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Supply Chain Networks and R&D Investments

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Supply Chain Networks and R&D Investments Supply Chain Networks and R&D Investments∗ Hyojin Songy November 2014 Abstract This paper presents the empirical evidence that supply chain networks play a cen- tral role in automotive parts suppliers' Research and Development (R&D) investment specifically in the Korean automotive industry. First, dynamic patterns show how net- work structures, defined by exclusiveness and the identity of partnerships, can affect the level of endogenous sunk costs consistent with Sutton (1991). Second, I test the sta- bility of the supply chain by eigenvalue approaches of the Laplacian matrix. Then the network effects on suppliers' R&D decisions are estimated using panel data techniques. Due to the stability of the supply chain, network measures are time-invariant and thus cannot be estimated by the fixed effects estimator. Instead, the Fixed Effects Filtered estimator (Pesaran and Zhou, 2013) is used which can estimate both time-variant and time-invariant regressors consistently. The results suggest that the identity of partners, exclusiveness and information flows between competitors are important factors in sup- pliers' R&D strategy. In addition, I find that internal R&D and external R&D are substitutes when supplier has an exclusive contract. Keywords: endogenous sunk costs, supply chain management, automobile industry JEL-Classification: L14, L22, L42, L62 ∗My advisor, Simon Wilkie provided invaluable guidance and supports. I would like to thank Cheng Hsiao, Hashem Pesaran, Greys Sosic and Yu-wei Hsieh for their helpful comments. I really appreciate Hangkoo Lee, KIET for allowing me to use invaluable data sets and advice. All remaining errors are my own. yDepartment of Economics, University of Southern California. Email:[email protected] 1 1 Introduction Consisting of more than 30,000 parts, vehicles are one of the most complicated consumer products. As contributions to the supply chain account for roughly three quarters of the content of a vehicle, controlling the quality of vehicles requires supply chain management. To maintain and improve the quality of vehicles, both automobile assemblies' Research and Development (R&D) investments and automotive parts suppliers' R&D are important. This paper investigates the role of supply chain networks in the automotive parts suppliers' R&D investment behavior. One main feature of supply chain is that information1, which is directly connected with R&D investment, flows as products and services move via the linkage. The hypothesis is that automotive parts suppliers have less internal R&D investments if knowledge is transferable from automobile assemblies due to the stability of the supply chain. If suppliers received blueprints from Hyundai based on their long-term partnership, they would have less incentives to invest in R&D to minimize their sunk costs. Before testing the hypothesis by panel data techniques, I investigate the importance of networks structures and test the stability of the supply chain. First, I present dynamic patterns as evidence that networks structures have to be con- sidered to connect theory with empirical data. Sutton (1991)'s endogenous sunk costs model shows that non-monotonic relationships between market size and market concentration can be found if endogenous elements in sunk costs play a role to enhance consumers' willingness to pay. From 1999 to 2010, the market size of the Korean automobile market grew through implementation of various Free Trade Agreements (FTAs).2 and the market size of the auto- motive parts industry increased because it is directly connected to the automobile industry. However, the number of automotive parts suppliers has not changed. There were 881 parts 1Grossman and Helpman (1991) demonstrated that R&D is an input of technology and two main charac- teristics of technology are non-rivality and partial nonexcludability. Partial nonexcludability is that the owner of technological information may not prevent others from using it and this characteristic creates spillovers. Information flows can be interpreted as partial nonexcludability in that sense. 2FTAs are in effect with Chile, Singapore, EFTA, ASEAN, India, Peru, the EU and the U.S. and under negotiation with Canada, Mexico, GCC, Australia, New Zealand, China, Vietnam and Indonesia. The main clauses in contracts pertain to taxes on vehicles and automotive parts. 