Document de travail IDP (EA 1384) n°2013

Document de travail IDP (EA 1384) n°2013-14
Income inequality and international trade: short and long-run
evidence and the specific case of the tourism services
Aurélie Cassette, Nicolas Fleury et Sylvain Petit
MDD – Mobilités et développement durable
Income inequality and international trade: short
and long-run evidence and the specific case of the
tourism services
Aurélie Cassette, Nicolas Fleury et Sylvain Petit
Aurélie Cassette
PRES Université Lille Nord de France, Université Lille 1, EQUIPPE, EA4018, Lille,
France
[email protected]
Nicolas Fleury
Centre Etudes et Prospective, Groupe Alpha et EQUIPPE, EA4018, Paris, France.
[email protected]
Sylvain Petit
PRES Université Lille Nord de France, Université de Valenciennes et du HainautCambrésis, IDP, EA1384 et EQUIPPE EA4018, Valenciennes, France
[email protected]
Income inequality and international trade:
short and long-run evidence and the specific case of the tourism services¡
Aurélie Cassette*, Nicolas Fleury** & Sylvain Petit***
Abstract: The paper analyzes the relationship between international trade and inequality.
We take into account the specific case of services and notably tourism services. We
distinguish short and long-run relationships by using a sample which covers 9 countries over
the 1967-1999 periods. For the first estimation, we estimate a dynamic panel data model and
for the second, we use the cointegration data panel method. We had decomposing trade in
services into three categories (tourism, transport and other commercial services) which
permits to uncover different impacts of trade on inequality. We first establish the existence of
a short-run and a long-run relationship between openness and inequality. We also find that
international trade in tourism services has a negative impact in inequality. Finally we
observe that the effect of openness varies by country.
Key words: International Trade; Income inequality; Services; Dynamic panel data model;
Cointegration.
JEL Classification: C23, F10, D31, L83
¡
We would like to thank Etienne Farvaque, Jean-Jacques Nowak, Alain Ayong Le Kama, Jérôme
Héricourt, Ali Shah Syed Sarfaraz and William Pearce for comments on a previous version of this
paper. We also thank the participants of the “International Trade: A Global Perspective for the 21st
Century” conference held in Stoke-On-Trent in December 2008.
*
Aurélie Cassette, Post-doctoral researcher, EQUIPPE-Universités de Lille (EA1048), University of
Science and Technology of Lille, F-59655 Villeneuve d’Ascq Cedex, France. Email address:
**
Nicolas Fleury, PhD Student, EQUIPPE-Universités de Lille (EA1048), University of Science and
Technology of Lille, F-59655 Villeneuve d’Ascq Cedex, France, Email address:
[email protected], Tel.: +33 3 20 43 66 12, fax: +33 3 20 43 67 68.
***
Sylvain Petit (corresponding author), PhD Student, EQUIPPE-Universités de Lille (EA1048),
University of Science and Technology of Lille, F-59655 Villeneuve d’Ascq Cedex, France/IDP Lien
(EA 1384), University of Valenciennes et du Hainault Cambresis, F-59300 Valenciennes Cedex,
France
Address for correspondence: [email protected]
Tel.: +33 3 20 43 66 12, fax: +33 3 20 43 67 68.
1. Introduction
The 1970’s crisis led to a deceleration in international trade growth. This entailed important
mutations and a second wave of globalization. Beginning in the first part of the 1980s, this
second wave is characterized by rising commercial flows, both in goods and services.
Liberalization in trade of services occurred primarily in the late 1970s for the United States,
and then progressively concerned all OECD countries (especially in transport services, which
reduced transport costs). Meanwhile, there has been an acceleration in the trade of goods,
notably helped by liberalization and international fragmentation in goods’ production, and by
the rising share of southern countries in international trade. The effect of globalization on
wages is not easily quantified. As pointed out by Baldwin (2006), it is difficult to assign
‘winners’ and ‘losers’. However, northern countries have encountered a global rise in
inequality.
Following the seminal empirical studies of Katz and Murphy (1992) and Lawrence and
Slaughter (1993), many papers attempt to provide empirical estimates of the impact of
international trade on inequality. The results are however relatively inconclusive, especially
in studies encompassing countries from all regions of the world (Higgins and Williamson,
2002; Figini and Görg, 2006; Gourdon et al., 2008). In our view, a potential limit is that
existing studies focus on international trade in manufactured goods, while, to our knowledge,
no paper examines the specific case of international trade of services and specially trade of
tourism services. This can be explained by the fact that most of the growing trade from
southern countries is in manufactured goods (both in exports and imports), and also by the
lack of data on trade in services, though their share in total international trade has strongly
increased, especially since the 1990s.
Data on international flows of services have been published largely after flows of
manufactured goods. This first comes from the fact that common international rules for the
trade in services and harmonization of computation for flows of services were not defined
until the GATS (General Agreement of Trade in Services) agreements under the Uruguay
2
Round (1994). A second reason is that international flows of services are notoriously difficult
to quantify1.
The new CHELEM database of the balance of payments, maintained by the CEPII, now
provides long-term data for the international flow of three categories of services: tourism,
transport and others commercials services. These data assess the growing weight of services
in international trade since 1967. Over the 1967-1999 period, the average annual growth rate
for the export of services is greater than 12%, whereas this rate is close to 10% for goods. In
the beginning of this period, services represented 20% of worldwide exports, reaching 25% in
1999. The role of services in international trade can thus no longer be discarded.
As the acceleration of international trade (and particularly in services) in the last decades is
also concomitant to surges in inequality for some countries, we investigate the link between
these two phenomena.
