Abstract
A distinctive feature of present globalization is the development of international production sharing activities, i.e. production fragmentation. The increased importance of fragmentation in world trade has created an interest among trade economists in explaining the determinants of intra-industry trade (IIT) in intermediate goods. In this study, the extent of IIT in Austria’s auto-parts trade is analyzed by decomposing Austria’s auto-parts trade into one-way trade, vertical IIT, and horizontal intra-industry trade IIT. Then, the development of vertical IIT in the auto-parts industry is examined as an indicator for international fragmentation of the production process between Austria and its 29 trading partners, and various country-specific factors suggested by the fragmentation literature are tested using panel econometrics as well as more recent data from 1996 to 2006. The results show that a substantial portion of IIT in the Austrian auto-parts industry is vertical IIT, and the econometric results mainly support the hypothesis drawn from the fragmentation theory. In particular, the findings show that the extent of Austria’s vertical IIT in auto-parts is positively correlated with average market size, differences in per capita GDP, and foreign direct investment, while it is negatively correlated with distance.
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Notes
Product fragmentation can be defined as division of production processes into different locations across different countries.
IIT is defined as the simultaneous export and import of products which belong to the same statistical product category. IIT of goods with a certain range of unit-price differentials between exports and imports is classified as horizontal IIT, while the rest is classified as vertical IIT.
Several empirical studies have analyzed the determinants of VIIT in the motor vehicle and auto-parts industry (Becuwe and Mathieu 1992; Ito and Umemoto 2004; Umemoto 2005; Montout et al. 2002). However, the drawback of these empirical studies is that they do not incorporate hypotheses stemming from newly developed fragmentation literature.
In the auto industry, global production networks involve intra industry trade both at the level of final products, and of intermediate goods.
A 2006 Survey by the Office of Aerospace and Automotive Industries’ Automotive Team ranks Austria among the top 20 countries in terms of auto-parts exports in the world in 2003.
Table 11 lists automotive assembly plants close to Austria by country.
As shown in Bhattacharya (2007), Austria’s trade links with Central and Eastern Europe have gathered momentum in recent years. In particular, the Central and Eastern European countries’ share of Austria’s total exports rose from 12.5% in 1991–1995 to 18% in 2001–2005, while its share of total exports increased from 8% in 1991–1995 to 14% in 2001–2005. According to Bhattacharya (2007), the shift in the commodity composition of exports and imports as well as the enormous increase in manufacturing products implies that intra-industry trade resulting from outsourcing activities has become much more important than before in Austria’s trade with the region. See also Egger et al. (2001).
The auto industry has organized itself into several tiers. Tier 1 sells directly to automakers or original equipment manufacturers (OEM), which assemble the final product. Tier 2 supply parts to tier 1, and those that sell parts to tier 2 are known as tier 3, etc. moving down the value chain. The term “tier” describes products rather than an entire firm, so that some firms may be tier 1 on one product and tier 2 on another.
More than 250 auto-parts producers including General Motors Powertrain, BMW Motoren, Magna Steyr, and MAN Nutzfahrzeuge supply components for the motor vehicle industry in Austria (see Table 10).
For instance, Magna Steyr, a subsidiary of Magna International and one of the leading auto-parts manufacturer in Austria, have assembled a total of more than 200,000 vehicles for various automakers on a contract basis, such as Daimler-Chrysler (Chrysler Voyager, Grand Voyager, Jeep Grand Cherokee, Jeep Commander), BMW (X3 Sports Activity), Saab (9-3 Cabriolet), and Mercedes-Benz (G-Class). Recently, Magna Steyr signed an agreement with BMW to manufacture the Mini series beginning in the year 2010. Further, Aston Martin has agreed a deal with Magna Steyr for the production of its forthcoming Rapide sedan in Austria starting at the end of 2009.
Currently, there are around 80 international auto-parts manufacturers, such as Delphi Packard Austria, Bosch, Magna International, and Johnson Controls, which coordinate their Eastern European operations from their bases in Austria.
The employment effects of outsourcing which had their origin in the accession of new Member states to the EU are also present in other Austrian manufacturing sectors. See Bhattacharya (2007).
For details, see Nunnenkamp (2005).
Table 6 lists core/periphery categorizations of countries used in the analysis.
For a more detailed discussion on the empirical analysis of fragmentation see Egger et al. (2001).
IPT is the duty relief procedure allowing goods to be imported into a country for processing and subsequent export outside the country without payment of duty, while OPT involves intermediate goods exports for further processing in a foreign country where the goods are shipped back to the home country under tariff exemption.
