Do all firms benefit equally from downstream FDI? The moderating effect of local suppliers capabilities on productivity gains

(2009) 40, 1095 1112 & 2009 Academy of International Business All rights reserved 0047-2506 www.jibs.net Do all firms benefit equally from downstream FDI? The moderating effect of local suppliers capabilities on productivity gains Garrick Blalock and Daniel H Simon Department of Applied Economics and Management, Cornell University, Ithaca, NY, USA Correspondence: G Blalock, Department of Applied Economics and Management, Cornell University, 346 Warren Hall, Ithaca, NY 14853, USA. Tel: þ 1 607 255 0307; Fax: þ 1 607 255 9984; E-mail: garrick.blalock@cornell.edu Abstract Using a panel data set on Indonesian manufacturers from 1988 to 1996, this paper examines how host-country firms capabilities influence their propensity to benefit from downstream foreign direct investment (FDI). We estimate local suppliers productivity response to multinational entry in downstream industries. We find that firms with stronger production capabilities benefit less than others. In contrast, firms with greater absorptive capacity benefit more. These results are largely robust to the inclusion of firm fixed effects, industryyear and region-year fixed effects, and other controls, and indicate the importance of firm capabilities in moderating the effect of downstream FDI on productivity. Finally, we also find some evidence, though less robust, that firms with greater complementary capabilities (proxied by firm size) also benefit more from downstream FDI. (2009) 40, 1095 1112. doi:10.1057/jibs.2009.21 Keywords: foreign direct investment; productivity; Indonesia Received: 22 December 2006 Revised: 16 May 2008 Online publication date: 18 June 2009 INTRODUCTION An important question in international business is whether foreign direct investment (FDI) has positive effects on the host economy. While a substantial literature has assessed the impact of FDI on host-country firms, most of these papers focus on within-industry FDI. The results of these studies have been very mixed, prompting many observers to question the existence of any impact. 1 Reviewing these studies, Rodrik (1999: 37) comments: Today s policy literature is filled with extravagant claims about positive spillovers from FDI, [but] the hard evidence is sobering. Recent research has focused on two possible explanations for the conflicting empirical evidence on FDI. First, several studies have demonstrated that host-country suppliers upstream of the multinational entrants may benefit more from FDI than within-industry rivals. 2 Second, firm capabilities may moderate any effect of FDI on local firms, and thus unobserved differences in capabilities may obscure any measured overall impact of FDI. 3 Our study examines the effects of local supplier firms capabilities on their productivity response to FDI in downstream industries, and thus lies at the intersection of these two research themes. FDI can affect the productivity of upstream firms in two ways. First, it can be a source of new technology (Caves, 1974; Chung,

1096 Downstream FDI and firm capabilities Mitchell, & Yeung, 2003), especially for firms in developing economies (World Bank, 1993). When multinationals enter an emerging market, they bring advanced technology, including managerial practices, production methods, and other tacit and codified know-how by which a firm transforms inputs into a product. Downstream multinationals may transfer technology to upstream firms that they select as local suppliers. This new technology may come in a variety of forms, including employee training, quality control, inventory management, as well as new product and process technology. Second, the new business opportunities created by downstream FDI may influence a local firm s productivity even if it does not become a supplier to an entering multinational. The competitive pressure to win the multinational s business may spur local firms to improve their performance in order to increase the probability of their winning a contract (Caves, 1974; Chung et al., 2003). Two main reasons explain why FDI is likely to have a greater effect on upstream local suppliers than on local competitors. First, whereas multinationals seek to minimize technology leakage to competitors, they have incentives to share their technology with their suppliers in order to improve their productivity. Moreover, to reduce dependency on a single supplier, the multinational may establish relationships with multiple vendors. Second, while the technology gap between foreign and domestic producers may limit within-industry technology transfer, multinationals are likely to procure inputs requiring less sophisticated production techniques, for which local firms are well suited. Reflecting these reasons, Blalock (2002) and Blalock and Gertler (2008) find that FDI has a positive effect on local upstream suppliers in Indonesia. Javorcik (2004) and Jabbour and Mucchielli (2007) obtain similar results in Lithuania and Spain, respectively. Likewise, Chung et al. (2003) find that downstream FDI by Japanese auto manufacturers has a positive effect on upstream suppliers in the US automotive components industry. However, Chung et al. find that US firms that supplied Japanese manufacturers did not increase productivity more than suppliers that did not contract with Japanese buyers. Therefore Chung et al. argue that their results do not provide evidence of technology transfer, but instead reflect competitive pressure created by the Japanese FDI. The analysis by Chung et al. (2003), comparing firms that did and did not supply Japanese manufacturers, raises the important possibility that local suppliers may not all benefit equally from FDI. We build on this notion, and posit that differences in firm capabilities may enable some local firms to benefit more than others from downstream FDI. Drawing from the literature on within-industry FDI, we identify three categories of capabilities that may influence local firms propensity to benefit from downstream FDI: production capabilities; absorptive capacity; and complementary capabilities and resources. Firms with weaker production capabilities may have a stronger incentive to adopt new technology because they can initially chose low-hanging technology with low marginal costs and high marginal returns. In contrast, firms with greater absorptive capacity are probably better able to exploit external knowledge (Cohen & Levinthal, 1990). Moreover, larger firms with greater complementary capabilities and resources may be more likely to win supply contracts from the entering multinationals. We examine the moderating effect of firms capabilities on their propensity to benefit from downstream FDI by estimating local suppliers productivity response to downstream FDI. We use a rich panel data set on Indonesian manufacturing facilities, with information on ownership, inputs, outputs, and a number of managerial attributes. The data set spans from 1988 to 1996, a period of foreign investment liberalization. The Indonesian setting provides two main advantages for our research. First, the liberalization of Indonesia s investment policy introduced substantial changes in foreign investment by industry, region, and year, and we can exploit this variation to identify the effects of FDI. Second, Indonesian firms are generally less technologically advanced than multinational entrants, and the variation in the capabilities of the Indonesian firms is large. This context is ideal for assessing the benefits of FDI, and whether differences in firms capabilities influence their propensity to benefit from FDI. The data set also offers three critical features. First, it provides exceptionally rich descriptions of establishments that allow identification of firm capabilities. Second, the data capture FDI at the level of the multinational firm, which enables us to identify local suppliers in the same region that potentially benefit from FDI. Third, panel data allow the use of within-establishment estimation,

Downstream FDI and firm capabilities 1097 which enables better identification of productivity changes than cross-sectional data. The results suggest that firms capabilities do affect their propensity to benefit from FDI. In particular, firms with stronger production capabilities benefit less than others. In contrast, firms with greater absorptive capacity tend to benefit more from downstream FDI. In addition, the results provide some evidence that larger firms with greater complementary capabilities tend to benefit more from downstream FDI. The results for the production capabilities and absorptive capacity are robust to the inclusion of firm fixed effects, industry-year and region-year fixed effects, and controls for exporting experience and other factors. The results for complementary capabilities are less robust. While the results are consistent with both explanations for the positive effects of FDI on local firms technology transfer and increased competitive pressure the positive moderating effects of local firms absorptive capacity suggests at least some technology transfer. That is, since absorptive capacity is the ability to exploit new knowledge, it should have no effect on productivity in the absence of new knowledge. In addition, Indonesia is a developing country in which local firms tend to be substantially less technologically advanced than the multinational entrants. This differential suggests that multinationals have both the ability and the incentive to transfer technology to their local suppliers. The central contribution of this paper is in providing evidence that firm capabilities can help to explain differential impacts of downstream FDI on host-country firms. Papers by Blalock and Gertler (2008), Javorcik (2004), Jabbour and Mucchielli (2007) and others assess the average effect of downstream FDI, but do not consider any differential impacts. Other papers have assessed the differential impacts of within-industry FDI. The only other paper of which we are aware that has assessed the differential impacts of downstream FDI is Chung et al. (2003), which does so in the context of the United States. This paper is the first to examine the differential impact of downstream FDI in an emerging market context such as Indonesia, where such issues are particularly important to economic development. Demonstrating that firm capabilities moderate the effect of downstream FDI helps policymakers better predict how FDI will influence host-country firms. For example, our results, which show that less productive firms gain more from downstream FDI, suggest that downstream FDI can aid laggard firms in catching up with the leading firms. This result is an attractive feature of downstream FDI for policymakers, particularly those in developing countries, because it directs productivity gains to firms that had previously performed poorly. Similarly, this paper is the first to show that absorptive capacity increases a firm s propensity to benefit from downstream FDI. Again, this result is particularly important for developing countries such as Indonesia, because it adds evidence to the argument that FDI produces technological spillovers. And, because it appears that at least some of the increase in productivity reflects technology transfer, we might expect these improvements to be relatively stable and long-lasting. Moreover, it is also important for managers, as it alters the incentive to invest in certain kinds of capabilities. For example, our results suggest that managers may have a greater incentive to invest in R&D, and in the human capital of their employees, as these investments improve absorptive capacity, which in turn increases the firm s ability to benefit from downstream FDI, and perhaps other sources of new technology as well. Finally, while Chung et al. (2003) find no evidence of technology transfer, our results suggest that multinationals do transfer technology to Indonesian firms. It seems likely that, relative to entering multinationals, US automotive component suppliers were much more technologically advanced than were Indonesian suppliers. Therefore Chung et al. may have found no evidence that Japanese manufacturers transferred technology to US suppliers because of the diminishing marginal utility of the new technology; US firms had relatively little to gain from Japanese technology, while less productive Indonesian firms stood to gain a lot from the new technology. The rest of the paper proceeds as follows. In the following section we examine how a local firm s capabilities influence the impact of downstream FDI on its productivity. We then provide some background on manufacturing and FDI in Indonesia. The subsequent sections detail the data, our identification strategy, and our results, including a variety of robustness checks. In the final section we offer some discussion and concluding remarks. THE INFLUENCE OF FIRM CAPABILITIES ON THE EFFECT OF DOWNSTREAM FDI As noted above, there are two main mechanisms through which downstream FDI may have a

