting e-mail, the networks are not necessarily comparable in other respects. For instance, the Internet, with specialized services such as the World Wide Web and remote log on, has more functional capacity than the others. Therefore, in each functional form perverse results are theoretically possible. For example, a country with relatively fewer nodes and thus a lower interconnectivity score could potentially have more communications capability than a country with relatively more nodes if a higher percentage of the former's nodes were Internet nodes.
In practice this is not likely to occur, and the analysis shows none of the potential degradation of this variable. There are several reasons why this theoretic possibility is not a practical problem. First, e-mail, not necessarily the other services, offers the specific capability that is hypothesized to have dynamic implications for democratization: multi-directional discourse across borders in a timely and inexpensive manner, unbounded by geographic and institutional constraints. Second, interconnectivity evolves. Less capable systems are similarly less expensive and easier to implement, so initially they are more prevalent. Improvements ultimately incorporate Internet capabilities. Thus, a general progression emerges in the enhancement of interconnectivity that this scale approximates. Furthermore, to the extent that interconnectivity as a predictor for democracy is measured imprecisely, the effect is reduced statistical significance of the predictor. Thus the conclusions would still be sound from the resulting a fortiori analysis.
The use of more than one functional form of the main exogenous variable offers two advantages in this research. Most obvious, perhaps, is that the researcher can choose the form best suited to each particular statistical test. Additionally, the independence of test results from any particular functional form further strengthens the validity of comprehensive conclusions.
Economic Development and EducationEconomic development, reported here as per capita GDP (and abbreviated simply as GDP), is measured as purchasing power parity. Education is commonly paired with economic development as a predictor of democracy. Direct causality is easy to imagine. An educated public is likely to be both more aware of political events and more capable of intervening to influence them. Indirectly, education conceivably enhances democracy by contributing to economic growth. The average number of years of schooling across the entire population is considered to be the best measure of education for analyses such as these (Rowen, 1995: 57).
Human Development and HealthHuman development and health indicators also are correlated often with democracy. Most prevalent in the literature are infant mortality rates and life expectancies. A causal argument could be posed that as citizens become more assured of their own well-being, they have more incentive and wherewithal to demand civil rights and political liberties. Although these two measures, infant mortality and life expectancy, are highly (and negatively) correlated, forward causality seems more plausible in terms of the latter.
Ethnicity and CultureCultural and ethnic factors also may have certain roles in democratization. Some have argued, for example, that "Homogeneous national entities may be more likely to evolve into peaceable democracies than states rent by harsh linguistic and cultural antagonisms" (Gottlieb, 1994: 101). The measure of ethnic homogeneity employed in this study is the percentage of the population that constitutes the largest ethnic group in a nation.
In multivariate analyses, cultural differences across countries are potentially more important than the internal cultural mix. Debates continue as to whether certain cultures or civilizations are favorably disposed or fundamentally disinclined to embrace democratic principles. In either case, it is not difficult to believe that cultural aspects influence the characterization of the political regimes and the appreciation of personal liberties. To account for these effects, the data set includes binary variables that indicate the culture with which each country most closely identifies. Demarcation between cultures can never be exact. Inexorably, the classification of some countries into any of the regional categories may be arguable.
Six regional categories were defined that incorporated elements of geography, history and religion: Africa, Asia, Eurasia, Latin America, Middle East, and Western Europe. Western Europe includes countries that are not on the continent but which have a dominant Western European heritage: United States, Canada, Australia and New Zealand. Israel also is included in the Western European category. The Middle East category is predominantly Muslim, includes the Islamic North African states and extends from Morocco to Pakistan. Africa is defined in fairly obvious geographic terms including South Africa, minus the northern states grouped into the Middle East. Asia includes the Confucian countries and the Pacific Islands, plus India and Japan, minus North Korea. Latin America stretches from Mexico through Argentina including all the Caribbean except Cuba. Cuba and North Korea, plus Albania and the splinter states of Yugoslavia, in addition to the members of the former Warsaw Pact countries, are all grouped in the Eurasian category.
Cultural influences may also shape the ways in which various people utilize communication technologies. Therefore, some of the regression models that follow include interaction terms that are the products of the binary regional variables and the interconnectivity scores. The resulting regression coefficients on this term describe region specific correlations between democracy and networked communication technologies.
PopulationPopulation completes this list of independent variables. Presumably, the size of a country can influence the type and effectiveness of governance. James Madison addresses the issues and effects of country size and population on democracy in the Federalist Paper Number 10 (48). This is also the focus of recent analytic research at the National Bureau for Economic Research (Alesina and Spoloare, 1995). Furthermore, very small countries may be anomalous. Therefore, data only for countries whose populations exceeded 1,000,000 (and for which data are available) in 1993 are included in this study. Above this threshold minimum, country populations have a skewed distribution that spans more than three orders of magnitude. Population, therefore, is best included as an independent variable in this study in its logarithmic transformation.
