Supply&Chain&Trade&and&Per&Capita&Income:&A&Cross9Country&Analysis&&
University*of*Groningen*
Faculty*of*Economics*and*Business*
Research*paper*International*Economics*and*Business*
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Name*Student:*Linda&Jongenburger*
Student*ID*number:*2360667*
Student*email:*lindajongenburger@gmail.com&
Date*Paper:*07.07.2014*
Name*Supervisor:*Dr.*A.&A.&Erumban&
CoCassessor:&Dr.&M.&S.&S.&Krammer*
ABSTRACT
The importance of integrating into supply chains for the development of less development countries has been often advocated in recent literature. This thesis attempts to understand the empirical link between supply chain trade and the stage of a country’s development, by identifying the additional gains of trade within supply chains and analysing its influence on a country’s income. Increasing fragmentation of supply chains has led to a gap between exports and value added of exports, making it important to measure the gains of supply chain trade in terms of value added in exports instead of gross exports. One way of analysing value added that is created and captured in the supply chain is global value chain analysis. We use the backward- and forward linkages of a country as an indication of the foreign- and domestic value-added content of a country’s exports, created by trade within GVCs. Subsequently, we measure the influence of the degree of integration in a value chain on productivity using the same backward- and forward linkages. Although the importance of increasing domestic value- added in a value chain by functional upgrading has been well established in the literature, it is less evident in this research. We find that it is also important to focus on inter-sectoral upgrading: ‘’using the knowledge acquired in particular chain functions to move into different sectors’’ (Schmitz, 2004).
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Keywords: Global value chain, value-added, forward- and backward linkage
1. INTRODUCTION
Globalisation’s first and second unbundling, -a rapid decline in transportation cost and the rapid fall in communication and coordination costs primarily due to onset of ICT-, has changed the level of competitiveness and thereby has led to the fragmentation of supply chains (Baldwin, 2006). The decreasing cost of transport made it possible to spatially separate production and consumption, which was more profitable due to scale economies and comparative advantage. The ICT revolution has made it possible to spatially separate different production stages, due to declining communication and co-ordination cost. Thus the second unbundling made it profitable for advanced nations to offshore the labour-intensive production stages of their supply chain to low-wage countries. The result has been an increasing fragmentation of supply chains of firms in advanced-nations, as international competitiveness occurs at the level of production stages. G7 countries witnessed a decline in their share of world exports. The G7 share of world exports declined from 52 per cent in 1991 to 32 per cent in 2011, implying an increase for all other countries, owing to the increase in offshoring of production stages (Baldwin, 2011).
This fragmentation of supply chains in advanced-nations is considered to be a stepping-stone for developing countries to industrialize and integrate into the world economy, because ‘‘developing countries can now join a supply chain rather than having to invest decades in building their own’’ (Gereffi and Fernandez-Stark, 2011). In a highly interconnected global economy, joining a supply chain and thereby integrating into the world economy is important for the economic development of developing countries (Gereffi and Lee, 2012). It is important for the economic development because it leads to an increase of the exports of a country and exports generally tend to have a positive and significant effect on productivity and economic growth (Bonelli, 1992; Dollar, 1992; Frankel and Romer, 1996,1999; Edwards, 1998). For instance, Edwards (1998) finds that total factor productivity growth is faster in more open economies. Also along with the decreasing G7 share of world exports, the share of world GDP decreased from 67 per cent in 1988 to 50 per cent in 2010 for the G7 countries (Baldwin, 2011). Which implies an increase in share of world exports and GDP for all other countries. Though earlier studies have established a positive effect of exports on economic growth, the second unbundling has made the volume and value of exports less significant on economic growth. Export of sophisticated manufacturing goods had a welfare enhancing effect, now exporting these goods may rather reflect a nation’s position in an international supply chain (Baldwin, 2011). A country’s export is therefore now
measured in value-added, as the value-added differs greatly between the production stages of a supply chain. A welfare gain can be achieved by increasing the domestic value-added of exports and not necessarily the value or volume of gross exports.
A concept used to analyse the international expansion and geographical fragmentation of supply chains and value creation and capturing therein is the Global Value Chain (GVC).
A global value chain highlights the relative value of all production stages in a supply chain.
The value-added trade flow is equal to the value-added paid to the factors of production in the exporting country (Koopman et al. 2010) and therefore differs between the production stages.
Differences in value-added between the production stages may imply that the distribution of gains between countries differ, in terms of countries’ shares in total value-added created by trade under GVCs (Banga, 2013). Baldwin and Lopez-Gonzalez (2011) suggest that there is an unexplored mechanism linking supply chain trade and stages of development. This thesis is an attempt to explore the relationship between the way a country is linked into the GVC and its real GDP per capita and productivity. The central research question addressed in this thesis is: How does supply chain trade influence a country’s stage of development and level of productivity.
Most studies conducted on GVCs so far are industry case studies, while a more aggregated view on a country level to explore the mechanism between supply chain trade and the stage of development and productivity of a country is hard to find. The added value of a research of this kind is that it provides insights on how value chains operate and what are the consequences of the distribution of gains in terms of domestic value-added for a country’s welfare. It is very important for developing countries and policymakers to understand how these value chains are structured as it has implications for policies on integrating into global value chains. By actively changing the way developing countries are linked to global value chains they can stimulate economic development.
