Reexaming FDI Horizontal Spillover Effects on Productivity Gain of Developing Countries: Theory and Evidence

Reexaming FDI Horizontal Spillover Effects on Productivity Gain of Developing Countries: Theory and Evidence Guo Yanran Osaka University January 31, 2016 Abstract This paper is motivated by the dichotomy between empirical studies and theoretical analyses about the horizontal spillover effects of Foreign Direct Investment (FDI). Numerous empirical studies have explored the linkage between multinational enterprises and indigenous firms. Generally, they got a similar conclusion that for most of the cases horizontal spillover effects are either negative or insignificant especially when the recipient is a developing country. Nevertheless, the preceding theoretical studies, like traditional international trade theories, provide support for the practice of attracting FDI, emphasizing that multinational activities could generate technology/knowledge externalities via facilitating more efficient technology and management practices to domestic firms even through the horizontal linkage.. So far little effort has been made to investigate this contradiction. This paper gains a new insight by thinking about learning cost and offers one explanation for this dichotomy. According to the research of this paper, in most of the existing empirical work, there is a measurement error in TFP that leads to FDI horizontal externalities generating negative effect on productivity gain of host country via the channel of learning investment. However, after taking learning cost into consideration and remeasuring TFP, FDI spillovers through horizontal linkage will augment economic growth of recipient country in the long run by accelerating productivity growth rate of recipient country. 1

1 Introduction According to a report from UNCTAD, the share and volume of foreign direct investment (FDI) inflows to developing countries has been increasing for the last three decades. After a decline of about 4% each year during 1980-1985, FDI to developing countries has substantially risen in the second half of the 1980s at a speed of 17% annually reaching $179 billion in 1999, which is almost 12-fold (World Bank, 2003). Even though a decrease in FDI inflows occurred due to the negative impact of financial crisis in 2008, FDI flows to developing economies remained resilient, and even for the first time ever, exceeded those to developed countries in 2011 (UNCTAD, 2013). When taking a close look at FDI inflows to developing countries, we will readily find that they are mainly concentrated in East Asia and Latin America. Interestingly, the FDI receiving booms of many countries in these two areas coincided with their miraculous achievement in economic development and prosperity, which also occurred in the past three decades 1. This coincidence makes people naturally associate economic growth and productivity gain with inward FDI. Furthermore, impressive claims such as World Bank (1993) saying that [FDI] brings with it considerable benefits: technology transfer, management know-how, and export marketing access. Many developing countries will need to be more effective in attracting FDI flows if they are to close the technology gap with high-income countries, upgrade managerial skills, and develop their export markets, are undoubtedly a strong impetus to policy makers in less developed economies to place attracting FDI high on their agenda. Under this background, detecting the true effect of FDI spillovers is extremely important for developing countries to make appropriate FDI policies. So far, vast amount of theoretical as well as empirical studies have been made on investigating effects of inward FDI. However the results obtained by such studies are not always consensual. Theoretical studies, especially traditional international trade theories, provide support for the practice of attracting FDI, emphasizing that multinational activities could generate technology/knowledge externalities via facilitating more efficient technology and management practices to domestic firms and then benefit the economic growth in recipient country 2. On the other hand, empirical analyses fail to find the positive spillover effects of FDI on developing countries 3. In order to obtain more accurate analysis about the influence of MNEs on productivity gain of domestic firms, the latest empirical work on this issue specifies spillover effects from FDI into horizontal spillovers (the effect that the presence of MNEs has on domestic firms in the same sector), and vertical spillovers (the po- 1 One of the most iconic examples is China. Since 1980s (right after its economic reform in 1978), Chinese per capita income has increased 8-fold with an average growth rate of 9%. Also at the same time, from an almost isolated economy, China has surpassed the US as the world s largest recipient of FDI for the first time since 2003 (UNCTAD, 2012) 2 See Vernon (1966), Glass and Saggi (2002), Markusen and Venables (1999) 3 See Aitken and Harrison (1999), Lopez Cordova (2002), Gorg and Strobl (2005) 2

tential influence from multinational firms on their domestic suppliers or consumers through vertical input-output linkages 4 ). By distinguishing between horizontal and vertical spillovers, several recent studies suggest that spillovers through vertical linkages are always positive whereas the horizontal spillovers are either negative or statistically insignificant 5. Even though empirical analyses have detected desirable vertical spillover effects which are consistent with preceding theoretical literature, the negative horizontal spillovers are in contradiction to the results of theoretical work. Little effort has been made to give a good explanation for such phenomenon. As far as I know, a large proportion of preceding research is concentrated on looking for the presence of productivity externalities without trying to fully understand the mechanism through which they occur. Therefore, this paper contributes to the existing literature by providing a reasonable explanation for the contradiction between theoretical studies and empirical work about FDI horizontal spillover effects. First, this paper constructs a theoretical framework utilizing the concept of learning cost. According to the properties of horizontal and vertical spillovers as well as some anecdotal evidence, one significant difference between intra-industry spillovers and inter-industry spillovers is whether learning cost is incurred or not. Foreign invested firms compete with their domestic rivals that operate in the same sector. In order to grab market share and maintain their competitive advantage, multinationals have an incentive to prevent technology leakage from taking place. As a result, if indigenous firms want to obtain sophisticated technology from MNEs conducting production activity in the same sector, they need to make great efforts. Searching for information, reversed engineering, et cetera, make learning costly and time consuming. How much resources should be devoted to learning is, therefore, essentially an investment decision. On the other hand, multinationals do not have strong incentive to prevent technology diffusion to their upstream suppliers, since they could benefit from the improved performance of their local intermediate input suppliers. Sometimes foreign invested firms even voluntarily help local suppliers to improve production efficiency 6. Therefore, many researchers argue that the learning process in vertical linkages is not that costly or even free 7. 4 Horizontal and vertical spillovers can also be called as intra-industry spillovers and inter-industry spillovers respectively. 5 See Kugler (2001), Lopez-Cordova (2003), Javorcik and Spatareanu (2011) 6 Vertical spillovers actually contain backward as well as forward spillovers. Backward spillovers of FDI refer to the spillover effects generated by foreign affiliates to their local suppliers through supply chains. Forward spillovers occur when domestic firms gain access to intermediate inputs that are less costly or with higher quality produced by MNEs in upstream industries. Many empirical researches show that compared with backward spillovers, even though the estimated coefficients on forward spillovers are positive, they are always statistically insignificant, implying that forward linkages can only generate very weak or even no cheering productivity enhancing impacts on indigenous firms (Harris, Robinson 2002). Therefore, this paper only refers to backward spillovers as vertical spillovers. 7 See more details in Section 2. 3

