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Who is my Neighbour? Cultural Proximity and the Diffusion of Democracy*

David Wong De-Wei

April 2015

Abstract

What explains the strong spatial and temporal clustering of democratization around the world? Most

theories of democratic diffusion focus on geography: the probability of a state’s democratization is

influenced by developments in geographically neighbouring states. However, this paper challenges

this view through the lens of cultural proximity. I argue that elites and society are influenced by

democratic developments in cultural neighbours because they look to them as reference states, and

relate with them through transnational religious/ethnic imagined communities. I demonstrate this

relationship using both qualitative historical evidence about the ‘third wave’ of democratization, and

quantitative statistical analysis of transitions globally from 1960-2008. The effect of cultural

proximity on democratization is strong and robust across various sample sizes and controls,

consistently removing the effect of geographical proximity.

                                                                                                               * Paper prepared for the International Association for Political Science Students (IAPSS) World Congress 2015 titled, “The Politics of Conflict and Cooperation” at Birkbeck University in London, United Kingdom, 14-18 April 2015. This paper received the IAPSS Award for Academic Excellency 2015 for the best paper presented. The abridged version is found here. Contact author for full paper at david.wong@some.ox.ac.uk

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Introduction

Over the past few decades, we have seen a dramatic rise in the number of democracies globally.

Between the 1960s to early 2000s, we see about 87 democratic transitions around the world

(Cheibub, Gandhi & Vreeland 2010; Houle, Layser, & Xiang 2013).

An interesting trend in these transitions is that it tends to be clustered across time, and across

geographical space. Countries that are close to each other tend to democratise together, giving

democratisation a ‘wave-like’ characteristic. A key research area has thus been to examine why this

is happening. Why are democratic transitions spreading in this manner? What conditions facilitate

this?

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Existing Literature

Most scholars that study this contagion argue that geographical neighbours influence

democratization (O’Loughlin et al. 1999; Wejnert 2005; Brinks & Coppedge 2006; Simmons,

Dobbin & Garrett 2008). They claim that neighbouring countries tend to have closer and deeper

network ties with each other, allowing democratic ideas to spread. The frequent contact and flow of

people, ideas, goods and services lead to a diffusion of norms. State and opposition leaders may also

look to what is going on in neighbouring countries as mental shortcuts to learn from (Weyland

2012).

Although this seems plausible, it remains theoretically underdeveloped. Why for example do

countries simply copy their geographical neighbours? Who do they look to as their neighbours, and

specifically who do they consider neighbours worth copying?

The presence of dense networks between neighbouring states may increase exposure to democratic

ideas, but it does not necessarily lead to the adoption of democracy; the link between exposure and

adoption needs to be explained.

Although most studies conceptualize space in a geographical sense, space can also be defined

culturally. Differences in language, ethnicity, religion, and colonial history can lengthen the cultural

distance between states, limiting the spread democracy.

A Theory of Cultural Proximity

Cultural distance can be conceptualised as the measure of how similar a state’s culture is from

another. People living in Kuwait and the UAE for example, are on average likely to share more

common values and worldviews, and be sensitive to similar symbols and metaphors, compared to

people living in Greece. Religion, ethnicity, language, and colonial history shape culture, and can be

used to proxy cultural distance between states.

But how does cultural proximity influence the spread of democratisation?

1. Cultural proximity allows for denser networks and thus more information flow. For

neighbouring countries to be affected by developments in other countries, they must receive

information about it, and this information has to be framed as a struggle for democracy, not say

youth violence (cf. Rogers 1995; Scott 2000).

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Direct Communication Indirect Communication (through translator)

Figure 1. All potential networks between DC and IC dyads.

Countries that share a common language will on average have denser networks compared to states

that speak different languages (Melitz 2008; see Figure 1). This allows more information to flow

between countries, and is less likely to be blocked by state officials or the co-optation of translators.

And by translators here, I mean media outlets, publishing houses, etc.

2. Cultural proximity also helps to bridge the gap between exposure and emulation through

shared identity. People hold multiple identities simultaneously. One may associate herself as an

Egyptian, an Arab, a Muslim, a bureaucrat, a mother, and a wife, each with different degrees of

importance.