The remaining of the paper is structured as follows. Section two presents a review of the relevant literature, explains the theory and states the hypotheses. The third section describes the methodology and the fourth section presents the data. The fifth section presents the results, which will be discussed in the sixth section. The final section concludes the thesis.
2. LITERATURE REVIEW
The nature of international trade has changed dramatically; the first and second unbundling of globalisation has led to an increase of vertical specialization (VS) – the use of imported inputs in producing goods that are exported (Hummels et al. 2001). Vertical specialization occurs when countries specialize only in particular stages of a good’s production sequence (Yi, 2003) It has been labelled extensively: ‘‘slicing up the value chain’’, ‘‘outsourcing’’,
‘‘disintegration of production’’, ‘‘multi-stage production’’ and ‘‘fragmentation’’. Indicators of vertical specialization developed by Hummels, Ishii and Yi (hereafter HIY, 2001) measure the value of imported inputs embodied in goods that are exported and has often been used to measure the extent of country’s international integration and participation in supply chain trade. They find that the growth of vertical specialization accounted for 30 per cent of the growth in exports of 10 OECD and four emerging countries. Several other authors have studied the impact of increased VS on factor prices, production and trade patterns, and welfare (Feenstra and Hanson, 1996; Feenstra, 1998; Campa and Goldberg 1997; Lawrence, 1994; Slaughter, 2000; Berman et al., 1994). Subsequently the HIY, 2001 VS indicator was also used to measure the extent of fragmentation (Amador and Cabral, 2009) and to trace the source of value-added captured in a country’s export.
The global value chain is a concept to analyse the international expansion and geographical fragmentation of supply chains and value creation and capturing therein. The value chain includes the full range of activities and processes that are needed to bring a product from conception through the intermediary stage of production to delivery to final consumers and final disposal after use (Banga, 2013). Figure 1 (Appendix) shows a global value chain in the 1970’s and a value chain of the 21st century. It shows that the value-added is unequally distributed between the different activities of the value chain. The activities in the pre- and post- fabrication stage have higher value added than the activities in the fabrication stage. The figure also shows that in the value chain of the 21st century, value-added has shifted away from the fabrication part to the pre- and post-fabrication part (Baldwin and Evenett, 2012).
This phenomenon is known as the ‘deepening of the smile curve’. Value-added depends on the payment to the factors of production in a country. The production stages are mostly labour intensive and were offshored to low-wage countries, which decreases the value-added in these stages leading to the deepening of the smile curve.
The focus of previous research on GVCs is mainly on case studies at industry level.
These researches analyse upgrading opportunities within sectors, taking into account the governance structure of the sector. The literature distinguishes between four types of upgrading: Process-, product-, functional-, and inter chain upgrading (Humphrey, 2004). The five governance structures used in the literature are: Market, Modular, Relational, Captive and Hierarchy (Gereffi et al. 2005). The type of governance in a sector depends on three important variables: the complexity of transactions, the codifiability of transactions and the competence of suppliers (Gereffi et al. 2005). Taking all these components of a value chain into account results in different upgrading opportunities per sector. Authors have found positive product and process upgrading in the textile and shoe industry in Latin America (Pietrobelli and Rabelloti, 2004), and in the apparel industry in East Asian countries. Functional upgrading took place in the apparel industry in Japan and Hong Kong, in the computer industry in Taiwan and in the electronics industry in South Korea (Gereffi et al. 1999). Especially functional upgrading has been indicated as a way to increase development in a country. Many emerging economies have shifted their development strategies from simple export-oriented industrialisation to an emphasis on gaining access to higher value-added activities (functional upgrading) in global value chains. While upgrading of product and processes does not necessarily lead to increased profits and sustainable incomes (Gereffi et al. 2001) and inter- chain upgrading has been less explored in the literature.
Integrating into international supply chains is important for the economic development of developing countries because it leads to an increase of the exports of a country. The positive relationship between exports and economic growth has been well established in the literature (Frankel and Romer, 1996,1999). Firstly, exports allow poor countries to exploit economies of scale (Helpman and Krugman, 1985). Secondly, export growth lead to enhanced efficiency, due to competitive pressures in world markets (Balassa, 1978; Bhagwati and Srinivasan, 1979; Krueger, 1980). Thirdly, exports increase the technical knowledge through spillovers and by learning by doing (Grossman and Helpman 1991). The influence of exports on income seems to be well explored in the literature, though several authors have suggested analysing the gains from trade by domestic value added rather than gross exports (Porter, 1985; Kogut 1985). An increase of VS has led to a gap between the domestic value-added content of exports and gross exports. Because ‘‘The assembly of imported materials, which involves a limited input of domestic labour and capital, results in less domestic value-added generation than ordinary exports do’’ (Pei, Oosterhaven and Dietzenbacher, 2012). One of the most famous studies that indicate the discrepancy between gross and value-added trade is the
Apple iPod case study by Dedrick et al. (2008). They find that whereas China exports $144 worth Apple iPod, Chinese value added in it is only $4. Vertical specialization increased tremendously in China and accounted for 51% of total trade in 1996. Therefore measuring the impact of growing exports, without taking value-added into account (Feder, 1982; Akyüz, 2011) may well lead to a bias. Pei et al. (2012) find that the contribution of the change in exports to value-added changes is 32 per cent larger when simple exports are used than when domestic value-added has been taking into account.
