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Effect of Political Instability on Corruption: An inter-state analysis
within United States
Ahmed Alnefeesi§ Shreyo Mallik‡ Irina Valenzuela†
June 2014
Abstract
This paper analyzes the effect of political instability on corruption, in particular,
we test whether there is a U-shaped relationship between political stability and
corruption as proposed by Campante, Chor and Do (2008), providing a within-
country evidence. It is expected that at low levels of stability, the incumbent
would find optimal to steal today than from the uncertain future (horizon
effect), whereas a higher level of stability, private sector is more willing to offer
bribe to stable incumbents. Using data from US states and performing a cross-
section and panel-data regression, we find that the estimated coefficients show
a U-shaped relationship between both variables, however such evidence is not
statistical significant.
Keywords: Corruption, Political Instability, Incumbent Tenure
Research paper for the course of Political Economy, Msc. Economics, Barcelona Graduate School of Economics. § [email protected] ‡ [email protected] † [email protected]
1 Introduction
In this paper we investigate the impact of political stability on corruption by public officials
in the United States. Our work extends on Campante, Chor and Do (2008), who brings us
some evidence about the U-shaped relationship between political stability and corruption,
based on a cross country comparison. In particular, the authors argue that at low levels of
stability, private firms have no incentive to offer bribes to public authorities since their
business may require longer time to be effective, for instance, resource’s concession, in
such way willingness of the private sector to pay bribes increases with political stability.
Such relationship, the authors named demand-driven effect and it’s related to long –term
horizon of the incumbent. On the contrary, a low levels of stability, the incumbent may
feel that it is likely that he will not continue in power, therefore would find optimal to steal
today instead of waiting the resources accumulate in the future with an uncertainty that that
he will be in power tomorrow, such relationship the authors name supply-driven effect.
Most of the empirical research relating corruption has been done at a cross-national level as
Alt and Lassen (2008) states, except for the work of Ferraz and Finan (2005), which bring
us evidence from Brazilian’s municipalities and Gamboa-Cavazos, Garza-Cantú and Salinas
(2007) which dealt with corruption across states in Mexico. Therefore, our contribution is
to increase the empirical evidence of the relationship between corruption and political
stability at a within-country level by analysing US states.
The aim of this paper is to investigate whether this U-shaped relationship between political
stability and corruption holds for elected state governors in the United States. To measure
corruption, we used two source of information, one from Boylan and Long (2003) based
on a survey to “state house” news reported carried out in 1989-1999 and the other one
from Maxwell and Winters (2004) based on data from corruption convictions. Regarding
political instability, we created our indicators based on Campante et al (2008) approach,
which are the average length of tenure of incumbent governors and the strength of
incumbents’ positions, measured the latter one as the difference of percentage of winner’s
votes and the runner-up. Performing a cross-section and panel data analysis, and by using
in particular Maxwell & Winters (2004) data of corruption, it is find that the coefficient
show a U-shaped relationship between corruption and political stability, however such
results are not statistically significant.
The remainder of this paper proceeds as follows: section 2 discusses the related literature,
section 3 presents the theoretical model, section 4 describes the data and empirical strategy,
section 5 discuss the estimation results and section 6 presents the conclusion.
2 Literature Review
The determinants of political corruption have been investigated extensively in the literature.
Meier and Holbrook (1992) find a significant association between corruption in American
states and political forces (particularly voter turnout and party competition), bureaucratic
forces (government size and policies that increase bribe opportunities) and structural
factors (e.g. campaign finance reporting requirements). Persson, Tableni and Trebbi (2003)
examine the impact of different electoral rules on corruption. They find that larger voting
districts – which entail lower barriers to entry for candidates - are associated with less
corruption, whereas more candidates being elected from party lists – which entails less
individual accountability – is associated with more corruption. Chang (2005) also
investigates the impact of electoral rules on corruption. His paper hypothesizes that under
an open-list proportional representation election system where individual votes are critical
for politicians to win elections, politicians’ uncertainty of election results drives them to
engage in corrupt behavior to finance campaigns. Using individual level data from pre-1994
Italy, Chang finds substantial evidence that electoral uncertainty can lead to corruption.
Ferraz and Finan (2007) examine the impact of an anti-corruption program in Brazil, which
randomly inspects municipal expenditures of federal funds. They find that disclosures of
local government corruption as a result of the program considerably reduced the likelihood
of re-election for corrupt governors, highlighting the impact of asymmetric information on
political accountability.
