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The present study usesfirm survey data of 1033 manufacturingfirms operating in Ethiopia in 2011 to examine the impact of Chinese outbound direct investment on the productivity of domesticfirms. Particularly, we attempt to answer two questions. Firstly, are Chineseowned (henceforth foreign)firms more productive than local ones? Secondly, does the presence of foreignfirms generate technology spillovers on domesticfirms operating in the same industry? Our empirical results show that foreignfirms are more productive and that their presence has different spillover effects on the productivity of domesticfirms. In particular, wefind that domesticfirms with higher absorptive capacity experience positive spillovers, while those with low absorptive capacity witness negative spillover. We alsofind that smallfirms and nonexporting firms benefit more from spillovers than do other types of domesticfirms. In this study, instrumental variables are used to address the potential endogeneity between foreignfirm presence and domesticfirm productivity
China Economic Review 33 (2015) 35–49 Contents lists available at ScienceDirect China Economic Review Technology spillovers from Chinese outward direct investment: The case of Ethiopia Mebratu SEYOUM a,1, Renshui WU b,⁎, Li YANG b,2 a b Department of International Economics and Business, School of Economics, Xiamen University, Xiamen 361005, Fujian, China The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen 361005, Fujian, China a r t i c l e i n f o Article history: Received 28 January 2014 Received in revised form 10 November 2014 Accepted January 2015 Available online January 2015 JEL classification: F21 D24 O33 L60 Keywords: Absorptive capacity China Ethiopia Outward direct investment Spillovers a b s t r a c t The present study uses firm survey data of 1033 manufacturing firms operating in Ethiopia in 2011 to examine the impact of Chinese outbound direct investment on the productivity of domestic firms Particularly, we attempt to answer two questions Firstly, are Chinese-owned (henceforth foreign) firms more productive than local ones? Secondly, does the presence of foreign firms generate technology spillovers on domestic firms operating in the same industry? Our empirical results show that foreign firms are more productive and that their presence has different spillover effects on the productivity of domestic firms In particular, we find that domestic firms with higher absorptive capacity experience positive spillovers, while those with low absorptive capacity witness negative spillover We also find that small firms and non-exporting firms benefit more from spillovers than other types of domestic firms In this study, instrumental variables are used to address the potential endogeneity between foreign firm presence and domestic firm productivity © 2015 Elsevier Inc All rights reserved Introduction Over the past decade, though insignificant in global terms,3 China's outward direct investment (ODI) flows to Africa have increased rapidly The increase has generated interest and concern over the effects of China's ODI on developing Sub-Saharan host economies Some argue that Chinese ODI provides an alternative source of capital, technology and skills and that it has been instrumental in fulfilling financial and technological gaps for Africa (Brautigam, 2009; Foster, Butterfield, Chuan, & Pushak, 2008) On the negative side, some contend that the primary objective of Chinese ODI in Africa is to find resources, and markets for their products where it drives African countries to resource-based economies and crowds-out local industries (Kaplinsky, 2008; Kaplinsky & Morris, 2009) China's ODI in Africa has generated considerable attention for several reasons One reason is the rapid pace at which China's ODI has risen and expanded in Africa.4 Second, small and medium private Chinese firms have recently become prominent investors in African manufacturing sector that there are uncertainties about the impact of their activities on the African economies where the investment ⁎ Corresponding author Tel.: +86 18959215421 E-mail addresses: msmebratu@gmail.com (M Seyoum), renshui.wu@gmail.com (R Wu), Yangli1997@hotmail.com (L Yang) Tel.: +86 13599515882 Tel.: +1 703 473 7308 The largest part of Chinese ODI goes to Asia, Latin America and Europe, respectively According to the Ministry of Commerce of China (MOC), China's ODI stock in Africa rose from 900 million dollars in 2003 to nearly 16 billion dollar in 2011 http://dx.doi.org/10.1016/j.chieco.2015.01.005 1043-951X/© 2015 Elsevier Inc All rights reserved 36 M Seyoum et al / China Economic Review 33 (2015) 35–49 is made.5 The third reason relates to the way in which the Chinese are perceived to invest; there is a belief that Chinese firms behave differently from other firms, either because the Chinese state is often behind ODI or because they have a different culture and institutional structure (Buckley et al., 2007) The purpose of this study is to examine the impact of Chinese manufacturing ODI on the productivity of the Ethiopian manufacturing sector More specifically, we analyse two issues The first issue is to examine whether foreign-owned firms exhibit higher levels of productivity than domestic ones Then, we investigate whether the productivity of domestic firms is correlated with the presence of foreign firms in the same industry.6 Identifying such effects would be consistent with the existence of intra-industry or horizontal technology transfer spillovers We further explore whether productivity gains stemming from horizontal spillovers vary with domestic firms' characteristics The study focuses on Ethiopia for two reasons First, Ethiopia has received a substantial amount of Chinese ODI in manufacturing, and is ranked among the top four recipient countries in Africa.7 And over the past few years, China has emerged as the largest source of FDI in Ethiopia in terms of number of investment projects, with over 970 projects as of end-2012 (Ethiopian Investment Agency [EIA] data) Second, to the best of our knowledge, no attempt has so far been made to systematically investigate the impact of Chinese manufacturing FDI on developing Sub-Saharan host economies.8 Using a more disaggregated dataset for Chinese FDI in manufacturing, this empirical study is, to our knowledge, the first to present a detailed analysis of the impact of Chinese FDI on a host country in Sub-Saharan economies The analysis is based on 1033 manufacturing firms operating in Ethiopia in 2011 The data come from the survey of large and medium scale manufacturing industries conducted by the Ethiopian Central Statistical Agency (CSA), Ministry of Finance and Economic Development (2012) The survey data covers firms in the formal manufacturing sector, which employ 10 persons and more and use power driven machinery The dataset contains detailed information on the basic information of the establishment, ownership structure, foreign equity participation, output, assets, employment, wages, input costs, location and other information The findings can be summarized as follows We find that foreign-owned firms are significantly more productive than their local counterparts, suggesting that there are direct benefits from Chinese FDI With regard to spillover effects, we find that Chinese FDI has different spillover effects on domestic firms dependent upon their characteristics More specifically, our empirical results reveal that: (i) domestic firms with high absorptive capacity (smaller technology gap with foreign firms) experience positive spillovers, while those with low absorptive capacity witness negative spillovers; (ii) small firms and non-exporting firms benefit more from spillovers than other types of domestic firms; and (iii) skilled labour of domestic firms does not enhance their capacity to attract FDI spillovers The remainder of the paper is organized as follows Section presents a brief overview of Chinese ODI flows to Ethiopia Section explains the theoretical framework for the role of foreign ownership on the productivity and technology spillovers Section introduces the econometric model, data and variable definitions In Section we present our regression results, while Section