CORRUPTION AND INTERNATIONAL TRADE: A COMPREHENSIVE

ANALYSIS WITH GRAVITY

Salvador Gil-Pareja* Rafael Llorca-Vivero

José Antonio Martínez-Serrano University of Valencia

Abstract

The aim of this paper is to analyze the impact of corruption on trade. To this end, we estimate gravity equations using a wide sample of countries for a long period of time and three different measures of corruption: Corruption Perception Index (CPI), Control of Corruption Index (CCI) and a Structural Corruption Index (SCI). Our results show that perception-based indexes (CPI and CCI) give quite different results with respect to the Structural index (SCI) and that the detected impacts are somewhat sensitive to the formation of regional trade agreements or the level of per-capita income of the trade partners. In particular, when using the CCI index and there is at least one middle or low income country in the pair a positive effect of corruption on trade appears. Moreover, the negative impact of differences in the level of corruption detected with the SCI index appears to be much more relevant when the two countries are high income.

Keywords: Corruption, Gravity equation, Income level, International trade,

Regional Trade Agreements.

JEL code: F14, F15.

• Salvador Gil-Pareja, Rafael Llorca-Vivero and José Antonio Martínez-Serrano are Full Professors in Applied Economics at the Departamento de Estructura Económica, Facultad de Economía, Av. de los Naranjos s/n, C.P. 46022, Valencia, Spain. Emails: Salvador.Gil-Pareja@uv.es, Rafael.llorca@uv.es (corresponding author), Jams@uv.es Tel. 34963828349. Fax 34963828354. This study is part of a research project financed by Ministerio de Economía y Competitividad (ECO2015-68057-R, partially funded by the European Regional Development Fund -ERDF) and Generalitat Valenciana (PROMETEO II/2014-053).

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1.- Introduction

Corruption in the public sector is probably one of the main elements of failures in

governance (Acemoglu and Verdier, 2000) which directly affects institutional quality

and, thereby, the well-being of the society. The World Bank and the International

Monetary Fund share the opinion that corruption, as one of the determinants of the poor

functioning of institutions, is a great obstacle to economic and social

development. Diverse authors show how a higher degree of corruption negatively affects

economic growth (Mauro, 1995), slows down the flow of foreign direct investment (Wei,

2000) or distorts tax revenues (Mauro, 1998). The uncertainty in the decision making by

economic agents, the poor law enforcement affecting business agreements because of the

insecurity around the legal solution to disputes or the inefficient allocation of resources

are the most common arguments that are behind this adverse impact.

However, other authors present corruption as a factor that, under certain

circumstances, may facilitate economic exchanges thus improving efficiency (see, for

example, Egger and Winner, 2005, for foreign direct investment or Dreher and Gassebner,

2013, for firm entry). The reason is that, in economic systems with extensive and complex

regulations as well as weak institutions, bribes can help companies to avoid formal

regulatory barriers. That is, corruption would serve as a mechanism for deregulation. It is

the “greasing the wheels” hypothesis.

Therefore, in general terms, there are reasons to believe that corruption can act in

one or another sense and the real impact will depend on the conditions under which it

operates, although on the set of empirical contributions the negative impact usually

predominates over the positive one. In this topic, the relationship between corruption and

international trade is a less explored area. The motives for expecting that the effect of

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corruption on international trade flows is favorable or detrimental are similar to those

outlined above. On the one hand, the improper functioning of the legal framework can

hinder the effectiveness of the contracts, a fact that discourages international transactions

by increasing the cost of exporting (Anderson and Marcouiller, 2002). For this reason,

corruption would have an impact that is similar to the establishment of a tariff. On the

other hand, if tariff and/or non-tariff barriers (for instance, complex administrative

formalities) are high, bribes could facilitate trade since they would help to overcome these

obstacles.

In this sense, Dutt and Traca (2010) show that the positive effect of corruption on

trade (evasion) will surpass the negative effect (extortion) in those markets in which

tariffs are relatively high (over 25%). A reasonable corollary would be that corruption

favors international trade in those cases in which destination markets present barriers that

exceed a certain threshold. In general, the various existing empirical researches find a

negative effect of corruption on international trade. This is the case of Levchenko (2007),

focusing on institutional quality, Pomfret and Sourdin (2010), showing that direct trade

costs increase with corruption, Zelekha and Sharabi (2012), discussing the case of Israel

and Masila and Sigue (2010), on a sample of 47 African countries. On the other hand,

Voraveeravong (2013) estimates a positive effect for ASEAN countries whereas

Akbarian and Shirazi (2012) find an inverted U relationship between corruption and trade

for a sample of countries of the Middle East and Latin America. This implies that, for

such countries, an increase of corruption from relatively low levels would increase trade

while the same phenomenon from relatively high levels would reduce it.

