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The Impact of Socio Political Integration and Press Freedom on CorruptionNicholas CharronaaUniversity of Gothenburg, Sweden

Online publication date: 17 November 2009

To cite this ArticleCharron, Nicholas(2009) 'The Impact of Socio-Political Integration and Press Freedom on Corruption',Journal of Development Studies, 45: 9, 1472 1493

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The Impact of Socio-Political Integrationand Press Freedom on Corruption

NICHOLAS CHARRONUniversity of Gothenburg, Sweden

Final version received June 2008

ABSTRACT The analyses in this study demonstrate a more nuanced understanding of apreviously understood phenomenon that openness has a negative relationship with corruption. Itis argued that this relationship is substantially influenced by the domestic context, a relationshipthat has been underdeveloped by previous empirical studies. Focusing on social and politicalintegration, I find that the effect of openness on corruption is conditioned by domestic institutions.The empirical evidence suggests that while political and social openness have a significant impactin combating corruption given a free press, the impact of such international forces are negligiblein cases where press freedoms are low.

I. Introduction

My message is: it is through openness and good governance at all levels of

society, right down to the grass roots, that people will be empowered, change

take root and development sustained. (Gordon Brown, 2006)

A popular government, without popular information, or the means of acquiring

it, is but a prologue to a farce or a tragedy; or, perhaps both. (James Madison,

1953[1822])

In recent years, numerous academic empirical studies have been devoted to

understanding the determinants of corruption. On the policy side, international

organisations (IOs) such as the World Bank, WTO and the IMF have made

significant strides in attempting to curb world-wide corruption, particularly in

developing countries.1 A consensus is emerging in the academic and policy worlds on

improving our understanding of corruption by using cross-national variations.

While institutional and cultural factors have received a considerable amount of

attention as key explanatory variables, a subset in the corruption literature has

Correspondence Address: The Quality of Government Institute, Department of Political Science,

University of Gothenburg, PO Box 100, SE-405 30 Gothenburg, Sweden. Email: nicholas.charron@

pol.gu.se

Journal of Development Studies,

Vol. 45, No. 9, 14721493, October 2009

ISSN 0022-0388 Print/1743-9140 Online/09/091472-22 2009 Taylor & Francis

DOI: 10.1080/00220380902890243

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focused on the effects of various aspects of international openness on government

corruption (Krueger, 1974; Ades and Di Tella, 1997, 1999; Wei, 1999; Sandholz and

Koetzle, 2000; Wei and Sheifler, 2000; Bonaglia et al., 2001; Torrez, 2002;

Lambsdorff, 2003; Sandholtz and Gray, 2003; Gatti, 2004). Among the analyses in

the opennesscorruption nexus, the empirical findings have mainly been supportive

of the positive relationship between openness and good governance. Thus among

many economists and political scientists there is an optimistic consensus in the

empirical literature that openness has a negative relationship with corruption.

However, as some scholars aptly point out, beginning with the work of Rose-

Ackerman (1978) and subsequently Sandholtz and Gray (2003), the effect of

openness on corruption can also come from normative effects. It is argued that

growing interdependence among states politically through international organisa-

tions (IGOs, NGOs, and so on) and peace-keeping missions and socially through

the diffusion of technology, media and migration might have a significant impact

on spreading quality of government (QoG) and anti-corruption norms and exposecorrupt leaders to domestic and international audiences. Along with a substantial

increase in economic interdependence over the past few decades, many states have

experienced a substantial rise in political and social interdependence. However, the

impact of the socio-political aspect of openness on corruption and good governance

has not received equal empirical attention as that of economic openness. While this

analysis also takes into account variations in economic openness, I contribute to the

opennesscorruption nexus by mainly focusing upon the effect of non-economic

factors on corruption. Such factors include IO membership, cross-border commu-

nications and UN mission participation.A second important contribution of this study to the literature is the attention to the

interplay between openness an international variable and domestic institutions of

transparency. Specifically, I argue that the level of domestic press freedom in a country

plays a conditional role in the spread of anti-corruption norms as social and political

interactions increase. Previous empirical studies have overlooked the potentially

significant interaction between openness and domestic institutions. Thus, this study

seeks to provide some answers to the following empirical question: does the impact of

international openness in influencing levels of corruption depend on the level of press

freedom present in the country? Further, do social and political openness havedifferent effects depending on the domestic context of the press, particularly in

developing countries? The empirical results demonstrate that prior claims regarding

social and political openness may have been overly optimistic.

Finally, this study contributes to the literature by focusing on a wide range of

states. I test these questions empirically on over 100 countries using two widely used

measures of corruption. Additionally, I employ stratified samples of exclusively

developing countries in order to check if the results hold without OECD states in the

model. I estimate the results with both cross-sectional and panel time series

regressions to assure a level of robustness in the results. The evidence I report

corroborates previous empirical studies in that there is a significant and negative

relationship between openness and corruption. However, this relationship is more

nuanced than previously reported. While socio-political openness has a strong,

negative impact on corruption scores in both the full sample and the sample of

developing states, such factors have little to no effect on corruption when press

Socio-Political Integration, Press Freedom and Corruption 1473

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freedoms are low. The results are robust to both cross sectional and time series

models using multiple indicators of corruption.

The analysis is developed as follows. First, I review the empirical literature on the

determinants of corruption, focusing primarily on the relationship between openness

and corruption and elucidate my testable hypotheses. Second, I discuss and display

recent trends in both socio-political openness and press freedoms over the past 10 to

15 years. Third, I discuss data and specifications of the models. Fourth, I present the

empirical findings. I end this study with some concluding remarks and interpreta-

tions of the results.

II. Determinants of Corruption

Whether focusing on domestic political institutions (Myerson, 1993; Persson et al.,

1997; La Porta et al., 1999; Treisman, 2000; Persson and Tabellini, 2003; Andrews

and Montinola, 2004; Dreyer, 2004; Charron, 2009), press freedoms (Brunetti andWeder, 2003; Chowdhury, 2004; Lindstedt and Naurin, 2005; Norris, 2008) or

factors of international openness (Kreuger, 1974; Ades and Di Tella, 1997, 1999;

Wei, 1999; Sandholtz and Koetzle, 2000; Treisman, 2000; Torrez, 2002; Sandholtz

and Gray, 2003; Gatti, 2004) there is a strong and consistent empirical consensus

that expanding power away from the executive, and increased accountability and

transparency, have a negative relationship with corruption. Scholars have generally

found that countries with strong executive branches, limited opposition parties, low

degrees of democratic accountability, low economic development and countries that

are relatively closed to international competition and ideas are more corrupt,ceterisparibus.