concludes Overview of Chinese ODI flows to Ethiopia China's ODI flows to the African continent have grown rapidly over the past decade and Ethiopia is a good example of this trend Fig shows the trends in China's ODI flows to Ethiopia from 2004 to 2010, using the Ministry of Commerce of China (MOC) 2010 Statistical Bulletin of China's Outward Foreign Direct Investment China's ODI flows to Ethiopia rose from virtually zero in 2004, reaching 24 million dollars in 2006 to a peak of 73.4 million dollars in 2009 before it had declined to 58.5 million dollars in 2010 According to MOC data, the stock of ODI from China to Ethiopia in 2010 was 368 million dollars The official statistics reported by MOC (Fig 1) seem to understate the true investment volume of Chinese ODI in Ethiopia According to the EIA figures, the accumulated stock of Chinese ODI in Ethiopia stood at nearly two billion dollars at the end of 2010.9 As shown in Table 1, the amount of annual ODI flows from China to Ethiopia has been increasing rapidly, albeit from a low base: from 181.71 million dollars between 2002 and 2004, to 414.29 million dollars during the period 2005–2007 and rose further to over one billion dollars between 2008 and 2010 Similarly, the total number Chinese investment projects in Ethiopia reached 944 projects in 2010, a whopping 782.24% increase from the period 2002–2004 According to EIA data, out of the 944 investment projects, 632 projects (66%) are engaged in the manufacturing sector Fig looks at China's ODI flows as a share of total FDI inflows for the past decade There is a visible trend exhibiting that Chinese ODI has been growing very fast, and that it is taking over the principal position in new FDI attracted to the country In terms of the number of investment projects, China's contribution rose from 11% in the period of 2000–2005, reaching 29% in 2007 to a peak of 32% in 2008 During the period 2006–2011, China took the top place contributing 25% of the total FDI attracted to Ethiopia According to Shen (2013) estimates, small and medium private Chinese enterprises, predominantly concentrated in manufacturing and service industries, accounted for 55% of all Chinese investment projects in Africa by the end of 2011 In our case, sectors are defined at a more aggregate level, hence some intra-industry spillovers may, in reality, capture vertical spillovers (see Table 2) According to Shen (2013) estimates, based on data from MOC and African host governments, Nigeria, South Africa, Zambia, Ethiopia and Ghana (in that order) are the top five Chinese ODI recipient countries in Sub-Saharan Africa We use “ODI” and “FDI” interchangeably It is fair to argue that the EIA data captures Chinese ODI in Ethiopia more comprehensively and accurately than the official data reported by MOC, simply because the EIA data covers all ODI projects, large or small, with the latter less likely to be captured or registered by MOC data M Seyoum et al / China Economic Review 33 (2015) 35–49 37 Table China's outward FDI flows to Ethiopia, 2002–2010 Source: authors’ computations based on data from the Ethiopian Investment Agency (EIA) Years Number of projects Value of projects (US$ millions) 2002–2004 2005–2007 2008–2010 Total 107 598 239 944 181.71 414.29 1279.0 1875 FDI and productivity spillovers: literature review FDI is considered to influence the productivity and competitiveness of host-country economic activities for at least two quite different reasons First, multinational firms bring to their host-country superior productive assets such as technological know-how, managerial and entrepreneurial skills, and marketing techniques Second, there are spillovers of technology transfer from foreign firms, which affect the productivity of domestic firms Spillovers occur when foreign firms cannot fully internalize all quasi-rents as a result of their productive activities 3.1 Are foreign firms more productive than local ones? The orthodox literature suggests that foreign firms undertake FDI to exploit firm-specific ownership advantages that arise from the possession of intangible assets such as better access to advanced technology inputs and more efficient organization in production and distribution In addition, they tend to operate on a lower (production and distribution) cost curve than domestic firms, which allows them to compute successfully with domestic firms who have intimate knowledge of local market conditions including customs, consumer preferences, legal environment and business practices If so, other things being equal, we expect foreign firms to be more productive than domestic ones Consonant with the conventional literature, a number of studies conducted in Sub-Saharan African countries confirm that foreign firms are more productive than domestic firms A recent Africa investor survey conducted by the United Nations Industrial Development Organization (UNIDO) in 2011 reveals that, on average for all surveyed countries, foreign firms are 11% more productive than domestic ones.10 For country-specific studies in Africa, Melese and Waldkirch (2011) use a sample of 6574 manufacturing firms for the period 2002 to 2009 to show that foreign-owned firms are more productive than domestically owned firms in Ethiopia In Kenya, Gachino (2013) employs firm survey data for 180 manufacturing firms, and finds similar productivity advantages in favour foreign firms Thus, the first issue examined in this paper is whether Chinese-owned firms exhibit significantly higher levels of productivity than domestic firms in Ethiopia If so, the presence of Chinese firms can be expected to impact positively on the host-country, because their higher productivity levels help productivity in their respective industries to shift upward, which is also reflected in the aggregate productivity of the host country (Caves, 1996) 3.2 Are there any spillover gains from FDI? There is a large literature on the existence and direction of spillovers of technology transfer from FDI The existence of spillovers is based on two assumptions The first assumption lies on the fact that multinational firms have better access to advanced technology and other advantages and have, therefore, higher levels of productivity (Caves, 1996) The second is related to the fact that technology-based assets transferred to the host economy have the characteristics of a public good (Romer, 1990) Dissemination and appropriation of their qualities may take place in the form of unintentional transmission or intentional transfer from multinational to local firms through demonstration effects, worker mobility or direct linkage with local agents Furthermore, multinational firms may inject a higher level of competitive intensity in the host-country, which may produce additional spillover benefits Foreign firms typically enter markets characterized by high entry barriers and consequently strong monopolistic distortions Their entry thus may reduce monopolistic distortions and raise the productivity of local agents by improving resource allocations in the host country (Caves, 1996) On the other hand, negative effects may arise from competition if foreign firms, which happen to produce at a lower marginal cost, gain market shares from local firms Aitken and Harrison (1999) argue that even if local firms benefit from technology spillovers, productivity of local firms may still decline (rise in average cost) if the output demanded from them is reduced as foreign firms take a large share of the market However, local firms may also respond to foreign competition by making better use of existing resources or investing in new technologies in order to maintain their market share and, therefore, drive a higher level of productivity on the process (Blomstrom & Kokko, 1998) Technical efficiency in the industry is thus improved Spillovers of technology transfer from FDI also depend on the characteristics of domestic firms, which shape absorptive capacity to internalize spillovers (Farole & Winkler, 2012) Domestic firms' absorptive capacity is their ability to recognize, assimilate and apply 10 The survey was carried out in Burkina Faso, Burundi, Cameroon, Cape Verde, Côte d'Ivoire, Ethiopia, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mali, Mozambique, Niger, Nigeria, Rwanda, Senegal, Tanzania, Uganda, and