Redistribution, Trade and Corruption:An Empirical Assessment

Antonia Reinecke∗ Hans-Jörg Schmerer†

Abstract

This paper explores the role of institutional quality in the trade and inequalitynexus. Does corruption shape the relationship between trade and inequalitythrough its impact on redistribution? Our answer to this question builds on thehypothesis that trade rises inequality through higher per capita income at the topof the income distribution. Motivated by recent theoretical evidence, we argue thatgovernments may intervene through appropriate redistribution schemes that aim attaxing the gains from trade in a way that reduces inequality. Corruption and badinstitutions may induce distortions that neutralize those positive effects: inequalityrises due to trade liberalization, if bad institutions prevent redistribution schemesby the government. We find that trade increases inequality but the effect hingeson the level of institutional quality. Quite to the contrary to common wisdom,we even find an income inequality reducing effect of trade in countries with highinstitutional standards as low corruption or a high level of regulatory quality.

∗FernUniversität in Hagen. E-mail: antonia.reinecke@fernuni-hagen.de†FernUniversität in Hagen, CESifo and IAB. E-Mail: hans-joerg.schmerer@fernuni-hagen.de

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I. Introduction

There is an emerging consensus in the academic debate that trade causes

inequality. Explanations for this result either focus on fair wage considerations

as in Egger and Kreickemeier (2009a) or search and matching with screening

costs as in Helpman, Itskhoki, and Redding (2010) and Helpman, Itskhoki,

Muendler, and Redding (2012). All of those studies are able to associate rising

wages at the top and stagnant or even declining wages at the bottom of the

income distribution with globalization.

Moreover, those more recent models have in common that they build on the

seminal heterogeneous firm framework with sorting of heterogeneous firms into

export. The presence of labor market frictions from different proveniences can

explain why trade increases within-group inequality due to some sort of rent

sharing. An otherwise identical worker earns a higher wage, if she is employed

at a more productive exporting firm. Going from autarky to free trade still

increases per capita income, albeit those gains are usually accompanied by a

more dispersed income distribution. Thus, the gains from trade and the type

of inequality discussed in the literature mentioned in the last paragraph are

different to the welfare gains known from the more canonical trade models.

Firstly, countries that open up its economy to international trade benefit from

an exit of the least productive firms as domestic competition becomes fiercer

due to entry of foreign exporters and the strengthening of domestic exporters.1

Secondly, more productive firms are more efficient, generate higher profits

and pay higher wages due to rent-sharing between workers and firms. This

adjustment process results in soaring inequality among unobservable attributes,

e.g. firm productivity or worker ability, rather than observable differences in

1Foreign and domestic establishments compete over market shares within a particularcountry.

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skills.

This result is in line with recent empirical evidence. The increased availability

of rich firm-level data that comprise information on both firms and workers

enabled researchers to show that exporting firms pay wage premia to their

employees. The seminal papers by Bernard, Jensen, and Lawrence (1995),

Bernard and Jensen (1999) and Bernard, Eaton, Jensen, and Kortum (2003)

motivated a large and growing literature that sheds light on the determinants

for this exporter wage premium. Schank, Schnabel, and Wagner (2010) argue

that part of the premium can be explained by unobserved worker heterogeneity.2

It is difficult to gauge the importance of inequality in the public debate

but there seems to be a widespread consensus that governments should in-

tervene using appropriate redistribution schemes. Especially the unexplained

differences in income are less easy to defend in the public debate. Is there a

channel through which governments can offset the negative labor market ef-

fects of globalization through redistributing the gains from trade? Appropriate

policy instruments include lump sum tariffs that are redistributed in form of

unemployment benefits (this link is explained in De Pinto (2015a) and De Pinto

(2015b)) or lump sum tariff payments that are redistributed equally among the

whole population as Egger and Kreickemeier (2009b) propose in their model.

The latter model goes one step further by investigating the possibility that

governments can reduce the inequality promoting effects of trade liberalization

by setting a tax that is high enough to reduce overall inequality without purging

the entire gains from trade. Thus, their paper can rationalize that governments

are indeed able to offset the negative effects of trade on inequality by applying

appropriate tax schemes that reduce inequality.

Moreover, Gozgur and Ranjan (2015) report some evidence that trade liber-

2See also Hauptmann and Schmerer (2013) and Felbermayr, Hauptmann, and Schmerer(2014) for more recent empirical evidence on the exporter wage premium.

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alization fosters redistribution.

Combining the arguments proposed by Egger and Kreickemeier (2009a),

Gozgur and Ranjan (2015) and Egger and Kreickemeier (2009b) one may have

some doubts about the unambiguity of the long run effects of trade on inequality.

We argue that those considerations call for a full-fledged empirical analysis that

aims at shedding light on the role of the government in the debate on trade

and inequality. Using macroeconomic data on openness and income inequality

we estimate the relationship empirically. Our main contribution is to take the

role of institutions into consideration when estimating the relationship between

trade and inequality. Redistribution may reduce the surge of inequality induced

by globalization but this positive effect may be distorted in countries with bad

institutions.

In compliance with Gupta, Davoodi, and Alonso-Terme (2002) the quality of

institutions has an influence on income inequality through different channels,

i.e. economic growth, biased tax systems, less targeted redistributional policies

as well as educational inequality. Corruption is one attribute of low institutional

quality. Mauro (1998) as well as Tanzi and Davoodi (2000) identify a negative

relation between the level of corruption and public spendings on education,

health care as well as social insurance and welfare payments, while they found

a positive relation between corruption and public military spending. These

findings suggest that public spendings are less equally distributed among

citizens. Additionally, Tanzi and Davoodi (2000) descry corruption affects

tax revenues and their productivity negatively, which is associated with less

progressivity. Corruption or weak enforcement power of a government may

lead to a situation, in which the gains from trade are not redistributed efficiently,

that magnifies inequality relative to the effect in countries characterized by high

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institutional quality.3

But not only the redistribution system itself is prone to corruption. Lamb-

sdorff (1998) shows that a country’s level of corruption also has a significant

influence on its trade volumes. Disadvantages can emerge through declin-

ing shares of more corrupt countries’ trade in the world market. Moreover,

De Groot, Linders, Rietvield, and Sumbramanian (2004) argue that low insti-

tutional quality increases transaction cost of trade by boosting insecurity and

diminishing the investors’ trust in business procedures. The authors state that

homogeneity of institutional quality between two observed trading partners

is an additional source of lower transaction cost due to reduced burdens on

market entry and more familiarity regarding business proceedings.