Specifically regarding openness, though different measures of corruption are

often employed in various empirical studies, the statistical relationship appears

robust countries that are more open, broadly defined, often exhibit less

corruption. A common argument asserts that in closed states, political elites can

more easily manipulate information and deal in bribes and patronclient-type

exchanges that are less visible relative to more open societies. In explaining this trend

in the data, scholars have mainly posited two somewhat compatible hypotheses.

First are rationalist, economic reasons as to why openness reduces corruption.Bonaglia et al. (2001) argue economic openness reduces corruption through three

distinct mechanisms: first, when trade restrictions become less restrictive (Krueger,

1974; Gatti, 1999); second, openness increases the level of foreign competition (Ades

and Di Tella, 1999); and third, this in turn draws in more international investors

(Wei, 2000; Wei and Sheifler, 2000). Ades and Di Tella argue that competition from

foreign firms reduces the rents enjoyed by domestic firms, and this reduces the

reward of corruption (1999: 998). They posit that as foreign competition increases in

a country, demand for more efficient business practices increases, which in turn

compels corruption to decrease. Rent-seeking and kickbacks, which can lead to sub-

optimal economic outcomes, are discouraged due to transparency, in what Gatti

labels the foreign competition effect (2004: 852).2

The socio-political (anti-corruption norm) hypothesis focuses mainly on

non-economic factors such as the spread of anti-corruption norms and rule-

following behaviours, for example through increased international interactions and

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membership in IOs and NGOs (Rose-Ackerman, 1978; Abbot and Snidal, 2001;

Bukovansky, 1999; Sanholtz and Gray, 2003). However, this side of the coin is less

empirically developed than the trade opennesscorruption nexus. Largely based on

developments from the constructivist perspective in international politics, transna-

tional actors project new norms and behaviour into the system and, as states become

more open to international influences, they become more open to accepting such

behaviour. According to this ideational-type hypothesis, it is through the norms

proliferated by the entrepreneurs in IOs (Finnemore, 1996; Finnemore and Sikkink,

1998; Sandholtz and Gray, 2003), or other forms of information proliferation (that

is, contact with foreign governments, UN participation, increase in Internet and

foreign communication sources, and so on) that pressure will be placed upon

governments to reduce corruption due to the diffusion of good governance norms.

Taken individually,politicalopenness (IOs, UN missions, embassy exchange) puts

external pressure on leaders from above so to speak. Increased exposure to other

foreign leaders and IO elites stresses incentives to conform to the rules and norms of theinternational community. In the age of the good governance movement (Burkovsky,

2002) external incentives to reform could be for such reasons as foreign aid or

international reputation. On the other hand, associalopenness increases, information

about anti-corruption norms may also spread. Yet, social openness puts pressure on

domestic elites from below in a sense, in that it provides channels for ordinary citizens

to obtain more information via a wider scope of sources coming from abroad. Through

the proliferation of the Internet, telephones, foreign newspapers, tourism, and contact

with foreigners, citizens have avenues to acquire more information about their

government and its practices. They are then better equipped to make demands forreform from their leaders. Though it argues through the lens of cultural and normative

reasoning rather than focusing primarily on economic incentives, the anti-corruption

norm hypothesis is certainly compatible and possibly serves as a complement rather

than a rival to the rationalist hypothesis of openness and corruption.

Previous research has demonstrated that the diffusion of norms in the

international system has an effect on states regarding a number of issues, such as

women and minority rights, land mines, weapon proliferation and decolonisation

(Finnemore and Sikkink, 1998; Dubois, 1994; Price, 1998). Pertinent to this analysis,

such scholars also speak to how domestic institutions and politics, mainlydemocratic, filter the effect of such international norms. Finnemore and Sikkink

(1998) write, International norms must always work their influence through the filter

of domestic structures and domestic norms, which can produce important variations

in compliance and interpretation of these norms and that there is a two-level norm

game occurring in which the domestic and the international norm tables are

increasingly linked (Finnemore and Sikkink, 1998: 893; see also Putnam, 1988).

Under this logic it is clear that certain domestic structures must be taken into

account when assessing the possibility of norm acceptance of a trend such as anti-

corruption.

III. The Conditional Effect: Press Freedom

The norm hypothesis postulated by Sandholz and Gray (2003) asserts that countries

with higher levels of transnational interactions, such as participation in IOs, are


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expected to be on average less corrupt. This hypothesis implies that

international factors have a significant influence on domestic behaviour. Yet this

type of openness is not directly related to economic market forces and is socio-

political in nature, thus its impact will be through the spread of information.

Building on important works Allison (1969) and Putnam (1988) that demonstrate the

interplay between international and domestic politics, I argue that previous

analyses have overlooked a nuanced relationship that mechanisms of

transparency (press freedoms) need to be in place for good governance norms to

proliferate after international openness is increased. Thus I maintain that there is a

degree of omitted variable bias in the models of a number of such analyses that argue

that international transparency (structural, interstate variables) determine change in

government behaviour (agent-centred, domestic variables) without accounting for

potentially conditional effects of a free press, which enable or prohibit the spread of

such ideas.

The theory in this analysis relies on the assumption that most citizens in any countrywould rather have high quality government institutions and as little corruption as

possible on the part of their leaders,ceteris paribus. Further, I assume that leaders who

seek to remain in power will take part in corruptive acts when the threat of being exposed

is relatively low, thus minimising the risk costs of their behaviour. That being said,

reforms that apply to good governance and reducing corruption can be costly to leaders

and clients who benefit from a more patronclient system. From a rational standpoint,

the pressures to undergo such reforms must be rewarding enough to leaders to outweigh

the benefits of the status quo environment. Pressure to reform can come internally from

opposition groups (political parties, religious or ethnic minorities, and so forth) orexternally (international forces economic, social or political). For either the internal or

external forces to apply ample pressure on leaders to curb corruption or improve

governance, such actors must be able to inform enough of the domestic constituency to

make a difference, which in turn will compel leaders to make reforms or else potentially

lose power. The most cost-effective way to inform the general public in any given country

about the behaviour of corrupt leaders is through the media. If the media is free and

impartial, then the threat of, or actual pressure, applied by internal and/or external

agents to inform the public of corruption and malpractice is expected to provide leaders

with strong incentives to change their behaviour in favour of better governance, giventhat they wish to remain in power. However, if the media is controlled by the government

or not truly impartial, corrupt leaders can essentially use their influence in the media to

block both internal and external pressures for reform. In sum, without certain domestic

institutions that can help foster the spread of such international norms as anti-

corruption, the effect of openness is expected to be negligible. Given the presence of a

relatively free and impartial media, the norm hypothesis is expected to play a significant

role in curbing corruption.