Numerous authors have also investigated the relationship between political stability and
corruption. For instance, a key insight from Campante et al. (2008) is that political stability
can have contrasting effects on the incentives for corruption. On the one hand, there is a
horizon effect, from which if an incumbent expects a short tenure in public service, then
he will have more incentive to embezzle public funds today rather than wait for funds
accumulate and steal from others at a later date. On the other hand, there is a demand
effect, from which if an incumbent expects a longer time horizon in tenure, then this can
lead to other forms of corruption that require long term relationships between an
incumbent and a third party, for example, when firms bribe public officials to make
concessions for long-term projects. Hence, as stability of an incumbent’s tenure increases,
the incentive to engage in direct embezzlement decreases, but the incentives to engage in
other forms of corruption such as receiving bribes increases. Using indices on corruption
perception and measures of political stability among countries, Campante et al. find a
robust U-shaped relationship between political stability and corruption. Countries with very
unstable or very stable regimes display more corruption than countries that fall in the
middle.
Olson (1991) associates the incentives of an autocrat to steal public assets to the stability of
tenure. Olson suggests that an autocrat with a stable tenure can secure funds in the form of
taxes, and will thus have an incentive to provide peaceful order and other public goods to
retain a stable source of taxes. On the other hand, if an autocrat expects a brief tenure, then
he will have an incentive to steal public assets if their tax yield during his tenure is less than
their total value. This is similar to the horizon effect in Campante et al. in that incumbents
with unstable tenures will have more incentive to embezzle public funds. Acemoglu (2005)
develops a model that also gives rise to a U-shaped relationship between a ruler’s incentives
to act against the public welfare and the strength of the state. In his model when a state is
excessively strong, then the ruler will have an incentive to maximize rent by imposing such
high taxes that economic activity is suppressed. On the other hand, if the state is
excessively weak, then anticipating that he will not be able to extract future rents, the ruler
will under invest in public goods. Gamboa-Cavazos et al. (2007), empirically examine the
impact of different political horizons on corruption. Using variation in gubernatorial office
terms in Mexico, they also find a U-shaped relationship in which corruption is more
intense during long and short political horizons and less intense in intermediate ones. They
associate this relationship to the incentives of incumbents to receive bribes and the
incentives of firms to give bribes over different political horizons. They suggest that during
short political tenures, incumbents prey more intensely on firms to extract bribes, whereas
during long tenures firms tend to bribe incumbents more because of the longer policy
horizons.
3 Theoretical Model
This section is based on the theoretical model presented by Campante et al. (2008), who
show how instability shapes the incentives for corruption. First, the model considers an
infinite-horizon with an initial pool of available resources (K0), whose allocation is
controlled by an incumbent. There is a probability of stability (α) that the incumbent
continues from one period to the next.
At any given point in time t, the resources can be diverted in either of the two following
ways:
(i) Embezzlement (Et): It refers to direct stealing.
(ii) Licensing (Lt): It involves providing the private sector firms the control over some of
the resources in exchange for an upfront bribe payment.
The incumbent chooses to spend some amount (Pt), out of his initial pool of resources in
order to boost his stability. Let π(Lt, α(Pt)) denote the ex-ante expected value of the
profits received by the firm from the license Lt. We assume that π is an increasing function
of stability and that π(Lt, 0) = 0, so that firms have no interest in bribing to an unstable
incumbent who has a zero probability of being in power in the following period. Another
assumption is that the incumbent has the ability to extract a fraction σ of expected profits
as a bribe payment for the license. In each period, the unutilized resources are transformed
into the pool of resources available in the next period, subject to diminishing returns:
Kt+1 = A(Kt − Et − Lt − Pt)γ
The incumbent maximizes his expected income, whose sequence problem is as follows:
α σπ α
γ
Corruption (Γt) is defined in each period as the amount of illicit income that the incumbent
receives, normalized by the resources available at the beginning of the period as follows:
Γt = [Et + σπ(Lt, α(Pt))]/K
Regarding stability, the variable Pt, referred to as the “public goods provision”, captures
the fact that the stability of the incumbent can be affected by the decisions regarding
resource allocation that he makes. Therefore, a higher public good’s expenditure boosts
the incumbent’s stability but also diverts resources away from his own benefit. Based
on this, stability is specified as a function of the incumbent’s choice of Pt, g(Pt), where g(•)
is increasing and concave, with g(0) = 0. Such function illustrates how effective public
goods provision strengthens the incumbent’s position.