Visual evidence of a correlation between democracy and interconnectivity is provocative. Figure 4.1 shows Freedom House democracy ratings for all the countries of the world. Darker shading indicate higher levels of democracy.
Figure 4.1-Democracy Rating
Figure 4.2 is a comparable world projection denoting prevalence of major worldwide e-mail exchanging computer networks.
Figure 4.2-Interconnectivity Scores
Darker shading indicates a greater level of interconnectivity. Corresponding regions of dark and light on every continent reveal striking similarities between the two charts. The pattern similarity suggests a correlation between democracy and electronic network interconnectivity and inspires more rigorous further examination.
The scatterplot with an accompanying regression line in Figure 4.3 displays this relationship graphically and the following correlation matrix in Table 4.1, numerically.
Figure 4.3-Democracy and Interconnectivity
Matrix Showing First Order Correlations
The correlation matrix exhibits a surprisingly powerful correlation between interconnectivity and democracy. The correlation coefficient on interconnectivity is not only large, it is substantially larger than that of any other traditional predictors of democracy in this first order analysis. The coefficient on per capita GDP, which has often been considered the most important of democracy's correlates, is more than 20 percent smaller.
Multivariate Regression AnalysisIn large complex systems such as international politics, simple relationships can rarely tell the whole story. Multiple linear regression can be a powerful technique to provide insight into the complicated interactions. As with other techniques, the answers are often influenced by the ways in which the questions are asked. In other words, regression results can be model specific. Therefore, versions of several models offer various perspectives that can be integrated to form a comprehensive understanding of the interactions. Ultimately, the multiple linear regressions in this research provide further evidence that this correlation cannot be dismissed. Regression results of six representative and most informative models are shown in Table 4.2. Models I and II show the resulting statistical output of ordinary least squares (OLS) regressions. Model I is an inclusive model involving six predictors.
It is immediately apparent that interconnectivity emerges again as the dominant correlate. The level of certainty that interconnectivity is a valid predictor for democracy is greater than 99.9 percent, higher than that for any other potential predictor. Furthermore, the coefficient on interconnectivity is large. A single point increase on the interconnectivity scale corresponds to an increase of 5 points in democracy rating.
The correlation of GDP with democracy in this model is also statistically significant although at a lower level. It is important to note, however, that the sign is negative. This result could support arguments of some scholars, as well as apologists for the Pinochet and Lee Kuan Yew economic development theories, that democracy is not costless. All else being equal, such as education, interconnectivity and population, greater economic development might be available only at the expense of democratization. Alternatively, a sufficient standard of living may serve to deflect popular demands for more political power. Another possibility may be that the relationship between economic development is not linear as assumed in the regression model. Perhaps there is a simple minimum threshold; or perhaps there is an "N" shaped progression in democratic and economic development (Lipset et al., 1993); or perhaps there is some other more complicated relationship.
Years of schooling and life expectancy also show statistical significance. In the case of the latter, the negative sign is more difficult to explain although this is the weakest of the significant predictors. The coefficient on population is also significant, but the size of a country's population, largely inaccessible to foreign intervention, offers scarcely few policy recommendations (except perhaps to shine a glimmer of hope on the fractious states of Yugoslavia and the former Soviet Union that potentially may have a more democratic future than their larger predecessors.)
Model II contains a more parsimonious model retaining only GDP, the log of population and the interconnectivity variable. These fewer variables continue to explain more than 50 percent of the variation in democracy for 141 countries. After excluding three predictors, the small drop in adjusted-R2 (0.047) underlines the relative importance of interconnectivity. Alternatively, when retaining those three variables and excluding interconnectivity, the goodness of fit measure decreases by more than twice as much. In other words, interconnectivity alone may be more important for predicting democracy level than these three independent variables combined.
The possible effects of multicollinearity also deserve attention. The correlation matrix in Table 4.1 indicates high correlations between many of the independent variables, particularly those of specific interest to this investigation: GDP, interconnectivity, and schooling. Collinearities between independent variables will tend to reduce the efficiency of predictors, but without bias. Reduced efficiency means that the reported statistical significance may be less than the actual because the standard errors will be excessively large. The absence of bias means that the estimated coefficients will be neither systematically higher nor lower than their "true" values. Correcting for the multicollinearity could result in an increase in the number of statistically significant predictors and further strengthen statistical inferences relative to those variables such as interconnectivity that are already significant. On the other hand, the effect of GDP might be unduly understated since some statistical significance is sacrificed to interconnectivity and to the other included variables with which GDP is collinear. The magnitude of the coefficient on GDP is, nevertheless, quite small and is presumably reported without bias in the inclusive Model I. Furthermore, comparing Models I and II, the coefficients do not vary much with the consecutive inclusion or exclusion of the other independent variables.
Regional AnalysisModels III and IV, with the addition of the regional interaction terms, are analogous to I and II, respectively. These next two models show that the positive correlation of interconnectivity with democracy is consistent across and within regional boundaries. In all the regions the coefficient is positive. In half of the regions, the coefficient is substantial and statistically significant.