The empirical results of De Groot, Linders, Rietvield, and Sumbramanian

(2004) support the hypotheses highlighted above: The estimated influence

of perceived institutional quality on bilateral trade is highly significant and

positive, whereas the divergence of government effectiveness between two

countries have a negative impact on bilateral trade.4

First glimpse at the data. Figure 1 compares the evolution of inequality, trade

and a proxy for redistribution over time. The graph shows averages across

OECD countries going back to the year 1970. The preferred measure of inequal-

ity is the net-Gini coefficient. The market Gini has little explantory power in our

application because it does not account for transfer payments by the respective

government. We are instead interested in the purchasing power of individual

3One has to admit that the suggested mechanism opposes the results in Itskhoki (2008) thatthere exists no optimal redistribution strategy that allows tackling income inequality inducedby trade without offsetting aggregated welfare gains.

4The effect of institutional quality and the homogeneity of institutional quality on bilateraltrade is estimated by a gravity equation. The World Bank Government indicators are used asproxy for institutional quality. Homogeneity of institutional quality is measured by a dummyvariable that takes the value one, if the difference of a respective institutional quality measurebetween two countries exceeds a previously defined share of the sample standard deviation.Those countries are defined as heterogeneous.

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households.

Figure 1: Openness and inequality

The stylized facts in the left panel of Figure 1 reveal a clear trend towards

higher globalization but the growth rates are declining in more recent years.

The right panel of Figure 1 allows to asses inequality and its evolution over time.

Omitting redistribution gives a positive correlation between trade liberalization

and soaring inequality: the market Gini grows from 0.4 shortly before 1980

to more than 0.45 around 2005. However, we get a different picture once we

look at the net-Gini, which fluctuates around the level 0.3. Thus, the overall

level of inequality in our sample becomes much lower once transfer payments

are accounted for and the positive trend is not as obvious anymore. This first

glance at the data can be considered as evidence that points towards a high

relevancy of redistribution.

Figure 2 illustrates the evolution of corruption and different countries degree

of redistribution. Thereby, it confronts corruption (CPI - Corruption Perception

Index) as our preferred measure of institutional quality and the degree of

redistribution, that is approximated by the difference between market and

net-Gini.

We argue that countries with higher corruption tend to redistribute less.

Corruption may very well magnify the negative effects of trade on inequality

by hindering governmental redistribution schemes. We observe a clear pattern

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Figure 2: Openness and redistribution

between corruption and redistribution. Countries with low CPI are more corrupt

and redistribute less.

Our empirical analysis tries to jointly estimate the relationship between the

three variables of interest. We expect some interrelationship between corruption,

trade and redistribution tested in the following empirical analysis.

II. Empirical Analysis

We test the hypothesized relationship derived from our theoretical considera-

tions using a dataset that covers 47 countries5. Inclusion of both developed and

emerging economies for a long period that covers the years 1995-2011 gives us

enough variation in trade and institutions so that we are able to disentangle the

total effect into its between and within components. Our preferred specification

5The countries included comprise the major developing and developed countries, namely:Argentina, Australia, Austria, Belgium, Bolivia, Bulgaria, Canada, Chile, China, Colombia,Croatia, Czech Republic, Denmark, Ecuador, El Salvador, Estonia, Finland, France, Germany,Greece, Hungary, India, Indonesia, Ireland, Israel, Italy, Japan, Latvia, Lithuania, Mexico,Moldova, Netherlands, New Zealand, Norway, Poland, Portugal, Romania, Russia, Slovakia,Slovenia, South Africa, Spain, Sweden, Thailand, Turkey, United Kingdom, United States.

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includes country and time fixed effects. The model estimated in our study reads

IEit = α + β1(G)it + β2(Q)it + β3(G×Q)it + βn(CV) + uit .

The dependent variable, IE, is the Net Gini coefficient in country i at time t.

The Gini coefficient is calculated on the basis of disposable income, which is

total income net of tax payments and other transfer payments received by the

workers in the respective country. Variable G describes the economy’s level of

globalisation. Our preferred proxy for openness is the KOF globalization index,

which comprises information on imports and exports as well as other indicators

of globalisation. The variable Q stands for the different institutional quality

measures considered in this study. To shed light on the role of institutions for

the link between trade and inequality we also include the interaction between

both measures. We expect that bad institutions magnify the effects of trade on

inequality towards more inequality. However, the effect of openness is expected

to be ambiguous and may be positive or negative depending on the degree of

redistribution. A negative impact of openness on income inequality may be

attributed to country specific characteristics, in particular the level of the re-

spective country’s development. Emerging economies are usually characterized

by a high level of agricultural production, low productivity rates, lower devel-

oped infrastructure as well as less targeted social redistribution systems, which

leads to higher levels of income inequality. In contrast, highly industrialized

countries are characterized by high productivity rates, a high level of industrial

production, and hence a distinctive export sector, as well as well developed

redistribution systems, which would lead to lower income inequality levels.

This is supported by the empirical results of Gozgur and Ranjan (2015) that

identify a positive effect of international trade on the level of public redistribu-

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tion by estimating an econometric model.6 The results in their study reveal a

positive and highly significant effect of trade on the degree of redistribution in

an economy. Consistently, high developed economies associated with a high

level of trade openness tend to be more engaged in redistribution than emerging

economies. With respect to the coefficients obtained from regressions that focus

on within income inequality, the expectations regarding the estimated coeffi-

cients are different. In compliance with Egger and Kreickemeier (2009a), trade

liberalization leads to firm selection and higher inequality but the impact of the

quality of institutions is expected to be negative. Thus, we expect a positive

coefficient of trade but a negative of the interaction term between openness and

institutional quality. The gains from trade are expected to be distributed more

equally in economies with better institutions. Consequently, we expect that the

potential positive effect of openness on income inequality can be attenuated, or

even overcompensated by strong institutions, which ensure that not only one

specific part of income distribution benefits from trade, but redistribute welfare

gains from trade among the whole distribution.