In the race of developing countries trying to link into GVCs, very little attention is being paid on measuring additional gains to the country by linking. Banga (2013) is one of the first that looks into the distribution of gains under GVCs in terms of value-added across countries, i.e.
the share of domestic value-added and foreign value-added in a country’s export. This is important, because the value-added differs per activity along the value chain (deepening of the smile curve) and linking into a global value chain can thus have different implications for additional gains created by trade within GVCs. Trade created in GVCs occurs when the production process involves at least two countries and the good crosses at least two international borders. The domestic value-added content of exports created by trade in GVCs refers to the share of domestic intermediates in a country’s export that are subsequently used by other countries to service world export demand. These are the value-added gains of participating in GVCs, as domestic value-added are the payments to production factors, e.g.
salary and thus have a direct effect on income. The domestic value-added is labelled as the forward linkage of a country. Whereas the foreign value-added content of exports created by trade in GVCs is defined as the share of imported intermediates in a country’s export and is labelled as the backward linkage of a country. Foreign value-added in a country’s export does not directly stimulate welfare. Though there might be channels through which a high backward linkage can enhance welfare, for instance, through increased productivity, caused by the linkages that arise between sectors within and across countries. This would suggest that foreign value-added is less welfare enhancing than domestic value-added in a country’s exports.
Banga’s (2013) focus is on the additional gains of linking into a GVC in terms of a country’s 'net value-added' created by trade within GVCs (Banga, 2013). The net value-added gain of trade within GVCs is the domestic value-added minus the foreign value-added of exports of GVC trade. Thus in terms of the forward- and backward linkages; a relative stronger forward linkage than backward linkage indicates a net value-added gain from linking
into GVCs. Subsequently, a relative stronger backward linkage than forward linkage indicates a negative net value-added gain from linking into GVCs (Banga, 2013). Thus a positive net value-added is an additional gain of linking into a GVC and is expected to have a positive effect on welfare.
Hypothesis 1: Foreign value-added in a country’s export is less welfare enhancing relative to domestic value-added in a country’s export.
Hypothesis 2: A net value-added gain has a positive effect on welfare
It is also therefore that many emerging economies have shifted their development strategies from simple export-oriented industrialisation to an emphasis on gaining access to higher value added activities in global value chains as it may not help to trade more without compensating gains linked to production activities and creation of domestic value-added.
The domestic- and foreign value-added content of exports created by trade in GVCs also indicates a country’s integration into the world economy and the fragmentation of supply chains (Banga, 2014). Increasing trade is argued to have a positive effect on productivity (Edwards, 1998; Bonelli, 1992). However, as argued before, trade measured in the conventional way does not imply it would have an impact on economic development of a country as the increasing fragmentation of production makes it essential to delineate the effect of domestic content of exports. This applies, perhaps to a larger extent, to the relationship between trade and productivity also. Firstly, increased fragmentation leads to an increasing amount of linkages across sectors within and across countries. These linkages, through intermediate goods, create a productivity multiplier. Higher productivity leads to more output, which in turn leads to higher productivity (Jones, 2008). Secondly, the second unbundling has lead to increased competition on the task level leading to higher productivity within the different tasks of a production process. Thirdly, developed countries that outsource certain production stages to developing countries cause technology transfers to those countries. These technology transfers help the developing countries to attain higher productivity. Lastly, the theory of upgrading in value chains implies that developing countries can specialize in the tasks that were outsourced by the developed countries through the ‘learning by doing’ effect (Gereffi, 1999; Gereffi et al. 2001; Humphrey and Schmitz, 2002; Holmes and Lopez- Gonzalez, 2011). Therefore, we conclude that both increased foreign- and domestic value-
added created by trade in GVCs have a positive effect on productivity. In terms of the forward- and backward linkage it follows:
Hypothesis 3: The backward- and forward linkages of a country have a positive effect on productivity.
In the literature we find the increasing importance of value-added of trade, because the fragmentation of supply chains has led to a gap between domestic value-added of exports and gross exports. This distinction is important as domestic value-added directly enhance welfare, whereas foreign value added might only enhance welfare indirectly through other channels than value-added. Productivity might be a channel as foreign value-added in a country’s export leads to increased linkages between sectors within and across countries. In the following section we will discuss the methodology adopted to test the above-stated hypotheses.
3. METHODOLOGY
The main theme of this thesis is the relationship between the distribution of gains in terms of domestic and foreign value-added created by trade in GVCs and its impact on income and productivity. In order to analyse this relationship we need to empirically evaluate the distribution of gains within a country, and relate it to the level of development and productivity. One approach followed in the literature to evaluate the distribution of gains created by trade in GVCs is to consider the backward linkage as a proxy for foreign value- added and the forward linkage as a proxy for domestic value-added. An indicator of trade created by GVCs is that the production is carried out in at least two countries and the goods cross at least twice the international border, which is the case for the backward linkage, often known as vertical specialization (HIY, 2001), and for the forward linkage. In our analysis we use an input-output (I/O) approach to measure the backward and forward linkage. I-O tables classify outputs from sectors according to their use, i.e. as an input into another sector’s production or as a final good. The data on trade of the World Input Output Database (WIOD) is in value added and distinguishes between intermediate goods and final goods and is therefore suitable for this research. Though the data set provides information per sector, this research will aggregate the sectors and examine the results on a country level.