In Africa, the coefficient on the interaction term is the highest, and the t-statistics correspond to a 1 percent level of significance or better. In Eurasia, the results are similar with a t-statistic also indicating 1 percent as the lowest significance level. The coefficient is also substantial for Latin America with a 10 percent significance level on model IV. The regression lines that accompany the six scatterplots in Figure 4.4 approximate these multivariate regression results for visual comparison. Western Europe shows the most paltry correlation. In this region, the high interconnectivity levels do not vary much and the high democracy ratings move even less.
Figure 4.4-Regional Regressions and Scatterplots
Longitudinal Time AnalysisIn the wake of the coincident revolutions, cross-sectional investigations reveal a strong correlation between democracy and interconnectivity. These one-moment-in-time pictures cannot show which countries took part in the recent democratic revolution nor whether or not these same countries were participants in the information revolution. The salient question that the cross-sectional regressions fail to answer is how much did democracy change in countries that increased interconnectivity? Addressing this question requires temporal comparisons, the data for which, unfortunately, is largely nonexistent.
International interconnectivity data was not recorded until electronic networks were already a global phenomenon. By the time Larry Landweber began his landmark surveys in 1991, almost 100 countries and territories were already connected. It was also not until 1991 that the Internet started sampling and recording country data. Nevertheless, there was an earlier date on which interconnectivity levels are known almost exactly: in the early 1980s when electronic networking was still an American experiment almost exclusively within the research community of the United States. At the end of 1983, for instance, the level of interconnectivity for almost every country, other than the U.S. was identically zero. In that year, FidoNet was invented and BITNET ("Because It's Time NETwork") was still in its early stages of domestic development. The European version of BITNET, EARN (European Academic and Research Network) was also established in 1983. In the following year, the British and Japanese implemented their own national versions of the Internet, JANET (Joint Academic Research Network) and JUNET (Japanese Unix Network) respectively. In 1983, the total number of American Internet hosts was still less than one thousand, but prepared to leap three orders of magnitude to more than one million ten years latter (Hobbes, 1996). This decade, from 1983 to 1993, although somewhat arbitrarily defined also brackets the major surge in the number of democratic states at the end of the 1980s.
The dependent variable in the following tests is defined as the change in democracy over the period from 1983 to 1993. To compute this difference across the decade, democracy data are extracted, as before, from the Freedom House surveys and then the democracy values for 1983/84 are subtracted from the 1993/94 values. Countries which appeared after 1983 assume the value of the of the predecessor state. Namely these are the republics of the former Soviet Union and the splinter states of Yugoslavia. States that merged take the value of the dominant partner, that is West Germany and North Yemen.
Possible values for the change in democracy over the decade are structurally truncated at both ends of the scale because the democracy scale is finite. In other words, if a country with the lowest democracy rating were to become less free, the value for the change would nevertheless be zero. The reasoning and value are the same if a country with the highest democracy rating were to become more free. It is, thus, apparent that comparison between values of the dependent variable can be valid only when the initial conditions are similar. Therefore, the following analysis is segmented into three groups of countries according to the Freedom House classification of countries in 1983/84: Not Free (defined by a democracy rating below 33), Partially Free (between 33 and 67) and Free (above 67).
Values of the independent interconnectivity variable can likewise be expressed in terms of change since the initial condition is universally zero in all countries except the United States. The change in interconnectivity is everywhere non-negative yet spans several orders of magnitude. Therefore, to show most clearly and represent most faithfully the relative relationships between changes in democracy and gains in interconnectivity, the latter is transformed as the square root of the number of networks per million population. The previous metric, a partially limited rank order in discrete levels from 0 to 16, mutes the magnitude effects that are important here. A natural log transformation, which will be used later, necessitates an undesirable separate treatment of the important data where there is identically zero network connectivity. A disadvantage of the square root transformation is that there is no ready physical interpretation or explanation. However, since the purpose of this study is neither to seek the "right" functional form nor to advocate any particular functional form, consistent results using various transformations can suggest that a strong correlation is evidence of an important relationship that exists between the dependent and independent variables rather than simply a statistical accident contingent on a certain functional form.
"Not Free" in '83Among the three groups, the countries in the "Not Free" segment are of greatest analytic interest, both from a theoretic and from a policy perspective. The theory suggests that an increase in connectivity is likely to be accompanied by an increase in democracy. These "Not Free" countries have the most room for democratic improvement. The "Not Free" countries are also those for which policy initiatives to support democracy can have the most dramatic impacts. While the need for programs to encourage democratic development in these countries is pressing, the success of such programs, historically, has been most elusive, otherwise they would have moved out of this bottom category. Additionally, this segment contains a majority of countries, more than double the number of either "Partially Free" or "Free" countries.