All regressions include per capita GDP, the age dependency ratio7 and

population (in millionen inhabitants) as further control variables.

IV strategy Potential endogeneity of trade and corruption likely biases the

benchmark OLS estimates. Controlling for country fixed effects already deals

with that issue but we go one step further using an instrumental variable (IV)

approach. Our main concern is that corruption in the underlying regression

analysis is endogenous due to potential reverse causality: While high corruption

is expected to distort redistribution schemes, and thereby fosters inequality, it

6The dependent variable in their study is specified by the difference between market Giniand the net Gini. This difference is positively correlated with openness.

7"Age dependency ratio is the ratio of dependents–people younger than 15 or older than64–to the working-age population–those ages 15-64. Data are shown as the proportion ofdependents per 100 working-age population.", World Bank, 2016.

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can be hypothesized that high inequality create an incentive for individuals to

become corrupt in order to receive personal advantages. Economic openness

and corruption as well as their interaction are instrumented by the interaction

between the Frankel and Romer trade share and the indicator of Government

Effectiveness (GE) as well as Regulatory Quality (RQ). Furthermore, we intro-

duce both indexes, GE and RQ, separately as instruments. Frankel and Romer

(1999) use a gravity approach to generate an instrument for trade openness. In

contrast to classical gravity estimates, they exclusively introduce geographical

characteristics determining bilateral trade as size measured as population and

area, distance between two countries, common boarder and landlockedness in

their regressions. Based on this first step they estimate a country-individual

geographic component of overall trade. They argue that geographic character-

istics of an economy are not determined by income or other aspects affecting

income (such as political measures). However, the Frankel and Romer (1999)

trade share is time invariant and thus not appropriate in a time-series panel

analysis. We tackle this problem by generating various interactions with other

exogenous variables. We are mainly interested in variables that are not affected

by the distribution of income across individuals, at least not through variables

that are not controlled for in the regressions. A government’s efficiency may be

influenced by inequality only if a crucial number of voters are unsatisfied with

the situation and try to put pressure on the government in order to change the

situation. Likely that voters are mainly concerned about the fact that govern-

ment inefficiency spurs corruption and thus inequality. A second channel may

go through redistribution. The same line of arguments holds for regulatory

quality. In order to assess the validity of our instruments we report all relevant

test statistics at the bottom of each regression table. The Sargan test allows to

test the exclusion restriction. The first stage F-statistics jointly tests whether

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the excluded instruments are significant. The rule of thumb is fulfilled as all

F-statistics are greater than 10. Moreover, Shea’s partial R-squared statistics are

also sufficiently high, which supports the validity of our instruments.

III. Data

Table 1 reports the first and second moments as well as minimum and maximum

values of all variables used in our study. The net Gini stems from ”The Stan-

dardized World Income Inequality Data” (SWIID) by Solt (2014), who proposes

a way to make the Gini coefficients from different countries comparable.8 We

use two different globalization measures in our study. The Penn World Table

provides openness meassured as the sum of imports and exports relative to

the respective country’s level of GDP9. Dreher (2006) argues that globalization

also depends on other indicators not included in im- or exports. The KOF

Globalisation Index introduced by Dreher (2006) is constructed out of three

components: Firstly, economic globalisation depending on trade in goods and

capital plus international money transactions. Secondly, social globalisation is

included through a country’s share of foreign population, tourism and Internet

users. Thirdly, political globalisation comprises association with membership

in international organizations, international treaties and participation in U. N.

security councils. The focus of our analysis is on the Economic Globalisation

Index and the overall Globalisation Index.

All variables are normalized so that their values range from zero to one.

A lower number indicates that the economy is less globalised, whereas the

value one corresponds to the maximum degree of globalisation. As proxy for

institutional quality we use the Corruption Perception Index (CPI) developed

8Solt (2014) uses a costumised algorithm that calculates missing values under usage of datafrom national statistical offices, regional data collection as well as academic studies.

9They use real GDP in million USD.

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Table 1: Summary Statistics

Variable Obs Mean St. Dev. Min Max

Net Gini 855 .347 .089 .203 .657KOF I 846 .717 .132 .369 .924KOF II 846 .690 .148 .260 .970(Imp+Exp)/GDP 799 .8659 1.754 .0741 36.368Imp/GDP 799 .470 1.1483 .039 24.316Exp/GDP 799 .395 .618 .034 12.052CPI 756 5.661 2.361 1.7 10RQ 705 .628 .226 1.76e-08 1GE 705 .540 .255 8.63e-09 1Ln(GDP/POP) 799 9.545 .954 5.753 11.066Dependency 893 .511 .074 .345 .808Population 799 87.716 239.449 1.340 1324.353

by Transparency International. The observations take values between zero and

ten. The lowest value zero is associated with the highest level of perceived

corruption, whereas the highest value indicates a zero perceived corruption

within the respective country. Furthermore, indicators from The Worldwide

Governance Indicators data set, developed by Kaufmann, Kraay, and Mastruzzi

(2010), allow to approximate other dimensions of institutional quality. The

World Bank dataset includes six dimensions that assist evaluating the state of

governance. Our study makes use of the variable Regulatory Quality (RQ).

All indicators in the original data set range from -2.5 to 2.5. We normalize the

measure in order to guarantee comparability. Thus, our measure takes values

from zero to one, with zero describing a weak level of institutional quality.

Economies that are characterized by high institutional quality take a value equal

to one.

As explained in the introductory part, the channels corruption affects in-

equality are diverse: by distorting the tax collection process, by influencing

public spending decisions, and thereby, effectiveness of redistribution, as well

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as by manipulating the patterns of trade. The measures of institutional quality

applied in this empirical analysis are aggregated indices, and hence, capture

institutional quality as a whole and not one specific channel. Consistently, we

estimate the overall effect of institutional quality.

The data of the control variable dependency ratio originates from the World

Bank. The per capita GDP is calculated based on data of the Penn World Table

(PWT) data, version 8.1.