Some of these identities link people with an “imagined community” beyond the confines of their

nation state (cf. Anderson 2006). Religious and ethnic groups, like states, use membership data,

maps of geographic religious/ethnic reach, museums, periodicals, and rituals to bolster a sense of

unity within their in-group, creating within the minds of people that they belong to a wider

community beyond the state.

Figure 2 shows an example of efforts by Christian organisations to construct this imagined

community. The map itself shows no state borders, but simplifies the world into areas that are

‘Christianised’ and those that are not. Maps like these help reinforce a transnational religious identity

by visualising the geographical spread of Christianity, giving Christianity a ‘geo-body’ (Winichakul

1994)

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Figure 2. A Christian ‘imagined community’.

When a state democratizes, its cultural neighbours are likely to observe developments, and may

identify with the struggle for freedom because they consider themselves part of the same

transnational community. This may inspire people, who see themselves in similar cultural contexts,

to push for democracy, amplifying the effect of neighbouring transitions.

In sum, cultural proximity influences democratic diffusion in at least two ways: through a network

effect, and an identity effect.

Historical Evidence: The Iberian-Catholic Wave

I apply this theory to what scholars have called the ‘third wave’ of democratisation from the late

1970s through the mid 1990s (Huntington 1991). The wave of democratization began in Spain and

Portugal, spread to Latin America, then the Philippines, and finally to Eastern Europe and Africa. In

the interest of time, I will only sketch the contagion effect between Southern Europe and Latin

America.

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In 1974, a group of military officers launched a coup aimed at halting Portugal’s colonial wars,

leading to the parliamentary elections of 1976 (Linz and Stepan 1996). Ruling elites in Spain

watched Portugal closely. Although Spain was not entangled in similar colonial wars, Spanish elites

knew that the institutional environment they operated in was similar. Both were Catholic-majority

states and faced heighted pressure for democracy after the Second Vatican Council, when the

Catholic hierarchy in Spain withdrew their support of the regime (Philpott 2004).

Under pressure, then-Vice President of Spain Manuel Fraga lamented that he “did not want to

become the Caetano [the dictator of Portugal] of Spain,” and signalled interest in guiding democratic

change; that he did not want to risk a coup (quoted in Linz and Stepan 1996, p. 76)

The ruling elites thus began negotiations with opposition parties for a top-down transition toward

democracy. The result was a “pact transition”: democratic elections were held in 1977, but only after

a Law of Political Amnesty was passed, which protected the ruling elites of the old regime from any

charges of war crimes or human rights abuses (Ibid; Encarnacion, 2008).

The first countries that experienced the contagion of Portuguese and Spanish transitions were not

geographically proximate Morocco or Algeria, but Latin America. These states were former colonies

of the two Southern European states and had similar inherited political institutions.

The influx of information into Latin America about Portugal and Spain’s transition created increased

societal pressures for democracy. As if mimicking the Spanish transition, Catholic churches began to

withdraw support of dictators, and spoke openly against the regime. (Edmonds 2010) Oppositional

elites across Latin America picked up on these opportunities, and used the institutional resources of

the Church to pressure the regime for democratization.

Ruling elites across Latin America, watching these developments unfold, began to recalculate how

best to preserve some of their personal power and wealth. The top-down transition of Spain provided

the ruling elites with a template that could minimize the cost of a democratic transition by providing

(a) amnesty from earlier war crimes or human rights abuses; (b) and a controlled pace of transition

(Encarnacion 2008).

Many Latin American ruling elites eventually chose this path and negotiated with opposition elites to

create ‘pact transitions’ with amnesty laws – i.e., Chile (1978), Brazil (Amnesty Law of 1979),

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Uruguay (Law Declaring An Expiration of the State’s Punitive Authority, 1986) followed swiftly by

elections.

This trend is especially noteworthy because elite-cantered and relatively peaceful ‘pact transitions’

were rare before Spanish democratization. The two earlier waves of democratisation did not take this

path.

