Political Corruption and Institutional Stability Hanne Fjelde & Håvard Hegre # Springer Science+Business Media New York 2014 Abstract This article is the first to statistically examine the reciprocal relationship between formal political institutions and political corruption. We argue that political corruption is an informal institution that allows nondemocratic leaders to build political support, act as a substitute for liberalizing concessions in the formal institutions of the state, and thereby extends the longevity of non-democratic regimes. Yet, whereas high corruption level will prevail in nondemocratic regimes, we expect the electoral con- stituency in democratic regimes to have the formal power to curb political corruption. We demonstrate that these expectations hold by estimating a dynamic multinomial regression model on data for 133 countries for the 1985–2008 period. Our model shows that high-corruption autocracies and hybrid regimes are more stable than their low- corruption counterparts, but that low-corruption democracies are more stable than high- corruption ones. For autocratic and hybrid regimes, the stability is due both to corruption making the formal institutions more resistant to democratization and that the formal institutions prevent reductions in corruption. Consistent democracies, on the other hand, are able to reduce corruption and become more stable as a result. Keywords Corruption . Regime type . Institutional stability . Regime change . Informal institutions Introduction The world is steadily becoming more democratic. In 1985, near half of the world’ s countries were autocratic, and about a quarter democratic. 1 In 2008, this picture was St Comp Int Dev DOI 10.1007/s12116-014-9155-1 1 Our classifications of democratic, authoritarian, and hybrid regimes are based on the SIP measure of democracy (Gates et al. 2006). The measure ranges from 0 (full autocracy) to 1 (full democracy), and the cutoff points for hybrid regime and democracy are set at 0.15 and 0.80, respectively. The Polity project shows the same trends (see http://www.systemicpeace.org/polity/polity4.htm). H. Fjelde (*) : H. Hegre Department of Peace and Conflict Research, Uppsala University, Uppsala, Sweden e-mail: [email protected]H. Hegre e-mail: [email protected]H. Fjelde : H. Hegre Peace Research Institute Oslo, Oslo, Norway
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Political Corruption and Institutional Stability
Hanne Fjelde & Håvard Hegre
# Springer Science+Business Media New York 2014
Abstract This article is the first to statistically examine the reciprocal relationshipbetween formal political institutions and political corruption. We argue that politicalcorruption is an informal institution that allows nondemocratic leaders to build politicalsupport, act as a substitute for liberalizing concessions in the formal institutions of thestate, and thereby extends the longevity of non-democratic regimes. Yet, whereas highcorruption level will prevail in nondemocratic regimes, we expect the electoral con-stituency in democratic regimes to have the formal power to curb political corruption.We demonstrate that these expectations hold by estimating a dynamic multinomialregression model on data for 133 countries for the 1985–2008 period. Our model showsthat high-corruption autocracies and hybrid regimes are more stable than their low-corruption counterparts, but that low-corruption democracies are more stable than high-corruption ones. For autocratic and hybrid regimes, the stability is due both tocorruption making the formal institutions more resistant to democratization and thatthe formal institutions prevent reductions in corruption. Consistent democracies, on theother hand, are able to reduce corruption and become more stable as a result.
The world is steadily becoming more democratic. In 1985, near half of the world’scountries were autocratic, and about a quarter democratic.1 In 2008, this picture was
St Comp Int DevDOI 10.1007/s12116-014-9155-1
1 Our classifications of democratic, authoritarian, and hybrid regimes are based on the SIP measure of democracy(Gates et al. 2006). The measure ranges from 0 (full autocracy) to 1 (full democracy), and the cutoff points forhybrid regime and democracy are set at 0.15 and 0.80, respectively. The Polity project shows the same trends (seehttp://www.systemicpeace.org/polity/polity4.htm).
H. Fjelde (*) :H. HegreDepartment of Peace and Conflict Research, Uppsala University, Uppsala, Swedene-mail: [email protected]
reversed, as half of the world was democratic, and only 14 % autocratic. These figuresreflect obvious improvements in governance in many parts of the world, but someaspects of the empirical pattern underlying the “third wave of democratization”(Huntington 1991) are puzzling. The proportion of hybrid regimes, i.e., those mixingauthoritarian and democratic institutions, has increased from 22 to 35 %, despite thefact that this regime type is known to be inherently unstable (Gates et al. 2006;Sanhueza 1999; Epstein et al. 2006). This democratization wave did not produce theanticipated reduction in corruption levels that much of the cross-country literature ongovernance would lead us to expect. The black line in Fig. 1 shows the increase inaverage democracy levels following the end of the Cold War.2 The gray line shows thatcorruption levels were slowly decreasing up to the mid-1990s, but then swiftlyincreased.3 This suggests that political corruption is not simply a system of malfeasancethat gradually disappears with liberalization and increased competition in the politicalsphere.
These empirical trends challenge both our conventional knowledge about regimestability and about the determinants of political corruption. In this article, we argue thatthese trends—the observed stability of hybrid regimes and the stickiness of corrup-tion—cannot be understood in isolation from each other. Rather, political corruptionshould be understood as an informal institution. In contrast to institutions that arecodified in law and/or officially sanctioned, such as the institution of elections, informalinstitutions are rules of political behavior “created, communicated and enforced outsideof officially sanctioned channels” (Helmke and Levitsky 2004, 727).4 Where present,they may reinforce the power of incumbents in authoritarian regimes and, in effect,counteract formal constraints on the executive in hybrid regimes. Hence, even ifexternal pressures on elites lead to democratization in the formal political institutionsof the state, elites can rely on political corruption to compensate for their loss in de jurepower. By increasing the de facto power of elites, political corruption may serve toreinforce the stability of nondemocratic regime types.
The idea that formal and informal institutions work together is not novel.Both the qualitatively oriented comparative politics and area studies literatureascribe a critical role to political corruption in explaining political outcomes(e.g., Rose-Ackerman 1999; Johnston 2005). Huntington (1968, p.64) calledcorruption “a substitute for reform” that would ensure groups became vestedmembers of the political system rather than its alienated opponents. Manyscholars working on Africa describe a system of governance where formaland informal institutions interpenetrate each other, as leaders’ ability to retainpower, maintain elite cohesion, and placate opposition rests on disbursement ofregime patronage through clientelist exchange (e.g., Lemarchand 1972; Brattonand de Walle 1994; Chabal and Daloz 1999; Englebert 2000; Hyden 2006). Thequalitative literature points to cases such as Haiti, the Philippines, and Côte
2 The data on corruption are from the PRS Group (2006). Both the democracy and corruption variable arenormalized to range from 0 to 1 in Fig. 1.3 Some of this increase in perceived corruption may reflect that the International Country Risk Guide (ICRG)now set higher standards for how public affairs are to be conducted than in the 1980s, but the figure stillindicates no corruption-reducing effect of democracy at the global level.4 See also Grzymala-Busse (2010).
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d’Ivoire to illustrate how the award of personal privilege can serve to fosterpowerful coalitions against democratization. In places where authoritarian re-gimes in recent decades have moved towards more open institutions in recentdecades, for example, Nigeria and Kenya, the incumbent regime’s efforts tobribe institutions for electoral oversight and control served to strengthen thepower of the incumbent and stifled further liberalization.
This article builds on these insights by simultaneously studying corruption levelsand patterns of institutional stability. We contribute to the cross-country literature oninstitutional stability by paying attention to how informal institutions such as corruptioninfluence political behavior and, in turn, trajectories of change in formal institutions(Helmke and Levitsky 2004; Acemoglu and Robinson 2006a, b). We also contribute tothe cross-country literature on political corruption, which up until now primarily hasfocused on how formal institutions determine corruption levels, predominantly withinthe context of democratic regimes. 5 We model the reciprocal relationship betweenpolitical corruption and formal institutions using dynamic multinomial logisticregression models. Our results show that high-corruption autocracies and hybridregimes are more stable than their low-corruption counterparts, but that low-corruption democracies are more stable than high-corruption ones. For autocratic andhybrid regimes, the stability is due both to corruption making the formal institutionsmore resistant to democratization and to the formal institutions preventing reductions incorruption, although the evidence is strongest for the latter. Consistent democracies, onthe other hand, are able to reduce corruption and become more stable as a result.Corruption thus seems to be part of a vicious circle that contributes to hinderingdemocratization in many countries, but democratic institutions do have a potential tobreak this cycle.
