Abstract
I examine how the structure of corrupt exchanges between voters and politicians—an important-yet-underexplored form of informal institutions—shapes voters’ electoral behavior toward corruption. I argue that when voters have a clear idea of whom to bribe to secure desired services and how much they need to offer, they are less likely to engage in corruption voting and hold corrupt incumbents electorally accountable for their malfeasance. Utilizing the World Business Environment Survey on corruption predictability and the Asian Barometer survey on voters’ electoral behaviors, I report empirical evidence that institutionalized corruption promotes greater electoral tolerance of corrupt politicians in Asian democracies. The results hold against a number of robustness checks. The paper thus furthers our understanding of the effect of informal political institutions on corruption voting as well as Asia’s corruption exceptionalism.
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Notes
Perhaps one of the most illustrative examples of this paradox is Tanaka Kakuei. The then-prime minister of Japan collected career-high votes from his constituency and was reelected to the Diet 15 consecutive times after the 1983 “Tanaka Verdict Election”, despite being tried in court for bribery during the infamous Lockheed scandal (Johnson 1986).
Reed (2005) finds that legislators in Japan who were convicted of corruption actually enjoy post-conviction vote-share increases. Similarly, Peters and Welch (1980) and Chang et al. (2010) find that, even in light of evidence of corruption, voters in American and Italian legislative elections failed to penalize corrupt legislators. Relatedly, Karahan et al. (2006) argue that as the value of office holding increases, politicians are more likely to spend more efforts enticing citizens to vote. Using a unique dataset from an FBI investigation in a statewide corruption scandal, they find that voters in Mississippi are more likely to vote in counties with higher levels of corruption.
Kurer (2001, p. 79), for instance, suggests that “…ignorance about the effects of corruption, about the alternatives available, and about the intentions of those standing for election are possible reasons for voters to support corrupt politics.” Olken (2009) examines Indonesian villagers’ knowledge and beliefs about local corruption, finding that Indonesian villagers are only vaguely aware of the existence and effects of corruption.
In their study of Brazil’s municipal elections, Ferraz and Finan (2008) find that incumbents subjected to anti-corruption audits before elections did not suffer significant electoral penalties. But after accounting for the level of corruption revealed in the audit, the authors find that corruption audits strongly reduce the incumbent’s chance of reelection. Similarly, Winters and Weitz-Shapiro (2013) show that voters are less likely to support corrupt candidates after receiving credible information about such behavior. Finally, Chang et al. (2010) show that Italian voters punished corrupt politicians only in the 1992–1994 legislature after the systematic corruption in Italy had been unearthed by the Clean Hands operation in the early 1990s.
The famous quote “[Y]es, but he’s our bastard” by Franklin D. Roosevelt illustrates this line of reasoning.
Philp (2002), for instance, highlights several basic components of corruption: it involves a public authority, it violates the public trust, it harms the public interest, and it benefits a third party by providing access to a good or service the third party could not otherwise obtain.
The other two are the frequency with which transactions recur and the condition of asset specificity.
Méon and Sekkat (2005) explicitly test and refute the “grease the wheels” hypothesis and show that corruption becomes even more detrimental to growth when the quality of governance is poor. Similarly, Cooray and Schneider (2018) further corroborate Méon and Sekkat’s study and find that corruption sands the wheels of financial sector activity.
The calculus of voting model takes the form of V = B*P − C, where the probability of a person voting (V) is a function of the benefits from her preferred candidate getting elected (B) and the probability of casting the decisive vote (P), minus the costs of voting (C).
Illustratively, consider that for Mr. Tanaka’s supporters in Niigata City, the term BI includes a high-speed train connecting Niigata to Tokyo, a new railroad station, and an atomic power plant (Johnson 1986).
Consistent with the principal-agent model, the calculus of corruption voting model assumes that voters believe that politicians desire to be corrupt. If politicians prefer corruption, then, voters have to decide if they want politicians who can coordinate competently with bureaucrats and make corruption predictable, or if they prefer politicians who cannot coordinate and consequently throw the corruption structure into chaos.
Authors' interview with a Taiwanese legislator.
As Levitsky (1998, p. 80) argues succinctly, “institutionalization is a process by which actors’ expectations are stabilized around rules and practices…. The entrenchment of ‘rules of the game’ tend to narrow actors’ behavioral options by raising the social, psychic, or material costs of breaking those rules.”