III. Empirical Results

In the following section, we present the benchmark regression outcomes for

our different specifications. The results document a fairly robust finding: The

effect of trade on inequality hinges on the quality of institutions. The overall

effect of openness turns out to be positive in regressions that were purged of

the between variation of the data but the interaction indicates that the marginal

effect turns negative in countries with good institutions. Quite to the contrary

of most of the existing studies we find some evidence that trade can reduce

inequality in the long run.

The role of corruption for trade and inequality

Our preferred globalization measure is the KOF index on globalization. The

coefficients associated with this variable are reported in the first row of Table

2. The third row reports the preferred proxy for corruption. The fifth row

contains the estimates for the interaction of both variables of interest. We always

include a bunch of controls and different combinations of fixed effects. In the

first column we estimate Ordinary Least Squares (OLS) without controlling for

any unobserved heterogeneity other then the time trend. Column (2) includes

country-level random effects and column (3) puts in country fixed effects. At

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first glance, our results seem to support the predicted positive coefficient of

openness, if unobserved heterogeneity is controlled for: trade can be associated

with more inequality. However, the marginal effect has to be interpreted together

with the coefficient of the interaction term, which is negative. Thus, the positive

effect is mitigated by the degree of corruption. A lower level of corruption

reduces the magnitude of the effect of openness on inequality.

We evaluate the marginal effect using the summary statistics reported in the

previous section. The marginal effect in column (1) is negative, which implies

that more open economies tend to have a lower level of inequality. Remember

that the first model is always OLS, which bases the point estimates upon both

between and within variation of the data. The between variation likely stems

from the fact that more open economies are located in more developed countries

with better welfare systems and lower overall inequality. Therefore, it is not

surprising that the coefficient likely reflects the impact of the between variation.

The interaction reveals that this effect becomes even stronger in less corrupt

economies. Comparing two countries with the same level of openness, we

find that the more corrupt economies are also having a more dispersed income

distribution. This low precision of the OLS estimates motivate the regressions

in column (2) and (3).

The inclusion of fixed- and random-effects on the country level absorbs

some or all of the between variation of the data. Fixed-effect estimates identify

the coefficients solely based on the within-variation over time. The sign of

the coefficient changes from negative to positive but the coefficient of the

interaction term remains negative. Both are highly significant. Evaluating

the marginal effect using the summary statistics reported above, we find that

the marginal effects in column (2) and (3) turn from positive to negative at

a level of perceived corruption equal to CPI∗RE = 0.236/0.047 = 5.02 and

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CPI∗FE = 0.115/0.017 = 6.7610. Those numbers are close to the mean value of

perceived corruption in the sample, which is around 5.66.

Quality of governance and regulations

Corruption is one source of distortion of the gains from trade. We argue that

corrupt societies distort the rent-sharing mechanism between firms and workers

through lowering the rents that can be redistributed between firms and workers.

The institutional quality of governance and regulations is a much broader

concept of measuring corruption. Countries with good regulatory institutions

may be less plagued by corruption so that redistributing the gains from trade is

likely more efficient.

Coefficients related to the KOF globalization indicator are negative in column

(4) but positive in (5) and (6). All are highly significant. The coefficient of

the interaction term is always highly significant and negative. The results

are identical to the ones discussed in the last paragraph. Countries with

better regulatory quality tend to benefit from globalization in terms of a lower

inequality. Only in countries with low regulatory quality trade causes more

inequality. The cutoff for which the marginal effects turns from positive into

negative is around 0.469-0.570.

Alternative measure of the KOF globalization index

As a first robustness check of our results we estimate our model with a narrower

concept of international trade and include exclusively the economic component

of the KOF globalisation Index. The Economic Globalisation Index represents

10All countries characterized by a CPI above this threshold are members of the OECD.Additionally, these countries belong to the high-income countries according to the World Bankclassification. These results support the hypothesis that especially industrialized countries arecharacterized by an efficient redistributing system that is not distorted by bad institutions.

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Table 2: Benchmark results: Inequality, trade and institutions

(1) (2) (3) (4) (5) (6)Net Gini Net Gini Net Gini Net Gini Net Gini Net Gini

b/se b/se b/se b/se b/se b/seKOF I -0.136** 0.236** 0.115* -0.215*** 0.237** 0.147**

(0.06) (0.11) (0.06) (0.08) (0.12) (0.07)CPI 0.032*** 0.027* 0.007

(0.01) (0.01) (0.01)KOF × CPI -0.044*** -0.047** -0.017*

(0.01) (0.02) (0.01)RQ 0.253*** 0.367** 0.213***

(0.08) (0.15) (0.07)KOF × RQ -0.327*** -0.505** -0.258**

(0.11) (0.22) (0.10)GDP/POP -0.004 0.028 0.083*** -0.005 0.019 0.069***

(0.00) (0.03) (0.01) (0.00) (0.03) (0.02)Dependency 0.405*** 0.337*** 0.386*** 0.360*** 0.301*** 0.359***

(0.04) (0.10) (0.04) (0.04) (0.09) (0.05)Population 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000**

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)Constant 0.261*** -0.233 -0.716*** 0.314*** -0.172 -0.640***

(0.06) (0.28) (0.14) (0.07) (0.27) (0.17)

Time FE x x x x x xCountry RE x xCountry FE x x

Obs. 754 754 754 609 609 609R-sq within 0.603 0.961 0.596 0.963adj. R-sq 0.591 0.958 0.584 0.958Standard errors in parentheses. Coefficients are significant at the 10 percent (* p<0.10), 5percent (** p<0.05) or 1 percent (*** p<0.010) level. The dependent variable is the net-Ginicoefficient. KOF I is our preferred economic globalization measure. CPI denotes CorruptionPerception Index, which is our preferred measure of institutional quality. RQ denotesRegulation Quality. GDP/POP is a control for per capita GDP and Dependency stands forDependency Ratio.

a more classical approximation of economic openness, but includes more di-

mensions than only imports and exports. It consists of actual flows11 and trade

11Trade including imports and exports, Foreign Direct Investment, Portfolio Investment andincome payments to foreign national as percent of GDP, respectively

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restrictions12 weighted by 50%, respectively. The estimation results including

the KOF Economic Globalisation Index instead of the total Globalisation Index

can be found in Table (3).