Additionally, the phenomenon of corruption presents different aspects that can

influence the degree in which it will affect international trade. This fact is highlighted by

Thede and Gustafson (2009). These factors come from the seriousness of such conduct

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(if the corruption is severe it can limit or even prevent transactions), its prevalence (which

increases the costs of looking for an honest partner), its function (obstruction of market

competition) or its predictability (the more predictable is the lower costs implies). Other

authors also highlight several factors that it is necessary to take into account. For instance,

De Jong and Bogmans (2011) outline the importance to determine if countries act as

exporters or importers. Horsewood and Voicu (2012) argue that differences in the ethical

standards of countries would also negatively affect trade and Knack and Azfar (2003)

pose the problem of sample selection and bias resulting from those databases that

incorporate a small number of countries.

Our aim is to shed some additional light on this issue. To this end, we have made

use of three different measures of corruption. Two of them belong to the so called

perception-based indexes. The other is the result of a structural modelling which takes

into account the causes and consequences of corruption across countries. The results

obtained differ depending of the type of index used, the income group of the trading

partners and the fact that the pair of countries eventually belong to a regional trade

agreement.

The rest of the paper is as follows. In the next section, we describe the

methodology used. Section 3 offers the data sources. Section 4 presents the results and

section 5 concludes.

2.-Methodology

The vast majority of the aforementioned papers analyzing the impact of corruption

on trade make use of the well know “gravity model”, which name proceeds from its

analogy to the Newtonian physics. Gravity equations relate bilateral trade flows to the

economic size of countries, the distance between them and other factors affecting trade

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barriers as, for instance, the fact that the partners share a common language, are part of

the same regional trade agreement, etc.1

Since the appearance along the 2000s of diverse influent papers, there are some

relevant points that it is necessary to take into account in the estimation of gravity

equations in order to avoid misspecification issues. In particular, Anderson and van

Wincoop (2003) outline the necessity to account for relative trade barriers by including

in the estimation “multilateral resistance” terms, which may vary over time in panel data

models (Klein and Shambaugh, 2006 or Baier and Bergstrand, 2007). Additionally,

specific factors in the pair of countries (unobserved heterogeneity) influencing their

mutual trade but which are invariant over time have to be also controlled for (Egger,

2002). The existence of a high volume of zero trade flows in extent samples2 (Helpman,

Melitz and Rubinstein, 2008) as well as econometric problems resulting from

heteroscedastic residuals (Santos Silva and Tenreyro 2006 and 2010) are the further

elements to be considered.

With the above comments in mind and following the proposal of Santos Silva and

Tenreyro (2006, and 2010) to control for zeroes in the dependent variable and

heteroskedastic residuals, we run a non-linear Poisson estimator (Pseudo-Maximum

Likelihood Estimation – PPML) to the following gravity equation:

𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽1𝑙𝑙𝑙𝑙𝑌𝑌𝑖𝑖𝑖𝑖𝑌𝑌𝑖𝑖𝑖𝑖 + 𝛽𝛽2𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖𝑖𝑖 + 𝛽𝛽3𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖𝑖𝑖

+𝛽𝛽4𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 + 𝛽𝛽5𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 + 𝜆𝜆𝑖𝑖+ 𝜇𝜇𝑖𝑖𝑖𝑖 + 𝐶𝐶𝑖𝑖𝑖𝑖𝑖𝑖

(1)

1 The first authors to apply gravity equations to trade flows were Tinbergen (1962) and Pöyhönen (1963), although lacking a theoretical basis. Anderson (1979) first provided the theoretical background. Subsequent developments came from Bergstrand (1985 and 1989), Deardoff (1998), Evenett and Keller (2002), Anderson and van Wincoop (2003) or Helpman, Melitz and Rubinstein (2008), among others. 2 This is because the existence of non-observable firm heterogeneity and fixed trade costs in line with the so-called new-new trade theory (Melitz, 2003).

