Corruption and the Inequality Trap*
    
    Eric M. Uslaner Department of Government and Politics University of Maryland–College Park College Park, MD 20742 Senior Research Fellow, Center for American Law and Political Science Southwest University of Political Science and Law Chongqing, China
    
    euslaner@gvpt.umd.edu
    
    Uslaner, “Corruption and the Inequality Trap” (1)
    
    Prepared for the Conference on Institutions, Behavior, and the Escape from Persistent Poverty, November 16-17, 2009, Cornell University, Ithaca, NY.
    
    Corruption flouts rules of fairness and gives some people advantages that others don’t have. Corruption transfers resources from the mass public to the elites–and generally from the poor to the rich (Tanzi, 1998). It acts as an extra tax on citizens, leaving less money for public expenditures (Mauro, 1997, 7). Corrupt governments have less money to spend on their own projects, pushing down the salaries of public employees. In turn, these lower-level staffers will be more likely to extort funds from the public purse. Government employees in corrupt societies will thus spend more time lining their own pockets than serving the public. Corruption thus leads to lower levels of economic growth and to ineffective government (Mauro, 1997, 5). Most accounts of the roots and remedies for corruption are institutional. Corruption, most academic and policy analysts argue, stems from bad governmental institutions–especially the lack of democracy, free and unfair elections, and an ineffective judiciary. I argue that institutional accounts of the roots–and the solutions–to corruption are lacking (Uslaner, 2008).. In an extensive six-equation model of corruption across a wide range of societies, I find little support for institututional accounts of corruption. Neither democracy, the structure of a country’s electoral system, whether government is centralized or decentralized (measured by federalism nor the share of a country’s government expenditures spent at the local or national
    
    Uslaner, “Corruption and the Inequality Trap” (2) level) significantly shapes corruption. I outline a different account of corruption here–what I call the “inequality trap.” Corruption rests upon a foundation of unequal resources and it leads to greater inequality in turn. I first present my overall argument and then I consider why transition countries at first seem to be exceptions, but then fit the thesis rather well. Next I argue that inequality and corruption both lead to lower levels of service delivery and that this effect exacerbates the inequality trap. I then show using both aggregate and individual-level survey data that service interruptions in transition countries reflect both inequality and corruption. Failures in public service in turn lead to reduced trust in government, higher tax evasion, and a weaker infrastructure–which in turn exacerbates inequality. Inequality and Corruption The link between inequality and corruption seems compelling. Corruption is exploitive. Not all corruption is linked to inequality. “Grand” corruption refers to malfeasance of considerable magnitude by people who exploit their positions to get rich (or become richer)–political or business leaders. So grand corruption is all about extending the advantages of those already well endowed. “Petty corruption,” small scale payoffs to doctors, police officers, and even university professors, very common in the formerly Communist nations of Central and Eastern Europe (and many poor countries) is different in kind, if not in spirit. Petty corruption, or “honest graft” as New York City political boss George Washington Plunkitt called it (Riordan, 1948), does not enrich those who practice it. It may depend upon an inequitable distribution of wealth–there should be no need to make “gift” payments in a properly functioning market economy.
    
    Uslaner, “Corruption and the Inequality Trap” (3) Inequality promotes corruption in many ways. Glaeser, Scheinkman, and Schleifer (2002, 2-3) argue: ...inequality is detrimental to the security of property rights, and therefore to growth, because it enables the rich to subvert the political, regulatory, and legal institutions of society for their own benefit. If one person is sufficiently richer than another, and courts are corruptible, then the legal system will favor the rich, not the just. Likewise, if political and regulatory institutions can be moved by wealth or influence, they will favor the established, not the efficient. This in turn leads the initially well situated to pursue socially harmful acts, recognizing that the legal, political, and regulatory systems will not hold them accountable. Inequality can encourage institutional subversion in two distinct ways. First, the havenots can redistribute from the haves through violence, the political process, or other means. Such Robin Hood redistribution jeopardizes property rights, and deters investment by the rich. Similarly, You and Kaghram (2005, italics in original) argue: “The rich, as interest groups, firms, or individuals may use bribery or connections to influence law-implementing processes (bureaucratic corruption) and to buy favorable interpretations of the law (judicial corruption).” Inequality breeds corruption by: (1) leading ordinary citizens to see the system as stacked against them (Uslaner, 2002, 181-183); (2) creating a sense of dependency of ordinary citizens and a sense of pessimism for the future, which in turn undermines the moral dictates of treating your neighbors honestly; and (3) distorting the key institutions of fairness in society, the courts, which ordinary citizens see as their protectors against evil-doers, especially those with more
    