A finding of no correlation between the changes in democracy and interconnectivity in these least democratic countries would cast substantial doubt on the underlying basis of the correlations noted earlier. However, a strong correlation, consistent with all previous results, is apparent from the following scatterplot and corresponding regression line in Figure 4.5. Interconnectivity proves to be a statistically significant predictor of the change in democracy for the "Not Free" group at a level of confidence of more than 99.9%.
Figure 4.5-Interconnectivity and Democratic Change in "Not Free" Countries
Sufficiency, on the other hand, is not so easily discounted in the absence of data to the contrary. The two countries closest to the edge of the "Empty Corner" are extreme cases; Croatia was at war in 1993 and South Africa suffers a bifurcated society consisting of a connected minority and a large unconnected majority. Even under these trying circumstances, both of these countries reported respectable democratic gains. Perhaps as these countries become more "normal" the "Empty Corner" will expand further.
The R2 for the regression suggests that interconnectivity can account for only about 20% of the change in democracy. The two outliers mentioned above degrade line fitting, but the points scattered at the top left-hand corner of the plot are the main cause of the relatively poor fit. The measure of goodness of fit for this regression is not as high as others have been but an increase in connectivity correlates with a steep increase in democracy level as indicated by the slope of the regression line and the magnitude of the regression coefficient. Introducing a hundred nodes per million population corresponds with a leap in democracy equal to a third of the scale.
The "Empty Corner" corroborates further the notion of the "Dictator's Dilemma," that greater connectivity can come only at the expense of political control. President Gorbachev provided the most dramatic example of an authoritarian leader who would seek economic benefits of information and communication while hoping to maintain political control. The record shown in Figure 4.5 indicates that neither he nor anyone else has yet been successful in exploiting networked communication technologies while simultaneously avoiding political liberalization.
The shatter of Gorbachev's Soviet Union resembles, in some aspects, a controlled experiment. The initial state was arguably monolithic. The constituent elements, subject to differing levels of intervention (interconnectivity and otherwise), diverged toward disparate outcomes. The results of this "quasi-experiment" are depicted in Figure 4.6. The point lying farthest to the left on this plot, representing Estonia, is one of the five points that hover around the top of the regression line in Figure 4.5. Analysis of these five points can offer additional valuable insight. Since they describe the regression line best, if all five were from the same cohort of countries, the generalizability of the regression would be doubtful. However, the five points represent countries from four different cohorts on two continents: the former Soviet Union, former Yugoslavia, Latin America and Eastern Europe.
It is subsequently instructive to compare these five points with the others within their respective cohort of countries, such as Figure 4.6 depicts for the countries of the former Soviet Union. If the elements within a cohort were to line up vertically, no inference could be drawn because there would not have been enough variance in the independent variable to reasonably test or determine correlation. If the elements were to line up horizontally, the inference would be one of two depending on whether the horizontal line was high or low. A low horizontal regression line would be sufficient to reject the correlation hypothesis within that particular cohort. It would indicate that the independent variable had no effect on the dependent variable because even high values of the former would correspond with no change in the latter. A high horizontal line is more ambiguous. An elevated but flat distribution of points could result if an outside factor instigated change uniformly throughout the countries of the cohort, thus implying that interconnectivity was irrelevant, or it could result if interconnectivity in fact contributed to democratic change in leading countries and then the other less connected states in the cohort were influenced by the example or proximity of the leaders. Alternatively, a correlation within a cohort characterized by a line that rose to the right would be consistent with the notion that among similar countries, those which were the most interconnected were also those that experienced the greatest democratic gains. Of the four cohorts of countries, a correlation that rises to the right is clear in three of them although the samples consist of as few as three elements.
Figure 4.6-Interconnectivity and Democratic Change in the Former USSR
The break-up of Yugoslavia, like that of the Soviet Union, provides an opportunity to compare clearly divergent outcomes from a nominally uniform initial condition. The similarity extends to the results of this "quasi-experiment" also. The Yugoslav results shown in Figure 4.7 repeat the Soviet experience. The correlation between interconnectivity and change in democracy level is striking although the sample size is too small for strong statistical inference. Historic, economic, demographic and cultural factors likely account for some of the correlation but perhaps not all.
Figure 4.7-Interconnectivity and Democratic Change in Former Yugoslavia
Only three countries in Latin America were classified as "Not Free" by Freedom House in 1983. There was no analogous unified starting point among these countries as there was for the Soviet Union and Yugoslavia, but the correlation shown in Figure 4.8 is again remarkably similar. Of the three countries, Chile alone became significantly interconnected. While the two unconnected countries, Haiti and Guatemala, recorded a democratic loss and minimal gain, respectively, the democracy level in Chile jumped up by more than half of the democracy scale. Of course, interconnectivity is only one of numerous plausible explanations why Chile might differ from the other two. In such a small sample, it is not possible to rule out all alternative hypotheses.
