¹ Mainwaring, Scott and Scully, Timothy, Building Democratic Institutions: Party Systems in Latin America (Palo Alto, Calif.: Stanford University Press, 1995)Google Scholar.

² Lijphart, Arend, Patterns of Democracy (New Haven, Conn.: Yale University Press, 1999)Google Scholar; Giliomee, Hermann and Simkins, Charles, The Awkward Embrace: One Party Domination and Democracy (Cape Town: Tafelberg Press, 1999)Google Scholar.

³ Bartolini, Stefano and Mair, Peter, Identity, Competition, and Electoral Availability: The Stabilization of European Electorate 1885–1985 (Cambridge: Cambridge University Press, 1990)Google Scholar; Roberts, Kenneth M. and Wibbels, Erik, ‘Party Systems and Electoral Volatility in Latin America: A Test of Economic, Institutional, and Structural Explanations’, American Political Science Review, 93 (1999), 575–590CrossRefGoogle Scholar; Tavits, Margit, ‘The Development of Stable Party Support: Electoral Dynamics in Post-Communist Europe’, American Journal of Political Science, 49 (2005), 283–298CrossRefGoogle Scholar; Mainwaring, Scott and Zoco, Edurne, ‘Political Sequences and the Stabilization of Interparty Competition: Electoral Volatility in Old and New Democracies’, Party Politics, 13 (2007), 155–178CrossRefGoogle Scholar.

⁴ Electoral volatility is most commonly measured using Pedersen’s index, which is calculated by summing the total change in the percentage of seats or votes won or lost by all parties between two (legislative or executive) elections and dividing by two (see Pedersen, Mogens, ‘The Dynamics of European Party Systems: Changing Patterns of Electoral Volatility’, European Journal of Political Research, 7 (1979), 1–26CrossRefGoogle Scholar). More formally, V_Pedersen = ∑|p_{i,t+ 1}−p_i,t|/2, where p_i,t is the vote (or seat) share of party i at the first election (t) and is the vote (or seat) share of party i at the second election (t + 1). Bartolini and Mair, Identity, Competition, and Electoral Availability, and Mainwaring and Scully, Building Democratic Institutions, follow this strategy, as do most other researchers of volatility. Sarah Birch offers an alternative, arguing that volatility involves two components that should be separately considered: volatility generated by parties coming and going, and volatility generated from voters leaving and joining existing parties. She calculates the former (‘party replacement’) as the sum of the vote (or seat) shares won by electoral contenders at election t + 1 who had not contested election t. She calculates the latter (‘volatility’) as ‘the amount of change observed within the set of parties … that contest two consecutive elections’ (see Birch, Sarah, ‘Electoral Systems and Party System Stability in Post-Communist Europe’ (paper presented at the Annual Meeting of the American Political Science Association, San Francisco, 2001), p. 4Google Scholar). More formally, V_Birch = ∑|c_{i,t +1} − c_i,t|/(∑c_i,t+1 + ∑c_i,t), where c_i,t is the vote (seat) share of continuous party i at the first election (t) and c_i,t+1 is the vote (seat) share of continuous party i at the second election (t + 1). I make use of both the Birch and Pedersen measures of volatility in this article, calculating them both for legislative votes and for legislative (lower house) seats. Most of the analysis that follows will focus on legislative seats, as the data are far more complete.

⁵ Mainwaring, and Scully, , Building Democratic Institutions, p. 22Google Scholar.

⁶ Giliomee and Simkins, The Awkward Embrace; Lijphart, Patterns of Democracy.

⁷ Bartolini and Mair, Identity, Competition, and Electoral Availability.

⁸ Mainwaring and Scully, Building Democratic Institutions; Tavits, ‘The Development of Stable Party Support’.

⁹ Mainwaring and Zoco, ‘Political Sequences and the Stabilization of Interparty Competition’.

¹⁰ Mainwaring and Scully, Building Democratic Institutions; Bielasiak, Jack, ‘The Institutionalization of Electoral and Party Systems in Postcommunist States’, Comparative Politics, 34 (2002), 189–210CrossRefGoogle Scholar; and Kuenzi, Michelle and Lambright, Gina, ‘Party System Institutionalization in 30 African Countries’, Party Politics, 7 (2001), 437–468CrossRefGoogle Scholar.

