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Freedom of Information Acts and

Public Sector Corruption

Monica Escaleras Shu Lin Charles Register*

August 2008

Abstract: Over the past several decades a number of countries have implemented Freedom of Information acts with the hope that greater transparency would assist in controlling public sector corruption. To consider the effectiveness of these acts, we analyze annual data on 128 countries during the period 1984 through 2003 using a variety of propensity score matching techniques. Overall, we find no significant relationship between the implementation of a FOI act and corruption. While this result holds for developed countries, in the developing world, enactment of FOI acts is significantly and consistently associated with rising rather than falling levels of corruption. As such, we conclude that implementation of these acts should not be seen as a panacea for the problem of corruption in the public sector. Keywords: Freedom of information, governmental transparency, and public sector corruption. JEL Classifications: D73, D78, H11, O10 ____________ *Monica Escaleras: (Corresponding Author) Florida Atlantic University, Department of Economics, 777 Glades Road, Boca

Raton, FL 33431; E-mail: [email protected]; Phone: (561) 297-1312; FAX: (561) 297-2542.

Shu Lin: University of Colorado-Denver, Department of Economics, Campus Box 181, Denver, CO 80217.

Charles Register: Florida Atlantic University, Department of Economics, 777 Glades Road, Boca Raton, FL 33431;

E-mail: [email protected]; Phone: (561) 297-3222; FAX: (561) 297-2542.

mailto:[email protected]

mailto:[email protected]

Yesterday’s guilty plea by Rep. Randy “Duke” Cunningham—make that former representative, since he resigned after entering the plea—reveals the most brazen bribery conspiracy in modern congressional history. A San Diego Republican and Vietnam War veteran who served on the House Appropriations defense subcommittee and the intelligence committee, Mr. Cunningham admitted taking $2.4 million in bribes from two defense contractors angling for government contracts and from two other co-conspirators. The court papers filed in the case, jaw-dropping as they are, don’t address a critical question: How could this happen? To some extent, it’s hard to guard against out-and-out corruption and criminality by someone bent on breaking the law; as the court papers describe it, the congressman and his co-conspirators worked to “conceal and disguise” their activities “by directing payments through multi-layered transactions involving corporate entities and bank accounts.” Mr. Cunningham also lied on his financial disclosure forms and filed false tax returns.

But there are also indications that the system failed. Mr. Cunningham’s ability to pull off the caper was helped by the fact that lawmakers don’t need to list their homes or mortgage debt on financial disclosure forms; such a listing might have provided an earlier clue to the wrongdoing. More fundamentally, the papers say that Mr. Cunningham used his influence in the congressional appropriations process to benefit the contractors and “took other official action to pressure and influence” Defense Department personnel to give contracts to his co-conspirators.1

1. Introduction

While, as the author states, the case of public sector corruption described above is objectively

“brazen” it is also unfortunately not isolated. At a local level, relatively minor cases, in a monetary

sense, of providing favors to local officials for the letting of contracts for items as seemingly trivial as

garbage pick-up and disposal are quite common. At the opposite extreme, very large sums of money

often line the pockets of corrupt public officials at a national level as contracts are let for the

construction of major water treatment or electric power generation projects. Similar exist at every

intervening level of government making bribery likely the most common form of public sector

1 Brazen Conspiracy, Staff Editorial, The Washington Post, November 29, 2005, p. A20.

1

corruption in that private parties have monetary interests in nearly all decisions made at all levels of

government. Thus, cases of public officials trading the power afforded them by their office for

personal gain can be found involving all levels of government, many if not most economic sectors,

and in all or nearly all countries, regardless of their level of development.2

Of course, bribery is but one common form of public sector corruption. If the specific case

referred to in the introductory piece is considered “brazen”, it pales in comparison to the numerous

instances of state leaders plundering their country’s resources and coffers. Consider, for example, the

case of the former Haitian leader Jean-Claude Duvalier who, it is estimated, stole what amounts to

about 3 percent of that country’s meager GDP each year while in office. Similar cases of looting

include: $15-35 billion taken by former Indonesian dictator Mohamed Suharto; $5-10 billion by the

Philippines’ Ferdinand Marcos; $5 billion by Mobutu Sese Seko of the Congo; $2.5 billion by Sani

Abacha of Nigeria; $1 billion by Serbia’s Slobodan Milosevic; and, about $600 million by Peru’s

Alberto Fujimori.3

The problem of corruption in government has been seen to be so severe and pervasive that

international actors such as the World Bank and the United Nations, among others, have taken

affirmative steps to attempt to reign in these practices. For example, the World Bank has a

Department of Institutional Integrity as part of its focus on improved governance. The department is

charged with investigating allegations of corruption in Bank-related operations. Since 2001, the

department has handled nearly 3,000 cases of alleged corruption resulting in sanctions being placed

2 Rose-Ackerman (1999) offers a good treatment of the literature on public sector corruption. Further, numerous case

studies on corruption can be found in Transparency International’s annual Global Corruption Report, published by London’s

Pluto Press.

3 Data taken from Transparency International’s Global Corruption Report 2004, Special Focus: Political Corruption.

2

on some 340 companies and individuals.4 For its part, the United Nations adopted a Convention

against Corruption in September, 2003, which took effect in December, 2005.5 This convention

includes a chapter specifically addressing the issue of the recovery of assets stolen by government

officials such as those noted above. To put teeth into this initiative, in September, 2007 the World

Bank and the United Nations established a joint program, the Stolen Assets Recovery Initiative,

charged with recovering the billions of dollars illegally looted annually from the public assets of

developing countries.6,7

While the actions of international players like the United Nations and the World Bank in

fighting public sector corruption are growing in strength, equally important are steps being taken at

the national level in many countries. Generally speaking these focus on enhancing the transparency of

governmental actions. The underlying idea here is simple: In the case of a public official being

bribed for the right, as examples, to erect a housing complex or water treatment facility in a

sub-standard way or perhaps in which the bribe targets a favorable zoning ruling, enhanced

transparency affords all interested parties the opportunity to see the corruption and, if empowered to

4 Further information on this and other recent World Bank activities in the area can be found at

http://go.worldbank.org/CI3TOJK4I0.