It is therefore the purpose of this analysis to test the impact of international social

openness on levels of domestic corruption, taking into account the level of a states

press freedom. I thus test the following hypotheses empirically on a large,

representative sample of states:

H1: As social openness increases, the level of corruption in a country is expected to

decrease, given that press freedoms are present,ceteris paribus.

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H2: As political openness increases, the level of corruption in a country is expected

to decrease, given that press freedoms are present,ceteris paribus.

IV. Trends in Openness, Press Freedom and Corruption

In the post-war era, and in particular in the post-Cold War era, states have become

more open by most measures of international openness. The KOF Index of

Globalisation (Dreher, 2006), which distinguishes among three components of

openness economic, social and political demonstrates that every region in the

world has experienced increases in their respective aggregate openness scores over

the last two decades. For example, between 1984 and, 2004, the aggregate score of

the African social openness index rose from 21 to about 36, a 71.5 per cent increase,

and political openness increased by 62.5 per cent, from 32 to 52. Similar increases

can be observed in every other developing area for both types of openness, which are

shown in aggregate form in Figure 1. Though OECD states have maintainedrelatively high openness scores in all areas since the beginning of the KOF data,

developing areas have seen substantial transformations in recent years. According to

the opennesscorruption hypothesis, whether economic or normative, this increase

should significantly reduce corruption in developing areas. While the rationalist and

economic hypotheses have been explored by a number of previous studies, the

impacts of cultural and political openness, especially when distinguished from each

other, have been less explored. Thus, I employ the two measures of openness social

and political to serve as proxies for exposure to international norms and ideas. The

full list of indicators in each index, along with respective weights, is located in theappendix. The aggregate trends for developing areas in social and political openness

from 1990 to 2004 are displayed in Figure 1.

Figure 1. Aggregate trends in social and political opennesss. Developing states: 19902004.


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Table 1 displays a brief preliminary analysis that is intended to demonstrate the

trends over the last decade in social and political openness in sub-sets of developing

countries concerning press freedoms free, partially free and not free.3 The number

of observations coded for each of the two groups is listed on the right-hand side of

the table.4 While countries with more press freedoms recorded significantly higher

social openness scores throughout the time period, aggregate scores in both groups

experienced substantial increases in openness over time. Concerning political

openness, the same can be said of the general pattern of increase. However, there

is no statistical difference in aggregate political openness scores between states with

press freedoms and those that lack a free press throughout the sample of developing

states. Therefore these figures demonstrate that there are no systematic increases in

either type of openness that have been skewed toward one of the two sub-sets of

states both developing countries with and without press freedoms in the aggregate

experienced increased exposure to social and political internationalisation. Further-

more, while states with greater press freedoms had higher social scores, the differencebetween the two groups in their IO participation, embassy count and UN Security

Mission participation was negligible. However, even though openness is expected to

reduce corruption and the aggregate openness levels have increased over time,

aggregate corruption scores have not followed this trend in developing states. Thus

the conditional factor of press freedom might help in resolving this puzzle.

In Figures 2 and 3, I separate press freedom and corruption scores into the

aggregate total by region.5 In Figure 2, clearly outside the OECD countries (Western

Europe, North America, Australia, New Zealand and Japan) significant variation is

observed. The data in Figure 2 for press freedoms have been inverted so that higherscores indicate more freedom. Clearly, Latin America (including the Caribbean), the

Pacific Islands and post-Soviet states and Eastern Europe rank among the highest of

the developing areas in terms of press freedom according to Freedom House data

from 1994 to 2004. Conversely, Middle Eastern (includes North Africa), South East

Table 1. Trends and annual means in a comparison of openness between free/partially freepress versus not free press: 19942004, 95 non-OECD states only

Level of social openness Level of political openness Observations

Year Free Not free T significance Free Not free T significance Free Not free

1994 36.5 27.7 2.49 46.1 43.5 0.43 73 221995 39.6 31.7 2.04 43.5 50.5 71.34 67 271996 42.2 31.6 2.71 43.0 50.9 71.54 69 261997 43.0 33.9 2.37 44.3 47.8 70.65 67 281998 44.4 34.5 2.62 43.7 50.3 71.31 67 281999 44.5 36.8 2.01 46.7 50.1 70.64 65 302000 45.8 36.0 2.60 47.3 48.5 70.14 65 302001 50.1 38.8 3.01 52.9 45.5 1.42 68 272002 50.1 38.9 3.01 51.9 46.7 1.02 68 272003 49.9 42.4 2.05 52.5 51.7 0.15 63 322004 49.7 42.4 2.03 55.4 53.0 0.49 61 34

Note: Data on press freedoms were taken from Freedom House while openness data weretaken from the KOF Index.

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Asian and sub-Saharan African countries rank among the lowest in terms of freedom

of the press, all having average aggregate scores below 50.

Moving to the dependent variable, a regional breakdown of the International

Country Risk Guide (ICRG) corruption scores is provided in Figure 3. Once more,

states from the West display scores significantly higher than those from developing

regions where variation is again significant among such areas. Corruption scores

Figure 2. Aggregate press freedom scores by region: 19942004.

Figure 3. Aggregate corruption scores by region: 19942003.


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over the decade between 1994 and 2003 show that developing areas range between

0.42 and 0.61 on a scale of 0 to 1. African states lag significantly behind, recording

the lowest average of any of the regions. Post-Soviet and East European and South

Asian states had the highest averages of all developing areas with approximately

0.61.