Zambia 38 M Seyoum et al / China Economic Review 33 (2015) 35–49 Fig China's outward FDI flows to Ethiopia, 2004–2010 Source: MOFCOM, 2010 Statistical bulletin of China's outward foreign direct investment outside knowledge to commercial ends (Cohen & Levinthal, 1989) It is argued that where local firms are constrained by their limited absorptive capacity, the expected productivity spillover benefits of FDI either not show up or are negative (the latter case may occur if the entry or presence of foreign firms shrinks local firms' market share) Conversely, the greater the local firms' capacity to absorb new technology and processes, the more productivity spillover benefits The factors that influence local firms' absorptive capacity include (i) technology gap between foreign and local firms; (ii) local firm's size; (iii) exporting behaviour of domestic firms; and (iv) share of skilled labour at the firm level The technology gap between foreign and local firms has been acknowledged as one of the most important moderating factors for the realization of FDI spillover potential (Findlay, 1978; Wang & Blomstrom, 1992; Kokko, Tanzani, & Zejan, 1996, among others) However, studies on the role of technology gap for spillover effects of FDI conflict For instance, some argue that a large technological gap between foreign and local firms should enhance positive spillovers, because the potential for improvement is large (Sjoholm, 1999; Wang & Blomstrom, 1992) Others argue that domestic firms need some minimum amount of technical capacity to be able to benefit from spillovers and thus smaller technology gap between foreign and domestic firm results in larger spillovers (Blomstrom, Globerman, & Kokko, 1999; Blomstrom & Kokko, 1998) While Blalock and Gertler (2009) suggest that if the technology gap between foreign and local firms is too large or too small productivity spillover benefits may not be realized Despite many attempts by others (cf Hale & Long, 2006; Kokko, 1994; Sjoholm, 1999) to examine the role of technological gap on FDI spillovers, results are inconclusive Kokko (1994) employs cross-section industry level data for Mexico to show that productivity spillovers are smaller because of high technology gap between foreign and local firms Similarly, Kokko et al (1996) extend Kokko's (1994) analysis for Mexico to examine the role of technology gap on productivity spillovers, using cross-section firm-level data for Uruguay They find evidence supporting the notion that smaller technology gap between foreign and local firms enhances productivity spillovers Hale and Long (2006) observe the same phenomenon in China On the contrary, Sjoholm (1999), using cross-section data for 8086 firms in Indonesia, shows that spillovers from FDI are found in sectors with a high degree of competition and less advanced technology Putting it differently, he finds that the larger the technology gaps between domestic and foreign firms, the larger the spillovers—it seems that larger technology gap leaves more ground for improvement Another important characteristic that affects the extent and nature of FDI spillovers is firm size (Dimelis & Louri, 2004; Sinani & Meyer, 2004) Larger domestic firms tend to have stronger capacity to compete with foreign firms and imitate their technology and management practices (Crespo & Fontoura, 2007) Moreover, they are better positioned to spread fixed costs of R&D over a larger sales base and hence are able to exploit economies of scale and scope in R&D activities (Cohen & Levinthal, 1989) They also pay better wages and therefore find it easier to attract workers employed by foreign firms (Markusen & Trofimenko, 2007) On the other hand, larger domestic firms may be competitive and hence operating at their maximum efficiency; therefore, the scope for technology transfer from foreign firms could be limited (Dimelis & Louri, 2004) Others also suggest that smaller firms may benefit more from multinational firms if, for example, they are endowed with higher proportion of skilled labour (Sinani & Meyer, 2004); moreover, they may operate at suboptimal efficiency level lacking the necessary technology and knowledge of productive assets and therefore can enjoy higher spillover benefits from FDI presence (Dimelis & Louri, 2004) Aitken and Harrison (1999) employ firm level panel data to assess the effects of foreign equity participation on the productivity of domestic firms in Venezuela They find significant negative spillovers from FDI for small enterprises only (less than 50 employees), which they attributed to market-stealing effect Likewise, the study by Boly, Coniglio, Prota, and Seric (2013) uses firm level data for 19 Sub-Saharan economies to show that large and young firms enjoy more positive spillovers than other type of firms In contrast, based on a sample of 3742 manufacturing firms operating in 1997 in Greece, Dimelis and Louri (2004) find that productivity M Seyoum et al / China Economic Review 33 (2015) 35–49 39 Fig The percentage of Chinese investment of total FDI Source: Shen (2013), based on EIA spillovers accrue mostly for small local firms Sinani and Meyer (2004) observe the same phenomenon in Estonia, especially for small enterprise with a higher proportion of skilled labour Exporting behaviour has been linked to a domestic firm's capacity to absorb new technology and management practices Two opposing views exist in the literature on the role of exporting behaviour in capturing FDI spillovers On the one hand, some argue that domestic exporting firms tend to have a stronger capacity that places them in a better position to mitigate negative spillovers of FDI—because they are generally characterized by higher productivity, be it via self-selection process where more productive firms become exporters or learning by-exporting (Melitz, 2003; Crespo & Fontoura, 2007) On the other hand, some suggest that local exporting firms have exposure to additional channels through which they can learn about advanced knowledge, skill and technology from their international connections and hence the potential for FDI-induced externalities is limited (Sinani & Meyer, 2004) In addition, exporting firms may already enjoy higher productivity and hence there may be little knowledge spillover to be transferred to them from FDI Besides, local exporting firms may not have the incentives to upgrade their technology if they face lower competitive pressure from FDI (assuming that foreign firms not export to the same market), which lowers the scope and magnitude of positive FDI spillovers In the bulk of existing empirical literature, studies find no clear evidence whether exporting behaviour enhances or reduces the productivity spillovers from FDI For instance, Barrios and Strobl (2002) employ firm level panel data for Spanish manufacturers from the period 1990 to 1998 to show that the gains from FDI spillovers are larger for exporting firms Girma, Gorg, and Pisu (2008) observe the same phenomenon in the U.K for intra-industry spillover So Schoors and Van der Tol (2002) in Hungary In contrast, several empirical studies find little or no productivity spillovers for exporting local firms (cf Sinani & Meyer, 2004; Blomström and Sjöholm, 1999, among others) A domestic firm's capacity to absorb new technology and managerial practices is also linked to its share of skilled labour The economics literature posits that investment in skilled labour and R&D not only increases innovation, but also raises a firm's ability to recognize, assimilate and apply outside knowledge to commercial ends (Cohen & Levinthal, 1989; Glass & Saggi, 2002; Sinani & Meyer, 2004) Blalock and Gertler (2009) apply panel data of Indonesian manufacturers from 1988 to 1996 to argue that the share of skilled labour (the percentage of workers with collage degrees) considerably increases domestic firms' productivity spillovers from FDI However, for highly skilled countries such as the U.K., Girma and Wakelin (2007) confirm such a finding for small enterprises only whereas Sinani and Meyer (2004) find that a larger share of skilled labour enhances positive spillovers for large enterprises in Estonia On the other hand, Cuyvers, Soeng, Plasmans, and Van den Bulcke (2008) find that a firm's human capital