    Uslaner, “Corruption and the Inequality Trap” (4) influence than they have (see also Glaeser, Scheinkman, and Schleifer, 2003; and You and Khagram, 2005). Economic inequality creates political leaders who make patronage a virtue rather than a vice, since it provided jobs for ordinary citizens. These leaders help their constituents, but more critically they help themselves. Inequality breeds corruption–and to a dependency of the poor on the political leaders. Inequality leads to clientelism–leaders establish themselves as monopoly providers of benefits for average citizens. These leaders are not accountable to their constituents as democratic theory would have us believe. There may well be the trappings of democracy, with regularly scheduled elections, so that the link between democratic and honest government may not be as strong as we might initially expect.1 The political boss is well entrenched in his position. His party reigns supreme in the area. Potential opponents don’t have the resources to mount a real challenge–and, even if they tried, the boss can count on the support of the legions whose jobs he controls through his patronage machine. Unequal wealth leads people to feel less constrained about cheating others (Mauro, 1998, 12) and about evading taxes (Oswiak, 2003, 73; Uslaner, 2003). Where corruption is widespread, people realize that they are not the masters of their own fate–and they lose faith that their future will be bright. People become resigned to their fate. In the World Values Survey waves 1-3 (1981, 1990, 1995-97), respondents who believed that corruption was widespread in their country were significantly less likely to believe that they could get ahead by hard work rather than by luck or having connections. The zero-order correlation is modest (as we might expect with a sample of almost 60,000, tau-b = .061)–but 34 percent of people in societies where
    
    Uslaner, “Corruption and the Inequality Trap” (5) corruption was seen as widespread thought the only way you could get ahead was by luck, compared to 29 percent in honest societies. Economic inequality is not the only distributional issue affecting corruption. Equality of treatment under the law matters as well. If people feel that they have been treated unfairly by the police or in the courts, they are less likely to have faith in the legal system. The justice system is especially important for two reasons. First, a corrupt court system can shield dishonest elites from retribution. Second, the courts, more than any other branch of the polity, is presumed to be neutral and fair. We appeal “unjust” decisions to the judiciary–and our vernacular includes the phrase “court of last resort,” suggesting that somewhere there must be justice. Rothstein and Stolle (2002) argue that there are two dimensions to the legal system: fairness and efficiency. Fairness is the key to the connection between law and corruption because it reflects the advantages that some people have over others. The efficiency of the courts should not matter so much for corruption–since rounding up the corrupt leaders and putting them in jail only makes room for a new group of miscreants, doing little to address the underlying causes of corruption. When the legal system is fair–when people see the courts in particular as fair–they will expect that the rich and poor will receive equal treatment and that corrupt officials will be unlikely to get away with their misdeeds. Some Preliminary Evidence While the dominant explanations for corruption are institutional, there is at least one key reason why structural accounts are wanting. Institutions have been rather malleable across the world in recent decades as democratization has spread across transition and developing nations. The r2 for political rights using the Freedom House data from 1973 to 2003 is .165 and for civil
    