V. Specification and Methodology

The primary focus of this analysis is to test whether social and political openness

hinder corruption and if there is an intervening effect on this relationship depending

on the level of a countrys press freedom. To present a parsimonious model, while

simultaneously reducing the likelihood of potential omitted variable bias, I include

the following indicators in the full regression.

Given that data on the dependent variable can be somewhat unreliable due to their

subjective nature (Williams and Siddique, 2007), I employ two common measures tocheck for robustness in the results. The first measure of corruption is taken from the

ICRG, the Political Risk Services (PRS) group of financial risk indicators. The PRS

group, a think tank specialising in economic and politicalrisk assessment internationally,

has published monthly data for business and investors on over 140 countries since 1980.

The PRS measure is primarily concerned with accounting for excessive patronage,

nepotism, job reservations, favour-for-favours, secret party funding, and suspiciously

close ties between politics and business.6 The period covered ranges from 1984 to 2003

and includes up to 139 countries. The data in the analysis have a finite range from 0 to 1,

with higher scores indicating lower levels of perceived corruption. There are severaladvantages to this measure. One, it is available for 20 years, which allows for any

institutional reform of a countrys vertical or horizontal power sharing structure or

structural shifts in the domestic fractionalisation. Second, it includes a wide range of

developed and developing countries so that the results of this analysis are highly

generalisable. It has also been used by other recent empirical studies (Ades and Di Tella,

1999; Persson and Tabellini, 2003; Gatti, 2004; Ba ck and Hadenius, 2008).

The second measure employed here to capture corruption comes from

Transparency International (TI), a non-partisan organisation that has created the

Corruptions Perceptions Index (CPI), which ranges from 0 to 10, with higher scoresindicating less corruption. For example, in the 2006 rankings, Finland ranks highest

(least perceived corruption) with a score of 9.6, while Angola ranks last (#142) with a

score of 2.2. The CPI score measures the perceptions of the degree of corruption as

seen by business people, risk analysts and the general public.7 The CPI ranks more

than 150 countries by their perceived levels of corruption in the public sector, as

determined by expert assessments and opinion surveys. The data range from 1996 to

2006. The TI data have been frequently used as well in a number of recent

publications (Triesman, 2000; Fisman and Gatti, 2002; Persson et al., 2003). Due to

incomplete data, I employ an unbalanced, pooled data set, which allows for greater

observations through more cases and thus increases reliability of the estimates

(Globerman and Shapiro, 2003).

The key domestic factor in the study is the level of a countrys press freedom.

Freedom House International has annually coded the level of press freedom from

1994 to 2006 on a scale of 0100, with lower scores indicating more freedom of the

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press. Freedom House has also trichotomised the data into not free (61100),

partially free (3160) and free (030). The organisation has recorded press

freedom scores for more than 190 countries. All three variables were taken from the

Quality of Government Institute database (Teorell et al., 2008). A more detailed

description of the summary statistics is located in the appendix.

Two of the primary international variables in the model are political and social

openness. I attempt to proxy this process in two ways using the KOF Index of

Globalisation data (Dreher, 2006). The first is social openness, which is an index of

three broad measures that account for the level of personal contacts, information

flows and cultural proximity (to other countries) (Dreher, 2006: 1093). This measure

is intended to capture whether the spread of ideas through personal and media

contacts influences the corruption level of a country. The second is political

openness, which tries to capture the diffusion of government policies through the

amount of interaction each state has with other states and with international

organisations. Included in the political openness index are the numbers ofinternational organisations to which each country belongs, the number of embassies

and high commissions in each country, and the number of UN peacekeeping

missions in which a country participates. A full description of each component of the

social and political openness indices is shown in the appendix. In order to aptly test

the two hypotheses stated earlier, I construct an interaction term between the level of

press freedom and each type of openness.8 For the hypotheses to be corroborated

empirically, the interaction term would need to be positive and significant, while the

individual coefficient for each type of openness is expected to be insignificant,

indicating that at the lowest levels of press freedom, socio-political openness has noimpact on corruption levels. Yet the positive and significant interaction term would

indicate that the openness variable would positively impact the dependent variable at

higher levels of press freedom.

Regarding the control variables in the model to account for rival hypotheses, studies

have shown that there is a strong and robust relationship between the strength of

democratic institutions and levels of corruption (Ades and Di Tella 1997; Fisman and

Gatti, 2002; Gatti, 2004; Sandholz and Gray, 2003; La Porta et al., 1999). I therefore

include a countrys democracy level from Polity (Marshall and Jaggers, 2002), which

measures the strength of democratic institutions from 0 to 10, with higher scoresindicating stronger democracy.9 Second, I account for the economic development of a

country, as measured by the log of GDP per capita taken from the United Nations

National Accounts data set. Most empirical studies demonstrate that higher degrees of

wealth are associated with lower degrees of corruption, thus I anticipate this

relationship to be robust in this analysis as well. Studies have also shown that highly

divided states are more prone to corruption compared to more homogenous states

(Mauro, 1998; Alesina et al., 2003; Charron, 2009), so I include a measure to account

for the level of a states ethnic fractionalisation, as coded by Alesina et al. (2003). I also

include a number of dummy control variables in the model, the first of which indicates

whether a country is involved in a conflict either domestic or external. I anticipate that

if a country is involved in some type of military conflict, then corruption is likely to

increase. Some scholars have asserted that internal and external conflicts have a

positive relationship with human rights violations. As for cases of conflicts, corrupt

practices and human rights violations might be the only way in which a government


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thinks it can sustain order (see Poe and Tate, 1994; Blanton, 1999). This factor is thus

controlled for. I also include a number of regional dummy controls to account for

geopolitical factors. Certain regions such as Africa have higher aggregate levels of

corruption scores than the mean scores in the full sample. Such differences need to be

accounted for in the model. Finally to check for the alternate hypothesis, economic

openness, I include an indicator that captures a countrys level of trade openness,

measured as imports plus exports/GDP, taken from the KOF Index of Globalisation.

A full list of the descriptive statistics is located in the appendix (Table A3).

When estimating the determinants of a variable such as corruption, careful

analysis is clearly needed. Thus I run multiple models in this analysis to test the

robustness of the results. The empirical field of scholarship mainly has reservations

about running time series panel analyses with corruption indicators as the dependent

variable though multiple years exist for each indicator. Therefore, I report the

estimated results of cross-sectional averages in the first section of the results.