intensity does not determine its ability to adopt foreign technology in Cambodian manufacturing sector Therefore, as discussed above, the characteristics of the recipient firms are important mediating factors for technology spillover potential to turn into actual technology spillovers The absence or presence of such firm-specific characteristics may crucially influence observed spillovers from FDI and thus not taking them into account can bias empirical results It is along these lines that in this study, we focus on how spillovers differ according to the characteristics of domestic firms in addition to estimating the productivity increase (or decrease) of domestic firms in the same industry FDI and productivity spillovers: estimation strategy, data and variables 4.1 Estimation strategy If the superior technology embodied in foreign-owned firms is diffused to local firms, productivity levels of local firms should increase To examine productivity spillovers from foreign-owned to locally owned firms, we follow an approach similar to that 40 M Seyoum et al / China Economic Review 33 (2015) 35–49 Table Distribution of firms with foreign capital by industrial sectors in 2011 Source: authors’ computations based on data from the Central Statistical Agency (CSA) of Ethiopia, Large and Medium Manufacturing and Electricity Industries Survey 2012 Industrial sectors All firms Food, beverage and tobacco Spinning, weaving and finishing of textiles Leather and footwear Paper, paper products and printing Chemical and chemical products Rubber and plastic products Other non-metallic mineral products Basic iron and steel Fabricated metal products except machinery and equipment 10 Manufacturer of oven 11 Bodies for motor vehicles, trailers and semi-trailers 12 Wood and furniture Total 199 26 79 96 57 92 156 32 85 201 1033 Domestic Foreign # % # % 182 19 72 90 43 81 146 28 77 198 943 91.5 73.1 91.1 93.7 75.4 88.0 93.6 87.5 90.5 75.0 66.7 98.5 91.4 17 7 14 11 10 90 8.5 26.9 8.9 6.3 24.6 12.0 6.4 12.5 9.5 25.0 33.3 1.5 8.6 taken by earlier literature and estimate an augmented Cobb–Douglas production function Following Dimelis and Louri (2004), we specify the following general form for the production function: α β γ X Y i ¼ Li K i M i e p X ip ỵ ỵ i iẳ1;2;;n; pẳ1;2 ;;P ð1Þ where Yi denotes the output of firm i, measured by gross sales; Li, Ki, Mi denote labour, fixed capital and intermediate material inputs used in each firm i; α, β, γ are the elasticities of output with respect to capital, labour and intermediate material inputs, respectively Xip denotes the p-th control variable which is not explicitly included, for example, the one correlated with FDI; λo is a constant parameter Finally, the error term, εi captures all other unobservable factors influencing output Log-linearizing Eq (1) yields the following estimation equation: lnY i ẳ ỵ lnLi ỵ lnK i ỵ lnMi ỵ p X p X ip ỵ i 2ị p¼1 Since our main objective relates to labour productivity, we transform Eq (2) to obtain its labour-intensive form ln Yi Li ẳ ỵ ln Ki Li ỵ ln Mi Li þ ðα þ β þ γ−1Þ lnLi þ p X p X ip ỵ i 3ị pẳ1 Eq (3) can be rewritten by adding several control variables, as follows: lnLP i ẳ ỵ lnKI i ỵ lnMIi ỵ lnLi ỵ SKILLi ỵ lnAGEi ỵ SCALEi ỵ C FORi ỵ i C FORi þ εi : ð4Þ LP stands for labour productivity, which is influenced by capital intensity (KI), material inputs intensity (MI), labour inputs (L), skilled labour (SKILL), firm age (AGE), firm scale (SCALE) and foreign presence (CFOR) Appendix A describes the explanatory variables adopted in the econometric investigation, which follow the theoretical and empirical literature discussed above We expect coefficients for the independent variables to be positive and significant A statistically significant positive coefficient correlated with CFOR implies that foreign firms have higher levels of productivity than their locally owned counterparts To examine whether the presence of foreign firms affects the productivity of local firms in the same industry, we build on the standard equation encountered in Eq (4) The difference now is that the left-hand side concerns only local firms, rather than all, firms: lnLP i j ¼ α þ α lnKIi j þ α lnMI i j ỵ lnLi j ỵ SKILLi j ỵ lnAGEi j ỵ SCALEi j ỵ FDI j ỵ ij 5ị where subscript j stands for sector j; LP, KI, MI, L, SKILL AGE and SCALE are same as Eq (4) FDI is the measure of foreign firms' presence in sector j M Seyoum et al / China Economic Review 33 (2015) 35–49 41 Table Number and size, exporting behaviour and productivity of domestic and foreign firms Source: authors’ computations based on data from the Central Statistical Agency (CSA) of Ethiopia, Large and Medium Manufacturing and Electricity Industries Survey 2012 Number and size distribution of firms Firm characteristics All firms Domestic firms Foreign firms # # % # % 710 323 1033 69 31 100 674 269 943 71 29 100 36 54 90 40 60 100 68 965 1033 Small Large Total % 93 100 54 889 943 94 100 14 76 90 16 84 100 Exporting behaviour of firms Exporting Non-exporting Total Productivity of firms All firms Y L (mean value ) Domestic firms Foreign firms 413.19 a 377.87 774.24 Small firm (b50 employees) a In thousand birr Following the literature, we employ the share of foreign firms' output as percentage total output at the 4-digit sectorial level as our measure of intra-industry FDI presence X f or Y i∈ j FDI j ¼ X Yi i∈ j Yi ð6Þ where i ∈ j indicates a firm in a given sector, Yi is firm-level output in a given sector, and Yfor is the output if the firm is foreign.11 A i statistically significant positive coefficient correlated with FDI would be consistent with the existence of intra-industry or horizontal technology spillovers To examine the effects of domestic firm's characteristics on technology spillovers, we will add four measures of absorptive capacity namely technology gap (TG), firm size (SIZE), skilled labour force (SKILL) and exporting behaviour (EXP) and the interaction term between these measures and our FDI presence as additional explanatory variables in Eq (5) TG is measured by the difference of the average labour productivity (the ratio of total sales to total employment, weighted by firm asset size) of foreign firms at the 4-digit industry level and the labour productivity of a local firm in the same industry, following Kokko et al (1996).12 A negative value for the individual domestic firm indicates that the local firm is more productive than the average foreign firms, while a positive value indicates that the firm is less productive We define a positive gap dummy that takes the value one when TG is positive and zero otherwise This dummy variable allows the isolation of local firms with low absorptive capacity We interact the positive gap dummy with FDI presence and expect it to have a negative effect Following the literature, firm size is defined by the number of workers employed in a firm where firms with 50 or more employees are considered as large and firms with less than 50 employees are categorized as small firms.13 Accordingly, we define a SIZE dummy that takes the value if the firm has more than 50 employees and otherwise We interact the SIZE dummy with our FDI presence to examine the effect of SIZE on technology transfer.14 In a similar vein, we include an interaction term between the individual domestic firm's share of high skilled-labour to its total workforce (SKILL) and our FDI presence to determine if SKILL has an effect on technology spillovers We further explore if FDI spillovers differ between domestic exporters and non-exporters A firm is considered to be an exporter if its export sales are equal to or greater than 5% of its total sales We consider this simple (and also most widely used) definition adequate for the sake of identifying differences in firm characteristics between domestic exporters and non-exporters Accordingly, we spilt the sample into exporting and non-exporting firms and estimate Eq (5) separately for each group 11 A firm is considered foreign if the share of foreign ownership is equal to or greater than 10% As an alternative measurement, we measured TG as the difference of the (un-weighted) average productivity of foreign firms at the 4-digit industry level and that of a local firm in the same industry The results obtained following this approach