Democratic Values in a Globalizing World:

A Multilevel Analysis of Geographic Contexts.[1]

 

 

 

John O’Loughlin

 

 

Institute of Behavioral Science and

Department of Geography

 

University of Colorado

Campus Box 487

Boulder, Co.  80309-0487

 

Email: johno@colorado.edu

 

 

 

 

 

 

 

 

 

 

 

 

 

Abstract

Geographers contend that regional and national contexts are important mediating and controlling influences on globalization processes.  However, to reach this conclusion, geographers have been forced to engage in rather convoluted statistical manipulations to try to isolate the so-called "geographic factor".  Recent developments in multilevel statistical modeling offer a more precise and suitable methodology for examination of contextual factors in political behavior if the data have been collected in a hierarchical manner with respondents grouped into lower-level and higher-level districts.  The World Values Survey data  (collected in three waves from 1980 to 1997) for 65 countries are ideally suited to examination of the hypothesis that democratic beliefs and practices are globalizing.  Using three key predictors (trust in fellow citizens, political interest, and volunteerism) for the sample of 91,160 respondents, it is evident that regional (for the 550 regions) and country settings (between 55 and 65 countries) are important predictors of political behavior, on the order of about 10% and 20% respectively.  Respondent characteristics account for about 70% of the variance explained.  Ideology is far more significant than many of the usual demographic characteristics in explaining political behavior cross-nationally.  Dramatic differences between established and new democracies clarify the political globalization process and global regions (Latin America, Eastern Europe, Western Europe, etc) also emerge as significant factors.  Multilevel modeling of survey data offers a compromise between the aggregate data analysis preferred by geographers and the emphasis on surveys in a non-geographic context preferred by political scientists.


 

Using data from 1946-1994 and a measure of democracy based on political authority characteristics,  democratization has proceeded in regular spatial and temporal diffusion patterns and distinct regional trends can also be observed (O’Loughlin et al., 1998).  Unlike the temporal diffusion suggested by Huntington’s well-known third “wave of democratization” model, the geographic pattern tends to be more complex.  One has to resort to regional-level explanations, rather than macro-structural ones, to account for the democratization  changes of the past half-century.  In reaching these conclusions, the geographic disparities in the global distribution and trends in democratization, barely mentioned in previous global-level analyses (see Lipset 1959 and 1994; Bollen, 1993 for examples), are certified.  Examining further the nature of the geographic factor generates many questions.  Was its presence simply an artifact of the approach that emphasized the “spatial and temporal diffusion of democracy”? Or was it a result of the special combination of place characteristics that mold a certain style of politics, as well as other social and cultural characteristics? Are the usual socio-demographic explanations sufficient to account for the global diffusion of democracy?  In this chapter, I take up this theme of teasing the geographic element and instead of using aggregate areal data to measure the characteristics of countries, I use survey data for individuals.  These data also have place information (region within countries as well as country identification) and allow me to demonstrate that understanding the global distribution of democracy and its causes cannot be separated from the locations in which citizens live, work, and take part in political life.  Democracy’s meaning is to some extent place-specific and sharp differences between places are evident within the overall globalization of democratic norms.

            The reversal to authoritarianism anticipated by Huntington (1991) after the “Third Wave” of democratization of the 1970s and 1980s did not happen in a dramatic manner but neither did the “wave” continue its upward slope.  Instead, at the beginning of the twenty-first century, we can note a period of stability (or stagnation, depending on one’s perspective) in the democratic trend.  As noted by Norris (1999c, 265), the percentage of independent states that were democratic (according to the Freedom House data on political and civil rights) was 34% in 1983, rising to 41% in 1997, where it has remained.  (In 1997, Freedom House counted 81 democratic states, 60 as semi-democratic and 53 as undemocratic).  What was especially noticeable about the trend in the 1990s was the strong macro-regional character of the overall process and of the nature of the political life in countries that shared borders.  Political scientists, including Lipset (1959) in his early study of political change, have frequently commented on these regional commonalities.  These regional comparisons have led to a debate about whether one can compare polities across regions (Inglehart and Carballo, 1997).  What is undeniable is that regional location matters in global political change. 

 

Location and Context in Global Political Change

 

Recognition of the importance of location as a factor in political developments continues to grow.  First, being part of the same region or sharing a border with a state undergoing profound political change increases the chances of political transformations in neighboring countries (Kopstein and O’Reilly, 2000; O’Loughlin, 2002).  Examples from sub-Saharan Africa and Latin America of this kind of spatial diffusion process are provided in O’Loughlin et al (1998).  For Africa, Joseph (1997) and Bratton and van der Walle (1997) note how authoritarian political elites in that continent were highly aware of the global and regional trends in regime change after 1990 and took steps to accommodate pressures for change by appearing to become democratic converts, while still managing to retain power.  Second, statistical analyses of the “requisites of democracy” have suggested that global regional divisions are significant in explaining the distribution of democratic governance, over and above the usual requisite factors - such as economic development, education, class, industrial sector, colonial history and cultural-religious factors (Lipset,1994; Lipset, Seong and Torres, 1993; O’Loughlin, 2001).  What these types of study lack is an integration of the regional and country characteristics so that one can visualize interactive effects that might be greater than the sum of the separate effects and more importantly, might allow a clarification of the conditions under which region becomes a significant factor within a global trend. 

A third trend parallels the methodological gap in political science between the comparativists, who tend to study one polity or examine a small set of countries, and the macro-structuralists, who engage in cross-sectional analysis of the countries that constitute the world system; their separate approaches do not really encourage a rapprochement on the basis of the shared interest in regional affairs.  While political scientists increasingly accept and use the geographic techniques of spatial analysis, there is still a significant knowledge lag between the disciplines about concepts of space and place.  Most geographers adhere strongly to notions of “place” as complex areal units that are shaped by human behavior, beliefs and values over a long period of time (Johnston, 1991).  Thus, their mix of characteristics are hard to convey in statistical analyses.  However, it is the spatial paradigm of the geographic discipline (distance, location, contiguity, cartographic form and shape, etc) that has been adopted by political scientists.  While this is a start in the recognition of the complexity of the world system’s mosaic, it remains a far cry from the preferred region-place approach of most geographers (O’Loughlin, 2000).  