However, it is impossible to establish any claims of a causal relationship using onlyspatial data (Granger, 1969). There is diachronic variance in all three primary

variables, thus I report the results of time series, cross-sectional regressions (TSCS).

A second advantage to the TSCS is the increased number of observations.

Additionally, due to the time series nature of the data, I include a time count

trend that begins with the first year that ICRG began to code their international risk

assessments. I do this for two reasons. First, as is common in TSCS data, the count

variable helps to avoid problems associated with spurious correlation when both the

dependent variable and the primary independent variables vary independently, but

in a constant trend over time (Tavits, 2005). This is the case with the dependentvariable (ICRG), press freedom and openness data, thus the count variable is

necessary to control for this tendency. Secondly, since the dependent variable is

based on subjective perceptions, the time count variable is expected to help us correct

for potential year-to-year differences in the administration of the PRS groups

surveys (for example, one can expect cross-time changes in the composition of the

respondents or in the way questions are framed) and trends in the systematic

diachronic changes.

The unit of analysis is thus the state-year in the TSCS data and I use a generalised

least squares (GLS) regression analysis for cross-sectional analyses that help accountfor the mulitcollinearity among the openness variables. For the time series data, and

to account for potential problems of first series autocorrelation, I follow the advice

of Beck and Katz (1995) and use panel corrected standard errors in all TSCS models

and specify for first order autocorrelation. Additionally, all openness and economic

variables are lagged by one year. The estimated model is thus:

Corruption bo b1(social or political openness) b2 (press freedom)

b3(social or political openness*press freedom) controls ei

VI. Cross-Sectional Results

Table 2 displays the cross-sectional analyses for the entire sample and the stratified

sample of developing states.10 Both ICRG and TI data are reported. As a reminder,

both corruption indices are coded such that positive coefficients indicate better

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Table2.Theeffectofsocialandpoliticalinterdependenceandopennesson

corruptionconditionedbylevelsofpressfreedom:crosss

ectionestimates

Socia

lopenness

Politicalopenness

ICRGdata

T.I.data

ICRGd

ata

T.I.data

Baseline

Full

sample{

De

veloping{

Baseline

Full

sample{

Developing{

Baseline

Full

sample

{

Developing{

Baseline

F

ull

sam

ple{

Developing{

Variable

1

2

3

4

5

6

7

8

9

10

11

12

Primaryvariables

Socialopenness

.007***

(11.47)

7.003*

(71.93)

7.004

(7

1.12)

.076***

(10.21)

7.39

(71.53)

7.035**

(72.04)

Politicalopenness

.0025***

(5.67)

7.002

(71.21)

7.001

(70.97)

.018***

(3.01)

7.018

(71.19)

7.003

(70.31)

Pressfreedom

.021***

(3.15)

7.048***

(73.16)

7.047**

(7

2.22)

.261***

(3.01)

.293**

(2.02)

.048

(0.33)

.059***

(9.21)

7.021

(71.49)

7.022

(71.45)

.656***

(8.54)

7.011

(70.30)

.174

(1.42)

Press*openness

.001***

(4.15)

.001**

(2.67)

.011***

(3.06)

.004*

(1.87)

.0005**

(2.59)

.0005*

(1.91)

.005**

(2.45)

.0013*

(1.72)

Controlvariables

Tradeopenness

.0007

(0.59)

.0004

(0.93)

.041**

(2.75)

.047***

(3.34)

.002***

(2.54)

.003**

(2.16)

.051***

(3.97)

.042***

(3.51)

Politicalrights

.006**

(2.25)

.007*

(1.93)

.003

(0.17)

.013

(0.62)

.007**

(2.66)

.006*

(1.93)

.002

(0.19)

.017

(0.69)

LogGDPpercap

.034***

(6.85)

.046**

(2.13)

.385*

(1.80)

.237*

(1.72)

.069***

(3.84)

.055**

(2.51)

.492**

(2.91)

.202*

(1.72)

Ethnicfractionalisation

7.041

(70.95)

7.044

(7

0.74)

71.03*

(71.84)

71.24**

(72.47)

7.063

(71.29)

7.065

(71.05)

71.31**

(72.45)

71.25**

(72.53)

Conflicts

7.031**

(72.25)

7.029*

(1.83)

7.259

(1.41)

7.112

(71.02)

7.024*

(71.80)

7.021*

(71.40)

7.152

(70.88)

7.068

(70.64)

Rsq.

.74

.84

.59

.71

.82

.67

.58

.83

.59

.49

.81

.65

Num.ofcountries

111

96

72

113

97

73

111

100

73

113

97

73

Notes:*p5

.10

,**p5

.05,***p5

.01.Dep

endentvariablesarePRSGr

oupMeasureofcorruption.Sampleofcountriesincludeo

nlynon-OECD

states.

Allstandarderrorsincross-sectionalmodelsarerobustandcorrectedforheteroskedasticityandautocorrelationintimeseries,p

anelregression.

Model1,fullrepresentsthefullsampleof

each.

Allmodelsrun

withareadummiestocontrolforgeography.AlldevelopingmodelsexcludeSingapo

re.

(t-statisticsinp

arentheses).

{IndicatesarobustcheckwasmadewithG

eneralizedLeastSquaresmo

del(xtglscommandinSTAT

Awithheteroskedasticpanels).


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governance or a reduction in corruption. As mentioned, there are a number of

empirical analyses that utilise cross-sectional data exclusively when studying

corruption as the dependent variable. Models 1 and 4 report the general bivariate

baseline relationship between social openness and corruption levels in developing

states, while models 2, 3, 5 and 6 report the estimates along with a series of control

variables to account for rival hypotheses, including the effect of trade openness.

Upon first glance, for either dependent variable models 1 and 4 demonstrate that

the effects of both social openness and press freedoms are associated with lower

corruption. For example, the two baseline models (1 and 4) predict that a one

standard deviation increase in social openness and press freedoms would result in a 7

per cent and between a 21 to 26 per cent decrease in corruption scores respectively.

