INTERNATIONAL
FRAGMENTATION OF
PRODUCTION IN THE EU
FOOTWEAR INDUSTRY
INTERNATIONAL FRAGMENTATION OF PRODUCTION IN
THE EU FOOTWEAR INDUSTRY
WHAT ARE THE TRENDS AND DETERMINANTS OF IFP IN THE EUROPEAN FOOTWEAR INDUSTRY?
Joanne Stegenga
Student Msc International Business and Management
S1508156
August 2007
University of Groningen
ABSTRACT
This thesis describes the trends and determinants of International Fragmentation of Production in the EU footwear industry, with special focus on four selected EU countries, which are Germany, France, Italy and Spain. In recent literature it can be read that the amount of IFP has increased the last one or two decades in many industries, among which the footwear industry. Measurement for IFP is Outward Processing Trade (OPT), which is data that registers temporary exports and re- imports. Trends in IFP in the footwear industry show indeed a peak between 1999 and 2003, but after that the amount in EUR decreases for every country with the exception of Spain. In addition, the selected EU countries have different trading partners, also in IFP, of which the possible determinants have been tested in both a multiple as a single regression analysis. Determinants tested are market (economic) size, factor endowment, geographical proximity and infrastructure variables of partner country like road, main phone lines and internet connections. As a distinction has been made between OPT export and OPT import both dependent variables have been tested for these independent variables. Result is that for OPT export the variables economic size, factor endowment, distance and road show significant results. The regression for OPT import almost shows the same results, with the exception of the variable road, which is not significant for OPT import.
ACKNOWLEDGEMENTS
1. INTRODUCTION
"The lion's share of the European footwear industry moved its production activities offshore a number of years ago”.” Only the final assembly process is often still done in Europe nowadays." (Paul Verrips, president of the Footwear Association of Importers and Retailchains (FAIR)
Over the last ten years, the European footwear market has faced more competition, mainly due to the pressure of imports from Asia. The footwear industry is a relatively labor-intensive industry where comparative advantage has shifted to low-wage labor abundant countries. The footwear industry is strongly influenced by globalization and fragmentation of production to low-cost countries due to low entry barriers, the use of standard technologies and its labor intensity. Increasing costs and competition from low-wage countries have forced the EU to specialize in niches or to shift production abroad. European footwear manufacturers that have shifted production are taking advantage of low-wage costs in labor abundant countries by moving the low-skill intensive parts of the production process abroad, which return to the home country to be finished. This is called International Fragmentation of Production (IFP), and is the main focus of this thesis.
This thesis firstly describes the international fragmentation of production in the footwear industry of the EU-15, and especially of four selected EU countries, using data on outward processing trade (OPT). From this it will be concluded what the main partner countries in IFP are for the EU and in what amount. In addition, a regression analysis has been made to test the variables that might influence the choice of the four European countries most active in IFP for their partner country in IFP. This leads to the following research question:
This paper is organized as follows. After a short look at the European footwear industry in general, an extensive overview of international fragmentation of production is given. After this theoretical framework the methodology is described. Section four contains the analysis, including the IFP trends in the EU, but especially the determinants for IFP of Germany, France, Italy and Spain, analyzed with data on Outward Processing Trade (OPT).
2. THEORETICAL FRAMEWORK
In this part the phenomenon of international fragmentation is explained. Before that, a short overview of the European footwear industry is given.
2.1 The European Footwear industry
FIGURE 1. Production of EU-25 Footwear 1999-2003, in millions of pairs
Source: EU market survey, 2004
The increase in competition has not lead to the same effect in the footwear markets of the European countries. Some EU countries have been able to maintain employment and output in footwear whereas others have suffered considerable losses (Brenton, Pinan and Vancauteren, 2000). For instance, Italy and Spain still account for more then 60% of the European footwear production (which can also be concluded from the above figure), while production in the Netherlands dropped with 37.8% in 2003 (compared to 2002), and production in the period of 1999-2003 declined even by 56.6%. (Shoenet, 2004).
2.1.1 Import
TABLE 1. EU-25 production versus total imports in % of total market share 1998-2004 % 1998 1999 2000 2001 2002 2003 2004 EU-25 production 52 47 44 42 38 32 25 Total imports 48 53 56 58 62 68 75 Source: Shoeinfonet, 2004
Countries where the EU is mainly importing its footwear from are listed in the table below.
TABLE 2. Import countries EU-25 1998-2005, in 1,000 pairs Imports - 1000 pairs 1998 1999 2000 2001 2002 2003 2004 20051 World 836,096 931,368 995,161 1,049,304 1,140,833 1,333,034 1,628,139 +18% China 357,510 398,301 434,568 474,275 541,143 566,632 788,186 +48% Vietnam 167,352 201,068 217,004 234,700 264,421 268,701 294,212 -12% Romania 33,390 42,540 50,267 59,917 64,810 70,179 70,626 +11% Indonesia 70,331 66,898 65,850 64,991 60,116 53,460 59,146 -9% India 23,323 28,154 29,148 32,018 35,131 41,104 51,214 0% Malaysia 5,883 6,641 9,406 12,984 14,810 20,449 41,821 -29% Thailand 39,252 35,285 36,427 35,519 36,468 34,451 31,992 -15% Macao 6,203 9,428 14,557 15,850 18,504 22,387 29,710 -72% Others 132,852 143,054 137,935 119,050 105,431 255,671 261,232 Source: Shoeinfonet, 2004
These countries seem to win from the CEE countries, where production has decreased the last years, with the exception of Romania (see table 3 below). Other CEEC producing countries are Poland, the Czech Republic, Hungary and Slovakia.