5 Available at: http://unodc.org/unodc/en/treaties/CAC/index.html.

6 Additional information about this program can be found at http://abcnews.go.com/International/wireStory?id=3616330.

7 A great deal of research has been directed toward identifying ill-effects of public sector corruption and shows, among

other things, that it significantly: 1) lowers investment and limits economic growth (Mauro 1995, Mo 2001, Pellegrini and

Gerlagh 2004, and Meon and Sekkat 2005); 2) causes countries to receive less foreign aid (Alesina and Weder , 2002); 3)

causes countries to be less attractive targets for foreign direct investment (Habib and Zurawicki, 2002); 4) increases fatalities

from traffic accidents (Anbarci, Escaleras, and Register, 2006); 5) increases fatalities from earthquakes (Escaleras, Anbarci,

and Register, 2007); and, 6) restricts access to clean drinking water and improved sanitation (Anbarci, Escaleras, and

Register, forthcoming).

3

http://go.worldbank.org/CI3TOJK4I0

http://unodc.org/unodc/en/treaties/CAC/index.html

http://abcnews.go.com/International/wireStory?id=3616330

do so, either intervene prior to the consummation of the conspiracy or, at a minimum, punish the

conspirators after the fact. It is the notion that transparency can lead to improved government

performance that has prompted recent implementation of laws providing greater public access to

internal government operations, know as Freedom of Information (FOI) acts, by a considerable

number of countries. In fact, a number of instances can be cited where the discovery of extreme cases

of corruption have lead directly to the implementation of FOI acts. Examples include: Japan’s

implementation of a FOI act in 1999 following a bribery case involving the Lockheed Corporation

and a recognized widespread culture of corruption in that country’s public works and construction

sectors; Canada’s 1982 implementation following scandals involving police surveillance as well as

the government’s general regulatory practices toward private business; and, Ireland’s 1997

implementation following public outcry over the government’s poor handling of the meat-packing

industry and public blood banks.8

A typical example of a FOI act is that of the U.S. which was implemented in 1966, but

significantly strengthened following the Watergate scandal of the early 1970’s. The law establishes

the presumption that all records of government agencies are to be made available to the public upon

request (though exceptions and time delays may be imposed by the executive for reasons of national

security, necessary secrecy surrounding the negotiations of international accords, and the like). While

differing in elements such as what and when the information is made available and how enforceable

the laws are, by the end of 2003 some 46 countries had implemented some form of a FOI act.

Essential to the rationale of enacting a FOI act is the belief that providing enhanced transparency

into governmental operations is tantamount to providing the public (or some other group such as

8 Information taken from Blanton (2002).

4

watchdog organizations, non-governmental agencies, and especially independent media

organizations) the tools necessary to effectively not just expose but punish both the public and private

parties who enter into corrupt compacts. One might be tempted to conclude that the combination of a

FOI act and free elections allowing the public to “kick the bums out” might be enough yet even in the

most democratic of countries, public scandals are far from infrequent. Similarly, public sector

corruption exists in countries with comparatively free presses—after all, it is through a free press that

knowledge of corrupt practices is typically made available even when no group in society has the

power to deter it. Thus, while the added transparency of a FOI act may prove effective in combating

corruption, its effectiveness is clearly limited by the ability of interested parties to act on the

information provided.

Even in environments where the enhanced transparency offered by a thoroughgoing FOI act is

coupled with the ability of interested parties to act on any corrupt public actions brought to light

exists, it must be recognized that such laws can have perverse effects. Bac (2001) provides an

excellent treatment of the two-faced nature of increased transparency resulting from policies such as

FOI acts. Specifically, while fully accepting the potential positive effects as discussed above, he

points out that FOI acts may actually lead to an increase in corruption as they provide information as

to whom within government to target for bribes. In this sense, the effectiveness of a FOI act is

determined by which effect dominates: the positive effect of providing actionable information on

corrupt governmental actions to the public or the negative effect of helping private parties identify and

connect with the government official who can deliver what they desire and who is most likely to be

amenable to a bribe.

In this paper, we consider the relationship of FOI acts and corruption by analyzing annual

5

data between 1984 and 2003 on 128 countries. We are aware of only two papers that address the issue

of FOI acts and government performance. Closest to our analysis is a working paper by Tavares

(2007) in which the author conducts a difference-in-difference test for up to 72 countries and finds a

consistent positive relation between the implementation of FOI acts and corruption in the public

sector. While the result is interesting, as discussed below, we do not find it fully persuasive due to the

assumption that the likelihood of implementing FOI acts is random across countries, which is at the

heart of the difference-in-difference methodology. Also related is Islam (2006) who considers the

relation between information flows and the quality of governance. As part of that analysis, Islam

reports that the existence of a FOI act is associated with better governance, that is, less corruption.

However, the precise issue we address—whether the enactment of a FOI act is associated with

improvement on the corruption front—is not addressed by Islam.

With respect to the difference-in-difference methodology used by Tavares (2007), it is our

contention that finding a relationship between the implementation of a FOI act and public sector

corruption does not necessary prove the case. There may be a classic self-selection problem in which

adopting countries would have experienced the same path of corruption whether or not the FOI act

had come into being. That is, unless it is assumed that the likelihood of implementing a FOI act is

random across countries simply looking at the level of corruption before and after implementation

within a given country may lead to misleading conclusions. We control for this possibility by using a

variety of propensity score matching techniques developed in the treatment effects literature. This

approach yields two outcomes of interest. First, for the entire sample, we find no significant effect of

the implementation of FOI acts on public sector corruption. Second, and more interestingly, when the

sample is divided into developed and developing countries, while no significant effect is found for the

6

developed countries, we do find the implementation of FOI acts to be positively and significantly

related to public sector corruption in developing countries.

In the following section, we describe the data used in our analysis. In the third section we

describe the empirical methods while the fourth section presents results. In the final section, we

offer a summary conclusion and discussion.