In light of the idea that local firms incur learning costs in order to learn from MNEs in the same sector, intuitively horizontal spillovers may have a negative effect on the current productivity level, since they need to sacrifice some production resources, like human capital, as the investment to get new technology; but a positive influence in the future, because such efforts for technology acquirement will help the future productivity capacity of domestic firms. By developing a multi-period profit maximizing model to capture the aforementioned scenario, I find that because the preceding empirical studies fail to take learning investment into consideration, there exists a measurement error in TFP. The presence of foreign invested affiliates will boost learning investment made by indigenous firms, and the measurement error in TFP for failure to take learning cost into account will lead to a decrease in productivity. After removing the negative indirect effect of horizontal spillovers via learning investment, the long-run effect of horizontal linkage is positive. Moreover, this paper also contains an empirical study with China s industry-level penal data over a period 2003-2012 in line with the basic logic presented in my theoretical model. Empirical estimation results are consistent with the theoretical study showing that horizontal spillovers exert significantly positive impact on learning investment 8. Then, in the case of domestic firms productivity level, by using the wrong measurement method for TFP as in the preceding empirical literature, estimated coefficient on learning investment is in negative direction, indicating that spillovers through horizontal linkage generate negative effect on productivity of recipient country via learning investment. However estimation results also show that the presence of MNEs will increase future productivity of domestic firms operating in the same sector. From here onwards, the paper is organized as follows: Section 2 considers the preliminary discussion about intra- and inter-industry spillovers from the perspective of learning cost. Theoretical framework in Section 3 offers a basis for empirical model to be discussed. Details about data is in Section 4. Empirical findings in correspondence with theoretical analysis are presented in Section 5. The paper concludes with Section 6. 8 In empirical estimation, I use the personnel engaged in R&D total labor force ratio to proxy learning investment. More details can be found in Section 3 and 4. 4

2 Preliminary Discussion from the Perspective of Learning Cost: Horizontal Spillovers vs. Vertical Spillovers Most standard models of FDI assume multinational firms possess some special assets, usually the knowledge assets such as production technology, improved organizational and managerial skills, and marketing know-how. Multinationals enjoy such technological advantages so that they could compete with domestic firms on the latter s own turf. Knowledge assets are believed to be the secret weapon for MNEs in the competition with indigenous firms who are more familiar with local market, possess complete supply chains and even have honest consumers of their own. When a foreign affiliate enters the local market, it is possible that some of its knowledge will spill over to domestic firms through the interaction with them. This is why the presence of MNEs is always considered to be the source of positive spillovers to domestic firms. 2.1 Horizontal Spillovers Horizontal spillovers occur when the presence of foreign affiliates enhances the productivity of local firms in the same sector. Three main channels for technology dissemination through intraindustry linkages are mentioned in the previous literature review section: imitation/ demonstration, labor mobility and competition. Ideally, if the abovementioned three channels work well, without any doubt, FDI can be viewed as a powerful engine for productivity growth. In the real world things are more complicated. As Kugler (2001) discussed in his paper, multinationals come to a new market all the way from their parent country not for generously helping the local firms with their productivity improvement. Maximizing profit is their sole and ultimate goal. Therefore, it is unlikely to be in the interests of MNEs to voluntarily share their firm specific advantages with domestic rivals. Quite the contrary, they will set up barriers to limit such spillovers as much as possible. To prevent knowledge leakage from taking place, MNEs will not give a comprehensive manual on goods production methods or management procedure to domestic firms. As foreign firms knowledge is often tacit or embodied in a product or process, it is not openly accessible to local firms. The scenario which is more likely to happen is that given the final goods produced by MNEs, local firms ask some workers, which could have been devoted to physical goods production, to figure out how to imitate the new production technique. This process is the so-called reverse engineering. The same principle applies to managerial and organizational innovations too, even though they may be easier to imitate. Taking this into account, imitation may be very costly. The more complex the products or processes are, the higher learning cost it will be. And such learning cost can be seen as a kind of 5