Also, it is included a“systemic” level of stability, which reflects an incumbent’s stability
can be affected by factors that are beyond his control, such as ethnic composition of the
population or cultural norms. Such exogenous factors are incorporated by assuming that
overall stability depends on the intrinsic stability of the polity:ζ ∈ [0, 1]. Therefore,
political stability is specified as α = α(ζ , g(P )).
Finally, the analysis of the comparative statics for corruption with respect to stability is
based on the steady state (t ≥ 1). Since Γt is a decreasing function in α, corruption is
decreasing in stability for α< α*. Thus, more unstable incumbents have a greater
incentive to steal resources currently instead of leaving them to future periods when they
might not be in power. While the firms have incentive to bribe the incumbent so long as α >
0, the expected returns from the licenses are small, in order that any bribes offered are
insufficient to persuade the incumbent to substitute away from purloining.
The influence of α on corruption involves a rich interaction between a horizon effect (since
the optimal amount of licensing also takes into care the trade-off with respect to leaving
resources to the future) and a demand effect (by which the firms are willing to pay higher
bribes to more stable incumbents). The latter effect leads to make corruption increase in
stability. We notice that when γ is sufficiently small (γ ≤1), diminishing returns set in
fast enough in the accumulation equation for Kt. As a consequence upon this, the
incumbent may not prefer to set resources aside for the future. Thus, the demand effect
unambiguously prevails over the horizon effect. If γ > 1, the demand effect still prevails
over some horizon of stability. This continues so long as licensing represents a sufficiently
large source of corruption rents for the incumbent. The horizon effect comes into the
scenario at the highest levels of stability because very stable incumbents find it
worthwhile to allow some resources to accumulate into the future instead of disbursing
more licenses. In short, corruption will increase over some range of stability, though not
necessarily decreasing in stability at the highest levels of α.
4 Data and Empirical Strategy
Our sample correspond to information from US states, which includes the period 1977-
2000.
For the variable of corruption, we used the database from Boylan & Long (2003) and
Maxwell & Winters (2006). In the case of the former, the authors constructed a corruption
indicator based on a survey carried out in 1998-1999. In particular, the authors sent a
questionnaire to state-house news reports, who are supposed to be well-informed of the
states’ government corruption. The questions included in such survey are in appendix 1.
Boylan & Long (2003) constructed two measures of state’s corruption, one based on just
the average of question nº 61 and the other one based on normalizing and then averaging
responses to question 3 to 8. In our study, we are using the latter one, since, as Boylan &
Long mention, there are some concern about whether by using the means of ordinal
measure (as by using just question 6) may lead to a loss of information within individual
data. Anyway, the authors find that both measures of corruption are highly and statistically
significant correlated (0.853), proving consistency between them. It is worth to mention
that since they do not receive response from 3 states (Massachusetts, New Hampshire and
New Jersey), they construct their indicator for only 47 states.
In the case of corruption’s index from Maxwell & Winters (2006), they replicate and
continue the work of Meier and Schlesinger (2002), that is constructing corruption
measured based on reports of “criminal abuses of public trust by government officials”.
The data is based primarily on the reports from US Attorney offices (Public Integrity
Section of the US Department of Justice). The authors divided the number of convictions
by the number of officials, from which the exclude Hawaii as an outlier in corruption. The
indicator is measured in logarithm terms.
The correlation between these two measures of corruption is 0.55.
1 They construct their measure of state corruption based on question 6 for two reasons: i) this question explicitly asks reporters to rank their state on overall corruption and ii) there is high level of agreement in the responses among reporters within same state. In particular, an individual score of 1 on question nº6, reflects that the State House reporter perceived government employees in her specific state as the least corrupt among all states, whereas a score of 7 reflects a perception of this state’s government employees to be the most corrupt. The authors, then, estimate the state score as the average among the report’s score within each state, and then rank states based on means scores.
For the variable of political instability, we are going to use to indicator as proposed by
Campante et al. (2008):
(i) Average tenure of incumbent: In our case, we are going to estimate how many years in
average an incumbent stays as governor within 10-years window, which is calculated as 10
divided by the number of different incumbent govern in such period. It is worth to
mention that we are not going to include as a alternative indicator the average tenure of
party, since in the case of US states there is usually two main parties: Democrats and
Republicans, and for the most of the states, they intercalate.
(ii) Strength of incumbent’s position: we create this variable as the difference of the
percentage of votes between the winning candidate and the runner-up in a certain election.