2. Data

The primary analysis draws on annual observations of 128 countries between the years 1984 and 2003

yielding a total sample size of 1,683.9 On average, FOI acts tend to have been in place longer in

developed countries. Of the 23 developed countries in the sample, 8 had such acts in place prior to

1984 and another 10 implemented such legislation during the sample period. Conversely, none of the

105 developing countries in the sample had a FOI act in place prior to 1984, though 28 implemented

such an act between 1984 and the end of 2003. Information on each country’s FOI status is presented

in Appendix 1 which also includes each country’s average corruption score (defined below) before

and after implementation of the FOI act with the only obvious pattern being the well-publicized fact

that, on average, developed countries score better (higher values for this index) with respect to public

sector corruption than do developing countries.

Table 1 presents descriptive statistics on the variables used in the primary analysis. Precise

summary definitions and sources can be found in Appendix 2. The key variables in the analysis relate

to the perceived level of public sector corruption within a country and its FOI act status. Our primary

measure of the level of public sector corruption (COR1) is taken from the International Country Risk

9 The sample is unbalanced since, for a country-year to be included, we required complete data.

7

Guide (ICRG), published by Political Risk Services Group, as assembled by the IRIS Center at the

University of Maryland. The specific variable taken from this source evaluates the overall level of

corruption in government. COR1 ranges from zero to six with larger values indicating less corruption,

as seen in whether high-level government officials are likely to require special payments and whether

such bribes are expected throughout all levels of government (see Knack and Keefer, 1995, for a

thorough treatment of this measure). Recalling Bac’s (2001) argument of the possibility that greater

governmental openness might lead to enhanced corruption through its tendency to provide

information on whom to offer a bribe, this index is particularly well-suited for our analysis. The mean

for COR1 is 3.136 with country-year observations taking on all potential values of zero to six.

Since the measure of corruption is key to the analysis, we take account of the fact that other

measures of corruption are available and commonly used in empirical work by running all models

with an alternative corruption index taken from Transparency International (TI). Like the ICRG

index, this alternate measure of corruption, COR2, is based on surveys and focuses on the misuse of

public power for private gain through acts such as bribe-taking. The index ranges from zero to ten

with ascending values suggesting less corruption. The mean for this variable is 4.633 and again

country-year observations can be found throughout the variable’s possible range of zero to ten. We

employ both COR1 and COR2 to provide some degree confidence that the results reported are not

being driven by the choice of this key variable though, since Alesina and Weder (2002) have shown

that there is very high correlation between each of the available measures of corruption and that each

tends to be very highly correlated over time, no such problems are expected. That is, we expect to find

results that are quiet similar for the two variables.

The other key variable in the analysis relates to a country’s FOI act status. Descriptive statistics

8

are given for two FOI variables: FOII and FOIA. The former of these identifies a country that has

not just adopted a FOI act but that has also implemented it while the later points to countries which,

while having adopted the act, have, as of the end of the sample period, not yet implemented it. We

take these variables from Banisar (2006) who provides the most comprehensive summary of FOI acts

available, including insightful discussions of the acts in the various countries. The 46 countries that

had implemented such acts by the end of 2003 are listed in Appendix 1. The means show that the

sample includes 333 country-year observations for which a FOI act had been adopted and 316 in

which it was implemented by the end of the sample period.

As our prime contention is that the decision as to whether to adopt a FOI act may not be a

random choice, we include the remaining seven political/institutional and economic variables of Table

1 in the primary analysis. Each is available for the entire sample period. These were chosen because,

while being likely related to the decision to adopt a FOI act, they have been shown to be significantly

correlated to public sector corruption.10 The first five of these are political and institutional in nature.

As a measure of the extent of democracy within a country, we use an index provided by

Polity IV. This scale, which runs from zero to ten, ranks countries annually as to the general openness

of their public institutions, with lower values indicating lesser degrees of openness. For the current

sample, the mean value of DEM is 5.255 and specific observations are found throughout the variable’s

potential range. The next is a measure of civil liberties (CIVIL) taken from Freedom House. The index

ranges from one to seven with higher values reflecting less civil freedom. The mean for the sample is

3.586 and again, the sample includes country-year observations at each point on the variable’s

potential range. The following three variables, FED, PROT, and BRIT are each taken from Treisman

)(DEM

10 See Treisman (2000) and Serra (2006).

9

(2000). FED is a dichotomous variable with a value of one when government is relatively centralized

and zero otherwise. Here, the mean country-year observation of 0.181 reflects a good deal of

decentralization in the sample, though the standard deviation clearly points to a fairly wide range.

PROT and BRIT reflect the percentage of a country’s population belonging to the Protestant religion

as of 1980 and whether the country’s legal origin is British, respectively. For the typical observation

in the sample, almost 14 percent of the population was protestant while 28.6 percent of the countries

had legal systems with British origins.

The final two variables in the primary analysis are economic in nature, GDPPC and TRADE.

GDPPC is measured in constant (2000) U.S. dollars, as reported in the World Bank’s World

Development Indicators. While the mean is a relatively modest $6,630, country-year observations

range from a meager $82 to nearly $55,000. Finally, TRADE indicates the country’s openness and

involvement with international transactions as it measures the percent of the sum of a country’s

imports and exports relative to GDP. This data is also taken from the World Bank’s World

Development Indicators and has a mean of about 77 percent with a broad range of 10.8 to 473.5.

3. Empirical Methods

3.1 Treatment Effect and Selection Bias

To estimate the average treatment effect of implementing a FOI act on the treated, that is, those

country-year observations in which a FOI act was implemented (ATT), we consider the following

equation:

]1|[]1|[ 01 =−== iiii DYEDYEATT (1)

where is a policy implementation dummy. D 1|0 =ii DY is the level of corruption that would

10

have been observed if an implementing country had not enacted a FOI act and is the

level of corruption actually observed in the same country. The fundamental difficulty in estimating

ATT is that the second term on the right-hand side is not observable. Corruption is only observed

under either the treated or control regimes, but never both. If a country’s policy implementation

choice is completely random, one can obtain the ATT by comparing the sample means of the

treatment group (countries that have implemented a FOI act) with those of the control group

(countries that have not implemented a FOI act). However, this method would generate biased

estimates if the policy implementation decision is not random. Specifically, if the decision to

implement a FOI act is systematically correlated with a set of observable variables that also affect

corruption, then we have a classic “selection on observables” problem, which makes linear regression

that relies on a targeting dummy unreliable.