As it is well know, tourism is a low-skill intensive sector (Sinclair and Stabler, 1997),
relatively to the others services and some manufactured goods sectors. Secondly, workers in
the tourism sectors are protected against foreign competition and the globalization cannot put
downward pressure on their wages. In this case, the development of international tourism
should to have no impact on income distributions. However, the scarce empirical studies
about tourism and inequalities (Blake, 2008; Wattanakuljarus and Coxhead, 2008) had
showed, by using social accounting matrix, that the development of tourism sector in
developing country has for consequences a rise of high incomes and a reduction of the
poorest incomes.
So, the aim of the paper is two-fold: (i) to re-examine the empirical evidence for the impact of
international trade on inequality, by using an annual panel data set for 9 northern countries
over 33 years; (ii) to include and compare the role of tourism services relative to goods and
the others commercials services in explaining rising or decreasing inequality. The long-term
dimension of our sample permits to take into account two relevant trade facts: the large
increase of trade with southern countries, and the striking growth of international trade
(especially of services).
First, we use a dynamic panel data model to take into account the sluggish nature of
inequality. Second, the long-term period of our dataset allows us to study the long-run
1
For example, there are still some technical difficulties to measure intermediate consumption of services. The
main existing sources are input-output tables, which incorporated many measurement problems and therefore
could give imprecise estimations (See Krugman, 2008, or OECD, 2004)
3
relation between openness and inequality. To our knowledge, this paper is the first attempt in
this field using panel data to deal with long run determinants of inequality.
Results of our empirical investigation are clear-cut. First, we show that the relation between
international trade and inequality differs between goods and services. Second, the results
change also according to the type of services. Third, we show that international trade in
tourism services is a specific case, being the only kind of international services trade leading
to a short-run decrease in inequality. However, in the long run and at the sample level, the
relation between each kind of international trade and inequality is positive when significant
but that differs among countries.
The paper is organized as follows. The next section presents the related theoretical and
empirical literature and section 3 provides the data. Section 4 describes the empirical
specification, the econometric procedure and the results of the dynamic panel data model. In
section 5, we show the methodology and the results of the cointegrated panel analysis. The
last section concludes.
2. From theory to empirics
Traditional explanations of the link between international trade and inequality are based on
trade in manufactured goods between southern and northern countries. In the seminal twocountry Heckscher-Ohlin-Samuelson (HOS) framework, one can find theoretical support for
rising inequality: as a country will export the good for which it uses the abundant factor
intensively, trade increases the price of this good. This entails an increase in the relative price
of the abundant factor in the production of the traded good (a decrease in the remuneration in
the scarce factor): for northern countries, this framework predicts a rise in the skill premium
(Stolper-Samuelson theorem). A recent extension of the HOS framework introduces one
southern country which only produces manufactured goods and one northern country that
produces non-traded services and R&D, provided by non-skilled and by skilled workers
respectively (Askenazy, 2005). Low skilled workers are thus partly protected against national
competition. If R&D has decreasing returns to scale and if households consume a large share
of non-traded services, then international trade reduces wage inequality.
4
However, new explanations have been developed (see the survey by Chusseau et al., 2008):
(i) international outsourcing, (ii) capital-skill complementary, (iii) competition-enhancing
trade liberalization. The latter is particularly relevant in the case of north-north trade. In this
framework, Manasse and Turrini (2001) and Neary (2002) both focus on the effect of the fall
in trade barriers consecutive to rising globalization. Considering a monopolistic competition
trade model, Manasse and Turrini (2001) suppose that exporting firms have access to a larger
market and must differentiate their product from foreign partners. Consequently, they employ
a growing share of skilled labor to produce goods or services of better quality, which boosts
the skill premium in these firms. This raises wage inequality at the aggregate level. Neary
(2002) proposes an oligopolistic competition model where trade liberalization enhances
strategic investment by incumbent firms in order to block the entry of foreign firms. As trade
liberalization makes foreign firms more competitive on the domestic market, the threat of
imports encourages domestic firms to produce with a higher ratio of investment to output. As
investment is skill intensive while output is unskilled intensive, trade liberalization raises the
skill premium and wage inequality.
Earlier empirical estimations in the 1990s, mainly based on the HOS framework, show that
international trade had a marginal impact on inequality (e.g. Katz and Murphy, 1992;
Lawrence and Slaughter, 1993). However, later studies find a more important influence than
previously thought. Notably, international trade seems to have a significant impact on
inequality for developed countries, especially by increasing the remuneration of higher skilled
workers relatively to lower skilled workers (e.g. Wood, 1994 and 1995; Goux and Maurin,
1997; Slaughter and Swagel, 1997; Dluhosch, 1998).
In recent years, new doubts on the actual impact of international trade on inequality have
been stressed upon. While some existing studies only focus on developed countries or on
developing countries, some encompass both kinds of countries.
Focusing on the European experience from the 1980s to 2000, Harjes (2007) underlines that
developments in income and wage inequality differ appreciably across developed countries,
and concludes that international trade may not be a major explanation for inequality. Using
top income share as an inequality variable, Roine et al (2009) confirm a difference across
groups of countries: increased trade is associated with increased top incomes in Anglo-Saxon
countries but not in continental Europe.
5
Using a sample of 70 developing countries, Meschi and Vivarelli (2007) show that total
aggregate trade is weakly related with income inequality. They also find that trade (at a
country level) with high income countries worsens income distribution for these developing
countries. Based on 16 countries located in Latin America, Asia and Africa, BahmaniOskooee et al. (2008) assess the short and long run effects of income and openness (measured
by imports as a percent of GDP) on Gini coefficients for each country (through an Error
correction model on time-series data). They conclude that the effects of both income and
openness vary across countries.
Turning to studies encompassing countries from all regions of the world, we can notably refer
to Jakobsson (2006), who finds that trade is weakly associated with higher inequality in the
90s, but not in the 80s. Higgins and Williamson (2002) find little support for the hypothesis
that international trade and globalization affect inequality. But they provide strong support for
cohort-size effects on inequality: this hypothesis means that fat cohorts tend to get low
rewards. In this case, fat young-adult cohorts create inequality whereas large mature workingage cohorts are associated with lower aggregate inequality. Replacing trade openness with
tariffs, Milanovic and Squire (2005) find that tariff reduction is associated with higher wage
inequality in poorer countries and lower inequality in richer countries, whereas Gourdon et al.