1098 Downstream FDI and firm capabilities positive effect on upstream local firms. First, foreign firms may transfer new technology to local suppliers. This new technology may come in a variety of forms, including employee training, quality control, and inventory management, as well as new product and process technology. Second, downstream FDI may create competitive pressure on local firms to improve their performance in order to win contracts with a downstream multinational. Although the empirical studies mentioned above demonstrate an overall benefit from downstream FDI, it seems likely that not all local suppliers will benefit equally. Firms vary in their capabilities, and these differences may enable some local firms to gain more than others from downstream FDI. Drawing from the literature on within-industry FDI, we identify three capabilities that may influence local firms propensity to benefit from downstream FDI: current production capabilities; absorptive capacity; and complementary capabilities and resources. These capabilities can affect the firm s ability to adopt new technology and/or its incentive to adopt new technology. Moreover, as we discuss below, some of these capabilities may influence a firm s propensity to adopt new technology, whereas in other cases these capabilities may influence the firm s propensity to win the business of the foreign entrants. The firm s current production capabilities may affect its incentive to adopt technology from FDI. Firms that are currently less productive have the most to gain from adopting new technology, as they have the most room for improvement. More specifically, if the technology has diminishing marginal returns, such that the benefits of technology adoption diminish faster than the costs, then firms that are less productive should have the highest expected returns from adoption. In contrast, firms with stronger production capabilities may lack the incentive to alter existing practices. These firms may have already picked the lowhanging technologies that have low cost and high returns, thereby making further cost-effective improvement more difficult. As a result, more productive firms stand to gain less from the new technology that downstream multinationals bring. In addition, some research suggests that more productive firms will gain less from FDI even in the absence of any technology transfer. Chung et al. (2003) provide evidence of adverse selection when foreign firms select local suppliers. In particular, the study shows that less productive US automotive component manufacturers were more likely to sell to Japanese auto manufacturers in the US. The study suggests that this adverse selection stems from differences in incentives. Whereas more productive firms are likely to enjoy strong demand, less productive firms may have idle capacity, and thus have strong incentives to improve so as to win new business from the multinational. Although it may seem counter-intuitive that more productive firms are less likely to benefit from FDI, production capabilities do not influence the firm s ability to learn. Rather, they influence only the firm s incentive to adopt the new technology that FDI brings. We posit that it is a firm s absorptive capacity, the ability to recognize the value of new information, assimilate it, and apply it to commercial ends (Cohen & Levinthal, 1990: 128), that should influence its ability to exploit external knowledge (Zahra & George, 2002). The effect of absorptive capacity can work through three mechanisms. First, firms with greater absorptive capacity will be better able to evaluate new technologies that multinationals bring. Lacking absorptive capacity, firms may not recognize the benefits of these valuable new technologies. Second, absorptive capacity helps the firm to assimilate the new technology (Zahra & George, 2002). Here, prior related knowledge is critical for being able to learn about and understand the multinationals technology (Lane, Salk, & Lyles, 2001). Third, absorptive capacity aids in the process of exploiting the new technology (Zahra & George, 2002). Those firms with greater absorptive capacity are likely to have a greater ability to disseminate internally the information learned from multinationals, and to incorporate the new technology into their existing routines and processes (Szulanski, 1996). By increasing local firms ability to adopt and exploit the technology brought by multinationals, absorptive capacity should increase a firm s ability to benefit from FDI. Reflecting these arguments, Blalock and Gertler (2009) show that absorptive capacity, as measured by both R&D expenditures and human capital, increases a firm s propensity to benefit from within-industry FDI. Similarly, Liu, Siler, Wang, and Wei (2000) find that the positive effect of within-industry FDI increases with the technical capabilities of the local firms, and Kinoshita (2000) finds that more R&D-intensive firms benefit more from within-industry FDI. Along with a firm s absorptive capacity, complementary capabilities and resources marketing know-how, distribution and logistics networks,

Downstream FDI and firm capabilities 1099 supply networks, etc. should also increase its ability to benefit from technology brought by downstream multinationals. Complementary capabilities could increase benefits in two ways. First, complementary capabilities should increase a firm s ability to win business from entering downstream multinationals. All else equal, entering multinationals are more likely to award contracts to those firms with complementary capabilities that facilitate vertical relationships. For example, local firms that lack strong distribution and logistics networks would be less attractive as potential suppliers for a multinational because of the increased transaction costs. Because technology would be transferred through the vertical relationships that the multinational establishes with local suppliers, firms with greater complementary capabilities should thus be in a better position to gain access to the multinationals technology. Second, among those firms that gain access to the multinationals technology, those with greater complementary capabilities should be able to exploit the technology better, and thus enjoy greater increases in productivity. For example, firms with better supply networks