5 See Gerring and Thacker (2004) and Treisman (2007) for excellent reviews.
.3.4
.5.6
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1984 1988 1992 1996 2000 2004 2008
Year
Mean democracy Mean corruption level
Fig. 1 Average global levels of democracy and corruption, 1985–2008
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Political Corruption and Political Transition
Political Corruption as an Informal Institution
The standard definition of political corruption is the abuse of public office for illegitimateprivate gain (e.g., Shleifer and Vishny 1993). Political corruption involves self-dealing bypublic officials who extract resources through graft and embezzlement (cf. Persson,Tabellini and Trebbi 2003). Through corrupt transactions, political elites capture privatebenefits from the public offices they hold. In the extreme case of kleptocracy, maximizingopportunities for illegitimate self-enrichment is the primary motive of government offi-cials (Manzetti and Wilson 2007). Yet, political corruption also involves a second, relatedprocess, the illegitimate use of state resources to retain and expand political power bythose holding political office (Rose-Ackerman 1999; Manzetti and Wilson 2007). Thishappens when public officials, as a way to solicit political support, rely on the illegitimatedispersion of private perks and privileges, for example, by offering selective taxexemptions, public appointments, land allocations, lucrative government contracts,discriminatory enforcements of the law, purchase votes, or condone graft from lowerlevel public officials. Office holders can also use bribes to manipulate public institutionsfor accountability and control, for example, through buying off electoral commissions orhigh courts. Nyblade and Reed (2008) refer to these two forms of political corruption as“looting” and “cheating,” respectively. These two processes of political corruption—onefor enrichment, the other for survival—reinforce each other since the politician who isadept at expropriating rents and collecting bribes also has more resources available to buypolitical support (Rose-Ackerman 1999).
When corrupt politicians reward their political base by means of illegitimate appro-priation of public resources, it generates networks of exchange that are closely associ-ated with the term patronage politics (Johnston 1986; Acemoglu, Robinson and Verdier2004). Leaders (or patrons) hold on to their positions through the distribution of rentsand other personal privileges, and clients exchange their political support for rewardsthat cannot easily be attained through legitimate economic channels (Arriola 2009).6
Through such ties of domination and dependence, patron-client exchange helps toperpetuate the hold on power by resourceful elites (Manzetti and Wilson 2007). Patron-client ties may or may not be corrupt, but when the patron has a public position orobtain rents from one in a public position, the two phenomena overlap. Bueno deMesquita et al. (2003, p. 203–204), for example, treat pervasive corruption as indicativeof a distributive regime wherein leaders buy their continuation in office through theprovision of private rather than public goods.
We see high levels of political corruption as a behavioral pattern associated withpatronage politics. Where political corruption is pervasive, public officials are guidedby private motives in this distribution, rather than some universalistic notion of thepublic good (O’Donnell 1996). However, as the discussion above suggests, politicalcorruption not only structures the distribution of economic resources but also shapes
6 The term clientelism is sometimes discussed as distinct from the phenomenon of political corruption bybeing explicitly confined to the electoral arena and involving a broader distribution of rents. Many researchers,however, see political corruption and rent seeking as an integral part of the concept of clientelism (see, forexample, Kitschelt 2000; Keefer 2007; Bratton 2007).
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patterns of political loyalty and the exercise of political power. Political corruption canthus be understood as an informal institution (see, e.g., Bratton 2007).7 Paraphrasing thedefinition of informal institutions in Helmke and Levitsky (2004, 727), we see politicalcorruption as reflective of rules of political behavior that is “created, communicated andenforced outside of officially sanctioned channels,” yet have powerful implications forthe working of formal institutions, such as elections and legal constraints on theexecutive. As noted by Bratton (2007, p.98), these formal and informal institutionscan be held distinct for analytical purposes, but must be recognized as thoroughlyintertwined when it comes to explaining political outcomes.
In the next section, we outline an argument for how the informal institution of politicalcorruption interacts with the formal political institutions that make up the different regimetypes (autocracy, hybrid regime, and democracy) to influence the trajectory of institutionalstability and change. We start from a model of institutional stability that focus on the dejure distribution power in the formal institutions, and discuss how the de facto powerallocated by political corruption influences the probability of institutional change—understood both as change in regime type and in corruption level. We argue that thedegree to which the formal and informal institutions compete with or reinforce each otherinfluences the institutional outcomes we observe.
Political Corruption and Institutional Stability
We start with a simple conceptual model of institutional stability from Gates et al.(2006). In their model, institutional stability refers to the persistence of institutionalcharacteristics that together define a regime type: democracy, autocracy, or a hybridregime. According to Gates et al. (2006), authoritarian and democratic regimes are self-enforcing equilibria, whereby the maintenance of the institutions are in the interest ofall actors with power to change them. Authoritarian regimes are inherently stablebecause the political institutions reinforce each other to concentrate power in the handsof the incumbent: there is no open participation in the political system and nocompeting institutions from where a broad-based opposition can challenge the powerbase of a nonelected leader (see also Gurr 1974). Democratic regimes are stablebecause democratic institutions reflect the preference of the citizenry: institutionalizedchannels of popular influence allow actors to alternate in power and keep the incumbentaccountable to them. Institutions that diffuse power, such as elections, limitations onexecutive authority, and institutionalized participation, reinforce each other to constrainincumbents and secure elite compliance (Przeworski 1991; Weingast 1997). Hybridregimes are regimes somewhere between the conventional, closed authoritarian regimeand a fully developed democracy or polyarchy (Diamond 2002; Schedler 2006;Levitsky and Way 2002).8 They are unstable and prone to change, since there is an
7 For more on informal institutions and patronage/political corruption as a subtype thereof, see Lauth (2000),Grzymala-Busse (2010), and Helmke and Levitsky (2004, 2006).8 Scholars have proposed a range of labels for hybrid regimes, including “inconsistent,” “semidemocracy,”“electoral authoritarianism,” or “semi-authoritarianism.” We use the label hybrid regimes from Levitsky andWay (2002)and Diamond (2002). Most of the hybrid regimes in our sample are electoral authoritarian systems,but some display another mix of democratic and autocratic traits, for example, combining strong democraticinstitutions with severe restrictions on suffrage. We expand on our discussion of these regime categoriesbelow.
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inherent tension between the authoritarian incumbent and democratic institutions thatdiffuse political power (Huntington 1968; Gurr 1974; Levitsky and Way 2002;Sanhueza 1999; Gates et al. 2006).
While the Gates et al. model has generated significant insights on patterns ofinstitutional stability, substantial variation exists within the different regime categoriesin terms of how resilient they are to change. We argue that some of this variation maybe understood by taking into consideration political corruption as an informal institu-tion that constrains and enables actors’ incentives and behavior, impacts on the de factobalance of power between them, and thus shapes the trajectories of regime stability.Such an integrated framework also enhances our understanding of varying patterns ofcorruption across different regime types. We thus extend the Gates et al. model toexplicitly incorporate corruption level as an informal institution.