Figure 4.8-Interconnectivity and Democratic Change in "Not Free" Latin America
Results from Eastern Europe (exclusive of the states of the former Soviet Union or of the former Yugoslavia) are the most ambiguous but they do not dispute either the other correlations or the hypothesis regarding interconnectivity and democratic change. The horizontal regression line in Figure 4.9 shows no evident correlation. However, since the regression line is high, it is not possible to conclude that interconnectivity had no effect. For example, the ability to communicate may have functionally supported democratic movements in Hungary and Poland while Albania, Bulgaria and Romania simply followed the examples set by their neighbors. Hungary and Poland were among the first to move in the direction of democracy in their region and were credible role models for the rest of Eastern Europe. Furthermore, the democracy scale does not account for the stability of democracy in a given country. The prospects for sustained democracy in Albania, Bulgaria and Romania are arguably less than in Poland and Hungary. It is quite possible that within a few years a correlation between interconnectivity and net democratic change within this cohort may become more apparent. More importantly, there is also a quantitative reason, which does not rely on speculations such as these, that attests to a correlation actually stronger than it appears in Figure 4.9. The initial conditions among these countries were not identical, nor nearly so, as was previously the case. In fact, this cohort comprises two sets of polar extremes within the "Not Free" classification. Albania, Bulgaria and Romania, residing at the very bottom of the democracy scale, had significantly more room for democratic improvement than did Hungary and Poland, which were just short of the "Partially Free" threshold. Ultimately, the more interconnected countries remained equal to or more democratic than the less interconnected countries.
Figure 4.9-Interconnectivity and Democratic Change in "Not Free" Eastern Europe
Interconnectivity in every other country of the "Not Free" segment (with the exception of South Africa already discussed above) was minimal or zero. Nowhere else was there enough variation in the independent variable to test the correlation hypothesis. Almost everywhere that the correlation was testable in this, the most important segment, it was strongly confirmed. A strong correlation between interconnectivity and democratic change was found in the complete population comprising 75 "Not Free" states and in three of the four salient subsets. In the single case in which correlation was not confirmed, it could not be rejected and there were statistical and speculative reasons why the correlation might actually be stronger than what was observed.
"Partially Free" in 1993The group of partially free states is the smallest and the most diverse segment. It includes 31 countries with roughly equivalent representation each from Africa, Asia, Latin America and the Middle East. Thus, characterizing this group with a small number of variables is difficult. This is the only segment of the three in which the statistical significance of the interconnectivity variable as a predictor for democratic change is less than 99.5%.
Asia is the only region within this group that has sufficient variance of interconnectivity for meaningful hypothesis testing on a regional level. However, the dominant outlier, Singapore, is also from Asia. Cohort analysis is, therefore, not helpful. The leverage of this one influential outlier is enormous. Excluding Singapore greatly alters the regression depicted in Figure 4.10. In its absence, the statistical significance on interconnectivity rebounds dramatically from about 60% to more than 95% and the R2 goodness of fit measure quadruples.
Figure 4.10-Interconnectivity and Democratic Change in "Partially Free" Countries
Despite the huge deleterious effect of Singapore on the correlation between democracy and interconnectivity, Lee Kuan Yew's model does not hold a key with which to escape from the "Dictator's Dilemma." In the first place, Singapore is exceptional in many regards; it is doubtful that any other country can replicate this unique city state model. In the second, the Singaporean model may be just a temporary anomaly; it is not clear how long Singapore can resist pressure for more democracy, or more pointedly, whether the model itself can survive its charismatic but aging founder. In the third place, and most important perhaps, Singapore does not represent a counter-example that refutes the policy arguments developed here, but rather this data point simply suggests a smaller less optimistic correlation coefficient. This realization is apparent upon examining Singapore's comparative position with other countries both in terms of interconnectivity and democracy. On a relative basis measured in percentiles with respect to the whole of the world, Singapore seems fairly well connected. On an absolute basis, however, measured in nodes per capita, Singapore is significantly less interconnected than almost all of the western democracies, some by nearly an order of magnitude. On the other hand, the level of democracy in Singapore, while not an ideal from the western perspective, would be a welcome improvement for a third of the countries and more than 40% of the world's population in 1993. In other words, if the interconnectivity level of "Not Free" countries were to be increased toward the level in Singapore, the expected value for the change in democracy in these countries would be positive whether the analysis includes Singapore or not.
"Free" in 1993
The segment of "Free" countries is probably the least interesting, at least from a policy point of view. Nevertheless, the trends, while subdued because these countries tend to have the most stable governments, are essentially the same as those in the previous two groups. Figure 4.11 exhibits a strong statistical significance in the correlation between interconnectivity and the change in democracy. The slope is lower because countries in this segment are less prone to dynamic political reversals. As before, some lesser connected countries improved on the democracy scale but none of the most connected countries registered substantial declines.
Figure 4.11-Interconnectivity and Democratic Change in "Free" Countries
Figure 4.11 can be combined with the corresponding Figures 4.5 and 4.10 from the other segments to produce a useful composite for comparison. In the progression from "Not Free" to "Free" in Figure 4.12, the location of the regression lines descends and the slopes of the lines decline. The latter attests to the decreasing political volatility as democracies take root. The former results from the truncation at both ends of the democracy scale. "Not Free" states have a lot of room for improvement but there is nowhere for "Free" states to move except down although few do.