Our estimation results are robust against the change of the globalisation

measure and the benchmark regression results can be restored: Controlling

for nothing else than the time trend, the effect of globalisation is negative and

significant, while the sign of the estimated coefficient changes controlling for

between variation. The coefficient of the interaction term is invariably negative

and highly significant. But, quite to our surprise, the cutoff is even higher for

the alternative KOF globaliation measure. Only the least corrupt countries with

CPI higher than 6.65 tend to benefit from trade liberalization, if inequality is the

main concern. For regulatory quality the marginal effect changes at the critical

value of RQ = 0.758

IV regression

Table 4 and 5 show of IV regression results, thereby the regression in Table 4

includes exclusively time fixed effects, while regression estimates in Table 5

include both time and country fixed effects. As a further control variable we

include redistribution13 in column (3) and (4), respectively. Our results still

exhibit the expected signs and are highly significant. Economic globalisation

fosters income inequality, while the interaction between high quality institutions

and globalisation has an income inequality reducing effect, indicating that

strong institutions are decisive for the effectiveness of the redistribution of gains

from trade. The high values of first stage F-statistics as well as of the partial

12Concealed import barriers, mean tariff rate, taxes on international trade in percent of currentvalues and capital account restrictions

13Redistribution is defined as the difference between Market Gini and Net Gini.

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Table 3: Benchmark results: Inequality, trade and institutions

(1) (2) (3) (4) (5) (6)Net Gini Net Gini Net Gini Net Gini Net Gini Net Gini

b/se b/se b/se b/se b/se b/seKOF II -0.022 0.206*** 0.154*** -0.023 0.213*** 0.156***

(0.05) (0.07) (0.04) (0.07) (0.08) (0.05)CPI 0.004 0.015* 0.011**

(0.01) (0.01) (0.01)KOF × CPI -0.017** -0.031** -0.023***

(0.01) (0.01) (0.01)RQ 0.056 0.190** 0.156***

(0.06) (0.08) (0.05)KOF × RQ -0.159* -0.281** -0.203***

(0.09) (0.13) (0.07)GDP/POP -0.017*** 0.038 0.083*** -0.022*** 0.019 0.068***

(0.00) (0.03) (0.01) (0.00) (0.03) (0.02)Dependency 0.459*** 0.313*** 0.378*** 0.388*** 0.275*** 0.339***

(0.04) (0.10) (0.04) (0.04) (0.09) (0.05)Population 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000**

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)Constant 0.319*** -0.289 -0.739*** 0.380*** -0.136 -0.612***

(0.06) (0.30) (0.14) (0.08) (0.26) (0.16)

Time FE x x x x x xCountry RE x xCountry FE x x

Obs. 754 754 754 609 609 609R-sq within 0.542 0.962 0.525 0.963adj. R-sq 0.528 0.959 0.511 0.959Standard errors in parentheses. Coefficients are significant at the 10 percent (* p<0.10), 5percent (** p<0.05) or 1 percent (*** p<0.010) level. The dependent variable is the net-Ginicoefficient. KOF I is our preferred economic globalization measure. CPI denotes CorruptionPerception Index, which is our preferred measure of institutional quality. RQ denotesRegulation Quality. GDP/POP is a control for per capita GDP and Dependency stands forDependency Ratio.

R2 suggest relevance14 of the used instruments. As a further test for weak

instruments we analyses the correlation between endogeneous variables and

instruments as well as between instruments. The correlation is high, which

14An instrumental variable must fulfill two conditions: Cov(z, u) = 0 and Cov(z, x) 6= 0.

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indicates strength of used instruments.15 Due to the fact that more instruments

than potential endogeneous variables find application, the regression model

is over identified. To test, if the overidentiying restriction is valid, we conduct

a Sargan test. The p-values always exceed the critical value of p ≥ 0.05,

consistently we can conclude that the overidentifying restrictions are valid.

15Results of the correlation analysis can be found in Appendix I.

19

Table 4: IV Regressions

(1) (2) (3) (4)Dependent Variable Net Gini Net Gini Net Gini Net GiniEstimator IV-REG IV-REG IV-REG IV-REGSample period (all years) (years>2000) (all years) (years>2000)KOF 0.598*** 0.538*** 0.791*** 0.781***

(0.18) (0.18) (0.16) (0.17)CPI 0.125*** 0.137*** 0.103*** 0.107***

(0.02) (0.03) (0.01) (0.02)KOF × CPI -0.176*** -0.185*** -0.148*** -0.152***

(0.03) (0.03) (0.02) (0.02)Dependency 0.435*** 0.319*** 0.473*** 0.418***

(0.08) (0.08) (0.06) (0.07)GDP/POP -0.020** -0.025** 0.006 0.008

(0.01) (0.01) (0.01) (0.01)Population 0.000** 0.000 0.000*** 0.000***

(0.00) (0.00) (0.00) (0.00)Redistribution -0.820*** -0.841***

(0.08) (0.09)Constant -0.073 0.050 -0.362** -0.352**

(0.17) (0.17) (0.15) (0.16)

Time FE x x x xCountry FE x

Sargan p-value 0.6090 0.9266 0.4626 0.5025

F-stat 1st stage: KOF 136.227 117.626 87.3945 71.1582F-stat 1st stage: CPI 380.899 371.212 319.686 309.404F-stat 1st stage: KOF× CPI 342.206 279.595 283.544 236.44

Partial R-sq: KOF 0.5295 0.5292 0.4298 0.4221Partial R-sq: CPI 0.7886 0.8048 0.7667 0.7898Partial R-sq: KOF× CPI 0.7679 0.7713 0.7332 0.7401

Number of obs. 523.000 417.000 523.000 417.000R-sq within 0.223 0.112 0.584 0.569adj. R-sq 0.195 0.079 0.568 0.552Standard errors in parentheses. Coefficients are significant at the 10 percent (* p<0.10), 5percent (** p<0.05) or 1 percent (*** p<0.010) level. The dependent variable is the net-Ginicoefficient. KOF I is our preferred economic globalization measure. CPI denotes CorruptionPerception Index, which is our preferred measure of institutional quality. RQ denotesRegulation Quality. GDP/POP is a control for per capita GDP and Dependency stands forDependency Ratio. We use Government Efficiency, Quality of Regulations and the interactionwith both a Frankel and Romer trade share as instruments for the KOF, CPI and the interactionbetween KOF and CPI.