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where i and j denote trading partners, t is time, and the variables are defined as follows:

Xij are the bilateral export flows from i to j, Y denotes Gross Domestic Product (GDP),

RTA is a binary variable which is unity if i and j belong to the same Regional Trade

Agreement, CU is a binary variable which is unity if i and j belong to the same Currency

Union, Corrup is an index of corruption (our variable of interest), λ t are time dummies,

𝜇𝜇𝑖𝑖𝑖𝑖 are country-pair fixed effects and uijt is the standard classical error term.3

Ideally, our PPML estimations should incorporate into the regressions country-

year fixed effects (accounting for variations in multilateral resistance over time) and

country-pair fixed effects (controlling for unobserved heterogeneity). However, two

serious problems appear. Firstly, country-specific corruption variables are perfectly

collinear with country-year fixed effects. Secondly, and more important, the PPML

estimation with country-year and country-pair fixed effects does not reach convergence

in any of the cases. Therefore, we have to make a choice. Our decision is to estimate by

including country-pair fixed effects. As stated by Baltagi et al. (2015, pp. 624 and 630):

”The lion’s share of the variance in log bilateral trade flows xijs is contributed by the

exporter-importer (time-invariant) component…” and “Empirically, country-pair effects

explain much more of the variation in bilateral exports or imports, foreign direct

investment, or migration than country-time effects.”. Moreover, country-pair fixed effects

adequately control for the main sources of endogeneity (see Baier and Berstrand, 2007 or

Gil-Pareja et al., 2016). The cost of this decision is to obviate the fact that multilateral

resistance may change over time, which probably involves some bias. However, from the

above analysis it seems that this is not really an important shortcoming.

3 Obviously, the effect of country-pair dyadic variables (distance, common language, etc.) are controlled for bilateral fixed effects.

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Apart from country-specific corruption variables we have incorporated into the

regression two additional pair-specific variables. The first one is the summation of the

values of the respective corruption variable for the exporter and the importer (we let S-

Corrup). It would reflect something like the “whole corruption” in the pair. Obviously,

in this case we are not able to distinguish trade flows between, for instance, two middle

corrupt countries and a high corrupt country and a low one. The second index correct this

shortcoming. It is the square of the difference between the corruption indices for the

exporter and the importer (let D-Corrup). Knack and Azfar (2003) have found that trade

is reduced as far as the differences in this institutional characteristic widens.

As noted before, some papers highlight the fact that corruption have a positive

effect in countries with high barriers to trade because the “evasion effect” exceeds the

“extortion effect”. One of the most striking events in the last two decades at international

scale is the massive proliferation of regional trade agreements all over the world,

regardless of their degree of development. This fact provides valuable information. The

hypothesis to test is whether corruption affects more those countries that form trade

agreements among them, since presumably only the negative aspects (misallocation of

resources, obstacles to competition, etc.) are prevalent, than those countries which hold

high regulations among themselves. If this is the case, a jointly priority would be

liberalization and eradication of corruption.

Therefore, a second equation to estimate is the following:

𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽1𝑙𝑙𝑙𝑙𝑌𝑌𝑖𝑖𝑖𝑖𝑌𝑌𝑖𝑖𝑖𝑖 + 𝛽𝛽2𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖𝑖𝑖 + 𝛽𝛽3𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖𝑖𝑖

+𝛽𝛽4𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖𝑖𝑖 + 𝛽𝛽5𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖𝑖𝑖 ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖𝑖𝑖 + 𝜆𝜆𝑖𝑖+ 𝜇𝜇𝑖𝑖𝑖𝑖 + 𝐶𝐶𝑖𝑖𝑖𝑖𝑖𝑖

(2)

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where the interaction of the pair-specific corruption variable with the dummy for RTAs

will capture the impact of liberalization on the relationship between corruption and trade.

3.- Data

The export flows from country i to country j come from the “Direction of Trade

(DoT)” dataset, which is constructed by the International Monetary Fund (IMF). The data

comprise bilateral merchandise trade between 139 countries and territories (see Table A1)

over the period 1975-2012. GDP data are from the World Development Indicators (World

Bank). The indicators of Regional Trade Agreements (RTA) have been built using data

from the World Trade Organization, the Preferential Trade Agreements Database (The

Faculty of Law at McGill University) and the website

http://ec.europa.eu/trade/issues/bilateral/index_en.htm. The indicators of currency unions

are taken from Reinhart and Rogoff (2002), CIA's World Factbook, and Masson and

Pattillo (2005). The sample includes around 200 regional trade agreements (plurilateral

and bilateral) and 17 currency unions.

In order to construct our variable of interest we have resorted to three different

sources. The first one is the well known Corruption Perception Index (CPI) from

Transparency International. This index is built since 1995 and goes from 0 (a highly

corrupt country) to 10 (a very clean country) although the scale have changed in last years

from 0 to 100.4 As defined in the website, countries score depending on “how corrupt

their public sectors are seen to be”. That is, it is based on values given by people with

founded opinions as analyst, businesspeople or experts in the area. Nowadays the index

4 Obviously, we have used the same scale for all the period.

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is available for the majority of countries in the world but at the outset there were only 41

countries ranked.