Figure 4.12-Interconnectivity and Democratic Change Worldwide
The locus of points demands a further explanation regarding the "Empty Corner." The triangular area that was empty in the "Not Free" segment representing the dilemma that dictator's face is now populated with several observations. The level of connectivity that associates with substantial democracy gains among "Not Free" states, such as 100 nets per million population, corresponds with a minimal gain in "Partially Free" states and even democracy losses in "Free" states. The general inference is that increasing levels of democracy have correspondingly greater communication requirements. The communication and information needs within established democracies, for instance, are quite profound. Meeting these needs would seems to be helpful in securing democracy. As shown above, for example, there are no points beneath the ordinate axis for levels of interconnectivity that exceed 2000 per million population. Conversely, failing to meet these needs leaves open the possibility of a democratic slide, perhaps a corollary to the Dictator's Dilemma. The countries that made a great leap into democracy recently might be expected to slip back somewhat unless or until their communication capabilities come on par with the communication needs that are associated with their new higher levels of democracy.
Multiple Endogeneity AnalysisIt may be tempting to infer causality from these strong correlations between interconnectivity and both the change and level of democracy and then conclude that interconnectivity influences democratization. However, to do so would be erroneous. Causality could, in fact, flow in the opposite direction. Democracies rely on an informed public and uninhibited communication and may therefore seek interconnectivity. One way to explore this possibility analytically is through a system of simultaneous equations with multiple endogenous variables solvable by two-stage least squares (2SLS) estimation. The two-equation model assumes that interconnectivity can influence democracy and also that democracy can influence interconnectivity. Then one can compare the relative statistical significance and sizes of the coefficients on these variables in each of the two equations. To perform these tests, both democracy and interconnectivity are both dependent variables in Model V. And to obtain a unique solution, at least one additional variable, called an "instrumental variable," must be included in the interconnectivity equation. Since electronic mail is text-based and travels over telephones lines, appropriate instruments are percent literacy and the number of telephone lines per capita. Independent variables in the democracy equation are, as before, related to economic growth, human development and ethnicity.
The resulting regression coefficients are also listed on Table 4.2. Interconnectivity remains a powerful predictor of democracy as before. The magnitude of the coefficient for interconnectivity on democracy is even greater than in the comparable OLS model. The level of significance remains exceptionally high. Democracy, however, does not prove to have any significant effect on interconnectivity. Thus, the suggestion that democracy leads to interconnectivity is not supported while the hypothesis that there is no positive effect cannot be rejected. The coefficient on population is still negative and significant. The coefficient on GDP is also still negative and nearly significant at the 10% level. The other outputs also closely parallel those of Model I.
The alternative explanation for the strong correlation between interconnectivity and democracy is that a third variable may influence both simultaneously. The obvious candidate is economic development, which many contend is an important prerequisite for democracy. The correlation between interconnectivity and GDP, at 0.84, is also quite high, further encouraging tests on a hypothesis that economic development is the third variable underlying the correlation between democracy and interconnectivity. In practical terms, equipment necessary to communicate electronically is expensive, especially for citizens of the Third World regions that Western democratization policy would be most eager to influence. The same economic resources that can finance participation in the communications revolution could conceivably fuel demands for personal rights and freedoms. In fact, in his seminal study, Lipset included measures of communication, radios, telephones and newspapers per thousand persons in his main index for wealth (1959: 75).
Again, a system of simultaneous equations can help unravel complex reciprocal effects. Model VI includes all three dependent variables: GDP, democracy and interconnectivity. The set of assumptions that this three equation model embraces are: economic development and interconnectivity predict democracy; democracy and economic development predict interconnectivity; and interconnectivity and democracy predict economic development. The relative effects of the predictors can be assessed and compared as before in Model V. The interconnectivity equation utilizes the same two instrumental variables. The independent variables in the democracy equation are the same as before except that schooling is used to serve as an instrument for economic growth in accordance with prevailing theory. Scholars surmise that education can influence democracy by increasing personal and national wealth, as discussed earlier. The 2SLS estimation results, shown in Table 4.2, are consistent with all those that preceded. Statistical test results do not support the hypothesis that economic development is a confounding third variable. Strongly to the contrary, the regression coefficients for interconnectivity on democracy and GDP are both substantial and statistically significant, again above the 0.1 percent level. Neither democracy nor GDP proves to influence interconnectivity strongly. GDP again shows a negative correlation with democracy at a 10 percent significance level.