TABLE 3. CEEC Footwear Producing countries million pairs, 1988-1999 CEEC Footwear Production 1998
Million Pairs 1999 Million Pairs ROMANIA 52.7 64 POLAND 70 57 HUNGARY 14 13 CZECH REPUBLIC 12 10 SLOVAKIA 10 9
Source: SATRA, as mentioned in Factbook, world leather market, 2000
2.1.2 Export
With declining production figures it can be expected that exports have fallen the last decade as well. Table 4 shows that this is indeed the case, at least from 1998-2004. When looking at exports to the world, it can be seen that exports have decreased by 28.8% in six years time. Most important recipients of EU footwear are the USA and Switzerland.
TABLE 4. Export EU-25 in 1,000 pairs, 1998-2004
2.1.3 Intra and extra industry trade
It is also interesting to look at the pattern of intra- and extra industry trade of footwear in the EU-15. Intra EU-15 industry trade refers to trade among the EU-15 countries, whereas extra EU-15 trade refers to trade other than the EU-15. As can be seen from the above tables countries outside the EU have become important trade partners for the EU. To show the trend in EU-15 intra- and extra industry trade the following figures of the intra EU-15 and extra EU-15 industry trade in (finished) footwear in 100 kg over the time frame of 1995-2005 are made. Figure 2 shows the trend in intra and extra EU-15 industry export, whereas figure 3 shows this for the import.
FIGURE 2. Intra and extra industry trade export footwear EU-15 1995- 2005
INTRA AND EXTRA INDUSTRY TRADE EXPORT FOOTWEAR EU-15
0 1000000 2000000 3000000 4000000 5000000 6000000 7000000 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 YEAR IN 1 00 K G intra-industry extra-industry
FIGURE 3. Intra and extra industry trade import footwear EU-15 1995-2005 INTRA AND EXTRA INDUSTRY TRADE IMPORT FOOTWEAR EU-15
0 2000000 4000000 6000000 8000000 10000000 12000000 14000000 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 YEAR IN 1 00 K G intra-industry extra-industry
Source: Eurostat Comext
In ten year time exports outside the EU-15 (extra EU-15 industry trade) shows a consistent line, there are no extreme changes over this time frame. For exports within the EU-15 (intra EU-15 industry trade) however, a sharp decrease in 1998 can be seen. This is probably due to the fact that the production of the EU-15 has also sharply decreased since then, due to competitive pressures from low-wage countries. This can also be seen in Figure 3, where an increase in import outside the EU-15 can be noticed after 1998, with even a very sharp increase after 2000. When looking at the intra industry import a consistent line is seen, but is slowly decreasing. In conclusion, exports of the EU-15 have decreased, especially within the EU-15, and imports have increased a lot due to a sharp increase in imports from countries outside the EU-15.
2.1.4 Challenges faced by footwear producers
The European footwear industry is characterized by a highly unstable demand, which changes rapidly due to changing demand patterns in response to changing fashion. Footwear producers nowadays cope with shorter product lifecycles as they need to present four to six collections per year (instead of two; summer-winter) in response to the market demand. In the footwear industry, consumers are price-sensitive, not loyal to a specific brand and want a high variety in style and price of footwear. In addition, there has been a stronger demand for new distribution channels like specialized shops, single-brand shops and footwear outlets (EC sector report, 2006).
Companies find it difficult to grow as the footwear market is mature, and are therefore looking for niche markets to differentiate themselves by means of innovative design or comfort shoes, for example. The footwear industry is also characterized by micro enterprises and SMEs. Big companies consisting of more than 500 employees only account for 15% of the workforce, which is in contrast to other manufacturing industries of which the big companies account for 33% of the workforce. The advantage of micro enterprises and SMEs is their adaptability and flexibility to respond to changes in demand. In contrast, the disadvantage of this high number of small companies is their vulnerability to external shocks and economic recessions. (European sectoral overview, 2006).
The footwear industry has been forced to restructure to cope with increasing market pressures. Besides looking for niche markets, the European Footwear industry has also fragmented the labor-intensive parts of their footwear production to third countries. This has allowed the companies to better control their production costs.
2.2 International Fragmentation of Production
especially labor cost differentials are one of the main driving forces of International Fragmentation of Production (IFP).
Fragmentation means that a production process is split into several production stages. This used to happen on a local or national base, but due to globalization, producers can take advantage of differences in technology and factor prices among countries, which have led to international fragmentation of production (Jones & Kierzkowski, 2005). International Fragmentation of Production (IFP) means that these several production stages are sent to other countries to be further processed. As Helg & Tajoli (2005) say: ‘IFP means that intermediate and unfinished goods are shipped from one country to another to combine manufacturing or services activities at home with activities abroad’. In literature, several names are used for this phenomenon, e.g. outsourcing, international fragmentation of production, delocalization, and vertical specialization. According to Hummels (2001), these terms all have the same idea, namely: ‘countries increasingly link sequentially to produce goods’.
EU footwear producers have adopted IFP to reduce their production cost, which is necessary due to the increase in competition from low-wage producers in, for example, CEEC and Asia. Logically, in order to reduce costs, international fragmentation of production should have lower costs than it would have at home; it should not increase total production costs. Transportation of goods between production locations, quality controls and communication costs, for instance, are extra costs when having production at different international locations (Helg & Tajoli, 2005).