The same relationship holds true when moving to models 7 and 10. Political

openness and press freedom when regressed independently of one another appear to

have a negative impact on corruption using either ICRG or TI data. Thus the

findings here support the notion that openness is bad for corruption.However, it is apparent with the full models, which include an interaction term

between both types of openness and press freedom, that this relationship is more

complex than what the baseline models indicate. It is important to remember that

when including an interaction term, if such an interaction is statistically significant,

the subsidiary coefficients (in this case social openness and press freedom) can only

be interpreted independently when the other (in this case press freedom) is at its

lowest value (in this case 0 for both). Therefore, in the social openness models 2 and

5 (the full samples), the results demonstrate that social openness has little to no

significant impact on corruption at the lowest levels of press freedom. Yet theinteraction term is positive and significant at the 99 per cent level of confidence in

models 2 and 5, meaning that social openness is in fact predicted to reduce

corruption, but only when a certain level of press freedom is reached. When moving

to the stratified models including only developing states, the results hold, albeit the

significance of the interaction term is reduced to the 95 per cent and 90 per cent levels

of confidence in the ICRG and TI models respectively. Interestingly, in model 6,

using the TI data, the impact of a one standard deviation increase in social openness

is expected to increase corruption by 3.5 per cent in the absence of press freedoms.

Conversely, when press freedom is held at its maximum value (10), an increase fromthe mean value of social openness in the sample to one standard deviation above the

sample mean results in about a 3 per centreduction of corruption in the data from

4.05 to 4.17.11

Looking at the effect of political openness on corruption, again the baseline

numbers show that a one standard deviation increase in this type of openness

produces an expected decrease in corruption of 2.5 per cent and 1.8 per cent from the

ICRG and TI data respectively. Yet in the interaction models, irrespective of the

sample used, the effect is insignificant at low levels of press freedoms, demonstrating

that the findings of previous results may have been slightly too optimistic. Again, the

interaction term is significant between openness and press freedom, indicating that

the effect one of these variables has on corruption is contingent upon the other for

the sake of interpretation. While the results are not quite as strong as those in the

social openness models, the impact is the same only at higher levels of press

freedom is the impact of political openness expected to reduce corruption levels. For

1484 N. Charron

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example, using the results from model 11, increasing political openness from one

standard deviation below the sample mean to the sample mean level, I find that the

predicted value of the dependent variable increases from 4.30 to 5.01 in the TI data

when press freedom is set at the maximum value. The same increase in political

openness (from one standard deviation below the sample mean to the sample mean

level) reduces the dependent variable from 3.33 to 2.89, if press freedoms are held at

the lowest value, meaninghigher levels of corruption. Thus, according to the cross-

sectional results, the two hypotheses are supported empirically in that the effect of

socio-political openness appears to be conditioned by the level of a countrys press

freedom, whether in the full sample or analysing the sample of developing states

only.

The baseline effects of press freedom in each of the models supports the general

findings in the empirical literature that the freer the press, the less corruption,ceteris

paribus. However, due to the significance of the interaction term, this variables effect

is conditioned by levels of political and social openness. Interestingly, in cases whensocial openness is extremely low, the impact of press freedom actually is expected to

increase the level of corruption. Yet the opposite is the case with low levels of

political openness, as corruption is predicted to go down as a function of press

freedom irrespective of political openness.12

A brief examination of the results of the control variables shows that all of them

produce coefficients in the expected direction. While some, including the democracy

score, ethnic fractionalisation and conflicts, go in and out of significance, they are

more or less robust irrespective of the corruption data used. Interestingly, with the

exception of models 2 and 3, the trade openness variable lends strong support toprevious findings that opening up an economy to foreign trade assists in fighting

corruption. This is most likely due to the higher level of multicollinearity between

trade and social openness than that of trade and political openness.13 Additionally,

the indicator for economic development (log of GDP per capita) is positive and

strongly robust throughout the models, adding further empirical evidence that

economic growth is important in alleviating public sector corruption.

VII. Time Series Results

Even though the cross-sectional results support the hypotheses of a conditional

relationship between openness and press freedom on corruption levels, only a time

series analysis can establish any sort of causal direction in the relationship. Thus

Table 3 reports results using corruption data from the ICRG data to test for

consistency in the estimates from Table 2. Due to the small time-frame available for

the TI data, only ICRG data are used in the TCSC analysis, which runs from 1994 to

2004.14 The models are designed similarly in that I show a basic relationship between

socio-political openness and press freedom on corruption without the interaction

term. Moreover, I again separate the sample into full samples and exclusively

developing states in order to test if the effects hold without the OECD countries.

First looking at social openness, the baseline results are quite similar to those in

the cross-sectional analyses, in that increases in such openness are predicted to

improve governance by reducing corruption. The estimates in models 1 and 3 show

that a one standard increase in social openness results in a 4 per cent reduction of


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Table3.Timeseriesestimates:theeffectofsocio-politica

lopennessoncorruptionco

nditionedbypressfreedoms

Socialopenness

Politicalopenness

Fullsample

Developing

only

Fullsample

Dev

elopingonly

Baseline

Interaction

Baseline

Interaction

Baseline

Interaction

Baseline

Interaction

Variable

1

2

3

4

5

6

7

8

Primaryvariables

Socialopenness

.004***

(17.80)

.0003

(.96)

.004***

(11.21)

.0004

(0.77)

Politicalopenness

.0008***

(6.62)

7.0005

(71.37)

.0006***

(3.02)

.0001

(0.60)

Pressfreedom

.017***

(5.93)

7

.010***

(74.40)

.004*

(1.79)

7.0031

(71.53)

.019***

(5.78)

.006**

(2.34)

.005*

(1.95)

.009**

(2.57)

Press*openness

.0006***

(15.21)

.0005***

(5.79)

.0003***

(4.34)

.0001*

(1.69)

Controlvariables

Tradeopenness

.00002

(0.09)

.0001

(0.39)

.0002

(0.17)

.0001

(0.61)

.002***

(7.26)

.002***

(7.24)

.002***

(6.26)

.002***

(7.16)

Politicalrights

.006*

(1.78)

.0004**

(2.08)

.005

(0.36)

.0004**

(2.09)

.003

(0.81)

.0003

(0.96)

.004

(1.40)

.004

(1.11)

LogGDPpercap

.599***

(20.50)

.045***

(7.74)

.032***

(10.80)

.021***

(3.34)

.077***

(19.22)

.059***

(10.14)

.055***

(9.84)

.041***

(9.06)

Ethnicfractionalisation

.058

(71.07)

7

.050***

(75.70)

7.026**

(72.26)

7.028**

(72.76)

7.061***

(73.42)

7.063***

(76.57)

7.029*

(71.68)

7.025*

7(1.91)

Conflicts

7.002

(71.20)

7

.004*

(71.84)

7.003*

(71.71)

7.002

(70.60)

7.005**

(2.38)

7.005

(71.61)

7.004**

(72.34)

7.0006

(70.28)

Yearcount

7.021***

(24.19)

7

.021***

(24.83)

7.021***

(19.20)

7.020***

(15.49)

7.018***

(18.27)

7.019***

(17.82)

7.019***

(15.63)

7.018***

(15.41)

Rsq.

