economic globalization, wages and wage … ben salha.docx · web vieweconomic globalization, wages...

2012 Cambridge Business & Economics Conference ISBN : 9780974211428

Economic Globalization, Wages and Wage Inequality in Tunisia: Evidence from the ARDL Cointegration Approach

Ousama BEN SALHALecturer in economics at the Higher Institute of Management of Sousse, Tunisia

Ph.D student at the Faculty of Economics and Management of Tunis, TunisiaMember of International Finance Group-Tunisia

E-mail: [email protected]

ABSTRACT

This paper seeks to test empirically the impact of economic globalization, i.e international

trade and foreign direct investments, on the level and structure of real wages in Tunisia. On

the one hand, we are interested in the effects of globalization on real wages in the whole

economy, and then a special attention is given to the manufacturing sector. On the second

hand, we analyze the effects of international trade and foreign direct investments on the

evolution of wage inequality. To do this, the recently developed autoregressive distributed lag

(ARDL) bounds testing approach is conducted on annual data covering the period 1970-2009.

Our results reveal that trade liberalization positively affects the average wages only in the

long-run. Nevertheless, FDI didn’t exert any effects on real wages. The second finding is that

trade openness and FDI positively affect the more exportable sector in the country, i.e. the

textile, clothing and leather industry. Finally, this paper support the predictions of the HOS

model since trade liberalization is found to reduce wage inequality between skilled and

unskilled workers in both the short-run and the long-run.

JEL Classification: J31, F16, F21

Keywords: Trade liberalization, FDI, Wages, Wages inequality, Tunisia, ARDL procedure.

June 27-28, 2012Cambridge, UK 1


1. INTRODUCTION

The effects of globalization on labor market dynamics had spurred various recent debates on

behalf of economists, policymakers and international institutions1. One can note that the

majority of academic studies on the consequences of economic globalization on labor market

dealt especially with developed countries (see Ghose, 2000, Gorg and Strob, 2002). Arbache

et al. (2004) noted that the exploration of labor market effects of trade openness in developing

countries is not a simple task. This complexity could be explained by the fact that many

developing countries have undertaken numerous economic reforms simultaneously, so that the

study of the relationship between these reforms and the labor markets should be deeply

analyzed. Then, the skill-biased technological change seems to play a significant role in the

relationship between economic globalization and labor markets in developing countries.

These factors, among others, make the empirical studies in contradiction with the theoretical

predictions of the standard theory of international trade (Arbache et al., 2004, p.F73). In

addition, most of papers on the subject tried to establish the link between trade liberalization

and employment/unemployment levels. The studies on the effects of economic globalization

on working conditions were generally confronted to data problems. As pointed out by

Jayasuriya (2008), defining and measuring working conditions is not easy. The same author

argues that the best and quantifiable measure of working conditions is the wage rate. Other

non-wage working conditions indicators, such as the bargaining power, the working

environment or the unionization, suffer from problems of data comparability between

countries. Once more, developed countries, and at less extent, Latin America countries, were

the subject of the majority of empirical investigations. But, what about the effects of

globalization on employment conditions in the Mediterranean emerging countries?

This region has been engaged for few decades in the international integration movement.

Among others, its proximity to the European continent and the abundance of its human capital



are considered as main attracting factors2. For the majority of these countries, the European

Union represents the principal trade and financial partner. This partnership was sustained by

the conclusion of several free-trade agreements between these countries and the European

Union. However, this region is usually ignored in the academic literature on the outcomes of

globalization on labor markets.

In this paper, we aim at filling this gap in the literature, by establishing both short and long-

run relationship between international trade, foreign direct investments (thereafter FDI) and

wage dynamics in Tunisia. This country was the first one in the south Mediterranean river to

join the World Trade Organization since its creation and signed a free-trade agreement with

the European Union in 1995. Consequently, this paper has triple objectives. First, we search

to determine the effects of economic globalization (international trade and FDI) on the

evolution of the average economy-wide wages in Tunisia. Second, disaggregated analysis of

the manufacturing sector will be conducted. The motivation to conduct such disaggregated

analysis is theoretical in nature, since wages in the importable and exportable sectors did not

react similarly to trade and capital flows liberalization (Edwards, 1988). The last objective of

this paper is to underline the impact of international trade and FDI on the evolution of wage

inequality. For our empirical investigation, we employ the newly developed autoregressive

distributed lag (ARDL) model developed by Pesaran et al. (2001). The use of this approach

will allow us to distinguish the short-run effects from long-run relationships between

economic globalization and wage dynamics. We think that this approach is the appropriate

one for our empirical investigation, since theoretical predictions advance that short-run effects

of economic globalization are different from those relative to the long-run.

Consequently, the originality of this paper resides in, at least, two points. Since the majority

of empirical studies on the effects of economic globalization on labor deals with developed

countries, we will be interested here in investigating this relationship in a small developing



country which has been engaged for three decades in several economic reforms. This study

represents one of the few empirical attempts to establish both short and long-run effects of the

neo-liberal economic policies on wage dynamics in Tunisia. In the best of our knowledge,

there is no prior attempt to deal with this purpose3. The second novelty is to understand the

relationship between economic globalization and wage dynamics at the disaggregated

industry-level in the manufacturing sector. This may be justified by the fact that the impact of

globalization may change across importable and exportable sectors. This analysis will allow

us to establish the industry-specific effects of international trade and FDI.

The remainder of this paper is structured as follows: section 2 provides an overview of

theoretical and empirical literatures treating the relationship between economic globalization,

wages and wage inequality. In section 3 and 4, we highlight, respectively, some stylized facts

on the Tunisian economy and data. Empirical results and discussion will be presented in

section 5. Section 6 concludes the paper.

2. THEORETICAL AND EMPIRICAL UNDERPINNINGS

Since the Ricardo’s theory of comparative advantage, the debate on the benefits of the free

international trade has witnessed the economic thought. The mainstream background used to

study the effects of international trade and FDI flows on wages and income inequality remains

the Heckscher-Ohlin model, or more specifically the Stolper- Samuelson Theorem. This “two

countries, two factors, two goods” model assumes that the North and the South are abundant,

respectively, in skilled and unskilled workers. The simplest version of this model announces

that, once trade barriers between the two countries are eliminated, each one will be

specialized in the production and the export of the good that uses intensively its abundant

factor and the import of the good that uses intensively its scarce factor (Leamer, 1995, Wood,

2002). Thus, the North will produce the skilled-intensive-good, while the south will produce

the unskilled-intensive-good. This specialization will induce a rise in the relative prices of the



unskilled labor-intensive product and the earning of unskilled workers in the South, reducing

then the gap of wages between them and skilled workers. However, for developed countries,

specialization will boost wage inequality, by increasing skilled labor wages and reducing

those relative to unskilled labor.

One can note here that, despite that the HOSS model seems to be theoretically robust, some

recent empirical studies failed to validate its predictions (Ghose, 2000, Spiezia, 2002),

especially in the case of less-income countries, where wage inequality has risen in the past

two decades. The first limit is the assumption of the homogeneity of the production functions,

the production factors and the produced goods in the two countries (Lall, 2002). Likewise,

Wood (2002) criticizes the fact that the HOSS model considers technological change as an

exogenous factor. Or, most of technologies used in developing countries are generally

transferred from developed countries through international trade and/or foreign direct

investments. In addition, Meschi and Vivarelli (2009) argue that the notion of skill is relative

to each country and that its significance may change especially between developing countries.

For example, compared to developed countries, middle-income countries are considered as

relatively unskilled labor abundant, but as a skilled labor abundant in relation to low-income

countries (Meschi and Vivarelli, 2009, p.288).

Anderson et al. (2006) assumed that globalization may reduce the telecommunication and

traveling costs, which will encourage the high-skilled workers in the North to cooperate with

those in the South. This cooperation, through trade and direct investments, will facilitate the

know-how transfer around the world. They suppose that two types of workers exist: the high-

skilled workers (K-workers) and the skilled and unskilled workers (L-workers). The main

findings are an increase of wage inequality between the K-workers and the rest of workers in

the North, but no final results were reached for the South. Feenstra and Hanson (1996)

focused on an economy composed by two skill categories: skilled and unskilled workers and



producing a continuum of goods. By transferring activities from the North to the South,

unskilled-intensive goods produced previously in the North will be considered as skilled-

intensive goods in the South. Consequently, this shift of the production will raise the demand

for skilled labor in both areas. This will increase the gap of earnings between skilled and

unskilled labors and the wage inequality as much in the North as in the South. Wood (2002)

announces that none of these theories succeeded in explaining the impact of globalization on

wage inequality, especially in developing countries. On the other hand, he proposes a

‘synthetic theory’ which may explain, as say Wood, the wage inequality effects of

globalization observed in developed or developing countries, without taking into account

others factors such as the technological change or the labor market institutions (Wood, 2002,

p 25).

The political economy partisans are based on the bargaining power argument to determine the

effects of trade liberalization and free capital movements on the evolution of absolute wages.

They argue that the transition from autarky to free trade is usually marked by a reduction in

the bargaining power of workers. In fact, in the post-liberalization period, capital is

considered as mobile, while labor is relatively immobile, that’s why liberalization will

generate a shift in the balance of power from labor to capital. The disequilibrium in the

balance of power between labor and capital is particularly observed in developing countries4,

where labor market regulations are weak, and sometimes non-existent. This may be explained

by the growing international competition between developing countries in order to attract

foreign capital flows. Low-cost labor is nowadays considered as one of the most important

FDI determinants in developing countries. Labor markets in these countries are generally

characterized by low levels of legal minimum wage, flexible labor laws and the quasi-absence

of trade unions activity. Particularly, the existence or not of trade unions seems to play an

important role when detecting the effects of economic globalization on wage levels and



structure. Moreover, Velde and Morrissey (2001) advance that the skilled workers are

relatively in stronger bargaining position than less-skilled ones. The authors explain this

difference in the bargaining situation by the scarce and abundant supply of, respectively,

skilled and unskilled workers, especially in developing countries. Freeman (1996) argues that

the fall of unionization power in the United States explained 20% of the US wage inequality

rise. However, in some European countries, where trade unions remain stronger, even wages

of unskilled workers did not decrease in the same period.

