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Lustration Matters: a Radical Approach to the Problem of Corruption

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Abstract

Do radical anticorruption measures such as lustration reduce corruption by systematically limiting the political participation of former authoritarian actors? While research has largely overlooked the role of transitional justice in addressing corruption, some scholars claim that lustration may increase corruption by reducing bureaucratic expertise. Analyzing original panel data from 30 post-communist states from 1996 to 2011, we find that lustration is effective in lowering corruption. Lustration disrupts the political, economic, and administrative malpractice of the preceding regimes by limiting opportunities for corruption of former communist elites. To illuminate the causal mechanism, we examine the cases of Estonia, which has adopted lustration and lowered corruption; Georgia, which has reduced corruption since first considering lustration; and Russia, which has not adopted lustration and maintains high levels of corruption. This study breaks new ground with a novel system-level explanation and an integrative approach to causation for the entire post-communist world.

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Notes

  1. Quotations are from interviews conducted in Russia in 2011; identity withheld at interviewees’ request.

  2. The Soviet nomenklatura emerged from the old Russian patrimonial rule, relying on a long-existing practice of “blat.” Based on personal networks for obtaining goods and for circumventing formal procedures (Ledeneva 2006), blat was seen as necessary to compensate for the inefficiencies of socialism and to adjust to a harsh state system. Similar informal networks existed in other communist societies.

  3. As we presume that lustration removes or constrains those usually responsible for corruption from not only office but also access to rents, two counter-arguments should be considered. First, a prior cause may be responsible for both higher lustration and lower corruption. However, research shows that, for example, the degree of rupture with the previous system, such as elite turnover (McFaul 2002) or displacement of former communist incumbents in first elections (Fish 1997), is a not significant predictor of lustration or corruption while controlling for each (Rožič 2012). Second, while lustration usually addresses high officials, lustration could miss its target as an anticorruption measure since many of these officials would have left politics for the economic sector. Such an argument is difficult to quantify. Lustrative provisions usually do not allow the assessment of whether a law’s respective targets meet their match. In the best of cases, such an approach would require a particularly thorough in-depth analysis. Moreover, we can further speculate that those post-communist officials who moved to the business sector need the support of those remaining in political office, the removal of which (the ladder) would affect the corrupt practices of the former.

  4. Our measure of lustration treats legislative discussions of lustration—even if the plan was rejected and not implemented—as an instance higher on the Lustration Index than instances where lustration was not even discussed. While we do not claim that having rejected a lustration bill leads to cleaner government, we show that the mere process of discussing or attempting lustration makes corrupt officials less secure and powerful.

  5. Without the possibility of minimally open competition granted through democratization, new political elites will have weaker incentives to challenge their old-regime rivals through lustration. Under this view lustration is an outcome of a particular politics of democratization. It may serve the transitional elites to fight political opponents in their power struggle and use lustration as a tool for political gain.

  6. We invert the corruption indices, as demonstrated in the Appendix (CPIN, CCIN, FHCN) and normalize the first two. The higher the score, the more corrupt a country is.

  7. As a legislative or executive decision, “lustration programs” include three characteristics: (1) suspected past involvement, based on collective responsibility for past abuses, which is determined on its own terms by specific lustration programs; (2) protected present or future public positions; and (3) specific methods or procedures (such as screening), which include a potential threat (such as removal or public exposure).

  8. The variation in the Lustration Index may raise theoretical concerns, particularly with regard to the combination of the adoption and screening parts of the variable. While an isolated use of the screening part would be beneficial for statistical analyses, the rich variation among the negative cases in the adoption level would be lost. In order to prove the index as internally consistent, we provide three different levels of statistical models, using separately the LI as well as its adoption and the implementation portions (see Tables 7 and 8, Appendix 2). Moreover, the combination of the two portions is empirically justifiable. Once lustration is debated and potentially adopted, it is usually enforced, and there is a high correlation between the three measures of lustration. Finally, the LI correlates reasonably well with currently available scales of lustration (Nisnevich 2012).