    Uslaner, “Corruption and the Inequality Trap” (6) liberties it is .263 (both N = 77). Even excluding countries that were Communist in 1973, the respective r2 values increase only to .264 and .375 (N = 67). More critically, changes in political rights and civil liberties from 1973 to 2003 are unrelated to changes in corruption from 1980-85 to 2004 ( r2 = .007 and .038 respectively, N = 38). Moving the democratization measures forward to 1988 does not improve the fit with changes in corruption ( r2 = .004 and .0005 for political rights and civil liberties, N = 39). The major components of the inequality trap–inequality, trust, and corruption–are rather sticky. They do not change easily because each breeds the other. The r2 between generalized trust from the 1980 and 1990-1995 World Values Surveys is .81 for the 22 nations included in both waves. Inequality similarly moves little over time. The r2 for the most commonly used measures of economic inequality (Deininger and Squire, 1996) between 1980 and 1990 is not quite as strong as the connection with trust over time, but it is still substantial at .676 for a sample of 42 countries. A new inequality data base developed by James Galbraith extends measures of inequality further back in time and across more countries.2 The r2 between economic inequality in 1963 and economic inequality in 1996 is .706 (for 37 countries). The r2 between the Transparency International Corruption Perceptions Index for 2003 and the ICRG measure for 1980-85 (even though they are not directly comparable) is .785 for 49 countries. More critically, changes in political rights and civil liberties from 1973 to 2003 are unrelated to changes in corruption from 1980-85 to 2004 ( r2 = .007 and .038 respectively, N = 38). Moving the democratization measures forward to 1988 does not improve the fit with changes in corruption ( r2 = .004 and .0005 for political rights and civil liberties, N = 39). The linkage between corruption and inequality is not much stronger. The r2 is a paltry
    
    Uslaner, “Corruption and the Inequality Trap” (7) .082 across 85 countries, suggesting no relationship at all between inequality and corruption. When I remove the former and present Communist regimes, there is a moderate fit between the two indicators ( r2 = .246, N = 62) when the former and present Communist countries are excluded. With a bivariate r2 of this magnitude, it should not take much effort to see it vanish in a multivariate analysis. So I suggested that the relationship between inequality and corruption is indirect–through generalized trust, trust in strangers. Inequality is the strongest predictor of trust across nations without a legacy of communism, in the United States over time, and across the American states (Uslaner, 2002, chs. 6, 8; and Uslaner and Brown, 2005). And high levels of trust are strongly related to low levels of corruption (Uslaner, 2008, 45-53).. Across nations, I thus suggest that the relationship between inequality and corruption is indirect. I estimated a six-equation model of corruption, inequality, trust, regulation of business, the overall risk of a country’s economy and polity, and a measure of government effectiveness (Uslaner, 2008, 63-74) and the fairness of the legal system is the only institutional variable that is a significant predictor of corruption. Legal equality is not the same as economic equality (the two are moderately correlated) nor the same as the efficiency or the size of the judiciary. The transition countries stand out as the major exception to my argument linking high inequality through low generalized trust to much corruption. Yes, they have high levels of corruption–former and the handful of still Communist countries on average are more corrupt than either the West (by far) or the developing nations. And yes, they have low levels of trust– slightly higher than developing countries but much lower than the West. Yet, they have on average the lowest levels of inequality–marginally less than the West but far lower levels than developing countries.3
    
    Uslaner, “Corruption and the Inequality Trap” (8) Yet, there are many reasons to believe that the inequality trap argument fits transition countries well.4 First, measures of economic inequality are based upon official statistics and do not take into account the great wealth of a handful of officials at the top, who had privileges unavailable to ordinary citizens. Second, while inequality has been historically relatively low in transition countries, there have been sharp increases in the uneven distribution of wealth since the Communist regimes fell in the late 1980s. Two different data bases tell largely the same story: The Rosser, Rossser, and Ahmed (2000) data on income distribution show an increase in economic inequality from 1989 to the mid-1990s for every country save one (Slovakia). The more recent WIDER estimates indicate substantial increases in inequality–an average change of 78 percent from 1989 to 1999–for each of 21 countries. The rise in inequality was accompanied by an increase in the shadow economy (Schneider, 2003). Even the best performing economies, Slovakia and the Czech Republic, had almost 20 percent of their revenue off the books. Three countries had a majority of their revenue in the informal sector (Ukraine, Azerbaijan, and Georgia) and 15 of 21 countries for which there are data have at least a third of their income in the shadow economy. Even more distressing is that 16 of the 18 countries for which there are data experienced increases in the shadow economy of at between 10 and 42 percent; only one country (Hungary) had a (very slight) decrease while another (Slovenia) experienced no change. Not only did inequality increase, but more people had to rely upon the informal sector. The greater the share of the economy beyond the reach of the state, the more difficult it will be for a government to marshall the resources to gain public confidence that the state can provide essential services. Overall, the average share of the shadow economy more than doubled
    