1|1 =ii DY

11

To shed light on the potential for non-randomization in the policy implementation choice, in

Table 2 we offer simple difference in means t-tests on the political/institutional and economic

variables presented in Table 1 for the treated and control sub-groups.12 The differences are striking:

for each of the seven variables, the two groups exhibit significant differences beyond the 0.05 level in

all but one case (which is still significant at the 0.1 level). Specifically, the treated group tends to

have higher levels of democracy, civil liberties, centralization, Protestant population, real GDP per

capita, and openness to international trade, while having slightly lower likelihood of having a British

legal origin. While not proving the existence of non-randomness in the policy implementation

11 See Dehejia and Wabba (2002) and Heckman et al. (1998) for detailed discussions. Also, note that the selectivity problem

here is neither selection on unobservables (omitted variables) nor a Heckman type sample selection problem.

12 These political and economic variables are commonly examined in the corruption literature. See, for example, Treisman

(2000) and Serra (2006).

11

process, such differences do suggest that the possibility of a selection bias should not be ignored.

3.2. Matching on Propensity Scores

To address the potential selection bias problem, we make use of a variety of propensity score

matching methods recently developed in the treatment effects literature. The central idea of matching

is to use a control group to mimic a randomized experiment. The key assumption needed to apply the

matching method is the conditional independence assumption ( XDYY |, 10 ⊥ ), which requires that,

conditional on X , the outcomes be independent of the policy implementation variable. Under this

assumption, equation (1) can be rewritten as

],0|[],1|[ 01 iiiiii XDYEXDYEATT =−== (2)

where we have replaced with],1|[ 0 iii XDYE = ],0|[ 0 iii XDYE = , which is observable.

As a first step, one might match the treated units to the control units having similar values of X .

As the number of covariates in X increases, however, this method creates a “high-dimension”

problem and becomes impractical to implement. To overcome this, Rosenbaum and Rubin (1983)

propose matching the treated units and control units using propensity scores, which are the

probabilities of policy adoption conditional on X which can be estimated using simple probit or

logit models. A further assumption needed to apply propensity score matching is the common support

assumption ( ), which requires the existence of some comparable control units for each

treated unit. Using propensity score matching, the ATT now can be estimated as:

1)( <iXp

)](,0|[)](,1|[ 01 iiiiii XpDYEXpDYEATT =−== (3)

We consider a variety of commonly used propensity score matching methods. The first method is

nearest-neighbor matching with replacement, which matches each treated unit to the control units

having the closest propensity scores. We employ two nearest-neighbor matching estimators:

n

1=n

12

and . The second matching method we use is radius matching, which matches a treated unit to

those control units with estimated propensity scores falling within some radius

3=n

r . We consider three

values for the radius: 1) wide radius ( 1.0=r ); 2) medium radius ( 05.0=r ); and, 3) tight radius

( ). Next we take advantage of kernel matching, which matches a treated unit to all control

units weighted in proportion to the closeness in the propensity scores between the treated unit and the

control unit. Finally, the last method we use is regression-adjusted local linear matching, developed

by Heckman et al. (1998).

025.0=r

4. Empirical Results

4.1 Estimating the Overall Treatment Effects Using the Pooled Sample

We first estimate the treatment effects of implementing a FOI act on corruption in the overall sample.

4.1a Estimating the Propensity Scores

As described above, propensity scores are estimated using a probit model where the dependent

variable is the policy implementation dummy FOII. The control variables included in the probit

regressions should be the characteristics that are likely to lead to non-random selection of policy

implementation.13 Therefore, we use the seven variables listed in Table 2.

The results are reported in the first column of Table 3 (ignore the remaining columns for the

moment). We find that all the control variables, except the BRIT dummy, are statistically significant.

The results suggest that the probability of implementing a FOI act is positively related to a country’s

degree of democracy, extent of civil liberties, degree of decentralization, percent Protestant, real GDP

13 It is important to note that the goal of estimating the propensity score is not to find a best statistical model to explain the

probability of policy implementation. According to the conditional independence assumption, it is not a problem not to

include variables that systematically affect the implementation probability but not corruption in the probit regressions. See

Persson (2001) for detailed discussions.

13

per capita, and openness to international trade (recall that CIVIL and FED are coded such that higher

values reflect lower levels of civil liberties and greater levels of centralization). The overall fit of the

regression is reasonable, with a pseudo R-square of 0.33.14 From this regression, the mean propensity

scores of the treated and control groups are calculated. The mean estimated propensity score of the

treated group is 0.466 while the mean estimated propensity score of the control group is only 0.140,

which again highlights the strong differences between the two groups.

4.1b Results from Matching

The matching results are presented in the first row of Table 4 (ignore the remaining rows for the

moment). The first two columns of the table show the results from the one-to-one nearest-neighbor

and 3-nearest-neighbor matching techniques. The next three columns report the results from radius

matching while the results from local linear regression matching and kernel matching are shown in the

last two columns of the table. The estimated ATTs are all quantitatively very small and have different

signs across different matching methods. Furthermore, no single estimated ATT is statistically

significant suggesting that, regardless of the matching technique applied the implementation of a FOI

act is not significantly related to corruption.

4.2 Estimating the Treatment Effects by Level of Development

Thus far, we have found that for the entire sample the effect of implementing a FOI act on corruption

is insignificant. In this section, we ask the question of whether this result holds for countries at

different levels of development. To test this idea, we split the full sample into developed and

developing sub-groups using the World Bank’s classification and replicate the analysis presented

above.

14A pseudo R-square around 0.2-0.4 is comparable to an ordinary least squares adjusted R-square of 0.7-0.9.

14

The probit estimates of propensity scores for the two sub-samples are shown in the second and

third columns of Table 3. Again, in both regressions, most of the control variables are statistically

significant. However, most of the political and institutional variables behave quite differently across

the two groups. and are positive and significant in the developing country regression

but negative and significant in the developed country regression, while

DEM CIVIL

BRIT is negative and

significant in Column 2 but positive and significant in Column 3. Further FED is found to be negative

and significant in the developing country regression but positive and insignificant in the developed

country regression. Finally, although is positive in both regressions, it is only significant for

the developed countries. The behavior of the two economic variables is more consistent across the

two regressions. and TRADE are positive in each regression though only the latter is

significant.