2.2.1 Theories behind International Fragmentation of Production
Since the 1990s new trade theories have been explored and have increased the attention for trade location. According to Brulhart (1998, as stated in Strobl, 2004), industrial location theory can now be divided into three different trade theories. The first one is the neo-classical trade theory, like the Heckscher-Ohlin and Ricardo model. The other two ‘schools’ are the new trade theory and new economic geography theory. Especially the first two theories have been used by researchers to explain and predict IFP. Neo-classical theory
The neo-classical trade model focuses on differences in productivity or factor endowments between countries and explains differences in specialization patterns among countries by means of their differences in comparative advantage. The models of Ricardo and Heckscher-Ohlin belong to this theory and assume perfect competition, homogenous products and non increasing returns to scale. The Heckscher-Ohlin (H-O model), where fragmentation can result from differences in factor endowments, is an important trade theory for IFP. The trade model of Ricardo is also sometimes discussed, which says that countries produce the range of intermediate inputs in which it has a comparative advantage. This neo-classical theory is about inter-industry trade; countries exporting and importing the products of different industries based on comparative advantage provided by their national characteristics or factor endowments. However, as this theory fails to explain trade among countries with the same factor endowments (e.g. Western Europe), a new trade theory has been developed.
The new trade theory
Emerging Economic Geography in EU accession countries’ Traistaru et al., 2003, call these three factors (increasing returns to scale, consumer’ preference variety and imperfect competition) second nature characteristics of the new trade theory. First nature characteristics are geography, factor endowments and technology of countries.
In addition, the new trade theory emphasizes the importance of country size to determine a trade location. Larger countries or regions in terms of economic size have the advantage of a good market access, which makes them attractive for production and export.
2.3.3 Determinants of IFP
Country size (New trade theory)
In the new trade theory important determinants for IFP is the market size and differences in factor endowments. Helpman (1987) researched the relation of new trade theory to data as first. He used the absolute value of differences in GDP per capita as a proxy for differences in factor endowments and each country’s GDP to measure the effect of (economic) size. He tested 14 OECD countries and came to the conclusion that the more similar countries are in their relative factor endowments and in economic size, the larger the bilateral trade flows (as stated in Cieslik, 2005). Although this was tested for trade in final goods, rather than intermediate goods, the indicators are good measurements for testing possible determinants for partner choice.
Egger and Egger (2005) also include market size and differences in factor endowments markets in their research as possible determinants for trade in IFP. They have measured the market (economic) size by analyzing the sum of real GDP (indicates the economic size of a country) of both the home and host country. Jones, Kierzkowski & Lurong (2005) state that the degree of fragmentation depends on the economic size of a country as well. According to them, a country’s GDP is a suitable indicator for the relation between market size and fragmentation volume, because economic growth encourages fragmentation of production. I will test the sum of real GDP of the home and host country to see if the economic size of a country is a determinant for partner choice. This leads to the following hypotheses:
H1: There is a positive relationship between the sum of the home country’s and partners’ GDP and the degree of IFP of the home country.
Endowment (Neo-Classical theory)
The Hecksher-Ohlin model describes factor proportions as a determinant of trade. Labor, capital and other cost factors of production are fixed in each country and differ across countries. These differences in factor endowments can predict patterns of trade of countries. The advantage of this model is that these predictions are testable; a change in relative factor endowment can explain a changing pattern of production and international trade (Dunn & Mutti, 2004, chapter 3). In addition, this model suits for explaining trade among developed en developing countries as they have different relative factor endowments, companies outsource (intermediate) goods to countries that have a relative abundance of factors which are scarce in the companies’ home country. I expect that this endowment variable is the most important determinant for IFP as the EU countries mainly fragment their production outside the EU-15; to countries with different factor endowments. Measurement for this variable is GDP per capita, which is based on the research by Helpman (1987) and Egger and Egger (2005). Helpman & Krugman interpret differences in GDP per capita as differences in capital-labor endowment ratio (as stated in Bergstrand, 1990), which means that the larger the difference in GDP per capita, the larger the difference in endowments.
H2: There is a positive relationship between the home country’s and partners’ difference in GDP per capita, and the degree of IFP of the home country.
Labor productivity (Neo-classical theory)
In their research to adjustment to globalization in the European Footwear industry, Brenton et al. (2000) show that labor productivity is an important issue in the international footwear competition. The labor productivity of a country tells something about the comparative advantage of that country and is measured by total production divided by total number of employees; it is the output per employee. High labor productivity indicates that, with the same amount of production, fewer employees are needed to do the same job, compared to others, which results in lower costs.
test this variable specifically. However, as labor productivity can also be measured by GDP per capita, which also indicates the countries’ endowment, labor productivity is indirectly tested by hypothesis 2 as well.
Geographical proximity (New trade theory / new economic geography)
Geography is an important characteristic of the new trade theory, even though that theory mainly refers to the size of a market. As easy access to a country is relevant for the choice of a trade partner, distance between home and host country can play an important role. In addition to the new trade theory, there is the theory of new economic geography. Distance and infrastructure are possible determinants for partner choice that belong to the new economic geography theory (Jones and Kierzkowski, 2003). Also Martin & Rogers (1995) and Egger & Falkinger (2003) discuss infrastructure and transportation costs in the context of new economic geography. Transportation costs are closely related to the distance among countries; the closer the distance between host and home country, the less transportation costs.
Geographical proximity is a possible determinant as some countries consider it important to have production close to home. This can also be necessary if a fast delivery is needed. Italy, for example, being a fashion country, produces trendy fashion at high speed, requiring skilful and fast delivery. Reduction in transportation costs is the most common mentioned motive for geographical proximity (e.g. Baldone et al. 2001).
H3: There is a positive relationship between the home country’s and partners’ difference in distance and the degree of IFP of the home country.
Infrastructure / service costs (new economic geography)
lower service costs promotes fragmentation, which refers to lower communication costs due to better phone lines and internet connections. Therefore, the last hypotheses are: H4: There is a positive relationship between the number of finished roads in km2 in
the partner country and the degree of IFP of the home country.
H5: There is a positive relationship between the number of phone lines per 1,000 people in the partner country and the degree of IFP of the home country.