    Uslaner, “Corruption and the Inequality Trap” (9) from 1989 to 1999-2000 (from 17 percent to 38 percent) and the average increase in the Gini index of inequality was 33 percent. Corruption remains a persistent problem. In 2004, every transition country had a higher level of corruption than any Western country. The 2005 scores show sharp leaps in honesty for Estonia and Slovenia (atypical for this index)–outranking Greece and Italy and tied with Israel among Western nations. However, excluding Estonia and Slovenia, the mean for East bloc countries is lower than for developing nations. All of the 11 formerly Communist countries ranked by Transparency International in 1998 had more corruption in 2004 (Uslaner, 2008, 105-106, 270). There is only a moderate amount of consistency from 1998 to 2005 (r2 = .543, N = 12), but far greater for the larger sample between 1999 and 2005 (r2 = .832, N = 24). The public in transition countries sees corruption as a long-term, insoluble problem: In a 2005 survey, just eight percent of Russians held that corruption can be eliminated “if dishonest leaders are replaced with honest ones,” while 26 percent hold that “Russia has always been characterized by bribery and embezzlement, and nothing can be done about it” (Popov, 2006; cf. Karklins, 2005, 59 for a more general statement on transition countries). Inequality seems to matter more for the transition countries as a determinant of corruption in more recent years. Inequality (together with perceptions that courts are not fair, GDP per capita, and the openness of the economy) is a significant predictor of corruption for the transition nations. There is a more powerful relationship between corruption and change in economic inequality: Corruption is a significant predictor as well of increases in inequality in these nations (Uslaner, 2008, 108-111).
    
    Uslaner, “Corruption and the Inequality Trap” (10) An unfair legal system predates the fall of Communism in Central and Eastern Europe. Under communism, legal fairness was a vain hope. As corruption and inequality have increased, so have perceptions that the legal system is unfair (Uslaner, 2008, 97-98). The growth of the informal economy is a sign that the transition to a market democracy did not lead to a more fair legal system. At the top of the shadow economy, the rich evade taxes. At the bottom the workers in the shadow economy have no legal rights. Rising economic inequality makes people more skeptical of the fairness of the legal system. People see a clear connection between the maldistribution of both income and legal fairness and corruption: I find strong support for these linkages in surveys in Romania and Estonia (Uslaner, 2008, chs. 5, 6). Ordinary citizens (far more than elites) believe that you can’t get rich without being corrupt and that corruption plays a large role in promoting more inequality. When Russian oil entrepreneur Mikhail Khodorkovsky confessed his sins of relying on “beeznissmeny” (stealing, lying, and sometimes killing) and promised to become scrupulously honest in early 2003, Russians regarded this pledge as “startling.” When he was arrested and charged with tax evasion and extortion under orders from President Vladimir Putin ten months later, the average Russian was unphased: About the same share of people approved of his arrest as disapproved of it (Tavernise, 2003). The arrest of Khodorkovsky stands out as exceptional: Corrupt officials and business people are rarely held to account. While crime spiraled in Russia after the fall of Communism, conviction rates plummeted (Varese, 1997). Service Failure, Corruption, and Inequality Corruption acts as a tax on the poor. The well-off can afford bribes, but the poor often do without basic services. And corruption robs the state of resources for providing basic services to
    