might be able to source more easily the necessary inputs to meet the design specifications of the multinational. Generally, larger firms are likely to have more of these complementary capabilities, which should give them an advantage in competing for supply contracts with downstream multinationals. Consistent with this argument, Chung et al. (2003) find that supplier size has a positive effect on the likelihood of establishing a vertical relationship with a downstream multinational. Reflecting these arguments and evidence, we offer the following hypotheses: Hypothesis 1: Current production capabilities weaken the positive effect of downstream FDI on host firm productivity. Hypothesis 2: Absorptive capacity strengthens the positive effect of downstream FDI on host firm productivity. Hypothesis 3: Complementary capabilities (proxied by firm size) strengthen the positive effect of downstream FDI on host firm productivity. INDONESIAN MANUFACTURING AND FOREIGN INVESTMENT POLICY Indonesia s manufacturing sector is an attractive setting for research on FDI and technology adoption for several reasons. First, Indonesia has the fourth largest population in the world, spread across thousands of islands, and abundant labor and natural resources to support a large sample of manufacturing facilities in a full supply chain, from raw materials to intermediate and final goods. Second, rapid and localized industrialization provides variation in manufacturing activity over time and geography. Third, the country s widespread island archipelago geography and generally poor transportation infrastructure create a number of local markets, which support our use of geographical variation in the analysis. Fourth, a number of institutional reforms of investment law, which we discuss briefly below, have dramatically increased the amount of FDI in recent years. The nature and timing of these reforms provide exogenous variation in FDI by region, industry, and time that we exploit in the econometric identification. Fifth, as noted above, Indonesian firms are generally much less technologically advanced than foreign-owned firms, and the Indonesian firms, themselves, vary greatly in the technological capabilities. These differences facilitate identification of technology transfer and enable us to assess whether differences in local firms technological capabilities influence their propensity to benefit from downstream FDI. Finally, Indonesian government agencies employ a number of well-trained statisticians who have collected exceptionally rich manufacturing data for a developing country. Changes in Foreign Investment Policy and Investment Following Initiation of Reforms Following the collapse of world oil prices in the mid-1980s, the Indonesian government began to seek outside investment more actively. From 1986 to 1994 it introduced a number of exemptions to existing regulations, which expanded the sectors and regions open for new foreign investment. In addition, the government increased the maximum allowable foreign equity in manufacturing operations. Finally, in 1994 the government lifted nearly all equity restrictions on foreign investment. 4 The reforms have been accompanied by large increases in both the absolute and the relative value of foreign production in Indonesian manufacturing. For example, the value added by multinational manufacturing in the province of Riau (the closest province to Singapore and home to the Batam bonded zone) is 2335 billion rupiah, or about 10% of the province GDP. Large foreign investment from 1988 to 1996 in chemicals, plastics, electronics

1100 Downstream FDI and firm capabilities assembly, textiles, garments, and footwear dramatically increased the foreign output in many areas. Similarly, in many regions the foreign share of value-added increased dramatically from 1988 to 1996, and accounted for more than half of the total in 1996. DATA Our empirical analysis is based on data from the Republic of Indonesia s Budan Pusat Statistik (BPS), the Central Bureau of Statistics. 5 The primary data are taken from an unpublished annual survey of manufacturing establishments with more than 20 employees conducted by Biro Statistik Industri, the Industrial Statistics Division of BPS. Additional data include the input output table and several input and output price deflators. The remainder of this section describes the data. The principal data set is the Survei Tahunan Perusahaan Industri Pengolahan (SI), the Annual Manufacturing Survey. The SI data set is designed to be a complete annual enumeration of all manufacturing establishments with 20 or more employees from 1975 onward. Depending on the year, the SI includes up to 160 variables covering industrial classification (five-digit ISIC), ownership (public, private, foreign), status of incorporation, assets, asset changes, electricity, fuels, income, output, expenses, investment, labor (head count, education, wages), raw material use, machinery, and other specialized questions. BPS submits a questionnaire annually to all registered manufacturing establishments, and field agents attempt to visit each non-respondent to either encourage compliance or confirm that the establishment has ceased operation. Because field office budgets are determined partly by the number of reporting establishments, agents have some incentive to identify and register new plants. In recent years, over 20,000 factories have been surveyed annually. 6 To derive inter-industry supply chains, we use input output (IO) tables published by BPS in 1990 and 1995. The tables show the value-added of goods and services produced by industry, and how this value is distributed to other industries. The IO tables divide manufacturing activity into 89 industries and allow us to identify downstream industries for each local firm. We define regions as each of Indonesia s 27 provinces. 