were consistent to the ones reported 13 The definition of large and small firms varies by country The Central Statistical Agency of Ethiopia follows similar classification where firms are defined as: small (between 10 and 19 employees), medium (between 19 to 49 employees) and large (greater than 50 employees) 14 An alternative approach is to split the entire sample into large and small firms and estimate Eq (5) separately for each group The results obtained following this approach were consistent to the ones reported 12 42 M Seyoum et al / China Economic Review 33 (2015) 35–49 Table Foreign firms by ownership type Source: authors’ computations based on data from the Central Statistical Agency (CSA) of Ethiopia, Large and Medium Manufacturing and Electricity Industries Survey 2012 Ownership type # Wholly (or majority) Minority Total % 80 10 90 89 11 100 Note: Minority ownership (b50) Given the cross-sectional nature of our dataset, solving the problem of estimating technology spillovers from FDI when FDI is endogenous is a major challenge The major challenge arises from identifying the direction of causality between foreign output share and domestic firm productivity Foreign firms may enhance the productivity of domestic firms through technology and knowledge transfers and spillover, but it may be also the case that they are attracted to certain industries that already exhibit higher productivity In the latter case, the estimated coefficient on foreign output share would be biased upward On the contrary, if foreign firms are attracted to or are mainly concentrated in industries that exhibit lower productivity, the estimated coefficient on foreign output share would be biased downward This endogeneity concern is addressed adequately by using instrumental variables two-stage least squares (IV 2SLS) approach, as explained in greater detail below 4.2 Data and instruments The data employed in this study is the annual firm survey data from the large and medium scale manufacturing industries conducted in 2012 by the CSA, Ministry of Finance and Economic Development This survey data was obtained directly from CSA The survey covers firms in the formal manufacturing sector, which employ 10 persons and more and use power driven machinery The survey data is well suited to examine spillover effects from FDI, because it contains information on variables that are commonly applied in econometric estimation of firm level production functions Particularly, the data includes financial information as well as a wide range of indicators on firm characteristics such as foreign ownership, employment and skills, exporting behaviour and region and sector of firms Sectors are defined according to the International Standard Industrial Classification (ISIC Revision-3.1), but in some cases are further aggregated The number of firms used in our econometric estimation is reduced to 1033 (out of 1937) The number of firms in the estimation is reduced for the following reasons: i) we omit firms for which we cannot calculate key variables due to missing information; and ii) we include only sectors that have foreign firms (seven sectors at the 4-digit level in the survey had no foreign presence) After these considerations, the final data consists of 943 observations for domestic firms and 90 for foreign firms.15 The sectoral distribution of firms with foreign capital in 2011 is presented in Table The relative presence of foreign capital and ownership is more visible, in terms of percentage shares, in bodies for motor vehicles, trailers and semi-trailers; spinning, weaving and finishing of textiles; and chemical and chemical products Table indicates that there is difference in the sector wise distribution among foreign firms implying the need to control for industry specific factors that influence firm level productivity Table below presents summary statistics on number and size distribution of firms, exporting behaviours of firms and productivity by firm ownership In terms of number and size distribution of firms, the majority of foreign firms (60%) are considered to be large firms (N50 employees), while the majority of domestic firms (71%) are categorized as small firms (b50 employees) Furthermore, about 93% of the domestic firms report that they not export some or all of their production Interestingly, and consistent with other survey findings (cf Shen, 2013; UNIDO, 2011; World Bank, 2012), Table suggests that the majority of Chinese investors in our survey data (about 84%) are essentially local market seekers, i.e., they not export some or all of their production In terms of labour productivity, Chinese firms are on average times more productive than domestic firms Moreover, there is a definitive preference for independent market entry among Chinese firms As reflected in Table below, 89% of the Chinese firms in the survey prefer wholly or majority ownership (N 50%) as opposed to minority ownership Only 11% of the surveyed firms are minority ownership with local partners This trend is consistent with other survey findings in Ethiopia (World Bank, 2012) and other African countries that show that Chinese firms tend to be wholly Chinese owned (Shen, 2013) To identify the causal relationship between FDI presence and domestic firm productivity, we instrument our measure of FDI presence using sectorial measure of sector targeting by the EIA The study of Harding and Javorcik (2012) was the first to utilize information on investment promotion efforts to attenuate endogeneity concerns So Farole and Winkler (2012) for a cross-sectional setting analysis of FDI and spillovers The choice for this instrument is based on two assumptions First, this variable must be correlated with 15 To maintain confidentiality, the dataset that was provided to us has no firm identifiers However, by following the information in the survey data on sector activity of firms, location (region, city/town, district, house no.) of firms, and phone address of firms, one is able to identify and determine firms' ownership M Seyoum et al / China Economic Review 33 (2015) 35–49 43 Table Impact of foreign ownership on productivity Dependent variable: ln ÀY Á L of domestic and foreign firms Independent variables (1) (2) Constant 3.469*** (0.270) 0.076*** (0.015) 0.672*** (0.025) 0.897*** (0.025) 0.151*** (0.030) – 3.506*** (0.270) 0.073*** (0.015) 0.668*** (0.025) 0.883*** (0.027) 0.161*** (0.030) 0.224*** (0.074) 0.114** (0.045) 0.091*** (0.015) 1033 0.899 lnKI lnMI lnL SKILL CFOR AGE SCALE Included observations Adjusted R-squared 0.094** (0.151) 0.089*** (0.015) 1033 0.894 Notes: Numbers in the parenthesis are the heteroscedasticity robust standard errors The symbols ***, ** and * indicate 1%, 5% and 10% significance levels, respectively All regressions were performed with 11 2-digit industry dummies, the inclusion of which was based on the F-statistics The excluded dummy corresponds to the last category of wood and furniture industries Please refer to Appendix A for the definition of lnKI, lnMI, lnL and CFOR variables FDI presence Second, it must be uncorrelated with domestic firm productivity We believe that sector targeting by the EIA is well suited as an instrument, as it is likely to meet both assumptions First, it is reasonable to assume that sector targeting by national investment promotion agencies (IPAs) is correlated with FDI presence, because the primary objective of such policy tool is to identify and promote investment opportunities in host countries to attract FDI (UNCTAD, 2001; UNIDO, 2011; Wells & Wint, 1990) Sector targeting is deliberated to be the best policy tool for investment promotion activities, because more intense efforts focused on a few priority sectors are likely to lead to greater FDI inflows than less intense across-the-board efforts to promote FDI (Loewendahl, 2001; Proksch, 2004) Employing a difference-in-differences approach, Harding and Javorcik (2011) show that targeting a particular sector by a national IPA leads to more than doubling of FDI inflows into the sector, with significant time lag effects between these two So Bobonis and Shatz (2007) and Charlton and Davis (2006) Second, Harding and Javorcik (2012) show that the choice of sector targeting by IPAs is less likely to be driven by the quality