human capital consumption. In summary, technology transfer via intra-industry linkages could generate significant learning cost. And how much resources should be devoted to learning is, therefore, essentially an investment decision. 2.2 Vertical Spillovers Backward spillovers refer to the technology transfer through supply chain from MNEs in the downstream to their upstream local intermediate input suppliers 9. As a number of researchers have argued in their work, MNEs may transfer technology to their suppliers deliberately as a strategy to build up efficient supply chains for overseas operations, since doing this can lower the cost of physical inputs (except for labor) for multinationals 10. The primary reason for foreign invested firms to provide technical assistance to suppliers is for the sake of intermediate products with higher quality and lower prices. Lall (1980) notes that multinationals might transfer knowledge to local firms in upstream in three ways: to help prospective suppliers set up production capacities; to provide assistance to raise the quality of suppliers products and to facilitate innovations; to provide labor training and help in management, organization. From these aforementioned characteristics of vertical linkages, there is unlikely to be a significant amount of learning cost when benefiting from inter-industry spillovers compared with the intraindustry level learning process. A clarification must be made here: I do not deny the existence of basic costs for acquiring new technology no matter from the foreign affiliates in the same sector or in the downstream sector. However from the perspective of learning cost and based on the properties of horizontal, vertical spillovers, it is also rational to conclude that horizontal spillovers may incur substantial amount of learning costs, whereas learning costs in vertical spillovers are comparatively minor or even negligible. In other words, to acquire superior technology from within-industry MNE subsidiaries, local firms are faced with a learning investment decision. This will be the essential assumption in my study. 9 Again in this paper we refer to vertical spillovers as backward spillovers. Note that forward spillovers which occur when domestic firms gain access to new or cheaper intermediate inputs due to the presence of MNEs in the upstream belong to vertical linkages as well. However as backward linkages are more robust and significant in empirical studies, here we only focus on backward spillovers. 10 See Blalock (2002), Javorcik (2004) 6

3 Theoretical Model In this model, domestic firms are allowed to have technology transferred from foreign invested firms either in the same sector or downstream sector. Knowledge transferred through the vertical linkage is denoted by V t, which is an exogenous variable. When foreign consumers increase, knowledge provided for the upstream local suppliers will rise correspondingly. Note that since the amount of V t is determined by MNEs rather than domestic firms themselves, the variable V t can also be considered as a proxy for the presence of foreign invested firms in downstream sector. The adjustable intangible knowledge based asset of each firm is obtained from MNEs operating in the same sector, denoted by H t. It can also be considered as human capital per worker. This variable is endogenous, meaning that domestic firms can choose the quantity and quality of technology provided by multinationals via deciding how much learning investment they are going to make 11. Production technique with larger quantity or higher quality incurs larger learning cost and consequently needs domestic firms to devote more human capital for acquirement. Then the production function is specified as Y t = AV t K α t [L t (1 I t )] β H γ t where A, K t, L t are the basic components of a standard Cobb-Douglas production function. A represents firm specific factors which does not change along with time. K t, L t are capital and labor inputs respectively. Since absorbing new technology through horizontal linkages calls for reverse engineering, local firms need to sacrifice some proportion of the human capital which could have been used to conduct productive activity. That is to say, in order to have access to new technology, some labor forces are taken away from production, resulting in a reduction in labor. I t represents learning investment. More specifically speaking, in this model it is the fraction of labor engaged in acquiring new technology. Thus, L t (1 I t ) is the amount of labor input chosen by each firm to conduct current physical goods production. Consequently, Q t is the net output after taking learning cost into account. In addition, we assume the elasticity of firm specific knowledge asset γ is bigger than β and α + β + γ = 1. The accumulation of firm intangible asset comes from three sources. First, it requires learning investment I t. The economic intuition is straightforward: active learners who are willing to devote more to learning investment will correspondingly acquire more advanced production technique; on the other hand, passive watchers will be left further behind in a changing world. The second source is denoted by G t, indicating the presence of MNEs that carry out productive activity in the same sector. Since it is the multinationals who possess superior technology, without them no matter how 11 The principle idea here is that technology transfer through intra-industry linkages does not take place automatically and it is a costly learning process. 7

much local firms invest there would be no knowledge transfer. Obviously, the accumulation of H t changes along with G t. Furthermore, as a number of theoretical as well as empirical studies have argued, if technic skills of indigenous firms do not reach a certain level, absorbing state of the art technology from MNEs remains out of reach. Hence it is necessary to think about the firm s ability to convert knowledge from the public pool into themselves. The stock of firm s intangible asset H t is utilized to represent its technical capability. Given these three sources, the accumulation equation of firm intangible knowledge based asset is defined as H t+1 = (1 + hi t G t )H t where h is an efficiency parameter. Therefore, in this model when a domestic firm acquires new technology, it bears an opportunity cost. Accumulating human capital H t involves time when goods production cannot be conducted, meanwhile it can augment the firm specific assets of next period leading to higher output in that period. Taking the future profit into account, domestic firms carefully make time allocation, so that the present value of their profit stream can be maximized. Based on the previous discussion, the firm profit maximization problem is defined as Max E o t=0 δ t [Y t w t L t rk t ] (1) s.t. H t+1 = (1 + hi t G t )H t H 0 is given. (2) G t+1 = Φ(G t, ϵ t+1 ) ϵ t+1 Q(ϵ t+1 ) G 0 is given. (3) V t+1 = Ψ(V t, e t+1 ) e t+1 Q(e t+1 ) V 0 is given. (4) where δ is discounting rate, α + β + γ = 1 and γ > β. The future presence of foreign affiliates in the same sector G t+1 depends on the current presence G t and some uncertainty ϵ t+1. ϵ t+1 has a distribution function Q(ϵ t+1 ) which is assumed to be a normal distribution. Besides, convexity is assumed for Φ(G t, ϵ t+1 ), implying that Φ > 0, Φ > 0. Assumptions about the future presence of multinationals in the upstream sector V t+1 are the same as G t+1. Note that w t L t + rk t stands for production cost. w t is real wage which is assumed to grow at a constant rate g. The assumption of small country is introduced, implying that interest rate (r) is 8