    Uslaner, “Corruption and the Inequality Trap” (11) all citizens, but especially the poor. People who turn to the informal economy have few legal rights (their employment is not legal and there are no contracts or unions representing workers in the informal sector). Corruption is particularly rampant on those services the poor most depend upon: the police, the schools, and the medical sector. Countries with high levels of corruption have poor service delivery. The failure of corrupt states with rising inequality to provide basic services illustrates the inequality trap: The wealthy have options to protect themselves against the failure of public services. They may bribe local authorities to ensure that their services are fixed first. They may not have to rely exclusively upon state-provided services. The poor cannot afford bribes. Nor do they have the option of using alternative services. When governments don’t have the resources to provide services, the poor will suffer more. I turn to three analyses to examine the linkage between service interruptions in transition countries and both corruption and inequality. First, I examine the measure of public service deterioration in the Failed States dataset for 2007.5 The Failed States index is a measure of the capacity of a polity to maintain order and to deliver essential services of the “vulnerability to collapse or conflict.”6 A key component of the index is the deterioration of public services, which is essential both for ordinary citizens and for businesses in a newly privatized economy. Does the failure of a state to provide essential public services stem from corruption and inequality? I also examine the 2005 Business Environment and Enterprise Performance Survey (BEEPS) of the European Bank for Reconstruction and Development and the World Bank.. BEEPS 2005 is a survey of business people in 26 transition countries (all except Turkmenistan). It is not a survey of the mass public, but it does focus on questions of service delivery and
    
    Uslaner, “Corruption and the Inequality Trap” (12) corruption, both of which are essential for the growth of business and the economy more generally. The survey asked respondents how many days a year they faced interruptions of service in water (low supplies), phones (no service), and electricity (power outages). I first estimate an aggregate regression model of levels of service interruptions. Then I turn to individual-level analyses of perceptions of poor service delivery in each of the three areas. For both the aggregate and individual-level models, my central focus is whether inequality and corruption–both the “objective” perceptions measures of Transparency International and the “subjective” perceptions of survey respondents–lead to higher levels of reported service interruptions. First, consider the aggregate model for the State Failure measure of service deterioration, available for 21 countries. I present the model in Table 1. I use three predictors, which collectively account for almost 90 percent of the variance: The 2005 Transparency International Corruption Perceptions Index (higher scores mean less corruption), the change in inequality from 1989 to 1999 as estimated by the United Nations University World Institute for Development Economics Research (WIDER).7 I chose the years 1989 and 1999 to get a measure of inequality at transition, first, and to maximize the number of available data points. Finally, I include the 2003 composite Freedom House democratization index to test for institutional effects. _______________ Table 1 about here The story is simple: All three variables matter. While democratization may not lead to less corruption, it does lead to better service delivery. But corruption and inequality change matter as well. Countries with higher levels of corruption have worse service delivery. And
    
    Uslaner, “Corruption and the Inequality Trap” (13) increasing inequality is also significantly associated with deteriorating public services. What is notable is that inequality per se doesn’t seem to matter but rising inequality has a strong impact on service delivery. As countries become more corrupt, they have fewer resources to deliver these services. As states become more stratified economically, there seems to be a lack of will to provide public goods to all. The pattern repeats itself in the aggregate model for the 2005 BEEPS data. There are three measures of service interruption: low water supply, lack of phone service, and power outages. I aggregated the survey data to the country level. I focus on three variables, one derived from the BEEPS data and indices of corruption and change in inequality.8 From the BEEPS 2005 data, I aggregated the perception of confidence that the legal system will enforce contracts and property rights. _______________ Table 2 about here Across all three measures of service interruptions, change in inequality and corruption are significant. The most powerful effect of inequality appears to be for low water supply–though the simple regression coefficient for power outages is the greatest (but so is its standard error). Service interruptions are not very common for any of the three measures, but power outages are more frequent (6.1 days a year) with phone interruptions the least common (1.5 days) and low water supply in the middle (2.6). Reported telephone outages are relatively rare–with a mean number of days of only 1.24, compared oto 4.72 for low water and 12.45 for power. Yet power outages are infrequent in most countries, with two outliers: Georgia at 57 days and Kyrgyzstan at 14.5. Of the three forms of service interruption, inequality matters least for telephone
    