The second measure of corruption is the “Control of Corruption Index” (CCI)

which is elaborated in the context of the project Worldwide Governance Indicators from

the World Bank. It combines data from a set of other sources (for instance CPI) and, as

such, it is based on the views of informed people and public and private organizations.

The range of values goes from -2.5 (very bad governance) to +2.5 (very good governance)

for around 200 countries and territories and for the years 1996, 1998, 2000 and 2002 to

2014.

Finally, we resort to the (what we call) “Structural Corruption Index” (SCI) by

Dreher et al. (2007). Contrary to perception-based indexes, which according to diverse

authors can be misleading, this index considers corruption as a latent variable connected

to its causes and consequences. Therefore, the respective values are obtained basing on

much more objective factors than just “opinions” which, additionally, are probably slow

moving over time even in the event of important social changes at the respective

countries. The index is constructed for around 100 countries for the periods 1976-1980,

1981-1985, 1986-1990 and 1991-1997. In our panel data sample we have decided to

assign the respective value to the year that is in the middle of each period, that is, we have

data for the years 1978, 1983, 1988 and 1994. Contrary to that occurs with the other two

indices (CPI and CCI) in this case higher values imply worse performance. For instance,

for the period 1991-1997 the least corrupt country (Switzerland) has a value of -0.9047

whereas the most corrupt country (Guinea-Bissau) presents a value of 0.3424.

4.-Results

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The results for the estimations of equation (1) for the three measures of corruption

considered are in Table 1. First of all, given that higher values for the CPI and CCI indexes

imply better performance and the contrary occurs with the SCI index, in our regressions

we have decided to change the sign of the two former variables for a clearer understanding

of the parameters estimated. In this sense, a negative value of the parameter for the

corruption variable in all the three cases implies an adverse impact of corruption on trade

flows and the contrary occurs with a positive value. As usual, the gravity equation works

well: trade flows increase with countries´ incomes and the fact that these countries

eventually share a regional trade agreement or a currency union.5

When using the CPI index (column 1) we observe that for both, the exporter and

the importer, higher levels of perceived corruption are related with lower volumes of trade

flows. The respective parameters are negative and significant at the 1% level. In order to

have an idea of the magnitude of this effect we consider the values for the best

performance6 (-10) and the worse one (-0.4). Doing this, our calculations reveal that the

potential loss (ceteris paribus) for being a totally corrupt country with respect to being a

totally clean country is around 36% for the exporter and 63% for the importer. Things

change a little when considering the CCI index. In this case, only the parameter for the

importing country is significant. Acting in the same way as before (in this case the best

performer shows a value of -2.59 and the worse one a value of 2.06) the estimated loss in

trade for the importer is around 77%, close to the impact observed with CPI. However,

things change a lot in the estimations with the structural index (SCI). As it is observed,

now trade flows increase with corruption for exporter and importer countries, although in

this last case the parameter is only significant at the 10% level. Doing the same

5 The differences observed between the regressions with CPI and CCI with that with SCI are dependent on important differences in sample size, especially with the time period considered. 6 Remember that lower values of the corruption variables means lesser corruption in our regressions.

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calculations as before, we obtain that the most corrupt country would export (ceteris

paribus) 65% more than the less corrupt one and this figure is going to be 33% for

importer countries. Although the number of observations for the regression with SCI is

much smaller than in the other two cases, this result seems strange and needs for a deeper

analysis.

Before doing this, we are going to analyze the results obtained with the pair-

specific variables. The corresponding parameters estimated are in Table 3. With respect

to the summation of the values of the index for the exporter and the importer (S-Corrup)

the pattern is the same as before: the higher the level of corruption in the pair the lower

the trade flows between partners when measuring corruption by the CPI and the CCI

indexes. The contrary occurs when we use the SCI index. However, when considering

differences in the level of corruption we detect no impact using as regressors the

perception-based indexes whereas a negative (and significant) effect is observed in the

case of the structural index. That is, to the extent that the two countries in the pair differ

in their level of corruption their mutual trade is lower. In particular, for the biggest

difference in the sample between a pair of countries trade decreases by 35%.

The next step is to check the premise about the influence of regional trade

agreements on the relationship between corruption and trade. The estimations of equation

(2) for the summation (S-Corrup) and the difference (D-Corrup) measures are in Table

3. Again, the results obtained with the CPI and CCI measures clearly differ from those

with the SCI index. With respect to the first measure, the estimations show that the

negative effect of corruption on trade is lower for the pair of countries belonging to a

regional trade agreement, contrary to our expectations. That is, the elimination of barriers

between a pair of countries also induces the drastic reduction of corruption as a barrier to

trade. However, no impact of regional trade agreements in the relationship of interest is

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detected for the SCI index. Relating to our second corruption pair-specific measure (D-

Corrup) regional trade agreements only affect the impact of corruption on trade with the

SCI variable (column 6) and in a very strong positive way.