PROT

GDPPC

Rows 2 and 3 of Table 4 present the matching estimates for the developing and developed

countries, respectively. Immediately apparent is the fact that, regardless of the matching technique

employed, the relationship between corruption and the implementation of a FOI act is quite different

across the two groups. In the developing countries, the estimated ATTs are all negative, quantitatively

large, and statistically significant, implying that implementing a FOI act leads to a significantly higher

level of public sector corruption. The average estimated ATT across the different matching techniques

on corruption is about -0.459. At the same time, in the developed countries, although the estimated

ATTs are all negative, they are much smaller in magnitude and never statistically significant. The

average estimated ATT across the different matching methods for the developed sub-sample is only

around -0.214. Apparently, while for the developed countries, implementing a FOI act has no

significant effect on corruption, in the developing world implementation is associated with rising

15

levels of corruption.

4.3 Robustness Checks

In this section we consider a number of robustness checks of our finding that implementing a FOI act

has different effects in developed versus developing countries. As a first check we restrict the sample

to ensure that our treated units and control units share the same support so that they are truly

comparable. To do this, we sort all the observations by their estimated propensity scores and then

discard all the control units whose estimated propensity scores are lower than the lowest score among

the treated units. The matching results are shown in Table 5 and prove quite consistent with those

presented in Table 4 for the developing and developed sub-samples. That is, while in both the

estimated ATTs are all negative, they are much larger and statistically significant only for the

developing sub-sample.

As a second robustness check we use the alternative measure of corruption, COR2, taken from

Transparency International. The results are reported in Table 6 and again point to the same

conclusion, that is, that while negative in all cases, the estimated ATTs are much larger in the

developing sub-sample and only statistically significant for this group. The average estimated ATT

across different matching methods in the developing countries is about -0.786, which is comparable to

the average ATTs obtained in Tables 4 and 5 based on the ICRG corruption index, COR1, which has a

scale of zero to six.

Next, we examine the robustness of our results by using the date when a FOI act is approved by

the legislature rather than the date when the act is actually implemented. The new probit regression

results are presented in columns 4 and 5 of Table 3. They are very similar to the results reported in

Columns 2 and 3. The matching results are shown in Table 7. Again, using the date when the policy

16

was adopted does not significantly affect our results. The treatment effect of adopting a FOI act is

again found to be insignificant in developed countries but negative and quite significant in developing

countries.

To determine whether our results are sensitive to the independent variables chosen for our probit

model specifications we add two additional explanatory variables: urban population as a share of total

population (URBP ) and population density ( ). The new probit estimates of propensity

scores using the two sub-samples are reported in the last two columns of Table 3. is found to

be positive and significant in both regressions while is negative in both columns but only

significant in the developed sub-sample. Nevertheless, using the alternative probit model specification

does not change our main result. As shown in Table 8, we again find that implementing a FOI act is

associated with a significant increase in corruption in developing countries while no significant effect

is found for developed countries.

PDEN

URBP

PDEN

15

Finally, we check the robustness of our results using a control function regression approach. The

results are presented in Table 9. In the first two columns, we simply compare the average level of

corruption between the treated group and the control group by regressing corruption on the policy

implementation dummy ( I) with no controls. The policy dummy is found to be negative and

insignificant in developing countries but positive and significant at the 5 percent level in developed

countries. In the next four columns, we include the estimated propensity scores obtained from the two

different probit specifications ( and , respectively) as a control function.

FOI

1PS 2PS 16 In developing

15 The same robustness checks have been conducted for the pooled sample. The estimated ATTs are all insignificant.

16 PS1 are the estimated propensity scores obtained from our baseline probit models (columns 2 and 3 of Table 3). PS2 are

the estimated propensity scores obtained from the probit models which include URBP and as additional

explanatory variables (columns 6 and 7 of Table 3).

PDEN

17

countries, the policy dummy becomes significant at the 1 percent level no matter which probit

specification we use. In developed countries, the policy dummy now becomes negative and even

marginally significantly, at the 10 percent, level if we control for but negative and insignificant

if we control for . In addition, in both country groups, the propensity scores are positive and

significant at the 1 percent level, which is evidence for strong selection bias. In the last four columns,

we add country and year fixed effects in the regressions to further control for unobserved

heterogeneity. The policy dummy still remains negative and significant in developing countries, but

loses its significance in developed countries even if we control for . The estimated coefficients on

the policy dummy in developing countries are -0.478 and -0.468 in column 7 and column 9, which are

quantitatively close to the ATTs obtained from matching reported in the previous tables. The

propensity scores are still significant in developing countries, but they are no longer significant in

developed countries after controlling for unobserved heterogeneity. The results from these regressions

are, therefore, quite consistent with the matching results, once we control for self-selection and

unobserved heterogeneity.

1PS

1PS

2PS

All in all, the results tell a very consistent story: Implementing a FOI act has significantly

different impacts on corruption for developing versus developed countries. While no significant

relationship is found between the implementation of a FOI act and corruption in developed countries,

consistently we find the implementation of FOI acts to be associated with increasing levels of public

sector corruption in the developing world.

5. Conclusion and Discussion

Public sector corruption has been linked to many economic and social ills including, but not limited

to, retarded economic growth and domestic investment, reduced foreign aid and foreign direct

18

investment, limitations on the development of social infrastructure, and rising death tolls from natural

disasters. For corrupt public officials to conspire with private parties for their mutual benefit, at the

expense of the general public, and with only a limited fear of reprisal, two factors must be present: 1)

there must be a lack of information as to the conspiracy available to the public, watchdog

organizations, non-governmental organizations, a free news media or other similar agents; and/or, 2)

those organizations must lack the ability to effectively punish the conspirators. Given this, laws

designed to provide the public with greater insight into governmental operations such as FOI acts

would be thought to at least be part of an effective anti-corruption campaign.

Our purpose has been to put this proposition to the test. That is, by the end of 2003, 46

countries had implemented FOI acts. What has been the experience of these countries with

respect to corruption relative to those countries who have failed to implement such laws?