    Uslaner, “Corruption and the Inequality Trap” (14) service–perhaps because telephones are not as ubiquitous as is reliance on water and power. And it matters most for power.9 Corruption seems to follow the same pattern: The greatest impact is on power, followed by water supply and phones. Regulating contracts and protecting property rights is only significant for power and water. Service interruptions, even if uncommon, are more frequent where inequality has been rising, corruption is rampant, and the courts do little to enforce rights. A weak legal system means that people have few opportunities to challenge service disruptions. Corruption robs the state of resources and provides for many opportunities for officials to withhold services unless they get bribes. Rising inequality means that some people are better able to get services restored–or to go outside the grid–than others. The aggregate results receive strong confirmation from the individual-level analysis of the survey data. I estimate a negative binomial model for the three forms of service disruption: The data are counts and there is substantial overdispersion (confirmed by the significant alpha and ln alpha measures), so neither tobit nor Poisson models are appropriate. I estimate two models for each form of service interruption. Common to the models are four measures from the survey: (1) whether courts are fair; (2) whether the Mafia is an obstacle to business; (3) whether economic instability is an obstacle to business; and (4) how often respondents have to make “gift” payments to public officials to obtain routine services. In the first set of models, I include the change in inequality and the change in the Transparency International Corruptions Perception Index from 2002 to 2004 as country-level variables. In the second set of models, I replace these measures with an indicator of the size of the unofficial economy from World Bank economists (Johnson et al., 1999, 184). The unofficial
    
    Uslaner, “Corruption and the Inequality Trap” (15) economy is strongly correlated with inequality change even in the survey data ( r = .766, N = 3772), so I estimate the models separately. I only report the coefficients for other variables in the first model, since there is not much difference for the other variables. I cluster the standard errors by country and report the results in Table 3.
    
    _______________ Table 3 about here There is much greater variation in reporting of service interruption in the individual level responses than in the aggregated data. Over 85 percent of respondents never reported experiencing interruptions for either water or phones, though a handful (.67 percent) said that they had “interruptions” every day. “Only” 58 percent of respondents never had power outages, while one percent lacked service every day. In the first set of models, there is clear support for the arguments that both change in inequality and change in corruption lead to more frequent reports of service interruption–for water, telephones, and power. Changes in corruption seem to matter more for low water supply and power than for phones–perhaps because the state has greater monopoly power over water and power than over phones. Change in inequality seems to affect phone service more–perhaps because telephone service may be less available to those at the bottom of the economic ladder. People living in countries with greater informal economies are substantially more likely to report sservice interruptions–especially for water and power. A larger informal economy means more people living off the grid–the power grid as well as the legal one. Indeed, one of the key indicators of the size of the informal economy is the “physical input” method, which
    
    Uslaner, “Corruption and the Inequality Trap” (16) compares the amount of electricity consumed with the amount billed (Schneider and Enste, 2000). A large informal sector means that public utlities will be “stressed” as people use more capacity than utilities routinely provide to their paying customers–and because there is no routine way for utilities to measure expected demand. People living off the grid but tapping into it may find their services “interrupted” when utitilies discover this poaching and cut down their illegal lines. On the other hand, telephone service is less susceptible to the informal economy because it is so much easier to block service to people who try to tap into a phone line. For two of the three measures, fair courts lead to fewer service interruptions. The exception is for power outages–with no obvious explanation except that this is the only service for which gift payments to public servants are significant. Petty bribes to public servants seem to reduce the level of power outages (which may reduce the need to go to court). They are not significant elsewhere. Organized crime leads to greater service interruptions only for low water supply. We see the same pattern for economic instability. It seems that low water supply may be a political and economic tool for corrupt leaders to ensure that businesspeople make the expected payments. Power outages may simply reflect poor infrastructure that may be traceable to corruption and uncertain demand. The larger story is that all three estimations point to the central role of both corruption and inequality in shaping poor service delivery. Corruption redistributes resources from the poor to the rich and thus is a great source of envy, especially in the transition countries where egalitarianism has long been a widely held value. Reprise The service interruptions in transition countries (and elsewhere) are not simply a matter
    