For the past two decades, geographers have manipulated aggregate data to demonstrate the small, but statistical significant, effects of the context in which political acts takes place.  In almost all instances, empirical analysis examines voting statistics, though spatial analyses of other kinds of political data such as international conflict behavior follow the same general modeling procedures (O’Loughlin, 1986; Kirby and Ward, 1987; O’Loughlin and Anselin, 1991).  Important extensions in the spatial analysis tool set in the 1980s and the integration of these methods with GIS (Geographic Information Science) visualization techniques allowed geographers to show how the usual regression equations of aggregate data were probably incorrect – biased and inefficient estimators and significant clustering (dependence) in the residuals – and that these kinds of aggregate data required the application of the specialized tools of spatial analysis.  MacAllister (1987) and King (1996, 1997) argued that adding the right kind of predictor variables, fitting the right kind of model (perhaps log-linear), avoiding the ecological fallacy, collecting the right kind of information to answer a specific question, or analyzing at the right scale (more localized analyses) will see the evaporation of the “geographic factor”.  Geographers, especially John Agnew (1996, 2001) and Ron Johnston (1991), have responded with a vigorous theoretical defense of the idea of place as context but until multiple clear demonstrations of the empirical effects of context appear, skepticism of established geographic practices is likely to continue in political science.

In recent years, geographers have increasingly turned to survey data for individuals to report the existence of small but significant contextual effects in political behavior and attitudes.  Pattie and Johnston (2000) and Shin (2001) have shown how logistic modeling of voters in Great Britain and Central Italy, respectively, benefits greatly from the addition of variables that measure the extent to which a voter feels part of a local political community.  The effects are both significant and interactive; one needs to consider the behavior of voters in their communities because their combination (measured by a multiplicative term) adds a powerful explanatory variable.  Public opinion pollsters know this interactive effect is important. They often conduct surveys using a stratified sample based on locations, just as exit polls on election night report the results from “key precincts” that have been identified as representing the whole state complexity.  Typically, however, location is simply treated as an independent dummy variable in regression analysis, thus minimizing the interactive elements that underlie its significance. 

Like other social scientists, geographers are beginning to move from a “dummy variable” to a multilevel modeling approach for individual level data.  If more than a few regions exist, use of the dummy variables becomes cumbersome and worse yet, the nuances of place are poorly captured.  Alternatively, one could fit the same model for different scales (individual, regional, national) if the requisite data were available but cross-scale effects are hidden in this approach.  In multilevel modeling, on the other hand, a single regression model handles the micro-scale (individuals), the meso-scale (regions or towns) and the macro-scale (states or countries).   Moreover, multilevel models allow relationships to vary according to geographic context, thus speaking to the heart of the division that separates geographers and political scientists.  Widely used in public health studies and educational research to determine the separate and interactive effects of the characteristics of people and their contextual settings (communities and schools), multilevel modeling is rapidly growing in use in all the other social sciences. With both the availability of specialized computer software and the growing recognition that many models are too general (fit for aggregate data across the varied contexts within a study site) or too specific (targeted to the characteristics of individuals with no attention to their environments), multilevel modeling can be expected to gain more adherents quickly.  

Jones and Duncan (1996) provide a list of contextual analyses that can be accommodated within the multilevel framework.  Consider the topic of this chapter, the explanation of the variation in democratic beliefs, measured by answers to the question of whether the respondent thinks that democracy is the best political system.  First, multilevel modeling can detect and measure contextual differences by considering simultaneously personal attributes (the micro-scale) and the macro-scale of the country where the respondents live.  Second, place heterogeneity can be measured so that we can see how different factors are related to democratic beliefs across the countries.  Third, perhaps the greatest potential of multilevel modeling is to take the interaction of place and individual socio-demographic attributes into account.  A respondent may answer quite differently about democracy depending on the ideology or government style of the country in which he/she is a citizen.  Whether through intimidation, pressure or conversion, ethnic or  class determinants of political attitudes can take on different dimensions in different countries.  Fourth, multilevel modeling does not assume that all voters of a particular class or other socio-economic group behave in the same manner.  Individual heterogeneity is also determined and measured.  Fifth, panel data of a longitudinal nature (same respondents at different times) can be modeled in a multilevel fashion so that each wave can be considered as a separate scale and the effects of changing context over time also measured.  Sixth, since it is probable based on previous research that voters have multiple contextual influences (home, work, church, neighborhood), multilevel modeling allows the measurement of these separate environments.  While all of these different modeling strategies can be accomplished using familiar multiple regression procedures, the adaptations to achieve them are cumbersome and require the use of multiple dummy predictors and a large number of terms in the equation. 

 

            Democracy and Political Values in a Globalized World

 

A truism highlights two contradictory trends that characterize contemporary citizens.  Across the globe, growing numbers of people express support for democracy as a value system while in the longest-established democracies, more citizens than ever are dissatisfied with democratic procedures and especially, with the performance of governmental regimes at all levels (Nye, Zellikov and King, 1997; Dalton, 1999; Pharr and Putnam, 2000).   Even in the new democracies (established after 1989), citizens are increasingly critical of their governments’ performances and while not taking to the streets to protest their dissatisfaction, they nevertheless are becoming “critical citizens” like their Western counterparts (Norris, 1999b).  The most vulnerable democracies (candidates for a reversal to authoritarianism after Huntington’s Third Wave) are those that are labeled “partly free” in the Freedom House lexicon, as they seem to be plagued with ethnic tensions, regional and religious polarizations, administrative corruption, controlled elections and weak mass media,  partly-functioning or brow-beaten legislatures, and un-consolidated party systems.  These “democracies” are in danger of not consolidating the gains of the 1980s and 1990s.

            In a major project to ascertain the state of democratic values world-wide, Norris and her colleagues derive three main conclusions from numerous studies of a wide range of democracies – a) political support is not one type and needs to be disaggregated into its different components, b) growing numbers of citizens are critical of government performance in rich countries and established democracies, and c) there is a growing tension everywhere between democratic ideals and reality.  The worry for promoters of democracy is that if support for democratic institutions is falling, then support for democratic values can also be jeopardized (Norris, 1999c, 26).  The terms “critical citizens” or “dissatisfied democrats” well describe the current state of play.  Explaining the variation in institutional confidence is not simple, with only a few variables (at the individual level) significantly related to it.  Conventional democratic participation (voting, volunteerism, etc), political attitudes, and national context explain some of the variation but there is only a weak correlation between institutional confidence and protest potential (Inglehart, 1999).

 The national context variable appears consistently as an important factor in setting the nature of democratic values leading Inglehart (1999, 266) to conclude that “we strongly suspect that a supportive political culture is necessary for democratic consolidation but the exact weight to be given to it is a matter of debate.”  Inglehart and his associates have tracked the rise of a change in social values that they call “post materialism” in many countries for over 30 years.  Though there is a correlation between materialist – post-materialist value ratios and economic fluctuations, the noteworthy trend is an inexorable rise in post-materialism in rich countries, a strengthening of materialist values in poor countries, and a generational change toward post-materialist values.  As the public favors more public voice in governmental decisions (part of a democratic culture’s expectations) due to rising levels of education, democratic institutions must adapt to these expectations or come under increased questioning by citizens.  The long-term prospect anticipates mass publics becoming increasingly supportive of democratic institutions as more countries become richer – though established democracies will have to be careful in how they respond to their citizenry (Abramson and Inglehart, 1995; Inglehart, 1997, 1999).