(2008) explicitly show that the conditional effects of tariffs on inequality are correlated with
relative factor endowments.
The literature acknowledges an effect of international trade on inequality. However, this
effect differs between developed and developing countries, and even among developed
countries and among developing countries. This literature examines short-run effects. Longrun results could be more homogenous. To our knowledge, this paper is the first attempt in
this field using panel data to deal with long-run determinants of the evolution of inequality2.
Whereas the literature mainly deals with global trade openness, we investigate the specific
influence of trade openness of tourism services.
3. Data
In this section, we present the variables used in this paper, while table A.1 in the Appendix
reports the summary statistics and the sources. The purpose of this paper is to test whether
2
Borjas and Ramey (1994) and Bahmani-Oskooee et al. (2008) examine long run effects in time series pattern.
6
international trade has an impact on inequality in OECD countries over the period 196719993. Although the size of the cross-section is limited to nine countries4, due to data
availability, this limitation also ensures that we study countries with comparable factor
endowments and technology levels (see Gourdon et al., 2008). Strong homogeneity in our
sample is ensured by harmonization and the reliability of the data.
3.1.
Dependent variable
A wide range of inequality indices have been developed. Some studies focus on the skill
premium for the inequality variable; others use Gini indices or shares of quintiles in the
distribution of income (e.g. Lundberg and Squire, 1999; Higgins and Williamson, 1999;
Dollar and Kraay, 2000; Milanovic, 2003; Attanasio et al., 2003; Meshi and Vivarelli, 2007).
Our estimations employ top income shares. These data correspond to the share of revenue
received by the highest portion of the income distribution. Using fiscal information, these
data were first calculated for France by Piketty (2001) and for the United States by Piketty
and Saez (2003). Following the same method, the panel data set was extended to 12 other
countries5. For this reason, this indicator supports comparisons between countries. Moreover,
Leigh (2007) shows that this indicator is a good substitute for the traditional inequality
indicators such as the Gini coefficient or the D9/D1 ratio. Top income shares are used in
Roine et al. (2009).
Two main stylized facts regarding inequality emerge from the 1967-1999 period. First, there
exists a strong heterogeneity in inequality both in terms of level and growth rate. High
income inequality is observed in Canada, in the United Kingdom and in the United States: in
1999, in these countries, the wealthiest 10% of the population held 40% of the total revenue,
whereas this rate was less than 30% for Australia, Sweden and the Netherlands. Second, for
all countries in our panel, inequality has ceased to decrease, and even increased in some
cases. As a consequence, inequality appears as a persistent phenomenon.
3
Data limitations for the inequality variable prevent us from investigating the recent evolution of inequality.
Australia, Canada, France, Germany, Netherlands, Sweden, United Kingdom, United States, New Zealand
5
New Zealand (Atkinson and Leigh, 2005), Australia (Atkinson and Leigh, 2007), Canada (Saez and Veall,
2005), Germany (Dell, 2005), Netherlands (Atkinson and Salverda, 2005), Sweden (Roine et Waldenström,
2006), Switzerland (Dell, Piketty and Saez, 2005), United Kingdom (Atkinson, 2005), Spain (Alvaredo and
Saez, 2006), China (Piketty and Qian, 2006), India (Banerjee and Piketty, 2005) Indonesia (Leigh and van der
Eng, 2007). Similar studies on other countries are in progress.
4
7
3.2.
Main variables of interest: Openness indicators
As pointed out by Edwards (1998), there is no generally accepted measurement of openness.
Two kinds of openness variables exist: incidence-based measures of tariff data (e.g. average
import tariff on manufacturing) and trade policy (like Sachs and Warner binary openness
index, average black market premium or average coverage of non-tariff barriers) and
outcome-based measures (like the standard trade/GDP variable). We turn to the well-known
trade/2GDP indicator. International trade is represented by an openness variable defined for
sector j, country i and at time t:
OPENijt with: OPENijt =
X ijt + M ijt
2 × GDPit
where X ijt and M ijt represent exports and imports, and GDPit the current Gross Domestic
Product. As we focus on developed countries, either positive or negative signs could be
expected. In the first case, if the share of north-south trade is relatively high, the traditional
Heckscher-Ohlin-Samuelson (HOS) framework applies. Then, low-skill-intensive products
are likely to be imported by rich countries, and so low-skilled wages decrease relatively to
high-skilled wages: a positive sign is expected. In the second case, when trade concerns
primarily low-skill intensive sectors, a negative sign is expected (Askenazy, 2005).
Table 1: Openness variables
Total trade
Goods
Services
Openness variable
OPENiTOTt
Detail
Goods and services
OPENiGt
Goods
OPENiSt
Services
OPENiTOURt
Tourism services
OPENiTRt
Transport services
OPENiOt
Other commercial services
Six openness variables are distinguished (see table 1) relative to total trade, trade in goods
and trade in services. In the CHELEM database, three principal entries of services can be
distinguished:
(i)
The first entry consists of travel services (i.e. tourism services). This aggregate
contains restoration services, accommodation services, animation services and
services through tour-operators. Registration from this entry corresponds to the
estimation of tourism expenses and receipts;
8
(ii)
The second corresponds to transport services. Flying, shipping and other charges
are estimated for passengers and for freight;
(iii)
The last entry represents “other commercial services”. It contains communication
services, construction, insurance, financial services, informatics and information
services, fees and patents, other services for firms, cultural services, and public
administrations.