The information about percentage votes and incumbent condition in each election (re-
elected, retired or term-limit) was collected from Wikipedia2. he controls variables include
in the regression are specified in the appendix 2.
Cross –section analysis
For the cross-section regression, it is going to be used two measures of corruption, one
that correspond to Bong and Lang (2004), as it is stated was based on a survey in 1998-
1999, and as a second measure of corruption, Maxwell and Winters’s indicator is used.
Regarding the political instability variable and in the case of cross-section analysis, it is
considered the average tenure of incumbent in the last 10 years 1989-1999 in order to
coincide with the previous 10 years to the data of corruption. The second indicator of
political instability to be considered in the cross-section analysis is the vote margin
corresponding to the previous election held prior to 1998. In the latter case, what we want
to evaluate is whether the level of corruption is associated with the strength a governor has
reflected in the percentage of vote his receives in elections. Finally, as a third measure of
political instability and related to the average tenure of an incumbent, we are considered the
2 Gao, Pengjie and Yaxuan Qi. “Political Uncertainty and Public Financing Costs: Evidence from U.S. Gubernatorial Elections and Municipal Bond Markets”. 2013, also collected data of US gubernatorial elections from Wikipedia.
number of times a re-election has occurred in the last 22 years (1977-1998), this measure
not only captures long-term effect of political stability. In this case, we are not going to
consider New Hampshire, Rhode Island and Vermont since the elections are held every
two years.
Our regression specification for the cross-section stands as follows:
Corrupi = β0 + βα*Stabαi + βα 2*Stabαi 2 + βX*Xi + εi
where i denotes state/territory. The dependent variable Corrupi is an index on corruption
for the various states and territories in the United States. Xi stands for the set of control
variables included in the specification that captures some additional factors that can affect
corruption.
Panel-data analysis
In the case of panel data structure, we are considered the data of corruption from Maxwell
and winters, since they collected such information in a year basis from 1977-2000.
Regarding the political instability variable, we are going to consider two measures. One is
the average incumbent tenure, which is constructed following Campante et al., for each
year in our sample; average tenure is calculated as 10 divided by the number of different
incumbent that governed in the previous 10 years. Therefore, for such regression we are
going to use the sample corresponding to the period 1986-2000.
The other variable of political instability to be considered is the vote margin, in such a case
we are going to consider the vote margin and a sample period from 1980-2000.
Next, we perform a panel regression in order to analyze the results, including year and state
fixed effects as well as clustered standard errors by state. Our specification for the panel
regression is the following:
Corrupi, t = β0 + βα*Stabi, t + βα 2*Stabi, t^ 2 + βX*Xi, t+ Dt + vi + εit
where i and t denote state/territory and time respectively. Since we have sufficient number
of observations per state/territory, we have included fixed effects in the model (vi) and
year fixed effects (Dt), and robust standard errors clustered by state/territory.
5 Empirical Results
In this section, the estimation results are presented from both the cross-section and panel
data analysis.
Cross –section regression
Table 1. Dependent variable: Corruption Scale Boylan and Long (2004)
(1) (2) (3) (4) (5) (6)
Avg. Incumbent tenure -0.087 0.256
(0.351) (0.456)
Avg. Incumbent tenure^2 -0.001 -0.024
(0.025) (0.031)
Vote margin -0.025 0.011
(0.020) (0.019)
Vote margin^2 0.000 -0.000
(0.000) (0.000)
Nº re-elections -0.048 -0.166
(0.166) (0.183)
Nº re-elections^2 -0.005 0.006
(0.017) (0.017)
District level competition -0.014 -0.018 -0.015
(0.011) (0.014) (0.011)
Real income per capita -0.000 -0.000 -0.000
(0.000) (0.000) (0.000)
Population -0.002 -0.002 -0.002
(0.002) (0.002) (0.002)
% of metropolitan population 0.017 0.019 0.015
(0.009) (0.010) (0.010)
% of population with high school diploma -0.060 -0.069 -0.054
(0.046) (0.041) (0.037)
Real tax revenue per capita 0.000 0.000 0.000
(0.000) (0.000) (0.000)
Socio-ethnic homogeneity 0.000 -0.000 0.000
(0.000) (0.000) (0.000)
Civic involvement -2.546 1.748 -8.372
(25.446) (24.460) (22.087)
% of protestan population -0.022 -0.019 -0.027*
(0.012) (0.012) (0.012)
R-squared 0.009 0.007 -0.002 0.294 0.258 0.302
F 2.333 1.125 0.615 3.847 2.869 4.389
Nº of observations 47 47 47 45 45 45
* p<0.05, ** p<0.01, *** p<0.001
Table 2. Dependent variable: log of convictions for public corruption per 1000 elected
officials (Maxwell & Winters)
According to our results, using the Boylan and Long’s corruption indicator, none of our
proposed political instability measure resulted with the expected coefficient nor statistically
significant at 5% level. On the other hand, by using the corruption indicator from Maxwell
& Winters, we find that, according to the sign of the coefficients from the variables average
incumbent tenure and vote margin, and its respective squared variable, a U-shape
relationship between corruption and political instability, however such result does not turn
to be statistically significant.
