The hypotheses listed above will be examined with the help of a regression analysis of PSI in 83 lower house elections of 19 Sub-Saharan African countries60. Thus, apart from PSI scores, our dataset includes a series of indicators to measure each independent and control variables. In this section we will present the precise way in which these variables were constructed. The full list of variables is presented in Table 4.1.
Social structure is measured by Alesina et al. (2003) ethnic fractionalization index (http://www.nsd.uib.no/macrodataguide/set.html?id=16&sub=1). The advantage of using this dataset is that it includes individual scores for ethnic, linguistic and religious fractionalization. Thus, it allows testing the combined and individual effect of the components of ethnic fractionalization. Other interesting complements to ethnicity could be the urban vs. rural divide (Tavits 2005), the union’s density or the size of the informal sector (Roberts and Wibbels 1999); yet, lacking comprehensive data prevents us from replicating these variables.
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To test the explanation of institutionalization over time we use two variables: time measured as the years since the first multiparty elections and polity durability measured as Polity IV “durable” variable, which indicates «the number of years since the last substantive change in authority characteristics»61 (http://www.systemicpeace.org/polity/polity4.htm). This differentiation matters since according to Roberts and Wibbels (1999) and Bartolini and Mair (1990), changes in the nature of political institutions can potentially affect the level of party system stabilization. Contrary to the first “time” variable, we expect this second one to have a more substantial effect on institutionalization, for where the contours of the polity are more stable political parties are likely to establish more stable patterns of interaction since they know the rules of competition and what they stand for.
Institutional design is measured with two variables. The first accounts for provisions guaranteeing and regulating party access to funding and is measured by an additive index that varies between 0= weakly regulated party funding/finance and 11= highly regulated party funding/finance. To create this index we used IDEA’s Political Finance Database (http://www.idea.int/political-finance/index.cfm), which has global data on party funding. The data is not temporal, but it is very rich since it covers more than 40 items. From this pool we have selected 11 items62, which are clustered in three dimensions, namely party funding/finance, regulations of spending; and reporting, oversight and sanctions. Since all items are coded “yes” or “no”, what we did was merely assigning the score “1” when the answer to the item was “yes” and the added all scores into an additive index.
The second is the country’s form of government, which is measured with a dummy variable for presidentialism. To create this variable, we first classified the countries according to their forms of government. The definition of presidential and parliamentarian regimes is quite consensual and straightforward «if the cabinet depends exclusively on the confidence of
61 Polity IV codebook describes DURABLE as follows: Regime Durability: The number of years since the most recent regime change (defined by a three point change in the POLITY score over a period of three years or less) or the end of transition period defined by the lack of stable political institutions (denoted by a standardized authority score). In calculating the DURABLE value, the first year during which a new (post-change) polity is established is coded as the baseline “year zero” (value = 0) and each subsequent year adds one to the value of the DURABLE variable consecutively until a new regime change or transition period occurs. Values are entered for all years beginning with the first regime change since 1800 or the date of independence if that event occurred after 1800(http://www.systemicpeace.org/inscr/p4manualv2012.pdf).
62 The 11 items/questions are: 1. Are there provisions for direct party funding/finance to political parties? 2. Are there provisions for free or subsidized access to media for political parties? 3. Are there provisions for free or subsidized access to media for candidates? 4. Is there a ban on vote buying? 5. Are there bans on state resources being used in favor or against a political party or candidate? 6. Are there limits on the amount a political party can spend? 7. Are there limits on the amount a candidate can spend? 8. Do political parties have to report regularly on their finances? 9. Do political parties have to report on their finances in relation to election campaigns? 10. Do candidates have to report on their campaigns finances? 11. Is information in reports from political parties and/or candidates to be made public?
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the majority of the assembly», we have a «system of parliamentary cabinets»; «if cabinets are accountable to the president – who, therefore, may dismiss them or individual ministers for strictly political reasons – we have a system of presidential cabinets [...]» (Shugart 1999, 56- 57). Nonetheless, the most hybrid variant, semipresidentialism, has been defined variously. Some use both normative and constitutional dispositions do define it (Duverger 1990), whereas others focus on the length of presidential powers and on the analysis of different subtypes of semipresidentialism, therefore discussing the relevance of the presidential, parliamentary and semipresidential trichotomy (Shugart and Carey 1992; Siaroff 2003; Lijphart 1997). To deal with the ambiguity of the existing definitions of semipresidentialism, Elgie (2004) proposes a new definition based on two constitutional dispositions: (i) a popularly elected fixed-term president and (ii) a prime minister and cabinet that are collectively responsible to the legislature. This conceptualization is more operative than the previous ones because it is minimal and purely constitutional. In this sense it minimizes the variance that occurs when more subjective criteria (notably the length of the presidential powers) are also considered in the definition of the regime.