Figure 1 illustrates the evolution of weight for these three sectors on the international trade of
services for our sample. Transport services prevailed until the mid-1970s. Then “other
commercial services” took the advantage, as a result of the service liberalization (notably for
the United States). In the mid 1980s, in line with deregulation of air transport, exports of
tourism services started to rise sharply, and even overwhelmed transport services.
Figure 1: Evolution of the three categories of services shares on the trade of services
0,6
Other commercial services
0,5
0,4
Tourism services
0,3
0,2
Transport services
0,1
0
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
Source: CHELEM database (CEPII)
3.3.
Control variables
International trade may hardly be considered as the only vector of inequality: several control
variables must be introduced.
9
Foreign direct investment FDI it : FDI corresponds to international financial flows leading to
the creation of direct, stable and long links between economies. FDI can be seen as a
substitute or complement for the other openness variables. Following Figini and Görg (2006),
it is better to use data on FDI stocks rather than flows because FDI can be viewed as
contributing to the stock of general-purpose technology available in the economy.
Unfortunately, this data is not available before 1980. As a consequence, we use inward FDI
flows as a percentage of GDP.
Education EDU it : We use a variable of skill supply, which corresponds to the average years
of schooling of the total population aged 25 and over. These data come from Barro & Lee
(1993/2000)6. Education is expected to have a negative impact on inequality: an increase in
education means more abundant skills in the economy, so a decrease in the relative
skilled/unskilled wage, and a decrease in the overall income inequality.
Gross domestic product per capita GDPCit : Gross domestic product per capita may have an
impact on inequality, according to the mechanism initially exposed by Kuznets (1955)7. The
relationship between inequality and economic development would follow an inverted Upattern: inequality within countries has been observed as increasing in the early stage of
development, and decreasing in the latter. Because there should be a quadratic (inverted U)
relationship between development and inequality, we also add GDP per capita squared
( GDPCQit ) to account for the non-linear relationship.
Inflation INFLit : We include the inflation rate to check for the macroeconomic environment
which is likely to affect income distribution. Inflation erodes real wages and
disproportionately affects those within the bottom percentiles of income distribution, thus
increasing inequality.
3.4.
Unit root tests
As time series dominate the cross section, we need to check the stationarity of our variables.
In recent years, several procedures have been advocated to test for a unit root with panel data
6
Missing data have been extrapolated from a time trend.
Another advantage of the GDP per capita variable is to make sure that inward FDI does not merely pick up the
impact of the level of economic development on inequality.
7
10
techniques. The main difference between them concerns assumptions about the heterogeneity
of the data-generating model. It appears that heterogeneity has first been introduced via fixed
effects and then via the autoregressive dynamic structures ȡi:
∆yi;t = α i + δ i t + θ t + ρi yi;t-1 + ς i;t
(1)
Im, Pesaran and Shin (2003) study the small sample properties of unit root tests in panels with
heterogeneous dynamics and propose alternative tests based on group mean statistics: under
the null hypothesis, all series in the panel are non stationary processes ( ρi = 0, ∀i ) and there
are no individual fixed effects ( αi = 0); under the alternative, a fraction of the series in the
panel are assumed to be stationary ( ρi < 0). In their group-mean panel unit root tests, the tstatistics from each ADF test are averaged across the panel. From the Im Shin Pesaran test
(table A.2 in appendix), all variables are difference stationary8 (except INFLit). This implies
that inflation is a short run determinant of inequality but has no long run effect. Other
explanatory variables potentially have both short and long run effects on inequality.
4. Short-run results
4.1.
Empirical specification
Due to the presence of unit roots in our main variables, we estimate the model in firstdifference. In this way, we look at short run effects on inequality. Due to the sluggish nature
of inequality, we include a time-lagged dependent variable to take into account the past
dynamics of inequality. The general specification of our empirical model is:
∆INEQi;t = α∆INEQi;t −1 + β1∆OPEN i;t + β2 ∆X i;t + ηt + εi;t (2)
where ηt and ε it are the time fixed effect and the error term respectively, INEQ represents
our dependent variable, the degree of inequality, OPEN is the trade openness indicator and X
is a vector of control variables.
4.2.
Econometric procedure
In equation (2), the presence of the lagged dependent variable together with fixed effects
requires the use of the GMM estimator suggested by Arellano and Bond (1991). However,
our data set contains a small number of observations in the cross section (9 countries) and a
relatively large time dimension (33 years): in this case, Judson and Owen (1999) show that
8
A variable is integrated of order I(1) if this variable is not stationary in level but stationary in first difference. A
variable is integrated of order I(0) (or level stationary) if it is stationary in level and in first difference.
11
the LSDVC (Least Square Dummy Variable Corrected) estimator, suggested by Kiviet (1995)
outperforms alternative estimators like the GMM-system estimator developed by Blundell
and Bond (1998). Kiviet’s LSDV correction (LSDVC) uses a consistent estimator in a first
stage of the estimation procedure. This consistent estimator could be obtained following
Blundell and Bond’s GMM (Generalized Method of Moments) system estimator.
4.3.
Results
Table 2 displays the results for the dynamic model in four columns which differ according to
the openness variable included (one column for total openness variable, one column for
openness in goods only, then one column with both openness in goods and services and one
column with both openness in goods and each entry of services9).