For instance, Chen et al. (2005) found that a significant portion of U.S. exports of manufactured goods carried out by U.S. multinationals which are sent to foreign manufacturing affiliates of the U.S. multinationals have mainly consisted of materials and components for further processing or assembly: the share of U.S. exports to foreign affiliates for further manufacturing increased from 15.6% in 1977 to 22% in 1999.
For instance, transport equipment group 78 does not include parts such as automotive tires, electronics, instruments, glass parts, or rubber parts, which are recorded under different headings.
See Türkcan (2009) for more on this issue.
For a more detailed discussion of trade in horizontally differentiated intermediate goods, see Ethier (1982).
Notice that exchanges of intermediates against intermediates (such as in our case, exports of parts of spark-ignition type engines and imports of assembled spark-ignition type engines) may result in one-way trade due to fact that trade leads to changes in a product's statistical category. However, the current analysis is internationally not so highly disaggregated such as 10-digit level, as to exclude this form of trade.
A number of studies, such as Feenstra and Hanson (1997) and Arndt (1997), have employed the Heckscher-Ohlin model to explain the pattern of vertical specialization in intermediates. In these models, the degree of vertical specialization will increase as countries’ incomes and factor endowments diverge.
Horizontal IIT through fragmentation would also be present if imported parts and components were exported with small unit price differentials embodied in the local market. However, this kind of trade does not seem to be important in Austria’s auto-parts trade.
The traditional G-L index is negatively correlated with a large overall trade imbalance. With national trade balances, the level of IIT in a country will be clearly underestimated. To avoid this problem, Grubel and Lloyd (1975) proposed another method to adjust the index by using the relative size of exports and imports of particular goods within an industry by weight.
Fontagne et al. (2006) compare between the G-L index and the two-way trade index using regression analysis in a quadratic form for all country pairs in the world in 2000 and find that the fit between two indices is good, but the two-way index is considerably larger than the G-L index. As pointed by Fontagne and Freudenberg (1997), a degree of caution must be used when comparing and interpreting the G-L index and the two-way trade index because these two methods are complementary rather than substitutes. The former method deals with the intensity of overlap, while the later method calculates the relative importance of each type of trade in total trade.
This method is called “the decomposition-type threshold method” by Ando (2006).
Following Klier and Rubenstein (2006), we employ the list provided by the Office of Aerospace and Automotive Industries' Automotive Team, part of the U.S. Department of Commerce's International Trade Administration in order to select the motor vehicle products and auto parts from the trade data. That team's definition of motor vehicle products and auto parts can be found at http://www.ita.doc.gov/td/auto.html.
The choice of 25 % is arbitrary. In trade literature, two common values are often employed, 15% and 25%. Greenaway et al. (1995), and Fontagne and Freudenberg (1997)’s empirical analysis suggest that the results are not very sensitive to the range chosen. The 15% threshold is generally used and considered to be appropriate when the unit value differences reflect only differences in quality. However, in case of production fragmentation the 15% threshold could be too wide and 25% threshold is considered to be more appropriate. Taking these considerations into account, this paper uses a rather narrower measure of vertical IIT in intermediates to more accurately measure the degree of international fragmentation.
Unfortunately, the G-L method still considers the minority flow below this 10 % threshold as two-way trade when the calculated G-L index is greater than zero.
Similarly, Ando (2006) provided empirical evidence that the auto industry trade in East Asia is mainly one-way trade thanks to import substituting policies in these developing countries, although vertical IIT has become important for auto-parts in recent years. On the other hand, Montout et al. (2002) demonstrated the importance of IIT in NAFTA’s auto parts trade, which represents approximately 70% of total trade in the 1990 s. Similarly, Jones et al. (2002) also found that the degree of IIT between the USA and Mexico in auto-parts rose substantially, from 67% in 1992 to 85% in 1999. However, Lall et al. (2004) argue that in the auto industry, fragmentation is more constrained than in other sectors, such as the electronics sector. While the auto industry has separable stages of production and parts with different scale, skill and technological needs whose production can be located in different countries, many components, such as body and chassis parts, are heavy, thus making their processing more suitable for relocation in closer areas rather than in distant areas, which in turn reduces the degree of intra-industry trade.
As seen in Fig. 2, except in 2006, the degree of VIIT in auto-parts has continuously increased since 1996. The reason for a drop in 2006 is due to the fact that missing data are often encountered in the last quarter of 2006.
From Tables 2 and 3, one can see that several countries such as Belgium, Iceland, and others, have extremely low levels of intra-industry trade in 1996, and also large changes in the levels of IIT from 1996 to 2006. These values are not in line with the values expected for a developed country such as Belgium. The low levels of IIT and large changes in IIT can be primarily explained by the fact that there are many missing or unrecorded observations in the OECD’s International Trade Commodity Statistics (ITCS) database, particularly in 1996.