not affected by domestic policies. Derive Bellman Equation V (H t, G t, V t ) = max { Y t w t L t rk t + δ } V (H t+1, G t+1, V t+1 ) dq(ϵ t+1 ) dq(e t+1 ) (5) Euler Equation is obtained from first order condition and envelop condition γy t + βy t(1 + hi t G t ) = γy {γyt+1 t + δ + βy t+1(1 + hi t+1 G t+1 ) } (1 + hi t G t ) dq(ϵ t+1 ) dq(e t+1 ) H t (1 I t )hg t H t H t H t+1 (1 I t+1 )hg t+1 H t+1 (6) Transversality condition should also be satisfied Rearrange Euler equation 0 = lim t δe 0 [γy t + βy t(1 + hi t G t ) (1 I t )hg t G 0, V 0 ] (7) 1 β δγh Yt+1 Y t (1 I t )G t H t H t+1 Yt+1 dq(ϵ t+1 ) dq(e t+1 ) + δ Y t (1 I t )G t H t (1 I t+1 )G t+1 H t+1 dq(ϵ t+1 ) dq(e t+1 ) (8) Yt+1 + δh Y t I t+1 (1 I t )G t H t (1 I t+1 )H t+1 dq(ϵ t+1 ) dq(e t+1 ) = 1 By utilizing first order condition and production function, Equation (8) can be rewritten as 1 β γh(1 I t+1) β γ (1 It ) 1 β γ Gt ( 1 ) 1 γ V 1 γ t+1 V dq(e t+1) (9) t + (1 I t+1 ) β γ 1 (1 I t ) 1 β γ Gt ( 1 ) 1 γ V 1 γ t+1 V dq(e 1 t+1) dq(ϵ t+1 ) t G t+1 + h(1 I t+1 ) β γ 1 (1 I t ) 1 β γ It+1 G t ( 1 ) 1 γ V 1 γ t+1 V dq(e t+1) t 1 δ (1 + g) β γ = 0 Denote Equation (9) as F, therefore F = 1 I t β γh(1 I t+1) β β γ (1 γ )(1 I t) β γ Gt ( 1 ) 1 γ V 1 γ t+1 V dq(e t+1) (10) t (1 I t+1 ) β γ 1 (1 β γ )(1 I t) β γ Gt ( 1 ) 1 γ V 1 γ t+1 V dq(e 1 t+1) dq(ϵ t+1 ) t G t+1 h(1 I t+1 ) β γ 1 (1 β γ )(1 I t) β γ It+1 G t ( 1 ) 1 γ V 1 γ t+1 V dq(e t+1) t < 0 9

F = 1 G t β γh(1 I t+1) β γ (1 It ) 1 β 1 γ ( ) 1 γ V 1 γ t+1 V dq(e t+1) (11) t + (1 I t+1 ) β γ 1 (1 I t ) 1 β 1 γ ( ) 1 γ V 1 γ t+1 V dq(e 1 t+1) dq(ϵ t+1 ) t G t+1 (1 I t+1 ) β γ 1 (1 I t ) 1 β γ Gt ( 1 ) 1 γ V 1 γ Φ1 (G t, ϵ t+1 ) dq(ϵ t+1 ) V t + h(1 I t+1 ) β γ 1 (1 I t ) 1 β 1 γ It+1 ) 1 γ V t > 0 t+1 dq(e t+1) V 1 γ t+1 dq(e t+1) G 2 t+1 F { 1 = V t β γh(1 I t+1) β γ (1 It ) 1 β γ Gt + (1 I t+1 ) β γ 1 (1 I t ) 1 β γ Gt } + h(1 I t+1 ) β γ 1 (1 I t ) 1 β γ It+1 G t { < 0 1 γ V 1 γ 1 t V 1 γ t+1 dq(e t+1) + 1 γ V 1 γ t } V 1 γ 1 t+1 Ψ 1(V t, e t+1 ) dq(e t+1 ) 1 G t+1 dq(ϵ t+1 ) (12) Then apply Implicit Function Theorem. Proposition 1 The increase of foreign affiliates will boost the learning investment made by domestic firms operating in the same sector Since the expansion of foreign invested firms increases marginal benefit of contemporaneous learning investment made by local firms, as a result, investment for new production technology in period t moves in the same direction as the presence of MNEs in the same period. Expressed by formula, I t G t = F G t / F I t > 0. Proposition 2 As to vertical linkage, the current presence of foreign invested firms in the downstream sector adversely affects the amount of learning investment made by local firms. According to implicit function theorem, I t V t = F V t / F I t < 0. V is the superior technology transferred through vertical linkage, and it also proxies the presence of MNEs in downstream sector as discussed earlier. There is a reciprocal relationship between learning investment and the contemporaneous presence of downstream multinationals, namely V t. Due to the production function Y t = AV t K α t [L t (1 I t )] β H γ t, when V t increases, it leads to an increase in firm s productivity. Therefore now in order to conduct an extra unit of learning investment, firms need to suffer from larger output losses. Briefly put, the opportunity cost for learning investment becomes higher due to the increase in V t. As a result, V t exerts negative impacts on I t by increasing marginal cost of I t. 10