In short, we argue that institutional outcomes—understood in terms of both the typeof regime and the level of political corruption—depend on the particular configurationsof both formal and informal rules. We identify three institutional equilibria: the high-corruption authoritarian regime, the high-corruption hybrid regime, and the low-corruption democratic regime. In authoritarian regimes, political corruption and author-itarian institutions are mutually reinforcing. High political corruption serves to com-plement the power of the incumbent and but tress the authoritarian regime, and theabsence of strong constraints on the leader implies that high corruption levels will alsoprevail. In hybrid regimes, political corruption similarly serves to strengthen the powerof the incumbent. Yet, in this case, political corruption clearly competes with the powervested in formal institutional arenas of popular influence. Because it shifts de factopower from the electorate to the incumbent, an important source of regime instability inhybrid regimes is reduced. Since incumbents can counteract the loss of power due toliberalizing reforms with an expansion of corruption, the pressure for authoritariantransition also goes down. In sum, corruption should render hybrid regimes moreresilient to regime change, and by stifling institutions for popular control, we are alsolikely to see high levels of corruption prevailing. Also, in democratic regimes, politicalcorruption competes with the institutionalized channels of popular influence and themechanisms that keep incumbents accountable to the citizenry and thus render demo-cratic institutions less resilient to authoritarian transitions. Yet, while the outcome of theformal and informal institutions diverge, the formal institutions more strongly empowerthe citizenry and we are more likely to see a reduction of corruption levels than a movetoward a more authoritarian regime. This discussion leads to specific empirical expec-tations regarding both the impact of regime type on corruption levels and on corruptionlevels on patterns of regime stability.
We elaborate on each of these in turn.
Autocratic Regimes
Political corruption as an informal institution is likely to strengthen the monopolizationof power in the hands of the authoritarian incumbent. Consequently, authoritarianleaders have a strong incentive to maintain the practice, supporting a high-corruptionautocratic equilibrium. Autocratic rulers need not solicit support from a majority ofvoters, but some rent sharing within the elite coalition is necessary to retain power(Wintrobe 1998; Gandhi and Przeworski 2006). Reliance on private goods consolidates
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the incumbency advantage vis-à-vis political opponents and reduces the opportunitiesfor political opponents to successfully vie for power (Bueno de Mesquita et al. 2003).All politicians can solicit support through promises of public goods that benefit allmembers of society, but only the incumbent can deliver on up-front promises of privaterewards to his political base. A narrow distribution of private goods through corruptionleaves more surplus for the incumbent’s own discretionary use than investment inpublic goods that enhance the welfare of all members of society. This makes rent-seeking behavior combined with clientelist-based distribution particularly appealing tothe incumbent. The incentives to expand political corruption are thus high. Meanwhile,authoritarian institutions provide few institutional constraints on the incumbent’s abilityto capture private benefits from public office, and use these to buy the necessarypolitical support to retain office. High corruption levels can thus prevail.
An important source of instability in authoritarian regimes is elite fractionalization(cf. O’Donnell, Schmitter and Whitehead 1986; Arriola 2009). Corrupt exchanges canbe used to co-opt pivotal groups that could otherwise launch a credible campaign todisplace the incumbent and threaten regime stability. These could, for example, beeconomic elites who, in return for bribes, enjoy irregular business opportunities,political elites who engage in illicit rent-seeking for private gain, subordinate stateemployees whose graft is condoned by their superiors in exchange for their compliance,or military officers receiving a “second salary” as a premium for their loyalty. Theconstituency that benefits from corruption is often narrow. 9 In Haiti, the Duvalierregime is said to have misappropriated 63 % of government revenue during the late1970s for the benefit of “just a few thousand people connected by marriage, family tiesand friendship to those in power” (Grafton and Rowlands 1996). By being allowed topursue illegitimate opportunities for rent seeking or allotted other privileges with theimplicit consent of the ruler, these groups are compensated for their lack of or limitedsources of political influence through formal institutions. Political corruption thusfacilitates intra-elite accommodation, and this reciprocal assimilation of elites—to usethe phrasing of Bayart (1993)—is likely to reduce the pressure for change in formalpolitical institutions stemming from societal groups with the economic, political, ormilitary power to otherwise threaten the regime (Arriola 2009). According to Wintrobe(1990, p. 854), patronage “act[s] as a premium to compensate the interest group for itssupport or loyalty to the party and serve as a deterrent to the shifting of loyalty.”Because these are private goods, they can be appointed and withheld in a manner thatpromotes personal loyalty to the leader (Bates 1981; Rose-Ackerman 1999; Darden2008). In the Philippines, for example, President Marcos created a system of cronycapitalism where the state provided monopolies for private accumulation within differ-ent spheres in the economy. Since the continued rule by Marcos was the guarantee ofcontinued privilege, this subcontracting of corruption created a vested interest in regimesurvival among these economic elites (Thompson and Kuntz 2006). This group, willingto substitute tangible rewards for democratic concessions, is pivotal for autocraticregime stability. Without successful co-optation of elites through such rent-sharingmechanisms, schisms within the authoritarian coalition could precipitate regime change(cf. O’Donnell, Schmitter, and Whitehead 1986).
9 Indeed, a large political base dilutes the value of the tangible reward for each client and, in turn, reduces therecipient’s obligations to support the leader.
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Against this coalition stand the interests of the broader citizenry who are notincluded in the patron-client networks. They prefer to check corruption. Corrupttransactions divert funds away from investment in public welfare, implying a redistri-bution of resources away from the median voter. However, the citizenry in autocraticregimes lack the institutional means to discipline the leadership. Political corruptionfurther reduces the de facto power of the potential electorate in autocratic regimes if astrong middle class, which could otherwise be a potential ally, also benefits from thecurrent order. Targeted and excludable benefits can be used for a strategy of “divide andrule” to break up citizens’ collective interest that could otherwise threaten the regime(Acemoglu, Robinson, and Verdier 2004). The problem of coordinating groups forcollective action might be the most critical barrier to change in political institutions(Acemoglu and Robinson 2006a). Hence, a key factor precipitating regime change isneutralized.
This discussion suggests that in high-corruption authoritarian regimes, theoutcomes of the formal and informal institutions converge. The informal behav-ioral rules implied by political corruption privilege the authoritarian incumbent,facilitate elite integration within the existing order, and aggravate collective-actionproblems for reformist actors. Political corruption reinforces the authoritarianregime equilibrium, and we expect authoritarian regimes with high levels ofpolitical corruption to be more resilient to democratic transitions than low-corruption autocracies. Conversely, because the outcomes of political corruptionaligns with the interests of those with the formal institutional means to check it,high levels of corruption can also be sustained and even expanded. This suggeststhe following hypotheses:
Hypothesis 1A: High levels of political corruption reduce the probability of demo-cratic transitions in authoritarian regimes.
Hypothesis 1B: Authoritarian regimes are less likely to reduce political corruptionthan hybrid or democratic regimes.
Hybrid Regimes
Similar to their autocratic counterparts, incumbents in hybrid regimes may use politicalcorruption to co-opt political opponents and facilitate elite cohesion. These regimesdisplay an inherent tension between the authoritarian incumbent and the existence ofinstitutional arenas for popular influence, such as elections and parliament (Levitskyand Way 2010; Gates et al. 2006). In hybrid regimes, incumbents often stifle theopposition and violate democratic principles to tilt the playing field in their favor(Schedler 2002; Levitsky and Way 2010). Because incumbents in hybrid regimes aremore constrained in using repression and persecution than an autocratic leadership(Smith 2014), the increase in the incumbent’s de facto power attained through corruptexchanges may be particularly important for his ability to retain his position. He mightbe able to exercise disproportionate influence in politics by invoking patronage-basedrelationships outside the formal political channels. Incumbents that face strong externalpressure for democratization may counteract the loss of power due to liberalizingreforms with an expansion of corruption. By accommodating the incumbent, political
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corruption may enhance the stability of formal institutions by dampening pressures forchange.