3.1 Measurement
3.1.1. Independent variable: Backward linkage and forward linkage
The backward linkage indicates the intermediate import intensity of a country’s exports and it therefore captures the foreign value-added content of exports that is created by trade in GVCs as a share of gross exports. We use the vertical specialization measure developed by Hummels et al. (2001), which has been widely used in the literature (Amador and Cabral, 2009; Breda et al. 2008; Minondo and Rubert, 2007; Zhang and Sun, 2007; Chen and Chang, 2006).
!"#! = ! ! !!"/!!!!!!!!!!
! ! ! ! ! ! ! (1)!
Where the BWLz is the backward linkage of country z with respect to the rest of the world, µ is a 1 x n vector of 1’s, where n is the number of industries, Pij is a n x n matrix of all imports
from industry i by industry j, Xi is the output in industry i, (Pij/Xi) thus presents a matrix of direct import coefficients. [I-A]-1 is the Leontief inverse that allows us to track the direct and indirect imports and Ei are the exports of industry i. The numerator of the equation 1 is thus the direct and indirect imports of intermediate goods in a country that is required for the exports of that country.
A country can be upstream, producing raw materials or intangibles involved at the beginning of the production process, such as research, or be downstream, doing assembly of processed products or providing customer services. Vertical specialization, measured using backward linkages, however, considers only the importance of foreign intermediates backward in the value chain, and therefore, ignores a country’s participation in GVCs by supplying inputs to other countries. This aspect is often considered as the forward linkage (HIY, 2001). The forward linkage reflects the domestic intermediate intensity in a country’s exports, which are subsequently used by other countries to service world export demand, as a share of gross exports. The forward linkage is also first devised by HIY (2001), which they call the VS1 indicator. However, HIY (2001) do not calculate this indicator due to lack of adequate data tracking intermediate goods across destinations. Holmes and Lopez-Gonzalez (2011) provides an extension of the HIY (2001) VS1 indicator, that allows for bilateral trade.
We use this extended indicator of forward linkage in this thesis, which is measured as:!
!"#!" = ! ! !"!",!"/!!" !!!!!!!"
!!" ! ! ! ! ! ! (2)!
!"#!= !!!"#!(!,!,!(..))! ! ! ! ! ! ! (2.1)!
Where FWLzk is the forward linkage of country z with respect to country k, BPiz,jk is a n x n matrix of imports of industry i from country z by industry j in country k. Xik is the output in industry i of country k and [I-A]-1 is the Leontief inverse of country k that allows us to track the direct and indirect imports from country z. Eik are the exports of industry i of country k and Eiz are the exports of industry i of country z. The numerator of equation 2 thus captures the imported intermediates by country k from country z that is used in the exports of country k. FWLz is the forward linkage of country z with respect to the rest of the world, ΣFWLz(k,q,l(..))
is the sum of the forward linkage of country z with respect to all other countries. For a good understanding of the forward linkage it is important to realize that the forward linkage is the mirror flow of the backward linkage. The backward linkage of country K with respect to
country Z is equal to the forward linkage of country Z with respect to country K. Hence the aggregate forward linkage of country Z with the world is the absolute sum of the backward linkage of all countries with respect to country Z divided by the exports of country Z, represented in equation (2.1) (Holmes and Lopez-Gonzalez, 2011). Figure 2 is a schematic representation of the forward- and backward linkage of a country with respect to the rest of the world.
Figure 2: Backward and Forward linkages
Source: Holmes and Lopez-Gonzalez, 2011
3.1.2 Independent variable: Total vertical specialization
Following Holmes and Lopez-Gonzalez (2011), we measure the total degree of participation in a GVC or Total Vertical Specialization (TVS) by the sum of backward- and forward linkages. However there is an issue of double counting; the backward linkage of the US with respect to China may already contain some American inputs that can be identified through the forward linkage. This double counting is small and will therefore not cause severe problems when calculating them separately. Holmes and Lopez-Gonzalez suggest a method to measure TVS avoiding double counting:
!"#! = !"#! 1 − !"#! +!!"#!(1 − !"#!)! ! ! ! (3)!
Where TVSz is the total vertical specialization of country z, BWLz is the backward linkage of country z with respect to the rest of the world and FWLz is the forward linkage of country z to the rest of the world.
3.2 Model
3.2.1 Income model
Once, the backward and forward linkages are measured using input-output tables, to examine the impact of gains created by trade in GVCs on income and productivity, we estimate two different models, where income and productivity are expressed as determined by backward and forward linkages along with other control variables identified in the literature. In the first model we regress the backward- and forward linkage on per capita income to capture the influence of a domestic value-added gain compared to foreign value-added, proxied by the backward- and forward linkages, on the developing stage of a country.
!"(!"#!) = ! !!+ !!!"#!!+ !!!"#!+!!!!!+ !!! ! ! (4)!
Where ln(GDPz) is the natural log of per capita GDP in country z, BWLz is the backward linkage of country z as identified in equation (1) and measures the foreign value-added content of a country’s exports as a share of gross exports, FWLz is the forward linkage of country z as identified in equation (2) and (2.1) and measures the domestic value-added content of a country’s export as a share of gross exports. Xz is a set of control variables that are identified as important determinants of per capita GDP. Following Lederman and Maloney (2003) we expect research and development spending (RD) to have a positive relationship with income, as it increase Total Factor Productivity (TFP). Also FDI of a country increases TFP through knowledge and technical spillovers (Hsiao and Shen, 2003).