Since almost all of the preceding empirical analyses ignored learning investment, following their measurement method of TFP, then Y t T F P t = Kt α L β t = AV t (1 I t ) β H γ t (13) Positive vertical spillover effects and negative horizontal spillover effects can be easily proved by differential equation dt F P t dv t = T F P t V t + T F P t I t I t V t > 0 dt F P t dg t = T F P t I t I t G t < 0 This result is consistent with most of the existing empirical work. However, based on the definition of TFP the portion of output not explained by the amount of inputs use in production, it is inappropriate to remove all the labor force when measuring TFP. Because some proportion of labor is devoted into acquiring new technology, which does not belong to production process. Therefore, the right way to measure TFP should be T F P t = Y t K α t [L t (1 I t )] β = AV th γ t (14) From this equation of TFP, positive vertical spillovers can be observed. About horizontal spillovers, Proposition 3 Even though the contemporary presence of multinationals in the same industry (G t ) will not immediately generate any influence on current productivity level of domestic firms, it will benefit the economic growth of recipient country in the long run by raising productivity growth rate. The TFP measurement equation can be expended as T F P t = Y t K α t [L t (1 I t )] β = AV th γ t (15) = AV t [H t 1 (1 + hi t 1 G t 1 )] γ = AV t [H t 2 (1 + hi t 2 G t 2 )(1 + hi t 1 G t 1 )] γ t 1 = AV t H γ 0 (1 + hi τ G τ ) γ τ=0 indicating that the presence of foreign invested firms does not affect the current productivity of domestic firms. However, an increase of foreign affiliates will boost the learning investment made by domestic firms operating in the same sector, and the increased learning investment will expand the 11

future intangible knowledge based asses of domestic firms. As a result, this will benefit productivity of recipient country in the future. More specifically speaking, this mechanism works through the channel of productivity growth rate. g T F Pt = T F P t+1 T F P t T F P t (16) = AV t+1h γ t+1 AV th γ t AV t H γ t = V t+1 V t (1 + hi t+1 Gt + 1) γ 1 dg T F Pt dg t = γ V t+1 V t (1 + hi t Gt) γ 1 hi t > 0 (17) Increase in learning investment will augment domestic firms intangible assets and consequently raise productivity growth rate, dg T F P t dg t > 0. As a result, indigenous firms benefit from a steeper path of productivity growth. Therefore at some point of time in the future, local firms will recover from the initial productivity loss and thereafter the net effect of horizontal spillovers is positive and keeps growing overtime, indicating that the long run effect of FDI horizontal spillovers is positive. This result coincides with the preceding theoretical work. Based on the above discussion, the preceding studies mistakenly include the labor force which is used for learning investment into the production process labor input. Due to the measurement error in TFP, many preceding empirical studies obtain the conclusion that the horizontal spillover effect of FDI is negative. However this result is not true. When taking the proportion of labor used for learning investment into consideration and redefine TFP, it can be found that horizontal spillovers do not affect current productivity but will exert favorable influence on the future productivity. In one word, FDI spillovers through horizontal linkage will augment economic growth of recipient country in the long run. 4 Data Description This section sets out to examine whether the above findings are warranted from the aspect of empirical analysis by utilizing panel data of 37 manufacturing industries over the period from 2003 to 2012 in China. The main data for this research, which span the population of Chinese industrial enterprises 12

above designated size 12, are drawn from China Statistical Yearbook, China Industry Economy Statistical Yearbook, China Statistical Yearbook on Science and Technology and Statistics on Science and Technology Activities of Industrial Enterprises published by China Statistic Press. From 1998, the complete enumeration has been used in industrial enterprises designated above size. It covered about 160000 enterprises in 1998, 280000 enterprises in 2005, 80% of all industrial value-added activities. To eliminate the influences of price fluctuation, the correlative price indexes are utilized. All price indexes used in this paper are drawn from Chinese Statistical Yearbook 13. Our sample spans the period from 2003 to 2012 and includes data for 37 two-digit manufacturing industries each year 14.The data contain information on the output value, value added, labor, original value of fixed assets, net value of fixed assets, personnel engaged in R&D. Table 1 exhibits definitions and summary statistics of the key variable used in subsequent estimation. 4.1 About Learning Investment, Labor and Capital Stock Personnel engaged in R&D sector is the proxy for learning investment level. Since in theoretical framework I t is the ratio between human capital devoted to acquiring new technology and total labor force, I measure this variable by using personnel engaged in R&D to labor ratio. Labor is measured by the total number of employees rather than labor hours because of the data limitation. To measure the real capital stock, I adopt the method which is very close to perpetual inventory method developed by Chow (1993). Perpetual inventory method requires historical information on annual gross investments. Due to data constraint, I can only adopt a similar approach by treating the deflated net value of fixed assets in 2003 (the initial year in this dataset) as the initial real capital stock. This kind of treatment is similar to Lichtenberg (1992). Then I derive the value of newly added fixed assets by taking the difference in original value of fixed assets between the current and preceding year. Given the initial real capital stock, I use the perpetual inventory method to measure capital stock in year t as K t = (1 d)k t 1 + I t where d is the depreciation rate. In the same fashion with Young (2000), I assume that the depreciation rate is 6%. To check robustness, I also apply different depreciation rates, 7%, and 10%, 12 Industrial enterprises above designated size is referred to all the sate-owned, and non-state-owned industrial enterprises with the annual sales revenue of over 5 million Yuan. 13 Producer price index for manufactured goods, purchasing price index for raw material, fuel and power, price index for investment in fixed assets are used to deflate in the current research. 14 There are 39 two-digit manufacturing industries in total. However the problem of missing data is very serious in mining of other ores and recycling and disposal of waste. Hence I exclude these two industries from our dataset. 13