            Generalized conclusions about democratic values, institutional performance and post-materialist developments are drawn from cross-national surveys.  In order to make comparative statements, it is first important to establish equivalence in the concepts, terms, phenomena and definitions used in the different national contexts.  In designing the “political values” project, Inglehart (1997, 63) picked a general strategy that designed broadly relevant questions in order to examine to what extent their structure, connotations, demographic correlates, and constraints are cross-culturally similar.   Factor analysis of the responses to the same survey questions across countries shows that key indicators line up in the same manner in different national settings (Klingenmann, 1999), allowing Inglehart to develop his 12-item post-materialist index.  However, there is no insistence on forcing similar interpretations onto different settings; interpreting results still requires an awareness of the differences in meaning across cultures.  For example, there is a noticeable difference in the meaning of post-materialism between Western and the former socialist countries and between industrial and low-income countries.  As van Deth (1997, 4) notes, “comparative research must start from the axiom that even similar phenomena are never identical.  The question is whether we can restrict the differences between the phenomena to intrinsic, non-relational properties irrelevant to the goals of our research.”

            The World Values survey has a twenty-year history, though its antecedents stretch back to the early years of the Eurobarometer surveys in the European Union states.  Three waves of surveys have now been completed (1981-84, 1990-93, and 1995-1997) and the temporal and spatial coverage is very impressive, covering 45% of the world’s population.  The survey relies on national teams but the nature of the voluntaristic group enterprise means that not all survey instruments are identical, not all sampling procedures are the same, and not all surveys are temporally coincident (Inglehart et al, 2000).  Despite these caveats, this enormous data set constitutes the best information for cross-national examination of political, social, cultural, religious, and ideological values and with its ancillary socio-demographic data, allows a check on assumptions about the spread of democratic values, the arrival of global norms to new settings, the regional concentration of cultural affiliations and traditions, the diffusion of post-materialism, the extent of critical citizenship and number of dissatisfied democrats, and the depth of democratic feelings in democracies, old and new, established and transitional.  It is the data set that I choose for the purposes of teasing out the extent to which national and regional contexts play a role in these global developments.  Global trends might be sweeping aside traditional regional and national value systems producing an “international political culture” or conversely, local attachments and historical memories and legacies continue to shape external values to produce a world of cultural mosaics and democratic diversity within political globalization.

 

The Multilevel Modeling Procedure

 

As is ordinary least squares regression, multilevel models operate on the principle that each response is a result of systematic components and fluctuations across the levels.  In the language of regression, each model thus has fixed and random parameters (Goldstein, 1995; Hox, 1995).  Critical to the application of multilevel models is a hierarchical data structure.  In this chapter, survey respondents are the first level, embedded in regions at the second level and these regions in turn are nested in countries at the third level; this is the structure of the World Values Survey data.  Minimum requirements of cases apply to each level and a rule of thumb suggests that there should be at least 15-20 cases per unit at the next highest level.  The selection of the World Values data for this study generated 91,196 cases at the first level, 550 regions at the second level and 65 countries at the third level (though the exact number of cases in each model depends on the mix of independent and dependent variables in the equation and their respective missing data values).   The most common usage of multilevel models has been in educational settings (e.g., how much of a pupil’s test score can be attributed to the pupil’s abilities and how much to the school environment?), public health (e.g., how much of a person’s lifestyle choices such as cigarette smoking can be attributed to the person’s social status and how much to environmental influences in the form of peer pressure and community practices?) and voting behavior (e.g., what is the relative importance of a voter’s socio-demographic characteristics and his/her community setting in determining voting choice?). 

In regression, a key assumption is independence of the observations.  Fitting an OLS model for individual data in the presence of autocorrelation within the groups violates one of the assumptions of regression – independence of the observations.  If the context in which the respondents live exercises a significant effect on their attitudes, this assumption is violated.  Ignoring clustering of individuals will generally cause standard errors of regression coefficients to be underestimated (elevating the significance of the predictors) when the variation could be ascribed to chance but in fact, is based on the groups (Kreft and de Leeuw, 1998; Rasbash et al., 2000).   Conflating the levels of analysis is also common so that inferences derived from one level are often applied to another, termed the ecological fallacy.  Specifically ordering the data in a hierarchical mode allows attention to the interactive effects between levels and promotes a clear understanding of where (which level) and how effects are occurring.  In multilevel analysis, the groups (countries in my case) at the second level are treated as a random sample of the population of groups.

            Building a multilevel model is an iterative process adding more explanatory variables onto the first model.  Typically, modeling begins by allocating variance to each of the levels, a purely random effects model.  If we adopted the usual regression approach, we would fit an explanatory model for each country (with dependent and independent variables for each respondent), thus yielding 65 separate equations.  In this procedure, we assume that each country has different intercept coefficients, β0j and different slope coefficients, β1j.  The random errors εij for each country are assumed to have a mean of zero and a variance of σj2.  In the multilevel model, however, we assume that the variance is the same in all countries and specify this common error variance as σ2.  The slope and intercept coefficients are assumed to vary across the countries.  Stating the assumption in verbal terms, for respondents with the same class status, a country with a high value of the intercept is predicted to produce higher democratic values (say, on the question of “do you trust your fellow citizens?”) than countries with a low value of the intercept.  Further, differences in the slope coefficient for the independent predictors are interpreted to mean that not all countries have the same relationship between the outcome (political value) and predictor variables.  Some countries, perhaps long term stable democracies, may have a strong effect while others, perhaps former Communist states in Eastern Europe, might show a weak effect. 

After demonstrating these varying effects, the next step in the multilevel modeling procedure is to introduce explanatory variables at the second level, countries in this example.  The multilevel modeling approach has the strong advantage that it allows us to see if political values are significantly affected by country residence and citizenship – or the converse, whether a person’s characteristics (education, age, gender, etc) are all that we need to know in order to account for the variance in political beliefs.  If countries matter, serious consideration must then be given to local factors in accounting for the institution and consolidation of democracy; if countries are unimportant, then we can anticipate a global spread of democratic beliefs (and practices) as income and educational gains diffuse across the globe and international norms of democracy are adopted without respect to country setting.  In this chapter, a dummy variable that distinguishes between stable democracies (over 20 years democratic) and other countries provided the only useful distinction at this stage of the analysis.

            The main aim of multilevel modeling is to separate and measure fixed and random effects.  Starting from a simple bivariate regression equation, we can extend it to a multilevel model.  In the bivariate regression equation, the subscript i refers to the individual respondent:

            yi = β0χ 0 + β1χ1i  + εi                                                                                   (1).