Regarding party and party system properties, fragmentation is measured as Laakso’s and Taagepera’s (1979) Effective Number of Electoral Parties (ENEP) in terms of vote share63. This is probably the most popular measure of party system fragmentation and it has been used in numerous studies about party system development in Third Wave democracies (Tavits 2005; Ferree 2010). Party institutionalization is measured by party age, which is calculated as the average age of political parties with 10% of the votes in the last lower house elections64. Imagine the following vote distribution for an election taking place in 1999: Party A wins 50% of votes; Party B 20%; Party C 15%; Party D 5% and other parties receive the remaining 10% of votes. To compute this indicator only Party A, B and C would be considered, since they won at least 10% of the votes. If party A was founded in 1950, party B in 1960 and party C in 1970 then, the average age of political parties with 10% of votes in 1999 would be = (49+39+29)÷3 = 39 years. This formula has been suggested by Mainwaring and Scully (1995) and has already been applied to African countries by Kuenzi and Lambright (2001) and, more recently, by Riedl (2008) who lowered the threshold to 5% of the votes.
63 For party system fragmentation Roberts and Wibbels (1999) use Remmer’s (1991) indicator which is computed as follows: take the percentage of the vote obtained by the top two parties in the previous election, and subtract it from 100, so that higher scoring will be associated with greater fragmentation.
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Due to the majoritarian nature of African political institutions and the resulting low party system fragmentation the 10% thresholds allows to include the main competitors of the political arena.
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To test the effect of economic performance on PSI, we consider GDP annual growth (%) using World Bank data (available online at http://data.worldbank.org/; accessed 05-08- 2011), and inflation, consumer prices (annual %); using the International Monetary Fund, World Economic Outlook data (available online at http://www.imf.org/external/data.htm; accessed 30-01-2013) 65. Electoral participation is measured as change in voter turnout, which expresses the differences in turnout in two consecutive lower house elections.
Table 4.1 – Independent and control variables: measurement and data
Explanation Variable: Indicator (data source)
1. Social Structure Ethnicity: ethnic fractionalization index; 0 = homogeneity, 1 = heterogeneity (Alesina et al.
2003)
2. Institutionalization over time
Time: years since the first multiparty election
Polity durability: years since last change in the authority/regime characteristics (Polity IV
Database)
3. Institutional design
Party funding/finance: 0 = weakly regulated party funding/finance, 11 = highly regulated
party funding/finance (IDEA Political Finance Database)
Presidentialism: 1 = if presidential, 0 = otherwise (categorized as Shugart 1999; Elgie 2004)
4. Party and party system characteristics
Fragmentation: ENEP (author’s own calculation as Laakso and Taagepera 1979)
Party institutionalization: party age (author’s own calculation as Mainwaring and Scully
1995; Kuenzi and Lambright 2001)
5. Economic performance
Short-term economic performance
GDP annual growth (%)(World Bank)
Inflation, consumer prices (annual %) (International Monetary Fund – IMF)
6. Electoral
participation Turnout change: change in voter turnout (IDEA Voter Turnout Database)
Controls
Lagged dependent variable of PSI (calculated in STATA) Number of Presidents
Dummy for majoritarian electoral system/ 1 = majoritarian electoral system, 0 = otherwise
(Norris 1997; Nohlen, Thibaut, and Krennerich 1999; IPU PARLINE Database on National Parliaments and ACE Electoral Network)
Individual components of ethnic fractionalization: ethnic, linguistic and religious
fractionalization; 0 = homogeneity, 1 = heterogeneity (Alesina et al. 2003)
Dummy for quality of PSI: 1 = adequate institutionalization, 0 = inadequate
institutionalization and overinstitutionalization
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Alternative indicators are unemployment rates, consumers’ confidence in the economy, purchase power, but these are not available for the entire period of analysis.