.75

.79

.49

.51

.74

.76

.44

.48

Numberofobservations

951

931

691

691

951

931

691

691

Numberofcountrie

s

98

96

72

72

98

96

72

72

Notes:*p5.10,**p5

.05,***p5

.01.DependentvariablesareICRG

measureofcorruption.DevelopingOnlyincludeo

nlynon-OECD

states,Allstandarderrorscorrectedforhe

teroskedasticityandautocorrelationintimeseries,pane

lregressionusingpanelcor

rectedstandard

errors(xtpcseinSTATA).Allmodelsrunw

ithareadummiestocontrol

forgeography.Allopenness

andeconomicvariableslagg

edbyoneyear.

Z-statisticsinp

arentheses.Timeseriesdata

from

19942004.

1486 N. Charron

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corruption, while holding all other variables constant. Yet, these results are again

misleading, as the interaction terms in models 2 and 4 are strongly significant,

indicating again that the effect of social openness is conditioned by press freedom.

For example, the impact of such openness on corruption drops out of any range of

acceptable significance at low levels of press freedom, yet its effect is interpretable

when considering the effect of press freedom. Using the estimates from the

developing sample in model 4, the predicted increase in the dependent variable from

one standard deviation below the sample mean to the mean value of social openness

(moving from 20 to 38 in the KOF index) when press freedoms are held at their

minimum value moves the corruption perception from .419 to .427. To demonstrate

the contrasting effects of the conditional relationship, when the exact same increase

in social openness is estimated, yet press freedom is set at its maximum value, the

dependent variable is estimated to increase from .455 to .561, a substantially larger

decrease in the perception of corruption.

The results for political openness are similar, yet the coefficients for the interactioneffects are weaker in significance than in the social openness models. Again, even

when considering the impact of a number of control variables in models 5 and 7 in

Table 3, political openness and press freedoms appear to have a strong impact on

reducing corruption levels in both the full sample and looking at developing

countries exclusively. However, the impact of political openness in models 6 and 8

has no discernable effect when press freedoms are absent, which supports H2. Thus

when analysing either spatial or panel data, the optimistic prediction that openness is

part of the cure for a corrupt country seems to be somewhat misleading. All of the

empirical results point to the notion that within countries with little to no pressfreedoms, the impact of increased exposure to international interactions, IOs,

foreigners and diplomacy have little impact in altering the level of corruption.

Briefly, all control variables in the eight models are robust with the exception of

trade openness, which is strongly significant in models 58 yet is statistically

indistinguishable from 0 in the first four models. Again, while economic growth and

democracy are associated with lower corruption, higher levels of armed conflicts and

ethnolinguistic fractionalisation are predicted to increase corruption, all things being

equal.

VIII. Conclusion and Discussion

This analysis has examined the relationship between two non-trade forms of

international openness and corruption while taking into account the level of press

freedoms for a large sample of countries. The results found in this analysis

demonstrate clearly the complex relationship between socio-political openness and

corruption. This study has contributed to the literature in a number of interesting

ways. First, while many authors have committed substantial empirical contributions

to the economic side of the opennesscorruption nexus (trade, trade barriers, capital

freedom, and so forth), this analysis gives further insight into other important

components of globalisation namely the spread of socio-political forces, norms and

ideas. Second, due to the data availability on social and political openness, I have

been able to parse out their individual effects on the dependent variable in

question. Third, this analysis pays specific attention to how international variables


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(socio-political openness) are conditioned by domestic institutions (the level of press

freedom) in their impact on government corruption, which demonstrates a more

nuanced relationship than previously discussed in the literature. Many earlier studies

have treated openness, whether economic or socio-political, as largely independent of

domestic institutions, ignoring a potential two-level game type of effect. This analysis

shows empirically that socio-political openness has little to no impact on corruption in

the absence of press freedom. Finally, this study reports stratified models of

developing states exclusively, thereby testing to see if the results hold when dropping

OECD states out of the analyses. It is widely known among scholars and policy

makers that the OECD 24 countries exhibit better scores on corruption no matter

which source of data is employed. Studies on combating corruption also mostly imply

that their purpose is to aid developing and transitioning countries with this dilemma.

Thus this analysis has sought to parse out the differences in the sample of states and

focus on developing countries to offer specific utility for transitioning states.

Social and political openness increase information to a number of new actors inpolitics. Yet according to the empirical findings their impact on corruption is

conditioned by domestic factors. I argue that pressures for reform can be internal or

external, the latter being the focal point of this study. However, rational leaders who

have enough control over media channels can more easily divert the spread of

information from external sources, either politically or socially, which call for good

governance reforms or attempt to expose their corrupt practices. On the specific

effects of the two types of openness analysed here, political openness (IOs, UN

missions, embassy exchange) puts pressure on leaders from above so to speak, in

that increased interaction with other leaders from the international communityapplies pressure for them to conform to the rules and norms of the international

community, possibly for the sake of such reasons as aid or international reputation.

On the other hand, as social openness increases, information about anti-

corruption norms may also proliferate. However, while direct contact with

international elites is made with increases in political openness, social openness

puts pressure on domestic elites from below, meaning that it relies on everyday

citizens becoming more and more informed through new channels of information

such as new technology from international sources. Through the increased use of the

Internet, telephones, foreign newspapers, tourism, and contact with foreigners,people obtain more information and are in turn more likely to pressure their

government to become less corrupt.