Onoran and Stockhammer (2008) argue that, according to the political economists, the effects

of trade and capital flows liberalization on the evolution of wages are not immediate and may

be observed in the medium run, contrary to the traditional international trade theory, which

expects long run effects. Many authors have argued that increasing integration into the global

economy may induce technological change, especially in the case of developing countries

(Singh and Dhumale, 2000, Palokangas, 2009).

Edwards (1988) analyzed the labor market short and long-run adjustments5 to trade

liberalization, which is marked by a shock on import prices, i.e. on terms of trade. The model

is designed for a small open economy, where two production factors (labor and capital) and

three production sectors (exportable (X), importable (M) and non-tradable (N)) exist. In the

short-run, capital is supposed to be fixed, while labor is perfectly mobile among sectors. In

the long-run, both labor and capital move freely. According to Edwards (1988), the intensity

of production factors varies between sectors: relative to labor, the importable sector is the

most capital intensive, followed by the non-tradable sector. The exportable sector is assumed

to have the lowest capital-to-labor ratio6. These differences in factor intensity between sectors

seem to reflect the situation of developing countries, where labor is abundant. The analysis of

the impact of a change in the terms of trade on employment and wages are done under two



postulations: in the absence of wage rigidities and in the presence of these rigidities,

manifested by the existence of minimum wage on the importable sector.

In the short-run7, if wage rigidities are inexistent, employment in the exportable sector will

increase due to the expansion of production. However, wages will decrease as the supply in

the exportable sector rises. In the importable sector, both employment and wages will

slowdown because of the decline of the output demand. Wages in the non-tradable sector will

fall too. Production in this sector may increase or decrease, depending on the degree of

substitution in production and consumption between the three goods (Edwards, 1988, p.173).

In the presence of wage rigidities, the importable sector is considered as ‘protected’ via the

implementation of minimum wage. The effects of a shock on the import prices on wages in

the exportable and non-tradable sectors are the same as in the first case, i.e. a fall in wages. In

the importable sector, employment will collapse, while wages will rise. In the long-run,

capital and labor can move freely across the three sectors. Thus, an increase in both

employment and wages are observed in the exportable and non-tradable sectors, due to the

increase in the production. The importable sector will experience a fall in the domestic

production, due to the reduction of import tariffs. Thus, labor will move to the exportable and

non-tradable sectors, so that employment in the importable sector would be reduced. Wages in

this sector will rise because of a reduction in labor supply. When minimum wage laws are

implemented, the long-run impacts of changing terms of trade will be an enhancement of

employment in the exportable sector, while those on wage are unclear. Fosu (2000) argue that

the ambiguity may be caused by the coexistence of two contradictory effects. On the one

hand, capital movements between sectors will climb wage in the exportable sector. On the

other hand, due to the reduction in the import tariffs and the existence of minimum wages in

the importable sector, unemployment will rise in this sector. Thus, the labor supply will

migrate from the importable to the exportable sector. Mixed with the growing production



supply in the exportable sector, trade liberalization will exert pressure on wage in the

exportable sector. Trade liberalization would enhance employment in the non-tradable sector,

while its effects on wages are not clear. Finally, the adjustment in the importable sector still

unchanged as in the short-run, a fall in employment and an increase in wages. Haouas et al.

(2005) advance that many factors may affect the labor market adjustment process, such as the

degree of heterogeneity of goods and sectors, the effects of trade liberalization on

competitiveness…

Dealing with the traditional international trade theory and the bargaining power analysis of

the political economists, Paus and Robinson (1997) mention that ‘little empirical evidence has

been presented in support of either side until now’. Many economists argue that the study of

labor market adjustments to trade and capital flows liberalization remains by far an empirical

issue. The detection of labor market outcomes of liberalization is generally dependent on

country specific characteristics, such as the structure of labor markets, the weight of each

sector in the economy, the labor market standards, the nature of liberalization process…

Agénor and Aizenman (1996) advance that, in spite of the diversity of theoretical background,

the empirical understanding of the effects of trade reforms on wages and employment is still

limited. Krueger (1983), dealing with the effects of international trade on employment and

wages in 10 less developed countries8, concludes that the HOSS theorem success in

explaining the nature of labor markets adjustments in the long-run. Employment seems to be

higher in open economies, and exportable sectors tend to use intensively less skilled workers.

Meschi and Vivarelli (2009) found contradictory empirical results. Dealing with the effects of

international trade on the evolution of income inequality in 65 developing countries between

1980 and 1999, the authors did not establish any significant relationship. When trade flows

are geographically decomposed by the origins/destinations, the authors conclude that both

exports and imports with developed countries rise wage inequality in middle-income



countries. Paus and Robinson (1997) examined the effects of export growth on average wages

in the manufacturing sectors of 32 developing countries between 1973 and 1990. Results

suggest that exports did not exert direct effects on wage growth. However, exports will

enhance the bulk of foreign exchange, which will be used to import capital goods needed in

local investment. Finally, higher investment will induce more growth, which will affect

wages. Egger and Stehrer (2001) found that both imports and exports of intermediate goods

exacerbate wage of unskilled labor in four Central and Eastern European countries. Trade in

intermediate goods with the European Union reduces the annual change of wage inequality

between skilled and unskilled workers by about 58% and 30%, respectively, in the Hungarian

and the Polish manufacturing sectors. Ramasamy and Yeung (2010) investigate the

relationship between FDI, wages and productivity in China. Employing vector autoregressive

and generalized method of moments on a sample of 28 Chinese provinces, the authors found

that FDI affect positively wages since a 1% expansion in FDI flows gives a rise of 0.048% in

average wages. However, a 1% increase in average wage will slowdown FDI by about 2.67%.

Thus, the ‘cheap labor argument’ seems to be verified in China, since wages could be

considered as a main FDI determinant. Janicki and Wunnava (2004) argue that cheap labor

costs represent one of the most important determinants of FDI flows in developing countries,

especially when these flows are coming from countries where wages are relatively high.

Empirical investigation shows that a change of $1 in the differential of wages between home

and host countries will induce a change of $17 278 in FDI flows. Onoran and Stockhammer

(2008) investigate the effects of FDI and trade flows with the European Union on the

evolution of real wages in five Central and Eastern European countries. In the short-run, FDI

exert positive effects on manufacturing wages, especially in capital intensive and skilled

sectors9. Trade flows with the European Union exert no effects on real wages. In the log-run,

the impact of FDI and exports on real wages are negative, while imports affect positively



wages. This may be explained by the fact that the important share of imports from the

European Union is composed by intermediate inputs, which is considered as complementary

to labor. These results are so interesting, because a great part of imports from developed to

developing countries are intermediate inputs, which will be transformed and then exported to

developed countries. Jaumotte et al. (2008) conclude that financial globalization (especially

FDI flows) and technology transfer have increased wage inequality within the sample

composed by 20 advanced and 31 developing countries. In contrast, trade liberalization has

reduced wage inequality between skilled and unskilled workers. Nevertheless, the authors use

the share of information and communications technologies capital produced domestically in

the total capital stock as a proxy of technology progress. Or, as mentioned by Damijan et al.

(2003), developing countries generally prefer importing technologies from advanced

countries, rather than to produce them. Arbache et al. (2004) point out that trade liberalization

induces a rapid transfer of foreign technology via FDI and imports flows. So, the measure

used by Jaumotte et al. (2008) may not take in consideration the imported technologies used

in the host country, and then, it will underestimate the real level of technology development.

Beyer et al. (1999) investigated the relationship between trade openness and wage premium in

the case of the Chilean economy over the period 1960-1996. Their finding shows that trade

liberalization amplified the gap of wages between skilled and unskilled labor. The authors

explain this deepening in wage inequality by skill-biased technological change, which favors

skilled workers. Taylor and Driffield (2005) found that FDI explained about 11% of wage

inequality in the United Kingdom manufacturing sectors over the period 1983-1992. Grenier

and Tavakoli (2006) conclude that the ratio of the average production earnings to the average

non-production earnings is moderately affected by imports and FDI in Canada. For a panel of

103 developed and developing countries, Figini and Gorg (2006) examine the possibility of

the existence of non-linear relationship between FDI and wage inequality over the period



1980-2001. Theoretical backgrounds are based on the Aghion and Howitt’s General Purpose

Technology model. The authors confirm the existence of an inverted-U shape curve in

developing countries, where FDI will enhance wage inequality between skilled and unskilled

workers in the first stage of capital flows entry, and then will reduce it in the long-run.

However, in developed countries, FDI seems to reduce wage inequality, whence the

relationship is linear. Bigsten and Durevall (2006) evaluate the effects of trade openness on

sectoral wage inequality between 1964 and 2000 in Kenya. Using VAR and VECM

estimations, the authors found that the reduction of trade tariffs seems to reduce the gap of

wages between the agricultural and the manufacturing sectors.

3. BASIC STYLIZED FACTS ABOUT ECONOMIC GLOBALIZATION AND LABOR

MARKET IN TUNISIA

Since the mid-1980, the Tunisian economy has recorded good performance. Most

macroeconomic variables, such as inflation rate and budget deficit were controlled10. Between

1980 and 2009, the average GDP growth rate was about 4.5%, including 7.8% in 1992. Per

capita GDP (in purchasing power parity) has more than doubled between 1980 and 2009,

from US$3616 to US$7511. As mentioned by the World Bank (2008), the Tunisian per capita

GDP is the second higher in the Maghreb, just after Libya (World Bank, 2008, p. 1). Starting

from the mid-1980, a set of reform policies have been initiated, with the implementation of

the Structural Adjustment Program in 1986. The decade of the 1990s brought a new

experience with the adhesion to the World Trade Organization since its creation. The next

major step was the signing of the free-trade agreement with the European Union in 1995. The

agreement, entered into force in January 1996, stipulates the gradual reduction of non-tariff

barriers on goods and services, the liberalization of investments and generally the promotion

of economic, social, cultural and financial cooperation between the two parties. While

Tunisia’s total exports were only US$3.3 billion11 in 1980, they became US$6.9 billion in



1995, and finally reached more than US$12 billion in 2008. At the same time, its share in the

total gross domestic product has significantly grown from 45% in 1995 to 60 % in 2008.