¹¹ Roberts and Wibbels, ‘Party Systems and Electoral Volatility in Latin America’.

¹² Tavits, ‘The Development of Stable Party Support’.

¹³ Mainwaring and Zoco, ‘Political Sequences and the Stabilization of Interparty Competition’.

¹⁴ Martin Lipset, Seymour and Rokkan, Stein, eds, Party Systems and Voter Alignments: Cross National Perspectives (New York: The Free Press, 1967)Google Scholar; and Bartolini and Mair, Identity, Competition, and Electoral Availability.

¹⁵ Roberts and Wibbels, ‘Party Systems and Electoral Volatility in Latin America’; Mainwaring and Zoco, ‘Political Sequences and the Stabilization of Interparty Competition’.

¹⁶ Tavits, ‘The Development of Stable Party Support’.

¹⁷ See Duverger, Maurice, Political Parties (New York: Wiley, 1963)Google Scholar; Ordeshook, Peter and Shvetsova, Olga, ‘Ethnic Heterogeneity, District Magnitude, and the Number of Parties’, American Journal of Political Science, 38 (1994), 100–123CrossRefGoogle Scholar; Amorim-Neto, Octavio and Cox, Gary W., ‘Electoral Institutions, Cleavage Structures, and the Number of Parties’, American Journal of Political Science, 41 (1997), 149–174CrossRefGoogle Scholar; Mozaffar, Shaheen, Scarritt, James and Galaich, Glen, ‘Electoral Institutions, Ethnopolitical Cleavages, and Party Systems in Africa’s Emerging Democracies’, American Political Science Review, 97 (2003), 379–390CrossRefGoogle Scholar; Robert Clark, William and Golder, Matt, ‘Rehabilitating Duverger’s Theory: Testing the Mechanical and Strategic Modifying Effects of Electoral Laws’, Comparative Political Studies, 39 (2006), 679–708CrossRefGoogle Scholar.

¹⁸ Mainwaring and Zoco, ‘Political Sequences and the Stabilization of Interparty Competition’.

¹⁹ Tavits, ‘The Development of Stable Party Support’.

²⁰ For a similar point, see Posner, Daniel, ‘Measuring Ethnic Fractionalization in Africa’, American Journal of Political Science, 48 (2004), 849–863CrossRefGoogle Scholar.

²¹ For a basic review, see Lijphart, Patterns of Democracy.

²² This assumes that there is some magic to having a group with a name and a story that endows greater stability to an electoral coalition than simply a ‘marriage of convenience’ between groups. That is, if reds and blues can call themselves purples and can tell a story about why they are united as a group, this provides their electoral coalition with greater stability than if reds and blues simply agree to vote for the same party, but have no meaningful story about how they together formed a group.

²³ Posner, Daniel, Institutions and Identities: Regime Change and Ethnic Cleavages in Africa (Cambridge: Cambridge University Press, 2005)Google Scholar; Chandra, Kanchan and Boulet, Cilanne, ‘A Language for Thinking about Ethnic Identity Change’, in Kanchan Chandra, ed., Ethnicity, Politics and Economics (manuscript in progress, 2006)Google Scholar; Bratton, Michael, Mattes, Robert and Gyimah-Boadi, E., Public Opinion, Democracy, and Market Reform in Africa (Cambridge: Cambridge University Press, 2005)Google Scholar.

²⁴ Cox, Gary, Making Votes Count (Cambridge: Cambridge University Press, 1997)CrossRefGoogle Scholar.

²⁵ These data come from Scarritt and Mozaffar and do not add up to one hundred because the authors focus only on groups that are politically salient (see Scarritt and Mozaffar, ‘The Specification of Ethnic Cleavages and Ethnopolitical Groups for the Analysis of Democratic Competition in Contemporary Africa’). Thus, the other sub-groups making up ‘southerners’ are not salient. Presumably, these people would instead identify as southern, not as part of a sub-group.

²⁶ Daniel Posner, ‘Measuring Ethnic Fractionalization in Africa’; Chandra and Boulet, ‘A Language for Thinking about Ethnic Identity Change’.