Through formal channels of political influence, such as elections and the parliament,the citizenry in hybrid regimes has a stronger platform for reforming institutions in thedirection of democracy than in authoritarian regimes (Levitsky and Way 2010).Political corruption, however, often undermines reformists’ attempts to institutionalizepopular influence (cf. Lauth 2000). First, political corruption creates venues for levyinginfluence and gaining privilege exempt from popular accountability. Political corrup-tion is characterized by the representation of narrow interests, whose influence is notfiltered by any mechanisms of checks and balances. The personal and uninstitu-tionalized nature of corrupt networks breaks the link between collective decisionmaking and the electorate’s possibility to influence these decisions (Johnston 2005;Warren 2004). Second, efforts to “cheat” may also target the public institutions foraccountability and control directly. The strategic co-optation of members of parliamentthrough private rewards is likely to be an integral part of such a strategy in a hybridregime. Under President Fujimori in Peru, for example, illicit “second salaries”were paid to the leadership of the parliament, the army, the judiciary, and otherstate institutions as an “informal inducement to loyalty and compliance” (Darden2008, 54). Political corruption thus undermines the de facto power of the elector-ate. These efforts compromise the power of popular institutions to provide a checkon corruption.
In addition, research suggests that the electorate in these hybrid regimes might beless antagonized by political corruption than in fully democratic systems. Where partiesare weak and parliaments are not perceived as independent from the executive branch,politicians have problems making political promises credible to voters. The electoratemight thus discount future promises of public-goods provision, knowing that institu-tions cannot bind politicians to their word. Patronage-based distribution throughhierarchical networks may turn more salient in response to such commitment problems(see Englebert 2000; Robinson and Verdier 2002; Keefer 2007). Such networks offerimmediate and tangible gains to voters and tie the citizenry’s continued benefits to theirassociation with the patron. Through vote-buying, politicians succeed in bribingvoters directly (Manzetti and Wilson 2007). Medina and Stokes (2002) theorizethat incumbents with monopoly control over economic and political resources aresuccessful in preserving status quo by establishing credible threats against clientswho may defect to the opposition, thereby undermining political competition. Thismay trap hybrid regimes in an institutional equilibrium wherein a sufficiently largenumber of individuals derive so substantial economic benefits from the corruptexchange and their association with patrons that their incentives to press for moredemocratic political institutions are small and the incumbent’s incentives forproviding them remain low.
Corrupt, hybrid regimes can also be more resistant to transitions toward autocracy.Since political corruption increases the de facto power of the incumbent, it decreaseshis incentives to change the formal political institutions in a more autocratic direction.As an informal institution, political corruption compensates the authoritarian incumbentfor the limits to executive power put in place by the formal institutions. Hence, assuggested by Helmke and Levitsky (2004), they reconcile the incumbent’s interest withthat of the formal arrangements and may dampen his demand for more authoritarian
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institutions. Political corruption may thus mitigate the instability of hybrid politicalsystems and make them more resilient to regime change.
This discussion suggests that political corruption directly competes with the demo-cratic norms prevailing in hybrid regimes and benefits the authoritarian incumbent. Assuch, political corruption stifles the pressure for democratization among the citizenry,while dampening the incumbent’s demand for authoritarianism. The high-corruptionhybrid regime is therefore an institutional equilibrium. High levels of corruption renderhybrid regimes more resilient to regime transition in both a democratic and authoritar-ian direction, and can be sustained because its outcome aligns with the interests of thosewith the institutional means to check it. This suggests the following hypotheses:
Hypothesis 2A: High levels of political corruption reduce the probability of demo-cratic and autocratic transitions in hybrid regimes.
Hypothesis 2B: Hybrid regimes are less likely to reduce political corruption thandemocratic regimes.
Democratic Regimes
Democratic leaders also derive payoffs from diverting public resources to their privateends, for example, using state resources for own electioneering. If political corruptionpersists in democracies, the informal and formal institutional outcomes diverge. Thepersonal and uninstitutionalized nature of corrupt networks breaks the link betweencollective decision making and the electorate’s possibility to influence these decisions(Johnston 2005; Warren 2004). This may erode democratic legitimacy and rendervoters more susceptible to clientelist appeals as described in the previous section. Itmay also hinder the rise of political competition by reinforcing the incumbencyadvantage inherent in patronage spending and over time increase the risk of anauthoritarian backlash.
However, democratic institutions influence the incentives of the incumbent toengage in political corruption (see, for example, Tavits 2007). For the incumbent,scheduled and free elections and open executive recruitment make political leadersaccountable to the citizenry. The formal institutions that allow the citizenry to votecorrupt politicians out of office provide incentives for the incumbent to align theirpolicy with the preference of the citizenry and thus abstain from illegitimate rent-seeking for political survival (Rose-Ackerman 1978). Several scholars report thathigher electoral competition is associated with less political corruption (Treisman2000; Montinola and Jackman 2002).
In addition to the accountability imposed by the electoral mechanism, scholars alsoargue that the incentives for leaders to rely on illegitimate patronage are lower indemocracies. Bueno de Mesquita et al. (2003), for example, argue that since ademocratic incumbent’s political base derives from the majority vote in elections,economies of scale lead incumbents to switch from selective accommodation of privateinterests to policies that enhance the welfare of all citizens in society. Because eachindividual’s value of private goods decreases as the number of clients goes up, privategoods offer small rewards to the “median voter.” For a given total expenditure onprivate goods for the patron, a large political base dilutes the value of the tangible
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reward for each client and in turn reduces the recipient’s obligations to support theleader. The turnover of power in democracies also implies that the incumbent cannotcredibly promise that the regulations that secure private actors extraordinary rentsmight continue into the future (Montinola and Jackman 2002). This lowers the incum-bent constituency’s willingness to endorse networks of corrupt exchanges.
For the citizenry, institutionalized democracy provides a low-cost option to checkcorrupt behavior and punish politicians through the electoral mechanism (Adzera, Boixand Payne 2003; Lederman, Loayza and Soares 2005; Persson, Tabellini and Trebbi2003). Freedom of information and association facilitates monitoring of public offi-cials, limiting their opportunities to engage in political corruption (Montinola andJackman 2002).
Based on this discussion, we argue that the low-corruption democracy is an institu-tional equilibrium. Whereas we might see some high-corruption democracies revert tonondemocratic regime configurations, the predominant pattern of change in suchdemocracies will be toward a reduction in corruption. Hence, to the extent that electoralchecks are effective, political corruption is unlikely to endanger the stability of thedemocratic regimes. Instead, political corruption is curbed, as democratic institutionsbring popular control from below.
Hypothesis 3A: High levels of political corruption increase the probability of auto-cratic transitions in democratic regimes.
Hypothesis 3B: Democratic regimes are more likely to reduce political corruptionthan non-democratic regimes.
Data
We test our hypotheses on a dataset covering 133 countries over the 1985–2008 period.For the formal political institutions, we use the “Scalar Index of Polities” (SIP) measureof democracy developed in Gates et al. (2006).10 The SIP index is based on a three-dimensional conception of democracy that takes into consideration the nature of therecruitment of the executive (e.g., open elections vs. hereditary designation), the extentto which the executive is constrained by other institutions, and the extent of popularparticipation (Eckstein 1973; Gurr 1974).11 The index combines the Polity index ofdemocracy (Jaggers and Gurr 1995) and the Polyarchy index of Vanhanen (2000).12 Fordetails regarding the construction of the index, see Gates et al. (2006, 897–898). Themeasure is normalized to range from 0 (perfect autocracy) to 1 (perfect democracy).
10 The dataset is available at http://havardhegre.net/replication-data/.11 Changes to political institutions often take time and are associated with a period of turmoil. The Polityproject codes these transition periods with a set of transition codes (−77, −88, −99). Such periods may last forseveral years. To take such transitions into account when observing countries annually, we replace transitioncodes with the regime type observed immediately before the transition.12 We have data at a finer temporal resolution than the year for both the corruption (quarterly series) anddemocracy indicators (in principle, daily series). We assign the value of the last observation within the year asthe annual observation.