following Liu (2008). 4.2 Proxies for Spillovers Following Aitken and Harrison (1999), the presence of foreign investment is defined by using employment weights. G it = F oreign employment it T otal employment it Each industry is represented by the sub-index i and t indexes the different years in the time frame. In line with notations in preceding theoretical model, G it captures the presence of foreign invested firms in the same sector. While measurement of horizontal FDI is straightforward, the presence of foreign affiliates in downstream is complicated. Following Javorcik (2004), Blalock and Gertler (2008), I define the proxy for it as V it = α ij G jt j ifj i where α ij is the proportion of sector i s output supplied to sector j taken from the input output tables. This variable is intended to capture the extent of potential contacts between domestic suppliers and multinational consumers. The greater the foreign presence in sectors supplied by industry i and the larger the share of intermediates supplied to industries with a multinational presence, the higher the value of α ij. Since relationship between sectors changes over time, multiple input-output tables are used. For V it in 1999, I utilize I O table of 1997; for V it in 2000 to 2003, I O table of 2002 is used; 2005 I O table is for V it in 2004 and 2005; 2007 I O table is for V it in 2006 to 2008; for the recent 4 years V it, we use 2010 I O table. 4.3 Measurement for TFP In order to show that due to the measurement error in TFP, FDI horizontal spillover effects are negative, I apply the same measurement method for TFP as many preceding studies. Hence I remove all the labor force from TFP without considering about learning investment. To tackle the endogeneity bias, I employ a semi-parametric approach suggested by Levinsohn 14

and Petrin (2003) 15. The underlying idea is that there exists a relationship between unobserved productivity on the one hand, and observable intermediate inputs and capital on the other hand. Therefore, intermediate input is used to proxy unobserved productivity shocks. A simplified version of the procedure involves the following assumptions and steps. Consider the Cobb-Douglas production function model: y it = β 0 + β K k it + β L l it + β M m it + ω it + µ it where y it, k it, l it and m it denote the logarithm of output, capital, labor and material inputs, respectively. ω it denotes productivity, and µ it stands for either measurement error or a shock to productivity that is not forecastable during the period in which labor can be adjusted. The difference between ω it andµ it is that ω it is a state variable in the firm s decision problem and thus affects the input demand, while µ it does not. Labor is treated as a variable input and capital is regarded as a fixed input. The fact that input decisions are determined in part by the firm s beliefs about ω it causes simultaneity bias making OLS estimates biased. The insight of the Levinsohn Petrin method is that observable characteristics of the firm can be modeled as a monotonic function of the firm s productivity. Assumption of monotonicity and of strict increase of materials inputs m it in ω it m it = m t (k it, ω it ) makes it possible to invert the intermediate input decision and to express unobserved productivity as a function of observables ω it = s t (k it, m it ) with s t ( ) = m 1 t ( ) Therefore estimation equation to be estimated in the first stage of the procedure is obtained: y it = β 0 + β K k it + β L l it + β M m it + s t (k it, m it ) + µ it Estimate it in OLS, where s t ( ) is approximated by a polynomial of intermediate inputs and capital, we then get the consistent estimates for β L and β K. Assuming first order Markov process on firm dynamics leads to the next stage equation where consistent β M are obtained. Once all the betas are 15 This method is first suggested by Olley and Pakes (1996), Levinsohn and Petrin made some modification later by using intermediate input as proxy for productivity shock. 15

obtained, the log-form TPF (tfp it ) can be computed as tfp it = y it β K k it β L l it β M m it 5 Data Description 5.1 Spillover effects of MNEs in the specific case of China In order to use China s data to prove the validity of the previous theoretical study, it is necessary to check first whether in the specific case of China spillovers through intra-industry linkage is indeed negative and such effects through inter-industry linkage is positive. Table 2 reports the estimation results with tfp it (the log-form TFP) as dependent variable and G it, V it as independent variables. When conducting perpetual inventory method to calculate capital stock, I utilized three different depreciation rate, 6%, 7% and 10%. Accordingly, estimated TFP varies when different capital stock is used. Therefore, the first two columns contain estimation results for TFP based on 6% depreciation rate. In the same vein, column 3 and 4 are for TFP based on 7% depreciation rate and the last two column show the case when TFP is estimated with a 10% depreciation rate. In all three cases, positive and significant coefficient on the proxy for spillovers through backward linkages (V it ) can be found. There is also evidence of negative impacts taking place through horizontal linkage. The G it variable bears negative sign and appears to be statistically significant at 1% level in every case. These estimation results indicates that China can serve as an example to empirically prove the conclusion of the previous theoretical discussion. 5.2 Estimation Results Some essential conclusions are brought forth in preceding theoretical model: when there exists measurement error in TFP, horizontal spillovers could possibly produce negative effects on productivity level via learning investment whereas vertical spillovers exert positive effects. To keep consistency with findings in theoretical work, the empirical estimation is carried out in two steps. 5.2.1 First Step Estimation Learning Investment This section seeks to check the reliability of Proposition 1 and Proposition 2. For the first step, I regress learning investment I t on the current presence of MNEs in the same sector G t and the 16

