1. The Impact of Corruption on Trade Costs: Labor-
Intensive Versus Capital-Intensive Industries
Gregory Brucchieri, Brian LeBlanc, Daniel Pulido-Mendez
Abstract
This paper uses highly disaggregated trade data from the US Cen-
sus to analyze the impact of corruption and institutions on the cost of
international trade. Through multiple specifications, this paper finds
corruption to be significantly correlated with higher trade costs, ceteris
paribus, re-affirming the results of Pomfret and Sourdin (2010) among
others. We then test this result at the product-level by analyzing the
heterogeneous impact of corruption on different product groupings.
Our results suggest that products that are more capital-intensive are
more susceptible to higher trade costs through corruption and bribery
than labor-intensive products.
1 Introduction
Contemporary research in international trade has been more focused on
its impact on national economic growth and trade costs, given the decline
in tariffs worldwide. On the former, studies tend to agree that increased
trade and open trade policies helps to improve a countries national economy
and growth prospects. Sun and Heshmati (2010) found that the Chinese
provinces that increased their participation in global trade greatly increased
their wealth and growth over other provinces. This effect was heightened in
1

2. the provinces that focused on high end technology exports.
So it would seem necessary for developing countries to try to increase
their participation in global trade to improve their situation. However, while
trade costs have been falling worldwide, they remain much higher for less
developed countries (The World Bank, 2013). Costs of transporting goods
are a major impediment to exports (WIlson & Otsuki, 2004). Lower trade
costs have been shown to increase exports, raise real wages in all countries
and increase sales revenue (Eaton, Kortum, & Kramarz, 2009).
One reason for higher trade costs is that poorer countries tend to have
more restrictive trade policies (Kee, Nicita, & Olarreaga, 2009). Research
indicates that open trade policies are positively correlated with growth
(Mbabazi, Milner, & Morrissey, 2004) and increased firm productivity (Topalova
& Khandelwal, 2011). Particularly troubling for less developed countries is
the result that restrictive trade policies result in less foreign direct invest-
ment (FDI) (Gorg & Labonte, 2012). Thus, these restrictive policies often
coincide with poorer institutional and infrastructure quality, other sources
for increased trade costs.
Another impediment to increased trade, possibly coinciding with restric-
tiveness, is government corruption. Corruption has been shown to hamper
trade and hurt the overall economy (Thede & Gustafson, 2012), while an ab-
sence of corruption increases economic growth (Serritzlew, Mannemar Son-
derskov, & Tinggaard Svenson, 2014). Even when it has been shown to help
certain industries avoid poor institutions and red tape, the effect was for
very limited sectors and still hurt the overall trade industry and economic
growth (Dutt & Traca, 2010; de Jong & Bogmans, 2011). Overall, decreas-
2

3. ing corruption and increasing transparancy creates significant potential for
trade and welfare gains (Abe & Wilson, 2008).
In this paper we will attempt to measure the direct effects of corruption
and institutional quality on trade costs. We use as a base for our model the
one designed by Pomfret and Sourdin (2012) in their paper Why Do Trade
Costs Vary?, which found that corruption and institutional quality have a
direct impact on trade costs. Instead of the Australian data they used, we
will be looking at US import data collected from the World Bank. We then
will look at the effects of corruption on specific industries by developing an
interaction variable. These ideas will be expanded upon later in the paper.
2 The Economic Model
As mentioned in the onset, the decline in artificial barriers to trade has
created increased interest in factors which impact the cost of trading across
borders. Numerous studies and surveys of exporters in both developing and
developed countries have highlighted different aspects of related to trade
costs 1. Pomfret and Sourdin (2010), for example, have modeled trade
costs as gravity-type equation where the costs of freight and insurance are
positively correlated with distance, poor institutional quality, and a variable
aimed to capture the bulkiness of the particular shipment. Others have
highlighted the importance of port and infrastructure quality on the cost of
shipping from a particular country or region.
1
For a summary of these surveys and studies see De, Prabir, ”Why Trade Costs Mat-
ter,” Asia-Pacific Research and Training Network on Trade Working Paper Series, No 7,
2006
3