The data on political corruption are from the International Country RiskGuide (PRS Group 2006). 13 No objective data on the extent of corruptionexist, and the ICRG annual index of perceived corruption builds on assessmentsby country experts. While such assessments are by definition “subjective,”different cross-national ratings of corruption tend to be highly correlated witheach other and with cross-national polls of business people’s and inhabitants’perception of corruption (Treisman 2007). The ICRG corruption rating has twoadvantages over other corruption measures. First, it is the one with the bestcoverage for cross-section time-series analysis. Second, while taking into ac-count financial corruption in the form of demands for special payments andbribes, it is primarily concerned with political corruption organized from above,which is the focus of the theoretical argument. The rating records “actualcorruption in the form of excessive patronage, nepotism, job reservations orquid pro quo deals, secret party funding, and suspiciously close ties betweenpolitics and business” (PRS Group 2006).
To set up a model that takes the reciprocal relationship between corruption andpolitical systems into account, we divided the democracy index into three categoriesand the corruption index into two. We define countries as nondemocratic if they haveSIP score less than or equal to 0.15, as democratic if the score is higher than 0.80, andas hybrid if they fall in between. We define countries as low corruption if their score isless than 4, and corrupt if higher or equal to 4. By combining these variables, we obtaina six-category dependent variable.
To control for the confounding effect of economic development, we include data onGDP per capita from Maddison (2007).14 We are interested in GDP per capita as anexogenous variable. Income levels, however, are affected both by corruption and bypolitical instability. To minimize endogeneity bias, we use observations of GDP percapita prior to the time frame for the analysis. We use data for 1983 or, in the few caseswhere the time series starts later, data for the first available year. We log-transform thevariable.
We also enter a measure of oil dependence in the model. For many economies,GDP per capita is a good proxy for the extent to which the country has a “moderndynamic pluralist” society, in the words of Dahl (1989). In economies that arehighly dependent on oil exports, however, the correlation is much lower betweenGDP per capita and factors that may affect corruption and regime stability—suchas urbanization, literacy, economic diversification, and the mobility of capital. Oileconomies may have high average incomes without any of these socialcharacteristics. Moreover, as discussed above, oil economies are by definitiondominated by a highly appropriable asset even when GDP per capita is high.This accentuates the need to distinguish these economies from other economies.We use an indicator variable from Fearon and Laitin (2003) that denotes whetheroil accounts for at least 33 % of export income.
13 For more information about the data and coding, see www.icrgonline.com. The data are available fromwww.countrydata.com.14 The Maddison GDP data are measured in 1990 International Geary-Khamis dollars. To reduce missingness,we have interpolated data as well as supplemented with GDP data from World Bank (2011) and Gleditsch(2002). For details, see Dahl et al. (2014).
The six-state matrix of observed transition frequencies for the 1985–2008 period isreported in Table 1. It shows a cross-tabulation of the observed state at year t with theobserved state the year before. Beneath the transition frequency matrix, we report theobserved distribution over the six states.
The observed transition probabilities can be calculated from the frequencies inTable 1.15 Figure 2 shows a graphical representation of these transition probabilities.The six circles represent the six states. The sizes of the circles are proportional to theprobability of remaining in the same state from 1 year to another. The arrows betweencircles reflect the probabilities of transition from one state to another. The widths of thearrows are proportional to the transition probabilities.
The patterns observed in Table 1 and Fig. 2 give preliminary support for ourhypotheses. Corruption is clearly sustainable in autocratic regimes, as stated in Hy-pothesis 1B. Only 3.5 % of the high-corruption autocracies transition into a low-corruption state. The corresponding figures for hybrid and democratic regimes are6.1 and 7.1 %. Moreover, a larger share—6.0 %—of low-corruption autocraciestransitions into high-corruption autocracies. High-corruption autocracies are also morestable than low-corruption autocracies (Hypothesis 1A)—87.1 % of the corrupt autoc-racies remain in the same state as compared with 84.6 % among the low-corruptionautocracies. Figure 2 shows that hybrid regimes also have a net flow toward more
15 The transition probability corresponding to cell j,i is the column proportions, or the probability of observinga state j at t given that a country was in state j at t−1.
Table 1 Matrix of observed transition frequencies for states of corruption and institutional types, 1985–2008
State at t−1 State at t Sum
High-corruption Low-corruption
Autocracy Hybridregime
Democracy Autocracy Hybridregime
Democracy
High-corruption autocracy 250 22 5 10 0 0 287
High-corruption hybrid 5 353 14 0 24 0 396
High-corruption democracy 3 2 324 0 0 25 354
Low-corruption autocracy 18 0 0 253 22 6 299
Low-corruption hybrid 0 37 0 8 380 19 444
Low-corruption democracy 0 0 47 2 8 909 966
Sum 276 414 390 273 434 959 2,746
Observed distribution 0.117 0.173 0.161 0.086 0.134 0.329
Steady-state distribution 0.043 0.111 0.304 0.026 0.085 0.432
Only country-years with data for all variables used in models below are included
St Comp Int Dev
corruption, as expected in Hypothesis 2B. As a consequence, hybrid regimes are morestable with high corruption levels than with low, 89.1 % remain hybrid as compared to85.6 %, supporting Hypothesis 2A. As predicted in Hypothesis 3B, the opposite patternshold for consistent democracies: 4.9 % of the low-corruption democracies transitioninto the high-corruption state, as compared to 6.0 and 8.3 % for autocracies and hybridregimes. Hypothesis 3A is also supported, as the risk of transition to hybrid or autocraticregime is higher in high-corruption democracies. Note, however, that there is not muchdata to support this inference. Over the 24-year period, there are only 15 transitionsfrom democracy to other regime types. None of these transition probabilities are largerthan 1 %.
No high-corruption systems change into a low-corruption system of a different typewithin the same year. In the upper-right and lower-left quarters of Table 1, there areobservations only on the diagonal. Both corruption and regime type change but infre-quently, and the likelihood of observing both transitions within the same year is very low.
Figure 2 also reflects the trend toward more democracy in the 1985–2008 periodseen in Fig. 1—all arrows pointing to the right (more democracy) are thicker than thosepointing to the left. The figure clearly shows how corruption interacts with this processof democratization: The arrows from autocracy to hybrid regimes are roughly of thesame size at both levels of corruption, but the arrows from hybrid regime to consistentdemocracy are thinner among the high-corruption countries. Political corruption thusseems to stifle democratization.
The figure also indicates that authoritarian leaders use corruption as a substitute forauthoritarian institutions. A thick arrow (7.4 % annually) leads from low-corruption
Fig. 2 Observed transition probabilities, all countries, 1985–2008
St Comp Int Dev
autocracy to low-corruption hybrid regime. From here, though, twice as many transi-tion into high-corruption hybrid regime as into consistent democracy. A pair of thicklines also go from low-corruption autocracy to high corruption hybrid regime via high-corruption autocracy.
What are the long-term trends implied by these transition probabilities? In thebottom row of Table 1, we report the steady-state distribution of the transition proba-bility matrix—i.e., the long-term distribution over our six states. 16 The strong netoutflow of transitions from the autocratic systems depicted in Fig. 2 results in a lowshare of autocratic systems—only 6.9 % are consistent autocracies in the long run giventhese transition probabilities. In line with our argument that corruption renders autoc-racies more persistent to change, most of the remaining autocracies are high-corruptionsystems (4.3 % as compared with 2.6 %). The hybrid regimes are not very stable either.In the long run, 11.1 % will remain high-corruption hybrid regimes, and 8.5 % will beof the low-corruption type. Only the democratic states have a net inflow of transitions.Most of these have low corruption, but the proportion of democracies with highcorruption will increase: In steady state, 30.4 % are high-corruption democracies and43.2 % low-corruption democracies.