20

Table 5: IV Regression, contd.

(1) (2) (3) (4)Dependent Variable Net Gini Net Gini Net Gini Net GiniEstimator IV-REG IV-REG IV-REG IV-REGSample period (all years) (years>2000) (all years) (years>2000)KOF 0.729*** 1.362*** 0.771*** 1.259***

(0.23) (0.33) (0.24) (0.32)CPI 0.040 0.142*** 0.053 0.126**

(0.04) (0.05) (0.04) (0.06)KOF × CPI -0.098* -0.253*** -0.124** -0.232**

(0.06) (0.09) (0.06) (0.09)Dependency 0.507*** 0.479*** 0.575*** 0.496***

(0.07) (0.09) (0.07) (0.08)GDP/POP 0.090*** 0.144** 0.103*** 0.132**

(0.03) (0.06) (0.03) (0.06)Population 0.000* -0.000 0.000 -0.000

(0.00) (0.00) (0.00) (0.00)Redistribution -0.311*** -0.133

(0.06) (0.09)Constant -1.074*** -1.901*** -1.239*** -1.738***

(0.39) (0.63) (0.40) (0.63)

Time FE x x x xCountry FE x x x x

Sargan p-value 0.9421 0.4044 0.7040 0.5860

F-stat 1st stage: KOF 8.651 6.086 8.673 6.1285F-stat 1st stage: CPI 7.121 8.412 7.930 9.374F-stat 1st stage: KOF× CPI 8.288 3.730 9.299 5.720

Partial R-sq: KOF 0.1010 0.0602 0.1033 0.0601Partial R-sq: CPI 0.0583 0.0960 0.0609 0.1004Partial R-sq: KOF× CPI 0.0736 0.0528 0.0762 0.0578

Number of obs. 523.000 417.000 523.000 417.000R-sq within 0.949 0.948 0.950 0.953adj. R-sq 0.942 0.939 0.943 0.944Standard errors in parentheses. Coefficients are significant at the 10 percent (* p<0.10), 5percent (** p<0.05) or 1 percent (*** p<0.010) level. The dependent variable is the net-Ginicoefficient. KOF I is our preferred economic globalization measure. CPI denotes CorruptionPerception Index, which is our preferred measure of institutional quality. RQ denotesRegulation Quality. GDP/POP is a control for per capita GDP and Dependency stands forDependency Ratio. We use Government Efficiency, Quality of Regulations and the interactionwith both a Frankel and Romer trade share as instruments for the KOF, CPI and the interactionbetween KOF and CPI.

21

The role of imports and exports

Dauth, Findeisen, and Suedekum (2012) argue that imports and exports may

have different effects on labor market outcomes. Their focus lies on labor

demand. Nevertheless, the effects on inequality may also be different for import

and export penetration. If a government is ambitious in redistributing gains

from trade, one would expect positive effects of soaring exports. Domestic

firms may gain from exports. If governments tax part of those new profits

from export, inequality may decrease. However, in countries where openness

is driven by imports, one may argue that offshoring of low skilled jobs lead to

a decline in wages at the bottom of the income distribution. Those losses are

hardly compensated, if firms do not export more. However, if trade is balanced

one may expect that both effects cancel each other out.

Interestingly, we find no significant effects for import and export measures

in the specification that include corruption as institutional quality measure.

The picture changes in columns (4) to (5). Inclusion of the regulatory quality

measures restores the pattern observed in the benchmark regression table. More

openness increases inequality but the effect changes to negative in countries

with good regulatory quality. However, the reducing effect of inequality shows

up for the interaction with export openness only. The interaction term between

imports and the institutional variable is insignificant. This result is in line with

the intuition described above. Only exports generate additional rents that can

be redistributed. Governments in countries with good regulations may tax some

of the profits generated through higher export volumes in order to redistribute

them to the citizens with low income.

22

Table 6: Econometric results Imports, Exports and Interaction

(1) (2) (3) (4) (5) (6)Net Gini Net Gini Net Gini Net Gini Net Gini Net Gini

b/se b/se b/se b/se b/se b/seImp -0.011 0.052 0.027 -0.065 0.092** 0.066*

(0.03) (0.04) (0.02) (0.05) (0.05) (0.04)Exp 0.074 0.090 0.033 -0.043 0.175** 0.116*

(0.05) (0.08) (0.04) (0.08) (0.08) (0.06)CPI 0.002 -0.005 -0.006***

(0.00) (0.00) (0.00)Imp× CPI 0.004 -0.009 -0.003

(0.01) (0.01) (0.00)Exp × CPI -0.022*** -0.014 -0.001

(0.01) (0.01) (0.01)RQ 0.011 0.071** 0.064***

(0.02) (0.03) (0.02)Imp × RQ 0.093 -0.145** -0.090

(0.08) (0.07) (0.06)Exp× RQ -0.058 -0.272** -0.147*

(0.11) (0.13) (0.09)GDP/POP -0.026*** 0.038 0.087*** -0.031*** 0.012 0.063***

(0.00) (0.03) (0.01) (0.01) (0.02) (0.02)Dependency 0.498*** 0.284*** 0.347*** 0.425*** 0.255*** 0.324***

(0.03) (0.10) (0.04) (0.04) (0.09) (0.05)Population 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000***

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)Constant 0.339*** -0.150 -0.661*** 0.421*** 0.036 -0.498***

(0.05) (0.27) (0.13) (0.05) (0.22) (0.15)

Time FE x x x x x xCountry RE x xCountry FE x x

Obs. 754 754 754 609 609 609R-sq within 0.591 0.962 0.577 0.963adj. R-sq 0.577 0.958 0.562 0.958Standard errors in parentheses. Coefficients are significant at the 10 percent (* p<0.10), 5percent (** p<0.05) or 1 percent (*** p<0.010) level. The dependent variable is the net-Ginicoefficient. Imp stands for import openness and Exp stands for export openness. CPI denotesCorruption Perception Index, which is our preferred measure of institutional quality. RQdenotes Regulation Quality. GDP/POP is a control for per capita GDP and Dependencystands for Dependency Ratio.