Chang and Kerr (2017) treat corruption tolerance as citizens’ proclivity to condemn a political actor’s engagement in corruption. Other studies also have used both elites’ and citizens’ willingness to sanction corruption as an indication of the extent to which corrupt acts are tolerable (Heidenheimer 2002).
Levitsky (1998, p. 88) defines value infusion as “a process in which organizational actors' goals shift from the pursuit of particular objectives through the organization to the goal of preserving or perpetuating the organization per se”.
Phrase borrowed from Janda (1980, p. 19).
Specifically, predictability = 7 − 0.5*(country average for Q.15 + country average for Q.16).
One reasonably can expect that the levels of predictability across different types of corruption are likely to be correlated since political elites have incentives to discipline all types of public official to maintain the overarching structure of the corruption network. Indeed, as Fisman and Golden (2017) suggest, bureaucrats often are organized into corruption networks by politicians and the mutual dependence between the two groups often forms a corruption hierarchy wherein political and bureaucratic corruption are strongly intertwined. However, other scholars can counter-argue that corruption predictability varies depending on the types of corruption and the participants involved.
To maximize the available information, I rely on the most current wave of ABS data and I focus on democracies that Freedom House considers free (Indonesia, Japan, Mongolia, the Philippines, South Korea and Taiwan) and partially free (Hong Kong, Malaysia, Singapore and Thailand). My results also hold if I drop the partially free countries and focus only on the free countries.
Each item is scaled on a 1–4 range (one = very difficult; four = very easy).
This proxy has obvious weaknesses. While one reasonably can assume that voters in predictable corruption structures will find public services accessible, the reverse might not hold, as many alternative factors might affect access to public services. Despite the potential measurement errors, the proxy remains informative as it taps into respondents’ perceptions of public services provision. At a more practical level, access to public services is the best option available for capturing the notion of corruption predictability in the ABS data. Statistically, it represents a conservative test as one would expect that a more accurate measure would yield even stronger results.
The variable asks citizens to rate the overall economic condition of their country today (one = very bad, five = very good).
The variable asks respondents how fair they think the distribution of income is in their country (one = very unfair, four = very fair).
The variable asks respondents how well they think the government responds to citizens’ needs (one = not responsive at all; four = very responsive).
The variable asks respondents whether they agree with the statement: “Between elections, the people have no way of holding the government responsible for its actions” (one = strongly agree; four = strongly disagree).
The binary variable female takes the value of one if the respondent is that sex.
To ease interpretation, I recode public services access into a binary variable.
For instance, when bureaucrats independently raise the bribe price for passing the written driving test, they can discourage citizens from taking the subsequent road test.
To facilitate interpretation, I recode the bureaucratic professionalism variable so that countries with values higher (lower) than the middle category are considered high- (low-) professionalism countries.
Graf Lambsdorff (2002) uses data from the World Bank to substantiate his arguments, showing empirically that corruption predictability indeed contributes to more corruption.
As Keefer (2007) notes, the fundamental challenge in governance for young democracies is political competitors' inability to make credible pre-electoral promises.
Lee and Oh (2007) classify countries into four different categories: high in pervasiveness, but low in arbitrariness (Indonesia and China); low in both pervasiveness and arbitrariness (Hong Kong and Singapore); high in both pervasiveness and arbitrariness (India); and low in pervasiveness, but high in arbitrariness (Malaysia).
One typical example is the exchange of legal campaign contributions for legislation favoring specific groups at the expense of general public. Dincer and Johnston (2015) show that most American citizens equate campaign donations with corruption.
For instance, politicians might find it more difficult to deliver a public procurement contract as large as originally promised.
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Acknowledgements
This research was supported by the National Research Foundation of Korea (NRF-2017S1A3A2066657). A previous version was presented in the Comparative Politics Workshop in the Department of Political Science at Michigan State University, the Department of Economics at Illinois State University, the Department of Political Science and Diplomacy at Sungkyunkwan University in South Korea, and the Institute of Political Science at Academia Sinica in Taiwan. I thank all of the participants for their helpful comments. Finally, I am indebted to Jessica A. Schoenherr for her indispensable research assistance.
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Chang, E.C.C. Corruption predictability and corruption voting in Asian democracies. Public Choice 184, 307–326 (2020). https://doi.org/10.1007/s11127-019-00760-x
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DOI: https://doi.org/10.1007/s11127-019-00760-x