One interesting point is whether the described pattern depends on the level of

income of countries. To this end, we have used interactive dummies considering, on the

one hand, only those pairs formed exclusively by trade flows among high income

countries (high-income) and, on the other hand, those formed by the rest of possible

combinations (rest of combinations). That is, the pairs among each high income and the

rest (middle and low income countries) and the pairs among middle and low income

countries. In order to group countries by level of income we have resorted to the World

Bank data source.7 The list of countries is in Table A1 in the Appendix.

The first point to consider is whether there are relevant differences in the level and

variability of the corruption variables between both samples. The descriptive statistics for

the three corruption measures are in Table 4. As observed, the sample containing all the

combinations of countries´ income except that between high-income countries is much

larger. As expected, the means of the respective variables reflect that developed countries

are less corrupt on average, a fact that is also reflected in the range of the maximum and

minimum values. However, the standard deviations are similar in all the cases implying

that differences in countries´ corruption across pairs are also relevant even between high-

income countries. Therefore, there is not a priori reasons to expect a different impact of

corruption on trade across the two considered samples unless this variable would impact

in a different way the cost of trading between countries depending on the level of income.

7 See http://data.worldbank.org/country.

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As observed, the consideration of the two alternative samples have some

consequences on the estimated parameters affecting our variable of interest. On the one

hand, when considering the CPI measure, the negative impact appears for corrupt

imported countries in the “high income” sample but for corrupt exported countries in the

“rest of combinations” sample. However, the summation (S-Corrup) measure gives

similar results in both samples. Another interesting difference is that, in the case of “high

income” countries, the existence of a regional trade agreement between the pair of

countries has a negative impact on mutual trade if the partners differ in their level of

corruption. That is, it seems that trade liberalization eliminates the “evasion” effect.

The most radical changes occur when considering the CCI measure, for which the

results obtained for the “high income” case are qualitatively identical to that of the CPI

measure. As observed, a positive and significant impact of corruption in the exporting

country on trade is detected in the estimations with the “rest of combinations” interactive

dummy. That is, in this case the “greasing wheels” hypothesis is accomplished. This

positive effect is also reflected in the summation pair-specific dummy.

Finally, we obtain interesting results when using the SCI variable. As observed,

the positive effect of corruption on trade occurs in both types of combinations of

countries. For the “high income” case this impact takes place when countries act as

exporters and importers but the significance is only at 10% level. For the “rest of

combinations” case the impact takes place only for countries acting as exporters and the

estimated coefficient almost doubles that for “high income” and is significant at 5%. The

summation dummy is positive in both cases but the contrary occurs: the level of

significance is higher for the “high income” case than for the “rest of combinations” case,

reflecting in this last case the non-significance for importers.

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In both cases (“high income” and “rest of combinations”) differences in the level

of corruption in the pair of countries have a negative impact on bilateral trade flows.

However, the magnitude is much bigger for “high income” countries implying that the

loss of trade for the larger difference in corruption across these partners in the sample is

around 75% whereas the value drops to around 25% for the alternative group considered.

This negative effect vanishes when countries form RTAs, especially for the “rest of

combinations” case for which it seems that differences in the level of corruption favors

trade if countries belong to a regional trade agreement.

Apart from the time period considered (we can do nothing) one of the important

differences between the estimations with the CPI and CCI indexes with respect to the SCI

index lies in the number of countries considered in the sample. In the two first cases we

have (depending on the year) till 139 countries, in the last case (also depending on the

year) just 85 countries, which are those with at least two years observations (in bold in

Table A1). Therefore, as an additional robustness check we have repeated the estimations

with the CPI and CCI indexes but the same sample of countries as in the case of the SCI

index. As before, the first estimations are done with dummies that consider all the possible

combinations of countries. The latest estimations divide these dummies considering, one

the one hand, only high income countries and, on the other hand, the rest of combinations.

As it can be seen, things do not change in a relevant manner and some of the most

interesting conclusions are reinforced. First, when considering the impact of corruption

on international trade for pairs of only high income countries the results are qualitatively

(and almost quantitatively) the same with the sole exception of the loss of significance of

the parameter which corresponds to the interactive dummy of differences in the level of

corruption with RTAs in the CPI case. Second, in the “rest of combinations” case there is

also one exception which corresponds to the negative and significant impact of

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differences in corruption levels across countries with the CPI measure, in line with the

result obtained with the SCI measure. More interesting, when considering the CCI index

the detected positive impact of corruption on trade for exporter countries is now strongest

and with a high level of significance as before (also positive for the importer country but

non-significant). This is also the case for the corresponding pair-specific corruption

dummy.