(Figure 1about here)

The overall weighted average tariff rates were cut from over 28% in 1990 to 25.6% at the end

of 2002 and 19.9% in 2006. At the disaggregated level, tariffs on imported manufactured

products have been slowdown by 40% between 1990 and 2006 (UNCTAD, 2010). Following

the reduction of tariffs, imports have more than doubled between 1980 and 2009.

Manufactured goods account for almost 80% of total goods imports in 2009. This may be

related to the fact that a significant share of Tunisian imports is composed by intermediate

goods, which will be transformed and then exported. Thus, the overall trade balance (both

goods and services transactions) has recorded large deficit, reaching one billion US dollars in

2001. This deficit was primarily caused by the goods balance deficit, which continued to be

deteriorated, reaching a record level of about 3 billion US dollars in 2001 and 2007.

Contrariwise, services balance has been characterized by regular surplus, which allowed the

reduction, in some extent, of the goods balance deficit. One should note that the European

Union countries were by far the most important trading partner, accounting for more than

74% of exports and more than 63% of imports in 2009.

On the other hand, since the beginning of the 1970’s, authorities have encouraged foreign

investors to outsource their activities in Tunisia. This was provided, especially, by the well-

known 1972 investment law, which offers many financial and fiscal incentives to foreign

investors (exemption of import duties on raw materials and equipments, partial/total tax

exemption for the first years of activity, no value added tax…). In the next two decades, many

factors have permitted the attraction of more capital flows, such as the geographic proximity,

the abundance of educated and low-priced labor and the relative political stability. Foreign

direct investments were especially encouraged, because they are not only more stable, but also



because of their ability to bring technological and managerial spillovers12. Consequently, the

surge in FDI flows has substantially improved over the past decades. In fact, FDI flows have

fluctuated up and down, with average annual flows of about US$54.6 million in 1970-1979

and US$157 in 1980-1989. This figure increased further to more than US$356 million in the

next decade. During the first decade of the 21th century, average FDI flows reached US$1.3

billion, with nearly US$3.3 billion in 200613. As a share of GDP, FDI flows have grown

continually from 1.75% in the 1980’s to 2.1% in the end of 1990, reaching finally 4.5%

between 2000 and 2009.

In spite of the good economic performance realized by the Tunisian economy and the neo-

liberal policies implemented by authorities, the labor market always suffers from high

unemployment rate (World Bank (2004), p.28)). As mentioned by Redjeb and Ghobentini

(2005), unemployment is considered as a structural phenomenon in Tunisia. During the last

decades, the nature of unemployment has changed over time. In fact, until the beginning of

the 1990’s, unemployment touched labor force with low or medium levels of education.

However, the last decades were especially marked by a sharp rise of university graduates

unemployment. While the overall unemployment rate was about 14.1% in 2007, the one

relative to young graduates (aged between 23 and 29 years) in economics, law and

management is about 47%14 (Stampini and Verdier-Chouchane (2011)). Youth15

unemployment rate reached 30.7% in 2005, with no important gender differences since the

rate is about 31.4% and 29.3%, respectively, for men and women.

Table 1 deals with the distribution of employed labor force in each sector according to the

level of education. The total employed labor force has increased at an annual growth rate of

2.72% between 1989 and 2007. At the same period, the labor market received, in average,

65800 new employees every year. Services represent the major recipient sector, with a share

evolving from 44% to 48.5% of total employment. While the number of employed workers in



industry has risen continually, the one relative to agriculture has fluctuated, due to its

dependence on the climatic circumstances. In regard to the educational intensity of

employment, one can advance that, except the illiterate workers, the demand for ones with

primary, secondary or university diplomas has grown over time. In 2007, primary and

secondary graduates represented more than 70% of total employment, whereas the

recruitment of university ones increased to reach 14% in 2007. Over time, the situation of

illiterate employers has been deteriorated, with a demand decreasing in all sectors. Even in

agriculture, the share of workers without schooling fell down from more than 60% in 1984 to

38% in 2007.

(Table 1 about here)

As shown in this table, this sector has used intensively workforce with low level of education,

since more than 80% of workers have, at the best, primary attainments. However, industry

uses medium-skill labor force. In 2007, about 85% of employers are from primary and

secondary schooling. This may be explained by the fact that the Tunisian industries are, in

general, medium-skill industries (textile, mechanical and electrical industries…).

Simultaneously, the number of university graduates working in manufacturing and non-

manufacturing industries has been multiplied by 5 between 1984 and 2007. Finally, services

have required more skilled workforce, since it employed the most important share (83.3%) of

total university graduates in 2007. Workers with secondary level represent the biggest part,

with 41% of the total employment in services.

As mentioned by the World Bank (2004), the Tunisian labor market is characterized by high

regulation. Laws related to dismissal measures are too strict, especially for permanent

workers16. Consequently, Tunisia offers a job security which is higher than the average in

OECD countries (World Bank, 2004, p.44). However, the labor law was revisited in the

1990’s, just to introduce some flexibility on it. Reforms dealt essentially with the economic



dismissal measures, which become more easily but still too costly for firms. The second wave

of reforms, implemented in 1996, simplified the recruitment procedures, since it allows to

firms to hire workers for partial duration, by using determined period contracts. This measure

was undertaken to encourage firms to recruit workers, since the unemployment rate has been

intensified. At the same time, it allows to firms to adapt themselves rapidly to the internal and

external shocks. Finally, the mechanism of determination of wages includes two major

intervenors: the government and the trade unions. As a consequence, wages are regularly

negotiated. As mentioned by Gouider (2010), wage policies are generally determined in

collective negotiations between the government and sectoral trade unions17. The minimum

wage is annually augmented, proportionally to the increase of productivity, so that the labor

cost still constant over time (World Bank (2008), p. 42). Proportionally, the average annual

real wage has grown from 5864 TDN in 1983 to 7675 TDN in 2009.


Table 2 deals with the average real wage for the whole economy and for the main economic

sectors. In addition, the total period is divided into two sub-periods: before and after the

conclusion of the free trade agreement with the European Union. This decomposition allows

us to make simple preliminary intuition on the implications of this agreement on the evolution

of wages. As can be seen from the table 2, the only sector for which the annual average wage

is higher to the whole economy annual wage is services. Agricultural wage is found to be the

lowest. In the first sub-period, we remark that the annual real wages are above the annual real

wages associated to the total period. This statement remains valid for all economic sectors.

While, for the post reform period, annual real wages have raised substantially, reaching levels

which are superior to the whole period annual real wages. Thus, comparing the two sub-

periods, it is clear that the real annual wages have grown for all sectors, even if the

enhancement is not identical for all sectors.



4. EMPIRICAL METHODOLOGY AND DATA

As mentioned previously, our objective is to empirically determine the nature of the short-run

and long-run relationships between trade openness, foreign direct investments and wage

dynamics in Tunisia. More precisely, our investigation will focus on three different

assumptions. The empirical study of each one is developed with respect to the theoretical

background, previously presented.

According to the political economy proponents, economic globalization induces a decrease in

the bargaining power of trade unions. Thus, it will deteriorate the situation of workers,

especially in developing countries. Thus, our first assumption is:

Assumption 1: Trade openness and foreign direct investments have deteriorated the working

conditions, especially wages. To test empirically this assumption, aggregated analysis

between trade openness, FDI and the annual real wage will be conducted during the period

1970-2009. Annual data on exports and imports as a share of GDP are extracted from the

World Development Indicators database (online, 2010). Foreign direct investment flows as a

share of GDP comes from the United Nations Conference on Trade and Development

(UNCTAD) online database. The average real wages are obtained from the Tunisian Institute

of Competitiveness and Quantitative Studies.

As pointed out by Edwards (1988), the effects of trade openness on wages are not the same

for all sectors. Wages in the exportable and importable sectors will react differently to trade

liberalization. This analysis is considered as the basis of our second assumption.

Assumption 2: The effects of trade and capital flows on annual real wages are not the same

for the importable and exportable sectors18. Methodologically, we will study the nature of the

relationship based on disaggregated industry-level data associated to the manufacturing

sector19. This will allow us to distinguish the industry-specific effects of economic

globalization. Manufacturing subsectors are classified into two groups: exportable and



importable. As in Haouas et al. (2005), the distinction between exportable and importable

sectors is essentially based on the size of the import to export share and on the market

orientation. Data, covering the period 1990-2008, comes from different sources. The national

accounts, published by the Tunisian National Statistics Institute, provide us with data on

disaggregated exports, imports and value-added in the manufacturing subsectors. While, data

associated to disaggregated FDI flows are obtained from the Tunisian Foreign Investment

Promotion Agency.

According to the HOS model, trade liberalization raises the wage of unskilled workers and

decreases the wages of skilled workers in developing countries. Thus, wage inequality will

fall. In addition, Hanson and Feenstra (1996) pointed out that trade liberalization enhances

wage inequality in these countries. Consequently, the third hypothesis we have the intention

to test is:

Assumption 3: Trade openness and foreign direct investments decrease wage inequality

between skilled and unskilled workers. Due to the lack of long time series data on wages by

occupation, we have constructed three different wage inequality indicators, based on annual

real wages. The first one, denoted WII1, has been inspired from Bigsten and Durevall (2006).

Thus, we will create an intersectoral wage inequality indicator, which is the ratio of the

average annual wage in the manufacturing sector divided by the average annual wage in

agriculture. These authors argued that workers in the manufacturing sector are generally more

skilled than those working in agriculture. Thus, this indicator can be considered as a proxy of

wage inequality between unskilled and relatively more skilled workers.