7 These provinces range in size from Jakarta at 661 square kilometers (roughly half the size of Rhode Island) to West Papua at 420, 540 square kilometers (roughly half the size of Montana). METHODS We want to examine whether the effect of downstream FDI on local firm productivity varies with the local firms capabilities. To do so, we estimate a translog production function with firm and year fixed effects, and a measure of downstream FDI interacted with measures of firm capabilities. The production function controls for input levels and scale effects. The firm fixed effects control for timeinvariant differences across industries and firms, and the year dummies control for economy-wide changes in productivity. Specifically, we specify the firm-level translog production function as follows: ln Y it ¼ b 0 Downstream FDI jrt þ b 1 Capability i Downstream FDI jrt þ b 2 ln K it þb 3 ln L it þ b 4 ln M it þ b 5 ln 2 K it þb 6 ln 2 M it þ b 7 ln 2 M it þ b 8 ln K it ln L it þb 9 ln K it ln M it þ b 10 ln L it ln M it þa i þ g t þ e it ð1þ where Y it, K it, L it, and M it are the amounts of output, capital, labor, and raw materials for firm i in year t, a i is a fixed effect for factory i, and g t is a dummy variable for year t. Downstream_FDI jrt is downstream foreign direct investment, which we derive below, in industry j, region r, and year t, and Capability i Downstream_FDI jrt is a placeholder for the three-firm capability variables, also discussed below, interacted with FDI. Note that we include the interaction of the capabilities with FDI but not the main effect of the capabilities. This is because we employ firm fixed effects, and we do not observe changes in capabilities over time (there is no t subscript on Capability i ). Therefore the main effects of the capabilities on productivity would be dropped from the estimation even if we did include them. Moreover, this approach is consistent with our focus in this paper, which is not whether these capabilities affect productivity, but rather if and how they moderate the effect of FDI on productivity. We initially assume that the residual, e it, is independent and identically distributed, but we later control for simultaneity bias that may arise if it is correlated with other right-hand-side variables. Output, capital, and materials are nominal rupiah values deflated to 1988 rupiah. Labor is the total

Downstream FDI and firm capabilities 1101 number of production and non-production workers. We estimate Eq. (1) on a sample of locally owned firms. Measurement of FDI We measure downstream FDI as the share of the total output of an industry and region that is sold to downstream foreign buyers across all industries. Our measurement choice, detailed further below, is driven in part by data limitations, because we do not know whether or how much each firm sold to foreign-owned buyers. Rather, we infer the amount sold to foreign-owned firms for each industry using the IO tables. If one wanted to measure only technology transfer, one might prefer to use the actual output sold to foreign buyers by each supplier. This would be a better measure of technology transfer if the firms selling to foreign buyers were the only ones that benefited from the technology transfer. However, Pack and Saggi (2001) argue that foreign buyers distribute their technology to many suppliers to avoid being held up by individual suppliers. If the technology becomes widely available, so that all firms might benefit, then a better measure would be the share of all output from the industry-region sold to foreign firms, in which case our average measure would more accurately reflect downstream FDI. In reality, the truth probably lies somewhere in between, that is, that the technology is distributed beyond those firms that sell to foreign buyers, but not to all firms. Nonetheless, our approach allows us to capture the total effect of FDI through both mechanisms: technology transfer and competitive pressure. Our estimator is then best interpreted as the overall effect of an increase in FDI on the average productivity of sellers in a particular industry in a particular region. To measure the share of industry j s output, in region r, that is sold to foreign firms in year t we use the IO tables. These tables indicate the amount that firms in one industry purchase from each of the other industries. From the IO tables we also know the share of output in industry j that is produced by foreign-owned firms, that is, within-industry FDI. If we assume that a firm s share of an industry s demand for a particular input is equal to its output share, then a measure of the share of industry j s output sold to foreign firms is the sum of the output shares purchased by other industries multiplied by the share of foreign output in each purchasing industry. For example, consider three industries: wheat flour milling, pasta production, and baking. Suppose that half of the wheat flour industry s output is purchased by the bakery industry and the other half is purchased by the pasta industry. Further, suppose that the bakery industry has no foreign factories but that foreign factories produce half of the pasta industry output. The calculation of downstream FDI for the flour industry would yield 0.25¼0.5(0.0) þ 0.5(0.5). Formally, Eqs. (2) and (3) express the calculation for industry j, in region r, at time t: Intra Industry FDI jrt P Foreign Output it i2jrt ¼ P Output it i2jrt Downstream FDI jrt ¼ X k a jkt Intra Industry FDI krt ð2þ ð3þ where a jkt is the proportion of industry j output consumed by industry k. Within-industry FDI is our measure of the share of an industry s output in a local market that is produced by foreign-owned firms. Values of a jkt before and including 1990 follow from the 1990 IO table, values of a jkt from 1991 through 1994 are linear interpolations of the 1990 and 1995 IO tables, and values of a jkt from 1995 on are from the 1995 IO table. Recall that a jkt does not have a region r subscript because the IO table is compiled for the entire national economy. The measure of downstream FDI varies by industry and time. Again, the approach appeals to Indonesia s vast island geography and poor inter-region transportation infrastructure in assuming local markets, that is, that intermediate goods output is consumed by firms in the same region. Finally, we note that we calculate downstream FDI at the region level but only have a national IO table. We cannot observe differences in the true input output relationship across regions. We believe that production technologies should not vary a lot by location, so we expect the IO table to be relatively stable across regions; but we cannot be sure. In short, like most empirical studies, ours certainly includes some measurement error. However, we do not believe that this error is correlated with our measure of FDI, or with any of our capabilities measures. Fixed effect estimation tends to exacerbate the attenuation bias from classical