H6: There is a positive relationship between the number of internet connections per 1,000 people in the partner country and the degree of IFP of the home country. It should be noted that the road variable and geographical proximity both influence transportation costs. I will take this into account in the regression analysis. Taxes
Egger and Egger (2005) use partners’ country tax rate as a cost variable that influence the choice for partner country as well. The lower the tax rate on profits and earnings in a certain country, the more attractive it is for the home country to produce in that country. However, when looking at the available data provided by the Worldbank Development Indicators only limited data is available; there is no tax information about most of the countries used in this research. This means that I cannot say anything valid with regards to this variable. When including this variable in the regression analysis, it would negatively influence the other variables and therefore I have decided not to include tax rate on profits and earnings in partner country.
Technological change
and technological innovations. ‘Without technological improvements it would not have been possible for some industries to separate production and to coordinate it’. In other words, technological change is also reflected in the OPT pattern itself.
2.1.2 IFP measurement
Most of the (limited available) empirical work that has been written on international fragmentation of production considers Outward Processing Data (OPT) as the most precise measurement of IFP (see e.g.Baldone, Sdogati & Tajoli, 2001; Amighini & Rabellotti, 2004; Egger & Egger, 2005). OPT is data that registers temporary exports and re- imports. Egger and Egger (2005) make this distinction very clear in their research; they have divided OPT into OPT export and OPT import. OPT export consist of ‘intermediate goods export for further processing in a foreign country, after which the goods are re-imported under tariff exemption’. These re-imports are the OPT imports. Data has been collected since 1988 at EU member country level and is available from the Eurostat Comext database (DVD supplement 2).
3. METHODOLOGY
In this part of the paper the methodology is described. It contains the main research question, sub questions, research strategy and data collection, and hypotheses testing. 3.1 Research question and sub questions
Based on the theoretical framework the following research question is created: Research question
What are the trends and determinants of IFP in the European footwear industry?
Sub questions
• Where and how much have the EU-15 fragmented their footwear production from
1988-2004?
• What EU-countries are most actively involved in IFP in the footwear industry? • Who have been the main trading partners in the footwear industry, with regards to
IFP, of these selected countries; EU, CEEC, Asian countries or others?
• Has there been a shift from CEEC to Asian countries in IFP?
• What are possible determinants for different partner countries in IFP?
The first four sub questions are descriptive to get an idea about IFP in the EU-15 footwear industry. The last sub question is the most important one and is the main focus of this thesis.
3.2 Research strategy and data collection
The strategy to answer the research question is to conduct a descriptive research. The research method for this thesis is deductive, ‘as it entails the development of a conceptual and theoretical structure prior to its testing through empirical observation’ (Gill and Johnson, 2002). Hypotheses are tested in a regression analysis by data provided by Eurostat and the Worldbank Development Indicators. I will analyze international fragmentation of production (IFP) with data on Outward Processing Trade (OPT), which is data that registers temporary exports (OPT export) and re- imports (OPT import). Data is collected from the Comext database from Eurostat.
TABLE 6. Research variables + sources
VARIABLE SOURCE
OPT
OPT export (OPTX ) OPT import (OPTM) OPT RATIOS
Eurostat Comext
Country size
- GDP, sum of home and partners’ country GDP (SGDP)
World Development Indicators
Endowment
- GDP per capita, difference in home and partners’ country GDP per capita (DGDPpc)
World Development Indicators
Geographical proximity
- Difference in distance in km2 between home country and partners’ capitals (DISTANCE)
World Development Indicators
Infrastructure (in partner country) - Finished roads in km of partner (ROAD) - Internet connections per 1,000 people in partner country (INTNET)
- Telephone lines per 1,000 people in partner country (PHONE)
Product
Based on other researches and the many different varieties mentioned in Eurostat, I will only use aggregated data, meaning footwear as a whole on the 6-digit code combined nomenclature.
Sample size
Sample in this thesis are the EU-15, which are the countries that joined the EU before 2004: EU-15 AT Austria GR Greece BE Belgium IE Ireland DE Germany IT Italy DK Denmark LU Luxemburg
ES Spain NL the Netherlands
FI Finland PT Portugal
FR France SE Sweden
GB Great Britain
For the first part of the analysis, describing IFP, I will look at all 15 EU countries. However, for the analysis of which determinants influence IFP, I will use the four countries most active in IFP and describe the OPT per country towards CEEC and Asian countries, and others.
CEEC Asian countries
CZ Czech Republic CN China
BG Bulgaria HK Hong Kong
HU Hungary ID Indonesia
PL Poland IN India
RO Romania VN Vietnam
3.5 Hypotheses testing
As already mentioned in the theoretical framework, the following hypotheses are tested:
H1: There is a positive relationship between the sum of the home country’s and partners’ GDP and the degree of IFP of the home country.
H2: There is a positive relationship between the home country’s and partners’ difference in GDP per capita, and the degree of IFP of the home country.
H3: There is a positive relationship between the home country’s and partners’ difference in distance and the degree of IFP of the home country.
H4: There is a positive relationship between the number of finished roads in km2 in the partner country and the degree of IFP of the home country.
H5: There is a positive relationship between the number of phone lines per 1,000 people in the partner country and the degree of IFP of the home country.
H6: There is a positive relationship between the number of internet connections per 1,000 people in the partner country and the degree of IFP of the home country. These hypotheses are tested in a regression analysis, according to the following regression model:
Yijt = 1SGDPijt + 2DGDPPCijt + 3DISTANCEijt + 4ROADjt + 5PHONEjt + 6INTNETjt
4. ANALYSIS
This part describes the main focus of this thesis, namely the analysis of OPT export and OPT import, also called temporarily exports and re-imports. At first OPT is analyzed for the EU-15, followed by the regression analysis for Germany, France, Italy and Spain. 4.2 Outward processing export and import
The Eurostat Comext extraction of OPT export and import of all the EU-15 over the time frame 1988-2005 shows that with regards to OPT there is hardly intra EU-15 industry trade. From 1988-1992 there is some intra EU 15- industry trade, but not comparable to the figures on extra EU-15 industry trade, and after 1992 there is no intra EU-15 industry trade at all anymore. In addition, not all EU-15 countries are active in IFP, even though the number of countries has increased since 1988. Table 7 gives a small overview of this.