The above comments are reflected in the estimations aggregating all the possible

combinations. The two main differences with respect to consider the whole sample of 139

countries are the following: For the CPI case, differences in levels of corruption between

countries negatively impact trade, for the CCI case, the pair-specific variable for

summation of levels of corruption in the pair, although with the same negative sign, losses

its significance level.

5.- Conclusions

The effects of corruption on international trade flows are ambiguous. On the one

hand, corruption may act as a barrier to trade increasing the cost of doing international

business. On the other hand, corrupt agents may facilitate trade in countries with strict

regulations (greasing the wheels hypothesis). To determine which factor dominates is an

empirical matter. In this paper, we have used three different measures of corruption in

order to analyze this point. Two of them, the Corruption Perception Index (CPI) and the

Control of Corruption index (CCI), belong to the known as perception-based indexes. The

third (SCI), constructed by Dreher et al. (2007), comes from a structural modelling which

takes into account causes and consequences of corruption.

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The results show that differences appear in the estimated impact depending on the

measure of corruption used. In general, we observe that corruption negatively impacts

trade when perception-based indexes are used whereas the contrary occurs when we

alternatively include into the regression the structural index. Additionally, in this last case,

differences in corruption levels in the pair of trading partners negatively affect their trade

flows.

These results have to be qualified when we take into account the level of per-

capita income of countries or the fact that they form part of a regional trade agreement.

In particular, when using the CCI index and there is at least one middle or low income

country in the pair a positive effect of corruption on trade appears. Moreover, the negative

impact of differences in the level of corruption detected with the SCI index appears to be

much more relevant when the two countries are high income. In general terms, regional

trade agreements counterbalance the negative effect of corruption on trade flows but there

are some exceptions.

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19

Table 1: Estimations of the impact of corruption on trade

Sample period: 1995-2012 for CPI, 1996, 1998, 2000 and 2002 to 2012 for CCI and 1978, 1983, 1988 and 1994 for SCI. Notes: Regressand: log of real bilateral exports. Robust standard errors (clustered by country-pairs) are in parentheses. *significant at 10%; **significant at 5%; ***significant at 1%. CPFE: country pair fixed effects. CPI: Corruption Perception Index. CCI: Control of Corruption Index. SCI: Structural Corruption Index.

PPML-CPFE Variables CPI CCI SCI LnYitYjt 0.697

(0.02)*** 0.685

(0.023)*** 0.663

(0.037)***

RTAijt 0.098 (0.047)**

0.085 (0.037) **

0.363 (0.037)***

CUijt 0.155 (0.044)***

0.151 (0.045)***

0.170 (0.158)

Corrupit -0.032 (0.011)***

0.017 (0.032)

0.400 (0.153)***

Corrupjt -0.051 (0.012)***

-0.123 (0.034)***

0.229 (0.123)*

Observations 176,038 208,782 16,456

20

Table 2: Estimations of the impact of corruption on trade. PPML-CPFE Variables CPI CCI SCI CPI CCI SCI LnYitYjt 0.697

(0.025)*** 0.686

(0.023)*** 0.662

(0.036)*** 0.711

(0.024)*** 0.691

(0.023)*** 0.642

(0.033)***

RTAijt 0.098 (0.047)**

0.086 (0.039)**

0.364 (0.036)***

0.118 (0.048)**

0.092 (0.039)**

0.382 (0.051)***

CUijt 0.157 (0.044)***

0.153 (0.045)***

0.171 (0.155)

0.167 (0.044)***

0.147 (0.044)***

0.169 (0.167)

S-Corrupijt -0.041 (0.009)***

-0.055 (0.025)**

0.320 (0.089)***

D-Corrupijt -0.001 (0.001)

0.000 (0.006)

-0.278 (0.113)**

Observations 176,038 208,782 16,456 176,038 208,782 16,456 Summation of the indexes of corruption in the pair (S-Corrupt) and Squared difference of the indexes of corruption in the pair (D-Corrup). Sample period: 1995-2012 for CPI, 1996, 1998, 2000 and 2002 to 2012 for CCI and 1978, 1983, 1988 and 1994 for SCI. Notes: Regressand: log of real bilateral exports. Robust standard errors (clustered by country-pairs) are in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. CPFE: country pair fixed effects. CPI: Corruption Perception Index. CCI: Control of Corruption Index. SCI: Structural Corruption Index.