This simple model at the individual-level is referred to as the micro-model and can be fitted for all countries in the sample with yi denoting a respondent’s score on a political trust variable, χ1i denoting age (a typical independent predictor), εi the individual-level residuals, and χ0 is the constant.   The two fixed parameters, β0 (intercept) and β1 (slope showing the change in political trust with increasing age) are interpreted as usual.  For multilevel modeling, the random effects captured in the εi are highly important and rather than simply allocating them to the “unexplained variance” category, their values can be used in further modeling.  A more realistic model that does not simply assume the error terms have a mean of zero and a constant variability can be developed by allowing the political trust measure to vary from country to country, at the higher-level (second level) of a macro-model.  Formally,

            β0j =     β0 + μj                                                                                                                           (2)

In equation (2) for the second level, β0j , the average value of the social trust variable in country j, is a function of the country-wide average, β0,  as well as a varying difference μj between each country and the overall countries average.  We can combine equations (1) and (2), the micro- and macro-models to make a two-level mixed model:

            yij = β0χ0 + β1χ1ij + (μj + εij)                                                                               (3)

with the subscript ij denoting respondent i in country j, and the terms inside the parentheses indicate the random part of the model.  We make the standard assumption that they follow a normal distribution so that it is sufficient to estimate their variances, σ2μ and σ2ε .  In this model, the same age-political trust holds for each country (same slope) but the intercept 0 + μj) varies according to country.  A further extension of the multilevel model allows the slopes to vary between countries so that the age-trust relationship can take on different forms according to the national context.  Another two-level model is needed for this relationship of the form:

            βij = β1 + Γj                                                                                                                                       (4)

where the country slope term is a global average plus the variation from country to country, Γj.  We can now combine equations (1), (2) and (4) generates the full fixed and random effects model of the form:

            yij = β0χ0 + β1χ1ij + (Γjχ1ij + μj + εij)                                                                   (5)

in which the slopes and intercepts are allowed to vary.  In equation (5), six values have to be estimated - the two fixed coefficients, three variances/covariances at level 2, and one variance at level 1.  In this paper, I estimate the values for this general class of model with fixed and random coefficients with the random terms allowed to vary at any level.  If the variances are small, then political trust is a function only of age with no contextual effects.  However, anticipating the results presented below, the variances are of a significant size and the conclusion must be that a combination of fixed parameters (reflecting the socio-demographic characteristics of the individuals) and random effects (the contextual and individual variances) is needed for an adequate explanation of the variation of political values across the world.

            In building a multilevel model, the usual procedure is to start with a variance components model to determine if there is any variance in the second and higher levels, in addition to the variance at the first level (the individual voters).  Should there be no evidence of higher-level variance, a simple regression model is appropriate since there is no geographic variance visible.  In the variance components model, only random parameters are present.  Depending on the nature of the information available and the quest for either model building or model testing, fixed parameters are added in a stepwise manner or all independent predictors are entered simultaneously.  The estimation procedure typically uses an IGLS (Iterative Generalized Least Squares), a maximum likelihood estimation procedure.   In the case where the intra-unit correlations are small, there is reasonably good agreement between the multilevel estimates and the OLS ones (Goldstein, 1995, 25).  IGLS starts from initial OLS estimates for the fixed coefficients and builds upon the residuals from the OLS model.  At each iteration, the weights are adjusted and changed and the residuals re-used in the next iteration.  All models were estimated using the specialized multilevel modeling software, MlwiN (Rasbash et al., 2000).

 

Data for Examining Civic Democracy

 

The number of countries sampled in the World Values survey varies from wave to wave and in order to have the most complete global coverage possible while still maintaining temporal consistency, I opted to combine information from the last two waves (1990-93 and 1995-1997) in this study.  Only the most recent information was taken for each country. For example, since the same questions were asked for a U.S. sample in 1990 and 1995, only the 1995 data are included.  In total, 65 countries and 550 regions within these countries are in the data set with a total sample size of 91,196.

            Choosing from among the myriad of questions asked in the cross-national survey, I probed three different elements of democracy.   Rather than focusing on formal democratic institutions and norms such as elections, I opted to examine the “civil basis of democracy”;  what extent are people connected to fellow citizens, are actively engaged in their communities through volunteerism and are interested in politics?  I was motivated by Robert Putnam’s (2000) recent work on the US civil society and especially on the data reported by Putnam that shows dramatic variations across the dozens of communities surveyed.  Why do some communities seem so engaged not only with formal governmental structures but also with informal and civil democracy?  Why are people in some communities seemingly so distrustful of governments while others are willing to accept the regimes, warts and all?   After comparing individual indicators for consistency with each other (an inter-correlation matrix indicated significant overlap between many of the dependent variables), I choose the three democratic values – trust in fellow citizens, volunteerism, and political interest – as my three key indicators of democracy across the globe.

            Trust in fellow citizens does not correlate well with trust in democratic institutions (Newton, 2001) but, nevertheless, trust is widely used as a measure of the communal basis of democracy and as a predictor of the chances for democratic consolidation.  In the former Communist countries of Eastern Europe and the former Soviet Union, the past decade has seen a concentration of research through numerous surveys on trust since it is generally assumed that a population deeply alienated by decades of Communism would find it difficult to develop and sustain trust of fellow citizens.  But as Mishler and Rose (2001) note, the concept of trust is highly complicated.  Depending on the measure and the question, a reservoir of trust emanated from the Communist period focused on family, close friends, and mutual assistance groups.  Newton (1999) examining the Eurobarometer data over time, distinguishes between “thick trust” (personal, particular, family) and “thin trust” (more impersonal, general and abstract).  The specific question asked in the World Values survey measures “thin trust”.  While the historical trend in Europe is one of fluctuations in levels of “thin trust”, rather than general decline, Putnam’s research suggests a significant decline in the US over the past quarter-century.  In this study, I compare these two regions of stable democracy to the rest of the world.

            Because yearly Eurobarometer data from the early 1970s and a recent surge in research in the former Communist countries and in the US have added to the pool of information, we know quite a lot about social trust and its dynamics.  In his model of transition to democracy, Rustow (1970) believed that in the last stage, the habituation stage, trust between competing elites and social-political groups as well as between ordinary citizens grows as a variety of groups participate in and get something out of political struggles.   Trust is a precondition for retaining the tenuous middle ground between imposed uniformity (authoritarianism) and implacable hostility (civil strife).  Though a normative expectation holds that they should be two sides of the same coin, there seems to be little correlation between social trust (thick trust, measured by attitudes to fellow members of the local community) and political trust (thin trust, measured by attitudes to political institutions).   “Social trust is regarded as a strong determinant or influence upon political support of various kinds, including support for the political community, confidence in institutions and trust in political leaders.  As a result, it is believed that the accumulation of social capital, in the form of social trust, will also result in the accumulation of political capital (Newton, 1999, 170; see also Newton 2001).  Just how complex the notion of social trust remains can be seen from statistical analyses of World Values data.  No relationship exists between volunteerism and social trust in four countries (United Kingdom, Germany, Spain and the Netherlands).  The US and France show a weak association and only Italy has a strong association.  Socio-demographic predictors of trust are likewise uneven and only a few countries show significant independent effects (age only in the US, UK and Spain; gender only in the UK and Italy; income only in France, Spain and Italy; etc).  Only education is significant for all countries – the more educated the population, the more trusting they are of fellow citizens and the highest values are found among educated, high-income, high socio-economic status males of the ethnic majority. 