The first ‘long wave’ of democratization that spread and evolved across Europe in the 18th and 19th

century was punctuated with violence and unrest. Following the French revolution, industrialist and

intellectuals rebelled against monarchs, and attempted to topple these regimes. Only after a long

conflict between state and society did continental Europe move toward some form of universal

suffrage and representative democracy (Spielvogel, 2009).

Moreover, the post-World War II ‘second wave’ of democratization was primarily a result of the

Allied powers imposing democratic institutions on fascist states, and departing colonizers

transferring power to colonial legislatures following independence, not elite pacts with those

currently in power (Huntington 1991).

We thus see that some form of copying and emulation of the Spanish model was actually happening;

that it was not just a coincidence or chance correlation that these countries were democratising at the

same time. The process itself was similar.

However, some may still be believe that this seeming pattern of democratisation is either

idiosyncratic or spurious – i.e., caused by geographical proximity or domestic factors like per capita

income or inequality or trade. I thus turn to statistical analysis to control for alternative explanations

and test the generalizability of my theory.

Statistical Analysis

I ran a survival analysis to test the association between cultural proximity and transitions to

democracy in a global sample of 129 states from 1960-2008.

[Table 2 at bottom]

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I controlled for various domestic variables that scholars often cite as important to democratisation.

Values that are positive indicate that this variable increases the likelihood of democratisation.

Negative values indicate they reduce the likelihood. Values that are highlighted yellow, and with

stars are variables that actually have an effect; that are statistically significant

Table 2 tests the effect of having democratic neighbours on the likelihood of transition. At the first

column, model 1, I ran a regression with just domestic variables. Model 2, 4, and 6 includes the

geographical neighbour influence variable, which we see is statistically significant before we control

for cultural proximity. I ran multiple models to ensure that the number of observations remains

unchanged, since we want to make sure that it is due to the variable, and not the change in sample

that is causing the results.

I include cultural proximity variables in model 3, 5, 7, seen through the proxies of religious,

language, and colonial neighbour. They are strongly statistically significant, and in two cases they

remove the effect of geographic proximity.

[Table 3 at bottom]

In Table 3, we look at the effect of having neighbours who experienced democratic transitions. We

see that in every case here, the moment we control for the democratisation of cultural neighbours, the

effect of geography is removed.

Now I am not saying that cultural proximity is the only thing that influences democratisation. Indeed,

we see that oil, trade, and other variables continue to have an effect. What I am arguing here is that

cultural proximity is one of the variables, and indeed an important one, influencing the likelihood of

democratic transitions in states.

These findings are substantively and statistically strong, and are robust even after controlling for

various domestic determinants of democracy and other means of diffusion. Indeed, they alter the

strength and significance of domestic variables often thought to influence democracy, implying that

analysis that omit cultural proximity variables suffer from omitted variable bias.

In contrast, the effect of geographical diffusion is consistently removed once cultural proximity is

controlled, implying that the effect of geography found in earlier studies may be spurious.

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The overreliance on geographic space and distance simplifies complex diffusion processes that are

often influenced by culture. As we see from the results, we continue to live in a world that remains

largely embedded in culture and identities beyond nation state. Studies that look democratisation and

other political dynamics should pay careful attention to these ‘softer’ elements, and not only on

‘hard’ economic or material factors.

References

Anderson, Benedict. 2006. Imagined Communities: Reflections on the Origin and Spread of Nationalism, New Edition. New York: Verso.

Brinks, Daniel, and Michael Coppedge. 2006. “Diffusion is No Illusion: Neighbor Emulation in the Third Wave of Democracy.” Comparative Political Studies, 39 (1): 463-489.

Cheibub, Jose, Jennifer Gandhi and James Vreeland. 2010. “Democracy and Dictatorship Revisited.” Public Choice 143: 67-101.

Edmonds, Amy. 2010. “Authoritarianism and the Catholic Church in Latin America.” PhD diss., Baylor University.

Encarnacion, Omar. 2008. “Reconciliation After Democratization: Coping with the Past in Spain” Political Science Quarterly 123 (3): 435-459.