These figures, however, are based on the observed transition probabilities andimplicitly assume that transition probabilities are independent of other factors. In thenext section, we will model the transition probabilities statistically as functions ofexogenous variables such as average income and oil dependence, allowing for thepossibility that autocratic regimes are more sustainable in Ethiopia than they wouldhave been in Switzerland.
Estimating Transition Probabilities as Functions of Explanatory Variables
The multinomial logit model allows relating a categorical dependent variable with Jcategories to a vector of explanatory variables. The estimated probabilities of observingeach of the J outcomes are modeled as
Pr Y ¼ jð Þ ¼ eXβ jð Þ
X
j¼1
J
eXβ jð Þð1Þ
for each outcome j. The six corruption and institution states constitute our outcomevariable. The transition probabilities are estimated when we include the states at t−1 asvariables in the linear component Xβ(j). By adding further control variables to the linearcomponent, we estimate the transition probabilities as functions of these variables andmay assess the extent to which differences in transition probabilities are due tosystematic patterns rather than random variation.
We include five exogenous variables: initial income per capita, oil dependence, anddummy variables that distinguish between four periods within the 1985–2008 time
16 The steady-state probabilities or the “stationary probability distribution” is the distribution across the sixoutcomes that emerge if the transition probability matrix is repeated many times. As such, it accounts for howall possible transitions affect this distribution. It can be generated by multiplying the matrix of transitionprobabilities with itself a high number of times. The steady-state distribution can also be obtained by solving asystem of linear equations; see Taylor and Karlin (1998, p. 247).
St Comp Int Dev
frame. These variables are regularly included in the literature on democratization andcorruption (see, for example, Przeworski et al. 2000; Ross 2001; La Porta et al. 1999;Treisman 2000).17 The model also includes all six states at t−1 as covariates and has noconstant term. The results are reported in Table 2.
High-corruption autocracy is the reference outcome and the five columns refer to thefive other outcomes of combinations of corruption and institutions. All estimates areinterpreted as how much the log risk of an outcome relative to the reference outcomechanges when the explanatory variable changes by one unit.18
The model has been simplified by two types of constraints. First, several parametersthat were very far from statistical significance were set to 0.19 These are marked withdashes in Table 2. Another set of parameters needed a different treatment. Theseparameters are closely associated with transitions where corruption level and politicalsystem change simultaneously. As is apparent in Table 1, these transitions are neverobserved. Such “empty-cell” observations lead to estimation problems. To obtainrobust estimates, we inserted two “artificial observations” and constrained all theseparameters to have the same value and obtained an estimate of −12.74 and a standarderror of 1.515 for the set.20
Testing Hypotheses: Equilibria
Our hypotheses cannot be formulated as tests of individual parameter estimates asreported in Table 2. They can, however, be evaluated on the basis of estimatedtransition probabilities and steady-state distributions.
The first step in our testing procedure is to estimate the transition probability matrixusing Clarify (King, Tomz, and Wittenberg 2000). Clarify is a simulation and predic-tion tool that draws a number of realizations of parameters based on the estimate vectorand the variance-covariance matrix for a model and reports the distribution of predictedprobabilities from the set of such realizations. The estimated transition probabilities arecalculated for a “typical” case within the 1997–2000 period where initial GDP per
17 The multinomial logit model with six categories for the dependent variable requires the estimation of a largenumber of parameters. We refrain from including additional variables to avoid over-fitting of the model. Wehave also estimated a model with a set of region dummies, reported briefly in Appendix A.2.18 The estimate of −2.055 for “oil” in the equation for low-corruption democracy (equation 5), for instance,means that the odds for oil economies of being in the low-corruption democracy state relative to the referenceoutcome is exp(−2.055)=0.12 times that of nonoil economies. Or, nonoil economies are about eight timesmore likely to be low-corruption democracies than the reference outcome. Given the constraints on the model,the reference outcome in this case is either high-corruption autocracy, high-corruption hybrid, or low-corruption autocracy.19 We set parameters for control variables to 0 if they were not significant at the 10 % level, and parameters forlagged dependent variables to zero if their p values were higher than 0.50.20 We used a random-number generator to change one arbitrarily selected country-year observation to atransition from low-corruption inconsistent to high-corruption autocracy and one from low-corruption democ-racy to high-corruption autocracy. These two artificial observations adds unbiased noise to the estimation, butallows the estimation of two parameters modeling the log odds of “empty-cell transitions.” All otherparameters estimating the log odds of empty-cell transitions were constrained to be equal to these, so thatthe estimates reflect the “average” log odds of such transitions. The large negative estimate we obtain is a goodapproximation to the low-probability event of changing corruption level and regime type in the same year.Without this adjustment, the parameters corresponding to empty cells are estimated to be very large negativenumbers, corresponding to relative risks approaching zero, with very large standard errors. The presence ofsuch poorly defined estimates also hurt the precision of other parameters in the model.
St Comp Int Dev
Tab
le2
Multin
omiallogitmodelestim
ates
oftransitionprobabilities,1985–2008
Equation1
Equation2
Equation3
Equation4
Equation5
High-corruptio
nhybrid
High-corruptio
ndemocracy
Low
-corruptionautocracy
Low
-corruptionhybrid
Low
-corruptiondemocracy
High-corruptio
nautocracy
−2.431***(0.222)
−6.090***(1.373)
−2.411***(0.341)
−12.74
a(1.515)
−12.74
a(1.515)
High-corruptionhybrid
4.330***
(0.385)
−1.279
(1.403)
−12.74
a(1.515)
–b−1
2.74
a(1.515)
High-corruptio
ndemocracy
–b2.341(1.469)
−12.74
a(1.515)
−12.74
a(1.515)
−5.434***(1.486)
Low
-corruptionautocracy
−12.74
a(1.515)
−12.74
a(1.515)
3.729***
(0.359)
−1.821***(0.513)
−9.341***(1.492)
Low
-corruptionhybrid
3.611***
(1.013)
−12.74
a(1.515)
3.729***
(1.092)
4.289***
(1.081)
−5.251**
(1.800)
Low
-corruptiondemocracy
−12.74
a(1.515)
0.782(1.532)
2.264*
(0.891)
–b−1
.971
(1.494)
Income
–b0.368*
(0.179)
–b0.356***
(0.0529)
1.159***
(0.172)
Oil
–b−1
.895***(0.493)
–b−0
.691*(0.275)
−2.055***(0.454)
Period
1990–96
–b–b
−0.949**
(0.320)
–b0.256(0.340)
Period
1997–00
–b−0
.948*(0.482)
−2.037***(0.467)
−1.701***(0.313)
−1.372**
(0.498)
Period
2001–08
–b–b
−3.909***(0.679)
−1.779***(0.286)
−2.208***(0.361)
N=2,746
Log
likelihood=−1
,027.47
Log
likelihoodnullmodel=−4
,920.17
High-corruptio
nautocracyisthereferenceoutcom
e.Standarderrorsin
parentheses
*p<0.05;**p<0.01;***p
<0.001
aThese
estim
ates
constrainedto
beequalto
each
other—
jointestim
atehaspvaluep<0.001
bAlltheseestim
ates
areconstrainedto
bezero
St Comp Int Dev
capita is at the median (4,100 in 1990 Geary-Khamis dollars) and oil dependence islow. We also requested 90 % confidence intervals for the predicted probabilities. Theestimated probabilities are represented in Fig. 3.21 Clarify is somewhat limited in termsof the predictions it calculates directly, however. In order to test our hypotheses, weused Clarify to generate a set of 2,000 realizations of the parameters in the modelreported in Table 2, and calculated the transition probabilities using Eq. 1 for each ofthese realizations. We then calculated the estimated steady-state probabilities as well asa set of differences in steady-state and transition probabilities for each of theserealizations.
Figure 4 shows the distribution of the six estimated steady-state probabilitiesemerging from this procedure when we set initial income to the typical case values.22
On average across parameter realizations, the steady-state proportion in low-corruptiondemocracy is about 0.58. The uncertainty in the figure stems from the uncertainty of theestimates reported in Table 2.