Yet the results of this analysis suggest that for such international pressure, either

political or social, to be effective in combating corruption, channels must first be in

place domestically to most cost-effectively disseminate these new norms and ideas

from abroad. In the absence of press freedoms, corrupt leaders can more easily divert

and conceal information which might incriminate them in front of their domestic

constituency, regardless of external pressures from above or below to do otherwise.

Yet with such press freedoms in place, the international socio-political pressures

have a strong effect on pressuring leaders to curb corruption. Thus, from a policy

perspective, press freedom around the world should continue to be highly

encouraged, particularly in developing countries. Further, this type of integration

should be seen as an effective means of fighting corruption in transitioning countries

when the press has a substantial degree of freedom from the government. If not,

1488 N. Charron

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advocating the flow of international information channels into states with limited

press freedoms for the sake of improving governance and fighting corruption is

shown to be ineffective, according to the results in this analysis.

Acknowledgement

Special thanks to Anette Ahrnens, William Belichick, Naghmeh Nasiritousi, So ron

Holmberg, Bo Rothstein and two anonymous referees of this Journal for their

helpful comments and support.

Notes

1. See Sandholtz and Gray (2003: 769773) for a thorough overview of IO commitment to fighting

corruption.

2. See Gatti (2004: 853).3. Freedom House allows for simplicitys sake the division of press freedoms into three categories free,

partially free and not-free. I employ this categorisation in Table 1 for a clear and parsimonious

comparison between the non-free and free/partially free groups of developing countries.

4. There is a small degree of variance in the numbers due to changes in press freedom scores over time as

recorded by Freedom House. Thus, some might be recorded as not free one year and then make

enough improvements to increase their score, or vice versa.

5. Regions are based on the data from Hadenius and Teorell (2007).

6. Seehttp://www.prsgroup.com/ICRG_Methodology.aspx

7. I would like to thank one of the anonymous reviewers from theJournal of Development Studiesfor this

recommendation.

8. I would like to thank a reviewer for this suggestion. Polity data were chosen over Freedom House dueto press freedoms being incorporated into their measure of democracy. To avoid multicollinearity, I

employ a separate measure altogether.

9. Developing states are those that were not in the OECD 24. A full list is located in the appendix. Such

models are run using GLS estimates which account for first order autocorrelation and

heteroskedasticity between panels.

10. Using the MFX function in STATA allows the researcher to set values of the independent variables in

the model in order to produce marginal effects of interaction terms. STATA then produces the

predicted values of the dependent variable based on the set values of the independent variables, which

I used to calculate the percentage difference reported here. Again, as the dependent variable increases,

this indicates that the perception of corruption is lower.

11. Though this is indeed an interesting puzzle in the results, explaining the effects of press freedom on

corruption given the level of socio-political openness is admittedly complex and outside the scope of

this analysis and further research in this area would be welcome.

12. The GLS model specified for heteroskedasticity can help remedy this problem, but the covariance

between the trade and social openness variables is over .8. While multicollinearity does not bias the results

of the models, it does reduce inefficiency, thus the standard errors could in fact be smaller in reality,

rendering the effect of the trade and social openness coefficients stronger in significance in all likelihood.

13. 1994 is the starting point due to the Freedom House data, which codes countries 0100 starting in this

year as opposed to trichotomously free, partially free and not-free. To obtain the maximum

variance possible, I use only these years.

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Appendix

Social and Political Openness Indicators

Table A1. KOF components of social and political openness indices

Openness Indicator % Weighted

1) Sociala) Data on 29

personal contact Outgoing telephone traffic 14Transfers 8International tourism 27Foreign population 25International letters 27

b) Data on 35information flows Internet hosts (per 1000 people) 20

Internet users (per 1000 people) 24Cable television (per 1000 people) 20Trade in newspapers (per cent of GDP) 14Radios (per 1000 people) 23

c) Data on 36cultural proximity Number of McDonalds (per capita) 40

Number of Ikea (per capita) 40Trade in books (per cent of GDP) 20

2) Political

Embassies in country 35Memberships in International Organisations (IOs) 36Participation in UN Security Council missions 29

Note: Source is the KOF Index of Globalization (Dreher 2006).

Table A2. List of states in developing models

Albania Fiji Oman

Algeria Gabon PakistanArgentina Ghana PanamaBahamas Guatemala Papua New GuineaBahrain Guinea-Bissau ParaguayBangladesh Guyana PeruBarbados Haiti PhilippinesBolivia Honduras PolandBotswana Hungary RomaniaBrazil India Russian FederationBelize Indonesia RwandaBenin Iran Saudi Arabia

Bulgaria Israel SenegalBurma/ Myanmar Jamaica Sierra LeoneBurundi Jordan SingaporeCameroon Kenya Slovakia

(continued)

1492 N. Charron

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Table A3. Summary of variables

Variable Obs. Mean St. dev. Min. Max.

ICRG Corruption 1590 .483 .175 .055 .944Transparency International Corruption 734 4.84 2.42 0.4 10Social Openness 3143 30.58 16.21 1.93 92.75Political Openness 3143 39.75 21.55 1 96.04Trade Openness 2689 46.30 20.44 4.25 97.78Log GDP 3178 6.88 1.26 4.04 10.31Democracy 2877 4.89 3.16 0 10Ethnic fractionalisation 3358 .494 .241 .001 .931Conflicts 3136 .337 .818 0 8Press Freedom 1055 48.32 21.01 7 100

High Press Freedom 1055 .299 .458 0 1Middle Press Freedom 1055 .405 .491 0 1Low Press Freedom 1055 .294 .456 0 1Africa 3358 .302 .459 0 1Middle East 3358 .135 .342 0 1SE Asia 3358 .065 .242 0 1Latin America 3358 .261 .439 0 1

Table A2. (Continued)

Central African Republic Korea, South SloveniaChad Kuwait South AfricaChile Latvia Sri LankaChina Lithuania Syria

Colombia Madagascar TanzaniaCongo Malawi ThailandCongo, Democratic Republic Malaysia TogoCosta Rica Mali Trinidad and TobagoCote dIvoire Malta TunisiaCroatia Mauritius TurkeyCyprus Mexico UgandaCzech Republic Morocco UkraineDominican Republic Namibia United Arab EmiratesEcuador Nepal UruguayEgypt Nicaragua Venezuela

El Salvador Niger ZambiaEstonia Nigeria Zimbabwe


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