In each model, without exception, interconnectivity positively correlates with democracy at high levels of significance. In each model, at lower but still high significance, the correlation with population on democracy is negative. Stories to explain both the country size and the interconnectivity phenomena may share a common plot. Smaller size and greater interconnectivity may similarly be conducive to democracy by facilitating coordinated civic action. Although perhaps a cliché, the often repeated analogy that information revolution technologies are shrinking the world offers appropriate insight. It is interesting to note that the most populous country that Freedom House labeled as completely "Free" became a democracy in 1776 when its population was only a fraction of its current size. At that time and at that size, available communication technologies, like pamphleteering, were sufficient to gel public support into popular action.
Comparative Media AnalysisResults presented above using advanced regression techniques involving two endogenous variables to ascertain more about the relative predictive power of each were not consistent with the hypothesis that causality flowed uniquely from democracy to interconnectivity. The various econometric models showed interconnectivity to be a statistically significant predictor of democracy, but not vice versa. Tangled collinearities pose an even greater problem to tease out the relative role of economic development as a third endogenous variable. Two stage least squares regressions consistently showed the predominance of interconnectivity, but the available instrumental variables such as literacy or number of telephones are all themselves highly collinear with both endogenous variables. Thus, isolating any relationship is nearly impossible since everything is connected to everything else. An alternative exploratory tack is to determine the relative effects of the various communication technologies on democratization. If uni-directional, bi-directional, and multi-directional technologies differ in their relative effects on democracy, the difference can be assigned to the attributes of the technology itself since each communication technology is similarly, though not necessarily identically, dependent on wealth.
As before, this analytic approach employs regression modeling to predict democracy as a function of economic development, education, and population. Metrics for the prevalence of traditional communication technologies are additionally included for comparison. Per capita numbers of television sets proxy for uni-directional communication, telephone instruments for bi-directional and e-mail nodes for multi-directional.
Several of the variables have largely skewed distributions with a few wealthy or large countries forming a long tail far to the right. These variables are economic development, population and the three telecommunication technologies: telephones, televisions and networks. All of these variables were similarly modified for linear estimation using a natural logarithm transformation. In 1993, thirty-eight countries had zero connectivity, so a binary variable on the presence of networks was also incorporated into the analysis. Nearly complete data was available for 140 countries. For sixteen of these, including nine former Soviet republics, there were no data on the number of television sets.
The analysis involves two forms of linear regression equations. Inclusive equations contain all three communication variables. Each of the three corresponding exclusive equations includes only one of the three communication variables. The rationale for this approach is to best interpret unavoidable collinearities among the supposedly independent predictors. As discussed earlier, within the inclusive equation, collinearities between the communication variables may cause reduced predictor efficiency but no bias. In the exclusive models, on the other hand, efficiency is improved but effects of wrongly excluded variables will be assigned incorrectly to the included variables resulting in systematic bias. Together the inclusive and exclusive models provide alternative vantage points to examine the data for a comprehensive understanding of the complex interactions.
In accordance with the theory of the "Dictator's Dilemma," one could anticipate the results of the investigation as follows: multi-directional communication technologies as simultaneous enhancers of both autonomy and influence will presumably be most highly correlated with democracy; uni-directional communication technologies as the dictator's preferred communication instrument of control are likely to be the least correlated or perhaps even negatively correlated with democracy; and bi-directional communication as essential for market development will probably be somewhere in between, inconclusive or not statistically significant. These predictable results are precisely the test outputs shown in Table 4.3.
Media Comparison Regression Results
|Ave. School Yrs.||3.82
t-statistics are in parenthesesThe first column reports the results of the inclusive model. The network variables, both the binary variable showing presence and the continual variable showing prevalence, predict democracy with the highest recorded level of statistical significance, greater than 0.1 percent. The high statistical significance is particularly notable because the collinearities with other variables would tend to artificially decrease the apparent statistical significance. In both cases the substantive values are also large. A one percent increase in networks associates with a 4 point increase on the democracy scale. The mere existence of networks in a country predicts a democracy level above half the entire democracy scale. The regression coefficient on the television variable is also statistically significant but the sign is negative. Increasing the number of televisions by 1 percent corresponds with a decrease in democracy on the same order that increasing networks corresponds with an increase in democracy. The coefficient on telephones is positive but not statistically significant, therefore no meaningful conclusions can be made regarding its magnitude or sign. The exclusive models repeat results nearly identical to those of the inclusive model. The similarity suggests that the collinearities between media as predictors for democracy, beyond the part that is related to wealth, are not great. In other words, each media might influence democracy in its own distinct way.
*** = Significance at the 0.1% level
** = Significance at the 1% level
* = Significance at the 10% level
The regression coefficient on economic development, on the other hand, oscillates dramatically between the models as a result of its collinearities with the communication variables. The only model in which GDP is statistically significant is the network exclusive model. This implies that the effects of collinearities between networks and economic development have less of a degrading effect on the slope estimate. This may indicate that the emergence of networks is less dependent on economic development. While the Maitland commission report found that, regarding telephones, "Telecommunications have often been seen as a luxury to be provided only after other investments" (Maitland 1984: 8), electronic networks might be viewed as more of a necessity which organizations, particularly those seeking to effect political change, purchase before other amenities. Notably, the sign on the regression coefficient for economic development is again negative. This inverse relationship recalls previous analysis hinting that there might be some mutual exclusivity in the policy choices to between supporting democracy and economic growth. All else being equal, more of one might be obtainable only at the expense of the other. Of course, the coefficient on economic development in this exclusive model is biased by the excluded variables, but telephones and televisions seem to operate in different directions. They may somewhat balance each other out reducing the net bias. In the unbiased inclusive model, the sign is also negative although the standard error on the GDP variable is large.