To report our testing procedure, we introduce a notation convention: we refer to thelow-corruption states using Greek letters (α, η, δ), and the high-corruption states withLatin letters (A, H, D), for autocracies, hybrid regimes, and democracies, respectively.We use pAα as shorthand for the probability of transition from high-corruption autoc-racy to low-corruption autocracy, and SSα for the steady-state distribution for low-corruption autocracies.
21 The matrix of predicted transition probabilities and corresponding 90 % confidence intervals are reported inAppendix Table 11. Because of a limitation in the Clarify software, we simplified the model reported inTable 1 and constrained the empty-cell parameters ex ante to have a value of −12.74.
22 Figures 5, 6, and 7 report the same distributions for other control variables.
St Comp Int Dev
We then proceed to compare the predicted steady-state distributions for our typicalcase. According to Hypothesis 1A,B, high-corruption autocracies are more stable thanlow-corruption ones. In Table 3, this is formulated as SSA>SSα—in the steady-statedistribution, the proportion of high-corruption autocracies is larger than that of low-corruption autocracies. The column labeled “Stable SS” reports the steady-state distri-bution for high-corruption autocracies (SSA). The “Unstable SS” column reports thesame for the constellation we expect to be less stable—low-corruption autocracies(SSα). In the simulations shown in Fig. 4, mean SSA=0.055 and mean SSα=0.014. Totest whether these proportions are different from each other, we calculate the differencein each of the simulations, extract the mean and standard deviation of these 2,000differences, and report the p value of a one-sided t test for these differences.23 Forhybrid regimes, mean SSH for high-corruption countries is 0.150, considerably higherthan for low-corruption countries (SSη=0.057). Low-corruption democracies, on theother hand, are more stable than high-corruption ones—SSδ=0.579>SSD=0.144. Allthese results are in line with our hypotheses, but there is considerable uncertaintyinvolved. Our tests show that the differences have p values ranging from 0.107 forhybrid regimes to 0.003 for democracies.
The uncertainty involved stems from the variety of transitions feeding into theestimated steady-state distribution (see fn. 18) in combination with sparse data. Arelated test that requires less of the data is a comparison of the estimated probabilityof remaining in the same state from 1 year to another. Line 2 in Table 3 compares the
23 For autocracies, the mean difference was 0.041, the standard deviation 0.027, the t value 1.54, and the one-sided p value 0.062.
05
10
15
De
nsity
0 .2 .4 .6 .8 1
Estimated SS distribution
High-corruption autocracy
02
46
De
nsity
0 .2 .4 .6 .8 1
Estimated SS distribution
High-corruption hybrid
02
46
8
De
nsity
0 .2 .4 .6 .8 1
Estimated SS distribution
High-corruption democracy0
20
40
60
De
nsity
0 .2 .4 .6 .8 1
Estimated SS distribution
Low-corruption autocracy0
51
01
52
0
De
nsity
0 .2 .4 .6 .8 1
Estimated SS distribution
Low-corruption hybrid
01
23
De
nsity
0 .2 .4 .6 .8 1
Estimated SS distribution
Low-corruption democracy
Fig. 4 Distribution of predicted steady-state distribution, median-income nonoil producers, 1997–2000
St Comp Int Dev
probability pAA of remaining high-corruption autocracy with pαα of remaining low-corruption autocracy. We obtained the estimated distribution of differences in theseprobabilities from the realized parameters as above, and calculate the same statistic. Allthe differences in transition probabilities are as hypothesized, and they are all statisti-cally significant.24
In sum, Table 3 provides support to all our hypotheses and most strongly toHypothesis 3A,B. High-corruption autocracy and high-corruption hybrid regime areequilibria, as is low-corruption democracy. The equilibria, however, may be due tothe effect of corruption on regime stability (our A hypotheses), or differences in regimetypes’ effect on corruption (B hypotheses). To distinguish between our A and Bhypotheses, we decompose them into sets of comparisons of the estimated transitionprobabilities shown in Fig. 3. Table 4 shows the comparisons of the transition proba-bilities that relate to the stability of regime types for different levels of corruption (Ahypotheses). By the same method as above, we obtain t tests for the relevant probabilitydifferences, allowing us to evaluate whether pairs of transition probabilities are differ-ent from each other.
According to Hypothesis 1A, high-corruption autocracies are more stable than low-corruption autocracies. One implication of this is that the probability of transition fromautocracy to hybrid regime is higher if corruption is low (pαη) than if corruption is high(pAH). The first line in the upper panel of the table shows that this is the case, but theestimated difference is minuscule, and not statistically significant. Likewise, as sug-gested by Hypothesis 2A, the probability of transition from hybrid regime to autocracyor to democracy is lower if corruption levels are high—pηα>pHA and pηδ>pHD. Theresults confirm both of these expectations. In particular, democratization is twice aslikely in low-corruption hybrid regimes (pηδ>pHD) as in high-corruption regimes. Asexpected by Hypothesis 3A, the probability of transition from democracy to hybridregime is higher if corruption levels are high. All these differences are in the expecteddirection, but only pηδ>pHD is coming close to statistical significance. An inspection of
24 These conclusions are not restricted to our “typical case,” a middle-income nonoil economy. AppendixTables 6 and 7 report the same tests for low-income and oil-producing countries. We obtain very similar resultsfor autocracies and hybrid regimes for all four combinations of income level and oil. The only exception is thatlow-income or oil-producing high-corruption democracies are more stable than their low-corruptioncounterparts.
Table 3 Comparing predicted steady-state proportions and same-state probabilities
Hypothesis Comparison Stable SS Unstable SS Difference p value
Steady-state distributions are calculated on basis of 2,000 simulated transition probabilities for medianincome,nonoil-dependent countries within the 1997–2000 period. Also, see Tables 6 and 7
St Comp Int Dev
Table 1 indicates lack of data is one reason for this. For instance, there are only eightobserved transitions for low-corruption hybrid regime to low-corruption autocracy andfive for the corresponding high-corruption pair. Such low transition frequencies limitour ability to draw strong conclusions from the data.
In the lower panel of Table 4, we compare the probabilities of remaining inthe same regime type for the two levels of corruption. In contrast to Table 3,we here disregard changes to corruption levels and focus only on regime type.If Hypothesis 1A is correct, the probability of remaining in the autocratic stateif corruption levels are high is higher than of remaining autocratic state ifcorruption is low, which is pAA+pAα>pαA+pαα. The first line in Table 4 showsthat this difference is in the expected direction, but not statistically significant.The support for Hypothesis 2A in the second line in the lower panel of Table 4is stronger—hybrid regimes are much more likely to persist when corruptionlevels are high (p value=0.070).
The corresponding transition probabilities for Hypothesis 3A are compared in thebottom line of Table 4. The difference here is small and runs in the opposite direction ofour hypothesis.25
To sum up, the results in Table 4 are in favor of Hypothesis 2A, but gives littlesupport to Hypotheses 1A and 3A. Again, some of the uncertainty is due to scarcity ofdata.26 Some of the difference in the steady-state distributions shown in Table 3 mustthen be due to differences in the sustainability of corruption in the three regime types,as stated in our B hypotheses. These imply that the different political systems differ inthe propensity for corruption levels to increase or decrease. These comparisons aregiven in Table 5. Here, we obtain very strong support for our expectations.27 In the
25 The comparison of transition probabilities in Tables 8 and 9 for other combinations of income level and oilshow that Hypothesis 2A has some support, but Hypothesis 1A and 3A do not.26 The results shown in Tables 8 and 9 show that this tendency is stronger in low-income oil economies.27 Table 1 shows that there are many more observations of transitions underlying these probabilities—e.g., 18from low-corruption to high-corruption autocracy and 47 from low-corruption to high-corruption democracy.