Table 2: Econometric results for the dynamic model (1967-1999)
INEQit
(1)
(2)
(3)
(4)
∆INEQit −1
0.16**
(2.42)
0.05**
(1.96)
0.16**
(2.40)
0.16**
(2.40)
0.22***
(3.75)
0.05**
(2.02)
0.05*
(1.85)
0.002
(0.01)
0.06**
(2.54)
∆OPENiTOTt
∆OPENiGt
∆OPENiSt
∆OPEN iTOURt
∆OPENiTRt
∆OPENiOt
∆FDIit
∆EDU it
∆GDPCit
∆GDPCQit
∆INFLit
-2.57***
(-5.69)
0.23
(1.24)
0.75***
(3.26)
0.11***
0.11***
0.11***
0.11***
(3.39)
(3.37)
(3.40)
(3.80)
-0.17
-0.17
-0.17
-0.44
(-0.35)
(-0.34)
(-0.34)
(-0.92)
0.0002*** 0.0002*** 0.0002*** 0.0002***
(3.46)
(3.46)
(3.41)
(4.27)
-3.56E-9*** -3.58E-9 -3.58E-9*** -4.78E-9***
(-3.78)
(-3.80)
(-3.60)
(-5.16)
-0.03
-0.03
-0.03
-0.03*
(-1.59)
(-1.59)
(-1.58)
(-1.83)
t-Student between brackets. Time dummies included
Level of significance: *** for 1%, ** for 5% and * for 10%.
9
Note that we assess that openness variables are not correlated.
12
We first note that the lagged endogenous variable is always very significant and takes a value
between 0.16-0.22 in all specifications. This result confirms both the consistency of the
autoregressive specification in Eq. (2) and the existence of some path dependence.
Interestingly, the influence of the openness variable changes according to the sector
considered. The first regression introduces the aggregate openness variable commonly used in
the literature to study the link between international trade and inequality. It shows that total
international trade has a significant and positive impact on inequality. As our sample is
composed of developed countries, several explanations can be provided, but at this aggregate
level, it is not possible to define which one dominates. Consequently it seems particularly
relevant to look at a more disaggregated level.
The second regression confirms this result for the goods sector. Growing north-south trade is
very likely to enhance inequality. It could confirm the HOS prediction. Alternative
explanations relying, for example, on the inclusion of outsourcing can also be provided.
Interestingly, the size of the effect is broadly the same as for the aggregate level: does it mean
that trade of services has no impact on inequality? Indeed, we observe no significant
influence of services on inequality (column 3).
Once more, further disaggregation prevents misleading interpretations. First, column (4)
indicates that tourism services are the only case that exhibits a negative relationship between
trade and inequality. Two main mechanisms can highlight this result. First, these services are
low-skilled intensive (Sinclair and Stabler, 1997) and an approach à la Askenazy (2005) can
be invoked: as workers in the tourism sector are protected against foreign competition,
globalization cannot put downward pressure on their wages. Second, trade in tourism services
mainly takes place between northern countries10, which entails low international competition
on salaries.
Column (4) shows also a high effect of other commercial services on inequality, even higher
than effects of manufactured goods. This kind of services concentrates on north-north trade
and highly qualified workers. Following Chusseau et al. (2008), the outsourcing phenomenon
could also be expected to raise inequality. Second, international trade in transport services has
no significant impact on inequality. Linked with transport liberalization in the 1980s, the
impact of transport trade on inequality is likely to occur on a recent period: this result will be
shown later in this section.
10
See Lee and Lloyd (2002) and Nowak, Petit and Sahli (2008). Both found that international flows of tourism
services are dominated by intra-industry trade for northern countries.
13
Another way to take into account the impact of globalization is to use FDI flows. In
developed countries, inward FDI is mainly concentrated in the tertiary sector (UNCTAD,
2008). The foreign direct investment variable is highly significant and has the expected
positive sign. This result differs from previous results by Figini and Görg (2006). Focusing on
the relationship between FDI inward stocks and wage inequality, they show that wage
inequality decreases with FDI in developed countries.
Concerning the control variables, the education variable has the expected negative effect on
inequality but the coefficient is never significant. This result is not surprising, as it is likely
that education has long-run effects on inequality rather than short-run effects. The coefficients
associated with the GDP per capita variables exhibit an inverted-U pattern. A previous paper
(Higgins and Williamson, 2002) already reports evidence that inequality follows the invertedU pattern described by Kuznets, whereas Figini and Görg (2006) find a negative sign (but not
significant), suggesting a Kuznets relationship in its second stage, when an increase in GDP
leads to a reduction in inequality. Finally, the inflation variable always has a negative impact
on inequality, but never significant.
As the liberalization of services occurred at the beginning of the 1980s, we perform
estimations on a smaller time period, 1980-1999 (see table A.3 in appendix), to assess the
impact of this liberalization. Whereas there is a small difference between the whole period
and the subset in the relations with total trade and trade in goods (columns 1 and 2), the
coefficient associated with transport service flows (fourth column) is now significant for the
sub-period 1980-1999. Finally, the impact of tourism services on inequality is more important
during the sub-period than for the whole period, which could indicate that liberalization of
services had an impact on the intensity of this relationship.
Though our sample only includes developed countries, the impact of international trade on
inequality could also depend on the country studied. If it is the case, this could explain the
inconclusiveness of the literature about the impact of international trade on inequality and the
non-significance of some variables with regard to openness in services. The methodology
developed in the next section will answer this question.
14
5. Long-run equilibrium relationship between international trade and inequality
Here, we examine the long-run equilibrium relationship between international trade and
inequality, using the Fully Modified OLS estimator (FMOLS) suggested by Pedroni (2000).
As we know from section 4 that our main variables are integrated of order I(1), Pedroni panel
cointegration tests are conducted to see whether there is a long-run equilibrium between our
variables. If some of our variables are cointegrated, there is a risk of fallacious regression
using standard estimation techniques, so we have to estimate the long-run relationship by the
means of the Fully Modified OLS.
5.1 Cointegration tests
Once the existence of a panel unit root has been established, the issue arises of whether these
variables are cointegrated and whether there exists a long-run equilibrium between them. If
two variables y and x are I(1) and a linear combination of these variables is stationary (I(0)),
then these two variables are cointegrated of order (1;1). We use Pedroni’s (1999)
cointegration tests as it allows for considerable heterogeneity in fixed effects, individual
deterministic trends and slope coefficients of the cointegrating vectors. Under the null
hypothesis of no cointegration, the residual is also integrated of order one ( ρi = 1, ∀i ):
yi;t = α i + δ it + γ t + β1x i;t +...+ β Mi x mit + ε it with ε it = ρ i ε i;t-1 + u
it
(3)
These tests run individual cointegrating regressions for each member, collect estimated
residuals and compute either pooled panel root test, or group mean unit root test. In each case,
rejection of the null hypothesis means that the variables under consideration are cointegrated.