Likewise, Fontagne et al. (2005) showed that Germany and Austria are the two trading partners in the world having one of the highest shares of IIT in their manufacturing products trade: 77% in 2000.
For similar results see Fidrmuc (2000), who showed that the level of IIT in EU trade with Austria dropped from 69% in 1990 to 66% in 1996.
For similar results see Fidrmuc (2000), who showed that the level of IIT in EU trade with Austria dropped from 69% in 1990 to 66% in 1996.
Similarly, Umemoto (2005) illustrated that higher-income countries, such as EU countries, experienced a sharp decline in vertical IIT in auto-parts whereas low-income countries, such as East Asian countries, experienced a sharp increase in vertical IIT during the late 1990 s.
Gabrisch and Segnana (2007) also found that the shares of vertical IIT between Austria and candidate countries in 1993 and 2000 were 22% and 42%, respectively. The sharp increase in vertical IIT clearly indicates how rapidly vertical IIT became an essential element of trade between Austria and neighboring periphery countries.
By contrast, Falvey and Kierzkowski (1987) have demonstrated that vertical IIT is positively associated with differences in market size, reflecting differences in factor endowments. At first sight, the expectation of a negative coefficient on the differences in market size appears to be contrary to the expectations of Falvey and Kierzkowski (1987). However, in the present study, vertical IIT is used as a proxy to measure the degree of fragmentation of production, instead of two-way exchanges of quality-differentiated products trade.
See Andersson and Fredriksson (2000) for a more detailed discussion on the relationship between a host country’s market size and intra-firm imports of imported intermediate goods.
A temporal causality between outward FDI stocks and trade may also exist in the other direction. There appears no consensus among researchers in the literature on this issue. For instance, the results reported in Alguacil and Orts (2002) and Türkcan (2007) suggest a causal relationship running from FDI to trade In contrast, Pfaffermayr (1994) indicates that the causality between FDI and trade runs significantly in both ways.
The magnitude of this effect on vertical IIT could be different across different product groups: final and intermediate goods. Considering trade in intermediate goods, small changes in transportation costs have a major effect on fragmentation decisions because of multiple border-crossing involved in the value added chain. In contrast, distance is less likely to affect the final goods trade in which goods pass the border once only.
For instance, the results of Arndt and Huemer (2005) indicate that U.S trade flows become much less sensitive to exchange rate changes as fragmentation induced trade expands. Similarly, Thorbecke (2008) recently investigated the impact of exchange rate volatility and changes in the bilateral exchange rate on fragmentation of the electronic component industry in East Asia, and found that the flow of these goods in East Asia is very sensitive to exchange rate volatility, but not to changes in bilateral exchange rates. In contrast, Swenson (2000) analyses the sensitivity of firms located in the U.S. foreign trade subzones to a dollar depreciation, and found a decline in the use of imported inputs in production by the U.S. firms as a response to depreciation.
The EU dummy consists of Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, and the United Kingdom.
For a detailed explanation of the estimation strategy, see Greene (2003).
As suggested by the tests for heteroscedasticity the likelihood ratio test (LR) and serial correlation (the Wooldridge test) reported in Tables 4 and 5, pooled OLS, the FE model, and the HT model are conducted using the Newey-West method which generates robust standard errors in the presence of autocorrelation within panels, and heteroscedasticity across panels. In addition, the RE model is estimated using the feasible generalized least squares (FGLS) method in order to account for heteroscedasticity and autocorrelation. Besides addressing the problem of heteroscedasticity and autocorrelation, collinearity among independent variables is also examined and reported in the Appendix, Table 9. After an examination of collinearity among explanatory variables in Table 9, it is found that none of the explanatory variables is strongly correlated with the others.
Test statistics of 9.46 and 3.56 for both concepts of vertical IIT index are much smaller than the critical value of a chi-squared with six degrees of freedom (12.59).
Someone might argue that if we cannot reject the hypothesis that individual effects are uncorrelated with the other regressors in the model, then there is no need to apply the HT model. However, Baltagi et al. (2003) show that there is a substantial bias in the RE estimators when there are time-invariant variables, and also endogenity among the regressors. Hence, they conclude that inference based on the RE estimators can be seriously misleading even when there is no correlation between the explanatory variables and the individual effects.
Although we do not report the detailed results here, we also checked the sensitivity of our results with respect to outliers. We consider a HS product as an outlier if its unit value in any year is more than two standard deviations away from the population mean. Where outliers were obvious they were replaced by average values for that 6-digit category. Excluding these outliers from the dataset did not influence the key coefficients of interest relating vertical IIT. Overall, it is concluded that the results seem to be robust to extreme outliers.
Cooney and Yacobucci (2005) suggest that the key determinant for location choices of auto-parts firms would be the location of the assembly plant itself and the associated transport infrastructure.