Following Barro and Lee (1993,1996) we also expect education to have a positive effect on income, as increased human capital leads to a higher TFP and income. The positive influence of total factor productivity on economic growth is well established in the literature (Baier et al. 2002; Abramovitz, 1956; Solow, 1956; Swan, 1956). Almost all studies on growth include the initial level of income as a control variable, so there appears to be a strong correlation between initial levels of income and current levels of income (Bosworth and Collins, 2003).
Equation (4) will be used to test the effect of backward and forward linkages on per capita income (hypothesis 1). In addition we measure if a country has a net value-added (NVA) gain by subtracting the backward linkage from the forward linkage. We insert the net value-added gain in the regression using a dummy variable (hypothesis 2). When there is a net value-added gain, i.e. the forward linkage minus the backward linkage is positive, the dummy takes the value of 1 and it takes the value of 0 for countries with a negative net value-added
gain. Holmes and Lopez-Gonzalez (2011) argue that the relationship between GDP and the backward- and forward linkage is non-monotonic and they find no significant linear relationship. Following this argument, we also include two squared terms of the backward and forward linkages. In order to control for the possibility of reverse causality between income and backward and forward linkages (see for instance Holmes and Lopez-Gonzalez, 2011) we also perform an instrumental variable (IV) two stage least square (2SLS) regression. We use the lagged backward linkage as an instrument for the backward linkage and do the same for the forward linkage.
3.2.2 Productivity model
The second model is similar to the first one, except that here we test the relationship between the domestic value-added and foreign value-added of a country’s exports and its labour productivity. More specifically, in the second part of this research I will measure the influence of the backward- and forward linkage and of the total vertical specialization on productivity using the following econometric specification:
!"! = ! !!+ !!!"#!!+ !!!"#!+ !!!!+ !!!! ! ! ! (5)!
Where LPz is labour productivity in country z, BWLz and FWLz are the backward- and forward linkage from country z to the rest of the world respectively. Xz is the same set of control variables that are used in the first model and were identified as important determinants of TFP and thus also labour productivity. Further we supplement this result by replacing BWL and FWL by the total vertical specialization variable.
4. DATA
To implement equations (1) to (5), we require data on GDP per capita (GDP per person), labour productivity (GDP per hour), secondary school completion rate (percentage of total population aged 15 and over), net FDI inflows (as a percentage of GDP), research and development expenditures (as a percentage of GDP) and trade data (in value added).
The GDP per capita for 1997-2007 and 1950 were taken from the Conference Board Total Economy Database (TED).1 We use GDP per capita in 1990 International dollar (Geary Khamis). The productivity of a country is also obtained from the same database, and is measured as value added per hour worked in 1990 international dollar (Geary Khamis).
To calculate the forward- and backward linkage we use the World Input-Output Database (WIOD), which provides time-series of input-output tables. The database covers 27 EU countries and 13 other major countries that play an important role in world’s trade patterns (see Appendix). We include all 40 countries in our study, however, because the database covers mainly highly developed and some emerging countries the data will not be normally distributed. The countries in the database do cover more than 85 per cent of global GDP in 2008. In addition, all the excluded countries are represented together as the Rest of the World (ROW). The WIOD provides time-series of world input-output tables from 1995 to 2009. However, we use the data only during 1997-2007, so that we cover 11 years and the crisis years will not influence our results. The database also provides National Input-Output Tables (NIOT), which include information on 35 intra-industry flows within countries. These tables have been used to calculate the backward linkage, whereas to calculate the bilateral trade flows for the forward linkage also the World Input-Output Table (WIOT) is used. The database provides data for 35 industries, according to NACE classification. We will use 34 sectors in our analysis. The industry ‘Private households with employed persons’ was excluded as this industry had only zeros for all but three countries.
The secondary school completion rate (as a percentage of the population of aged 15 and over) was taken from the Barro-Lee dataset. This data set covers data on every 5 years.
We used the years 1995, 2000 and 2005, to cover the years of our interest, 1997-2007. More specifically, 1995 covers the year 1997, 2000 represents 1998-2002 and 2005 represents 2003-2007.
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1*This database has been originally developed by the Groningen Growth and Development Centre (University of Groningen) and transferred to The Conference Board as of 2007.*
The variables R&D and FDI were taken from the World Bank Development Indicator Database. These data combined provides a panel data set that covers 11 years, from 1997- 2007, and 40 countries. Table 1 provides an overview of all variables including the control variables and their scale of measurement, label and expected sign.