To address this question, we have analyzed annual data on 128 countries during the period

1984 through 2003. Given the possibility of selection bias associated with the decision to

implement such policies, we applied a variety of propensity score matching techniques

developed in the treatment effects literature. The results we find are consistent and not

encouraging. Overall, we find no significant relationship between the implementation of a

FOI act and corruption. But more disturbing, we find that while this overall result holds for

developed countries, in the developing world, implementation of FOI acts is actually

significantly and consistently associated with rising levels of corruption. Clearly,

implementation of such acts is not a panacea for the problem of corruption in the public

sector.

While it is beyond our scope to determine why these acts have not proven to improve

19

public sector corruption, we do wish to offer some possible reasons and in doing so perhaps

offer some pathways for future research. As a first possibility, it must be acknowledged that

while differing in degree, all FOI acts allow someone or group within government discretion

concerning what and when to disseminate information to the public. In some cases, the

requested information is simply not released, perhaps due to national security reasons

claimed by government. In other cases the requested information is released but only after a

significant delay in time. Interesting in this regard is a 2006 survey by the Open Society

Justice Initiative. 17 The report is based on about 2,000 identical requests for public

information presented to 14 countries. The results indicted that within countries with

implemented FOI acts, 38 percent of the time the requests went unanswered.

It is especially important to note the timeliness of responses to requests. In some instances even

if the information flows to the public in response to FOI acts, it may have little value upon

arrival—for example, if the corrupt officials are no longer in office or for whatever reason outside the

reach of punishment. And even when the information arrives, it must be recalled that the simple flow

of information to the public is never enough, on its own, to effectively reign in corruption. Some

actors, be they the general public, an independent news media, or whomever, must have the power to

respond to the information in an effective way. Finally, it should be recognized that while a number of

the countries that have implemented FOI acts did so expressly in an attempt to limit corruption, others

did so with less noble intentions. The U.S.’s implementation in 1966 of its FOI act is a prime

example. Rather than being implemented as a curative for government corruption, the act was the

17 The Open Societry Justice Initative’s report, Transparency and Silence, is available at:

http://www.justiceinitiative.org/db/resource2?res_id=103424.

20

http://www.justiceinitiative.org/db/resource2?res_id=103424

outgrowth of partisan politics as the democratic controlled congress simply wanted access to the

deliberations of the executive branch of the prior republican administration of President Dwight

Eisenhower. And this is not an isolated case. In fact, one of the oldest FOI acts in existence is that of

Sweden, adopted in 1776, which also was the result of competition for political power.18 To the extent

that such acts are put in place for reasons other than as an anti-corruption measure, perhaps their lack

of effectiveness in the fight against corruption should not be surprising. No doubt, in many instances,

one of these possibilities has some explanatory power in the result of no significant relationship being

found in the overall sample.

None of these points, however, really gets at the fundamental contribution of this paper: That

FOI acts are positively and significantly related to corruption in the developing world. Here we offer

three possible explanations. First, it should be recognized that many of these acts are rather young in

nature. Perhaps not enough time has passed, on average, for the acts to show their full effectiveness.

Given the fact of the vintage nature of many of the acts that exist in the developed world, however,

this possibility does not seem especially promising. Perhaps of more potential value, however, is the

dichotomy offered by Bac (2001) with respect to the relation between greater transparency in

government and corruption. Specifically, he points out that while increasing the flow of information

to the public creates an impulse toward improved corruption as it at least identifies the corrupt actors,

by doing so it also creates a countervailing effect of providing improved information to the public as

to who within government to connect with to pursue corrupt acts. The latter is expected to have

relatively great weight in circumstances where public actors have relatively little ability to effectively

change government or in which there is weak dissemination of the information to the public—perhaps

18 See Blanton (2002).

21

due to a less than completely free press. This unfortunately is a good description of many developing

countries. Alternatively, our result may reflect a perverse effect of the pressure that has been applied

recently by international agents such as the World Bank and the International Monetary Fund for

recipient nations to take concrete anti-corruption steps. It could possibly be the case that some

developing nations adopt FOI acts at least partly to provide a deliverable on the anti-corruption front

but that this ultimately results in little more than window dressing for the international agents,

providing the recipient nation with a cloak for even more corruption. Such an outcome seems

particularly likely in relatively authoritarian developing countries.

How much of the effects reported here are due to any of these justifications is unknown and

perhaps beyond the reach of studies such as this which rely on international comparisons. As such,

potentially fruitful research may require more of a case study approach. Regardless, and given the

standard caveats, one thing seems apparent: FOI acts on their own are not likely to prove an effective

remedy for public sector corruption.

22

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24

Appendix 1. Adoption/Implementation of FOI Laws and Corruption

Country FOIA FOII Corruption before FOII

Corruption after FOII

Developed Countriesa

Australia *** *** 4.95 Austria 1987 1987 5.00 4.76 Belgium 1993 1994 5.28 3.87 Canada *** *** 5.84 Denmark 1985 1985 6.00 5.96 Finland 1999 1999 6.00 6.00 France *** *** 4.62 Germany … … 5.03 Greece 1999 1999 4.63 3.92 Iceland 1996 1997 6.01 5.63 Ireland 1997 1998 4.91 2.72 Italy … … 3.35 Japan 1999 2001 4.51 3.36 Luxemburg … … 5.64 Netherlands *** *** 5.89 New Zealand *** *** 5.68 Norway *** *** 5.63 Portugal 1993 1993 4.52 4.61 Spain 1992 1992 4.25 3.50 Sweden *** *** 5.95 Switzerland … … 5.57 United Kingdom 2000 … 5.14 United States *** *** 4.67

Developing Countriesa

Albania 1998 1998 3.67 20.7 Algeria … … 2.71 Angola 2002 2002 2.67 Argentina … … 3.22 Armenia 2003 2003 1.86 1.50 Azerbaijan … … 2.00 Bahamas … … 2.28 Bahrain … … 3.14 Bangladesh … … 1.11 Belarus … … 3.03 Bolivia … … 2.21 Botswana … … 3.51 Brazil … … 3.46 Brunei … … 4.17