    Uslaner, “Corruption and the Inequality Trap” (17) of going without basic amenities for a few days a year. Because they stem from corruption and inequality, they add to the mounting inequalities we see developing in these states. The rich are less likely to be affected by service interruptions and to be affected negatively by corruption overall. Beyond increasing inequality, poor service delivery leads to a loss of faith in the political system and a greater likelihood of tax evasion. A principal cause of withholding tax payments is the belief that one isn’t getting quality services from government (Hanousek and Palda, 2000; Torgler, 2003; Uslaner, in press). In the 2005 BEEPS individual-level data, more frequent service interruptions are strongly related to tax evasion. Respondents who reported more frequent low water supplies, lack of phone services, and more frequent power outages were more likely to say that they paid less than 50 percent of their income to tax authorities ( r = .321, .474, and .386, respectively, N = 7085). Ironically, the strongest zero-order correlation is for lack of phone service–which is the least likely of the three services to be provided by government. Many people may see it, especially in this day when communications are increasingly done by phone and online, as the most essential for a business. People may target their anger at government even when it is not the main culprit. If people withhold their taxes, this adds to the burden of government in providing services. High levels of corruption may lead to poor services, but poor services lead to less confidence in government (Citrin, 1974) and in turn to greater levels of tax evasion–and then in turn to poorer levels of service and more inequality and more corruption.... And to the neverending inequality trap.
    
    Uslaner, “Corruption and the Inequality Trap” (18)
    
    Uslaner, “Corruption and the Inequality Trap” (19)
    
    Table 1
    Determinants of Public Service Deterioration in Transition Countries: State Failure Data
    
    Public Service Deterioriation Variable
    Corruption (TI 2005) Change in Inequality (W IDER) Democratization (Freedom House 2003) Constant R2 S.E.E.
    
    Coefficient
    -.418*** 1.473** -.777*** 4.973**** .896 .480
    
    Standard Error
    .130 .620 .251 .964
    
    t Ratio
    -3.22 2.38 -3.09 5.16
    
    N = 21 * p < .10 ** p < .05 *** p < .01 **** p < .0001
    
    Uslaner, “Corruption and the Inequality Trap” (20)
    Table 2 Determinants of Service Interruption in Transition: Aggregate Models from BEEPS 2005 (Robust Standard Errors)
    
    Low W ater Supply Variable b S.E. t Ratio 4.25 1.69
    
    Lack of phone service b S.E. t Ratio b
    
    Power outages S.E. t Ratio 2.11 2.35
    
    Change in Gini index (W IDER) 1989-1999 Confident legal system enforce contracts & property rights TI Corruption Perceptions Index 2004
    
    5.84**** 3.026**
    
    1.371 1.79
    
    1.520*** .476
    
    .619 .824
    
    2.45 .58
    
    15.220** 19.893**
    
    7.211 8.459
    
    1.577**** -13.368**
    
    .357
    
    -4.20
    
    -.484***
    
    ,199
    
    -2.43
    
    -5.998***
    
    2.029
    
    -2.96
    
    Constant R2 RM SE
    
    6.308 .684 2.030
    
    -2.12
    
    -1.497
    
    3.054
    
    -.49 .424 .981
    
    -72.787**
    
    30.177
    
    -2.41
    
    .535 10.526
    
    * p < .10 ** p < .05 *** p < .01 **** p < .0001
    
    N = 21
    
    Uslaner, “Corruption and the Inequality Trap” (21)
    Table 3 Determinants of Service Interruption in Transition: Individual-Level Models from BEEPS 2005: Negative Binomial Regressions with Standard Errors Clustered by Country
    Low Water Supply Variable Change in Gini index (WIDER) 1989-1999 Courts are fair TI Corruption Perceptions Index Change 2002-2004 Make gift payments to public servants Mafia is obstacle to doing business Economic instability is obstacle to doing business Size of informal economy (World Bank)# Constant alpha ln alpha Wald Chi square Log pseudolikelihood N b 3.091*** -.137*** -.514*** .032 .161* .157** .068**** -2.637 27.156**** 3.302**** S.E. 1.287 .057 .190 .107 .103 .091 .013 2.306 2.470 .091 51.61 -3717.96 4388 * p < .10 ** p < .05 *** p < .01 **** p < .0001 # Estimated in separate model without change in Gini index or change in corruption perceptions index. Other coefficients show little difference. t Ratio 2.40 -2.43 -2.71 .30 1.56 1.73 5.19 -1.14 Lack of phone service b 1.340**** -.095** -.147** .187* .181 .069 .027*** -1.675* 19.431**** 2.967**** S.E. .355 .056 .087 .137 .144 .128 .009 .918 2.142 .110 46.28 -3832.64 4383 t Ratio 3.77 -1.69 -1.69 1.36 1.25 .54 3.15 -1.82 b 1.809** -.032 -.537**** .135** -.113 .088 .066**** .387 6.200**** 1.825**** Power outages S.E. .920 .067 .115 .067 .077 .090 .011 1.548 .571 .092 60.26 -8722.76 4478 t Ratio 1.97 -.08 -4.67 2.01 -1.48 .97 6.21 .25
    