Table 1: Overview of all variables
Variables Proxy Label Type Database Expected
sign Stage of development LN of GDP per capita in 1990
International dollars (GK)
LNGDP Dependent Total Economy Database Labour productivity Output per hours worked in
1990 International dollars (GK)
LP Dependent Total Economy Database Domestic value-added Forward Linkage as a
percentage of gross exports
FWL Explanatory WIOD +
Foreign value-added Backward Linkage as a percentage of gross exports
BWL Explanatory WIOD +
Research and development R&D expenditure as a percentage of GDP
RD Control World Bank WDI +
Foreign direct investment Net inflows of FDI as a percentage of GDP
FDI Control World Bank WDI +
Level of education Secondary school completed as a percentage of population aged 15 and over
EDUC Control World Bank WDI +
Initial level of income GDP per capita in 1950 in 1990 International dollars (in hundred thousand GK dollars)
ILI Control Total Economy Database
+
5. RESULTS
5.1 Descriptive statistics
In table 2, we present the descriptive statistics of all the variables. The number of observations is high; none of the variables have less than 400 observations. The results on the backward and forward linkage are very similar, with only a small standard deviation. The standard deviation of the initial level of income variable is high, indicating that the data is spread out. This indicates that in 1950 a large gap existed between high-income countries and low-income countries, in the sample. Due to the rise of many emerging countries, the amount of middle-income countries increases and the gap decreased over time, proven by the small standard deviation of current levels of income, which is in line with the literature. However, the overall data looks good and it will not create a problem in our data.
Table 2: Descriptive statistics
Obs Min Max Mean SD
LNGDP 440 7.4 10.6 9.4 .7
LP 403 3.3 38.3 19.8 9.1
BWL 440 .1 .7 .3 .1
FWL 440 .1 .7 .2 .1
FDI 419 -55.1 74.8 4.9 7.3
EDUC 440 .7 69.8 29.9 13.5
ILI 440 .5 10.9 4.4 3.0
5.2 Econometric issues 5.2.1. Multicollinearity
In order to see whether the independent variables are correlated, so that the problem of autocorrelation might exist, in Table 3, we provide the correlation between all the independent variables. Most variables are less correlated hence posing any serial correlation problem. The highest correlation of 0.5 is between the level of education (EDUC) and research and development expenditures (RD), however it is not too high and will not create a problem for our econometrics.
5.2.2. Heteroskedasticity
The error term should have a constant variance, when the variance is not the same for all observations, heteroskedasticity is present and robust standard errors should be applied. We have tested the data for heteroskedasticity using the White test. The test results show that there is no heteroskedasticity problem at a 5% significance level.
Table 3: Correlation matrix
BWL FWL RD FDI EDUC ILI
BWL 1.0
FWL .2 1.0
RD -.3 .2 1.0
FDI .4 .1 -.1 1.0
EDUC -.1 .2 .5 .1 1.0
ILI .1 .3 .3 0 .2 1.0
5.3 Model selection
To decide if the random or fixed model fits the data best, we have used the Breusch-Pagan test. This test shows that there are random effects in the data, indicating that a random effect model is appropriate. Furthermore we perform the Hausmann test to check for any correlation between the error component and the regressors in the random effects model. This test compares the coefficient estimates from the random- and fixed effects model. When the estimators of both the random- and fixed effect model are consistent, we can conclude that there is no correlation between the error term and the explanatory variables. The result shows that there is no significant difference between the random and fixed model at a 5%
significance level. Therefore we will use the random effect model. We prefer the random effect estimator for two reasons. Firstly, the model gives a more precise estimation. Secondly, the estimates of the effects of variables that are time-invariant, the initial level of income, are permitted.
5.4 Results income model
Regression results for the per capita income equation, using a random effects model, are presented in table 5. In column 1 of Table 5, we have the basic model, only with backward and forward linkage. The fitness of the model is high with an R-square of 0.42, suggesting that it explains 42 per cent of the variation in GDP per capita. The forward linkage is significant and has the expected sign. The backward linkage also has a positive and significant sign, but is larger in magnitude, respectively 1.6 and 0.5. Though we expected the forward linkage to have a larger positive effect on income per capita compared to the backward linkage, therefore these results do not support our first hypothesis. In the second column we test the second hypothesis by adding a dummy variable of the net value-added (NVA), to measure the effect of a net value-added gain. The coefficient of the net value-added gain is negative and significant. This is in contrast with our expectations that a net value- added gain would have a positive effect on income per capita. In column 3 we replace the backward- and forward linkage for their lagged terms. The lagged terms of the backward- and forward linkage have both a positive and significant effect, which indicates a strong relationship between the backward- and forward linkage and income per capita. Also the explanatory of the model increases to .43. In column 4 we add two squared terms of the backward- and forward linkage, as Holmes and Lopez-Gonzalez (2013) suggest there might be a non-monotonic relationship with income. However the backward linkage becomes insignificant and the forward linkage becomes negative, which contradicts the previous models and is not in line with our expectations. In the last two columns we use an IV 2SLS method to control for the possibility of reverse causality between the linkages and income per capita, also suggested by Holmes and Lopez-Gonzalez (2013). The lagged terms are used to instrument the backward- and forward linkage. The results are comparable to the previous model, including the squared terms of the linkages. The coefficient and sign of the backward linkage do not change in both models. Whereas the coefficient of the forward linkage is still significant but changes signs and becomes negative in both models. All control variables are significant and gained the expected sign in all models. The backward- and forward linkage are positive and significant in all models, except when we instrument the linkages with the lagged terms and when we add the squared terms of the linkages, where the forward linkage becomes negative. This suggests that domestic value-added in a country’s export would have a negative effect on welfare, which is highly unlikely. Thus the results might imply that a non- monotonic relationship it not the case and reverse causality is not a problem.