2 suppliers in 2001 and 898 in 20133. This non-monotonic relationship implies that the Korean automobile market exhibits endogenous sunk costs. However, the Korean automotive parts industry does not seem to be R&D intensive, which is not consistent with Sutton's model. To explain this contradiction, I focus on the stability of the supply chain structure. The stability of supply chains has been selected as the strength of the Korean automobile market (Lee, 2010). The hypothesis is that automotive parts suppliers can have less internal R&D if knowledge is transferable between automotive parts suppliers and automobile assemblies and this relationship is stable over time. I divide automotive parts suppliers into four segments by the level of knowledge trans- ferability. As a proxy for knowledge transferability, two network measures, exclusiveness and the identity of the partnerships, are used. exclusiveness is defined as suppliers who have one partner to trade. The four segments are the exclusive with Hyundai-Kia segment (Exc-HK ), the exclusive with Non Hyundai-Kia segment (Exc-NonHK ), the non-exclusive with Hyundai-Kia segment (NonExc-HK ), the non-exclusive with Non Hyundai-Kia segment (NonExc-NonHK ). As the market size of all four increases, the market concentration moves in different directions for each segment. The data set shows a non-monotonic relationship for the NonExc-HK segment and a monotonic relationship for the NonExc-NonHK segment. If the patterns were consistent with Sutton's insights, suppliers in the NonExc-HK segment would focus more on the quality-sensitive products, and the Nonexc-NonHK segments are more likely to produce homogeneous products. The component-related data allows me to capture the component distribution in each segment. I find the results are consistent with Sutton's insights. Automotive parts suppliers in the NonExc-HK segment produce more technology-oriented products such as steering, and power generating systems. NonExc-NonHK segment are more likely to produce less quality-sensitive products such as lamps, and parts (aluminum). I also find that the exclusive segments produce more design-oriented products such as molding and leather. 3The number of suppliers did not change over time. There were 878 suppliers in 2003 and 794 in 2008. 3 Second, I test the stability of the supply chain networks by the observed networks of the Korean automobile supply chain in two different time periods. Theoretically, the substitute conditions are crucial to guarantee the existence of pair-wise stable allocations (Hatfiled and Milgrom, 2005; Halfield and Kominers 2010). However, the substitutes do not hold for automotive suppliers. Hatfield and Kojima (2008) show that stable allocation may exist even if contracts are not substitutes. A naturally arising question is then whether it is possible to test the stability of networks empirically. The stability of the supply chain is based on the long-term relationship between automotive parts suppliers and automobile assemblies. However, there is no existing literature to test the stability of supply chain networks in the automobile industry due to the inability of data set. The observed network data from two time periods, 2008 and 2013 allow me to measure the distance of two networks and test the stability. Since networks structures can be defined by adjacency matrices, distance measures should be based on the graph spectra. The distance measures, which I use, are the eigenvalue approach of the Laplacian matrix. The Laplacian matrix is defined as the difference between the degree matrix and the adjacency matrix. To test stability, I calculate eigenvalues of the normalized Laplacian matrix of two network structures in 2008 and in 2013 and then derive the distance measures. The measures show that the supply chain networks in the Korean automobile industry are very stable. Third, I estimate the effects of network measures on automotive suppliers' R&D invest- ments using panel data. I define the network measures to capture the information flow between upstream and downstream firms and among upstream firms: the degree with length 1, the degree with length 2, the number of competitors, and exclusiveness. In the panel data analysis, a main concern is the unobservable firm-heterogeneity. If the unobservable firm-heterogeneity were correlated with regressors, the OLS results would be biased and in- consistent. The fixed effects estimator cannot be used here since all the network measures are time-invariant due to the stability of network structures. I applied the Fixed Effects Filtered (FEF) estimator (Pesaran and Zhou, 2013) to estimate both time-variant and time-invariant 4 regressors consistently with the correct covariance matrix. The Hausman and Taylor (HT) estimator is considered but cannot be identified due to the restrictions of the data set. The results show that exclusiveness and the level of price competition play an important role concerning the level of investment committed to R&D. This paper contributes to three strands of the literature. The first contribution addresses the role of endogenous sunk costs on the relationship between market structure and market concentration (Sutton, 1991). Empirical analysis has been done for different industries: newspapers and restaurant industry (Berry and Waldfogel, 2010), the supermarket industry (Ellickson, 2007, 2013) and the mutual fund industry (Gavazza, 2011; Park, 2013). In the U.S. mutual fund industry, Park (2013) divides the exogenous sunk costs market and the endogenous sunk costs market by segments with and without loads and Gavazza (2011) uses the retail and institutional funds industry. Second, this paper contributes to empirical literature on networks. Estimating the net- work effects is related to measurement issues because it is not obvious how networks should be measured. Typically defining the network is simply defining the neighborhood.
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