  9. This study’s level of analysis is a domestic and a systemic one. We locate the causes of corruption within specific states and within a system-wide level in all post-communist states. The unit of analysis is a country and observation is a country in a particular year. Case section represents a theoretical leverage since scope conditions are considered as constants and do not feature in the model. We select cases sharing similar structural and historical constraints on corruption. Case selection is partly limited by data availability: corruption indexes are mostly available from 1996.

  10. The random effects use is justified by relevant literature (Podestà 2006) and the Breusch-Pagan multiplier test. For example, for model 2 (Table 2), the test shows that {Prob > chi2 = 0.000}, meaning that there is within-unit variation requiring a random effects approach. The Hausman tests are less useful here due to the invariant nature of several terms in our models for which the fixed-effects estimator cannot provide separate estimates of the parameters. Unlike fixed effects, random-effects coefficients represent average change within units, estimated from all units whether they experience change or not. We nevertheless provide statistical results for fixed-effect models in Table 9 (Appendix 2). Moreover, we evaluate the impact of lustration on corruption through a marginal-effects analysis (Tables 3 and 4 and Fig. 2).

  11. Computing marginal effects means that predictions are calculated at every observed value of x. In order to compute the LI’s marginal effect at different levels, i.e., to uniquely identify margins, the factor variable of i.lustration is used. This also means that with this approach, the LI is treated as a categorical variable, which addresses the concern about the potentially noncontinuous nature of the lustration index as used in the panel regression analysis above.

  12. Lustration as well as other mechanisms of transitional justice have pointed to this nondemocratic tradeoff based on ethnicity. For example, property restitution became a way for political leaders to bolster their competitive advantage. In the Baltics, even political leaders not known for their nationalist tendencies could profit from a restitution program that primarily benefitted the titular nationality (e.g., Estonians in Estonia, Lithuanians in Lithuania) at the expense of ethnic minorities, particularly Russians (Rožič and Grodsky 2015).

  13. In 1992, Deputy G. Starovoitova authored a bill “On the ban on the profession for conductors of policies of the totalitarian regime” for the Supreme Council of the RSFSR that was approved by the III Congress of the movement “Democratic Russia.” In 1997, she again tried to bring this bill to the State Duma, without success.

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Acknowledgments

The authors would like to thank Ana Bračič, Jane Curry, Anna Khakhunova, Tina Letić, Naomi Levy, Kat McCormick, Liz Schwerer, Jack Siberski S.J., and the members of the panel “Economic Corruption and Nepotism in Russia” at the 2013 “Beyond Transition?” conference in Lund, Sweden, as well as the two anonymous readers of SCID for their valuable assistance and suggestions.

Financial Disclosure/Funding

This work was supported by “The National Research University Higher School of Economics Academic Fund Program in 2013-2014” under Grant №12-01-0150; “The Arts and Sciences’ ‘Dean Grant’ at Santa Clara University.” We are not aware that our employers have any financial interest in or a financial conflict with the subject matter or materials discussed in the manuscript.

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Authors and Affiliations

  1. Department of Political Science, Santa Clara University, 500 El Camino Real, Santa Clara, CA, 95054, USA

    Peter Rožič

  2. Department of Politics, Higher School of Economics, Moscow, Russia

    Yuliy A. Nisnevich

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  1. Peter Rožič
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Correspondence to Peter Rožič.

Appendices

Appendix 1: Coding of the Variables and Summary Statistics

Table 5 Overview of variables, definitions, coding, and data sources
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Table 6 Summary statistics of the variables and bivariate Pearson correlation with corruption (CPIN)
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Appendix 2

Fig. 3
figure 3

Two-way quadratic prediction plot between CPIN and LI with confidence interval

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Table 7 Influence of lustration adoption and alternative factors on corruption perceptions
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Table 8 Influence of Lustration Implementation and Controls on Corruption Perceptions
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Table 9 Influence of lustration and controls on corruption perceptions (fixed-effects models)
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Rožič, P., Nisnevich, Y.A. Lustration Matters: a Radical Approach to the Problem of Corruption. St Comp Int Dev 51, 257–285 (2016). https://doi.org/10.1007/s12116-015-9179-1

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