5.5 Results productivity model
We also examine the impact of backward and forward linkage on productivity. The results are presented in Table 6. In column 1, we have again the basic model, only with the backward and forward linkage. The fitness of the model is 0.25 suggesting that it explains 25 per cent of the variation in labour productivity. The backward- and forward linkage have the expected sign and are significant, though the forward linkage is only significant at a 10% significance level. In the second column we replace the BWL and FWL for the lagged terms of the linkages. The backward linkage does not change, however the forward linkage becomes insignificant. In the last column we include the total vertical specialization, which is the sum of the BWL and FWL. The R-square increases to 0.31, suggesting that it explains 31 per cent of the variation in labour productivity. The coefficient of the TVS measurement is high, positive and significant (24.4). This is consistent with the positive backward- and forward linkage. Overall the control variables are again positive and significant, except for FDI, which has the expected sign but is not significant. The backward linkage is again consistent positive and significant in all models, though the forward linkage is less consistent and only significant in the first model. In general, the coefficient of backward linkage is quite stable, positive and significant across models, while that of forward linkage is less significant in most models.
Table 5: Results random effect model on GDP per capita*
LNGDP 1.1 1.2 1.3 1.4 1.5 1.6
BWL 1.641*** 1.483*** 1.464** .917***
FWL .470** .809*** 1.906*** -.726***
NVA !.135***
Lag BWL 1.856***
Lag FWL .305*
BWL2 .162
FWL2 -2.661**
IV BWL .956***
IV FWL -.776***
EDUC .006*** .006*** .006*** .006*** .015*** .015***
ILI .052** .054** .051** .055** .037*** .038***
RD .254*** .249*** .262*** .240*** .293*** .291***
FDI .002** .002* .002** .002* .001 .002
R-squared .42 .39 .43 .40 .57 .42
*, **, *** Indicate a significance level of 1, 5 or 10%, respectively
&
&
Table&6:&Results&random&effect&model&on&labour&productivity&
LP 2.1 2.2 2.3
BWL 22.146***
FWL 4.39*
Lag BWL 22.767***
Lag FWL 3.178
TVS 24.428***
EDUC .091*** .109*** .106***
ILI .652* .616* .574
RD 2.975*** 2.914*** 3.133***
FDI .009 .007 .012
R-squared .25 .25 .31
*, **, *** Indicate a significance level of 1, 5 or 10%, respectively
6. DISCUSSION AND LIMITATIONS 6.1 Discussion
The results of the first model, where we examined the relationship between per capita income and the distribution of gains, did not fully support our expectations. We hypothesized that the forward linkage would have a larger positive effect on per capita income than the backward linkage. However, the results suggest that the backward linkage positively influences income to a larger extent than the forward linkage, respectively 1.6 and 0.5. This indicates that a 1%
increase of foreign value-added due to a link in a GVC as a share of gross exports increases per capita income by 1.6%, while a 1% increase of domestic-value added would only lead to a 0.5% increase in per capita income. These results, though changing slightly in magnitude, hold for all models, except the model including the IV regression. Which might indicate that there is no reverse causality in the model. An explanation of these results might be that we only measure domestic- and foreign value-added of exports that is created by trade in GVCs.
The results do not imply anything about the amount of value-added in gross trade. While especially GVC trade creates linkages across countries and between industries. An increasing forward linkage also implies a deeper integration in the global market, though to a lesser extent compared to the backward linkage. Therefore it might be that the volume and quantity effect of the backward linkage is larger to bring a positive impact on welfare. As indicated in the theory section the backward linkage might have an indirect effect on welfare through a productivity channel, created by the linkages between industries and across countries that arise. This might be particularly the case as we see even higher effects when taking the lagged effect, so perhaps there is a spillover productivity effect. Also our second hypothesis, where we examined the relationship between a net value-added gain and per capita income, is not supported by the results from the first model. We hypothesized that a net value-added gain would have a positive effect on per capita income. However, the net value-added gain has a negative coefficient. The arguments above cannot explain these results. It would then imply that a net value-added gain is only less welfare enhancing than a negative net value-added gain, but not negative. It might be that there is a bias in the data caused by the high level of aggregation. Vertical specialization, or the increasing import of intermediate for processing exports, might be taking place at lower level of disaggregation, as it would require finer specialization. For instance, low-wage countries like China might be specializing in labour intensive processing, which might be masked due to the high aggregation of sectors.
The results of the second model, where we examined the relationship between labour productivity and the forward- and backward linkages, fully support our expectations. We hypothesized that both the forward- and backward linkage would have a positive effect on labour productivity. Increasing backward- and forward linkages leads to more linkages between sectors within and across countries, which eases technology transfers, gains increased competition, allocative efficiency and productivity. This is confirmed for the backward linkage and in a lesser way for the forward linkage, because the forward linkage becomes insignificant in the model taking the lagged effect. Though the magnitude of the effect of the backward linkage, 22.1, is extremely high. The large difference in magnitude of effects between the backward- and forward linkage on productivity could be because the degree of integration is higher for the backward linkage compared to the forward linkage. A high degree of integration creates links between sectors and across countries. As reasoned above these linkages increase productivity through different channels.