current extent of multinationals presence V t. Table 3 reports results for the first step estimation. Time dummy is included in all the cases. The first four columns show the case in which G t is the only independent variable. The four columns in the middle represent the case where V t is the single independent variable. The last four columns show the case when including both G t and V t. For each of the case, the first two columns report the results of random effect model and fixed effect model respectively. The following two columns are the cases with robustness. Hausman test favors random effect model in all the three cases. As expected, G t bears a positive and statistically significant coefficient all the time, implying that the presence of MNEs is positively correlated with the contemporaneous learning investment of their local competitors. Even though the sign in front of V t is negative, the explanatory power of V t is not that strong. This may because when the number foreign affiliates in downstream sector increases in this period, domestic firms will form the prediction that there is very likely to increase again in the next period. An increase in V t+1 will affect marginal benefit of learning investment in the current period, lending to a higher I t. Therefore, the negative direct effect of V t on I t and the positive indirect effect may offset each other, resulting in the insignificancy of V t when regressing I t on it. 5.2.2 Productivity Level Effect and Growth Rate Effect This section sets about the second step, looking into linkages between learning investment and productivity level. For preliminary analysis, I regress log-form TFP obtained in Section 4.3 on I t directly. Table 4 presents the results of this estimation. When conducting perpetual inventory method to calculate capital stock, three different depreciation rate were utilized, 6%, 7% and 10%. Accordingly, estimated TFP varies when different capital stock is used. Therefore, the first four columns contain estimation results for TFP based on 6% depreciation rate. In the same vein, column 4 to 8 are for TFP based on 7% depreciation rate and the last four column show the case when TFP is estimated with a 10% depreciation rate. In each of the three cases, the first two columns are basic random effect and fixed effect model, and the following two columns are models with robustness. Time dummy is included in all the cases. No matter how the depreciation rate varies, coefficient on learning investment is always significantly negative. Especially when employing fixed effects model, the explanatory power of learning investment increases: all of them are statistically significant at 1% significance level with negative signs. This result indicate that when there is measurement error in TFP, FDI horizontal spillovers will generate detrimental effect on TFP of recipient countries through the channel of learning in- 17

vestment. With this preliminary analysis about learning investment and productivity, think the measurement equation of TFP again. From the definition equation of total factor productivity suffering from measurement error Y t T F P t = Kt α L β t = AV t (1 I t ) β H γ t (18) = AV t (1 I t ) β H γ t 1 (1 + hi t 1G t 1 ) γ Taking log of both sides, lnt F P t = lna + lnv t + βln(1 I t ) + γlnh t 1 + γln(1 + hi t 1 G t 1 ) (19) Then taking first difference, lnt F P t = lnt F P t lnt F P t 1 (20) = lnv t lnv t 1 + βln(1 I t ) βln(1 I t 1 ) + γln(1 + hi t 1 G t 1 ) lnv t lnv t 1 βi t + βi t 1 + γhi t 1 G t 1 Based on this, the second step estimation equation is lnt F P t = α 0 + α 1 lnv t + α 2 lnv t 1 + α 3 I t + α 4 I t 1 + α 5 I t 1 G t 1 + µ t (21) According to equation (20), α 1 should be positive while α 2 should be negative. Since α 3 is the coefficient on I t, in line with the previous discussion in Section 3, it should be negative. On the contrary, α 4 is supposed to be positive. α 5 represents how the past FDI horizontal spillovers influence future productivity of developing country. Based on Proposition 3. it should be positive. Table 5 reports estimation results for equation (21). Dependent variable is first difference of log-form TFP calculated by 6% depreciation rate 16. Year dummy is only included in the last four columns. For the two cases, with or without time dummy, the first two columns are random effect model and fixed effect model respectively. Hausman test goes for random effect model. The last two columns represent the models accounting for robustness. All the coefficients bear the expected signs. Since the estimated coefficient of I t 1 G t 1 is positive, it proves that horizontal spillovers will benefit future productivity. Hence in the long 16 Since the estimation results are similar in the case of 7% depreciation rate and 10% depreciation rate, hence I only present the case of 6% depreciation rate. 18

run, FDI horizontal spillovers will accelerate economic growth in the recipient country. Despite the fact that most of the variables do not possess too much explanatory power, F-test shows that independent variables of V t and V t 1 are jointly significant. So do I t and I t 1. The three variables V t, V t 1 and I t 1 G t 1 are jointly significant as well. By and large, estimation results in Table 5 support conclusions drawn in the previous theoretical analysis part. Based on equation (20), the coefficients in front of V t and V t 1 are not only opposite but also equal to each other in absolute value. So is the case of I t and I t 1. For a more rigorous study, the following estimation sets out to test whether the sum of coefficients of V t and V t 1 is 0, and whether it also holds in the case of I t and I t 1. According to estimation equation (21), if α 1 and α 2 are opposite in signs and equal in absolute value, then their summation should be 0. Also if it holds for α 3 and α 4, then adding them together will give 0. Let the sum of α 1 and α 2 be θ 1, the sum of α 3 and α 4 be θ 2. Therefore, estimation equation (21) can be rewritten as lnt F P t = α 0 + α 1 (lnv t lnv t 1 ) + θ 1 lnv t 1 + α 3 (I t I t 1 ) + θ 2 I t 1 + +α 5 I t 1 G t 1 + µ t (22) The null hypotheses are θ 1 = 0, θ 2 = 0 respectively. Table 6 exhibits estimation results for equation (22). Dependent variable is the first difference of log-form TFP calculated by 6% depreciation rate. Two situations are considered: with or without time dummy. Each column represents the same model as that in Table 5. For all the cases, the estimated coefficients of lnv i,t 1 and lni i,t 1 are not significant even at 15% significance level, implying that the null hypotheses cannot be rejected. The above-mentioned estimation results prove plausibility of my theoretical model and give insights about the true effect of horizontal spillovers on recipient country s productivity. Like most of the existing empirical analyses, if mistakenly include the labor force used as learning investment into the goods production labor input, then FDI horizontal spillovers will cause decrease in productivity via the conduit of learning investment. On the other hand, when TFP is calculated by taking learning investment into account, then it can be found that even though FDI horizontal spillover does not exert immediate effect on the contemporaneous productivity, it will accelerate economic growth of the host country in the long run. 19