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    Uslaner, “Corruption and the Inequality Trap” (24) Uslaner, Eric M. 2002. The Moral Foundations of Trust. New York: Cambridge University Press. ____________. 2003. “Trust and Civic Engagement in East and West.” In Gabriel Badescu and Eric M. Uslaner, eds., Social Capital and the Transition to Democracy. London: Routledge. _____________. 2008. Corruption, Inequality, and the Rule of Law: The Bulging Pocket Makes the Easy Life. New York: Cambridge University Press. Uslaner, Eric M. and Mitchell Brown. 2005. “Inequality, Trust, and Civic Engagement,” American Politics Research, 31:868-894. Varese, Frederico. 1997. “The Transition to the Market and Corruption in Post-Socialist Russia,” Political Studies, 45:579-596. You, Jong-sung and Sanjeev Khagram. 2005. “A Comparative Study of Inequality and Corruption,” American Sociological Review, 70 (February): 136-157.
    
    Uslaner, “Corruption and the Inequality Trap” (25) NOTES * This paper derives from and extends Uslaner (2008). I am grateful to the Russell Sage Foundation and the Carnegie Corporation for a grant on a related project that is encompassed in my work on the United States and to the General Research Board of the University of Maryland–College Park, for a Faculty Research Award in the Spring 2006 semester; and to Bo Rothstein, Mark Warren, Jong-sung You, Gabriel Badescu, Ronald King, Paul Sum, Kems Adu-Gyan, Michael Bratton, Nick Duncan, John Helliwell, Karen Kaufmann, Lawrence Khoo,, Mark Lichbach, Anton Oleynik, Jon (Siew Tiem) Quah, and Leonard Sebastian for helpful comments and discussions and to Mitchell Brown for research assistance. I am also grateful to the many comments I received as I presented my work at forums across the world. 1. The r2 between the 2003 Transparency International Corruption Perceptions Index and the trichotomized 2003 Freedom House index (not free, partially free, and free) is just .216. 2. The Galbraith data can be obtained at The data can be obtained at http://utip.gov.utexas.edu/web/ . 3. For the full set of nations ranked by Transparency International in 2005, former and present Communist countries averaged 3.42 on the Corruption Perception Index, compared to 7.97 for the West and 3.50 for developing (other) nations (N = 29, 21, and 110, respectively). On trust (imputed), the East bloc averaged .234, developing nations .220, and the West .388 (N = 25, 39, and 30, respectively). For the World Bank Gini index, the present and former Communist countries average .308, the West mean is .319, and developing nations average .443 (N = 23, 23, and 42, respectively).
    
    Uslaner, “Corruption and the Inequality Trap” (26) 4. 5. 6. 7. 8. This section is based upon Uslaner (2008, 105-106). http://www.fundforpeace.org/programs/fsi/fsifaq.php. http://www.fundforpeace.org/programs/fsi/fsifaq.php. Available at http://www.wider.unu.edu/wiid/wiid.htm. I use the 2004 Transparency International corruption ratings here rather than 2005 because the BEEPS data come from 2005 and a time lag is justified. 9. In Alex Dreher’s globalization data for 2003, the number of telephone main lines per 1000 people in a country ranges from 1.04 for Albania to 5.10 for Slovenia among transition countries, compared to a range of 5.15 (Portugal) to 8.95 (Norway) for Western nations. Dreher’s data come from the World Development Indicators of the World Bank. and are available at http://globalization.kof.ethz.ch/static/rawdata/globalization_2009_long.xls.

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