(1) (2) (3) (4) (5) (6)
Avg. Incumbent tenure -0.175 -0.116
(0.302) (0.315)
Avg. Incumbent tenure^2 0.007 0.004
(0.021) (0.022)
Vote margin -0.032** -0.015
(0.010) (0.011)
Vote margin^2 0.000 0.000
(0.000) (0.000)
Nº re-elections -0.049 -0.054
(0.114) (0.123)
Nº re-elections^2 -0.006 -0.001
(0.010) (0.011)
District level competition -0.005 -0.001 -0.005
(0.007) (0.007) (0.007)
Real income per capita -0.000 -0.000 -0.000
(0.000) (0.000) (0.000)
Population 0.001 0.001 0.001
(0.001) (0.001) (0.001)
% of metropolitan population 0.008 0.006 0.007
(0.004) (0.004) (0.004)
% of population with high school diploma -0.050* -0.041* -0.056***
(0.020) (0.018) (0.013)
Real tax revenue per capita 0.000* 0.000 0.000*
(0.000) (0.000) (0.000)
Socio-ethnic homogeneity -0.000 -0.000 -0.000
(0.000) (0.000) (0.000)
Civic involvement 2.184 -2.869 1.973
(12.979) (12.845) (10.430)
% of protestan population -0.007 -0.005 -0.010*
(0.004) (0.005) (0.004)
R-squared 0.037 0.241 0.092 0.595 0.569 0.573
F 2.117 9.703 5.274 9.029 8.184 13.747
Nº of observations 49 49 49 48 48 48
* p<0.05, ** p<0.01, *** p<0.001
Panel data results
Table 3. Dependent variable: log of convictions for public corruption per 1000 elected
officials (Maxwell & Winters)
In the case of our panel data regression, we only find the expected sign for the variable
vote margin as a measure of political instability, however even such evidence is not
statistically significant.
6 Conclusions
The objective of this paper is to evaluate whether the U-shaped relationship between
corruption and political stability shown as evidence from cross-country empirical analyze
holds for a within-country. By using information of US states, we perform two
econometric strategies, one based on cross-section and panel data regression, by using two
measures of corruption, the one from Boylan and Long (2003) and the other from Maxwell
(1) (2) (3) (4)
Avg. Incumbent tenure 0.310 0.264
(0.384) (0.368)
Avg. Incumbent tenure^2 -0.026 -0.024
(0.029) (0.028)
Vote margin -0.001 -0.002
(0.013) (0.013)
Vote margin^2 0.000 0.000
(0.000) (0.000)
Population 0.144 0.112
(0.107) (0.057)
Fractional % of states' populations -0.000 0.000
(0.001) (0.000)
% of College graduates -0.047 -0.051
(0.092) (0.082)
Real income p.c. -0.000 -0.000
(0.000) (0.000)
R-squared 0.422 0.443 0.420 0.442
Nº of observations 734 1029 734 1029
* p<0.05, ** p<0.01, *** p<0.001
and Winters (2004), we find the U-shaped relationship is reflected in our estimated
coefficients, however such evidence results to be not statistically significant.
The results of the present paper open a path to inquire in a deeper way whether it may be
needed to get a better estimates of political stability or whether the corruption indicators
are not so precise as it should be, or to analyze whether the corruption in US states may be
present in other ways not related with political instability. In the former case, we can
mention that Alt and Lassen (2008) show some concern about both sources of data. For
instance, corruption perception based on survey to state house news reporters may be
biased if in some states the reporters have some political preference as well as the different
effort in covering corruption news. In the case of data based on convictions, it also can be
biased, for instance, “the number of federal convictions on corruption charges depends on not only on the
level of corruption in a given states but also crucially on the priority and amount of effort that prosecutors
devote”.