23

Motivated by this decomposition into the effects of imports and exports

we ask, if both work into the same direction by lumping them together into

openness defined as imports plus exports. Total trade openness is positively

associated with inequality in the OLS regressions exclusively controlling for the

time trend. Including random or fixed effects destroys significance of openness

in all regressions for both the direct and the interaction effects of openness. The

effect of the institutional measures itself is ambiguous.

Further robustness checks

As a robustness check we include both institutional variables, the CPI as well as

RQ in our regression. The number of observations reduces due to some missing

values in both variables. The results turn out to be robust against inclusion

of additional variables and/or changes in the number of observations. The

coefficients mainly replicate the results documented in Table (2). Economic

globalization can be associated with lower inequality in regressions that identify

the effects on both between and within variation of the data. However, the sign

of the coefficients turn from negative to positive in the random and fixed effects

regressions. All coefficients are significant. Corruption is significant only in

the random and fixed effects specifications. The sign changes depending on

the interaction terms included. Specification (1) to (3) include the interaction

between KOFI and CPI, whereas specification (4) to (6) include the interaction

between KOFI and RQ index. The critical level of the CPI index for which

the marginal effect turns from positive to negative lies around 4.7 to 6.8 in

regressions (2) and (3). In regressions (4) to (6) we find a critical value of

RQ between 0.53 and 0.78. Those estimates are very much in line with our

benchmark results. Thus, a further loss in observations does not change the

overall picture of our results.

24

Table 7: Econometric results Imports+Exports

(1) (2) (3) (4) (5) (6)Net Gini Net Gini Net Gini Net Gini Net Gini Net Gini

b/se b/se b/se b/se b/se b/seTrade 0.046*** 0.000 -0.009** 0.042*** 0.005 -0.002

(0.01) (0.01) (0.00) (0.01) (0.01) (0.00)CPI 0.003 -0.006** -0.006***

(0.00) (0.00) (0.00)Trade × CPI -0.014*** -0.000 0.002*

(0.00) (0.00) (0.00)RQ 0.023 0.049* 0.047**

(0.02) (0.03) (0.02)Trade × RQ -0.113*** -0.015 0.002

(0.02) (0.01) (0.01)GDP/POP -0.026*** 0.038 0.089*** -0.032*** 0.013 0.067***

(0.01) (0.03) (0.01) (0.01) (0.03) (0.02)Dependency 0.497*** 0.279*** 0.346*** 0.418*** 0.248*** 0.331***

(0.03) (0.10) (0.04) (0.04) (0.08) (0.05)Population 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000***

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)Constant 0.340*** -0.139 -0.672*** 0.418*** 0.049 -0.525***

(0.05) (0.27) (0.13) (0.05) (0.22) (0.15)

Time FE x x x x x xCountry RE x xCountry FE x x

Obs. 754 754 754 609 609 609R-sq within 0.590 0.962 0.572 0.963adj. R-sq 0.577 0.958 0.559 0.958Standard errors in parentheses. Coefficients are significant at the 10 percent (* p<0.10), 5percent (** p<0.05) or 1 percent (*** p<0.010) level. The dependent variable is the net-Ginicoefficient. Trade denotes import + export openness from the Penn World Table. CPI denotesCorruption Perception Index, which is our preferred measure of institutional quality. RQdenotes Regulation Quality. GDP/POP is a control for per capita GDP and Dependencystands for Dependency Ratio.

As a further robustness check we estimate the impact of globalisation and

institutional quality on redistribution, directly. In compliance with Gozgur and

Ranjan (2015), redistribution is quantified by the difference between market

and net-Gini coefficient. Results can be found in table (6) and (7). The KOF

25

Table 8: Econometric results CPI and RQ

(1) (2) (3) (4) (5) (6)Net Gini Net Gini Net Gini Net Gini Net Gini Net Gini

b/se b/se b/se b/se b/se b/seKOF I -0.318*** 0.274** 0.170* -0.332*** 0.316** 0.211***

(0.08) (0.11) (0.09) (0.09) (0.13) (0.07)CPI 0.015 0.034** 0.010 -0.005* -0.008** -0.007***

(0.01) (0.02) (0.01) (0.00) (0.00) (0.00)KOF × CPI -0.026* -0.058*** -0.025*

(0.02) (0.02) (0.01)RQ 0.068** 0.048* 0.053*** 0.218** 0.444*** 0.259***

(0.03) (0.03) (0.02) (0.09) (0.14) (0.07)KOF × RQ -0.207 -0.597*** -0.311***

(0.14) (0.22) (0.10)GDP/POP -0.003 0.015 0.074*** -0.002 0.019 0.076***

(0.00) (0.03) (0.02) (0.00) (0.02) (0.02)Dependency 0.337*** 0.335*** 0.402*** 0.335*** 0.311*** 0.391***

(0.04) (0.10) (0.06) (0.04) (0.10) (0.05)Population 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000**

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)Constant 0.391*** -0.163 -0.702*** 0.399*** -0.197 -0.736***

(0.07) (0.26) (0.19) (0.07) (0.27) (0.19)

Time FE x x x x x xCountry RE x xCountry FE x x

Obs. 591 591 591 591 591 591R2 within 0.611 0.964 0.610 0.964adj. R2 0.598 0.960 0.597 0.960Standard errors in parentheses. Coefficients are significant at the 10 percent (* p<0.10), 5percent (** p<0.05) or 1 percent (*** p<0.010) level. The dependent variable is the net-Ginicoefficient. KOF I is our preferred economic globalization measure. CPI denotes CorruptionPerception Index, which is our preferred measure of institutional quality. RQ denotesRegulation Quality. GDP/POP is a control for per capita GDP and Dependency stands forDependency Ratio.

Globalization Index exhibits a positive significant sign in all regressions. Hence,

we can conclude that globalisation fosters redistribution, and thereby, support

the empirical results of Gozgur and Ranjan (2015). This holds also controlling

for the time trend for KOFII, the economic globalisation index. The estimated

26

coefficient of the CPI is insignificant using KOF I but highly significant and

positive estimating the model including KOF II. In contrast, the index for

regulatory quality is highly significant and positive using time fixed effects

in column (4) for both globalisation indices: Countries characterized by high

regulatory quality are associated with higher levels of redistribution. It is not

surprising, that the institutional quality measures are not significant controlling

for between variation because the within variation of both indices does not

fluctuate at a high magnitude over time. The same holds for the gini coefficients,

and consequently, also for redistribution. Therefore, globalisation is the driving

component analyzing the interaction between globalisation and institutional

quality on within country inequality. The estimated sign of regulatory quality

changes from positive to negative including country fixed effects. This result is

counterintuitive, and requires further research. One possible explanation could

be outliers.