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In addition to these independent variables, we have incorporated several control variables in our analysis as suggested by the literature. As Table 4.1 also shows, we control for (i) the type of electoral system, (ii) the number of presidents elected during the election period covered, (ii) the individual effect of the components of ethnic fractionalization and, finally (iii), the different qualities or types of PSI (adequate, inadequate and overinstitutionalization). The general hypothesis about the role of electoral systems is that majoritarian systems are good for government effectiveness and accountability, while proportional systems promote greater fairness to minority parties and more diversity in social representation. This assumption stems from studies that focused on Western countries (Lijphart 1994; Blais and Massicotte 1996; inter alia), but also in African countries (Lindberg 2005). Despite this general proposition, several other studies have shown that especially in new democracies, and divided societies such as the Africans ones, the effects of electoral systems (namely conflict resolution, representation, fragmentation and stability) are largely contingent or context-dependent. For example, according to Reilly and Reynolds (1999):
where ethnicity represents a fundamental political cleavage, particular electoral systems can reward candidates and parties who either act in a cooperative, accommodatory manner to rival groups; or they can punish these candidates and instead reward those who appeal only to their own ethnic group. However, the “spin” which an electoral system fives to the system is ultimately contextual and will depend on the specific cleavages and divisions within any given society (Reilly and Reynolds 1999, 6-7).
In similar ways, Bogaards (2007) has argued that the old controversy around proportional or plurality elections loses most of its relevance in ethnically divided societies because of the territorial concentration of ethnic groups. Ferree (2010), however, found no significant effect of the electoral rules, namely of district magnitude over the degree of party system stability in Africa. Since there is not a consensual hypothesis about the role of electoral systems we include it as a control rather than as an independent variable.
Considering the number of presidents a country has had since the first multiparty election also seems relevant to us, since the interruption of president tenure by death, impeachment, resignation or unlawful removal from office, can potentially entail party system instability. This is more evident in presidential regimes – and to lesser extent semi- presidential regimes – since the president holds powers of cabinet formation and dismissal
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and competition for government is primarily determined by the results of the presidential elections.
Following Posner’s (2001; 2005) cautionary notes regarding the use of ethnicity as a unitary concept exclusively, without considering its different layers, we control for the components of ethnic fractionalization individually66. Finally, in light of the findings presented in the previous chapter, we control for whether the association between the main independent variables and the dependent variable holds when we contrast adequate to inadequate and overinstitutionalized party systems.
Finally, we introduced a lagged dependent67 variable which apart from delivering a temporal dynamics over the data, allows us to control some of the shortcomings usually associated with TSCS data, namely the independence of observations and serial correlation. Several approaches were used to treat this kind of data68 , but the one introduced by Beck and Katz (1995) and Beck (2001), namely the Ordinary Least Squares (OLS) with Panel Corrected Standard Errors (PCSEs), is considered as the “de-facto-standard” approach precisely because it allows dealing with both homoscedasticity and serial correlation:
In particular, for OLS to be optimal it is necessary to assume that all the error processes have the same variance (homoscedasticity) and that all of the error processes are independent of each other. The latter assumption can be broken down into the assumption that errors for a particular unit at one time are unrelated to errors for that unit at all other times (no serial correlation) and that errors for one unit are unrelated to the errors for every other unit (no spatial correlation) (Beck and Katz 1995, 634).
With Beck and Katz’s (1995) PCSEs, the assumption of equal variance of errors is not violated and it is also possible to deal with serial correlation by fitting lagged values into the model. In our data serial correlation could occur if the current level of PSI is strongly influenced by its past value. Thus, the inclusion of a lagged dependent variable allows for
66 Posner (2001, 2) argued that «[...] under the umbrella term ‘ethnic’, communal conflict can take many forms. Sometimes competition takes place along religious lines. At other times competing groups are distinguished from one another by language. At still other times in-group/out-group distinctions are made on the basis of tribal affiliation, clan membership, geographic region of origin, or race. Within a single country, each of these distinctions may serve, in different situations, as a potential axis of social differentiation and conflict». This “fluidity” particularly applied to the Zambian case where the author found out that the relevant ethnic axis at the local level was the tribe while at national level language (Posner 2005).
67 With the creation of a lagged variable we include not only the current level of PSI but the past (lag) level of PSI as an independent variable.
68
Namely OLS regression with cluster-robust standard errors, first differencing, random effects and fixed effects (for a comprehensive discussion see Wooldridge 2000; Allison 2009; Cameron and Trivedi 2009).
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controlling this effect. Even though there are some problems with the use of lagged variables, they remain, for the most part, highly recommended. Achen (2001), one of the supporters of Beck and Katz’s approach, sustains that the specification with the lagged variable is always preferred even if the values and the direction of the coefficients change drastically. Similarly, Keele and Kelly (2006) note that, although «the lagged dependent variable is inappropriate in some circumstances, it remains an appropriate model for the dynamic theories often tested by applied analysts». Beck and Katz OLS with PCSEs can be estimated in STATA with the command xtpcse, while the lagged dependent variable must be computed manually and enter the model along with the other independent variables69.