²⁷ This logic applies both to the elite (party) level or the mass (voter) level. In the case of a single winning group, winning parties have no incentive to seek an alternative electoral constituency and voters have no incentive to abandon them. Challenger parties may attempt to break apart the majority, but are unlikely to be successful and hence should have little impact on volatility levels. In the cases of no winning coalitions and multiple winning coalitions, elites have incentives to offer different alternatives to the electorate and voters are likely to cycle between them. In the empirical section of this article, I explore voter and elite level volatility by examining the differences between Birch’s measure of volatility, which only considers movement between existing parties, ignoring volatility generated by elites offering new parties, and Pedersen’s measure, which combines both types of volatility (see fn. 4).

²⁸ On electoral systems, see Cox, Making Votes Count. On presidentialism, see Soberg Shugart, Matthew and Carey, John M., Presidents and Assemblies: Constitutional Design and Electoral Dynamics (Cambridge: Cambridge University Press, 1992)CrossRefGoogle Scholar, and Mainwaring, Scott and Shugart, Matthew, Presidentialism and Democracy in Latin America (Cambridge: Cambridge University Press, 1997)CrossRefGoogle Scholar.

²⁹ van de Walle, Nicolas, ‘Presidentialism and Clientelism in Africa’s Emerging Party Systems’, Journal of Modern African Studies, 41 (2003), 297–321, at pp. 304–305CrossRefGoogle Scholar.

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³¹ Posner and Simon, ‘Economic Conditions and Incumbent Support in Africa’s New Democracies’; Bratton, Mattes, and Gyimah-Boadi, Public Opinion, Democracy, and Market Reform in Africa.

³² Ferree, ‘Explaining South Africa’s Racial Census’.

³³ Bratton, Michael, ‘Second Elections in Africa’, Journal of Democracy, 9 (1998), 51–66CrossRefGoogle Scholar.

³⁴ Lindberg, Staffan I., Democracy and Elections in Africa (Baltimore, Md.: Johns Hopkins University Press, 2006)Google Scholar.

³⁵ Like most previous studies, I focus on legislative volatility levels rather than executive volatility levels. This ensures greater comparability across presidential and parliamentary systems and avoids complications created by two-round majority run-off presidential systems (which are common in Africa). I calculate legislative volatility based on seats in the lower house because the results of most African elections are reported in seats, not votes. Therefore, data on seat volatility levels are far more complete than vote volatility levels. For example, I was able to compile ninety-five observations over thirty-three countries of legislative seat volatility, but only fifty-five observations over twenty-two countries of legislative vote volatility. In order to control for any bias created by using seats instead of votes, I include a measure of average district size in my models. I also perform robustness checks on the smaller sample of legislative vote volatility scores; results are substantively similar for both measures. See fn. 49 for more details.

³⁶ Pedersen, ‘The Dynamics of European Party Systems’.

³⁷ Birch, ‘Electoral Systems and Party System Stability in Post-Communist Europe’.

³⁸ See fn. 4. In dealing with electoral coalitions, I followed the example of Birch, ‘Electoral Systems and Party System Stability in Post-Communist Europe’, and used the following coding rule: if there was a coalition in either election, treat the parties as if they were in coalition in both. Only elected (not appointed) seats were used in calculations. ‘Independents’ were treated as a single party.

³⁹ Scarritt, James R. and Mozaffar, Shaheen, ‘The Specification of Ethnic Cleavages and Ethnopolitical Groups for the Analysis of Democratic Competition in Contemporary Africa’, Nationalism and Ethnic Politics, 5 (1999), 82–117CrossRefGoogle Scholar. All electoral data through 1998 come from Nohlen, Dieter, Krennerich, Michael and Thibaut, Bernhard, Elections in Africa: A Data Handbook (Oxford: Oxford University Press, 1999)CrossRefGoogle Scholar. After that, data come from the African Election Archive (AEA) at http://africanelections.tripod.com/about.html.

⁴⁰ Kuenzi and Lambright, ‘Party System Institutionalization in 30 African Countries’.