25

Bulgaria 2000 2000 3.93 2.31 Burkina Faso … … 3.03 Cameroon … … 2.38 Chile … … 3.34 China … … 3.02 Colombia 1985 1985 3.00 2.67 Congo, DR … … 0.39 Costa Rica … … 4.75 Cote d'Ivore … … 3.01 Croatia 2003 2003 2.90 Cyprus … … 4.00 Czech Republic 1999 1999 4.10 3.30 Dominican Republic … … 3.15 Ecuador … … 2.96 Egypt … … 2.28 El Salvador … … 2.73 Estonia 2000 2000 5.00 3.85 Ethiopia … … 2.35 Gabon … … 1.39 Gambia … … 3.08 Ghana … … 2.65 Guatemala … … 2.51 Guinea … … 3.37 Guinea-Bissau … … 2.00 Guyana … … 1.99 Haiti … … 1.29 Honduras … … 2.04 Hong Kong … … 4.45 Hungary 1992 1992 4.11 4.51 India … … 2.65 Indonesia … … 1.45 Iran … … 3.25 Israel 1998 1998 4.80 3.12 Jamaica 2002 2002 2.35 Jordan … … 3.36 Kazakhstan … … 2.12 Kenya … … 2.71 Kuwait … … 2.73 Latvia 1998 1998 No data 2.57 Lebanon … … 2.00 Liberia … … 1.03 Lithuania 2000 2000 3.00 2.73 Madagascar … … 3.98 Malawi … … 3.27

26

Malaysia … … 3.79 Mali … … 1.96 Malta … … 3.57 Mexico 2002 2002 2.85 2.00 Moldova 2000 2000 2.00 1.73 Mongolia … … 3.62 Morocco … … 2.80 Mozambique … … 3.54 Namibia … … 3.47 Nicaragua … … 4.36 Niger … … 2.38 Nigeria … … 1.71 Oman … … 2.93 Pakistan … … 2.02 Panama 2001 2001 2.00 2.00 Papua New Guinea … … 2.75 Paraguay … … 1.34 Peru 2002 2002 2.97 2.42 Philippines 1987 1987 0.56 2.51 Poland 2001 2001 4.30 2.00 Romania 2001 2001 2.98 2.38 Russia … … 2.08 Saudi Arabia … … 2.22 Senegal … … 2.95 Sierra Leone … … 2.07 Singapore … … 4.50 Slovakia 2000 2000 3.80 2.92 Slovenia 2003 2003 3.71 3.00 South Africa 2000 2000 4.88 2.60 Sri Lanka … … 3.27 Sudan … … 1.61 Suriname … … 2.68 Taiwan … … 3.56 Tanzania … … 3.01 Thailand 1997 1997 3.00 2.05 Togo … … 1.97 Trinidad & Tobago 1999 1999 2.77 2.53 Tunisia … … 2.90 Turkey 2003 2003 2.75 Uganda … … 2.32 Ukraine 1992 1992 2.03 Uruguay … … 3.00 Venezuela … … 2.82 Vietnam … … 2.44

27

Yemen … … 2.81 Zambia … … 2.54 Zimbabwe 2002 2002 2.83 0.00 Notes: … indicates a country did not adopted/implement FOI laws during 1984-2003 *** indicates a country adopt/implement FOI laws before 1984. a World Bank classification of Develop and Developing countries.

28

Appendix 2. Definition and Sources of Variables Variable

Definition

Source

FOI Implemented (FOII)

A country has adopted and implemented a Freedom of Information law

Basinar (2006)

FOI Adopted (FOIA)

A country has only adopted a Freedom of Information law

Basinar (2006)

CORRUTION-ICRG Index (COR1)

Annual surveys with 6 indicating least corruption and 0 most corruption

International Country Risk Guide

CORRUPTION-TI Index (COR2)

Annual surveys with 10 indicating least corruption and 1 most corruption

Transparency International

DEMOCRACY (DEM)

Scale of 0-10 with higher values indicating more thoroughgoing democratic institutions

Polity IV database

CIVIL LIBERTIES (CIVIL)

Scale of 1-7 with higher values indicating less freedom

Freedom House

FEDERALISM (FED)

Dichotomous variable with a value of one when government is relatively centralized and zero otherwise

Treisman (2000)

POTESTANT (PROT)

Share of the total population of Protestant faith in 1980

Treisman (2000)

BRITISH LEGAL ORIGIN (BRIT)

Dummy variable indicating that the legal origin of the country is British

Treisman (2000)

GDP per capita (GDPPC)

Real GDP per capita, expressed in constant (2000) U.S. dollars

World Development Indicators

OPENESS TO TRADE (TRADE)

Sum of exports and imports of goods and services measured as a share of GDP


URBAN POPULATION (URBP)

The share of the total population living in areas defined as urban


POPULATION DENSITY (PDEN)

Population of a country per square kilometer


29

Table 1. Descriptive Statistics

Variable Mean Std. Dev Minimum Maximum

1COR 3.136 1.392 0 6

2COR 4.633 2.392 0 10

FOII 0.188 0.391 0 1

FOIA 0.198 0.399 0 1

DEM 5.255 4.110 0 10

CIVIL 3.586 1.801 1 7

FED 0.181 0.385 0 1

PROT 13.850 22.911 0 97.8

BRIT 0.286 0.452 0 1

GDPPC 6,630.4 9,231.5 82.158 54,778.65

TRADE 76.597 47.357 10.831 473.510

30

Table 2. Sample Characteristics by Treatment Status

Variable Mean Std. Dev Minimum Maximum

Treated

DEM 9.301 1.331 0 10

CIVIL 1.898 1.215 1 7

FED 0.236 0.425 0 1

PROT 26.677 32.069 0 97.8

BRIT 0.258 0.438 0 1

GDPPC 14,683.030 11,240.520 274.371 40,947.42

TRADE 80.212 36.772 17.175 184.121

Control

DEM 4.483 4.008 0 10

CIVIL 3.934 1.702 1 7

FED 0.163 0.370 0 1

PROT 10.203 17.946 0 96.6

BRIT 0.293 0.455 0 1

GDPPC 4,800.599 7,604.916 82.158 54,778.65

TRADE 75.775 49.419 10.831 473.510

31

Table 3. Probit Estimates of Propensity Scores Baseline model

(FOII as dependent variable) FOIA as dependent variable Adding more controls to Baseline model