4. In formulating the model, we categorize variables prior research has
found to impact trade costs into three sections: product-specific costs, in-
stitutional related costs, and costs related to geography of the exporter. We
discuss these briefly in turn.
Product-specific costs are those which can be assumed to be felt the same
across all exporters, despite the country of origin, the infrastructural quality,
or any other factor which may impact the cost of trade not directly related to
the product. For example, the weight and dimension of the shipment should
be expected to be positively correlated with the cost of shipping irregardless
of other factors which may impact the cost of that shipment. Similarly, the
total value of the good being shipped would also fall into this category, since
intuition tells us the cost of insuring a shipment is an increasing function of
the value of the good being insured.
Geographical-related costs are factors such as the remoteness of the ex-
porter or the distance the good has to travel before reaching its final des-
tination. For example, one would reasonably expect that shipping a good
from China to the United States should cost more than shipping the same
good from Canada to the United States, ceteris paribus. Other research
has highlighted the impact of being landlocked or not having access to an
established shipping route as having a negative correlation with trade.
Lastly, institutional-related costs - which is where this paper centers its
focus - are costs which the trader incurs due to things such as beaurcratic
inefficiencies, corruption, or any other governance-related factors that im-
prove or hinder the trading environment. This paper focuses most of the
analysis on corruption, but other institutional factors will be discussed as
4

5. well. Summarizing, our model is as such:
TradeCosts = f(P, I, G)
Where P, I, and G are vectors of product-specific variables, institutional-
quality variables, and geographic-related variables. We now discuss the data
and the econometric specification.
3 Econometric Model
3.1 The Data
The data employed by this study is a highly-disaggregated dataset of import
transactions into the United States during the year 2012, provided by the
US Census. Whereas most studies have used similar types of datasets, this
is the only one the authors are aware of that analyses data at the 10-digit
HTC level, the most disaggregated international classification of trade. The
benefit of using the most disaggregated level of import data is that allows
us to take into account very detailed product-level information which is lost
at broader classifications.
An immediate issue with the dataset, however, is that there are a good
amount of observations which are appear to be measurement error. For
example, an observation which is listed as costing $1 to transport 10,000kg
worth of good from Nigeria to the United States is most likely a sampling
error than worthwhile information. Thus, we cleaned the data along the
5

6. lines of Pomfret and Sourdin (2010). After cleaning, the trade cost variable
appears to conform to a log-normal distribution, which is in-line with prior
research on variable trade costs.
3.2 Econometric Specification
Similarly to previous models of trade costs, this paper uses a semi-log model
with log of total trade costs as the dependent variable and a number of inde-
pendent variables representing the product-specific, institional-related, and
geopgraphical factors mentioned in the last section. The list, description,
and summary statistics of the variables identified as relevant dependent vari-
ables can be found in Table 1 in the Appendix. Our first specification we
model a simple linear regression as follows:
log(TCi) = α1Airi + α2log(Disti) + α3log(Weighti) + α4log(V aluei) + α5Landlockedi
+ α6Contigi + α7DaysExporti + α8Logisti + α9Corruptioni + i
Where TC is trade costs, Air is dummy variable representing if the
export was shipped via air, Dist measures how far the product travelled
before reaching the US, Weight measures the weight of the shipment, V alue
represents the total value of the shipment, Landlocked is a dummy variable
that captures if the country touches a coastline, Contig is a dummy variable
for if the country is Mexico or Canada, DaysExport is the average number
of days it takes for a firm to export a product in the originating country,
Logist is an index measuring the overall logistical quality in the exporting
6

7. country, Corruption is an index from the World Bank measuring the level
of corruption in the country, and is the residual term.
An immediate issue with this specification is that intuition tells us that
there are a lot of unobservable characteristics of the trade shipments we are
trying to model which cannot be captured and put into a regression. For a
concrete example, consider the costs associated with transporting a shipment
of live animals to the United States compared to the costs associated with
shipping a crate full of feathers. All else held equal, one would expect the
shipment of live animals to come with additional costs since they may require
a more accommodating shipping environment and potentially an in-transit
handler.
To deal with the bias introduced by the unobservable characteristics of
the different commodities groups being shipped, we re-run the regression
above using dummy variables for each 10-digit HS code. Thus, if there
are any commodity-specific factors which are influencing the slopes of the
coefficients in the original mode, the will be captured by these dummies.
The new regression model is below.
log(TCi) = α1Airi + α2log(Disti) + α3log(Weighti) + α4log(V aluei) + α5Landlockedi
+ α6Contigi + α7DaysExporti + α8Logisti + α9Corruptioni +
x
i=1
δi + i
Where δi are dummy variables capture the fixed-effects of the commodity
groups.
It is also worth mentioning that this is the first paper the authors are
7