⁴¹ These are derived from the data in Scarritt and Mozaffar, ‘The Specification of Ethnic Cleavages and Ethnopolitical Groups for the Analysis of Democratic Competition in Contemporary Africa’.

⁴² Roberts and Wibbels, ‘Party Systems and Electoral Volatility in Latin America’; Mozaffar, Scarritt and Galaich, ‘Electoral Institutions, Ethnopolitical Cleavages, and Party Systems in Africa’s Emerging Democracies’; Tavits, ‘The Development of Stable Party Support’; and Mainwaring and Zoco, ‘Political Sequences and the Stabilization of Interparty Competition’.

⁴³ Mainwaring and Zoco, ‘Political Sequences and the Stabilization of Interparty Competition’.

⁴⁴ Per capita income comes from Alan Heston, Robert Summers and Bettina Aten, Penn World Table Version 6.2, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania (September 2006).

⁴⁵ Roberts and Wibbels, ‘Party Systems and Electoral Volatility in Latin America’; Tavits, ‘The Development of Stable Party Support’; and Mainwaring and Zoco, ‘Political Sequences and the Stabilization of Interparty Competition’.

⁴⁶ Golder, Matt, ‘Democratic Electoral Systems around the World, 1946–2000’, Electoral Studies, 24 (2004), 103–121CrossRefGoogle Scholar. Average district magnitude is the total number of seats allocated to the lowest tier of the legislature divided by the total number of districts in that tier. Golder provides thirteen African cases in his dataset. I calculate the remaining cases using the data in Nohlen, Krennerich and Thibaut, Elections in Africa.

⁴⁷ An alternative, probably superior, measure of electoral quality is Lindberg’s coding of the freeness and fairness of elections (see Lindberg, Democracy and Elections in Africa, Appendix 4). Unfortunately, Lindberg’s data extend only to 2003. Using it drops twenty observations from the election-by-election dataset. For this reason, I use the Freedom House measure instead but run robustness checks with the Lindberg data (see fn. 49). The correlation between country averages of the Lindberg and Freedom House measures was 0.73, suggesting that any distortions caused by using the latter should be small.

⁴⁸ Niger’s Pedersen volatility score was 95.2, by far the highest of any in the sample and very different from its other period scores (8.4 for 1993–95 and 11.1 for 1999–2004). The 1996 election followed on the heels of the early dissolution of the 1995 legislature and a coup. During the 1996 election, a new party (the National Union of Independents for Democratic Renewal) won 71 per cent of the vote. There were boycotts of the election and irregularities that probably affected the results (see Lindberg, Democracy and Elections in Africa). By 1999, the party had disappeared and the parties that dominated the elections prior to the coup returned. Hence, the high volatility score reflects the rapid rise and collapse of a party during a period of conflict. The Birch method of calculating volatility – which only looks at shifts between parties around in both the beginning and ending election – provides the more modest score of 33.

⁴⁹ I ran four robustness checks. First, I used random effects models instead of OLS. The results were nearly identical to the pooled OLS model (Table 1). Secondly, I included a lag of the dependent variable. This produced coefficient estimates very similar to the original pooled model, although standard errors were larger, perhaps because including the lags reduced the sample size by around one-third. Of greater importance, the lagged dependent variables were never close to significant, alleviating worries about serial correlation. Thirdly, I substituted Lindberg’s measure of electoral quality for the Freedom House measure in all specifications using the latter. Like the Freedom House measure, the Lindberg measure never approached significance (t-statistics were always well below 1). The substitution involved dropping twenty observations in the election-by-election regressions (Table 1). In both the Pederson and Birch specifications, this had little effect on the NWC variable (if anything, it strengthened it), but it did significantly diminish the multiple winning coalitions variable – almost certainly because of the loss of observations. The substitution had no effect on the averaged election regressions (Table 2). Fourthly, I collected as complete a dataset as possible for legislative vote volatility levels (using the same sources used for the seat volatility dataset). The result was a dataset with fifty-five total observations (eleven observations of no winning coalitions, thirty-nine observations of single winning coalitions, and five observations of multiple winning coalitions). In spite of the drastic loss of data (and its disproportionate absence from the no winning and multiple winning categories), replications of the regressions in Tables 1 and 2 using vote volatility scores in place of the seat volatility scores show remarkable consistency, especially for the distinction between no winning and single winning coalition cases. In the pooled specification of Table 1, the coefficient on the no winning coalition variable was very similar regardless of which version of the dependent variable was employed and it remained significant in three out of four models (it failed to be significant in the baseline Pederson model of column one, Table 1). The coefficient on the multiple winning coalition variable (Table 1) was less robust, but remained the same sign in all four models and maintained significance in two of them. Given the drastic loss of data for this category, its fragility is not surprising. Results were even more consistent for the averaged specification of Table 2: the coefficient on the no winning coalition variable remained steady and significant across specifications for the vote volatility model; once again, the coefficient on the multiple winning coalition variable was less robust but maintained sign and significance across two specifications. All results are available from the author upon request.