1102 Downstream FDI and firm capabilities measurement error. So, we may be understating the effects of FDI and the interaction terms. Measurement of Capabilities We calculate the firm s current production capabilities using the fixed effect, a i, obtained from estimating a translog production function (like that described in Eq. (1), but excluding FDI and the capabilities interaction terms). Because some industries have overall higher productivity than others, we normalize each firm s production capability around the mean for its industry. Specifically, we subtract the mean of the fixed effects for all firms in the industry from each firm s a i. The production capability is thus a measure of a firm s technical competence relative to its rivals. To avoid the obvious endogeneity of the production capabilities measure that the production capabilities and the firm s current productivity are jointly determined we have divided each firm s duration in the panel into two periods. We calculate the production capabilities measure using only the first 3 years of observations for each firm (pre-intervention period). We then estimate the effect of FDI interacted with the production capabilities measure using only observations after the first 3 years a firm was in the panel (postintervention period). It is important to separate the panel into two periods to avoid confounding prior production capabilities with those acquired from FDI. To see this, consider the extreme case where initially lowproductivity firms enjoyed immediate and massive productivity gains from FDI. Further, suppose that FDI affected no other firms. Since the later (extremely high productivity) years would outweigh the early (low productivity) years, the initially laggard firms would appear highly productive if we calculated productivity over the entire period. We would thus see a positive correlation between production capabilities and FDI, and conclude that FDI benefits firms with greater production capabilities when the exact opposite was true. By separating the panel, we obtain a measure of prior technological competency in the pre-intervention period. We then see how this production capability affects the firm s propensity to benefit from FDI in the post-intervention period. We measure absorptive capacity in two different ways. Cohen and Levinthal (1990) argue that a firm can build absorptive capacity by engaging in organizational activities requiring prior related knowledge, such as basic related skills, a common language, or familiarity with scientific and technical developments in the field. Therefore we use a firm s investment in research and development as one measure of absorptive capacity. Research and development expenditures are available in the 1995 and 1996 surveys. Experience with the SI data indicates that financial reporting is often noisy. Hence, and because the ratio of R&D expenditures to total costs is typically low, we do not distinguish between levels of expenditures. Instead, we use a discrete measure that equals 1 if the firm spent any amount on R&D in either 1995 or 1996, and 0 otherwise. 8 Cohen and Levinthal (1990: 131) also argue that an organization s absorptive capacity will depend on the absorptive capacity of its individual members. Therefore we use the firm s human capital as a second proxy for the firm s absorptive capacity. We measure human capital as the percentage of employees with senior high school or higher degrees. 9 Data on employee educational attainment are available for 1995 and 1996. If firms reported educational attainment in both years, we used the highest percentage. We dropped from the sample firms that did not report educational attainment. It is important to note that we observe these measures of absorptive capacity only in the last two years of the sample. One might be concerned that a firm s decision to invest in absorptive capacity is endogenous to its prior performance: firms that benefit from FDI may also invest in R&D and/or in their human capital. Although we cannot rule out the possibility that investments in both absorptive capacity measures are driven by productivity gains in the early years of the panel, we believe this potential endogeneity is much weaker than that of the production capability measure. Investments in R&D and highly educated employees are assets that are unlikely to change substantially from year to year. Therefore we believe it reasonable to assume that firms with high absorptive capacity retain this attribute over time. Finally, we use firm size, measured by number of employees, as a proxy for the firm s complementary capabilities. As we note above, larger firms are likely to have greater complementary capabilities, including distribution and logistics facilities, a network of suppliers, and marketing capabilities. As with the other capabilities, there is the potential for endogeneity: firms that benefit from FDI may also hire more employees. And, unlike R&D expenditures and educated managers, one might expect the

Downstream FDI and firm capabilities 1103 number of production workers to vary significantly from year to year, given Indonesia s abundance of unskilled labor. Fortunately, because we have annual data on the number of employees in each firm, we are able to use a similar approach to that which we use for the production capabilities measure. We observe firm size (in employees) in the pre-intervention period and assess its effects in the post-intervention period. Our measures of firm capabilities are likely to be correlated with other firm characteristics that also influence firm productivity. We include firm fixed effects to control for these unobserved time-invariant firm characteristics. Because our capabilities measures are also time invariant, their main effects are dropped in the fixed effects specification. Therefore, as mentioned above, the capabilities measures enter the model only through their interactions with downstream FDI. RESULTS Our analysis starts from 1988, the first year for which data on fixed assets are available. To avoid measurement error in price and other uncertainties introduced by the 1997 1998 Asian financial crisis, the last year of analysis is 1996. 