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

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

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.

1For 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

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

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

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 + εi

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

country, Corruption is an index from the World Bank measuring the level

of corruption in the country, and ε 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 + εi

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

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

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(yi ≥ 0) = Pr(Xiβ + ei)

= 1 − Pr(ei < −Xiβ)

= 1 − Pr(ei/σ < −Xiβ/σ)

= 1 − Φ(−Xiβ/σ)

= Φ(Xiβ/σ)

9

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) = −n2log(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

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 ... 0.7591Adj 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.

2An interesting point about the negative coefficient on the Landlocked variable is thata similar result was found in the Pomfret Sourdin paper.

11

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 + εi

The results for this model are reported in Table 2. As with the aggre-

12

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

Table 2: Regression Results: Interaction Variable for HTS Sections

Variable Coef P-value

Air 1.240 0.000log(Dist) 0.140 0.000log(Weight) 0.464 0.000log(Value) 0.405 0.000Landlocked -0.106 0.000Contig 0.284 0.000DaysExport 0.047 0.000Logist 0.020 0.000I: Animal Products -0.014 0.000II: Vegetable Products -0.020 0.000III: Animal or Vegetable Oils -0.042 0.000IV: Beverages and Tobacco -0.029 0.000V: Mineral Products -0.050 0.000VI: Chemicals -0.036 0.000VII: Plastics -0.015 0.000VIII: Leather -0.024 0.067IX: Wood -0.025 0.000X: Paper -0.020 0.000XI: Textile 0.002 0.000XII: Footwear 0.019 0.044XIII: Stone and Glass -0.021 0.000XIV: Precious Stones -0.006 0.002XV: Base Metals -0.046 0.000XVI: Machinery and Electronics -0.058 0.000XVII: Transport Equipment -0.042 0.000XVIII: Optical and Precision -0.064 0.000XIX: Arms -0.104 0.000XX: Miscellaneous -0.001 0.751XXI: Works of Art -0.048 0.000XXII: Special Classification -0.066 0.000Intercept -1.788*** 0.000

No.Obs 1,599,076

14

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+ εi

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

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 0.000log(Dist) 0.133 0.000 0.145 0.000log(Weight) 0.479 0.000 0.472 0.000log(Value) 0.379 0.000 0.393 0.000Landlocked -0.112 0.000 -0.104 0.000Contig 0.261 0.000 0.464 0.000DaysExport 0.05 0.000 0.058 0.000Logist -0.021 0.0300 -0.023 0.000FactorIntensity -0.001 0.000LaborIntensityXCorruption -0.011 0.000 0.007 0.000CapitalIntensityXCorruption -0.047 0.000 0.045 0.000Intercept -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

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

included. It would be interesting to determine when countries reduce their

trade cost and what forces are behind the shift.

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Appendix

Figure 2: Capital Intensity Index

21