As mentioned before, a complete assessment of GVC participation and its impact on income and productivity may be attained by combining backward- and forward linkage, reflecting both the intensity of foreign input use and the role as a supplier of intermediate to foreign producers. Our hypothesis (4) that total vertical specialization has a positive and significant effect on labour productivity is confirmed by our results. This is also consistent with the positive coefficient of the backward- and forward linkage. Thus being part of a value chain and thereby increasing the integration in the world economy leads to a higher productivity.
Earlier we argued that there might be other channels than value-added in a country’s export, through which GVC trade can increases per capita income. Firstly, productivity might be one of the other channels. This argument is supported by the results of the model on labour productivity, where we found a large and positive effect of the backward linkage on productivity. Productivity arises due to the increased linkages between sectors within and across countries and productivity also appears to be an indicator of the income of a country (Jones, 2008). A second channel through which a high backward linkage can increase income is through financial development. Finance is able to facilitate growth through enabling efficient intertemporal allocation of resources, capital accumulation and technological innovation (Beck, 2002; Sachs and Warner, 2005). Other factors, such as trade induced economic reforms, as emphasized by Sachs and Warner (1995) could also be playing an important role here. Integration into the world economy that is caused by increased trade leads to institutional harmonization with regard to trade policy, legal codes, tax systems,
ownership patterns, and other regulatory arrangements. International norms often play a large role in defining the terms of the reform policy. Other authors have stressed numerous other channels through which trade promotes income; efficient resource allocation according to comparative advantage, diffusion of international knowledge through trade, heightened domestic competition as a result of international competition and increased specialization, also often indicated by VS (Young, 1991; Grossman and Helpman, 1991; Eicher, 1993; Lee, 1993).
6.2 Limitations
The level of aggregation of this research is almost unique in the field of value chains, which has always had the focus on industry- and firm level. One of the contributions of this research is that it looks at the general distribution of gains created by trade in GVCs. Though it might also be one of the limitations of this research, as we have no detailed information on industries in itself. Secondly, the period of research ends before the economic crisis starts, while a high degree of integration can have many disadvantages during an economic crisis.
The results on the forward linkage, but especially on the backward linkage may therefore be less positive in the period following the period of this research. The last limitation concerns the calculations of the forward- and backward linkage and the WIOD. The WIOT, which is used to calculate the backward linkage, takes besides the 40 countries also the rest of the world into account. For the forward linkage I had to use both the WIOT and the NIOT, which are national tables. The rest of the world of course does not have a national table and could therefore not be taking into account for the calculations of the forward linkage. This might cause a small bias in the data. The WIOD does include the 40 countries that are most important in world’s trade patterns and therefore the influence of the rest of the world on these calculations will be nihil.
7. CONLUSION
Recent years, the importance of value added in a country’s export has been emphasized in the literature. The first and second unbundling has lead to the fragmentation of supply chains and caused a gap between a country’s exports and the value added in these exports. This research tried to link supply chain trade to the development stage of a country, by examining the influence of the distribution of gains in the value chain on income and productivity. The value chain indicates the value that is added per stage of production. Higher domestic value-added would lead to a higher income. Our results do lend partial support to the expectations that the distributions of gains, i.e. the share of domestic- and foreign value-added in a country’s export have a significant impact on the development of that country. High domestic value- added, created by trade in GVCs does seem to have a positive influence on the income of a country. However, foreign value-added, created by trade in GVCs also seems to have a positive influence on the income of a country, but to a larger extent. Obviously supply chain trade does have a positive impact on income and therefore increasing supply chain trade seems important for attaining higher per capita income.
Clearly this research shows the importance for developing countries to integrate into the world economy by entering a value chain. While the implications of our results for the importance for developing countries to upgrade along the value chain is less evident, evidence for the importance of inter-sectoral upgrading: ‘‘using the knowledge acquired in particular chain functions to move into different sectors’’ (Schmitz, 2004) is present. By moving into different sectors a country is entering into more supply chains and thereby increasing trade and the degree of integration. Far less literature is available on this type of upgrading.
Though the importance of upgrading along the value chain and thereby increasing value added has been well established in the literature, our research shows that it is also important to increase inter-sectoral upgrading. Therefore policy makers in less developed countries should not solely focus on increasing value added in a certain value chain but also on policies that facilitate entering into new value chains. Reforming trade policies, as lowering trade barriers, could attract more trade. Institutional policies can play an important role as well, by improving legal codes, tax systems and ownership patterns a country could, for instance attract international firms.
An implication for further research is to perform this research on a lower level of aggregation.
This could provide insight into the influence of the distribution of gains within a certain global value chain in a specific industry on the income of that industry. It might be that domestic value-added within an industry appears to be more important on a sector level. Also this thesis concluded that on a country level, not just functional upgrading but also inter- sectoral upgrading is important, though little research has been done on this type of upgrading. Therefore research is needed on the possibilities of inter-sectoral upgrading and on which policies can stimulate this type of upgrading. Lastly, it might also be interesting to replicate this research in a period of economic crisis. Because strong integration into the world economy has many disadvantages during an economic crisis and results might be less positive.
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APPENDIX
1. Figure 1: Smile curve
Source: Baldwin and Evenett, 2012
2. List of WIOD countries
European Union: Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Sweden, United Kingdom
North America: Canada, United States Latin America: Brazil, Mexico
Asia and Pacific: China, India, Japan, South Korea, Australia, Taiwan, Turkey, Indonesia, Russia
*