For a sufficient time dimension (T>20), these tests have a standard normal distribution and
give the same quality of results.
We proceed in two steps: first we check if the dependent variable is cointegrated with each
independent variable, then we test if the independent variables are cointegrated with each
other (we cannot put two cointegrated independent variables in the same regression). After
applying the cointegrating tests, we cannot accept the null hypothesis of no cointegration
between each independent variable and the inequality variable (table A.5 in appendix).
Independent variables (openness variables, education and FDI) are cointegrated between each
other and must not be included simultaneously: we will estimate the long-run relationship
between inequality and each independent variable separately.
15
5.2 The Fully Modified Group Mean Estimator to estimate a long-run relationship between
international trade and inequality
When variables are cointegrated, we have to estimate the long-run relationship between them.
Pedroni (2000) suggests a method based on the fully modified OLS that can capture the
heterogeneity across countries (slope and intercept heterogeneity) and permits short run
dynamics. Since this estimator takes into account the endogeneity of the regressors and
correlation and heteroscedasticity of the residuals, the estimator is asymptotically unbiased.
He argues that by doing this, inferences can be made regarding common long-run
relationships, which are asymptotically invariant to the considerable degree of short-run
heterogeneity that is prevalent in dynamics, typically associated with panels that are
composed of aggregate national data. The Fully Modified Group Mean Estimator is simply
the average of the individual FMOLS for each country. Therefore, we use group mean
FMOLS as suggested by Pedroni (2000) to estimate a cointegrating relationship between
inequality and international trade.
Table 4 gives the fully modified estimates of the long-run equilibrium relationship among
openness, education and inequality for the 9 OECD countries and the panel group fully
modified estimates over the period 1967-1999.
Firstly, total international trade and trade in goods have similar effects on inequality, at the
individual level and at the sample level. Coefficients in the first two columns have the same
sign, size and significance. International trade exhibits a positive long-run effect on
inequality.
Secondly, international trade in services has no significant impact on inequality at the sample
level. The effect differs according to the kind of services: while international trade in tourism
and transport increases inequality in the long run, the other commercial services have no
impact on inequality. Comparing short and long-run results, we observe that international
trade in other commercial services strongly affects inequality in the short run but there is no
effect in the long run. While openness in tourism services leads to short-run decreases in
inequality, we find positive long-run effects.
Thirdly, at the sample level, the positive relationship between international trade in tourism
services and inequality is largely higher than in the case of the transport services, which could
16
be explained by the fact that tourism services use unskilled labor more intensively, in relation
to transport services.
Fourthly, we look at long run effects by countries. Whatever the kind of openness, when it is
significant, this variable always has a positive impact on inequality for Australia, the United
States, France and the Netherlands. On the contrary, for Canada, the long-run relationship
between international trade and inequality is always negative when significant. For Sweden
and the UK, the effects differ according to the sector considered: the long run relationship is
mostly negative but is positive in Sweden for transport and in the UK for tourism. For New
Zealand and Germany, the individual long-run relationship is never significant. We must
stress the fact that, when significant, the impact of a type of trade (goods/services/services at
disaggregated levels) is always of the same sign (excepted in the United Kingdom and
Sweden). International trade in tourism services has the same impact as that of transport
services on inequality, except in two cases. First, in the United Kingdom, tourism services
have a positive and significant impact on inequality whereas transport services have a
negative and significant effect on inequality. It is the contrary for Sweden.
Our results provide evidence for an impact of international trade on inequality, but only for
some countries (United States, Netherlands and Australia). We have to stress the difficulties
when dealing with panel regression of large samples of countries which correspond to a large
range of specificities (technologies, trade sector, sub-sector, main trade partnership, country’s
average level of income, etc.): the absence of consensus in the existing literature may partly
emerge because the link between international trade and inequality is likely to change
according to the country, or the trade sector11.
Concerning the other variables, there is a negative long run relationship between FDI inflows
and inequality but the group-mean coefficient is only significant at 10%. According to Figini
and Görg (2006), this may suggest that developed countries are already at high levels of
technology. Further inflows of technology through FDI imply that technology becomes more
widespread and easier to use so that more workers are able to reap the benefits in terms of
increased wage premium. This result suggests that the positive impact of FDI on inequality
only applies in the short run. In the long run, a negative effect is found.
11
Note that some primary tests showed no link between commercial balance (or even comparative advantage)
and sign (positive or negative) for the long-run relation between international trade and inequality
17
Finally, education, as expected, significantly reduces inequality (though for the United States
and Canada, the effect is positive but not significant). Education has long-run effects on
inequality while in the previous section we found no significant short-run effect.