⁵⁰ See fn. 49.

⁵¹ Prior work by Pedersen, ‘The Dynamics of European Party Systems’; Bartolini and Mair, Identity, Competition, and Electoral Availability; Roberts and Wibbels, ‘Party Systems and Electoral Volatility in Latin America’; Tavits, ‘The Development of Stable Party Support’; and Mainwaring and Zoco, ‘Political Sequences and the Stabilization of Interparty Competition’.

⁵² However, when legislative vote volatility is used instead of seat volatility (robustness check in fn. 49), the coalition dummy variable was significant (and positive) across all specifications, and average district magnitude was negative and significant for the pooled Birch specification.

⁵³ Roberts and Wibbels, ‘Party Systems and Electoral Volatility in Latin America’; Tavits, ‘The Development of Stable Party Support’; and Mainwaring and Zoco, ‘Political Sequences and the Stabilization of Interparty Competition’.

⁵⁴ I thank an anonymous reviewer for this insight.

⁵⁵ All demographic figures come from Scarritt and Mozaffar, ‘The Specification of Ethnic Cleavages and Ethnopolitical Groups for the Analysis of Democratic Competition in Contemporary Africa’.

⁵⁶ The NPP also won the presidential election in 2000, taking close to 57 per cent of the vote. In 1996, the NDC had won with nearly the same percentage. Hence, volatility also marked the presidential election.

⁵⁷ van Walraven, Klaas, ‘The End of an Era: The Ghanaian Elections of December 2000’, Journal of Contemporary African Studies, 20 (2002), 183–202CrossRefGoogle Scholar.

⁵⁸ See Nugent, Paul, ‘Ethnicity as an Explanatory Factor in the Ghana 2000 Elections’, African Issues, 29 (2001), 2–7CrossRefGoogle Scholar, Table 1, for a breakdown of seats.

⁵⁹ Nugent, , ‘Ethnicity as an Explanatory Factor in the Ghana 2000 Elections’, p. 3Google Scholar.

⁶⁰ Gyimah-Boadi, E., ‘A Peaceful Turnover in Ghana’, Journal of Democracy, 12 (2001), 103–17, p. 114CrossRefGoogle Scholar.

⁶¹ Nugent, , ‘Ethnicity as an Explanatory Factor in the Ghana 2000 Elections’, p. 3Google Scholar.

⁶² Nugent, ‘Ethnicity as an Explanatory Factor in the Ghana 2000 Elections’.

⁶³ Van Walraven, ‘The End of an Era’.

⁶⁴ Mattes, Robert, The Election Book: Judgement and Choice in South Africa’s 1994 Election (Cape Town: Idasa, 1995)Google Scholar; Ferree, ‘Explaining South Africa’s Racial Census’.

⁶⁵ Johnson, R. W. and Zulu, Paulus, ‘Public Opinion in KwaZulu-Natal’, in R. W. Johnson and Lawrence Schlemmer, eds, Launching Democracy in South Africa: The First Open Election, April 1994 (New Haven, Conn.: Yale University Press, 1996), pp. 189–211Google Scholar; Lodge, Tom, Consolidating Democracy: South Africa’s Second Popular Election (Johannesburg: Witwatersrand University Press, 1999)Google Scholar.