TABLE 7. Number of EU-15 countries active in IFP 1988-2005 No. of EU-15 countries / year 1988 1989 1990 1995 2000 2005 Intra-industry Export Import 4 2 3 3 3 3 0 0 0 0 0 0 Extra- industry Export Import 6 6 7 8 8 8 14 13 11 13 13 10
Source: Eurostat Comext
countries are in their relative factor endowments and in economic size, the larger the bilateral trade flows2.
For this research I use data on the extra-industry (outside the EU-15) trade; there is hardly anything to analyze in the intra-industry trade. EU-15 countries with the highest economic values (in ECU) in OPT import are Italy, Germany, France, Denmark and Spain. In OPT export Italy, Germany, France, the Netherlands, Denmark and Spain show the highest values. As Italy, Germany, France and Spain are also important footwear production countries in Europe, I have chosen these four countries for the regression analysis later this chapter. Before only focusing on the four selected EU countries a graph is made to show the trend in OPT imports in extra-industry trade from 1988-2005 of all 15 EU countries, in 1,000 ECU (LOG).
FIGURE 4. OPT extra-industry export and import EU-15
OPT EXTRA INDUSTRY EXPORT AND IMPORT
0 5 10 15 20 25 30 35 40 45 50 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 YEAR 1, 00 0 E C U (L O G ) TOTAL EXPORT TOTAL IMPORT
2 I have assumed that intra EU-15 trade is trade across similar countries and that extra EU-15 trade is trade
Source: Eurostat Comext, own calculation
This graph perfectly shows the difference between OPT export and import. Countries export a part of the production process with a certain value to a country where value is added to the shoe by e.g. stitching the heel to the shoe. In this graph the line of both export and import increase in quite the same pace. The only remarkable point is 2001/2002, where the export value increase sharply for a moment and value of the (re-) import decreases sharply. It is interesting to see that the total amount of both OPT export and import decreases after 2001/2002. In recent literature it can be read that there have been enormous increases in IFP lately in several industries, among which the footwear industry. For that reason I expected an increasing line in this graph.
So far it can be concluded that the EU-15 does not fragment their production in each others countries, but outside the EU-15. Based on the figures on the regular (finished) footwear (see also theoretical framework) trade I expected an increase in IFP with Asian countries, but this is not the case. The selected Asian countries, as mentioned in the methodology, do not play a role in IFP of the European footwear industry. To get an idea, I have made an overview of the absolute values in EUR of OPT import of the 4 EU countries from the selected Asian countries:
ASIA 0 99,670 252,780 339,620 0
IT
1988 1990 1995 2000 2004 CEEC 128,100 1,106,760 73,722,980 267,933,810 172,890,390 ASIA 0 11,420 1,007,330 990,810 2,475,740
Source: Eurostat Comext
Country specific information
As mentioned earlier, Germany, France, Italy and Spain have been selected to test determinants for partner choice. Before conducting the regression analysis some information about the footwear industry of these countries is given.
Footwear can be divided into various product groups, namely footwear with leather uppers, footwear with plastic/rubber uppers, footwear with textile uppers, footwear with other uppers, parts of footwear and others (EU market survey, 2004). As these materials have different costs this might lead to different fragmentation strategies. However, as within the EU the footwear with leather uppers account for the largest import share this distinction will not be made. In addition, there is not much difference in imports in finished footwear among Germany, France, Italy and Spain, which can be seen in Table 9 below.
TABLE 9. Product groups in footwear industry of the selected EU countries in %, 2003 PRODUCT
GROUP/ COUNTRY
GERMANY FRANCE ITALY SPAIN
leather uppers 61.4 56.3 43.3 50.7 plastic/rubber uppers 16.0 17.5 10.5 24.0 textile uppers 11.8 16.2 14.2 14.7 Parts of footwear 7.1 6.7 27.7 6.8 Others 3.7 3.3 4.3 3.8 Total imports 100 100 100 100
Source: EU market survey footwear, 2004
In addition, the Eurostat Comext database regarding OPT footwear divides footwear into about 50 different product types and are not that clearly grouped as the product groups in the above table. Therefore, I have decided to test footwear at an aggregated level.
Ratio OPT export/total footwear and OPT import/ total footwear import
To find out how much Germany, France, Italy and Spain export and import for processing, the ratio relative to total export and import in footwear have been calculated. The results can be found in the next graphs, Figure 5 for the ratio OPT footwear export / total footwear export and Figure 6 for the ratio OPT footwear import / total footwear import (based on research by Helg and Tajoli, 2005). Unfortunately, for the total export and import only data from 1995 was available, therefore the time frame is 1995-2004. As can be seen in the below figures, there are differences in trend in both the export and import ratios among the countries.
FIGURE 5. OPT export ratio footwear 1995-2004
Ratio OPT export / total export footwear
Source: Eurostat Comext, own calculations
In 1995, Germany has by far the largest export ratio, which decreases every year, with the exception of an increase between 2001 and 2003. Italy has relatively consistent export ratios, whereas Spain is starting relatively late with fragmentation, but increases every year. From 2003, Spain even has the highest OPT ratio of the four countries. France does not have very high OPT export ratios and this decrease already in 2000 to a very low level.
