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. § ahmad.alnefeesi@barcelonagse.eu ‡ shreyo.mallik@barcelonagse.eu † irina.valenzuela@barcelonagse.eu

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”.

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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)