Table 4 Comparing transition probabilities: stability of regime type
Hypothesis Comparison Stable p Unstable p Difference p value
Probability of transition between selected regime types by level of corruption
H1A (autocracies) pαη>pAH 0.081 0.079 0.001 0.49
H2A (hybrid) pηα>pHA 0.019 0.014 0.005 0.31
H2A (hybrid) pηδ>pHD 0.067 0.033 0.034 0.097
H3A (democracies) pDH>pδη 0.011 0.007 0.004 0.28
Stability of high-corruption state versus low-corruption state
Transition probabilities are calculated for median-income, nonoil-dependent countries within the 1997–2000period. All transition probabilities are reported in Table 11. Also, see Tables 8 and 9
St Comp Int Dev
upper panel of Table 5, we see that the predicted probabilities of increases incorruption levels are significantly higher in autocracies and in hybrid regimesthan in democracies. The estimated probability pαA of increase in corruption inautocracies is 0.140, and pηH in hybrid regimes is 0.118. Both of these aremuch higher than pδD=0.033 in democracies. The difference is clearly signifi-cant. In the lower panel, we show that the probability of reducing corruption isalso much lower in autocracies and hybrid regimes (0.012 and 0.043, respec-tively) than in democracies (0.169). The probability of reducing corruption isalso somewhat higher in hybrid regimes than in autocracies.
Conclusion
This is the first article to examine statistically the reciprocal relationshipbetween corruption and political institutions. Much of the existing literaturefocuses on how political institutions influence corruption levels. Since theincumbent and the citizenry have conflicting interests with regard to corruptexchanges, the level of corruption is a function of how states’ formal politicalinstitutions regulate these actors’ access to power and the ability of the citi-zenry to monitor public officials (Persson, Tabellini and Trebbi 2003; Gerringand Thacker 2004; Tavits 2007). Simultaneously, however, political corruptionworks as an informal institution that generates incentives and constraints forpolitical actors and thus also shape institutional outcomes. We argue thatincumbents rely on corrupt exchanges to strengthen their own hold on powerand that political corruption can entrench a regime faced by pressures toliberalize by substituting for loss of power in the formal political institutionsof the state.
To model the reciprocal relationship between political corruption and institutions,we estimate a multinomial logistic regression model where six combinations of regimetypes and corruption levels are the outcomes and transitions between these six states aremodeled “dynamically” with lagged dependent variables. In line with the rich qualita-tive literature, the results indicate that high-corruption autocracies and hybrid regimesare more stable than their low-corruption counterparts, but that low-corruption
Table 5 Comparing transition probabilities: sustainability of corruption
Hypothesis Comparison Unstable p Stable p Difference p value
Probability of increase in corruption levels
H1B,3B autocracy vs. democracy pαA>pδD 0.140 0.033 0.107 0.010
H2B,3B hybrid vs. democracy pηH>pδD 0.118 0.033 0.085 0.0012
Probability of decrease in corruption levels
H1B,3B autocracy vs. democracy pDδ>pAα 0.169 0.012 0.157 0.0009
H2B,3B hybrid vs. democracy pDδ>pHη 0.169 0.043 0.126 0.0069
Transition probabilities are calculated for median-income, nonoil-dependent countries within the 1997–2000period. All estimated transition probabilities are reported in Table 11. Also, see Table 10
St Comp Int Dev
democracies are more stable than high-corruption ones. The model allowsdistinguishing between the two possible directions of causation. For hybrid regimes,the estimates suggest that corruption stabilizes the formal institutions, but corruptionhas a limited impact on the sustainability of democracy and autocracy. We find verystrong evidence that regime type affects corruption, however. Democracies are muchmore likely to decrease corruption than the autocratic and hybrid regimes, and alsomuch less likely to see increased corruption.
Our argument and the results shed new light on the third wave of democ-ratization. The remarkable increase in the number of democracies during thetime frame of our analysis has been accompanied by an increase in the numberof hybrid regimes and possibly an increase in corruption levels as measuredhere. According to our argument, this is not an anomaly, but a deliberateadaption from powerful elites under international pressure to democratize. Thethird wave of democratization may be less strong than it appears from studyingincreases in average levels of formally democratic institutions—an increasingproportion of systems seem to transition into high-corruption hybrid or demo-cratic regimes. Democratic institutions do succeed in curbing corruption, butthis is a fairly slow process. The results have implications for studies of theeffects of democracy on a variety of outcomes that do not take the informalinstitutions into account.
Appendix
Graphs of Steady-State Distributions
05
10
15
De
nsity
0 .2 .4 .6 .8 1
Estimated SS distribution
High-corruption autocracy
01
23
4
De
nsity
0 .2 .4 .6 .8 1
Estimated SS distribution
High-corruption hybrid
01
23
4
De
nsity
0 .2 .4 .6 .8 1
Estimated SS distribution
High-corruption democracy
01
02
03
04
0
De
nsity
0 .2 .4 .6 .8 1
Estimated SS distribution
Low-corruption autocracy
05
10
15
20
De
nsity
0 .2 .4 .6 .8 1
Estimated SS distribution
Low-corruption hybrid
02
46
De
nsity
0 .2 .4 .6 .8 1
Estimated SS distribution
Low-corruption democracy
Fig. 5 Distribution of predicted steady-state distribution, low-income nonoil producers, 1997–2000
St Comp Int Dev
02
46
8
Density
0 .2 .4 .6 .8 1
Estimated SS distribution
High-corruption autocracy
02
46
Density
0 .2 .4 .6 .8 1
Estimated SS distribution
High-corruption hybrid
010
20
30
40
Density
0 .2 .4 .6 .8 1
Estimated SS distribution
High-corruption democracy0
10
20
30
40
Density
0 .2 .4 .6 .8 1
Estimated SS distribution
Low-corruption autocracy0
510
15
20
25
Density
0 .2 .4 .6 .8 1
Estimated SS distribution
Low-corruption hybrid
050
100
150
Density
0 .2 .4 .6 .8 1
Estimated SS distribution
Low-corruption democracy
Fig. 6 Distribution of predicted steady-state distribution, low-income oil producers, 1997–2000
02
46
810
De
nsity
0 .2 .4 .6 .8 1
Estimated SS distribution
High-corruption autocracy
01
23
45
De
nsity
0 .2 .4 .6 .8 1
Estimated SS distribution
High-corruption hybrid
010
20
30
De
nsity
0 .2 .4 .6 .8 1
Estimated SS distribution
High-corruption democracy
010
20
30
40
De
nsity
0 .2 .4 .6 .8 1
Estimated SS distribution
Low-corruption autocracy
05
10
15
De
nsity
0 .2 .4 .6 .8 1
Estimated SS distribution
Low-corruption hybrid
05
10
15
De
nsity
0 .2 .4 .6 .8 1
Estimated SS distribution
Low-corruption democracy
Fig. 7 Distribution of predicted steady-state distribution, middle-income oil producers, 1997–2000
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St Comp Int Dev
Hanne Fjelde is Assistant Professor at the Department of Peace and Conflict Research, Uppsala Universityand senior researcher at the Peace Research Institute Oslo (PRIO). Her research interests include regimestability; the relationship between political institutions and armed conflict; electoral violence; and the behaviorof warring actors in civil wars. Her research has been published in Journal of Conflict Resolution, Journal ofPeace Research, and Political Geography.
Håvard Hegre is Dag Hammarskjöld Professor of Peace and Conflict Research at Uppsala University andResearch Professor at Peace Research Institute Oslo (PRIO). His research interests include democratization,the relationship between democratization, development, and armed conflict, economic, social, and politicalconsequences of armed conflict, forecasting of political events, and statistical methodology. His work has beenpublished in American Political Science Review, American Journal of Political Science, Journal of ConflictResolution, and Journal of Peace Research. Also see http://havardhegre.net.