of domestic firms or industries in a host country, particularly in the context of developing countries like Ethiopia Instead, IPAs choose targeting a particular set of sectors in the hope and expectation that greater FDI inflows into these sectors can make a positive contribution to economic growth by generating jobs, bringing additional capital, and transferring new technologies and expertise to host-countries In the past decade, under different national strategy plans (Plan for Accelerated and Sustained Development to End Poverty [PASDEP] and Growth and Transformation Plan [GTP]), the Government of Ethiopia and with the help from the World Bank and other multilateral institutions has identified a particular set of priority sectors for investment aimed at attracting greater FDI inflows into these sectors Consequently, the Ethiopian government has identified leather and leather products, textile and garment, and agroprocessing industries as priority sectors to potential international investors Also, under the GTP, the list of priority sectors was extended to include metal and engineering, and chemicals and pharmaceuticals The primary selection criterion for these sectors is to enhance their competitiveness internationally and increase domestic value addition and sales in line with the country's latent comparative advantage (World Bank, 2011) For instance, the country has a strong latent competitive advantage in producing leather and leather products due to the outstanding quality and quantity of local leather and extremely low labour costs Moreover, Ethiopia's large population (about 92 million), cheap and abundant labour and a fast growing economy (averaging 10.6% per year over the past decade) add to its attraction as a FDI host economy in textile and garment, and food processing industries (World Bank, 2012) The reason for the inclusion of metal and engineering, and chemicals and pharmaceuticals as priority area for investment is to reduce the country's heavy dependent on imports of these products (World Bank, 2011) These industries have continued to receive special interest and extensive support programs from policymakers Accordingly, the Government of Ethiopia has implemented various reforms and continuously provided a basket of incentives, such as tax holidays and tariff-free policies for FDI equipment imports to attract and nurture investments in these priority sectors As can be seen from our survey data (Table 2), the sectoral distribution of foreign firms is concentrated on these priority areas, which suggests that the survey provides a good characterisation of the general trends of FDI in Ethiopia 44 M Seyoum et al / China Economic Review 33 (2015) 35–49 Table FDI presence and productivity spillovers Dependent variable: ln ÀY Á L of domestic firms only Ordinary least squares Independent variable (1) C 3.687*** (0.298) 0.0710*** (0.016) 0.652*** (0.028) 0.886*** (0.028) 0.163*** (0.029) 0.119** (0.047) 0.0874*** (0.015) 2.054** (0.948) −1.798*** (0.635) lnKI lnMI lnL SKILL AGE SCALE FDIY j FDIY*TG j FDIY*SKILL j Instrumental variables two-stage least squares (2) 3.489*** (0.287) 0.070*** (0.016) 0.670*** (0.027) 0.879*** (0.028) 0.194*** (0.031) 0.124*** (0.047) 0.090*** (0.015) 1.128* (0.603) 3.535*** (0.297) 0.072*** (0.016) 0.667*** (0.027) 0.871*** (0.034) 0.162*** (0.031) 0.123*** (0.047) 0.0913*** (0.015) 0.698 (0.639) (4) 4.052*** (0.346) 0.0573*** (0.016) 0.625*** (0.033) 0.874*** (0.031) 0.159*** (0.036) 0.121** (0.051) 0.0888*** (0.015) 11.19*** (2.338) −5.249*** (1.391) −0.423*** (0.149) FDIY*SIZE j Included observations Adjusted R-squared (3) 943 0.898 943 0.897 (5) 3.549*** (0.297) 0.0635*** (0.016) 0.671*** (0.027) 0.853*** (0.032) 0.155*** (0.050) 0.130*** (0.048) 0.0973*** (0.015) 5.941*** (1.420) (6) 3.736*** (0.336) 0.0659*** (0.016) 0.664*** (0.029) 0.797*** (0.052) 0.165*** (0.043) 0.132*** (0.049) 0.102*** (0.016) 5.481*** (1.427) 0.0184 (0.486) 0.169 (0.482) 943 0.897 943 0.877 943 0.887 1.747 (1.162) 943 0.884 Notes: Numbers in the parenthesis are the heteroscedasticity robust standard errors The symbols ***, ** and * indicate 1%, 5% and 10% significance levels, respectively All regressions were performed with 11 2-digit industry dummies, the inclusion of which was based on the F-statistics The excluded dummy corresponds to the last category of wood and furniture industries Length of sector targetingLST as instrument for FDIY in columns to Another instrument variable, Sector targetedST (not reported) was also used as instrument for FDIY j i The results obtained were consistent to the ones reported The estimations were also run by combining all of the variables in columns 1–3 and 4–6 to check for robustness The results obtained (not reported) are consistent with the results in each column Please refer to Appendix A for the definition of each variable Following Harding and Javorcik (2012), we instrument our foreign output share variable with two sectorial measures of sector targeting by the EIA The first measure is an indicator variable (referred to as Sector targetedST) that equals one if the sector has been targeted by EIA in a certain year, and zero otherwise Since our foreign output share variable reflects the presence of foreign firms over a longer period of time, the second measure is constructed by aggregating the dummies over the period 2007–2011 to obtain a continuous variable, referred to as Length of sector targetingLST The sum may vary from (no targeting over the period under study) to (continuous targeting over the period under study) To control for non-linearities, we use the second measure in log form (adding before taking the log) We believe that utilizing information on sector targeting by EIA is well suited as an instrument, as it is uncorrelated with domestic firm productivity,16 but is correlated with foreign output share variable, especially since there is a time lag between these two measures In Subsection 5.2, we report the first stage statistical results for instrument validity Empirical findings In this section, we introduce the results of the empirical analysis All regression results follow various estimations of Eq (4) and include industry dummies at the two-digit level to control for productivity differences across industries All reported standard errors are robust to heteroscedasticity We start by presenting productivity differences between foreign and domestic owned firms, and proceed showing the results for productivity spillover effects employing ordinary least squares (OLS) and instrumental variables two-stage least squares (IV 2SLS) procedures 5.1 Estimation results Table presents the estimation results for the labour productivity differences for firms in the Ethiopian manufacturing sector by ownership Column is the regression of labour productivity on all independent variables used in the estimation, except the foreign 16 It is possible that the government of Ethiopia has been selective in allowing FDI in sectors, which are more or less productive than others If so, our instruments may have possible biases because the exclusion restriction will probably not be satisfied But due to data limit, we are not able to fully address this issue in the current paper M Seyoum et al / China Economic Review 33 (2015) 35–49 45 Table Productivity spillovers and trade orientation Dependent variable: ln ÀY Á L of domestic firms only Ordinary least squares Instrumental variables two-stage least squares C Non-exporting firms Exporting firms Non-exporting firms Exporting firms6 (1) Independent variable (2) (3) (4) 2.946 (1.910) 0.389** (0.170) 0.467*** (0.093) 0.791*** (0.111) 0.0787 (0.055) 0.164 (0.156) 0.142** (0.070) −0.111 (1.597) 54 0.643 3.659*** (0.296) 0.055*** (0.015) 0.674*** (0.028) 0.845*** (0.028) 0.164*** (0.043) 0.114** (0.049) 0.094*** (0.015) 5.614*** (1.457) 889 0.897 2.788 (2.714) 0.398* (0.215) 0.476*** (0.095) 0.757** (0.319) 0.0982 (0.152) 0.166 (0.150) 0.145⁎ AGE 3.605*** (0.292) 0.062*** (0.015) 0.674*** (0.028) 0.867*** (0.027) 0.172*** (0.034) 0.109⁎⁎ SCALE (0.048) 0.087⁎⁎⁎ lnKI lnMI lnL SKILL FDIY j Included observations Adjusted R-squared (0.015) 0.793 (0.605) 889 0.896 (0.085) 0.558 (6.486) 54 0.642 Notes: Numbers in the parenthesis are the heteroscedasticity robust standard errors The