With regard to the second wage inequality proxy, denoted WII2, we have followed the same

methodology, but by dividing the average annual wage in administration by the average

annual wage in agriculture. We think workers in the administration are more skilled than



workers in the manufacturing sector, and thereafter than those in the agriculture. For example,

workers with higher education represent respectively 57.7%, 7.46% and 1.25% of total

workers in the banking and insurance industry, in the manufacturing sector and in agriculture.

Thus, our second wage inequality indicator can be written as:

Finally, the third wage inequality indicator (WII3) deals essentially with wages in the

manufacturing subsectors. The first step consists in classifying the six subsectors of the

manufacturing sector into two groups: low technology unskilled industries (the pottery, glass

and other construction materials industry, the agro-food industry, the textiles, clothing and

leather industry and the other manufacturing industries) and medium-high technology skilled

industries (the chemical industry and mechanical and the electrical and electronic industry).

The classification of sectors is essentially based on NACE Rev. 2 at 3-digit level and on

Landesmann et al. (2004) and Onaran and Stockhammer (2008). Once subsectors

classification done, we generate the series of the third wage inequality indicator, using the

following formula:

Based on these definitions, three wage inequality indicators are calculated. Figure 2 deals with

the evolution of the wage inequality indicators over the period 1970-2009.

(Figure 2 about here)

As shown in the first and second figures, average annual wages in the manufacturing sector

and administration were, respectively, five and seven times as much as wages in agriculture in

the beginning of the 1970s. Then, wage inequality continually decreases over time. Thus, one

can note that the intersectoral wage inequality between skilled and unskilled workers has

constantly declined between 1970 and 2009. However, the last figure shows that wage



inequality have fluctuated up and down during the same period, but the general statement is

that it decreases slowly starting from the mid-1990s.

To test empirically the cited assumptions, one should distinguish between the short and long-

run effects. For this reason, we decide to use the ARDL bounds testing procedure introduced

initially by Pesaran and Shin (1999) and developed by Pesaran et al. (2001). Compared to

other cointegration approaches, such as the Johansen and Juselius (1990) one, the ARDL

modeling has several advantages. Firstly, the long-run relationship between two variables can

be estimated even if they have different orders of integration (integrated of order zero (I(0)),

integrated of order one (I(1)) or fractionally integrated). Secondly, as mentioned by Duasa

(2007), the ARDL procedure is more appropriate when the sample size is relatively small.

Finally, Harris and Sollis (2003) advance that the use of ARDL bounds testing approach

allows to obtain unbiased estimates of the long-run relationship. In addition, Amusa et al.

(2009) report that even if some of variables integrated in the regression are endogenous, the

estimated long-run coefficients seem to be unbiased.

Consequently, our empirical methodology involved four major stages. The first step is to

investigate the stationarity properties of times series. This is important because it is necessary

to ensure that none of our variables is integrated of order 2 or above. In fact, the ARDL model

is not applicable if one of variables integrated in the regression is I(2). In this study, three

different unit root tests will be computed: the ADF test, the Philips-Perron test and the Ng-

Perron test. The aim of using various unit root tests is to guarantee that our results are not

sensitive to a given test. Thus, if the hypothesis of stationarity is accepted by at least two unit

root tests, we can admit that the series are stationary. Otherwise, the same tests will be applied

on differenced time series.

The second step consists in testing the existence of long-run relationship between variables.

Thus, bivariate and multivariate cointegration analysis between wage and economic



globalization variables is conducted. Decision about the existence of cointegration or not is

made on the basis of the joint F-statistics. For each confidence level, Pesaran et al. (2001)

provide two different sets of asymptotic critical values: a lower bound critical value (I(0)) and

an upper bound critical value (I(1)). Three cases are possible. If the computed F-statistics is

below the lower bound critical value, the null hypothesis of no cointegration cannot be

rejected. If the F-statistics is higher than the upper bound, the null hypothesis of no

cointegration is rejected, and finally, if it lies between the lower and the upper bounds, the

result is inconclusive. If series are cointegrated, we move to the third step by estimating the

long-run relationship. Finally, the Vector Error Correction Model (VECM) is used to examine

the short-run dynamics.

5. EMPIRICS

5.1. Trade openness, FDI and the overall real wage

As mentioned previously, implementing unit root tests is a preliminary exercise before testing

for the existence of the long-run relationship among variables. Table 3 presents the results of

unit root tests for the level and first-difference series. As shown in this table, variables

associated to the average real wage, imports and trade openness are non-stationary in levels,

but stationary in first-difference (I(1)). Contrary, the unit root tests yield that FDI and exports

are stationary in level, or I(0).


Having established that each of the five variables is integrated of order zero or order one, the

long-run equilibrium between annual real wage and variables reflecting the extent of

economic globalization (FDI, exports, imports and trade) are checked using the ARDL

procedure. In order to ensure the robustness of our results, six different models have been

estimated, i.e. both bivariate and multivariate cointegration.




The computed F-statistics as well as the critical values at 5% and 10% significance levels are

summarized in table 5, panel A, while the panel B deals with various diagnostic tests

associated to the estimated ARDL model. Results drawn from panel A show that the null

hypothesis of no-cointegration cannot be accepted at the 5% significance level for model 3

and 5 and at the 10% significance level for model 1 and 4. Contrariwise, the same panel

displays that the null hypothesis of cointegration cannot be rejected for model 2 and model 6.


Based on these results, two main statements can be advanced. On the one hand, there is strong

evidence of a long-run relationship among variables associated to the degree of trade

openness and the average annual wage. For the bivariate analysis, cointegration among these

variables exists whether exports and imports are taken separately or together (the sum of

exports and imports, i.e. trade) with average real wages. In fact, the calculated F-statistics

associated to model 3, 4 and 5 are all higher than the upper critical values. On the other hand,

foreign direct investments seem not to have a long-run relationship with the annual average

wage. When taken together (models 1 and 2), the ARDL bound testing suggests the existence

of a long-run convergence relationship between international trade, FDI and wages but not

between exports, imports, FDI and wages. I fact, the calculated F-statistics associated to

model 2 ranges between the lower bound and the upper bound. In this case, no conclusion

about the existence of cointegration can be advanced. It is important to note that, according to

results presented in the table 5, panel B, the six estimated ARDL models well pass all the

computed diagnostics tests (the Breusch–Godfrey serial correlation test, the heteroscedasticity

test and the functional form test). As suggested by Pesaran and Pesaran (1997), the

cumulative sum of recursive residuals (CUSUM) is used to check for parameters stability.

According to graphs, not reported here, the CUSUM plots cross the 5% critical bounds,

suggesting the absence of any structural instability in the estimated ARDL models.



We move now to the estimation of the long-run relationships between the economic

globalization indicators and the annual wages for models for which cointegration exists. Thus,

models 2 and 6 are excluded from the analysis at this stage of the study. Results are displayed

in table 6.


It is clear from the estimation of the first model that the coefficient associated to trade

openness is positive and statistically significant at 1% level, while that of FDI is negative but

no statistically significant. The estimated trade openness coefficient implies that a 1%

increase in trade openness will result in about 2% increase in the annual real wages. These

findings are in line with the HOS predictions which stipulate that trade openness improves the

absolute wages in the long-run. Despite the coefficient associated to FDI is not significant, it

seems to have the sign anticipate by the political economy partisans. More FDI induce a

reduction in the bargaining power of local trade unions, and consequently a fall in wages. The

positive effects of trade openness on annual wages are confirmed in models 3, 4 and 5. In fact,

the estimation of the bivariate regressions between trade, exports, imports on the one hand

and the average annual wage on the other hand shows that the associated coefficients are all

positive and statistically significant. Both exports and imports affect positively the average

wage in the long-run. Based on a sample composed by 5 Central and Eastern European

countries, Onoran and Stockhammer (2008) find that imports from the European Union

enhance the real wages in long-run. They argue that intermediate goods constitute a big part

of imports, which will be transformed and then exported. We think that Tunisia plays the

same role such as these countries. For example, the textile and the electrical industries

constitute the two main exporting sectors in the country. The big share of imports in these two

sectors is composed by intermediate inputs which will be transformed by local firms or by

outsourced companies coming from the European Union.



Having determined the stable long-run relationships between economic globalization

variables and real wages, we proceed now to the estimation of the short-run effects, via the

implementation of the error correction model. It is important to note here that the error

correction term represents the speed of adjustments to return to the long-run equilibrium. In

other words, this term gives an idea about the extent with which the deviation from the long-

run equilibrium is corrected through partial short-run adjustments. Results are displayed in

table 7.


Results displayed in this table suggest that the lagged error-correction term obtained from the

long-run equilibrium relationship is negative and statistically significant at 1% confidence

level in all cases. These findings substantiate those relative to the bounds testing technique for

cointegration previously presented. Approximately, 25% of the disequilibria from the long-

run relationship between FDI, international trade and real wages are corrected. Similar results

are found in models 3, 4 and 5. In addition, the speed of adjustment is found to be higher in

the import-equation (-0.234) than that in the export-equation (-0.194). Coefficients associated

to trade, FDI, exports and imports are negative but not statistically significant. Thus, these

variables didn’t exert any effect on real wages in the short-run.

5.2. Trade openness, FDI and real wages in the manufacturing sector

As for the moment, the same methodology employed in the section 5.1 will be applied, but for

the manufacturing sub-sectors. This allows us to empirically test the validity of our second

assumption, based essentially on the developments conducted by Edwards (1988).

The first step is to classify the subsectors into exportable and importable sectors. To do this,

we were referred to Haouas et al. (2005). Thus, only the textiles, clothing and leather industry

and the pottery, glass and other construction materials industry are considered as exportable

sectors, while other sectors are qualified as importable. The data covers only the period 1990-



2008. We were confronted to a lack of data on disaggregated FDI by sub-sectors, which exist

only for this period. To start, we test the stationarity status of all variables included in the

regressions. Table 8 reports the results from the ADF, PP and Ng-Perron unit root tests.