10 Because we focus on the impact of FDI on local firms, we include only domestic firms in our estimations. Table 1 displays descriptive statistics for both foreign and domestic firms in 1988. As one would expect in a developing country such as Indonesia, there is a wide gap between foreign and domestic firms. On average, foreign factories are bigger (as measured by value-added, employees, and capital), more capital intensive (as measured by capital per Table 1 Mean statistics for domestic and foreign firms in 1988 Domestic firms Foreign firms log(output) 12.06 15.14 (1.91) (1.76) Employees 139.43 366.89 (539.04) (457.35) log(capital) 11.28 13.97 (2.00) (2.00) log(materials) 11.29 14.31 (2.15) (1.95) Value added per employee 2898.23 16505.16 (7638.85) (20382.73) No. firms 12,618 461 Standard deviations are in parentheses. All monetary values are in thousands of 1988 rupiah. Table 2 Mean statistics for domestic firms capabilities Production capabilities (normalized around zero for each industry) Absorptive capacity (binary indicator of R&D expenditures) Absorptive capacity (share of employees with senior high school degree) Complementary capabilities (log of employees) Standard deviations are in parentheses. 0.00 (0.37) 0.14 (0.35) 0.21 (0.26) 4.13 (1.11) employee), and more productive (as measured by value-added per employee). Although it is difficult to compare productivity levels across industries at different stages in the value chain, these differences between foreign and domestic firms suggest the potential for foreign firms to transfer new technology to local suppliers. Table 2 displays means and standard deviations of the capability measures. Note that the mean of the production capabilities measure is zero, because we normalize the measure around the mean in each industry. A measure of, for example, 0.1 suggests that the firm produces 10% more than the mean firm in the industry given the same inputs. Likewise, a measure of 0.1 would indicate that the firm is 10% less productive than the mean firm. The mean value for our first measure of absorptive capacity, investment in R&D, is 0.14. Because this measure is a binary indicator, the mean value tells us that 14% of firms made some investment in R&D. The mean for the second indicator of absorptive capacity, human capital, is 0.21. This value indicates that 21% of the average firm workforce has completed senior high school. 11 Table 3 reports the correlation matrix for the four capabilities. As one would expect, firms with strong production capabilities also tend to have more absorptive capacity and complementary assets. Table 4 reports our initial set of results. Column 1 of Table 4 shows the results of estimating Eq. (1), excluding the capabilities interactions. The coefficient of 0.086 on downstream FDI suggests that local firm output increases by about 9% (exp (0.086) 1) if downstream FDI moves from one extreme, 0, meaning no foreign firms downstream, to the other extreme, 1, meaning only foreign firms downstream. 12 In practice, changes in downstream FDI of about 0.25 (indicating a 25% increase in foreign firm presence downstream) are common,

1104 Downstream FDI and firm capabilities Table 3 Correlation matrix of firm capabilities Production capabilities (normalized around zero for each industry) Absorptive capacity (binary indicator of R&D expenditures) Absorptive capacity (share of employees with senior high school degree) Absorptive capacity (binary indicator of R&D 0.15 expenditures) Absorptive capacity (share of employees with senior 0.12 0.13 high school degree) Complementary capabilities (log of employees) 0.25 0.29 0.30 Table 4 Estimations by capability Dependent variable: log(output) Model 1 Model 2 Model 3 Model 4 Model 5 Downstream FDI 0.086*** 0.016 0.033 0.078* 0.139 (0.023) (0.035) (0.037) (0.043) (0.128) Within-industry FDI 0.009 0.016 0.015 0.014 0.016 (0.012) (0.017) (0.017) (0.017) (0.017) Production capabilities Downstream FDI 0.346*** (0.108) Absorptive capacity (R&D) Downstream FDI 0.216** (0.098) Absorptive capacity (human capital) Downstream FDI 0.384*** (0.135) Complementary capabilities (firm size) Downstream FDI 0.030 (0.027) Log(labor) 0.442*** 0.512*** 0.512*** 0.513*** 0.513*** (0.019) (0.033) (0.033) (0.033) (0.033) Log(capital) 0.016* 0.015 0.014 0.014 0.014 (0.009) (0.016) (0.016) (0.016) (0.016) Log(materials) 0.691*** 0.704*** 0.704*** 0.704*** 0.704*** (0.008) (0.013) (0.013) (0.013) (0.013) Log(K) log(k) 0.005*** 0.006*** 0.006*** 0.006*** 0.006*** (0.000) (0.001) (0.001) (0.001) (0.001) Log(L) log(l) 0.004* 0.004 0.004 0.004 0.004 (0.002) (0.004) (0.004) (0.004) (0.004) log(m) log(m) 0.006*** 0.005*** 0.005*** 0.005*** 0.005*** (0.000) (0.000) (0.000) (0.000) (0.000) log(k) log(m) 0.010*** 0.008*** 0.008*** 0.008*** 0.008*** (0.001) (0.001) (0.001) (0.001) (0.001) log(k) log(l) 0.016*** 0.015*** 0.015*** 0.015*** 0.015*** (0.002) (0.003) (0.003) (0.003) (0.003) log(l) log(m) 0.024*** 0.037*** 0.037*** 0.037*** 0.037*** (0.001) (0.002) (0.002) (0.002) (0.002) Constant 2.693*** 2.992*** 2.979*** 2.983*** 2.983*** (0.072) (0.133) (0.133) (0.133) (0.133) Within R 2 0.774 0.685 0.685 0.685 0.685 F-stat of FDI variables joint significance 3.71** 1.90 2.99** 0.68 No. of observations 101,269 42,061 42,061 42,061 42,061 No. of establishments 23,047 10,758 10,758 10,758 10,758 Standard errors in parentheses. *po0.10; **po0.05; ***po0.01. Year and firm fixed effects are included but not reported.

Downstream FDI and firm capabilities 1105 in which case the realized productivity gain would be about 2% (exp(0.086 0.25) 1) in an industryregion. However, this estimation does not shed light on whether the effect of downstream FDI varies across firms. We consider this possibility in the next four models of Table 2. To test Hypotheses 1 3, models 2 5 of Table 4 test how each of the three capabilities affects the firm s propensity to benefit from downstream FDI. In each case, the capabilities measure is timeinvariant, and therefore its main effect is dropped from the fixed-effects model. Model 2 displays the results of estimating Eq. (1) with the firm s static production capabilities, as measured by its prior productivity, interacted with FDI. The negative interaction term suggests that firms with greater production capabilities acquire less technology from downstream FDI. For example, a firm with a production capability of 0.1 (meaning it produces 10% more than the average firm given the same inputs) benefits by about 3.4 percentage points (exp( 0.346 0.1) 1) less from FDI. This result provides support for Hypothesis 1, which posits that local firms with greater productive capabilities will benefit less from downstream FDI. Note that the sample size is reduced because we extracted the first 3 years for which each firm was in the panel (the pre-intervention period) to construct the production capability measure. Model 3 