symbols ***, ** and * indicate 1%, 5% and 10% significance levels, respectively All regressions were performed with 11 2-digit industry dummies, the inclusion of which was based on the F-statistics The excluded dummy corresponds to the last category of wood and furniture industries Length of sector targetingLST as instrument for FDIY in columns and Another instrument variable, Sector targetedST (not reported) was also used as instrument for j Y FDIj The results obtained were consistent to the ones reported The estimations were run including all variables of absorptive capacity appearing in Table 7, but only the coefficients for trade orientation are reported for simplicity Results for exporting firms not include industry dummies due to small sample size; hence caution should be exercised when interpreting the results Please refer to Appendix A for the definition of lnKI, lnMI, lnL and FDIY variables j ownership variable, CFOR All variables register statistically significant coefficients with the expected signs Coefficients on capital intensity, intermediate material inputs, labour inputs, skilled labour force, age and scale of operation are positive, and highly significant at the 1% significance level, indicating that these variables are important determinants of labour productivity In column 2, CFOR is entered to control for the effect of foreign ownership on labour productivity The coefficient of foreign ownership CFOR is positive and statistically significant at the 1% level, indicating a significant positive effect of FDI on productivity The coefficient on CFOR implies that foreign owned firms are about 100 (.224) = 22.4% more productive than domestic firms, ceteris paribus All other explanatory variables exhibit statistically significant coefficients with the expected signs Thus, our findings confirm the high number of studies pointing toward a significant labour productivity advantage in favour of foreign firms Table presents the regression results for productivity spillover effects on domestic firms Columns to show estimates using OLS, while columns to report the results applying IV 2SLS procedure When examining the effects of local firm's absorptive capacity on technology spillovers, we must preclude the possibility that the observed effect of a firm's absorptive capacity does not capture another absorptive capacity for which we not instantaneously control The correlation coefficient matrix (available upon request) shows that the correlation between the four measures of absorptive capacity is reasonably low implying that there are no substantial multicollinearity problems; all of the pairwise correlation are less than 0.33, with only one correlation being greater than 0.33 (skill level and firm size show a correlation of 0.43), which is still within an acceptable range Foreign output share reveals a positive and significant impact on labour productivity, in all specifications Given the OLS and IV 2SLS results, it is not surprising to note that technology spillovers in the OLS and IV 2SLS estimates are quite different, indicating the existence of potential endogeneity between FDI presence and domestic firm productivity Capital intensity, material inputs, labour, skilled labour force, age and scale of operation have a positive and significant effect in all specifications Domestic firms' characteristics in terms of their absorptive capacities are interacted with our FDI presence variable In Eq (5) of Table 6, we interact a dummy, TG, with our FDI measure to validate the effect of technology gap on the extent of technology spillovers The TG dummy takes the value one if the technology gap variable is positive and zero otherwise, as defined in Section 4.1 As shown in column of Table 6, the coefficients on FDI presence are positive and significant, while the coefficients of the interaction terms with technology gap (TG) are negative and significant The coefficient estimates on FDI presence imply that the presence of foreign firms exercise positive spillover effects on domestic firms with high absorptive capacity (when the dummy TG = 0), while the coefficients on the interaction terms (when the dummy TG = 1) suggest that domestic firms with low absorptive capacity suffer 46 M Seyoum et al / China Economic Review 33 (2015) 35–49 Table First stage for FDIY and its interaction terms i Instruments (4) FDIY i [LST] [LST*TG] 0840*** (.0185) 0218*** (.0076) (5) FDIY*TG i 0286*** (.0171) 1034*** (.0076) FDIY i 1028*** (.0196) −.0060** (.0025) [LST*SKILL] (6) FDIY*SKILL i −.0298** (.0414) R No of obs F test Kleibergen-Paap Wald rk F statistic 0.8637 943 14.76 74.13 0.8126 943 90.43 0.8607 943 14.54 87.67 1036*** (.0192) FDIY*SIZE i 0004 (.0139) 1077*** (.0294) [LST*SIZE] FDIY i 0.5582 943 6.73 −.0132** (.0052) 0.8614 943 16.08 85.44 0713*** (.0062) 0.6663 943 66.60 Notes: [LST] is the length of sector targeting Numbers in the parenthesis are the heteroscedasticity robust standard errors The symbols ***, ** and * indicate 1%, 5% and 10% significance levels, respectively The estimations were run including all control variables appearing in Table 6, but only the coefficients for the instruments are reported for simplicity Kleibergen-Paap Wald rk F statistic is based on heteroscedasticity robust standard errors The Stock-Yogo weak identification test critical values for two endogenous variables and two instruments is 7.03 for 10% maximal IV size under the desired maximal nominal size of a 5% Wald test Please refer to Appendix A for the definition of each variable negative spillover effects Our results argue against the findings of other studies stressing that larger technology gap between foreign and domestic firms is beneficial for domestic firms, since their catching up potential increases (Findlay, 1978; Wang & Blomstrom, 1992), and instead lends strong empirical support for the notion that too large technology gap deters the likelihood of positive spillover (Blomstrom et al., 1999) The interaction term with a domestic firm's skill level of employees (SKILL) does not influence domestic labour productivity (column 5), confirming the results by Sinani and Meyer (2004) who find that firms' own human capital does not increase their ability to benefit from positive spillovers Putting it differently, while the SKILL level of employees contributes to productivity, the interaction term with FDI indicates that skill level of employees and FDI not mutually facilitate productivity among local firms It suggests that a certain skill intensity threshold must be met in order for a firm to modify and apply the advanced technology of foreign firms As shown in Table 6, when firm's SIZE is taken into account, the coefficient on FDI presence is positive and significant, while the interaction term with SIZE is insignificant (column 6) The coefficient estimates suggest that while small firms enjoy positive spillovers, large firms not seem to be influenced by the presence of foreign firms.17 Our results are in line with Sinani and Meyer (2004) and Dimelis and Louri (2004) who find that productivity spillovers accrue mostly for small firms in Estonia and Greece, respectively Moreover, as shown in Table above, the overall results indicate that FDI spillovers in the OLS estimates are downward biased than those reported by IV 2SLS; for instance, the FDIY variable in the IV 2SLS estimate is 11.19 (column 4), which is greater than the OLS i estimate of 2.054 (column 1); similarly, the IV 2SLS estimate of 5.481 (column 6) is greater than the 0.698 estimate reported by the OLS (column 3) Exporting firms tend to have stronger capacity than non-exporting ones, which puts them in a better position to enjoy positive spillover effects that arise from foreign firms’ presence (Crespo & Fontoura, 2007) On the other hand, exporting firms have additional channels through which they can learn about advanced knowledge, skill and technology from their international connections and hence the potential for FDI-induced spillovers is limited (Sinani & Meyer, 2004) Thus, we spilt the sample into firms that export and firms that sell only for the domestic market, and perform separate regression estimates to investigate spillover effects in each group The separate estimates are presented in Table The signs of the coefficients