8. aware of the models for the fixed-effects of the different commodity groups
despite the obvious influence they may have on the regression. The reason
for this is that it requires a very large dataset that hasnt been available
to researchers in previous studies. For example, there are a total of 16,000
dummy variables which enter the above regression as dependent variables,
almost exceeding the number of total observations in the Pomfret Sourdin
paper mentioned earlier. The dataset employed by this study has more
than 1.5 million observations, which makes including so many dependent
variables in the regression a possibility. As made evident in the regression
tables, included the vector of dummy variables has an almost negligible
impact on the adjusted R-squared coefficient.
Upon running the regression, however, it becomes clear that the model
suffers from heteroskedasticity, so robust-standard errors are presented be-
low in the regression tables. More interestingly, upon viewing the scatter
plot of the residuals against the fitted values, it becomes clear that for some
observation, the residuals are always above a linear function of the fitted
values (see Figure 1). The reason for this is that the dependent variable in
the regression is a non-negative variable, however there are instances where
the predicted coefficients of the linear regression produces negative values.
Since the truncated distribution of the dependent variable is not ac-
counted for in the OLS regressions above, the coefficients may be biased.
To correct for this, we run a maximum likelihood estimation (MLE) which
forces the distribution of the predicted values to be positive, corresponding
to the distribution of the actual dependent variable. We assume the error
terms are normally distributed similar to the OLS case.
8

9. Figure 1: Residual vs. Fitted Values: OLS Regression
To solve the MLE, we first find the probability the predicted values are
greater than or equal to zero, the lower bound of the distribution of the true
dependent variable.
Pr(ˆyi ≥ 0) = Pr(Xiβ + ei)
= 1 − Pr(ei < −Xiβ)
= 1 − Pr(ei/σ < −Xiβ/σ)
= 1 − Φ(−Xiβ/σ)
= Φ(Xiβ/σ)
9

10. The resulting density of the true dependent variable can thus be written as:
σ−1φ[(yi − Xiβ)/σ]
Φ(Xiβ/σ)
And the corresponding log likelihood function is thus:
(y, β, σ) = −
n
2
log(2π) − nlog(σ) −
1
2σ2
n
i=1
(yi − Xiβ)2
−
n
i=1
logΦ(Xiβ/σ)
Notice how the first three terms are identical to the loglikelihood function
corresponding to the OLS regression with no truncated distribution. The
last term represents the probabilities that an observation with the regression
function lies above zero.
Lastly, a few of our variables are indexes which are used to proxy the
impact of corruption and the logistical quality of the exporting country,
which could introduce a measurement error and endogeneity problem. The
OLS model also failed the ommited variable test, which could be related to
endogeneity in some of the variables.
To correct for this, we instrument the two variables and run a two-
stage least squares regression. This does not correct for the truncation
problem discussed above, however the similarity of the coefficients between
the OLS and MLE estimators suggest the bias introduced by the truncated
distribution of the dependent variable is small.
As instruments, prior research has used ethnolinguistic-fractualization
as an instrument for corruption, and we instrument Logist with the level
of GDP per capita in the exporting country. While one wouldn’t expect
10

11. ethnolinguistic-fractualization to impact trade costs directly, there could be
an argument that GDP may have an impact on trade costs. However, the
authors figure that the affect of GDP on trade costs probably occurs indi-
rectly through poor institutional and logistical quality within the country
as opposed to having a direct impact. Endogoneity and weak instrument
tests suggest the two variables are endogenous and that the two instruments
proposed are strong.
Table 1: Regression Results
Variable OLS-FE MLE-TRUNC 2SLS-FE
Air 1.269*** 1.240*** 1.261***
log(Dist) 0.143*** 0.139*** 0.106***
log(Weight) 0.447*** 0.453*** 0.442***
log(Value) 0.430*** 0.417*** 0.431***
Landlocked -0.097*** -0.103*** -0.027***
Contig 0.316*** 0.290*** 0.298***
DaysExport 0.043*** 0.046*** -0.011***
Logist 0.019*** 0.020*** 0.049***
Corruption -0.034*** -0.034*** -0.068***
Intercept -1.986*** -1.122*** -1.262***
R-squared 0.7983 ... 0.7591
Adj R-Squared 0.7962 ... ...
No.Obs 1,603,879 1,599,076 1,558,711
3.3 Discussion
In all three specifications, the product-related factors all had the expected
sign and significance, the only exception being the landlocked variable which
had the wrong sign 2. Distance, a geographical-related variable, also had
the correct sign and was statistically significant in all three specifications.
2
An interesting point about the negative coefficient on the Landlocked variable is that
a similar result was found in the Pomfret Sourdin paper.
11