21

Table 3: Estimations of the impact of corruption on trade. PPML-CPFE Variables CPI CCI SCI CPI CCI SCI LnYitYjt 0.693

(0.026)*** 0.686

(0.023)*** 0.659

(0.037)*** 0.711

(0.024)*** 0.691

(0.023)*** 0.627

(0.033)***

RTAijt 0.517 (0.099)**

0.144 (0.043)***

0.376 (0.059)***

0.105 (0.052)**

0.105 (0.043)**

0.273 (0.030)***

CUijt 0.167 (0.043)***

0.149 (0.046)***

0.168 (0.154)

0.169 (0.044)***

0.147 (0.044)***

0.176 (0.169)

S-Corrupijt -0.057 (0.009)***

-0.074 (0.027)***

0.311 (0.089)***

S-Corrupijt* RTAijt 0.036 (0.008)***

0.045 (0.022)**

0.025 (0.083)

D-Corrupijt -0.002 (0.002)

0.002 (0.007)

-0.341 (0.113)**

D-Corrupijt* RTAijt -0.001 (0.002)

-0.005 (0.011)

0.782 (0.169)***

Observations 176,038 208,782 16,456 176,038 208,782 16,456 Summation of the indexes of corruption in the pair (S-Corrupt) and Squared difference of the indexes of corruption in the pair (D-Corrup). Interaction of corruption with Regional Trade Agreements S-Corrup* RTA and D-Corrup* RTA. Sample period: 1995-2012 for CPI, 1996, 1998, 2000 and 2002 to 2012 for CCI and 1978, 1983, 1988 and 1994 for SCI. Notes: Regressand: log of real bilateral exports. Robust standard errors (clustered by country-pairs) are in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. CPFE: country pair fixed effects. CPI: Corruption Perception Index. CCI: Control of Corruption Index. SCI: Structural Corruption Index.

22

Table 4: Summary Statistics for corruption variables Variable Index Sample Obs. Mean Std. Dev. Min. Max.

Corrupit Corrupjt

CPI

High income

34216 -6.714 1.995 -10 -1.7

Rest 228772 -4.080 2.149 -10 -0.4

Corrupit Corrupjt

CCI

High income

30926 -1.089 0.924 -2.59 1.71

Rest 239290 0.170 0.957 -2.59 2.06

Corrupit Corrupjt

SCI

High income

5499 -0.181 0.201 -0.905 0.258

Rest 38703 0.068 0.208 -0.905 0.345

S-Corrupijt

CPI

High income

30372 -13.358 2.828 -20 -3.4

Rest 189608 -7.897 2.573 -16.39 -0.8

S-Corrupijt

CCI

High income

30926 -2.180 1.308 -5.18 3.42

Rest 239016 0.340 1.211 -3.35 4.12

S-Corrupijt

SCI

High income

3501 -0.373 0.303 -1.809 0.515

Rest 22439 0.152 0.272 -1.377 0.691

D-Corrupijt

CPI

High income

30372 7.833 9.985 0 62.41

Rest 189608 10.332 14.110 0 90.250

D-Corrupijt

CCI

High income

30926 1.704 2.373 0 17.98

Rest 239016 2.195 2.995 0 19.62

D-Corrupijt

SCI

High income

3501 0.085 0.149 0 1.351

Rest 22439 0.110 0.198 0 1.562

Notes: High income means that both countries in the pair are high-income countries. Rest are the complementary pairs of High-income. CPI: Corruption Perception Index. CCI: Control of Corruption Index. SCI: Structural Corruption Index.

23

Table 5: Estimations of the impact of corruption on trade by the level of development. PPML - CPFE

Only High Income countries Rest of combinations

Variable CPI CCI SCI CPI CCI SCI

Corrupit -0.002 (0.018)

-0.032 (0.042)

0.318 (0.175)*

-0.056 (0.010)***

0.095 (0.041)**

0.596 (0.275)**

Corrupjt -0.077

(0.017)*** -0.228

(0.041)*** 0.312

(0.188)* -0.026

(0.015)* -0.028 (0.046)

0.333 (0.307)

S-Corrupijt -0.041 (0.013)***

-0.130 (0.030)***

0.312 (0.089)***

-0.042 (0.010)***

0.061 (0.035)*

0.474 (0.246)*

D-Corrupijt -0.001 (0.002)

0.003 (0.013)

-1.040 (0.162)***

-0.002 (0.002)

-0.001 (0.007)

-0.197 (0.116)*

S-Corrupijt -0.064 (0.014)***

-0.158 (0.031)***

0.295 (0.087)***

-0.051 (0.009)***

0.049 (0.039)

0.477 (0.251)*

S-Corrupijt* RTAijt 0.038 (0.008)***

0.056 (0.021)***

0.032 (0.077)

0.037 (0.012) ***

0.038 (0.032)

-0.082 (0.269)