Houle, Christian, Mark A. Kayser, and Jun Xiang. 2013. “Diffusion or Confusion? Debt and Democratization Around the World.” Paper presented at conference, “Think Globally, Act Locally,” at the London School of Economics, 6-7 March, 2013.

Huntington, Samuel. 1991. The Third Wave: Democratization in the Late Twentieth Century. Norman: University of Oklahoma Press.

Linz, Juan, and Alfred Stepan. 1996. Problems of Democratic Transitions and Consolidation: Southern Europe, South America, and Post-Communist Europe. Baltimore: John Hopkins University Press.

Melitz, Jacque. 2008. “Language and Foreign trade,” European Economic Review 52: 667-699. O’Loughlin, John, Michael Ward, Corey Lofdahl, Jordin Cohen, David Brown, David Reilly,

Kristian Gelditsch, and Michael Shin. 1999. “The Diffusion of Democracy, 1946-1994.” Annals of the Association of American Geographers 88 (4): 545-574.

Philpott, Daniel. 2004. “The Catholic Wave.” Journal of Democracy 15 (2): 32-46. Rogers, Everett. 1995. Diffusion of Innovations, Fourth Edition. New York: Free Press.

Scott, John. 2000. Social Network Analysis: A Handbook, 2nd Edition. London: Sage Publications. Simmons, Beth, Frank Dobbin and Geoffrey Garrett. 2008. “Introduction: The Diffusion of

Liberalization.” In The Global Diffusion of Markets and Democracy edited by Beth Simmons, Frank Dobbin and Geoffrey Garrett. Cambridge: Cambridge University Press.

Spielvogel, Jackson. 2009. Western Civilization, Volume II: Since 1500, 8th Edition. Boston: Wadsworth.

Wejnert, Barbara. 2005. “Diffusion, Development, and Democracy, 1800-1999.” American Sociological Review 70 (1): 53-81.

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Weyland, Kurt. 2012. “The Arab Spring: Why the Surprising Similarities with the Revolutionary Wave of 1848?” Perspectives on Politics 10 (4): 917-934.

Winichakul, Tonhchai. 1994. Siam Mapped: A History of the Geo-Body of a Nation. Honolulu: University of Hawaii Press 1994.

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Table 2. Predicted Likelihood of Democratic Transition from 1960 to 2008 Based on Proportional Measure Concept Explanatory Variable 1 2 3 4 5 6 7 Domestic Variables

Log GDP Per Capita .178 (.171)

.189 (.180)

.174 (.174)

.280 (.193)

.284 (.185)

-.068 (.211)

-.204 (.204)

Urbanization .007 (.009)

.002 (.009)

-.000 (.009)

-.003 (.010)

-.017+ (.009)

.013 (.010)

.009 (.010)

Log Economic Openness

1.94+ (1.06)

1.63 (1.05)

1.42 (1.07)

1.90 (1.26)

1.11 (1.25)

1.87 (1.17)

1.76+ (1.03)

Log Economic Openness2

-.341* (.146)

-.294* (.145)

-.258+

(.147) -.329+ (.174)

-.187 (.169)

-.326* (.162)

-.301* (.144)

Major Oil producer -1.38** (.450)

-1.27** (.449)

-1.10* (.457)

-1.38** (.497)

-.879+ (.496)

-1.02* (.454)

-1.02* (.448)

Island state -.023 (.315)

-.285 (.325)

-.457 (.330)

-.591 (.376)

-1.20** (.387)

-.081 (.331)

-.368 (.336)

Landlocked state -.365 (.301)

-.272 (.306)

-.387 (.308)

-.245 (.339)

-.612+ (.357)

-.088 (.343)

-.125 (.337)

Geography Geographical neighbour influence

.013*** (.004)

.012*** (.004)

.013*** (.004)

.000 (.004)

.012** (.004)

.003 (.004)

Cultural Proximity

Religious neighbour influence

.011* (.005)

Linguistic neighbour influence

.056*** (.007)

Colonial neighbour influence

.035*** (.006)

Time at risk 3996 3996 3996 3346 3346 3466 3466 No. of Subjects 129 129 129 100 100 110 110 No. of Transitions 87 87 87 76 76 73 73 Log Likelihood -133.17 -127.55 -124.79 -105.58 -63.71 -115.37 -99.99 Notes: Weibull Survival Model; Coefficients shown, standard errors in parenthesis. Coefficients need to be exponentiated (ex) in order to obtain hazard ratios. The effect of a one unit increase in the explanatory variable = ex – 1. Significant level: + ≤.1, *≤ .05, **≤ .01, ***≤.001.