12. The institutional variables, Corruption and DaysExport also had the
correct signs and significance in all three specifications, with the exception
that DaysExport had an incorrect sign when we used instrumental variables
for Corruption and Logist.
4 Impact of Corruption on Different Industries
4.1 Impact of Corruption by HTS Groupings
The previous section showed that corruption seems to have a negative impact
on the cost of trade across all commodity groups when pooled, however this
effect seems to have a heterogeneous impact across different commodities.
The next step in the analysis is to see how this effect varies at the product
level.
In order to explore this question we made use of the HTS classification.
The HTS is grouped in 22 main sectors and we created a dummy variable per
sector . Then we created an interaction term, SectorDummyXCorruption,
to show the impact of corruption across the 22 broad categories of goods in
place of the Corruption variable in the original model.
log(TCi) = α1Airi + α2log(Disti) + α3log(Weighti) + α4log(V aluei) + α5Landlockedi
+ α6Contigi + α7DaysExporti + α8Logisti + α9Corruptioni
+
22
n=1
SectorDummynXCorruptioni + i
The results for this model are reported in Table 2. As with the aggre-
12

13. gate model, corruption increases trade cost in the vast majority of sectors
with high significance levels. Section 11 (Textile and Textile Articles) and
Section 12 (Footware) are the interaction terms that are positive, though
the coefficients are low relative to the other sectors. Section 20 (Footwear)
is the section with an insignificant coefficient.
A high level analysis reveals that the sections more affected by corrup-
tion are clustered at the bottom of the list. To ease analysis we bold the
coefficients lower than -0.050. The HTS sections follow roughly an order
from the simplest to the most complex, with animal and vegetable products
on top and machinery and precision instruments near the bottom. In other
words, the HTS sections follow roughly an order from labor intensive to
capital intensive. We now test this observation formally.
4.2 Impact of Corruption: Labor Intensive vs. Capital In-
tensive
In order to test the hypothesis that capital intensive products are more
affected by corruption than labor intensive products we employ the Revealed
Factor Intensity index developed by the United Nations Conference on Trade
and Development. The index reports down to the 6 digit HTS code the
capital intensity of a product. The researchers compute the number by
looking at which countries export what and compare this information with
the relative endowment in each country. For example, if Bangladesh that
is a labor-rich country exports lots of clothing this indicates that textile
industry is labor intensive.
We took the mean value of the index in each one of the sections and
13

14. Table 2: Regression Results: Interaction Variable for HTS Sections
Variable Coef P-value
Air 1.240 0.000
log(Dist) 0.140 0.000
log(Weight) 0.464 0.000
log(Value) 0.405 0.000
Landlocked -0.106 0.000
Contig 0.284 0.000
DaysExport 0.047 0.000
Logist 0.020 0.000
I: Animal Products -0.014 0.000
II: Vegetable Products -0.020 0.000
III: Animal or Vegetable Oils -0.042 0.000
IV: Beverages and Tobacco -0.029 0.000
V: Mineral Products -0.050 0.000
VI: Chemicals -0.036 0.000
VII: Plastics -0.015 0.000
VIII: Leather -0.024 0.067
IX: Wood -0.025 0.000
X: Paper -0.020 0.000
XI: Textile 0.002 0.000
XII: Footwear 0.019 0.044
XIII: Stone and Glass -0.021 0.000
XIV: Precious Stones -0.006 0.002
XV: Base Metals -0.046 0.000
XVI: Machinery and Electronics -0.058 0.000
XVII: Transport Equipment -0.042 0.000
XVIII: Optical and Precision -0.064 0.000
XIX: Arms -0.104 0.000
XX: Miscellaneous -0.001 0.751
XXI: Works of Art -0.048 0.000
XXII: Special Classification -0.066 0.000
Intercept -1.788*** 0.000
No.Obs 1,599,076
14