FIGURE 6. OPT import ratio footwear 1995-2004
Ratio OPT import/total import footwear
0 5 10 15 20 25 30 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 YEAR R A TI O GermanyFrance Italy Spain
Source: Eurostat Comext, own calculations
hardly results for 1995-1999, but its OPT import ratio increases steadily after that. In 1995, Germany’s OPT import ratio was a bit more than 10% of total footwear imports and decreases to about 4% in 2004. Italy has relatively high OPT import ratio, with the peak in 1999, where 25% of total footwear imports is OPT import. This is in contrast to their OPT export ratio, which accounts in general for 3 or 4% of total footwear exports. When looking at Table 9, Italy shows a high percentage for importing parts of footwear. Italy probably imports a lot of parts for further processing and finishing at home, but does not export those parts to be processed (which is OPT export).
Not that much background information is available about the footwear industries of Germany, France, Italy and Spain, but some information has been found to further explain the differences in trends3.
Germany
German footwear production is suffering from footwear competition; production has dropped considerably between 1998 and 2003, namely by 33.9%. It has also suffered from low demand, as German footwear has relatively high footwear prices. Result is that factories have been shut down, a number of producing companies have faced acquisitions, whereas some smaller companies have disappeared completely.
France
France is also suffering from the competition in footwear. Production between 1999 and 2003 declined even more than production in Germany. Footwear production in France almost halved in this period; it decreased by 47.1%. Production has been shifted abroad due the increase in competition, but also due to the strong regulations regarding employment France is facing.
Italy
The Italian footwear producers were able to cope with the intense competition for a long time, but also Italian footwear production has dropped by 20.4% from 1999-2003.
Due to increasing competitive pressures, Italian footwear producers have decided to specialize in high-quality goods, which do not directly compete with quality / low-priced footwear. Italian footwear is traditionally located in the upper segment of the footwear market and is knows for its quality and fashionable footwear.
Spain
Also Spain’s footwear industry has difficulties to compete with low-cost countries. Production over 1999-2003 declined a bit less compared to the other EU countries, it decreased by 19.4%. Traditionally Spain was a low-cost centre for footwear production as well. To cope with competitive pressures, Spanish footwear producers have changed their business strategy and became producers of trendy and fashionable footwear instead of cheap products.
Nevertheless, Germany, France, Italy and Spain are active in OPT, but what determines their choice for partner country? The regression analysis gives some insight in OPT determinants.
4.2.2 Regression analysis
In this part the formulated hypotheses are tested for Germany, France, Italy and Spain by means of a regression analysis. At first some descriptive statistics of the data set is given, followed by the least square method regression analysis.
Table 10 and Table 11 summarize the large amount of data of OPT export and OPT import respectively and the independent variables by means of a descriptive analysis.
TABLE 10. Descriptive analysis OPT export (LOG)
SAMPLE: 312
OPTX SGDP RLFAC ROAD PHONE INTNET DISTANCE Mean 2.95 22.62 0.80 4.95 2.26 0.95 3.04 Median 3.29 22.61 0.78 5.10 2.34 1.15 3.02 Maximum 4.86 24.56 1.51 6.79 2.86 2.66 4.08 Minimum -1.39 21.44 -0.39 2.75 1.12 -1.62 2.44 Std. Dev. 1.24 0.51 0.39 0.54 0.33 0.99 0.33
TABLE 11. Descriptive analysis OPT import (LOG)
SAMPLE: 285
OPTM SGDP RLFAC ROAD PHONE INTNET DISTANCE Mean 3.77 22.52 0.93 4.94 2.22 0.95 3.02 Median 3.93 22.58 0.93 5.01 2.29 1.16 3.03 Maximum 5.36 24.60 1.88 6.79 2.78 2.48 4.08 Minimum -0.88 15.34 -0.39 4.10 1.12 -1.62 2.44 Std. Dev. 1.03 0.73 0.32 0.49 0.31 1.01 0.27
between the highest and the lowest values. The mean and median are almost similar, which indicates that the variables are normally distributed. The minimum and maximum values indicate how widely spread the data for a certain variable is and help to understand the median. The standard deviation also measures the dispersion in a data set; the smaller the standard deviation, the more similarity between data points, and the more reliable the outcome of the analysis. For both OPT export and OPT import the variables with the largest dispersion in data are the dependent variables itself and the independent variable internet. This might be due to reason that Internet has developed very fast in a short period in some countries. However, in general, it can be seen that data are quite similar en that there is not much dispersion in the variables.
The next step is to analyze the independent variables relatively to the dependent variables OPTX and OPTM. A multiple regression in Eviews is conducted as there are more than one explanatory variables to test. A distinction have been made between OPT export (OPTX) and OPT import (OPTM). I have entered OPTX followed by all the independent variables and OPTM with the same independent variables separately in the Equation Estimation dialog. Both analyses include the symbol ‘C’, to include an intercept, which gives a possible correction for non-normalities in the residual of equation. The Least Squares method gives the following results; Table 12 for OPT export and Table 13 for OPT import:
TABLE 12. Regression analysis for OPT Export (LOG)
DEPENDENT VARIABLE: OPTX Method: Least Squares
Included observations: 213 after adjustments (sample : 312)
DISTANCE -15.16 0.00 C 1.55 0.12 Adjusted R²: 0.62
F-statistic: 56.33 P-value F-statistic: 0.00
Source: Eurostat Comext and Worldbank Development Indicators, own calculations
When looking at the t-statistic and its probability value (p-value) it can be concluded that for OPT export the variables DGDPpc and distance are influencing the choice for an OPT partner country. The other variables have too low t-values with insignificant p-values. (Eviews illustrator, chapter 3, 2007)
TABLE 13. Regression analysis for OPT Import (LOG)
DEPENDENT VARIABLE: OPTM Method: Least Squares
Included observations: 187 after adjustments4 (sample: 285)
Variable T-STATISTIC P-VALUE SGDP 1.78 0.07 DGDPpc 4.49 0.00 ROAD -0.51 0.60 PHONE 0.46 0.64 INTNET -0.41 0.68 DISTANCE -10.78 0.00 C 2.64 0.00 Adjusted R²: 0.42 F-statistic: 23.52 P-value F- statistic: 0.00
Source: Eurostat Comext and Worldbank Development Indicators, own calculations
4 Spain is in the beginning of the time frame not very active in OPT import, therefore there are less
The above regression result is for the OPT import and actually shows one more significant result than the results for the export. From this table it can be concluded that distance and DGDPpc are explanatory variables for the choice of partner country as well. Moreover, in this case also the economic size of a country matters. When officially calculating the critical value to compare the t-statistic with, the t-statistic should be larger than 1.653, which it is. As the p-value of SGDP is 0.07 I accept this hypothesis, but noting that it is weakly significant.