Table 3: Long-run relationship between international trade and inequality:
Individual and Panel Estimates
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Independent OPENiTOTt OPENiGt OPENiSt OPENiTOURt OPENiTRt
OPENiOt FDI it
variables
0.52***
0.45*** 0.45*** 0.42***
0.38***
-0.02
0.05
US
(6.14)
(4.66)
(5.14)
(5.07)
(12.33)
(-0.13)
(1.42)
0.21***
0.20*** 0.18 *** 0.02
0.14
0.07*** -0.008
Australia
(2.89)
(2.69)
(3.12)
(0.57)
(1.36)
(3.04)
(-0.63)
-0.09
-0.09
-0.11*
0.01
-0.06
-0.06** 0.02**
Canada
(-1.21)
(-1.18) (-1.78)
(0.39)
(-0.95)
(-2.09)
(2.47)
0.04
-0.27
0.28 *** 0.27 **
0.03
0.13*** -0.06
France
(0.15)
(-1.09)
(2.72)
(2.12)
(0.25)
(2.97)
(-1.35)
0.62 *** 0.57*** 0.26
0.20***
-0.10
-0.09
0.009
Netherlands
(2.68)
(2.92)
(0.89)
(3.27)
(-0.79)
(-1.08)
(0.15)
New
0.04
0.07
-0.04
-0.007
-0.12
-0.009
-0.06**
Zealand
(0.23)
(0.57)
(-0.41)
(-0.07)
(-0.99)
(-0.18)
(-2.12)
-0.50
-0.07
-0.97*** -0.83***
0.90***
-0.11** -0.02
Sweden
(-0.86)
(-0.14)
(-2.57)
(-4.54)
(4.75)
(-2.04)
(-1.58)
-0.74*** -0.66*** -0.44*** 0.54*
-0.23*** -0.36*** -0.06
UK
(-4.60)
(-2.60)
(-8.53)
(1.94)
(-10.45) (-3.71)
(-0.89)
0.06
0.05
0.08
0.02
0.03
-0.02
-0.02***
Germany
(0.72)
(0.59)
(1.18)
(0.29)
(0.93)
(-0.33)
(-2.64)
Fully Modified Group Mean Estimator
With
0.08**
0.09**
-0.002
0.07***
0.03**
-0.02
-0.01*
temporal
(2.05)
(2.13)
(-0.07)
(3.02)
(2.15)
(-1.19)
(-1.72)
dummy
t-Student between brackets. Level of significance: *** for 1%, ** for 5% and * for 10%.
All variables in logarithm
6.
(8)
EDU it
1.14
(1.24)
-0.009
(-0.07)
0.54
(0.72)
-0.80***
(-5.46)
-0.93
(-1.14)
-0.25
(-0.71)
-1.05**
(-2.30)
-3.64***
(-3.65)
-0.63**
(-2.10)
-0.49***
(-4.49)
Conclusion
Our aim was to distinguish the effect of international trade in goods and different categories
of services on inequality to determine if the recent rise in international trade in services
explains growing inequality in OECD countries. For this paper, we used two databases
(CHELEM and Top Incomes Share) that provide a long time dimension, appropriate to
perform short and long-run estimations. We obtain the following results. First, there is
18
evidence of a significant impact of international trade on inequality both in the long run and
in the short run. In addition, in some cases, the effect differs in the short and long run. For
example, international trade in tourism services strongly decreases inequality in the short run
but has a positive impact in the long run. Second, in the long run, the impact of international
trade in goods on inequality is stronger than for the international trade in services whereas the
inverse holds in the short run. This result indicates that it is relevant to take into account the
specific case of services and specially the tourism services which has a very different impact
on inequality. Third, the long-run effects of international trade differ among countries.
Results change among countries and the fact that some of them have a more significant share
of tourism services on international trade could explain this result. Cointegration analysis
provides some heterogeneous results according to the country. This shows that careful
attention should be paid when ‘general’ suggestions are given on economic policies. For
further research, we recommend testing the influence of trade on inequality at a disaggregated
and country level.
Finally, the result concerning international trade of tourism services and inequality needs
further research as for example to study this relationship for the developing countries.
Throughout the analysis, we have worked with data concerning aggregate income inequality.
However, it would be interesting to use disaggregated tourism data to have a better
understanding of our result. For this way, the only solution is to study each country separately
by using Tourism Satellite Account.
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23
Appendix
variable
Inequality
variable
International
trade variables
Table A.1: Sources of the data and summary statistics
Source
Name
Mean
Top Income Database:
31.5
INEQit
Piketty (2001), Piketty and Saez (2003),
Atkinson and Leigh (2005), Saez and Veall
(2005), Dell (2005), Atkinson (2005), Roine
and Waldenström (2006), Atkinson and Leigh
(2007), Atkinson and Salverda (2007)
CHELEM-CEPII
Std.Dev
4.3
OPENiTOTt
24.9
11.1
OPENiGt
19.9
9.1
OPENiSt
5.0
2.3
OPENiTRt
1.8
1.1
OPENiTOURt
1.3
1.1
OPENiOt
1.9
1.0
2.1
Inward FDI flows IMF-International Financial Statistics
(in% GDP)
IMF-International Financial Statistics
Gross Domestic
Product per
capita ($1000s)
CHELEM IMF-International Financial
Inflation
FDI it
1.5
GDPCit
13524 9180.3
INFLit
5.8
4.2
Education
variable
EDU it
9.3
1.5
Statistics
Barro & Lee (1993/2000)
International Data on Educational Attainment
Number of observations: 297
Table A.2: Im-Pesaran-Shin stationarity test: tb statistic with one lag