displays the results of estimating Eq. (1) with the first measure of absorptive capacity, R&D expenditures, interacted with FDI. The positive interaction term suggests that firms with greater absorptive capacity acquire and/or exploit more technology from FDI. Specifically, firms that report R&D expenditures enjoy about 24 percentage points (exp(0.216) 1) greater productivity benefits from FDI than firms that do not. Model 4 displays the results of estimating Eq. (1) with the other measure of absorptive capacity, the percentage of employees who have completed senior high school, interacted with FDI. As in model 2, the positive interaction term suggests that firms with greater absorptive capacity benefit more from FDI-related technology. As the share of highly educated employees increases by 10%, the productivity benefit of FDI increases by almost four percentage points (exp(0.384 0.1) 1). These results provide support for Hypothesis 2, which posits that local firms with greater absorptive capacity will benefit more from downstream FDI. Finally, in model 5, we interact FDI with the firm s size (log of number of employees), as a measure of complementary capabilities. The positive coefficient is statistically insignificant, failing to provide support for Hypothesis 3, which posits that larger local firms, with greater complementary capabilities, will benefit more from downstream FDI. Finally, note that we include within-industry FDI as a control in all models in Table 4. Because it is statistically insignificant in all models, we drop it in subsequent models. 13 Although the results in Table 4 provide support for all three hypotheses, Table 3 indicates that the capabilities are correlated with each other, making inference difficult when the interactions are included one at a time. Therefore in Table 5 we include all three capabilities interactions jointly. We do so in three different ways. Model 1 includes FDI interacted with absorptive capacity, measured by R&D expenditures, and with production and complementary capabilities. All three interaction terms are statistically significant and in the hypothesized direction. In model 2 we use the alternative measure of absorptive capacity, the firm s human capital, and rerun the full interaction model. Again, all three interaction terms are statistically significant and in the hypothesized direction. Finally, in model 3 we include both measures of absorptive capacity. The results are again very similar, though the firm size interaction effect narrowly fails to achieve statistical significance (t-stat¼1.48). Nonetheless, all of the interaction effects in Table 5 are as large as or larger than they are when they are included separately in Table 4. Consistent with these results, we note that the F-statistics for the joint significance of FDI and the interaction terms are statistically significant in all three models in Table 5, even though two of the four F-statistics are statistically insignificant in Table 4, where we include only one interaction term at a time. Taken together, the results in Tables 4 and 5 provide strong support for Hypotheses 1 and 2, while providing more tentative support for Hypothesis 3. Moreover, the results indicate that correlations among the capabilities measures actually induce a downward bias when we estimate their effects individually, as we do in Table 4. Robustness Tests To further examine the robustness of our results, we consider a variety of robustness tests in Table 6. First, one might be concerned that our results reflect the influence of local firms exporting experience. That is, it might be that the capabilities

1106 Downstream FDI and firm capabilities Table 5 Joint estimations of all capabilities Dependent variable: log(output) Model 1 Model 2 Model 3 Downstream FDI 0.403*** 0.375** 0.347** (0.146) (0.147) (0.148) Production capabilities Downstream FDI 0.537*** 0.521*** 0.541*** (0.123) (0.122) (0.123) Absorptive capacity (R&D) Downstream FDI 0.234** 0.234** (0.101) (0.101) Absorptive capacity (human capital) Downstream FDI 0.396*** 0.395*** (0.145) (0.145) Complementary capabilities(firm size) Downstream FDI 0.078** 0.062* 0.049 (0.032) (0.033) (0.033) log(labor) 0.513*** 0.514*** 0.514*** (0.033) (0.033) (0.033) log(capital) 0.015 0.015 0.014 (0.016) (0.016) (0.016) log(materials) 0.705*** 0.704*** 0.704*** (0.013) (0.013) (0.013) log(k) log(k) 0.006*** 0.006*** 0.006*** (0.001) (0.001) (0.001) log(l) log(l) 0.005 0.004 0.004 (0.004) (0.004) (0.004) log(m) log(m) 0.005*** 0.005*** 0.005*** (0.000) (0.000) (0.000) log(k) log(m) 0.008*** 0.008*** 0.008*** (0.001) (0.001) (0.001) log(k) log(l) 0.015*** 0.015*** 0.015*** (0.003) (0.003) (0.003) log(l) log(m) 0.037*** 0.037*** 0.037*** (0.002) (0.002) (0.002) Constant 2.985*** 2.990*** 2.985*** (0.133) (0.133) (0.133) Within R 2 0.685 0.685 0.685 F-stat of FDI variables joint significance 6.09*** 6.61*** 6.36*** No. of observations 42,061 42,061 42,061 No. of establishments 10,758 10,758 10,758 Standard errors in parentheses. *po0.10; **po0.05; ***po0.01. Year and firm fixed effects are included but not reported. that we measure are correlated with exporting, and that it is actually exporting that moderates the effect of FDI. We consider this possibility in two different ways. In model 1 of Table 6 we exclude all firms that exported during their first 3 years in the sample (the pre-intervention period in which we estimate the production capabilities measure). In this way we preclude the possibility that exporter status is correlated with our capabilities measures. As can be seen, our results are very similar to those that we report in Table 5, although the interaction effect of firm size is smaller and is not statistically significant. In model 2 we control directly for the moderating effect of exporting by including an interaction between exporting and FDI. Once again, our results remain largely unchanged, indicating that exporting does not appear to underlie our results. Similarly, one might also be concerned that our capabilities measures are correlated with unobserved industry characteristics that influence the effect of FDI. For example, it may be that local firms in high-tech industries benefit more from FDI and tend to have higher absorptive capacity. To consider this possibility, in model 3 we allow the effect of FDI to vary by industry. Specifically, we interact FDI with indicator variables for each of the 10 two-digit ISIC code industries. The results are again