The Adjusted R-squared coefficient (R²) indicates how well a model fits the observed data in a regression analysis. It is the percentage of the variance in the dependent variable that can be explained by all of the independent variables taken together. If the R² is 1, it can be concluded that the variables tested in the regression analysis fully, for 100%, influence the dependent variables. The R² variables are quite high in both regressions, indicating that the independent variables tested have an influence on the dependent variable. The F statistic tests the hypothesis that none of the independent variables say anything about the dependent variable, it tests if all the other hypotheses equal zero. From these regressions it can be concluded that the variables are not equal to each other and could be used to predict the dependent variable. This is indicated by the high F-statistic with a low p-value.
Regression analysis per variable
regression is the number of observations. The multiple regression only observes the data that are complete for all the independent variables. As sometimes for some years and/or some countries data is missing the total number of observations is less than the sample size. This is reduced in the single regression, which analyses the number of data of one independent variable. For example, OPT export has a sample size of 312, but the multiple regression analysis has tested 213 observations, which is a difference of almost 100 observations. The number of observations in the single regression is much higher. The results are summarized in the following table:
TABLE 14. Single Regression result OPT export and OPT import EU-4 together
OPT export OPT import
VARIABLE EU-4 VARIABLE EU-4 SGDP SGDP DGDPpc DGDPpc ROAD ROAD PHONE PHONE INTNET INTNET DISTANCE DISTANCE
Regression analysis per country
In the previous regression analyses I have tested the independent variables in relation to the dependent variable for all four countries at once. It can be the case, however, that some independent variables are more important for one country than the other. Therefore, I have tested the independent variables in a single regression for the four EU countries separately as well. The regression tables can be found in Appendix D, of which I have summarized the results in Table 15 and 16.
TABLE 15. Results OPT export per country
DGDPpc ROAD PHONE INTNET DISTANCE
TABLE 16. Results OPT import per country
VARIABLE GERMANY FRANCE ITALY SPAIN SGDP DGDPpc ROAD ( weakly) PHONE INTNET DISTANCE
For OPT export it can be concluded that there are some differences among the four EU countries in determinant for partner countries. However, distance seems to be important for most of the countries, except France, which was also the result of the analyses of all countries together. Remarkable is that the variable Road is not significant for any of the countries separately, where it is significant when testing it for all countries at once. On the other hand, the distance to the partner country is important for three out of four countries.
5. DISCUSSION AND CONCLUSION
To summarize and discuss the results of the regression analysis, the following tables are created, one for the Single Regression of Germany, France, Italy and Spain together (combination of Table 15 and 16) and Table 18 with the results for the four countries separately.
TABLE 17. Single Regression result OPT export and OPT import EU-4 together
OPT export OPT import
Variable EU-4 Variable EU-4 SGDP SGDP DGDPpc DGDPpc ROAD ROAD PHONE PHONE INTNET INTNET DISTANCE DISTANCE
TABLE 18. Single Regression result OPT export and OPT import separately.
OPT export OPT import
VARIABLE DE FR IT ES VARIABLE DE FR IT ES SGDP SGDP DGDPpc DGDPpc ROAD ROAD 5 PHONE PHONE INTNET INTNET DISTANCE DISTANCE
As can be seen in the above tables there are differences in results of what determines the choice for IFP partner country. I already have discussed some variables in the analysis part. Another difference in the tables is the variable internet, which shows
significant results for both OPT export and OPT import for the countries separately, but is not significant when measuring it for the countries together. These differences might be due to the fact that the regression analysis for the countries together consists of a lot more observations than the single countries. Spain, for example, has for the variable road for OPT import only 27 observations, whereas road for OPT import for the countries together include 229 observations. Another example is the difference in GDP per capita, which has 305 observations for the countries together, and about 82 observations for Germany, France and Italy, and 58 for Spain. As the regression analysis of the four EU countries combined have more observations and therefore provides more reliable information, I have decided to base my conclusions on these results. I will draw these conclusions from the single regression, to avoid the chance that the independent variables influence each other.
Conclusion
Germany, France, Italy and Spain have been chosen for the regression analysis because of the combination of their high involvement in OPT and their importance as footwear producing countries within the EU. However, also the UK, the Netherlands and Denmark are EU countries that are actively involved in OPT. With regards to the trend in IFP it can be said that the trend was at its highest point between 1995 and 2003. After that IFP decreases for the EU-15, but also for the four selected EU countries. Only Spain’s OPT increases after 2003. In my opinion this has to do with the fact that the increase in competition is growing and therefore, footwear producers decide more and more to produce the whole shoe abroad instead of only some production parts.