For instance, Kimura et al. (2007) reports that the machinery parts and components trade in Europe is discouraged by difference in GDP per capita, as a proxy for both differences in wages and location advantages, while their influence on East Asia appears to be positive.
A number of MNEs in the auto industry such as Magna, Renault, or Volvo have chosen Austria as the headquarters for their Eastern and Central European operations.
See Bhattacharya (2007).
Cooney and Yacobucci (2005) claim that distance may limit China’s role in the U.S. auto industry as a major supplier for auto-parts producers (particularly the original equipment industry) using the “just-in-time” production model.
Before the latest enlargement of the EU, the Austrian auto-parts industry had traditionally been linked to the German automobile industry.
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Acknowledgements
I would like to thank Martin Falk, Yushi Yoshida, and Aysegul Ates and the conference participants at the European Trade Study Group (ETSG) 10th Annual Conference held in Warsaw for their valuable comments and suggestions. The author started this project as a visiting researcher at the Austrian Institute of Economic Research (WIFO) in summer 2008. I wish to thank WIFO for its hospitality and research support.
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Appendices
Appendix A: Definitions of auto-parts industry trade and explanatory variables
Definition of auto-parts industry trade
The annual bilateral trade flows data in the 6-digit HS (Harmonized System 1996) used in this study were obtained from the OECD's International Trade Commodity Statistics (ITCS). There are about 6,784 items at the 6-digit level of the HS. For the measurement of IIT in the automobile industry, we chose to identify 18 items as motor vehicle products, and 92 items as auto parts from the 6-digit level of HS.
This database provides detailed annual bilateral trade data for commodity exports and imports in value ($US at current prices) and quantities at the 6-digit level of the HS. Unit values at the 6-digit product level of the HS are then constructed as the value of imports and exports of the product divided by the corresponding quantities. In this source, export values are recorded on a f.o.b. basis, while import values are recorded on a c.i.f. basis. Following Ando (2006), we multiplied the export values by 1.05 in order to adjust the discrepancy between export and import values. Thus, calculated unit price differentials do capture a trade in the auto industry that is entirely due to differences in quality or international fragmentation.
Definition of explanatory variables
Country-level variables for Austria and the 29 OECD countries are mainly retrieved from the OECD database that can be downloaded from http://www.sourceoecd.org. The full list of countries included in the analysis is shown in Table 6. In addition, we divided our sample of countries into core and peripheral countries using the categorization drawn up by the World Bank.
Market size (GDP kt ) is proxied by the log of the average GDP of Austria and its trading partner, expressed in current US dollars. In addition, DGDP kt is the log of the absolute difference in market size, expressed in current US dollars. In line with Balassa and Bauwens (1987), we calculated the difference in market size as:
where \( w = \frac{{GD{P_{ht}}}}{{GD{P_{ht}} + GD{P_{kt}}}} \), h and k are Austria and its trading partners, respectively. This index obtains a value between 0 and 1, and increases as the relative inequality between two countries increases.
The log of the absolute difference in per capita GDPs of Austria and its trading partner k is defined as \( DPGD{P_{kt}} = \left| {PGD{P_{ht}} - PGD{P_{kt}}} \right| \), expressed in current US dollars.
Moreover, FDI kt is the log of Austria’s outward FDI stock into its trading partner k, measured in current US dollars. As a measure of multinational activity in the host countries, outward FDI stock data is chosen rather than outward FDI flows since stock data is more complete than the flows data. Some researchers argue that outward FDI stock is an imperfect proxy for multinational activity because multinational companies may also engage in many activities in the host countries which one would not expect to have any relationship with fragmentation of production, such as real estate investment. Nonetheless, considering the limited availability of data, outward FDI stock data may be best available proxy.
DIST k is the log of direct distance between Austria’s capital and its trading partner’s capital, and is taken from the CEPII’s Distance Database that can be downloaded from http://www.cepii.fr. Finally, the bilateral exchange rate in this study is defined as the number of foreign currency units per unit of domestic currency, so that EXCH kt falls with depreciation of the domestic currency, namely the Euro. The explanatory variables, their predicted signs, and their sources are summarized in Table 7. Table 8 provides the summary statistics for different concepts of the IIT index and explanatory variables, while Table 9 presents the correlation matrix between explanatory variables.
Appendix B: Key figures and stats for the auto industry in Austria
Motor vehicle production in Austria (units), 1997–2006.
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Türkcan, K. Vertical Intra-Industry Trade and Product Fragmentation in the Auto-Parts Industry. J Ind Compet Trade 11, 149–186 (2011). https://doi.org/10.1007/s10842-010-0067-0
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DOI: https://doi.org/10.1007/s10842-010-0067-0