Pooled Developing Developed Developing Developed Developing Developed

0.176*** 0.259*** -1.533*** 0.251*** -1.431*** 0.260*** -1.059*** DEM (0.028) (0.036) (0.450) (0.034) (0.422) (0.036) (0.390)

-0.107** 0.185*** -0.770*** 0.174*** -0.707*** 0.200*** -0.815*** CIVIL (0.053) (0.059) (0.207) (0.056) (0.200) (0.061) (0.208)

-0.206** -1.240*** 0.079 -1.042*** -0.067 -1.353*** -0.470*** FED (0.099) (0.380) (0.146) (0.292) (0.146) (0.396) (0.162)

0.006*** 0.0003 0.012*** 0.002 0.009*** -0.001 0.001 PROT (0.002) (0.004) (0.003) (0.003) (0.003) (0.004) (0.004)

-0.039 -0.595*** 0.622*** -0.521*** 0.752*** -0.326* -0.082 BRIT (0.089) (0.188) (0.149) (0.161) (0.157) (0.194) (0.192)

3.3*10-5*** 1.8*10-5 7.1*10-6 2.5*10-5 2.2*10-5** 2.0*10-5 2.9*10-5** GDPPC (5.7*10-6) (2.0*10-5) (1.1*10-5) (1.8*10-5) (1.1*10-5) (3.0*10-5) (1.4*10-5)

0.004*** 0.005*** 0.005** 0.005*** 0.006*** 0.006*** 0.007*** TRADE (0.001) (0.002) (0.002) (0.001) (0.002) (0.002) (0.002)

0.011** 0.037*** URBP (0.005) (0.008)

-0.0001 -0.004*** PDEN (0.0001) (0.001)

No. of Obs. 1683 1269 414 1269 414 1269 414 Pseudo- 2R 0.33 0.25 0.21 0.24 0.21 0.25 0.27

Notes: Constant terms are included but not reported. Robust standard errors are reported in parenthesis. *, **, and *** indicate the significance level of 10%, 5% and 1%, respectively.

32

Table 4. Matching Estimates of Average Treatment Effect (Baseline Model) Matching Method

Radius Matching

Nearest Neighbor Matching

3-Nearest Neighbor Matching r=0.025 r=0.05 r=0.1

Local Linear Regression Matching

Kernel Matching

-0.009 -0.015 -0.002 0.002 0.037 0.050 0.003 Pooled

(0.128) (0.122) (0.093) (0.077) (0.075) (0.092) (0.079)

-0.523** -0.555*** -0.411*** -0.422*** -0.425*** -0.456*** -0.421*** Developing (0.218) (0.183) (0.154) (0.126) (0.124) (0.141) (0.007)

-0.231 -0.234 -0.186 -0.221 -0.157 -0.247 -0.221 Developed

(0.247) (0.229) (0.189) (0.167) (0.119) (0.237) (0.182)

Notes: A 0.06 fixed bandwidth and an Epanechnikov kernel are used for kernel and local linear regression matching. Bootstrapped standard errors are reported in parenthesis. They are based on 500 replications of the data. *, **, and *** indicate the significance level of 10%, 5%, and 1%, respectively.

33

Table 5. Matching Estimates of Average Treatment Effect (using more restrictive sample) Matching Method

Radius Matching




Kernel Matching

-0.610*** -0.553*** -0.413*** -0.425*** -0.429*** -0.455*** -0.424*** Developing (0.216) (0.185) (0.149) (0.130) (0.122) (0.141) (0.134)

-0.288 -0.235 -0.179 -0.226 -0.155 -0.265 -0.222 Developed

(0.241) (0.222) (0.190) (0.171) (0.122) (0.217) (0.175)


34

Table 6. Matching Estimates of Average Treatment Effect (using alternative measure of corruption) Matching Method

Radius Matching




Kernel Matching

-0.810* -0.774** -0.672** -0.815*** -0.650*** -0.974*** -0.808*** Developing (0.477) (0.365) (0.314) (0.300) (0.215) (0.366) (0.279)

-0.390 -0.326 -0.158 -0.274 -0.195 -0.390 -0.338 Developed

(0.374) (0.373) (0.340) (0.321) (0.284) (0.411) (0.347)


35

Table 7. Matching Estimates of Average Treatment Effect (using date of adoption, FOIA, as dependent variable) Matching Method

Radius Matching




Kernel Matching

-0.574*** -0.523*** -0.476*** -0.466*** -0.474*** -0.455*** -0.468*** Developing (0.202) (0.176) (0.135) (0.125) (0.117) (0.130) (0.121)

-0.061 -0.100 -0.062 -0.083 -0.119 -0.027 -0.086 Developed

(0.200) (0.196) (0.173) (0.159) (0.111) (0.197) (0.166)


36

Table 8. Matching Estimates of Average Treatment Effect (adding more explanatory variables to the Baseline Model) Matching Method

Radius Matching




Kernel Matching

-0.422* -0.503*** -0.577*** -0.459*** -0.424*** -0.444*** -0.467*** Developing (0.224) (0.177) (0.265) (0.126) (0.121) (0.136) (0.137)

0.019 0.508* 0.098 0.224 0.302 0.164 0.199 Developed

(0.386) (0.275) (0.249) (0.276) (0.244) (0.374) (0.285)


37

38

Table 9. Results from Control Function Regressions No controls Control for estimated propensity scores Further control for country and year fixed effects

Developing Developed Developing Developed Developing Developed Developing Developed Developing Developed

-0.016 0.237** -0.502*** -0.195* -0.510*** -0.040 -0.478*** -0.115 -0.468*** -0.114 FOII (0.103) (0.090) (0.110) (0.102) (0.110) (0.111) (0.110) (0.109) (0.109) (0.109)

2.074*** 2.062*** 1.334*** 0.184 1PS

(0.337) (0.237) (0.359) (0.267)

2.088*** 1.199*** 1.391*** 0.100

2PS (0.332) (0.212) (0.362) (0.261)

Notes: Constant terms are included but not reported. Robust standard errors are reported in Parenthesis. *, **, and *** indicate the significance level of 10%, 5% and 1%, respectively.

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