            The first dependent variable measured social trust and was derived from another of the World Values survey questions (http://wvs.isr.umich.edu/index.html).   To construct the binary dependent trust variable, I added the “you have to be very careful” and the “don’t knows” together.  The average score for all sample countries was 26.3% trust with a range from 2.8% in Brazil to a high of 57.7% in Denmark.  The world pattern for this variable is regionally coherent.  High values are found in the rich countries (Western Europe and North America) and low values in the new democracies and in poor countries, confirming Newton’s summary of the trends in Europe and his expectations that trust and income are highly correlated. (See Figure 1).

The second variable, volunteerism, varies dramatically across societies as a result of traditional attitudes, religious practices, cultural expectations, and recent experiences of authoritarianism.  The American democratic model from the early nineteenth-century has promoted civic engagement in the form of grassroots activism and a strong non-state sector as antidotes to overweening regime power.  Since Putnam’s (1993) book on Italian civil society, investigation of the nature, extent, depth and developments in civic engagement has been carried out in many of the new democracies, especially in Eastern Europe.  Compared to the US and Western Europe, little engagement in the form of membership of organizations and volunteerism is visible (O’Loughlin and Bell, 2000).  Nevertheless, a tenet of post-Soviet studies holds that sustainable democracy in former Communist states depends on the emergence of an autonomous citizenry - independent from formal state institutions and able to mobilize voluntarily on the bases of shared social and economic interests.  Successful transitions to democracy are therefore predicated on the consolidation of a civil society underlain by civic culture, a common set of values and beliefs.  Civil society thus creates the conditions under which the formal structures of democracy can take root.  Beyond the focus on the former Communist bloc, the concept of social capital is coming under increasing skepticism about its consistency  despite claims for its importance in sustaining successful societies and democracies (Fukuyama, 1995; Tarrow, 1996).  This chapter will help to understand the effects of national contexts in its distribution and causes.

With the rise in “critical citizenship” in rich countries, interest in formal politics is waning as attention shifts to more local, grassroots civic activism.  In the former Communist countries, older people have often experienced four types of regimes, including Nazism and Communism, and democracy is now evaluated in comparison to these discredited political alternatives (Mishler and Rose, 2001).  Alienation from the political system is growing and electoral turnout rates are falling as many regimes are blamed for declines in living standards. Mishler and Rose therefore conclude that support for the new democracies is relative and contingent.  If one only uses electoral turnout rates as the indicator of political interest, it appears as if the traditional democracies are unable to engage their citizens in politics and that the newer democracies are repeating the experience of a long slow steady decline in electoral participation.  Held (1993) in Prospects for Democracy asks if democratization is essentially a Western project or something of wider universal appeal.  As Western democratic societies lose interest in politics, will other regions follow suit?  Democracy relies on an active, informed citizenry for its successful operation.  If political interest continues to decline as it has in Europe and the US over the past generation, even this minimal requirement is unlikely to be met.  

The organizations for the volunteerism index are church or religious organization; sport or recreation organization; art, music or educational organization; labor union; political party; environmental organization; professional association; charitable organization; or any other voluntary organization.  To construct the binary dependent variable, I counted any active membership as volunteerism (respondent answered yes to any of the organizational membership questions).  In summary, 61.8% of all respondents stated that they volunteered in some organization, with a range across countries from a low of 25.1% in Bulgaria to a high of 96.7% in Norway.  The map in Figure 2 shows the distribution of volunteerism across the 65 countries in the World Values survey.  It is clear that no regional concentration of high values exists; instead, the map is very country-specific or to use the language of spatial analysis, there is little spatial autocorrelation in these data.  High values for countries like Ireland, Switzerland and Norway lie cheek-by-jowl with low values in France, Denmark, Germany and Austria.  Elsewhere, Latin America exhibits a similar checkerboard pattern and only the formerly Communist countries of Eastern Europe and the former Soviet Union, as well as the few Mainland Asian sample states, show any consistent regional affiliation.

            The final dependent variable examined here is political interest.  It is derived from the answers to the question:  “Please say, for each of the following, how important is it in your life” Options for responses were very important, rather important, not very important, not at all important, and don’t know.  Among the list of interests was “politics”.  In order to form the dummy dependent variable, I combined “very important” and “rather important” as political interested and “not very important”, “not at all important” and “don’t know” as not interested in politics.  Figure 3 shows the average country value for this indicator, with values less than 2.5 indicating high interest.   Highest values are seen in North America, some European countries (Scandanavia and central Europe) and Japan.  Lowest values are evident in the new democracies - South America, Eastern Europe/former Soviet Union, and Southern Europe.  Using a dummy indicator (interested in politics or not), the global average is only 44.8% with a range from 77.8% in West Germany and a low of 17.9% in Romania.

All of the surveys in the World Values project were carried out through face-to-face interviews, with a sampling universe consisting of all adult citizens, aged 18 and over, in the participating countries.  In the usual sampling design, a multi-stage random selection of sampling points within each country was developed with a number of points being drawn from all administrative regional units after stratification by region and degree of urbanization.  In each sampling point, a starting point address was drawn at random.  Further addresses were selected by random route procedures.  Some weighting was initiated to account for expected response rates by region, ethnic group and urbanization (see the World Values website http://wvs.isr.umich.edu/index.html for details).

 

The Political Geography of World Democratic Values

 

The analyses of the three dependent variables (social trust, volunteerism and political interest) will be reported separately, though it should be remembered that democratic values are rarely so finitely defined and disconnected.  For most individuals and for most societies, scores on the separate democratic values are consistent and overlapping.  I considered  the option of constructing dimensions of democratic values using the individual variables and analyzing the resulting principal components scores.  While attractive in principle,  the interpretation of the multilevel modeling results of these aggregated and complex scores would be difficult.  The option of selecting individual scores carefully to reflect some of the range of democratic values was followed instead. 