D-Corrupijt 0.004 (0.002)

0.019 (0.013)

-1.117 (0.154)***

-0.003 (0.002)

-0.001 (0.008)

-0.249 (0.118)**

D-Corrupijt* RTAijt -0.009 (0.003)***

-0.042 (0.020)**

1.078 (0.211)***

0.003 (0.003)

-0.000 (0.012)

0.667 (0.189)***

Observations 176,038 208,782 16,456 176,038 208,782 16,456

Notes: Regressand: log of real bilateral exports. Robust standard errors (clustered by country-pairs) are in parentheses.* significant at 10%; ** significant at 5%; *** significant at 1%. CPFE: country pair fixed effects. CPI: Corruption Perception Index. CCI: Control of Corruption Index. SCI: Structural Corruption Index.

24

Table 6: Estimations of the impact of corruption on trade for the CPI and CCI measures using the same sample of countries as for the SCI measure.

PPML - CPFE

All combinations Only High Income countries

Rest of combinations

Variables CPI CCI CPI CCI CPI CCI

Corrupit -0.036 (0.012)***

0.045 (0.038)

-0.001 (0.019)

-0.025 (0.048)

-0.065 (0.011)***

0.142 (0.049)***

Corrupjt -0.061 (0.013)***

-0.098 (0.041)***

-0.081

(0.020)*** -0.225

(0.049)*** -0.041

(0.013)*** 0.074

(0.050)

S-Corrupijt -0.048 (0.009)***

-0.027 (0.031)

-0.043 (0.015)***

-0.125 (0.038)***

-0.054 (0.010)***

0.108 (0.040)***

D-Corrupijt -0.004 (0.001)**

-0.009 (0.007)

-0.004 (0.003)

-0.012 (0.019)

-0.003 (0.001)**

-0.008 (0.007)

S-Corrupijt -0.063 (0.011)***

-0.044 (0.034)

-0.066 (0.017)***

-0.155 (0.040)***

-0.058 (0.011)***

0.100 (0.046)**

S-Corrupijt* RTAijt 0.034 (0.009)***

0.034 (0.025)

0.033 (0.009)***

0.051 (0.023)**

0.030 (0.012)**

0.021 (0.031)

D-Corrupijt -0.004 (0.001)**

-0.008 (0.008)

0.001 (0.003)

0.019 (0.022)

-0.004 (0.002)**

-0.009 (0.009)

D-Corrupijt* RTAijt -0.002 (0.002)

-0.003 (0.011)

-0.006 (0.004)

-0.053 (0.029)*

0.004 (0.003)

0.002 (0.012)

Observations 86,493 90,736 86,493 90,736 86,493 90,736

Notes: Regressand: log of real bilateral exports. Robust standard errors (clustered by country-pairs) are in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. CPFE: country pair fixed effects. CPI: Corruption Perception Index. CCI: Control of Corruption Index. SCI: Structural Corruption Index.

25

Appendix A Table A1: List of Countries

High Income Middle and Low Income Australia Austria Bahrain Belgium-Luxembourg Canada Chile China – Hong Kong China – Macao Croatia Cyprus Czech Republic Denmark Equatorial Guinea Estonia Finland France Germany Greece Iceland Ireland Israel Italy Japan Korea Kuwait Latvia Lithuania Malta Netherlands New Zealand Norway Oman Poland Portugal Qatar Russia Saudi Arabia Singapore Slovak Republic Slovenia Spain Sweden Switzerland United Arab Emirates United Kingdom United States of America Uruguay

Albania Algeria Angola Argentina Armenia Azerbaijan Bangladesh Belarus Benin Bolivia Bosnia and Herzegovina Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Cape Verde Central African Republic Chad China – Mainland Colombia Congo, D.R. Congo, Republic of Costa Rica Côte d’Ivoire Djibouti c(L) Dominican Republic Ecuador Egypt El Salvador Gambia Georgia Ghana Guinea Guinea Bissau Guatemala Guyana Haiti Honduras Hungary India Indonesia Iran Jamaica Jordan Kazakhstan

Kenya Kyrgyz Republic Lebanon Macedonia Madagascar Malaysia Mali Mauritania Mexico Mongolia Morocco Mozambique Nepal Nicaragua Niger Nigeria Pakistan Panama Paraguay Peru Philippines Romania Rwanda Senegal Serbia and Montenegro Sierra Leone South Africa Sri Lanka St. Tome and Principe Sudan Swaziland Syria Tajikistan Thailand Togo Tonga Tunisia Turkey Uganda Ukraine Venezuela Vietnam Yemen Zambia Zimbabwe

Note: In bold the sample of countries used for estimations with the SCI measure.