Statistic t-bar
INEQit
Variables in level
-1.691
Variables in first difference
-3.727***
Result
I(1)
OPENiTOTt
-1.891
-2.862***
I(1)
OPENiGt
-2.015
-2.993***
I(1)
OPENiSt
-1.693
-2.785**
I(1)
OPENiTRt
-2.099
-3.422***
I(1)
OPENiTOURt
-1.491
-2.141**
I(1)
OPENiOt
-2.260
-3.750***
I(1)
EDU it
-0.891
-2.643***
I(1)
FDI it
-1.315
-3.715***
I(1)
GDPCit
-1.060
-2.120**
I(1)
-2.814**
-4.546***
I(0)
INFLit
Temporal fixed effects and trends are introduced in each specification ADF. The IPS test
statistic, in the limit, follows a standard normal distribution. *, ** and *** implies rejection
of null hypothesis of a unit root at the 10%, 5% and 1% level
24
Table A.3: Dynamic model over the 1980-1999 period
INEQit
∆INEQit −1
∆OPENiTOTt
∆OPENiGt
∆OPENiSt
∆OPENiTRt
∆OPEN iTOURt
∆OPENiOt
∆FDIit
∆EDU it
∆GDPCit
∆GDPCQit
∆INFLit
(1)
0.16*
(1.95)
0.06**
(2.28)
(2)
(3)
(4)
0.15*
(1.87)
0.19**
(2.39)
0.23***
(2.75)
0.06**
(1.96)
0.10*
(1.88)
-0.05
(-0.26)
0.12***
(2.64)
0.91*
(1.88)
-2.72***
(-5.22)
0.73***
(2.68)
0.10**
0.09**
0.12***
0.11***
(2.29)
(2.31)
(2.65)
(3.10)
0.20
0.27
-0.20
-0.56
(0.16)
(0.21)
(-0.16)
(-0.48)
0.0002*** 0.0002*** 0.0002*** 0.0003***
(2.95)
(2.86)
(3.30)
(4.65)
-3.36E-9*** -3.30E-9*** -4.03e-9*** -6.5E-9***
(-2.93)
(-2.98)
(3.30)
(-5.43)
-0.03
-0.02
-0.02
-0.03
(-1.10)
(-0.92)
(-0.79)
(-0.90)
Level of significance: *** for 1%. ** for 5% and * for 10%. Time dummies included
25
3.26***
2.67***
1.74**
-0.57
OPENiTOTt
4.30***
3.21***
2.14***
3.56***
2.75***
1.82**
-0.71
OPENiGt
3.87***
2.86***
1.96***
2.80***
2.25**
1.42*
-0.11
OPENiSt
2.74***
2.47***
1.98***
2.97***
2.08**
1.34*
0.02
OPENiTRt
3.86***
3.07***
2.42***
2.87***
1.83**
0.74
1.17
OPENiTOURt
3.32***
1.86**
1.13
2.82***
1.66**
0.89
0.28
OPENiOt
1.82**
1.25
1.48*
1.84**
0.52
0.56
0.39
EDUit
1.09
1.33*
1.46*
0.31
0.40
0.19
-0.04
FDIit
0.54
0.73
0.65
1.03
0.35
-0.04
0.85
GDPCit
-1.13
-0.80
-3.15***
-0.93
-0.97
-3.10***
9.63***
OPENiTOTt
-1.29*
-0.94
-2.94
-1.19
-1.23
-3.34***
9.45***
OPENiGt
-0.52
-0.48
-2.45***
-0.56
-0.52
-2.06***
6.83***
OPENiSt
-1.78**
-2.01***
-2.78***
-1.66**
-2.27***
-3.89***
7.97***
OPENiTRt
-0.73
-0.52
-2.54***
-0.51
-0.58
-1.66**
4.32***
OPENiTOURt
-1.23
-1.42*
-1.68**
-1.34*
-1.45*
-2.09***
4.03***
OPENiOt
6.00***
5.38***
3.11***
5.08***
4.28***
2.54***
0.53
FDIit
-2.76***
-1.56*
-0.83
-2.66**
-1.98**
-1.97**
3.64***
OPENiTOTt
-1.55*
-1.17
-1.69**
-1.96**
-1.80**
-2.90***
5.74***
OPENiGt
-0.65
-0.48
-0.88
-0.86
-0.94
-1.75**
3.26***
OPENiSt
-1.62*
-2.30***
-3.43***
-1.69**
-2.08***
-3.33***
5.14***
OPENiTRt
0.15
-0.26
-0.73
-0.12
-0.45
-1.04
1.50*
OPENiTOURt
-2.28***
-1.36*
-0.49
-1.90**
-1.66**
-1.74**
1.69**
OPENiOt
FDI it
2.04***
Table A.4 : Cointegration tests
INEQit
EDU it
3.13***
Dependent variable
Independent
variables
Panel v
stat
Panel rho stat
Panel pp
stat
Panel adf stat
Group rho stat
Group pp stat
Group adfstat
A deterministic intercept and a deterministic trend are included for the Pedroni tests.
Level of significance: *** for 1%. ** for 5% and * for 10%.
3.97***
ITIS – Innovation, territoires et inclusion sociale
MDD – Mobilités et développement durable
RIO – Risque, information, organisation
DOBIM – Droit des obligations et activités bancaires et immobilières
THEMOS – Théorie, Modèles, Systèmes
Documents de travail récents
ü
Naïké Lepoutre, « L’européanisation du contentieux des étrangers en situation irrégulière »,
[2013-04].
ü
Romain Gosse, « L’exemple du principe d’intégration en droit de l’environnement », [2013-05].
ü
Gabriela Condurache, « Européanisation par influence horizontale : l’exemple du statut des
agents publics », [2013-06].
ü
Nadia Beddiar, « L’Européanisation par influence de règles incitatives, l’exemple du droit
pénitentiaire », [2013-07].
ü
Aurélien Fortunato, « Les finalités de l’européanisation du droit – créer un modèle commun :
l’exemple des clauses restrictives de concurrence dans les contrats d’affaires », [2013-08].
ü
Yves Mard et Ludovic Vigneron, « Does public/private status affect SMEs earnings management
practices? A study on French case », [2013-09].
ü
Joseph Hanna, « De la transposition des modèles alternatifs de l’économie dans la théorie pure
du commerce international », [2013-10].
ü
Kirill Borissov, Joseph Hanna et Stéphane Lambrecht, « Heterogenous agents, public investment
and growth in an intertemporal voting equilibrium model », [2013-11].
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Sylvain Petit, « Allers et retours entre théorie et empirique dans la literature du commerce
international: l’exemple du commerce intrabranche », [2013-12].
ü
Mohamed Ramdani et Ludovic Vigneron, « Pecking order versus trade off theory and the issus of
debt constraint problem », [2013-13].
Responsable de l’édition des documents de travail de l’IDP : Sylvain Petit ([email protected])