The variance components model, the usual starting point for multilevel modeling, is presented in  Table 1.  The fixed parameter for “thin trust” (trust in people) refers to the intercept value (-1.124) and reflects the log-odds of trusting fellow citizens.  When transformed, the odds are .896 of an individual trusting his/her fellow citizens (“Most people can be trusted a lot”).   All of the random effects for the social trust model are significant indicating that the three levels (individual, region and country) must be considered in the model.   As might be expected, the variance at the level of the individual is most prominent but that for the country level (third level) is also very important while the regional factor is less so.  Proportionately, without factoring in any independent predictors, it can be stated that about 66% of the total variance is at the individual level, about 9% at the regional level, and about 25% at the country level.  The last component is particularly noteworthy and suggests that use of the World Values data and similar surveys in predictive models without special regard for the national contexts is likely to overstate the nature of the socio-demographic relationships in the equations.  Much of the explanation is incorporated in the grouping of the data into countries and this context needs to be explicitly tallied in any modeling.   Clearly there are large and significant differences in social trust between the countries in the sample.  Attention to the regional and country residuals will be given after the multilevel modeling is completed.  Though mapping and graphing of the residuals from the variance components model can help in the selection of independent variables, enough is known about the correlates of social trust from the work of Putnam (2000) and Newton (1999; 2001) that we can proceed to the fitting of the models.

Unlike social trust, the variance components model for volunteerism shows no significant coefficient at the regional level (see Table 1) and thus, one can proceed to a model with only two levels, individual and country.  Surprisingly, the variance components suggest that the national level is more important than the individual for this factor – stated another way, the country where a person lives is more important in understanding the variation among individuals in volunteerism than the characteristics of the persons surveyed.  It is evident from the map (Figure 2) that strong national discrepancies in volunteerism exist due to cultural traditions, political regime character, religious affiliations and the strength of the non-governmental sector. The variance components model confirms this and produces the surprising finding that country-level factors are more significant than personal differences.  The intercept (.359) is also significant and when converted from the logit form, shows that the odds of respondents engaging in some volunteerism  as .494 (the binary outcome variable measures any active voluntary membership). 

 

 

Table 1:  Variance Components Model for the Trust in People, Volunteerism and Political Interest

a) Trust in People

 

Parameter

Estimate

Standard Error

Fixed Parameter

β1jk

-1.124

.089

 

 

 

 

Random Effects Level

 

 

 

3- Country

v1k

.344

.077

2- Region

u1jk

.141

.019

1- Respondent

e0ijk

1.00

.000

 

b) Volunteerism

 

Parameter

Estimate

Standard Error

Fixed parameter

β1jk

0.359

.026

 

 

 

 

Random Effects Level

 

 

 

3- Country

v1k

.999

.091

2 – Region

u1jk

.000

.000

1 – Respondent

e0ijk

.793

.020

 

 

c) Political Interest

 

Parameter

Estimate

Standard Error

Fixed parameter

β1jk

-0.032

.087

 

 

 

 

Random Effects Level

 

 

 

3- Country

v1k

0.238

.020

2 – Region

u1jk

0.137

.020

1 – Respondent

e0ijk

0.934

.010

 

            The final variance components model for political interest shows, unsurprisingly, that the odds of political interest are small.  When the fixed component (-.032) is converted from its logit form, the proportion of respondents showing political interest is only .225.  Since the question was asked in combination with a range of other possibilities in the social and cultural sphere, this low value is not too surprising – and again the map (Figure 3) and summary statistics show a large range in political interest between states.  The variances of the random terms are all highly significant and suggest a three level model.  Proportionately, 71% of the variance is attributed to the individual level, 10% to the regional level, and the remainder (19%) to the country level.  For most democratic and social values, one might expect these sorts of ratios. About two-thirds to three-quarters of the variance is attributed to individuals and the remainder split between the regional and national levels, with the bulk of this remainder associated with the national contexts.

            In modeling the variance of the respective dependent variables (social trust, volunteerism and political interest), I opted to use the predictors that had been found to be related significantly to these sorts of political outcomes, as reported in the book edited by Norris (1999b).  Given the presence of collinearity, I dropped the predictor with the weakest correlations; in general, I was looking for a model that met the theoretical specifications of the democracy literature, was parsimonious (an especially crucial factor in multilevel modeling with many random terms), met the requirements of the multilevel method and reported only significant coefficients.  The results of the final models are presented in Table 2; these models include dummy terms (established and new/non-democracies) for social trust and volunteerism that were included in the model as a result of the patterning in the residuals.  The penultimate models did not have these dummy variables. 

            In the first model for social trust, left-right self-placement on an ideological scale has a negative coefficient – those self-identified as leftists are less trusting (Table 2).  Social trust is also strongly and positively related to life satisfaction, a clear replication of the Newton (1999) finding abut the relationship between trust and income.  A further piece of evidence of this class basis of social trust is the negative relationship with subjective social class – those self-identified as working class are less trusting than the middle and upper classes.  Trust is also negatively related to societal change, an indicator that measures a respondent’s evaluation of the direction of his/her society.  A negative coefficient indicates that those most dissatisfied with the societal direction are more trusting.  Those who do not attend a place of worship or do so infrequently are less trusting than regular attendees and finally, a very clear split emerges between countries that have been democratic for 40 years and the other states in the study.  

            The results for social trust reported in Table 2 are not surprising given the extensive previous work on the subject in the United States and in Europe.  What is significant is that the conclusions of Putnam (2000) and Newton (1999) can be generalized to the rest of the countries in the World Values survey.  Trusting individuals are more religious, wealthier and of higher social status, are more conservative politically, want a change of the current direction of society, and reside in established democracies.  The presence of a “virtuous circle” is one of the key conceptual expectations of the democratic theory literature; egalitarian social policies will encourage more citizens to be part of the political process (Huber, Rueschemeyer, Stephens, 1999).  Clearly, such a concept works for a society where a sizeable majority of people have the characteristics of those who trust their fellow citizens.  Whether it can be emplaced in a polarized society is questionable.  In fact, there is a real danger of a “vicious cycle” developing in such contexts in which inegalitarian policies and the poverty resulting from them engender problems of marginalization and a corrosion of rights, producing a “delegative democracy” (Huber, Rueschemeyer, Stephens, 1999).  Putnam (2000) has bemoaned the decline in trust in the United States over the past generation and believes that this decline, unless it is arrested and reversed, will have strong negative consequences for the operation and strength of the American political system.  Combine this decline in community trust with growing alienation and misgivings about the fairness of the political apparatus and a growing challenge to the efficacy of the two hundred year old democracy can be envisaged.  In newer democracies without a history of trust and a legacy of misrule and authoritarianism, the establishment of the virtuous cycle is more problematic.  It is by no means yet certain that the democratic gains of the past decade will not be eroded in political and economic competition between groups.