Not only do networked communications appear as the most statistically significant predictor, they also predict the greatest percentage of the variance in democracy relative to the other communication technologies, almost sixty percent. Observing the adjusted R2 values, the exclusive model with television predicts just over half of the variance in democracy and with telephones, even less. The inclusive model produces the highest adjusted R2, nearly two-thirds, again suggesting that the collinear effects among the communication technologies are not severe.
The four other models examine the opposing cutting edge of the "Dictator's Dilemma." Economic development replaces democracy as the dependent variable. Again, the empirical results are consistent with theory. Networks correlate with economic development at high levels of statistical significance, as do the other communication variables. The magnitudes of the coefficients for networks, however, are substantially less than those of either telephones or televisions. In the inclusive model with networks, the adjusted R2 is the lowest of the four models. In effect, this corroborates the notion that networking may be less dependent on wealth than the other communication technologies. Also this lower correlation may be at least partially due to the accepted use policy (AUP) of the NSFNet. The main backbone for main networks, the NSFNet, having evolved in the research community forbade use of the networks for commercial purposes. It is expected that as policies and capabilities become more favorable to the conduct of business and trade, this correlation may strengthen over time (Jacobson, 1994).
Results of the media specific analyses lead to two important conclusions. First, national wealth cannot be a primary source of the significant correlation between democracy and interconnectivity. If it were, presumably the prevalence of telephones and televisions would produce similar statistical profiles. Multi-directional networks are less tied with economic development than either bi-directional telephones or uni-directional televisions, yet of the three, only networks exhibit a powerful positive correlation with democracy. Second, strengthening the relationship between networks and economic activity could well tighten the vice grip of the "Dictator's Dilemma." As such, this formulation of the choices that face anti-democratic regimes offers stimulating policy suggestions for western democracies who have national security interests in promoting global democracy.
 Many of the measurement and statistical difficulties are addressed in considerable depth by Inkeles (1990) and Dahl (1971).
 For several examples see, the World Bank (1991), Starr (1991), Helliwell (1992), Lipset, Seong and Torres (1993), Muller and Seligson (1994), Boone (1994), and Rowen (1995).
 For similar precedents, see Rowen (1995) or Muller and Seligson (1994)
 "Interconnectivity" is a term popularized by Larry Landweber for his measures of the proliferation of global email networks.
 For examples see Lipset (1959), Helliwell (1992), Lipset, Seong and Torres (1993), and Rowen (1995).
 For examples, see the World Bank (1991) and Boone (1994).
 United Nations Development Programme (1993) provided all the economic, education and health data which are used in these analyses.
 These data are published in the Central Intelligence Agency World Fact Book (1994). In a few cases, mostly in Northern Europe and Africa, these data were not available. Where applicable, the percentage of largest religious affiliation substituted for the missing data.
 For characteristic arguments from both sides of the debate, see Huntington (1993) and Schifter (1994).
 Data were either missing or relative to inconsistent entities for many of the new countries resulting from the recent breakups of Czechoslovakia and Yugoslavia. Therefore excluded from this study are Slovakia, the Czech Republic, Bosnia-Herzogovina, and in some cases Croatia, Slovenia and greater Serbia (called Yugoslavia.) Additionally, critical missing data precluded the inclusion of Taiwan.
 "INT*" indicates an interaction term as the product of the interconnectivity variable and the categorical region variable.
 For more discussion on the potentially negative economic consequences of democratization, see Shin (1994) or Rothstein (1991).
 The first international connections to the ARPANET were England and Norway in 1973 but even in these countries, interconnectivity was very close to nil until after 1983 (Hobbes).
 As mentioned earlier, missing or inconsistent data entries precluded the inclusion of the Czech and Slovak Republics along with Bosnia-Herzogovina in these analyses. Additionally, Namibia, which did not become an independent country until March 1990, is now also among the exclusions.
 The seminal work on this topic is Lipset (1959) but the literature is large. Also see, for example, Helliwell (1992), Lipset, Seong and Torres (1993), and Rowen (1995).
 The enormous size of India, nominally a democracy although Freedom House gives it only a "Partially Free" label, stands out as an obvious exception. As such, it exemplifies the epistimological difficulty of statistical inference to determine the causes of a complex effect like democracy. Close British tutelage as an important colony and the personality and presence of Matahma Gandhi are likely key factors. However, the objective of this dissertation is rather more modest, to examine the effects of a single cause.