References
Acemoglu, D. Constitutions, Politics and Economics: A Review Essay on Persson and
Tabellini's" The Economic Effect of Constitutions" (No. w11235). National Bureau of
Economic Research. 2005.
Alt, James E. and David D. Lassen. Political and Judicial Checks on Corruption: Evidence
from American State Governments. Economics & Politics, Vol.2, 2008.
Adsera, Alicia, Carles Boix and Mark Payne. Are You Being Served? Political
Accountability and Quality of Government. The Journal of Law, Economics, &
Organization, Vol. 19, No. 2., pages 4445-490, 2003.
Besley, Timothy and Anne Case. Political Institutions and Policy Choices: Evidence from
the United States. Journal of Economic Literature, Vol. XLI, pages 7–73, 2003.
Boylan, Richard T. and Cheryl X. Long. Measuring Public Corruption in the American
States: A Survey of State House Reporters. State Politics and Policy Quarterly, Vol. 3, No.
4, pages 420-438, 2003.
Campante, Filipe R., Davin Chor, and Quoc-Anh Do. Instability and the Incentives for
Corruption. Economics and Politics 21(1): 42-92, 2009.
Chang, E. C. Electoral Incentives for Political Corruption under Open‐List Proportional
Representation. Journal of Politics, 67(3), 716-730. 2005.
Ferraz, Claudio and Frederico Finan .Reelection Incentives and Political Corruption:
Evidence from Brazilian Audit Reports. Paper prepared for presentation at the American
Agricultural Economics Association Annual Meeting, Providence, Rhode Island, 2005
Gamboa-Cavazos, Mario, Vidal Garza-Cantú and Emiliano Salinas. The Organization of
Corruption: Political Horizons and Special Interests.
http://kuznets.fas.harvard.edu/mgamboa/papers/corruption.pdf, 2007.
Holbrook, Thomas M. and Emily Van Dunk. Electoral Competition in the American states.
The American Political Science Review, Vol. 87, No. 4, pages 955-962, 1993.
Maxwell, Amanda E. and Richard F. Winters. A Quarter-Century of (data on) Corruption
in the American States. Paper prepared for delivery at the annual meeting of Midwest
Political Science Association, Chicago, Illinois, 2004.
Meier, K. J., & Holbrook, T. M. “I Seen My Opportunities and I Took'Em:” Political
Corruption in the American States. The Journal of Politics,54(01), 135-155. 1992.
Olson, M. Dictatorship, Democracy, and Development. American Political Science Review,
87(03), 567-576. 1993.
Persson, T., Tabellini, G., & Trebbi, F. Electoral rules and corruption.journal of the
European Economic Association, 1(4), 958-989. 2003.
Serra, Danila. Empirical determinants of corruption: A sensitivity analysis. Global Poverty
Research Group (GPRG-WPS-012), 2004.
Treisman, Daniel. The causes of corruption: a cross-national study. Journal of Public
Economics 76, pages 399–457, 2000.
Wikipedia. http://en.wikipedia.org/wiki/United_States_gubernatorial_elections,_1990 (for
different years)
Appendix 1
Table A1
Appendix 2
Control Variables
• Percentage of Protestant Population: It measures the percentage of the population
that is follows Protestantism.
• Population: It gives the population count (in millions) in the various/states for the
respective years.
• Ethnic Share: It gives the fractional share of states' populations by Black, Hispanic,
Asian-American, and residual "other".
• Count of people with a College Graduate Degree or Higher: It measures the
number of college graduates or higher for the years 1990 and 2002 respectively.
• Civic Involvement Index: It is an index for civic involvement for each
state/territory corresponding to each respective year. It varies between zero and unity, both
exclusive.
Nº Topic Question
Q1 News Coverage Media Coverage of public corruption
Q2 Prosecutorial effort How high a priority corruption investigation is for federal prosecutors
Q3 Fraudulent expense reportEstimate of percentage of governement employees submitting
fraudulent expense reports
Q4 BribaryEstimate probability of firms getting similar tax breaks as those giving
campaign contributions to state legislators (bribary)
Q5 Governement employee corruption Estimate of governement employee corruption
Q6 Overall corruption Estimate the level of overall public corruption in the state
Q7 % corrupt emloyees Estimate the % of government employees who are corrupt
Q8 % corrupt legislators Estimate the % of state legislators who are corrupt
Source: Boylan & Long (2003)