15. organized the sections from less capital intensive to more capital intensive
. We designated the first 11 sections as labor intensive and the last 11 as
capital intensive. We then created a dummy variable to represent labor,
and capital, intensive product and lastly created the interaction variable
FactorIntensityXCorruption. The altered model is thus:
log(TCi) = α1Airi + α2log(Disti) + α3log(Weighti) + α4log(V aluei) + α5Landlockedi
+ α6Contigi + α7DaysExporti + α8Logisti + α9LaborIntenseXCorruption
+ α10CapitalIntenseXCorruption + i
The results are reported in Table 3. We can see that the effect of cor-
ruption on capital intensive goods is about 5 time higher than the effect
on labor intensive goods. Formally testing the null hypothesis that these
two coefficients are equal against the alternative hypothesis that the capital
intensive coefficient is higher than the labor intensive coefficient produces
a chi-square with one degree of freedom of 13175.97 with p-value=0.0000.
The null is thus rejected in favor of the alternative hypothesis. It is worth
pointing out that the most labor intensive section is Footwear which had a
positive coefficient in Table 2. This suggest that for the truly labor intensive
goods corruption not only is less of a problem but it actually reduces trade
cost.
For robustness, we recognize that the interaction between factor intensity
and corruption may be influenced by the fact that factor intensity was a
relevant variable omitted form the original regression. To address this, we
present regressions include and excluding the factor intensity index for each
15

16. individual observation. As evident from Table 2, controlling for the factor
intensity does not impact the conclusion reached above.
Table 3: Regression Results: Interaction Variable for HTS Sections
Variable Coef P-value Coef P-value
Air 1.233 0.000 1.252 0.000
log(Dist) 0.133 0.000 0.145 0.000
log(Weight) 0.479 0.000 0.472 0.000
log(Value) 0.379 0.000 0.393 0.000
Landlocked -0.112 0.000 -0.104 0.000
Contig 0.261 0.000 0.464 0.000
DaysExport 0.05 0.000 0.058 0.000
Logist -0.021 0.0300 -0.023 0.000
FactorIntensity -0.001 0.000
LaborIntensityXCorruption -0.011 0.000 0.007 0.000
CapitalIntensityXCorruption -0.047 0.000 0.045 0.000
Intercept -1.557 0.000 -1.719 0.000
No.Obs 1,599,076 1,386,377
5 Results and Conclusions
Our model confirms an idea that has been thoroughly studied in economics:
institutions matter. After controlling for the characteristics of the goods
and the geography of the countries the institutional variables remain signif-
icant. According to our model, an increase of one unit in the World Bank
Corruption Index can reduce trade cost by 0.7
Our findings that firms which produce more capital intensity goods may
be more susceptible to higher trade costs through corruption and bribes
re-affirms qualitative observations of Pomfret and Sourdin who wrote some-
thing similar about manufactured goods. This could have an impact on
16

17. a countrys development path as they begin to switch from labor intensity
production to capital intensive production. Although international trade,
and more specifically the cost of trade, is only one contributing factor of
development, our results suggest countries which better institutions in place
will have an easier time making this transition than countries with poor
institutions.
6 Further Research
Our findings are consistent with previous studies, specifically the paper from
Pomfret and Sourdin (2012) that used trade information from Australia.
These studies have focused on trade cost which is only a fraction of the final
cost of a good. It would be interesting to see how corruption affects goods
throughout their life cycle, from the factory or farm to the final consumer.
We could use this study as a proxy and make the argument that in the same
way corruption affects capital intensive goods during transportation, it does
too during manufacturing and final consumption. The theoretical basis for
this hypothesis is as follows: capital intensive industries require stable and
efficient institutions.
While this study shows that corruption particularly affects capital inten-
sive goods, it doesnt explore the concrete mechanisms by which the effect
occurs. Further research should aim to understand why this effect occurs.
Lastly, the study was limited to information at only one point in time.
It would be interesting to have access to time-series data and expand the
model. Variables like the price of oil and international conflicts could be
17

18. included. It would be interesting to determine when countries reduce their
trade cost and what forces are behind the shift.
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21. Appendix
Figure 2: Capital Intensity Index
21