27

Table 9: Regression results with redistribution as dependent variable

(1) (2) (3) (4) (5) (6)redistr. redistr. redistr. redistr. redistr. redistr.

b/se b/se b/se b/se b/se b/seKOF I 0.354*** 0.156 0.114** 0.318*** 0.159** 0.098*

(0.03) (0.10) (0.06) (0.04) (0.07) (0.06)CPI 0.002* 0.002 -0.001

(0.00) (0.00) (0.00)RQ 0.047*** -0.044 -0.077***

(0.02) (0.03) (0.02)GDP/POP 0.013*** 0.019 -0.029* 0.014*** 0.034*** -0.005

(0.00) (0.01) (0.02) (0.00) (0.01) (0.02)Dependency -0.013 0.143* 0.138*** -0.003 0.181** 0.212***

(0.03) (0.08) (0.04) (0.03) (0.08) (0.05)Population -0.000*** -0.000*** -0.000 -0.000*** -0.000*** -0.000

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)Constant -0.245*** -0.242*** 0.252* -0.241*** -0.364*** 0.035

(0.03) (0.09) (0.13) (0.03) (0.10) (0.18)

Time FE x x x x x xCountry RE x xCountry FE x x

Number of obs. 754.000 754.000 754.000 609.000 609.000 609.000R-sq within 0.650 0.914 0.653 0.917adj. R-sq 0.640 0.906 0.644 0.907Standard errors in parenthesis. Coefficients are significant at the 10 percent, (* p<0.10) 5percent (** p<0.05) or 1 percent (*** p<0.010) level. The dependent variable is redistribution,measured as difference between market and net Gini. KOFI is our preferred globalisationmeasure. CPI denotes Corruption Perception Index, which is aour preferred measure forinstitutional quality. RQ denotes Regulatory Quality. GDP/POP is a control for per capitaGDP and Dependency stands for Dependency Ratio.

28

Table 10: Regression results with redistribution as dependent variable contd.

(1) (2) (3) (4) (5) (6)redistr. redistr. redistr. redistr. redistr. redistr.

b/se b/se b/se b/se b/se b/seKOF II 0.171*** 0.020 0.008 0.132*** 0.034 0.025

(0.02) (0.04) (0.02) (0.02) (0.04) (0.03)CPI 0.005*** 0.003 -0.001

(0.00) (0.00) (0.00)RQ 0.084*** -0.037 -0.074***

(0.02) (0.03) (0.02)GDP/POP 0.028*** 0.030*** -0.014 0.027*** 0.043*** 0.003

(0.00) (0.01) (0.01) (0.00) (0.01) (0.02)Dependencyl -0.062** 0.144* 0.145*** -0.030 0.173** 0.205***

(0.03) (0.08) (0.04) (0.03) (0.08) (0.06)Population -0.000*** -0.000*** -0.000 -0.000*** -0.000*** -0.000

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)Constant -0.247*** -0.256*** 0.183 -0.253*** -0.370*** 0.016

(0.04) (0.09) (0.12) (0.04) (0.11) (0.17)

Time FE x x x x x xCountry RE x xCountry FE x x

Number of obs. 754.000 754.000 754.000 609.000 609.000 609.000R-sq within 0.610 0.913 0.616 0.916adj. R-sq 0.599 0.905 0.605 0.907Standard errors in parenthesis. Coefficients are significant at the 10 percent, (* p<0.10) 5percent (** p<0.05) or 1 percent (*** p<0.010) level. The dependent variable is redistribution,measured as difference between market and net Gini. KOFII is the Economic GlobalisationIndex. CPI denotes Corruption Perception Index, which is aour preferred measure forinstitutional quality. RQ denotes Regulatory Quality. GDP/POP is a control for per capitaGDP and Dependency stands for Dependency Ratio.

29

IV. Conclusion

This paper explores the role of institutional quality in the trade and inequality

nexus. We find that corruption has a significant impact on the relationship

between trade and inequality through redistribution of the gains from trade.

Trade rises inequality through higher per capita income: wage growth at the

top of income distribution is more pronounced than wage growth at the bottom.

Governments can offset those effects through appropriate redistribution schemes

that aim at taxing the gains from trade in a way that reduces inequality. However,

we argue that corruption and bad institutions may induce distortions that

neutralize those positive effects: inequality rises due to trade liberalization, if

bad institutions prevent redistribution schemes by the government. Our results

suggest that trade increases inequality in countries with bad institutions only.

Quite to the contrary and to our big surprise, we find negative effects of trade

on inequality in countries with high institutional standards as low corruption or

a high level of regulatory quality. Both results are highly significant and robust.

30

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V. Appendix

I. Appenix I - Correlation Analysis

Table 11: Correlation Analysis KOF I

KOF I GE*Grav RQ*Grav GE RQ

KOF I 1.000GE*Grav 0.7561 1.000RQ*Grav 0.7382 0.9693 1.000GE 0.8232 0.7825 0.6998 1.000RQ 0.8232 0.7195 0.7209 0.9171 1.000

Table 12: Correlation Analysis CPI

CPI GE*Grav RQ*Grav GE RQ

CPI 1.000GE*Grav 0.7345 1.000RQ*Grav 0.6632 0.9705 1.000GE 0.9449 0.7820 0.7017 1.000RQ 0.8866 0.7193 0.7202 0.9194 1.000

Table 13: Correlation Analysis CPI*KOF I

KOF I*Grav GE*Grav RQ*Grav GE RQ

KOF I*Grav 1.000GE*Grav 0.7898 1.000RQ*Grav 0.7393 0.9705 1.000GE 0.9219 0.7820 0.7017 1.000RQ 0.8902 0.7193 0.7202 0.9194 1.000

34