It can been observed that, except the other manufacturing industries, the unit root tests reveals

that real wages, international trade and FDI are either integrated of order zero or order one.

For the other manufacturing industries, all unit root tests reveal the non-stationarity of the

sectoral real wages, even in first difference, which means that this variable is integrated of

order two or more. Thus, the ARDL bounds testing procedure cannot be applied for this

subsector. The next step is to check for the existence of cointegration relationships among

variables. For each sector, three different models are estimated, so that both bivariate and

multivariate cointegration tests are implemented.


The panel A of table 10 reports the calculated F-statistics and the appropriate asymptotic

critical values at 5% and 10% statistical levels. We start by searching for long-run

relationships between economic globalization and real wages for the whole manufacturing

sector. The F-statistics is found to be below the 5% and 10% lower bounds, which suggest

that no long-run relationships between trade, FDI and real wages exist. We move then to the

application of the ARDL procedure to the five sub-sectors constituting the manufacturing

sector.


As shown in this table, cointegration between international trade, FDI and real wages seems

to be verified only in the textiles, clothing and leather industry. Once we estimate bivariate

versions of the ARDL model (models 2 and 3), we found that only trade openness and real

wages are cointegrated at 10% level. For the other subsectors, the calculated F-statistics are



generally too small and never exceeded the upper bound. These results are rigorous, since the

battery of diagnostic tests shows that the estimated ARDL models are adequately specified.

The CUSUM test proves that the estimated parameters are stable for all sectors. Thus, one can

advance that the long-run equilibrium relationship between international trade and real wages

in the textiles, clothing and leather industry is robust. The estimation of the long-run

coefficients will either confirm or invalidate this statement. Table 11 summarizes the long-run

coefficients for the textiles, clothing and leather industry.


Results drawn from this table display that, in the long-run, both international trade and FDI

exert a positive impact on real wages in the textiles, clothing and leather industry. The

coefficient associated to international trade is more important and statistically more

significant than the one associated to FDI. The positive impact of international trade on real

wages is confirmed even when we estimated the second model. Coefficient associated to

international trade did not vary significantly in the two regressions. Thus, as in the whole

economy, we conclude that international trade positively impacts real wages in the long-run.

The novelty here is that FDI seems to enhance the real wages in the long term only in sector.

The final step in our analysis is to conduct the short-run dynamics between international trade,

FDI and real wages. The table 12 deals with coefficients obtained from the error correction

model estimation.


Based on the estimation of the first model, one can advance that FDI inflows were able to

improve the real wages in the textiles, clothing and leather industry in the short-run. The

coefficient assigned to international trade is not statistically significant. Even when we

estimated the second model, its coefficient is significant only at 10% level. The error

correction term has the expected sign and is statistically significant. The speed of adjustment



is relatively high, since 56.1% of the disequilibria from the long-run relationship between real

wages, FDI and international trade are corrected. These findings support the results of the

bounds test for cointegration previously presented.

These findings support the fact that both FDI and international trade positively impacts real

wages in the textiles, clothing and leather industry in the short-run and in the long-run. As

mentioned previously, this sector is considered as an exportable sector. The predictions of the

Edwards model are only verified in the long-run, since international trade enhances the real

wages. In the short-run, the same model predicts negative effects of international trade on real

wages, a prediction not verified in our case. At the same time, foreign direct investments have

supported the real wage in the same sector during the last four decades. The collapse of real

wages induced by the deterioration of the bargaining power hypothesis (due to the surge of

FDI) is not confirmed in the case of the Tunisian textile, clothing and leather industry. As

suggested by Onoran and Stockhammer (2008), the predictions of the political economists

may be observed only in the medium-run. Even if the chemical industry and the electrical and

electronic industry still considered as the most important manufacturing recipients of foreign

direct investments (in 2009, these two sectors received about 60 % of total manufacturing

foreign investments), their effects on real wages still obvious.

5.3. Trade openness, FDI and wage inequality

The final assumption we have the intention to investigate treats the effects of international

trade and FDI on the dynamics of wage inequality over the period 1970-2009. The HOS

model stipulates that trade liberalization reduces the wage inequality between skilled and

unskilled workers, while Feenstra and Hanson (1996) support the opposite point of view.

As mentioned in the previous section, three different wage inequality indicators are

constructed, in order to check for the robustness of our results. The stationarity of these

indicators, as well as of the proxies of economic globalization are presented in the table 13.




The ADF, PP and Ng-Perron unit root tests indicate that the three wage inequality indicators

and the trade openness are stationary in first-difference, which means that they are all

integrated of order one. Nevertheless, FDI are found to be stationary in level. We can now use

the ARDL procedure to test for the existence of cointegration relationships among them. For

each wage inequality indicator, three different models are estimated.


The table 15 reports the F-statistics computed from the ARDL model and the related

validation tests.


From the results reported in this table, it is clear that the F-statistics associated to the first and

second wage inequality indicators (WII1 and WII2) are characterized by low values, which

not exceed the upper bounds. Thus, the null hypothesis of no cointegration cannot be rejected.

Bigsten and Durevall (2006) employed the same wage inequality indicator (WII1) for the case

of Kenya between 1964 and 2000. Their results demonstrate that trade liberalization reduces

wage inequality between skilled and unskilled workers. The divergence of results may be due

to the choice of the trade liberalization indicators. We use the sum of exports and imports to

GDP in our study, while these authors employ the trade tariffs. The estimated ARDL bounds

testing models for cointegration associated to the third indicator (WII3) shows mixed results.

When both trade openness and FDI variables are regressed together on the wage inequality

indicator, the calculated F-statistics falls within the two bounds, then we cannot make

conclusive decision about the existence of cointegration or not. However, FDI are not

cointegrated with wage inequality. Finally, our results reveal that international trade and wage

inequality converge together to a unique long-run equilibrium. According to a range of

diagnostic tests, it seems that there are no violation problems in all models. As for the



moment, we will estimate the long-run parameters associated to FDI and trade openness

variables for the bivariate and multivariate models. Since cointegration relationships exist

only when we have used the third wage inequality indicator, our analysis will be based only

on this indicator.


An interesting finding is that the estimates of the trade openness parameters in the long-run

wage equation are moderately significant with a negative sign in both the bivariate and

multivariate analysis. This suggests that trade openness seems to reduce wage inequality

between skilled and unskilled workers, which corroborate with the predictions of the HOS

model. Contrariwise, the coefficient associated to FDI is negative too, but not statistically

significant. Finally, we move to the estimation of the error correction model, in order to check

for the short-run dynamics among variables. In table 17, results drawn from the estimation of

the error correction model are reported.


Results are similar to those found in the long-run, since FDI have no effects on wage

inequality and trade openness reduces its extent. It is important to note that the magnitude of

the coefficients associated to trade openness in the long-run estimates is higher than that

found in the error correction model, which imply that the variables have a stronger impact in

the long-run. The error correction term is negative and statistically significant at 5% level,

which means that any disequilibrium due to a short-run shock on the relationship will be

adjusted to converge to the long-run equilibrium. Moreover, the negative magnitude of the

error correction term, ranging between -0.14 and -0.17, reflects a low adjustment speed from

the short-run disequilibrium towards the state of long-run equilibrium. With respect to WII1

and WII2, the absence of cointegration between them and economic globalization indicators is

explained by the fact that they deal essentially with intersectoral wage inequality relative to



agriculture, a sector not considered in the free trade agreement concluded between Tunisia

and the European Union. We think that the wage inequality indicator based on the

manufacturing sector is more representative of the skills/wages relation in general and more

appropriate for the Tunisian context.

6. CONCLUDING REMARKS

This paper has triple aims. The first one is to check for the effects of trade liberalization and

foreign direct investments on the real economy-wide wages in Tunisia between 1970 and

2009. The second objective is to establish the industry-specific effects of trade liberalization

and FDI in the manufacturing subsectors. Finally, we search to determinate the implications

of both trade liberalization and FDI on the dynamics of wage inequality. The ARDL bounds

testing procedure has been used in order to distinguish between short-run effects and long-run

effects. Our results suggest that trade liberalization have enhanced the real wages in Tunisia

only in the long-run. No effects have been detected in the short-run. At the same time, foreign

direct investments flows are not cointegrated with real wages. The second finding is that trade

liberalization and FDI positively impacts real wages only in the textiles, clothing and leather

industry. So, the predictions of the Edwards model are verified in the Tunisian manufacturing

sector. When moving to the third objective, the analysis suggests that trade liberalization, FDI

and wage inequality are cointegrated. There is a strong support to the HOS model predictions,

since trade liberalization reduces wage inequality between skilled and unskilled workers in

the long-run. On the other hand, we found that FDI didn’t interact with wage inequality.

Thus, one can conclude that trade liberalization has been beneficial for the Tunisian labor

market, in the sense that it has improved the level and the distribution of wages among

workers.



REFERENCES

Agénor, P. R. & Aizenman, J. (1996). Trade liberalization and unemployment. The Journal of

International Trade and Economic Development, 5(3), 265-286.

Amusa, H., Amusa, K., & Mabugu, R. (2009). Aggregate demand for electricity in South

Africa. Energy policy, 37(10), 4167–4175.

Anderson, E., Tang, P. & Wood, A. (2006). Globalization, co-operation costs and wage

inequalities. Oxford Economic Paper, 58(4), 569-595.

Ang, J. B. (2008). Determinants of foreign direct investment in Malaysia. Journal of policy

Modeling, 30(1), 185-189.

Arbache, J. S., Dickerson, A. & Green, F. (2004). Trade liberalisation and wages in

developing countries. The Economic Journal, 114(493), F73-F96.

Beyer, H., Rojas, P. & Vergara, R. (1999). Trade liberalization and wage inequality. Journal

of Development Economics, 59(1), 103-123.

Bigsten, A. & Durevall, D. (2006). Openness and wage inequality in Kenya, 1964–2000;

World Development, 34(3), 465-480.