 

Table 2: Final Multilevel Models for Trust in People, Volunteerism and Political Interest

a)  Trust in People

Variable

Estimate

Standard Error

Fixed Terms

 

 

Intercept

-.565

.135

Left-right Self Placement

-0.014

.007

Life Satisfaction

.040

.006

Subjective Social Class

-0.104

.016

Societal Change

-.118

.029

Church Attendance

-.036

.008

Est.-New Democracy

-.674

.183

 

 

 

Random Terms

 

 

3- Country

.325

.074

2- Region

.147

.020

1- Respondent

1.000

.000

 

b) Volunteerism

Fixed Terms

Estimate

Standard Error

Intercept

.415

.128

Life Satisfaction

.010

.006

Employment Status

.015

.006

Est.-New Democracy

-.537

.271

 

 

 

Random Terms

 

 

3- Country

.914

.177

1- Respondent

1.00

.00

 

c) Political Interest

Fixed Terms

Estimate

Standard Error

Intercept

..244

.155

Material/Post Materialist

.236

.026

Subjective Social Class

-.179

.018

Gender

-.436

.030

Democracy Indecisive

.137

.019

Marital Status

-.019

.007

Age

.013

.001

Societal Change Direction

-.162

.028

 

 

 

Random Terms

 

 

3- Country

.245

.056

2-Region

.126

.019

1- Respondent

1.000

.000

 

In the final model for social trust, the intercept value (-.565), converted to an odds ratio, yields an average level of trust by the stereotypical individual of .633.  More importantly, there is a large and significant variation across countries.  Examining the “caterpillar plot” of the residuals from the penultimate model (same as the final model but missing the democracy dummy variable) shows the clear trend.  In Figure 4, the confidence interval bands that do not intersect the mean value (horizontal line at 0.0) show countries where social trust is significantly over – or under-predicted.  Eleven countries show significant average under-prediction (more trust than would be expected on the basis of the predictors) whilst another eleven show significant over-prediction (less trust than would be expected).  In rank-order, the under-predicted values are for Norway (highest), Sweden, Canada, Netherlands, Japan, Finland, Taiwan, Northern Ireland, Ireland, West Germany and Australia.  With the exception of Taiwan, all are established democracies in rich continents.  At the opposite end of the residual scale are the over-predicted set, with Brazil showing the highest residual followed by Peru, Puerto Rico, Turkey, Philippines, Macedonia, Colombia, Venezuela, Slovenia, Bangladesh and Nigeria.  The over-representation of the Latin American countries in this list is noteworthy and offers support to the thesis of Diamond and Linz (1989), Bermeo(1999) and Inglehart and Carballo (1997) that countries in Latin America have seen a trend towards democracy without witnessing any deepening of democratic values.  The other countries on the list are recent transitional countries and while it is clear that each national context helps to create a legacy of trust or suspicion, these countries share a recent history of authoritarianism.  Incorporation of the dummy variable that distinguished between established democracies (more than 40 years old) and other states eliminated this patterning in the residuals.  Plotting the residuals for the nearly 550 regions in rank order showed no evident pattern beyond the one described for the countries.  Further analysis of this complex pattern is hardly warranted by the relatively weak contribution of the regional element to the variance explained. 

As noted earlier, the final multilevel model for volunteerism did not require the inclusion of a second-level coefficient for region and therefore, a two level model (individual and country) is presented in Table 2.  The intercept value (.415) translates into a volunteering odd-ratio of .534 for individuals (active in any organization).   With the introduction of the fixed terms in the model, the contribution of the random terms to the variance is about equal (.914 and 1.0).  But as noted above, there are dramatic country to country differences in this ratio of volunteerism.  Volunteerism at the individual level was significantly related only to two predictors; those who have a greater life satisfaction volunteer more, as do those with a higher employment status.  These relationships are not surprising since it is expected that volunteerism would be higher for those with the time and the means to take part in such activism.  Putnam (2000) has noted this phenomenon in the US and the pattern is replicated on a global basis by the World Values surveys.  None of the usual socio-demographic characteristics (age, gender, education, small town, rural or urban residence, etc) are significantly related to the propensity of volunteer activism.  A much stronger indication of the nature of the variance of this element of a democratic society is evident from the distribution of the residuals of the penultimate model (Figure 5).  The rank-order from low (volunteerism over-predicted) to high (volunteerism under-predicted) reads like a schema describing the scale of established democracy.  Volunteerism is most embedded in older stable democracies and weakest in the transitional societies especially those undergoing both dramatic economic and political changes.  In a society under severe dislocation due to the new economic regime, such as the former Soviet Union states, a clear gap has emerged between (economic) winners and losers.  For the “losers”, the vast majority of the population, multiple jobs and daily stress accompany the attempt to strive for a minimal quality of life and little time and energy is available for even the barest of non-essential activities (O’Loughlin and Bell, 2000). 

Twenty-five residuals on the over-predicted end of the rank-order and twenty-two on the under-predicted end of the graph do not have confidence limits that overlap 0.0. (Figure 5).   The wider range of values and the increase in the number of significant residuals is unsurprising given the nature of the model - The most over-predicted country is Slovenia, followed in order by Moldova, Bulgaria, Turkey, Austria, West Germany, France, Taiwan, Georgia, Latvia, Estonia, Bosnia, Chile, Lithuania, Colombia, Ukraine, Uruguay and Serbia.  At the other end of the rank-order (volunteerism higher than expected on the basis of the two predictors) is Argentina, followed by Norway, Mexico, Australia, Brazil, Hungary, Northern Ireland, Nigeria, Poland, United States, Netherlands, Macedonia, Ireland, Puerto Rico, Finland, Croatia, South Africa, and Sweden.   The residual pattern for the 57 countries in the analysis, while generally conforming to expectations, offers a few surprises.  Austria, France and West Germany are lumped with new democracies of the former Communist regions of Eastern Europe and the former Soviet Union.  Among the under-predicted states, Macedonia, Croatia and Nigeria – countries with severe economic and political stress in the past decade – are found.  The exact causes for these exceptions are rooted in local traditions and relations between the state and non-governmental sectors and are certainly worthy of further investigation.  The addition of the dummy predictor (established democracy or not) to the model eliminates the pattern that is evident in the caterpillar plot of the residuals in Figure 5, though it obviously does not account for all the variation.   

            The final multilevel model was also the most complex with seven independent predictors included in the equation (Table 2).  Many of the relationships are relatively weak, though significant.  The log-odds ratio of political interest, .415, derived from the transformation of the intercept value (.244) is small; this may be attributed to the nature of the question which posed political interest against a range of other interests of the individuals surveyed.  Higher political interest was expressed by individuals with a more post-materialist orientation (using the 12 point scale of Inglehart, 2000), by individuals with a (subjective) higher social class, by men, by those who disagree with the statement that democracy is indecisive, by married individuals, by older voters, and by those who believe that a change in the societal direction is needed (Table 2).  There are no surprises in this list. Once again, interest in the functioning of a democracy is expressed by those with the time, inclination (as a result of social status) and resources to pay attention to politics.  As is clear from surveys in former Communist countries, political interest is strongly related to personal resources.  In a time of stress, those caught by the changing nature of economic life are unable to take part. 