6 Conclusion This paper is built upon the emerging literature about spillover effects generated by foreign direct investment on productivity gain of host countries. Numerous empirical studies have explored the linkage between foreign investment projects and indigenous firms. They get a similar conclusion that, for most of the cases horizontal spillover effects are either negative or insignificant. This result is inconsistent with many of the earlier theoretical studies. Theoretical research in this field offers a myriad of views stating the potential benefits from intra-industry FDI and neither of the views can be ruled out. The big diverging results imply that there may be an erroneous inference about horizontal spillovers. So far little effort has been made to investigate this contradiction. This paper gains a new insight by thinking about learning cost which is neglected by the preceding studies and offers one explanation for this dichotomy between the existing empirical economics literature and international trade theory about FDI horizontal spillovers. Starting from a theoretical framework with the basic assumption of learning costs, this paper stresses two key insights. One is that it is the measurement error in TFP that leads to FDI horizontal externalities generating negative effect on productivity gain of host country via the channel of learning investment. Since in order to acquire advanced technology from foreign subsidiaries in the same sector, indigenous firms need to allocate some labor for this as a kind of learning investment. And as a result, this amount of labor cannot conduct production process. Therefore, failing to notice the existence of learning investment will lead to a measurement error in TFP by mistakenly adding this labor force used for getting new technology to the total production labor amount. With the wrongly calculate TFP, FDI intra-industry spillovers exert negative productivity effects on host country. Nevertheless, after taking these acquiring new technology labor force into consideration and remeasuring TFP, FDI spillovers through horizontal linkage will augment economic growth of recipient country in the long run by accelerating productivity growth rate of recipient country. 20

TABLE 1 Definitions and summary statistics of key variables used. Variable Definition Mean Standard Deviation Y (100 million yuan) K (100 million yuan) L (10000 persons) M (100 million yuan) Value-added in constant price Real values of capital stock The number of employees Intermediate input 2032.106 2056.218 3205.6 5911.39 156.162 131.05 5699.31 6632.403 G The presence of foreign investment in the same sector 0.271 0.176 V The presence of multinationals in downstream sector 0.308 0.339 I Personnel engaged in R&D / total labor force 0.01904 0.01937 Sample Size 370 Note: In actual practice for estimating TFP by Levinsohn-Petrein method, since I utilized the readymade STATA command levpet for the value-added case, therefore in my dataset Y is value added rather than gross output. 21

TABLE 2 Check the horizontal spillover effects and vertical spillover effects in the case of China. lntfp based on d = 6% lntfp based on d = 7% lntfp based on d = 10% VARIABLES (1) (2) (3) (4) (5) (6) Random effects Fixed effects Random effects Fixed effects Random effects Fixed effects G t -0.564*** -0.809*** -0.565*** -0.811*** -0.564*** -0.813*** (0.162) (0.183) (0.162) (0.183) (0.163) (0.184) V t 0.418** 0.548*** 0.426** 0.557*** 0.444** 0.579*** (0.191) (0.201) (0.191) (0.201) (0.192) (0.202) Constant 4.624*** 4.662*** 4.635*** 4.673*** 4.667*** 4.705*** (0.125) (0.0744) (0.125) (0.0745) (0.125) (0.0747) Time Dummy Yes Yes Yes Yes Yes Yes LM Test RE RE RE Hausman Test RE RE RE Observations 370 370 370 370 370 370 R-squared 0.903 0.905 0.910 Number of sector 37 37 37 37 37 37 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 22

TABLE 3 Use I t as the dependent variable, G t and V t as the independent variables. VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Random Fixed Robustness Robustness Random Fixed Robustness Robustness Random Fixed Robustness Robustness Effect Effect RE FE Effect Effect RE FE Effect Effect RE FE G t 0.0153*** 0.0146*** 0.0153*** 0.0146*** 0.0153*** 0.0147*** 0.0153*** 0.0147*** (0.00305) (0.00350) (0.00513) (0.00496) (0.00306) (0.00350) (0.00515) (0.00499) V t -0.000366-0.00224-0.000366-0.00224-0.000393-0.00253-0.000393-0.00253 (0.00241) (0.00299) (0.00229) (0.00283) (0.00231) (0.00291) (0.00227) (0.00277) Constant 0.00329** 0.00355*** 0.00329*** 0.00355** 0.00714*** 0.00794*** 0.00714*** 0.00794*** 0.00340** 0.00428*** 0.00340*** 0.00428** (0.00142) (0.000932) (0.000890) (0.00141) (0.00149) (0.000930) (0.00158) (0.000859) (0.00157) (0.00126) (0.00102) (0.00159) Time dummy Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 350 350 350 350 350 350 350 350 350 350 350 350 R-squared 0.486 0.486 0.457 0.457 0.487 0.487 Number of sector 37 37 37 37 37 37 37 37 37 37 37 37 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 23