Damijan, J. P., Knell, M., Majcen, B. & Rojec, M. (2003). Technology transfer through FDI

in Top-10 transition countries: how important are direct effects, horizontal and vertical

spillovers?. William Davidson Institute Working Paper Series n° 17.

Dickey, D. A. & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time

series with a unit root. Journal of the American Statistical Association, 74(366), 427- 431.

Duasa, J. (2007). Determinants of Malaysian trade balance: An ARDL bound testing

approach. Journal of Economic Cooperation, 28 (3), 21-40.

Edwards, E. (1988). Terms of trade, tariffs, and labor market adjustment in developing

countries. World Bank Economic Review, 2(2), 165-185.



Egger. P., & Stehrer, R. (2001). International Outsourcing and the Skill-Specific Wage Bill in

Eastern Europe. The Vienna Institute for International Economic Studies, Working Paper No.

17.

Feenstra, R. C. & Hanson, G. H. (1997). Foreign direct investment and relative wages:

Evidence from Mexico's maquiladoras. Journal of International Economics, 42(3-4), 371-393.

Figini, P. & Gorg, H. (2006). Does foreign direct investment affect wage inequality? an

empirical investigation. IZA Discussion Paper n°. 2336.

Fosu, A. K. (2000). International trade and labor market adjustment in developing countries.

Paper presented at the International Economic Association

Ghose, A. K. (2000). Trade liberalization and manufacturing employment. ILO Employment

Paper n° 03.

Gorg, H. & Strob, E. (2002). Relative wages, openness and skill-biased technological change.

IZA Discussion Paper n° 596.

Gouider, A. (2010). Le marché du travail en Tunisie entre segmentation et flexibilité.

Revue Européenne du Droit social, 4(1).

Grenier, G. & Tavakoli, A. (2006). Globalization and wage inequality in the Canadian

manufacturing sector: a time series analysis. Global Economy Journal, 6(2).

Haouas, I., Yagoubi, M. & Heshmati, A. (2005). The impacts of trade liberalization on

employment and wages in Tunisian industries. Journal of International Development, 17(4),

527-551.

Harris, R., Sollis, R. (2003). Applied Time Series Modelling and Forecasting. West Sussex:

Wiley.

International Labour Organization (2011). Trade and employment: from myths to facts.

Janicki, H. P. & Wunnava, P. V. (2004). Determinants of foreign direct investment: empirical

evidence from EU accession candidates. Applied Economics, 36(5), 505-509.



Jaumotte, F., Lall, S. & Papageorgiou, C. (2008). Rising Income Inequality: Technology, or

Trade and Financial Globalization?. International Monetary Fund Working Paper n°. 185.

Jayasuriya, R. (2008). The effects of globalization working conditions in developing

countries: An analysis framework and country study results. World Bank Employment Policy

Primer n° 9.

Johansen, S. & Juselius, K. (1990). Maximum likelihood estimation and inference on

cointegration-with applications to the demand for money. Oxford Bulletin of Economics and

Statistics, 52(2), 169- 210.

Krueger, A. (1983. Trade and Employment in Developing Countries, 3 Synthesis and

Conclusions. Chicago: University of Chicago Press.

Lall S., (2002). The employment impact of globalisation in developing countries. QEH

Working Paper Series n°. QEHWPS93.

Landesmann, M., Vidovic, H., & Ward, T. (2004). Economic restructuring and labor market

developments in the new EU member states. WIIW Research Report, n°. 312.

Leamer, E. L. (1995). The Heckscher-Ohlin model in theory and practice. Princeton Studies

in International Finance n°. 77.

Lee, E. & Vivarelli, M. (2006). The social impact of globalization in the developing countries.

International Labour Review, 145(3), 167-184.

Meschi, E. & Vivarelli, M. (2009). Trade and income inequality in developing countries.

World Development, 37(2), 287-302.

Ng, S., & Perron, P. (2001). Lag length selection and the construction of unit root tests with

good size and power. Econometrica, 69(6), 1519-1554.

Onaran, O. & Stockhammer, E. (2008). The effect of FDI and foreign trade on wages in the

Central and Eastern European Countries in the post-transition era: A sectoral analysis for the

manufacturing industry. Structural Change and Economic Dynamics, 19(1), 66-80.



Paus E. A. & Robinson M. D. (1997). The implications of increasing economic openness for

real wages in developing countries, 1973–1990. World Development, 25(4), 537-547.

Peneder, M. (2001). Entrepreneurial competition and industrial location. Edward Elgar,

Northampton.

Pesaran, M. H., Shin, Y. & Smith, R. (2001). Bound testing approaches to the analysis of

level relationships. Journal of Applied Econometrics, 16(3), 289-326.

Pesaran, M. H., Pesaran, B. (1997). Working with Microfit 4.0: Interactive Economic

Analysis. Oxford: Oxford University Press.

Pesaran, M. H., Shin, Y. (1999). An autoregressive distributed lag modelling approach to

cointegration analysis. In Strom, S. (Ed.), Econometrics and Economic Theory in the 20th

Century: The Ragnar Frisch Centennial Symposium. Cambridge University Press, Cambridge.

Philips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regressions.

Biometrika, 75(2), 335-46.

Ramasamy, B. & Yeung. M. (2010). A causality analysis of the FDI-wages- productivity

nexus in China. Journal of Chinese Economic and Foreign Trade Studies, 3(1), 5-23.

Redjeb, M.S. & Ghobentini, M. (2005). L’intermédiation sur le marché du travail en Tunisie.

Cahiers de la Stratégie de l’Emploi n°.19, Unité de recherches et analyses sur l’emploi,

Département de la Stratégie en matière d’emploi.

Singh, A. & Dhumale, R. (2000). Globalization, technology, and income inequality.

UNU/WIDER Working Paper n°. 210.

Stampini, M. & Verdier-Chouchane, A. (2011). Labor Market Dynamics in Tunisia: The Issue

of Youth Unemployment. Review of Middle East Economics and Finance, 7(2).

Taylor, K. & Driffield, N. (2005). Wage inequality and the role of multinationals: evidence

from UK panel data. Labour Economics, 12(2), 223-249.

UNCTAD (2010). International merchandise trade indicators.



Velde, D. W. & Morrissey, O. (2001). Foreign ownership and wages: Evidence from five

African countries. CREDIT Research paper n° 19.

Wood, A. (2002). Globalization and wage inequalities: A synthesis of three theories. Review

of World Economics, 138(1), 54-82.

World Bank (2004). Republic of Tunisia Employment Strategy, Vol.1.

World Bank (2008). Tunisia’s global integration: second generation of reforms to boost

growth and employment. Draft Report n°. 40129-TN.

Zivot, E. & Wang, J. (2002). Modelling financial time series with S-PLUS. New York:

Springer.



Table 1. Jobs distribution according to economic sectors and the level of educational attainment 1989 1994 2000 2007

level % level % level % level %Whole economy 1900.6 100% 2186.4 100% 2552.8 100% 3085.2 100%

no schooling 619.6 32.6% 505.7 23.1% 443.1 17.3% 392.5 12.7%primary 730.1 38.5% 859.1 39.3% 1035.8 40.6% 1137.1 36.8%secondary 452 23.8% 654.6 30% 815.8 32% 1114.4 36.1%university 91.9 4.8% 161.4 7.4% 257.6 10.1% 436.8 14.1%

Agriculture 478.6 100% 462.5 100% 499.5 100% 565.9 100%no schooling 289.1 60.4% 244.7 53% 226.4 45.3% 214.2 37.8%primary 159.1 33.2% 173.8 37.6% 212.3 42.5% 245.4 43.4%secondary 28.2 5.9% 40.5 8.7% 57.4 11.5% 98.6 17.4%university 1.2 0.3% 2.7 0.6% 3.4 0.7% 7.1 1.2%

Industry 634.8 100% 727.2 100% 870.9 100% 993.9 100%no schooling 178.3 28.1% 146.1 20% 125.3 14.4% 87.0 8.8%primary 310.1 48.9% 373.7 51.4% 455.9 52.4% 469.7 47.3%secondary 133.7 21.1% 187.5 25.8% 285.5 32.8% 375.5 37.8%university 10.9 1.7% 18.7 2.6% 31.2 3.6% 61 6.2%

Services 757.6 100% 960.9 100% 1162.7 100% 1496.2 100%no schooling 144.8 19.1% 108.0 11.2% 90 7.8% 85.5 5.7%primary 250.2 33% 298.1 31.1% 358.1 30.8% 411.9 27.5%secondary 282.6 37.3% 415.2 43.2% 492.8 42.4% 613.7 41%university 76.1 10% 137.2 14.3% 221.6 19.1% 364 24.3%

Source: The Tunisian Institute of Competitiveness and Quantitative Studies


Table 2. Annual real wage before and after the conclusion of the FTA with the European UnionAverage

(1983-2009)Pre-liberalization

(1983-1995)Post-liberalization

(1996-2009) Whole economy 6050,840 5373,364 6679,926 Agriculture 2414,298 1606,403 3164,487 Manufacturing sectors 5797,539 5673,037 5913,147 Non-manufacturing sectors 4514,475 3798,826 5179,006 Services 6596,701 6306,164 6866,485Notes: Data are expressed in constant Tunisian dinar and are obtained from the Tunisian Institute of Competitiveness and Quantitative Studies.


Table 3. Unit root tests

Variables ADF PP Ng-PerronMZa MZt

AW -1.752 -1.760 -4.570 -1.434FDI -3.751** -3.652** -15.765* -2.806*

EXP -4.047** -3.459 -18.700* -3.041*

IMP -2.419 -2.361 -6.082 -1.717TRD -3.341 -2.665 -7.519 -1.911Δ (AW) -5.669** -5.650** -18.930** -3.075**

Δ (FDI) -7.905** -18.757** -17.516** -2.922**

Δ (EXP) -5.516** -6.552** -18.731** -2.902**

Δ (IMP) -5.430** -5.419** -18.626** -2.920**

Δ (TRD) -5.272** -5.112** -18.864** -2.890**

ADF and PP refer, respectively, to the Augmented Dickey-Fuller and the Phillips-Perron unit root tests. MZa and MZt are two statistics related to the Ng-Perron unit root test. AW, FDI, EXP, IMP and TRD stand respectively for annual average wage, foreign direct investments, exports, imports and trade. Δ is the first difference operator. The max lag order is set to be 9 for the ADF and the Ng-Perron tests. The Schwarz information criterion and the autoregressive GLS-detrented spectral estimation method are used to select the optimal lag length, respectively, for the ADF and the Ng-Perron unit root tests. The Newey-West Bandwidth method using Bartlett kernel is used for the PP test. Critical values for the ADF and PP tests are obtained from McKinnon (1996), while those associated to the Ng-Perron test are from Ng and Perron (2001). The null hypothesis is the existence of unit root. ** and * denote, respectively, the rejection of the null hypothesis at 5% and 10% significance levels. All specifications include a constant term, but the deterministic trend is included only in levels.


Table 4. Estimated models Model Specification Model 1 AW=f (TRD, FDI) Model 2 AW=f (EXP, IMP, FDI) Model 3 AW=f (TRD) Model 4 AW=f (EXP) Model 5 AW=f (IMP) Model 6 AW=f (FDI)


Table 5. Bounds F-tests for cointegration with diagnostic testsPanel A: Bounds testing for cointegration

Models F-statistic

Critical bounds0.10 0.05

Lower bound I (0)

Upper bound I (1)

Lower bound I (0)

Upper bound I (1)

F (AW|TRD,FDI) 4.788* 3.354 4.343 4.101 5.256F (AW|EXP,IMP,FDI) 3.759 2.923 4.036 3.560 4.836F (AW|TRD) 6.965** 4.229 5.059 5.266 6.159F (AW|EXP) 5.134* 4.229 5.059 5.266 6.159F (AW|IMP) 6.959** 4.229 5.059 5.266 6.159F (AW|FDI) 3.816 4.229 5.059 5.266 6.159Panel B: Diagnostic tests of the underlying the ARDL model

LM test ARCH test RESET test CUSUM Adjusted R²Statistic p-value Statistic p-value Statistic p-valueModel 1 (AW-TRD-FDI) .148 .700 .431 .511 .001 .986 Stable .961Model 2 (AW-EXP-IMP- .072 .787 .481 .488 .008 .926 Stable .961Model 3 (AW-TRD) .213 .644 .507 .476 .054 .816 Stable .961Model 4 (AW-EXP) .287 .592 .534 .465 .640 .424 Stable .958Model 5 (AW-IMP) .011 .913 .514 .473 .033 .855 Stable .960Model 6 (AW-FDI) .017 .895 .755 .385 2.055 .152 Stable .948The Schwarz information criterion is used to select the optimal lag length for the ARDL model. ** and * denote the presence of cointegration at the level of 0.05 and 0.10, respectively. LM test, ARCH test and RESET test refer, respectively, to the Breusch–Godfrey Lagrange multiplier test for residual serial correlation, the autoregressive conditional heteroscedasticity test and the Ramsey's test for functional misspecification. CUSUM is the test of parameter instability (decision based on graph inspection).


Table 6. Long-run coefficients estimatesRegressors Coefficients Standard Error ProbabilityModel 1 (AW-TRD-FDI)FDI -.079 .120 .515TRD 1.938*** .406 .000constant .145 1.757 .935Model 3 (AW-TRD)TRD 1.783*** .3554 .000constant 0.775 1.5838 .627Model 4 (AW-EXP)EXP 1.675*** .4375 .001constant 2.535 1.6271 .129Model 5 (AW-IMP)IMP 1.747 *** .3530 .000constant 2.051 1.3435 .136Notes: the ARDL lag selection is based on SBC. ***, ** and * denote, respectively, the statistical significance at the 1%, 5% and 10% levels.


Table 7. The error correction representation model Regressor Coefficients Standard error T-ratio ProbabilityModel 1 (AW-TRD-FDI)Δ FDIt -.0198 .031 -.631 .532Δ TRDt -.0952 .222 -.428 .671ECTt-1 -.2498*** .068 -3.641 .001Model 3 (AW-TRD)Δ TRDt -.1045 .2196 -.4758 .637ECTt-1 -.2363*** .0645 -3.6593 .001Model 4 (AW-EXP)Δ EXPt -.0558 .1948 -.2867 .776ECTt-1 -.1945*** .0624 -3.1169 .004Model 5 (AW-IMP)Δ IMPt -.1318 .2183 -.6040 .550ECTt-1 -.2345*** .0639 -3.6842 .001***, ** and * denote, respectively, the statistical significance at the 1%, 5% and 10% levels.


Variables ADF PPNg-Perron

MZa MZtAll manufacturing industriesAW -1.695 -1.720 -2.864 -1.173FDI -2.854 -2.139 -32.359** -4.013**

TRD -1.345 -1.271 -16.352* -2.673*

Δ(AW) -3.271** -3.271** -7.918* -1.915*

Δ (FDI) -6.251** -5.880** -6.851* -1.849*

Δ (TRD) -3.420 -3.365** -8.064** -1.976*

Pottery, glass and other construction materials industryAW -1.683 -1.772 -4.443 -1.482FDI -1.625 -3.981** -8.974 -2.104TRD -3.006 -3.003 -5.844 -1.520Δ (AW) -3.616** -3.600** -8.105** -1.924*

Δ (FDI) -9.016** -10.042** -4.449 -1.486Δ (TRD) -3.126** -6.238** -7.037* -1.833*

Agro-food industryAW -1.818 -1.914 -4.551 -1.507FDI -1.778 -1.868 -5.764 -1.610TRD -3.323 -3.328 -8.488 -1.982Δ (AW) -3.140** -3.089** -8.305** -1.918*

Δ (FDI) -5.887** -5.727** -7.627* -1.931*

Δ (TRD) -6.984** -7.263** -6.803* -1.825*

Mechanical, electrical and electronic industryAW -2.051 -2.051 -7.353 -1.728FDI -3.713** -1.457 -3.163 -1.083TRD -2.269 -2.358 -6.737 -1.834Δ (AW) -3.990** -3.945** -8.365** -1.853*

Δ (FDI) -3.214** -3.230** -7.471* -1.886*

Δ (TRD) -4.016** -4.117** -8.386** -2.045**

Chemical industryAW -2.833 -2.848 -8.004 -1.978FDI -3.713* -3.703** -8.045 -1.925TRD -1.218 -1.149 -1.700 -.607Δ (AW) -4.662** -5.007** -8.075* -1.901*

Δ (FDI) -6.736** -7.008** -6.472* -1.787*

Δ (TRD) -3.471** -3.471** -5.360 -1.431Textiles, clothing and leather industryAW -1.779 -1.727 -2.603 -1.063FDI -2.718 -2.069 -21.228** -3.212**



TRD -2.789 -2.789 -6.787 -1.836Δ (AW) -3.934** -3.934** -8.105** -2.012**

Δ (FDI) -3.455** -3.128** -8.249** -1.963*

Δ (TRD) -5.274** -5.326** -7.921* -1.651*

Other manufacturing industries AW -.983 -1.606 -3.352 -1.245FDI -3.859** -2.074 -28.814** -3.793**

TRD -1.796 -.369 -3.003 -.909Δ (AW) -2.063 -4.829** -3.679 -1.293Δ (FDI) -4.269** -2.939* -33.944** -4.104**

Δ (TRD) -.851 -4.382** 5.303 5.104For notes and abbreviations, see table 3.


Models F-statistic


Lower bound I (0)

Upper bound I (1)

Lower bound I (0)

Upper bound I (1)

All manufacturing industriesF (AW|TRD,FDI) 1.479 3.809 4.925 4.904 6.240F (AW|TRD) 1.492 4.640 5.523 5.951 7.082F (AW|FDI) .936 4.640 5.523 5.951 7.082Pottery, glass and other construction materials industryF (AW|TRD,FDI) .395 3.809 4.925 4.904 6.240F (AW|TRD) .206 4.640 5.523 5.951 7.082F (AW|FDI) .636 4.640 5.523 5.951 7.082Agro-food industryF (AW|TRD,FDI) .636 3.809 4.925 4.904 6.240F (AW|TRD) .677 4.640 5.523 5.951 7.082F (AW|FDI) .616 4.640 5.523 5.951 7.082Mechanical, electrical and electronic industryF (AW|TRD,FDI) 2.694 3.809 4.925 4.904 6.240F (AW|TRD) 4.118 4.640 5.523 5.951 7.082F (AW|FDI) .143 2.479 3.688 3.750 4.796Chemical industryF (AW|TRD,FDI) 3.031 3.809 4.925 4.904 6.240F (AW|TRD) .367 2.479 3.688 3.750 4.796F (AW|FDI) 4.137 4.640 5.523 5.951 7.082


Table 9. Estimated models Model Specification Model 1 AW=f (TRD, FDI) Model 2 AW=f (TRD) Model 3 AW=f (FDI)


Textiles, clothing and leather industryF (AW|TRD,FDI) 13.520** 3.809 4.925 4.904 6.240F (AW|TRD) 6.400* 4.640 5.523 5.951 7.082F (AW|FDI) .309 4.640 5.523 5.951 7.082

Panel B: Diagnostic tests of the underlying ARDL modelLM test ARCH test RESET test CUSUM Adjusted R²Statistic p-value Statistic p-value Statistic p-value

All manufacturing industriesModel 1 (AW-TRD-FDI) .219 .639 .525 .819 .134 .714 Stable .892Model 2 (AW-TRD) .006 .936 .095 .757 .102 .749 Stable .880Model 3 (AW FDI) .198 .656 .508 .476 2.024 .155 Stable .875

Pottery, glass and other construction materials industryModel 1 (AW-TRD-FDI) .802 .370 .584 .444 .004 .945 Stable .894Model 2 (AW-TRD) .453 .501 .340 .559 .041 .839 Stable .860Model 3 (AW FDI) .641 .423 .510 .475 .030 .860 Stable .899

Agro-food industryModel 1 (AW-TRD-FDI) .209 .647 .562 .453 .582 .445 Stable .859Model 2 (AW-TRD) .320 .572 .367 .544 .103 .748 Stable .845Model 3 (AW FDI) .383 .536 .224 .635 .811 .368 Stable .854

Mechanical, electrical and electronic industryModel 1 (AW-TRD-FDI) 1.138 .286 .179 .672 .012 .912 Stable .367Model 2 (AW-TRD) .552 .457 .705 .401 .047 .827 Stable .346Model 3 (AW FDI) .347 .556 1.090 .296 3.466 .063 Stable -.146Chemical industryModel 1 (AW-TRD-FDI) .981 .322 .610 .435 3.698 .054 Stable .298Model 2 (AW-TRD) .921 .337 .010 .918 6.108 .011 Stable -.423Model 3 (AW FDI) 1.711 .191 1.084 .298 1.725 .189 Stable .249

Textiles, clothing and leather industryModel 1 (AW-TRD-FDI) .478 .489 .395 .530 .137 .711 Stable .977Model 2 (AW-TRD) .005 .943 1.085 .297 2.160 .142 Stable .931Model 3 (AW FDI) .052 .819 .110 .740 .0003 .979 Stable .871

For notes and abbreviations, see table 5.