⁶⁶ Sipho Maseko, ‘The PAC, Azapo, and the UDM’, and Piombo, Jessica, ‘The UCDP, Minority Front, ACDP and Federal Alliance’, both in Andrew Reynolds, ed., Election ’99 South Africa: From Mandela to Mbeki (New York: St Martin’s Press, 1999), pp. 125–132Google Scholar and 133–46, respectively.

⁶⁷ One might question whether South Africa’s distinctive political institutions – parliamentarism combined with large district magnitude PR – confounds its utility as a case to exemplify a non-institutional thesis. In other words, perhaps South Africa’s low volatility is a function of its institutions, not its demographics. To the contrary, however, South Africa represents a ‘tough case’ for the social explanation put forth in this article: its institutional features push in the opposite direction from its social features, i.e. towards greater volatility. Parliamentarism and proportional representation should produce a high ENP in South Africa, which is associated with high volatility. Moreover, PR should permit non-majority ethnic groups to carve out specialized electoral niches that create co-ordination problems for the African majority as a whole. Yet neither of these outcomes has occurred. Thus, there is low volatility in South Africa in spite of – not because of – its institutions, lending more credibility to the socially based theory proposed in this article.

⁶⁸ Scarritt and Mozaffar, ‘The Specification of Ethnic Cleavages and Ethnopolitical Groups for the Analysis of Democratic Competition in Contemporary Africa’, list the Fon as 55.5 per cent of the total population of Benin. This figure is difficult to confirm. Other estimates (see Battle and Seeley, ‘It’s All Relative’) put the number somewhat lower.

⁶⁹ This smaller Fon group is frequently described as the largest ethnic group in Benin (with 25 per cent of the population), but this clearly misses the more complex nested structure into which this group fits. Other groups in the broader Fon grouping include the Aizo, Goun, Mahi, Oueme and Torri (see Battle and Seeley, ‘It’s All Relative’). I will distinguish between the narrow Fon grouping and the broad Fon grouping by calling the former ‘Dahomey’ Fon, reflecting their city of origin in the historic kingdom of Dahomey. For a historical discussion of these groups, see Ronen, Dov, ‘People’s Republic of Benin: The Military, Marxist Ideology, and the Politics of Ethnicity’, in John W. Harbeson, ed., The Military in African Politics (New York: Praeger, 1987), pp. 93–122Google Scholar.

⁷⁰ See Magnusson, Bruce, ‘Representation and Territorial Administration: Reconciling Ethno-Regionalism with Democracy in Benin’, MacArthur Consortium Research Series on International Peace and Cooperation, Working Paper No. 6 (Global Studies Program, University of Wisconsin, Madison, 1996)Google Scholar; and Magnusson, Bruce, ‘Democratization and Domestic Insecurity: Navigating the Transition in Benin’, Comparative Politics, 33 (2001), 211–230CrossRefGoogle Scholar, for discussions of Benin’s institutions.

⁷¹ Economist Intelligence Unit, ‘Country Report: Benin’ (July, 2003), p. 10Google Scholar.

⁷² Battle and Seeley, ‘It’s All Relative’.

⁷³ See discussions in Battle and Seeley, ‘It’s All Relative’; Seely, Jennifer C., ‘It’s All Relative: The Importance of Ethnicity in Benin’s Elections’ (paper presented at the Annual Meeting of the American Political Science Association, Philadelphia, 2003)Google Scholar; Seely, Jennifer C., ‘The Presidential Election in Benin, March 2006’, Electoral Studies, 26 (2007), 196–231CrossRefGoogle Scholar; Degboe, Kouassi, Elections et realités sociologiques au Benin (Cotonou: Intermonde Editions, 1995)Google Scholar; and Magnusson, ‘Representation and Territorial Administration’.

⁷⁴ This pattern is not unique to the 1999–2003 period. RB support seems to vary quite widely. In the 1991 elections, for example, Soglo (who went on to join RB, the party created by his wife) won the presidency on the basis of a unified southern vote.

⁷⁵ Birch, , ‘Electoral Systems and Party System Stability in Post-Communist Europe’, p. 2Google Scholar.

⁷⁶ See Chandra and Boulet, ‘A Language for Thinking about Ethnic Identity Change’, and Posner, Institutions and Identities.