TABLE 19. Results hypotheses
HYPOTHESIS RESULT
H1, size Accepted for OPT export, rejected for OPT import H2, endowment Accepted for both OPT export and OPT import H3, distance Accepted for both OPT export and OPT import H4, Road Accepted for both OPT export and OPT import H5, Phone Rejected for both OPT export and OPT import H6, Internet Rejected for both OPT export and OPT import
From this it can be concluded that the endowment variable is indeed important for the choice of IFP partner country and is therefore a determinant for the differences in choice of EU countries. This is in accordance with the Heckscher-Ohlin model; the neo-classical trade theory. Also difference in distance is an important factor for EU countries to decide where to produce particular parts of their production. As can also be seen in Appendix B, Germany, France, Italy and Spain have different partner countries, even though some countries can be seen in all four EU countries. These are, Hungary, the Former Republic of Yugoslavia, Czech Republic, Romania, Poland and Bulgaria, for example. These countries are relatively close to the EU, especially compared to Asian countries, which hardly have been mentioned. Infrastructure variables phone and internet of partner country seem not to influence the choice of EU countries, probably due to the fact that the top-5 of the EU partner countries are also EU countries and have relatively well-developed infrastructures.
the European footwear industry. The independent variables are macro economic variables and not specific for the footwear industry. For this reason I wanted to include wages in footwear and labor productivity for footwear, of which I could not get data.
A suggestion for further research is to take into account the UK and the Netherlands as well. This provides more data and maybe therefore also different results. In addition, I have taken the top 5 IFP partners, but another possibility would be to enter all the IFP partners of the EU countries in the analysis. This also results in more variables and maybe different results. It might also be interesting to have a more in dept look at a country’s political situation and international trade relations to explain the pattern of IFP.
REFERENCES
Amighini, A. and Rabellotti, R. 2006. How do Italian footwear industrial districts face globalization? European Planning Studies. Vol. 14 Issue 4, p485-502
Amiti, M. 1998. Inter-industry trade in manufactures: does country size matters? Journal of International Economics, Vol. 44, p 231-255.
Baldone, S., Sdogati F. and Tajoli L. (2001), ‘Patterns and Determinants of International Fragmentation of Production: Evidence from Outward Processing Trade between the EU and Central andEastern European Countries’, Weltwirtschaftliches Archiv, 137, 1, 80– 104.
Barnett, L., Rahimifard, S., Newman, S. 2004. Distributed scheduling to support mass customization in the shoe industry. International Journal of Computer Integrated Manufacturing; Oct/Nov2004, Vol. 17 Issue 7, p623-632
Brenton, P., Pinna, A.M., Vancauteren, M. 2000. Adjustment to globalisation: A study of the Footwear industry in Europe. Research project EC; ‘Globalisation and Social Exclusion.
Cieslik, A. 2005. Intra industry trade and relative factor endowments. Review of International Economics, Vol. 13, Issue 5, p 904-926
Datamonitor. 2006. Footwear in Europe, industry profile. www.datamonitor.com. Accessed via Business Source Premier.
De Simone, G. 2004. The effects of International Fragmentation of Production on trade patterns: An empirical assessment. Working paper
Dunn, R.M., Mutti, J.H. 2004. International Economics. London: Routledge
Egger, H., Egger, P. 2005. The determinants of EU processing trade. Blackwell Publishing Ltd.
Egger, H., Falkinger, J. 2006. The role of public infrastructure and subsidies for firm location and international outsourcing. European Economic Review, Nov2006, Vol. 50 Issue 8, p1993-2015, 23p
European Commission, 2006. European Industry: a technical update. http://web2006.idepa.es/flash/carnico/SECTOR_CARNICO.pdf. Accessed April 3rd, 2007.
EU market survey Footwear 2004. CBI, Center for the Promotion of Import from developing countries. http://www.cbi.nl/?pag=126§or=27&search=Go. Accessed on April 3rd, 2007.
Eviews Illustrated. Startz, R. 2007. Chapter 3. Getting the Most From Least
Squares.http://www.eviews.com/illustrated/EViews%20Illustrated%20-%20Chapter%203.pdf, Accessed on August, 17th 2007.
Factbook, world leather market. 2000. The Leather Global Value Chain & The World Leather footwear market.
http://www.factbook.net/leather_opportunities.php. Accessed on April 12th, 2007. Gill, J. & Johnson, P. 2005. Research methods for Managers. Third edition. London, SAGE publications.
Grossman, G. M. and Helpman E. (2004), ‘Outsourcing in a Global Economy’, NBER Working Paper 8728, Review of Economic Studies (forthcoming).
Hanzl-Weiß, D. 2004. Enlargement and the Textiles, Clothing and Footwear Industry. Blackwell Publishing Ltd
Helg, R. and Tajoli, L. 2005. Patterns of international fragmentation of production and the relative demand for labor. North American Journal of Economics and Finance. Vol. 16 (2005) 233–254
Hummels, D.L., D. Rapoport and K.-M. Yi (1998), ‘Vertical Specialization and the Changing Nature of World Trade’, Federal Reserve Bank of New York Economic Policy Review, 4, 2, 79–99.
Jones, R.W. ; Kierzkowski, H. ; Lurong, C. 2005. What does evidence tells us about fragmentation and outsourcing? International Review of Economics and Finance; 14, 305-316.
Keller, G. ; Warrack, B. 2003. Statistics for Management and Economics. USA; Pacific Grove. Brooks/Cole – Thomson Learning.
Lüthje, T.2005. Vertical specialization across developed countries. International Trade Journal. Fall2005, Vol. 19 Issue 3, p193-216, 24p
Shoeinfonet, Worldwide Info Center, 2004. Stats about the Footwear Industry from
1998 to 2004.
Http://www.shoeinfonet.com/about%20shoes/stats/stats_footwear_1998_2004.htm. Accessed on March 24th, 2007.
Strobl, E. 2004. Trend and determinants of the Geographic Dispersion of Irish Manufacturing Activity 1926-1996. Regional Studies, Vol. 38 Issue 2, p191-205.