Table 12. The error correction representation model


Table 11. Long-run coefficients for the ARDL modelRegressors Coefficients Standard Error ProbabilityModel 1 (AW-TR-FDI)FDI .121** .051 .041TRD 3.064*** .399 .000constant -9.960*** 2.387 .002Model 2 (AW-TR)TRD 3.220*** .485 .000constant -10.822*** 2.895 .003



Regressor Coefficients Standard error T-ratio ProbabilityModel 1 (AW-TR-FDI)ΔFDIt .081*** .021 3.783 .003Δ TRDt -.029 .302 -.099 .923ECTt-1 -.561*** .161 -3.466 .005Model 2 (AW-TR)ΔTRDt .629* .346 1.815 .093ECTt-1 -.2656*** .0776 -3.4208 .002



Variables ADF PP Ng-PerronMZa MZt

WII1 -3.130 -3.050 -12.794 -2.519WII2 -2.922 -2.922 -11.448 -2.389WII3 -1.879 -1.879 -2.680 -.954FDI -3.751** -3.652** -15.765* -2.806*

TRD -3.341 -2.665 -7.519 -1.911Δ (WII1) -7.839** -16.289** -18.1.6** -2.995**

Δ (WII2) -7.842** -9.055** -18.483** -3.013**

Δ (WII3) -6.184** -6.184** -18.970** -3.027**

Δ (FDI) -7.905** -18.757** -17.516** -2.922**

Δ (TRD) -5.272** -5.112** -18.864** -2.890**

WII1, WII2 and WII3 stand for wage inequality indicators. FDI and TRD are respectively foreign direct investments and trade. For notes, see table 3



Table 14. Estimated models Model Specification Model 1 WII=f (TRD, FDI) Model 2 WII=f (TRD) Model 3 WII=f (FDI)


Models F-statistic


Lower bound I (0)

Upper bound I (1)

Lower bound I (0)

Upper bound I (1)

F (WII1|TRD,FDI) 1.1249 3.354 4.3436 4.1018 5.2566F (WII1|FDI) .6785 4.2292 5.0595 5.2666 6.1598F (WII1|TRD) 1.7338 4.2292 5.0595 5.2666 6.1598F (WII2|TRD,FDI) 1.4537 3.354 4.3436 4.1018 5.2566F (WII2|FDI) 1.0538 4.2292 5.0595 5.2666 6.1598F (WII2|TRD) 2.1731 4.2292 5.0595 5.2666 6.1598F (WII3|TRD,FDI) 3.6610 3.354 4.3436 4.1018 5.2566F (WII3|FDI) 2.7062 4.2292 5.0595 5.2666 6.1598F (WII3|TRD) 5.1471* 4.2292 5.0595 5.2666 6.1598

Panel B: Diagnostic tests of the underlying ARDL modelLM ARCH RESET CUSUM R-

squaredStatistic p-value Statistic p-value Statistic p-valueModel 1A (WII1-TRD-FDI) 1.8778 .171 1.4821 .223 .2509 .616 Stable .879Model 2A (WII1-FDI) 1.9503 .163 1.5010 .221 .5091 .476 Stable .877Model 3A (WII1-TRD) 1.9285 .165 1.5049 .220 .2553 .612 Stable .879Model 1B (WII2-TRD-FDI) 2.6587 .103 1.2704 .260 .5037 .478 Stable .853Model 2B (WII2-FDI) 1.9151 .166 1.8732 .171 .2966 .586 Stable .832Model 3B (WII2-TRD) 2.6566 .103 1.2634 .261 .4883 .485 Stable .853Model 1C (WII3-TRD-FDI) 1.1037 .293 .0329 .856 .1158 .734 Stable .848Model 2C (WII3-FDI) .0560 .813 .8011 .371 2.4774 .115 Stable .809Model 3C (WII3-TRD) 1.3028 .254 .0137 .906 .0240 .877 Stable .843WII1, WII2 and WII3 stand for wage inequality indicators. FDI and TRD are respectively foreign direct investments and trade. For notes, see table 5.


Table 16. Long-run coefficients for ARDL modelRegressors Coefficients Standard Error ProbabilityModel 1C (WII3 -TRD-FDI)FDI -.0921 .0873 .299TRD -.8658* .4931 .088constant 4.1987* 2.1607 .060Model 3C (WII3-TRD)TRD -1.2105* .6140 .057constant 5.6498** 2.0834 .045For notes, see table 6



Table 17. The error correction representation model Regressor Coefficients Standard error T-ratio ProbabilityModel 1C (WII3 -TRD-FDI)ΔFDIt -.0159 .0164 -.9675 .340Δ TRDt -.1497*** .0510 -2.9353 .006ECTt-1 -.1729** .0763 -2.2659 .030Model 3C (WII3-TRD)Δ TRDt -.1736*** .0446 -3.8910 .000ECTt-1 -.1434** .0699 -2.0519 .048For notes, see table 7.

0.0E+00

5.0E+09

1.0E+10

1.5E+10

0

20

40

60

80

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Imports of goods and services Imports as a share of GDP (%)Exports of goods and services Exports as a share of GDP (%)

1

2

3

4

5

6

1970 1975 1980 1985 1990 1995 2000 2005

WII1

2

4

6

8

10

1970 1975 1980 1985 1990 1995 2000 2005

WII2

0.8

1.0

1.2

1.4

1.6

1970 1975 1980 1985 1990 1995 2000 2005

WII3


Figure 1. Exports and imports of goods and services in Tunisia (1961-2009)

Notes: The left axis is for imports and exports of goods and services in constant 2000 US dollar, while the right one is for imports and exports of goods and services as a share of gross domestic product (%). Data are from World Development Indicators online, March 2011.

Figure 2. The evolution of wage inequality in Tunisia according to three indicators (1970-2009)



ENDNOTES

Several recent reports emphasize importance of labor markets impacts of economic globalization, such as the World Economic Outlook (International Monetary Fund, April 2007), the OECD Employment Outlook (Organization for Economic Co-operation and Development, 2007) or the project on ‘Trade and employment: challenges for policy research’ (International Labor Office and the World Trade organization, 2010).2 As motioned by the World Bank (2008), the quality of human capital is considered as one of the comparative advantages of Tunisia in the industrial activities (World Bank (2008), p. 96-97)). 3 Exception is the study of Haouas et al. (2005), but which deals, only, with the effects of trade liberalization on wages using data for the period 1971-1996, i.e. before the signature of the free-trade agreement between Tunisia and the European Union. So, it cannot capture the effects of this agreement on the evolution of wages.4 ILO (2011), Trade and employment: From myths to facts.5 Edwards treated the effects of falling import prices on wages and employment during the transition period too. For details, see Edwards (1988), p.175-176.

6 The factors intensity of sectors is such a way that: 7 Since capital is immobile in the short term, Edwards argue that four production factors are used: labor and sector -specific capital: capital in exportable, capital in importable and capital in non-tradable.8 The sample is composed by: Brazil, Chili, Colombia, Ivory-Coast, Indonesia, South Korea, Pakistan, Thailand, Tunisia and Uruguay.9 In fact, the authors classified manufacturing sectors into four sub-groups, depending on two criterions: the capital/labor intensity and the level of skill needed in the industry. Sub-sectors are capital intensive using unskilled labor, capital intensive using skilled labor, labor intensive using unskilled labor and capital intensive using skilled labor. For the classification, authors are based on Peneder (2001) and Landesmann et al. (2004).10 According to the World Bank, in 2010, Tunisia’s fiscal deficit and public debt were, respectively, about 1.3% and 40% of GDP. Starting from 1990, inflation was generally between 2% and 5%.11 Values expressed in constant 2000 US dollars12 For an overview of the determinants of FDI in developing countries, see Asiedu (2002) and Ang (2008).13 In 2006, the national telecommunication company was privatized, 35% of its capital (3052 million dinars, the equivalent of 2290 million US$) was transferred to the Tecom-Dubai Investment Group.14 Statistics are based on a study conducted by the World Bank and the Tunisian ministry of employment and professional integration of youth. The global sample is composed by 4763 youth graduates. 15 According to definition used in the Key Indicators of the Labour Market database, youth are persons aged between 15 and 24 years.16 According to Doing Business.17 Until January 2011, all trade unions are centralized in the Union Générale des Travailleurs Tunisiens (UGTT). After this date, two supplementary institutions have been created.18 For further information, see the development previously presented dealing with the Edwards model (1988).19 Six subsectors are considered in this study, i.e. pottery, glass and other construction materials industry, agro-food industry, mechanical, electrical and electronic industry, chemical industry, textiles, clothing and leather industry and other manufacturing industries.


economic globalization, wages and wage … ben salha.docx · web vieweconomic globalization, wages...

Documents