for the spillover variable (FDIY) are similar for both exporting and non-exporting firms, although the size of coefficient is not significant i for the former, indicating that the presence of foreign firms in the same industry exercises a positive and significant influence on the productivity of non-exporting firms Furthermore, as seen from Table 7, FDI spillovers in the OLS estimates are downward biased than those provided by IV 2SLS For non-exporting firms, FDI spillover in the IV 2SLS is 5.614 (column 3), which is greater than the OLS estimate of 0.793 (column 1) The same is true for exporting firms 5.2 Instruments validity results Table below presents the first stage statistical results for instruments validity corresponding to Table (columns to 6) The coefficients of our instruments (length of sector targeting [LST] and its interaction terms) are significant at the 1% significance level, and the first stage F-statistics in columns to substantially exceed the conventional critical value of 10; similarly, the Kleibergen-Paap Wald rk F statistics for the weak identification test are far greater than the critical value of 7.03 Thus, we reject the null hypothesis that the instruments are weak, confirming the validity of our instruments M Seyoum et al / China Economic Review 33 (2015) 35–49 47 Table First stage for FDIY i [LST] R No of obs F test FDIY i FDIY i (3) Instrument (4) 1128*** (.0230) 0.8697 889 24.02 0524*** (.0165) 0.3164 54 10.07 Notes: [LST] is the length of sector targeting Numbers in the parenthesis are the heteroscedasticity robust standard errors The symbols ***, ** and * indicate 1%, 5% and 10% significance levels, respectively The estimations were run including all control variables appearing in Table 7, but only the coefficients for the instruments are reported for simplicity Please refer to Appendix A for the definition of FDIY variable i Similarly, for the estimation sample of exporting and non-exporting firms (Table 7), our instruments are not weak, with first stage F-statistics substantially exceeding the conventional critical value of 10 (Table 9) Concluding remarks Using a sample of 1033 Ethiopian manufacturing firms operating in 2011, 8.6% of which are foreign owned, this study investigates the impact of Chinese ODI on domestic firm productivity Besides OLS, this study also employs IV 2SLS procedure to tackle the potential endogeneity between FDI-induced spillover effects and domestic firm productivity This empirical study is, to our knowledge, the first to present a detailed analysis of Chinese ODI spillover effects on a host country in developing Sub-Saharan economies We find that increases in foreign equity participation are positively associated with increases in productivity, indicating that foreign-owned firms enjoy higher productivity levels due to the possession of superior productive assets, such as technological know-how and management skills We also find that the productivity of domestic firms is positively correlated with the presence of foreign firms in the same industry, providing new solid empirical evidence on spillovers from Chinese ODI in manufacturing Our findings for positive FDI spillovers in Ethiopia are consistent with the conventional wisdom that FDI is superior to foreign portfolio investment, that it produces positive spillover effects not internalized by any agents in the economy When domestic firm characteristics are taken into account, we obtain the following results: (i) FDI has a positive spillover effects on local firms when the technology gap between foreign and domestic firms is smaller, and that domestic firms with lower absorptive capacity suffer negative spillover effects from FDI; (ii) skilled labour of domestic firms does not enhance their capacity to attract FDI spillovers; and (iii) small firms and non-exporting firms benefit more from spillovers than other types of domestic firms The importance of domestic firms' absorptive capacity in influencing FDI spillovers highlighted in our study also helps shed light on the appropriate government policies to pursue regarding FDI To fully benefit from the positive FDI spillovers, the Ethiopian government might wish to pursue proactive strategies to promote and broaden linkages between domestic firms and FDIs through intermediate inputs and technology upgrading For instance, the EIA could design supplier/buyer-identification programs to facilitate foreign firms locate potential domestic supply/buyer sources Similarly, the government could devise mechanisms and incentive packages to encourage foreign investors to undertake local supplier development programs to increase the prospects of domestic firms becoming suppliers to them Furthermore, creating a conducive and enabling investment/business environment is crucial in promoting FDI as well as boosting the likelihood of linkages with domestic firms Further research is needed to examine vertical (inter-industry) spillovers from Chinese ODI in manufacturing in Ethiopia or other African countries If firm survey data become available, the effect of Chinese ODI on downstream (local customers or buyers) sectors or upstream industries (local suppliers of intermediate inputs) should be considered to further enhance our understanding on the nature and impact of Chinese ODI on developing Sub-Saharan host economies Acknowledgements We thank Seyoum Mesfin for helpful conversations and comments We are indebted to Long, Cheryl Xiaoning and three anonymous referees for useful comments and suggestions We are thankful to Huajian International Shoe City (Ethiopian) P.L.C for financial support in covering fieldwork and data collection expenses in Ethiopia, including travel and accommodations The authors would also like to thank Michal Bernert for excellent research assistance Any remaining errors are the sole responsibility of the authors 48 M Seyoum et al / China Economic Review 33 (2015) 35–49 Appendix A Variables definitions Variables Dependent variable (Labour productivity, LP) Capital labour ratio (KI) Material input labour ratio (MI) Total labour force (L) Human capital intensity (SKILL) SCALE (within firms) Firm's age (AGE) Exporting behaviour (EXP) Size of firm (SIZE) Foreign-owned firms (CFOR) Proxy for foreign presence (FDIY) j Technology gab (TG) Industry dummies Definitions Gross sales as reported by firms in the large and medium scale manufacturing industries survey (2012), in log form The ratio of fixed assets to total employment in the individual firm (in log form) The ratio of material input purchases to total employment in the individual firm (in log form) Total number of employees (in log form) The ratio of managers, scientist, engineers and technicians, and clerical and office workers to the total number of workers The individual firm's output relative to the average output in the sector at 4-digit industry level to which the firm belongs Firm's age in year 2011 (in log form) A firm is considered to be an exporter if its export sales are equal to or greater than 5% of its total sales A binary variable equal to if the firm has N50 employees and otherwise A dummy variable equal to for a firm with foreign equity (majority or minority) and otherwise Following the literature, we consider firms as foreign with a foreign equity of 10% or more The ratio of the output of foreign firms to total gross output in each sub-sector at the 4-digit industry level The technological gap between foreign and local firms, as defined in Section 5.1 12-digit industry dummies are used in the regression to control for industry specific effects not captured by the above explanatory variables Notes: We would have preferred to use gross value added but it was not reported in the survey, nor was it possible to get from another source Arguments for the use of gross sales when 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China Economic Review 33 ( 2015 ) 35–49 37 Table China'' s outward FDI flows to Ethiopia, 2002–2 010 Source: authors’ computations based on data from the Ethiopian Investment Agency (EIA) Years Number of. .. Spillovers of technology transfer from FDI: The case of Estonia Journal of Comparative Economics, 32(3), 445–466 Sjoholm, F (1999) Technology gap, competition and spillovers from direct foreign investment: