TECHNOLOGY SPILLOVERS FROM FOREIGN DIRECT INVESTMENT: KENYA AND MALAYSIA COMPARED, Bethuel Kinyanjui Kinuthia.

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1 TECHNOLOGY SPILLOVERS FROM FOREIGN DIRECT INVESTMENT: KENYA AND MALAYSIA COMPARED, 225. Bethuel Kinyanjui Kinuthia PhD Student, Tracking Development Project, Africa Studies Centre Affiliate, Leiden University, the Netherlands and School of Economics, University of Nairobi, Kenya. Draft Paper Abstract This paper investigates the existence of productivity spillovers and their channel of transmission in both Kenya and Malaysia. Literature on spillovers suggest that productivity spillovers may be one of the most important effects that foreign owned firms impart on local firms in domestic countries. Using firm level panel data for the manufacturing sector for the period 225, the results reveal in both countries evidence of negative productivity spillovers through the competition effects. However, in Malaysia there is also evidence of positive spillovers from the demonstration effects. In addition, there is evidence of mutual spillovers from foreign owned firms to domestic firms through the backward linkages with the former having negative effects. Spillovers in both countries are also found to be sensitive to technology gap. In Kenya exporters are found to be more productive than non exporters, unlike in Malaysia where they are found to be similar in productivity. Keywords: productivity spillovers; transmission mechanism; Kenya; Malaysia. 1. Introduction This paper investigates the existence of productivity spillovers from foreign owned firms to domestic firms in Kenya and Malaysia. The two countries, apart from sharing the same colonial history, ethnic diversity, strategic location among others things, have relied to a large extent on foreign direct investment for the development of their industrial capabilities over the years. Whereas, Malaysia s industrial development in recent times has been attributed partly to foreign multinational (MNCs) activity 1, Kenya s slow growth has been blamed partly on its inability to not only attract large foreign direct investment (FDI) inflows but also, failure to create local linkages with the domestic industries 2. The literature on productivity spillovers or external effects from FDI has argued that foreign owned firms can benefit domestic firms by breaking up supply bottlenecks, demonstrate new technology and train workers who later take up employment in local firms, break down monopolistic industrial structure and stimulate competition and efficiency, transfer techniques for inventory and quality control and standardization to their local suppliers and distribution channels, and force local firms to increase their managerial efforts and adopt marketing techniques used by multinational corporations 1 See Lall(1995) 2 GOK(1994) 1

2 (MNCs). These activities may introduce new knowhow and intensify competition and hence contribute to productivity gains. If productivity gains outweigh competition losses, there will be positive productivity spillovers. Otherwise, the impact of the foreign presence on the host economy will be negative (Blomstrom and Kokko, 1998; Saggi, 22). More recently, this literature has also acknowledged that productivity spillovers can emanate from the host country; hence FDI may be motivated by technology sourcing. Firms may decide to invest abroad not only to exploit advantages in their possession, but also to acquire new technological knowledge. The reverse spillovers hypothesis suggests that firms might benefit from technological spillovers when they locate close to market leaders. This however tends to be more concentrated in developed countries (Wei et.al., 28; Driffield and Love, 23). However, empirical evidence has been inconclusive due to methodological and measurement issues, and more recently conceptual issues (Gorg and Strobl, 21; Smeet, 28). Moreover, apart from the inconclusiveness associated with these studies, few explore the channels of transmission. As Gorg and Strobl, (25a) have noted, one of the drawbacks of these studies is that the specific mechanisms by which spillovers are supposed to occur are treated as a black box. In spite of the importance of spillovers in industrial development and the possibility of transferability to developing countries, comparative studies have been rare yet they may provide some useful insights in terms of understanding the right kind of policies that ought to be pursued by such countries. In addition, studies on productivity spillovers are not only few in both Kenya and Malaysia, but also rarely consider the transmission channels (Khalifah and Adan, 29; Menon, 1998; Gachino 21). This paper intends to bridge this gap by investigating the presence of productivity spillovers and their transmission channels from foreign owned firms to domestic firms in both countries using firm level panel data from the manufacturing sector for the period 225. The advantage of using panel data is controls for longitudinal characteristics that cannot be controlled when using cross sections for individual years. This may be important in case of reverse causation between multinationals and some of the firms characteristics, which can be reduced using panel techniques. The rest of the paper is organized as follows. In the next section a review of FDI and the development of the manufacturing sector in both countries is presented. Section three discusses the literature review, while a discussion on data and the empirical estimation follows in section four. The empirical results are presented and discussed in section five. The last section contains the conclusion and policy implications of this study. 2. Overview of FDI and the development of the manufacturing sectors in Kenya and Malaysia. Kenya and Malaysia have relied on foreign direct investment in their industrial development. Graph 1 below shows that at independence the manufacturing sector accounted for less than 1 percent of GDP and that it was largely dominated by firms operating in the light industries as indicated by tables 1 and 2 below. 2

3 % Graph 1: Manufacturing value added in Kenya and Malaysia Source: World Development Indicators (21) Years Manufacturing value added(% of GDP) in Malaysia Annual growth of manufacturing value added in Malaysia Manufacturing value added(% of GDP) in Kenya Annual growth of manufacturing value added in Kenya In both countries, the production of manufactured goods during the early period of the 196s was concentrated mainly in sectors involved in food products, beverage and tobacco, textile and apparel, which accounted for more than 6 percent of manufactured output in Kenya and about 3 percent in Malaysia. In addition, the basic chemicals and petroleum sectors also contributed about 14 percent of manufactured output in Kenya, while rubber and fabricated metal products contributed 44 percent of manufactured output in Malaysia. These industries existed during this period under the import substitution industrialization strategy (ISI), which involved the domestic production of previously imported goods. Due to the limited nature of domestic firms in spearheading industrial development in these countries, the governments had relied much on foreign direct investment, in addition to government established entities as the initial drivers of the industrial process (Swainson, 198: 123,211; Alavi, 1995:35). Table 1: The composition of the manufacturing sector in Kenya (%) Sectors Food Products Beverage and Tobacco Textile and Clothing Footwear( Leather) Wood and Furniture Paper and Printing Leather and Rubber(Plastics) Clay and Grass Basic Chemicals and Petroleum Cement and Other minerals Metal products Machinery and Ship building/repair Railway rolling stock and motor vehicles Micro and small enterprises Export Processing Zones 5 4 Miscellaneous Total Source: Kenya National Bureau of Statistics 3

4 Table 2: The composition of the manufacturing sector in Malaysia (%) Sectors Food, beverage and tobacco Textiles and Apparel Leather and footwear Wood & Wood Products Paper and Printing 2 3 Chemicals & Plastic products Petroleum and coal products 2 16 Rubber products Non metallic & glass products Basic metals Fabricated metal products General machinery Electric/electronics machinery Transport equipment Precision equipment 1 1 Other manufactured goods Total Source: Rasiah and Shari (21, table 5); Ministry of Finance, Malaysia The growth of the manufacturing sector in both countries began in the 196s, following the successful implementation of ISI (Graph 1). There was a significant turn in the composition of manufacturing in GDP in Malaysia from 1967, when the country began plans to implement the export oriented industrialization strategy (EOI), after it became apparent that ISI was losing its impetus as a growth strategy. By the early 197s, government efforts to encourage export oriented industries were in full swing, and established free trade zones and licensed manufacturing warehouses were established, with a special emphasis on labor intensive foreign owned firms. The ISI was however retained in the resource rich sectors (Kinuthia, 211). Kenya during this period continued pursing the ISI, relying much on foreign capital as well as government established entities. However, the ISI discouraged learning and most of the firms were inefficient and could not compete internationally after the collapse of the East African community in Furthermore, Table 1 and 2 above, show that towards the end of the 197s, the composition of the manufacturing sector remained unchanged in Kenya, while in Malaysia, the entry of FDI in the electrical and electronic sector was already making significant contributions. In addition, the discovery of oil in the early 197s in Malaysia also resulted in the manufacture of petroleum and coal products. There was also an increase in the production of basic metals. These features show evidence of industrial transformation taking place and Malaysia beginning to produce intermediate products, in addition to consumer goods. At the start of the 198s, Malaysia embarked on the second round of ISI, which was later abandoned in 1985 in favor of the EOI. The government also embarked on a liberalization program, which saw the removal of restrictions on FDI and the privatization of state owned enterprises. As a consequence, there was a huge influx of FDI inflows, which resulted in a significant increase in the production of the manufacturing goods as presented in graph 1. Furthermore, there was a significant technology shift in the sector towards the production of high tech manufactured goods as shown in table 2. By 211, light industries accounted for approximately less than 4

5 1 percent of manufactured output and the sector was now dominated by chemical and plastic products and the electrical and electronic goods, which accounted for more than 45 percent of the output. In Kenya on the other hand, the 198s began with the country pursuing structural adjustment programs (SAPs), owing to the economic turmoil experienced in the 197s attributed to external shocks, fiscal irresponsibility, natural calamities and the high expenditure associated with the implementation of ISI, which had resulted in severe balance of payment problems (Fahnbulleh, 26). The aim of SAPs was to remove government involvement in the productive sector, orientation of the manufacturing sector towards exports, and the liberalization of the economy. However, these efforts did not yield much success (due to unwillingness by the government to implement reforms as well as lack of enough capacity, see O Brien and Ryan, 1999) and the manufacturing sector experienced limited transformation as shown in table 1. Towards end of the 199s, Kenya established export processing zones, aimed at attracting foreign firms to produce for manufactured goods for exports. At the same time, there were various legislations aimed at supporting small and medium enterprises, in anticipation of linkages arising from their interaction with the foreign owned firms. By 28, although the share of consumer goods has reduced, there was increased involvement in the production of cement and chemicals and other minerals and the manufacturing sector was yet to undergo significant transformation. Thus while there is evidence of transformation of the manufacturing sector from the production of light commodities to hitech goods in Malaysia, there was limited evidence of transformation in Kenya. In this process, both countries have relied on FDI which has been obtained mainly from the USA and the UK. Japan, Singapore and Taiwan have also been major sources of FDI in Malaysia, while countries within the EU and more recently China and India have also been important FDI sources in Kenya (Kinuthia, 21; UNCTAD, 25). 2. Literature Review 2.1. Theoretical Literature Review Endogenous growth theory views innovation as the main source of productivity growth, although it may be associated with either internal or external factors (Romer, 199; Lucas, 1988) and foreign direct investment (FDI) is often considered the strongest conduit for international technology transfer. According to Blomstrom and Kokko (1998) productivity spillovers occur, because foreign owned firms when investing in host countries bring with them some amount of proprietary technology that constitutes their firm specific advantage and allows them to compete successfully with other MNCs and local firms that presumably have superior knowledge of local markets, customer preferences and business practices. In addition, their entry disturbs the existing equilibrium in the market and forces the local firms to take action to protect their local market shares and profits. Both of these changes are likely to cause various types of spillovers that lead to productivity increases of local firms. Hence, productivity spillovers are said to take place, when they lead to productivity or efficiency benefits in host country s local firms and the foreign owned firms are not able to internalize the full value of these benefits. 5

6 Gorg and Strobl (25b) and Aitken and Harrison (1999), have also observe that foreign owned firms may be a source of negative productivity spillovers. By producing at lower marginal costs than host countries firms, they have an incentive to increase output and attract demand away from those firms. This will cause host country rivals to cut production that, if they face fixed costs of production, will raise their average cost. Likewise, to the extent that the presence of MNCs leads to higher wage demand in the economy, this will increase the local firms average costs. In addition, studies on productivity spillovers have also shown that the degree of productivity spillovers depends on both the technology gap between the foreign and local firms and the technology capabilities of local firms. Cantwell (1989, 1995) argues that if local firms do not lag too far behind multinationals they can embark on a catchup process and benefit from the presence of FDI. Local firms may also be so far behind foreign firms that it is impractical for them to benefit substantially from foreign firms knowledge and consequently they fall even further behind. Crespo and Fontoura (27) further observe that domestic firms must have a moderate technology gap vis a vis MNCs in order to benefit from the higher technology associated with MNCs. If the technology gap is too small, MNCs will transmit few benefits to the domestic firms. Thus, whether the effects of foreign owned firms on productivity of host firms is positive or negative, is therefore ambiguous and needs to be decided empirically. Scholars such as Javorcik (28); Gorg and Greenaway (24); Saggi (22) and Bloomstrom and Kokko (1998) in various literature surveys on spillovers have identified several channels through which productivity spillovers may occur. First, productivity spillovers may occur through demonstration effects. In its simplest form, the demonstration effects argument states that exposure to superior technology of foreign owned firms may lead to local firms updating their own production methods. This is based on the assumption that it may be too costly for local firms to acquire the necessary information for adopting new technologies if they are not introduced into the local economy by foreign owned firms. As part of this argument, this literature observes that geographical proximity is a vital part especially in developing countries, which might not have many alternatives for absorbing technologies. Since this is largely an industry level argument, relating to industrylevel variation, research and development (R&D) expenditures to the extent of FDI is one method of checking whether local adoption efforts are encouraged through FDI. A second important channel of productivity spillovers identified in literature is through labor turnover. This differs from other channels because knowledge embodied in the labor force moves across firms only through physical movement of workers. The relative importance of labor turnover is difficult to establish because it would require tracking individuals who have worked for foreign owned firms, interview them regarding their future job choices, and then determine their impact on the productivity of new employers. Foreign owned firms may also poach the best workers from their local competitors and make access to credit more difficult, because they may be lower risk borrowers than their competitors. Both channels create negative pecuniary externalities that affect local firms. While, pecuniary externalities have a negative impact on affected local firms, they lead to more efficient outcomes for the economy as a whole. As a result of increased competition in product, labor and credit markets, resources are reallocated from less efficient firms to those in a better 6

7 position to benefit from them. This may in turn benefit consumers through lower prices. Productivity spillovers may also occur though the competition effect, which is the third channel. The entry of foreign owned firms increases the level of competition within the industry as long as the share of their output is sold in the host country. Production of goods and services within a liberalized environment lowers both transportation and labor costs. This may lead to a reduction in the prices of commodities benefiting customers in the host markets. In the long run, this provides an incentive to local producers to improve their performance; it also leads to the exit of worst performers and an increase in the average productivity level in the industry. In the short to medium term, however, weak firms may experience a decline in their performance as the market size shrinks. The fourth mechanism through which productivity spillovers may occur is through linkages with the domestic firms, hence affecting the demand of intermediates. Gorg and Strobl (25a) observe that MNCs can be expected to only have a minimal effect the domestic economy if they operate in enclave sectors with no contact with domestic economy. Hence, it is unsurprising that the importance of backward or forward linkages between MNCs and domestic suppliers/or customers has also been emphasized in this literature. These authors have noted that local firms may be able to improve their productivity as a result of forward or backward linkages with MNCs affiliates. However, local firms can also influence the productivity of MNCs through such linkages. If expansion of MNCs forces local firms to exit, the MNCs use local inputs less intensely, a negative effect would be observed in upstream sectors as well as industries using these inputs. A final important productivity spillover channel identified in literature is through exporting. Greenway and Kneller (28) and Bigsten, et. al., (24) among others, observe that firms can raise their levels of productivity once they begin exporting. Here, learning from best practice follows experience in the export markets. Exporters find new ways of improving products and processes. On the other hand, Melitz (24) and Bernard et.al., (23) among others have shown that it is more productive producers in an industry who become exporters. 2.2 Empirical Literature Review There exists a rich array of empirical studies investigating the presence of productivity spillovers as well as their transmission channels. Jarkovic and Spatareau (211) find evidence of productivity spillovers through the backward linkages in Romania. In addition, they observe that investor s origin may matter for spillovers to domestic producers supplying intermediate inputs through the distance between the host and source country and the existence of preferential trade agreements. They use several proxies to measure foreign presence in the same sectors and in downstream industries. The level of competition in an industry j, was measured using the Herfindahl index, while the share of an industry j s output produced by firms with at least 1 percent foreign equity measured the importance of foreign presence in a sector s output a proxy for horizontal (within sector) spillovers. In addition, vertical (across sectors) spillovers were calculated as the proportion of sector j s output used 7

8 by sector k taken from the inputoutput matrix pertaining to year t. 3 Moreover, since knowledge spillovers from foreign presence may take time to manifest themselves, these measures of foreign presence were lagged one period. In a similar approach, Du et.al., (211) using panel data for Chinese firms, finds evidence of productivity spillovers from foreign owned firms to domestic firms through backward and forward (vertical) linkages, but not through horizontal linkages. Jordaan (211) in a cross sectional analysis finds evidence that FDI firms and Mexican firms do not differ in their use of local suppliers of material inputs and production services. However, FDI firms are significantly more supportive of domestic suppliers than Mexican firms. Todo, et. al., (211) find evidence of technology diffusion from R&D stock of foreign owned firms to domestic firms in the same industry in China s Silicon Valley. They also find that the size of knowledge spillovers is larger when the technology gap between source and recipient firms is larger. Driffield et.al., (21) using firm level data found that multinational investments in Italy, found that R&D and investment in capitalembodied technology played a significant role in determining the nature of interfirm technology flows. However, they observed that the basis for any spillovers arising from MNC affiliates does not arise from codified knowledge associated with R&D but rather from the productivity of the affiliate. Suyanto, et.al., (29) using firm level data for Indonesian chemical and pharmaceutical industries found evidence of intra industry productivity spillovers through competition effects from foreign presence in the industry. In addition, firms with R&D expenditure receive more productivity spillovers than those without. 4 Similarly, Arnold and Javorcik (29) found that foreign ownership does not only lead to significant improvements in the acquired plants in Indonesia, but also enhances the integration of plants into the global economy through increased exports and imports. Yasar and Paul (27) also found evidence that plants with foreign ownership are the most productive, followed by plants that export, especially with other forms of international technology transfer. Eriksson (211) shows that knowledge spillovers via the mobility of skilled labor are primarily a sub regional phenomenon in Sweden and that the only inflows of skills that are related to the existing knowledge base of plants and come from fewer than 5 kilometers away, have a positive effect on the plant performance. Menghinello, et.al., (21) using firm level data on Italian firms found that while inwards FDI does generate a productivity increase in the home country, in particular it s the locations with higher levels of agglomeration and a significant degree of industry specialization which are best placed to benefit from inward investment and the associated new technology. On exporting as a source of productivity spillovers, Alvarez and Lopez (28) using data for Chile, found strong evidence that domestic as well as foreign owned exporting firms improved the productivity of local suppliers. They found evidence that positive productivity spillovers are not only generated by the presence of foreign owned exporting plants, but also by the exporting activity of domestic firms. 3 Vertical_Origin= k j kjt Horizontal_Origin kt 4 In this study spillover is defined in terms of the share of foreign firms output in three digit ISIC industry. The HerfindahlHirschman index is used to measure concentration or the competition effect while R&D is measured as the using dummies variables i.e. 1 if firm spends on R&D activities during the observed years and otherwise. 8

9 In Kenya, several studies have examined various aspects of productivity, while others have examined various transmission channels through which foreign owned firms affect domestic firms. Fukunishi (29) found no significant gap in the average technical efficiency between firms in Kenya and Bangladesh in the garment industry although unit costs differed greatly between them and that productivity had little to do with stagnation of the industry in Kenya. Rasiah and Gachino (25) found evidence that foreign owned firms have higher labor productivity means than local firms in textile and garment manufacturing. Lundvall and Battese (2) found evidence of technical efficiency being positively correlated to firm size in food, wood and textile and metal sectors in Kenya. Pack (1987) also found that total factor productivity of Kenyan firms was less than in developed countries, but as productive as firms in the Philippines. On the transmission channels, Kamau et. al., (29) observed that within the export processing zones (EPZ) in Kenya, there is evidence of spillovers, where employees leave mainly the garments firms after acquiring training and experience, teaming up with local investors to establish other garment factories or even starting their own small scale garment firms. However, Phelps et. al., (29) found no evidence of vertical linkages between foreign firms and local firms in the clothing sectors. Graner and Isaksson (29) found evidence of export participation yielding learning effects in Kenyan manufacturing firms. Gachino (26) found evidence of technology spillovers from multinational to domestic firms through demonstration effects. Jenkins (199) found that foreign entry into the Kenyan footwear industry led to increased competition and changes in the production techniques of local firms. Gershenberg (1987) also found evidence that multinational enterprises in Kenya, train indigenous managers and spread know how in Kenya. Langdon (1981), in a study of FDI in the Kenyan soap industry, reported that the entry of foreign MNCs also introduced mechanized production, and local firms found themselves unable to sell handmade soap in the urban markets. Instead, they were forced to introduce mechanized techniques to stay in business, hence improving their productivity. Similarly in Malaysia, there are several studies that have investigated productivity in the manufacturing industry. Khalifah and Adam (29) found no significant difference in labor productivity among wholly foreign owned firms and locally owned establishments. They also found that their results were sensitive to the different measurements of the extent of foreign holding as well as proxies used for foreign presence whether in terms of net output, capital or labor, were important in determining the incidence of productivity spillovers from foreign to local establishments. Similarly, Oguchi et.al., (22) found evidence of similar total factor productivity between foreign and domestic firms. Mahadevan (22) using a data envelop analysis technique found evidence that the annual productivity growth in Malaysia for the period was low at.8 percent and was driven by small gains in both technical change and technical efficiency. Dogan et.al., (211) found that exporters in Malaysia are more productive than non exporters and that exporters turnover contribute to the aggregate productivity growth. Ahmed (29) and Menon (1998) found that productivity growth in Malaysian manufacturing sector is inputdriven rather than total factor productivity growth driven. On the transmission mechanisms, Athukorala and Menon (1996) found evidence of limited backward linkages and direct technology transfer from foreign owned firms, 9

10 but observe that they appeared to be increasing. Similarly, Giroud (27) observed that the impact of FDI in the host country depends to a large extent upon competence of the local firms. The study noted that Malaysia has now reached a second stage in the investment development path where there is evidence of creation of linkages and subsequent knowledge and technology transfer. Iguchi (28) found evidence of significant linkages between MNCs and local suppliers in the electrical and electronics industry. In addition, they found that subsidiary s level of autonomy and local sourcing rates, as well as other factors such as location, are all positively related to the intensity of backward linkages. Thus, although there is a considerable amount of studies investigating various aspects of productivity in the manufacturing sector in both countries, few studies have focused on productivity spillovers and their transmission mechanisms, which is the focus of this paper. In addition, existing studies reviewed provide positive evidence of productivity spillovers from various countries. However, the channels of transmissions are not clear. Indeed, as Gorg and Strobl (25a) have observed, spillovers are difficult to measure since they do not leave a paper trail by which they may be measured or tracked. Hence, the approach taken mainly in empirical literature therefore largely avoids the question of how spillovers occur, and instead focus on the simpler issue of whether or not the presence of foreign owned firms affects productivity of domestic firms. This is also evidenced in Kenya and Malaysia where few studies have addressed these issues. This study contributes to this literature by examining with various measures of foreign presence and spillover proxies, not only the existence of productivity spillovers, but also their transmission channels using firm level panel data for the period 225 in both countries. 3. Data & Empirical Estimation 3.1 Data The datasets used in this paper are based on annual surveys of manufacturing industries in both countries for the period 225. In Malaysia this dataset is collected by the Department of Statistics, while in Kenya this used to be conducted by the Ministry of Trade and Industry until 25, when the exercise was stopped for unknown reasons. The surveys cover all establishments above a specific employment cutoff, which varies from industry to industry. The data for the period 2 and 25 was based on a census in Malaysia. In Kenya the data set was based on a set of registered firms with the Ministry. Firms were registered until the year 24; hence in Kenya it s difficult to infer anything about representativeness of the true population. The principal statistics for each firm complied include type of ownership, value of gross output, costs of inputs, vale added, value of fixed assets and number of workers. A breakdown of this dataset is contained in tables 3 and 4 below. From table 3, it s apparent that Malaysia has more firms in this dataset than Kenya. A further breakdown is contained in table A.1 and A.2 in the appendix. For the purpose of this study, firms with at least 1 percent of their nominal capital owned by foreigners are defined as foreign owned firms while the rest are considered locally owned. 5 As de 5 This definition was adopted because the Kenya national authorities use the same benchmark. This definition follows that of OECD and UNCTAD. 1

11 Mello (1997) has observed, this definition is restrictive since FDI comprises of bundles of capital stocks, knowhow, and technology among other things which are not taken into account. Furthermore, firms today can exercise various forms of control over distance enterprises without direct ownership (Winder, 26). Data deflation is a necessary condition especially in time series analysis in order to remove data fluctuations that might exist due to inflationary effects over time in the economy. Due to lack of suitable deflators, the GDP deflator is used to deflate both output and export values and is obtained from the World Development Indicators. 6 Table 3: Classification of firms according to ownership K M K M K M K M K M K M Foreign Local Total Notes: (i) KKenya and MMalaysia. (ii) Years 2 and 25 a complete census was conducted in Malaysia. Finally, Table A.1 in the appendix presents a detailed comparison of the sample and the registered firms with the ministry in Kenya. On average the firms used for analysis in this paper represent about 5 percent of those registered by the ministry, and were selected on the basis of data availability. However, firm representation varies across sectors. Foreign owned firms are over represented in four of the sectors with more than 5 percent of the firms contained in the register. They are least represented in the wood and wood products sector with about 31 percent of the firms contained in the register. Domestic firms on the other hand, are over represented in five sectors with more than 5 percent of firms contained in the register. They are least represented in the food, beverage and tobacco and the textile, apparel and leather sectors with 31 percent of firms in the register. This representation is taken into account when estimating the sectoral level measures of foreign presence 7. Similarly, Table A.2 presents a detailed comparison of the firms in Malaysia across sectors. Most of the foreign owned firms are concentrated within the chemical, petroleum, plastic (36) and the fabricated metal products, machinery and equipment (38) sectors. They are well represented across sectors over the years even during the period between 21 and 24 when a complete census was not conducted. This is however not the case, when domestic firms are considered. Whereas they are evenly distributed across the sectors, they are under represented by 45, 49, 44 and 56 percent in the years 21, 22, 23 and 24 respectively. This under and over representation in the two countries is taken into account when estimating the sectoral level measures of foreign presence. In addition, Table 4 above shows that firms in Malaysia are larger, have a higher profitability, turnover, productivity and fixed assets than Kenyan firms. Firms in Kenya are also on average older than those in Malaysia. Foreign firms in both 6 This approach was followed by Gachino (26) except that the export values were deflated using export price indices for manufactured goods. Biggs et. al., (24) use firm specific deflators based on export shareweighted averages of the domestic and international prices to control for variations in the exchange rates. 7 There is no way of establishing the sampling frame used and therefore the register may or may not be representative of firms. Besides, to the best of my knowledge, the number of firm establishments in Kenya is unknown. 11

12 countries are superior in all variables compared to domestic firms. In Malaysia however, foreign firms have a lower productivity and are of a lower age compared to domestic firms. Moreover, in general wages firms in Malaysia have higher wages than those in Kenya. Table 4: Mean characteristics of the firms for the sample period (225) Kenya Variables Foreign Domestic Wage (usd ) Profit(usd ) Turnover(usd ) Labor productivity (usd ) Fixed Assets (usd ) employees Age(years) R & D ( ) Malaysia Variables Foreign Domestic Wage (usd ) Profit(usd ) Turnover(usd ) Labor productivity (usd ) Fixed Assets (usd ) employees Age(years) R & D ( ) Source: Author s calculation based on the datasets 3.2 Empirical estimation and Variables The paper empirically examines the existence of productivity spillovers and their transmission channels in a panel of Kenya and Malaysia manufacturing firms for the period 225. Following after Takii (25) a production function of firms both foreign and local is assumed as a following translogtype: ln( V LK ) A ln( L ln( L ) ln( K ) L ) ln( K K ) LL 2 2 ln( L ) ln( K ) where V is value added, and L and K are the number of workers and capital stock respectively 8. A refers to the efficiency level of a firm. At first, the efficiency is assumed to be decomposed into three components, firm type (whether foreign or domestic), firmspecific factors ( i : the individual effects), industry and year specific factors which are captured using dummies. The firm specific factors are decomposed into timeinvariant factors and timevariant factors. The former are the individual effects, while the latter include several measures of foreign presence. Due to data KK (1) 8 L and K are each divided by its mean value. The translog production function can be regarded as a second order approximation of arbitrary function at one point. When estimating the translog production function, it is common to use the approximation at point (L; K) = (1;1). Takii (25) used the approximation at point (mean of L; and mean of K) because it appeared to yield better results. 12

13 availability, four measures of foreign presence are considered in the case of Kenya and five in the case of Malaysia. These channels have been identified by Crespo and Fontoura (27) as demonstration, labor turnover, exports, competition and vertical linkages. However, due to the difficulties of measuring and obtaining data on labor turnover this variable is not considered. Moreover, for the Kenyan data vertical linkages are not considered since this data is not available, while in Malaysia only data on backward linkages is available. Demonstration effects are proxied by the proportion of expenditure of research and development carried out by foreign owned firms in a given sector (RDF). This captures the contribution of foreign owned firms to the available stock of technological knowledge, on the assumption that the more innovation activities carried out by these firms, the larger the potential for imitation from which domestic firms can benefit. Second is the relative weight of foreign owned firms in total employment in a sector (FEM). It accounts for the relative importance of these firms at the sector level in the domestic market. The greater their relative importance, the stronger the competitive pressure on domestic firms 9. Third is the relative importance of foreign owned firms in value addition in a sector (FVD). It is assumed that the greater their importance in the value addition of a given sector, the higher the scope for domestic firms to benefit from information externalities. Finally, for Malaysia the variable DF is included to capture the importance of vertical backward linkages created by foreign owned firms through purchases of direct raw materials from domestic firms. Positive coefficients are expected for RDF, FEM, FVD and DF. These variables are also obtained from the firm level data. Equation (1) is estimated without the various spillover channels and is based on the full sample. It establishes whether there is a difference between the productivity of foreign and domestic firms. Several equations are thereafter estimated following after equation one to include the spillover variables. These equations are now based on the domestic firms only, since the objective of the study is to establish the impact of foreign presence on domestic firms. However, due to the expected correlation between these variables as shown by the correlation matrices in table A.3 and A.4 in the appendix, the effect of each one of them on productivity is estimated both separately and jointly. These equations in general take the following form: ln( V KK ) i 2 ln( K ) ln( L ) ln( K ) FDI FDID LK S s j YD ln( L YD t L ) ln( K K ) LL ln( L where FDID dummy is = 1 if the firm is foreign and otherwise. S is the sectoral dummies, while YD is year dummies for the years has been chosen as the year of reference and the food, beverage and tobacco sector (31) has been identified as the sector of reference. The rest of the variables are as defined above. 2 ) (2) 9 Similar spillover measures have been used in several studies to include Aitken et. al., (1997) and Greenaway et. al., (24). However, other measures of spillovers include vertical and horizontal industrial linkages as used by Kneller and Pisu (27). 13

14 ln( V KK FVD ) S i 2 ln( K ) ln( L ) ln( K ) FVD st s j DF YD LK DF t st t ln( L L ) ln( K K RDF RDF st ) LL FEM ln( L FEM ) st 2 (3) where S j captures the sectoral dummies and the rest of the variables as defined above. It has been established in literature that productivity spillovers are dependent on the existence of technology gap. Following Takii (25), three measures of technology gap are considered, the average wage gap, labor productivity gap and the capital intensity gap. One possible important cause of spillovers occurs when locally owned firms employ skilled workers away from foreign owned firms by offering relatively high wages. If the wage gap between the foreign and local firms is large, it s difficult for domestic firms to entice these workers because they can t offer a large wage premium and hence spillovers are likely to be small. The median of these gaps is obtained and the data is divided into high and low technology gap using the three measures and equation 3 is then estimated for each group. Several econometric issues arise when estimating the productivity spillovers from FDI. Ordinary Least Squares (OLS) is inappropriate for estimating the impacts of labor and capital on productivity, since the factors of production should be treated endogenously. It is however a more important issue for the spillover variables. If this assumption is not fulfilled it may lead to biased estimates. In the case where FDI is attracted to industries (or subsectors) with high productivity growth, estimates using OLS could be biased upwards. Alternatively, foreign firms may be attracted to slowgrowing industries in order to gain a greater competitive advantage, which means that the OLS estimates will be biased downwards. In dealing with the possibility of an endogeneity bias, this study following Suyanto et. al., (29) uses lagged measures of spillovers. Lags may be appropriate because spillovers take time to materialize. It is also possible that spillovers may also be flowing from domestic firms to foreign firms. This is taken into account by estimating equation 3 using data on foreign owned firms and using similar spillover measures for domestic firms in both countries. Finally, the role of outliers is also examined in this study by estimating equation 3 with and without outliers. 4. Empirical Results 4.1 Differences in productivity differentials The first estimation is based on equation 1 above. It aims at establishing whether there is a difference between the productivity of foreign and domestic owned firms in the two countries. The estimation is based on a pooled sample, random effects and fixed effects and the results are presented in table 4 below. The results show that foreign owned firms in Kenya have a positive and statistically significant different level of productivity compared to domestic firms at the 1 percent level. The results also suggest positive significant relationship between labor, capital, and their squares on productivity. The impact of the interaction term of labor and capital on productivity is negative and also significant at the 1 percent level. The last column containing results based on a CobbDouglas (CD) specification show 14

15 consistency with other results obtained. Similar results are observed for Malaysia. However, the fixed effects model shows that the square of capital although positive is not significant at the 1 percent level. In addition, the foreign owned firms in Malaysia do not differ with the domestic owned firms in terms of their impact on productivity. Similar results are obtained using the CD specification in the last column. Table 4: Regression results for differences in productivity of foreign and domestic owned firms Kenya Pooled Random effects Fixed Effects CD Variables Coefficient t value Coefficient t value Coefficient t value Coefficient t value lnl *** *** *** *** lnk *** *** *** *** lnl *** *** *** lnk *** *** *** lnlk *** *** *** FDI *** *** *** *** _cons *** *** *** *** Sector Dummy Yes Yes Yes Yes Year Dummy Yes Yes Yes Yes Hausman test *** Observations R Malaysia Pooled Random effects Fixed Effects CD Variables Coefficient t value Coefficient t value Coefficient t value Coefficient t value lnl *** *** *** *** lnk *** *** *** *** lnl *** ***.1 5.8*** lnk *** *** lnlk *** *** *** fdi *** *** _cons *** *** *** Sector Dummy Yes Yes Yes Yes Year Dummy Yes Yes Yes Yes Hausman test *** Observations R *** denotes significance at the 1 percent level. CD= CobbDouglass specification. The difference in the levels of productivity between foreign and domestic owned firms in Kenya identified above is consistent with findings by Rasiah and Gachino (25) who found that foreign firms are more productive than domestic firms. In Malaysia, the result that there is no significant difference between the productivity of foreign and domestically owned firms is consistent with findings by Oguchi, et. al.,(22) who find that the total factor productivity for the manufacturing industry as a whole is approximately the same for foreign and domestic owned firms for the period It is however, inconsistent with findings by Khalif and Adam (211) although in their case the dependent variable was labor productivity. The results especially for Malaysia are sensitive to model specification and therefore since 15

16 the Hausman test is significant for both countries, the remaining results are estimated using the fixed effects model. 4.2 Productivity spillovers from foreign to domestic owned firms The second estimation is based on equation 3, which includes the various proxies for productivity spillovers. Due to high correlation between the spillover variables, the effect of each spillover variable on productivity is included separately initially and then jointly. The results are presented in table 5 below. The last column presents the results when all the spillover variables are considered together. According to table 5, the variable RDF and FVD affect productivity negatively in Kenya but insignificantly at the 5 percent level, while the effect of FEM on domestic firms productivity in Kenya is negative and significant at the 1 percent level. Similar results are obtained when these variables are considered jointly. Hence, in Kenya there is evidence of negative productivity spillovers from foreign firms to domestic firms through the competitive effects. In Malaysia on the other hand, there is evidence of positive productivity spillovers from foreign owned firms to domestic firms through the demonstration effects as given by the RDF variable, which is positive and significant at the 5 percent level. There is also evidence of negative spillovers through competition (FEM) and the backward linkage (DF) channels, whose impact on domestic firms productivity is negative and significant at the 5 percent level. The FVD proxy for information spillovers channel is negative but insignificant at the 1 percent level. Moreover, when the effect of the spillover variables is considered jointly on domestic firms productivity, the RDF variable remains significant at the 5 percent level, while FEM is negative and significant at the 1 percent level. The rest of the spillover variables are insignificant. Hence, in Malaysia there is evidence of positive productivity spillovers through the demonstration effects. There is also evidence of negative productivity spillovers through the competition and backward linkages channels. The evidence of negative spillovers through competition effects is consistent with the observations by Gorg and Strobl (25) and Aitken and Harrison (1999). It is however inconsistent with results obtained by Suyanto, et.al., (29) for Indonesia. The result of negative productivity spillovers through backward linkages in Malaysia is inconsistent with findings by Jarkovic and Spatareau (211) for Romania and Du et.al., (211) for China. Similarly, the evidence of demonstration effects in Malaysia is consistent with results obtained by Todo, et.al., (211) for China and Driffield et.al., (21) for Italy. Table 5: Regression results for spillover effects Kenya Variables Coefficient t value Coefficient t value Coefficient t value Coefficient t value lnl * * * * lnk * * * * lnl * * * * lnk * * * * lnkl * * * * RDF

17 FEM * * FVD _cons * * * * Sector Dummy Yes Yes Yes Yes Year Dummy Yes Yes Yes Yes Observation R Malaysia Variables Coefficient t value Coefficient t value Coefficient t value Coefficient t value Coefficient t value lnl * * * * * lnk * * * * * lnl * * * * * lnk lnkl * * * * * RDF ** ** FEM ** FVD DF **.6 1 _cons * * * * Sector Dummy Yes Yes Yes Yes Yes Year Dummy Yes Yes Yes Yes Yes Observation R *, ** significance at the 1 and 5 percent level respectively 4.3 Spillover variables, the technology gap and exports The next estimation aims at investigating the importance of technology gap in productivity as observed by Cantwell (1989, 1995) and Crespo and Fontoura (27). Following after Takii (25) three measures of technology gap are used, the average wage gap, the average labor productivity gap and the capital intensity gap. These indices are calculated as the ratios of averages of foreign and domestically owned firms. In addition, productivity can also depend on the exporting behavior of the firm as observed by Greenway and Kneller (28) and Bigsten, et. al., (24). This is examined by introducing a dummy variable if a domestic firm exports or not. These results are reported in table 6 below. 17

18 Table 6: Spillover variables, technology gaps and exports Kenya Malaysia Average Wage Technology Gap Average Wage Technology Gap Individually Jointly Chow Test Individually Jointly Coefficie Coefficie t nt t value nt value Spillover Variables Coefficie nt t value Coefficie nt t value RDFHIG H RDFLO W *.8 4.3* Chow Test FEMHIG H * * FEMLO W * * ** * FVDHIG H FVDLO W DFHIGH ** DFLOW ** ALL 2.85* 3.73* Labor Productivity Technology Gap Labor Productivity Technology Gap RDFHIG H * RDFLO W ** * FEMHIG H * * * FEMLO W * * ** * FVDHIG H FVDLO W DFHIGH ** DFLOW **.4.5 ALL 2.76** 3.33* Capital Intensity Technology Gap Capital Intensity Technology Gap RDFHIG H * * 4.41** RDFLO W FEMHIG H * * * FEMLO W * * ** FVDHIG H * FVDLO W * DFHIGH ** DFLOW *.5.59 ALL 2.34** 5.54* EV.23 Exporters 1.34 *.23 Exporters 1.25 * * * RDF **.3 FEM * * ** FVD DF ** * and ** denote significance level at 1 and 5 percent 18

19 The coefficients of the spillover variables for firms with high and low technology gap defined in terms of average wage, considered both individually and jointly are not significant at the 5 percent level with the exception of the measure of competition effects (FEM) for Kenya. In Malaysia however, there is evidence of positive demonstration effects (RDFLOW) and negative competition effects (FEMLOW) for firms with a technology gap defined in terms of average wage at the 5 percent level of significance when considered both separately and jointly. There is also evidence of negative productivity spillovers through the backward linkages both for firms with a high and low technology gap at the 5 percent level only when considered individually. They are significant differences between the coefficients of the spillover measure in Malaysia as given by the chow test, with the exception of the information channel (FVD), while in Kenya there is no significant difference between the coefficients of the spillover measures. However, when all the coefficients of the spillover measures are considered jointly, they are not only dissimilar but also different from zero at the 1 percent level of significance. Similar results are obtained when the labor productivity is used as a measure of the technology gap in both countries. The results do not change in Kenya when capital intensity is considered as a measure of technology gap. However, in Malaysia there is evidence that demonstration effects are dominant in firms that have a high technology gap when considered both individually and jointly. Negative competition effects are concentrated in firms that have low technology gap when considered individually. Similarly, negative information spillovers are concentrated in the firms with low technology gap when all spillover variables are considered jointly. Thus, there is evidence of negative productivity spillovers from competition effects in both countries across all the three measures of technology gap. However, in Malaysia this is concentrated in firms that have a low technology gap. In addition, there is evidence of positive productivity spillovers through demonstration effects concentrated in firms with a low technology gap defined in terms of average wages and labor productivity in Malaysia. When technology gap is defined in terms capital intensity demonstration effects are concentrated in firms with a high technology gap and there is also evidence of negative productivity spillovers through the information channel when all spillover variables are considered jointly. There is also evidence of negative spillovers through the backward linkages for all measures of technology gap. The evidence of productivity spillovers in both high and low levels of technology gaps is inconsistent with findings by Cantwell (1989, 1995) and Crespo and Fontoura (27). Moreover, as observed in table 5.6 above, the effect is dependent on how technology gap is measured. Finally table 6 shows that whereas exporting domestic firms in Kenya have higher impact on productivity than non exporting firms, in Malaysia this is not the case, where the productivity of exporting firms is not different from that of non exporting firms. It can also be observed that the spillover measures have similar effects on both exporting and non exporting firms. Similar results are obtained in table 5 with the exception of the size of the spillover coefficients. The results for Kenya are consistent with findings by Greenway and Kneller (28) and Bigsten, et. al., (24), that firms can raise their levels of productivity once they begin exporting. However, this does not appear to be the case in Malaysia. 19

20 4.4 Robustness check Equation 3 above is estimated using the lags of spillovers in an attempt to deal with the endogeneity issue following after Suyanto et. al., (29). In addition, the evidence of productivity spillovers from foreign owned firms to domestic firms is further examined by estimating equation 3 for foreign firms using similar spillover measures used in the analysis but for domestic firms in these countries. The results are presented in table 7 below. There is evidence of negative productivity spillovers through the lag of the competition effects. Similarly, there is also evidence of positive productivity spillovers through information spillovers in Kenya. These spillover variables are significant at the 1 percent level. Hence, the concentration of firms in some sectors lead to information sharing which affects the productivity of the domestic firms. In Malaysia, there is evidence of positive productivity spillovers from demonstration effects. However, there is also evidence of negative productivity spillovers from both the competition and the backward linkages channels, which are significant at the 5 percent level. Table 7: Robustness check Kenya Spillover Variables Lag Spillovers Domestic spillovers to foreign owned firms Individually Jointly Individually Jointly coefficient t values coefficient t values Variables coefficient t values coefficient t values LagRDF RDD LagFEM *** DEM LagFVD *** DVD Malaysia LagRDF **.3 2.3** RDD LagFEM ** ** DEM LagFVD DVD LagDF **.6.93 DD ** *** *** and ** denote significance at the 1 and 5 percent level The results of the estimation of spillovers from domestic firms to foreign firms as presented in table 7 above for Kenya and Malaysia. The results suggest that domestic firms do not significantly affect foreign owned firms productivity. Hence, it can be concluded that it is the foreign owned firms that affect the productivity of domestic firms in Kenya. In Malaysia, there is evidence of domestic firms affecting the productivity of foreign firms indirectly through the creation of backward linkages with other domestic firms. This is consistent with findings by Wei et.al., (28) who find the existence of mutual productivity gains between foreign and domestic owned firms in China. Finally the value added plots graphs 9 1 a, b, c and d, generated using a linear regression are presented below and contain outliers. The removal of the outliers does not affect the results hence they can be considered robust Graph 9(a) RDFKenya Graph 9(b) FEMKenya Graph 9(c) FVDKenya 2