            Unlike the other two models, the display of the residuals did not help to clarify the regional or country aggregations of over- and under-prediction (Figure 6).  In contrast to the previous two displays, the confidence intervals are narrow; fourteen countries have significant positive values (under-prediction) and seventeen have significant negative values (over-prediction).  The rank order (highest over-prediction) runs from Chile (highest) to Venezuela, Argentina, Spain, Uruguay, Moldova, Ireland, Brazil, Nigeria, Finland, Macedonia, Switzerland, Serbia, Russia and Azerbaijan.  East Germany is the most under-predicted value, then West Germany, followed by Norway, Turkey Russia, Bangladesh, Belarus, Japan, United States, Armenia, Latvia, India, Lithuania, Georgia, South Korea, Estonia, and Poland.  The clustering of four Latin American states near the top of the over-predicted list of residuals (less political interest than expected on the basis of the characteristics) falls in line with the expectations of Boeninger (1997, but otherwise, the country sets are mixed in terms of the age and strength of the democracy, the economic stress indicators, the regional locations, and the nature of the state.  In this case, unlike the two previous models, individual characteristics are significantly more important in determining political interest than country location.

            This multilevel analysis has indicated a range of geographic effects.  In all three models, the national context is an important presence in determining the outcome of democratic opinions on social trust, volunteerism and political interest.  In two of the three models (for social trust and political interest), the regional level had a significant presence and required the estimation of three level models.  Part of the explanation of the varying presence of region as a factor could be due to the inconsistent definition of region in the World Values survey.  Region size is highly variable from country to country (ranging from the nine large Census regions in the United States to small counties in Ireland).  In some countries, regions are often not much more than administrative devices and do not hold much influence on the attitudes of citizens.  Some exceptions can be noted when regions are commonly associated in the public mind with ethnic groups, such as Quebec and French-Canadians, the Süd-Tirol and German-speakers in Italy, or the breakaway mixed ethnic region of the Trans-Dniester Moldovan Republic in eastern Moldova.  Given the possible different meanings of region, it is somewhat surprising that the concept has as much importance as is evident in the analysis.

            The compositional correlates of political attitudes were consistent with previous studies and offered no surprises.  What is most impressive from the multilevel model fitting is the residual pattern for countries  that seems to have taken on a macro-regional form on a global basis and also one that corresponds to the disparity between old and new democracies.   While the pattern in the residuals can be explained by the addition of a dummy variable separating states into these two types, it is entirely possible that further addition of explanatory variables at the country level will assist in accounting for any remaining variance.  Since the main purpose of this paper was to establish the nature of the geographic effects in the distribution of democratic values, this further analysis is left for a later paper.

 

Conclusions

 

Geographers have insisted for over three decades that the patterning or spatial autocorrelation visible in the distribution of political phenomena cannot be explained away by the distribution of socio-demographic variables or the clustering of individuals of similar socio-demographic character.  Controlling for these compositional effects offers one insight into the “geography” that remains but a better alternative is to directly model the spatial element.  Multilevel modeling offers a compromise between the usual alternatives, blending individual and aggregate data and allowing a consideration of the multi-scalar effects rather than separate consideration of geographic effects at different scales.  Since most social scientific data are hierarchically organized in a nested fashion, the multilevel approach is tailor-made for modeling this type of information.

            In the case of democratic values, the scant evidence from social science surveys over the past 3 decades is that a diffusion of belief in democratic principles has spread at the same time as the growth in the ratio of “dissatisfied democrats” has been noted (Norris, 1999c).  Public opinion surveys show strong attachment to principles of free expression, civil liberties and political choices in the new democracies, even though many of them have neither little historical memory of such traditions nor much experience of freedom.  Efforts of non-governmental agencies based in Western countries to promote grassroots democracy in the form of non-governmental organizations and advocacy groups for women’s rights, the environment, minority and human rights, and protection of constitutional gains have been evident in the new democracies.  Though their contact with the citizens of the new democracies is relatively small, their efforts are not going unnoticed by the regimes.  By training cadres of educators, it is hoped to spread the notions of Western-style democracies and imbue the newly-democratized societies with the values that have helped to sustain the Western democracies.  Continuous pressure from powerful states and the threat to withhold foreign aid to repressive regimes act as powerful incentives for governments and elites at least to feign democratic credentials.  While the ratios expressing beliefs in democratic values are still relatively small compared to their Western counterparts, they are nevertheless growing and further diffusion might bridge the gap that is currently evident between the West and the rest.  The so-called “democratic deficit” applies not only to the gap between citizens and their governments but also to the disparity between the old and new democracies.  

            While there seems to be a growing acceptance of the value of democratic governance and the principles that underlie it across the globe, this study has highlighted the country-specific character of democracy.  The evidence in the multilevel regression equations is strong and consistent that the attitudes of individuals are conditioned by their location – in regions and countries of specific character.  Clear and unambiguous geographic effects in the equations and residuals supports the position of geographers that place matters in the sense that it shapes the local debates and political character and this historical memory remains embedded in the political expressions.  This is not to claim that place effects are unchanging and inviolate; rather, geographers hold that places both shape the attitudes and behavior of their residents and in turn, are shaped by the collective expression of this popular will in a reciprocal manner.  Countries or nation-states as they are frequently mislabeled are the most powerful territorial expression and their power to shape identity and political behavior remains unparalleled, despite claims of the demise of the state in a globalized world.   A full account of citizen preferences, practices and values requires not only knowledge of the compositional characteristics of the individual but also one further characteristic – where she or he lives.

 

 

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Figure 1: Global Distribution of Trust of Fellow Citizens in the 1990s.  Data are from the World Values survey questions.  The value for each country is computed on a 5-point scale from responses to the question “Generally speaking, would you say that most people can be trusted or that you can't be too careful in dealing with people?”



 

 

 

 

Figure 2: Volunteerism in Non-Governmental Organizations in the 1990s.  The World Values question posed for involvement in non-governmental organizations was: “Now I am going to read off a list of voluntary organizations; for each one, could you tell me whether you are an active member, an inactive member or not a member of that type of organization?. 

 



 

 

Figure 3: Political Interest in the 1990s.  The index is derived from the World Value survey answers to the question:  “Please say, for each of the following, how important is it in your life” Options for responses were very important, rather important, not very important, not at all important, and don’t know.  Among the list of interests was “politics”.


 

Figure 4: Residuals from the Final Model for Social Trust (three level  model with  5 predictors).


 

Figure 5:  Residuals from the Two-Level Model of Volunteerism


Figure 6: Residuals from the 2 level model of political interest


 



[1]  This research was supported by a grant from the National Science Foundation and was conducted in the context of the “Globalization and Democracy” graduate training program in the Institute of Behavioral Science at the University of Colorado, Boulder.   My IBS colleagues and students continue to offer a stimulating work environment.  Frank Witmer made the maps.  Thanks to participants at the “Interrogating the Globalization Project” conference at the University of Iowa, November 2001, for comments on an earlier draft of this paper.