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Table 3 Predicted Likelihood of Democratic Transition from 1960 to 2008 Using Change in Proportion of Democratic Neighbours

Explanatory Variable Religious Language Colonial Lag 1 Lag 2 Lag 1 Lag 2 Lag 1 Lag 2

Dom

estic

Var

iabl

es

Log GDP Per Capita .189 (.171)

.082 (.175)

.201 (.171)

.120 (.172)

.288 (.185)

-.151 (.194)

.301 (.185)

-.077 (.195)

-.061 (.203)

-.032 (.204)

-.047 (.202)

.038 (.206)

Urbanization .006 (.008)

.011 (.009)

.005 (.009)

.008 (.009)

.001 (.009)

.013 (.009)

.000 (.009)

.009 (.010)

.017+

(.010) .021* (.009)

.016+

(.010) .017+

(.009)

Log Economic Openness 1.90+ (1.05)

1.77+ (1.05)

1.85+ (1.05)

1.67 (1.05)

2.27+ (1.29)

5.82** (2.05)

2.19+ (1.28)

2.91+

(1.50) 2.24+ (1.19)

3.81* (1.67)

2.18+ (1.18)

3.10* (1.53)

Log Economic Openness2

-.334* (.145)

-.306* (.144)

-.327* (.145)

-.289* (.143)

-.385* (.177)

-.79** (.271)

-.375* (.176)

-.430* (.203)

-.382* (.164)

-.554* (.221)

-.374* (.164)

-.467* (.204)

Major Oil producer -1.36** (.450)

-1.35** (.451)

-1.34** (.450)

-1.32** (.451)

-1.47** (.500)

-.888+

(.497) -1.44** (.500)

-.985* (.497)

-1.10* (.454)

-2.55*** (.748)

-1.08* (.454)

-2.12*** (.635)

Island state -.013 (.316)

.006

(.316) -.007 (.316)

-.052

(.317) -.363 (.372)

-.044

(.372) -.357 (.373)

-.132

(.371) .130

(.316) .114

(.323) .142

(.323) .056

(.324)

Landlocked state -.353 (.301)

-.700* (.343)

-.345 (.302)

-.666* (.335)

-.316 (.331)

-.520* (.335)

-.310 (.331)

-.623+

(.334) -.197 (.336)

-.045 (.340)

-.189 (.336)

-.073 (.340)

Diff

usio

n

Democratization of Geographic Neighbour

2.06+ (1.08)

.856 (1.18)

2.12** (.779)

1.27 (.840)

2.09+ (1.1)

-4.22** (1.40)

2.17** (.790)

-.757 (1.12)

.993 (1.33)

.020 (1.40)

1.58+

(.878) .626

(.933) Democratization of

Religious Neighbour 10.58*** (1.00) 10.31***

(1.03)

Language Neighbour 7.46*** (.667) 4.85***

(.472)

Colonial Neighbour 8.38*** (.974) 7.62***

(.879) Time at risk 3946 3946 3896 3896 3308 3308 3270 3270 3421 3421 3376 3376 No. of Subjects 129 129 129 129 100 100 100 100 110 110 110 110 No. of Transitions 87 87 87 87 76 76 76 76 73 73 73 73 Log Likelihood -131.58 -101.75 -129.86 -100.90 -108.87 -54.45 -107.16 -68.45 -118.67 -86.86 -117.21 -89.40 Notes: Weibull Survival Model; Coefficients shown, standard errors in parenthesis. Coefficients need to be exponentiated (ex) in order to obtain hazard ratios. The effect of a one unit increase in the explanatory variable = ex – 1. Lags refer to the change in proportion of democratic neighbors over x years. Significance level: + ≤.1, *≤ .05, **≤ .01, ***≤.001.