Foreign Direct Investment in the Agricultural Sector of Thailand and its Effect on Productivity

Transcription

1 European Journal of Scientific Research ISSN X Vol. 91 No 3 November, 2012, pp EuroJournals Publishing, Inc Foreign Direct Investment in the Agricultural Sector of Thailand and its Effect on Productivity Waleerat Suphannachart Department of Agricultural and Resource Economics, Faculty of Economics Kasetsart University, 50 Paholyotin Road, Chatuchak, Bangkok, Thailand fecowrs@ku.ac.th Tel: ; Fax: Abstract This paper analyses foreign direct investment (FDI) in the agricultural sector of Thailand over the period It has two main objectives. First is to analyse the characteristics of FDI in the Thai agricultural sector comprising crops, livestock, fisheries and forestry, using the two main sources of FDI data namely the Bank of Thailand s FDI inflows and the Board of Investment s promoted foreign investment. Second is investigating the impact of FDI on agricultural total factor productivity (TFP) using error correction modelling technique. The results conform to the previous findings that FDI in the agricultural sector has been very small but the investment value has increased markedly in recent years. Livestock has been the largest recipient of FDI in the agricultural sector, followed by crop, fisheries and forestry. These FDI have concentrated in export-oriented activities and largely operated in the form of joint venture. As for the FDI impact on the TFP, econometric results show that the FDI inflows, representing the overall investment, do not have a significant impact on the TFP while the promoted foreign investment, representing FDI projects approved by the Board of Investment, has a positive and significant impact on the TFP. This suggests the technology transferred through FDI channel is observed more clearly in the promoted group that received privilege schemes. Despite the very small value of FDI in the agricultural sector, its benefits on productivity can still be partially observed and shall be strengthened with a clear emphasis of investment policies. Keywords: Foreign Direct Investment, Agricultural Productivity, Total Factor Productivity 1. Introduction FDI has long played an important role in economic development in a number of host countries including Thailand. Since 1970 the role of FDI has been recognized as sources of local employment, capital, know-how, and advanced technology that can enhance economic growth in Thailand. A number of previous studies have investigated the determinants and impacts of FDI in the Thai economy but mostly concentrated in the manufacturing sector and the overall economic growth (Tambunlertchai and Ramstetter, 1991, Kohpaiboon, 2003, Kotrajaras, 2010). There has been very limited empirical evidence on the pattern and impact of FDI in the agricultural sector despite the fact that Thailand is an agriculture-based economy and there has long been foreign investment in

2 Foreign Direct Investment in the Agricultural Sector of Thailand and Its Effect on Productivity 394 agricultural production. It is therefore interesting to examine the pattern and characteristics of FDI in the agricultural sector of Thailand. In addition, since FDI is widely viewed as a channel of technology transfer it is interesting as well to investigate its impact on technology-induced productivity in the Thai agriculture. An understanding of the FDI characteristics and its impact on agricultural productivity can shed lights on policy measures that can help sustain long-term growth of the agricultural sector and the overall economy. This paper has two main objectives. First is to analyze the characteristics of FDI in the agricultural sector over the four decades ( ). This should give a clear picture of the changing pattern of FDI in the agricultural sector, notably in terms of investment value, sources of FDI and subsector decomposition. Second is to investigate the impact of FDI on agricultural total factor productivity (TFP). The TFP is employed as a crude proxy of technological change, which may result from technology transfer through FDI channel. The scope of this study focuses on the inward FDI in the primary agricultural sector including mainly crops, livestock, fisheries and forestry. Time series data at an aggregate level are employed covering the period from 1970 to The next section discusses previous research, methods and data, results and conclusion. 2. Previous Research Regarding the detailed study on the characteristics of FDI in Thai agriculture, there has been very limited number of empirical studies. Netayarak (2008) is the only one study focusing on the role of FDI in the agricultural sector, both primary agriculture and processed agricultural goods. His study discussed the characteristics and impact of FDI on agricultural production in Thailand during The findings are that the value of FDI in the agricultural sector (excluding the agro-industry subsector) over the studied period was very small, accounting for less than 1% of the total FDI. These FDI were accounted for 6% of the total foreign investment under the BOI promotion. FDI were mostly in the form of joint ventures and export oriented. The presence of FDI in the agricultural sector is small. As a result, there are limited empirical studies on the role of FDI in agricultural growth and productivity (Furtan and Holzman, 2004, Sattaphon, 2006). FDI has been widely recognized as an important channel bringing in capital, new technology and know-how that can enhance the technological capability of the host country firms. In Thailand context, earlier studies have investigated the FDI impact on technology transfer to both agricultural and manufacturing sectors. Netayarak (2008) investigated the impact of FDI on agricultural production emphasizing on the role of technology transfer using primary data, collected by interviewing 20 firms. The study found evidence that the diffusion rates of new agricultural technologies introduced to Thai farmers were very high. Most agricultural R&D projects were conducted by private firms financially supported by their parent companies or subsidiaries abroad. In particular, the Thai agro-industries have benefited greatly from the technology transfer during the past decades (Netayarak, 2008). However, Kohpaiboon (2006) argued that technology transfer to the agricultural sector occurs mostly through non-fdi channels. Private companies, particularly the Charoen Pokphand (CP) Group, have played an important role in transferring technology to Thai farmers. The CP Group has been instrumental in the research and development of broiler and shrimp cultivation, seed technology and a new variety of freshwater fish (Poapongsakorn, 2006, p.35). Nonetheless, statistical analysis has not been conducted in either study. The technology transfer effect of FDI varied among empirical case studies. Kohpaiboon (2006) investigated linkages between FDI and technology spillover using Thai manufacturing as a case study, some of which include food products, beverages, rubber and wood products. The study found that gains from FDI technology spillover is conditioned by the nature of the trade policy regime, meaning that to maximize gains from FDI technology spillover liberalizing investment policy has to go hand in hand with liberalizing the trade policy (Kohpaiboon, 2006). Although his study did not specifically measure the gains from FDI technology transfer it has important policy implications. Agricultural trade

3 395 Waleerat Suphannachart policy in Thailand has to be liberalized to induce the type of FDI inflows that likely introduce technology spillover. As regards to the linkage between FDI and productivity, productivity growth can result from technology developed in other countries. Many developing countries have relied on agricultural research and technology primarily conducted by a small group of developed countries, especially the United States, France, Germany and Japan (Pardey et al., 2006). Access to foreign technology can be gained directly through imports and FDI or indirectly through international cooperation. Foreign agricultural technology can be directly imported in the form of capital goods and raw materials, such as machinery, livestock breeds, seeds and fertilizers. Alternatively, FDI is another important channel bringing in new technology and know-how that can improve production efficiency and productivity in host countries. FDI also expands market channels and encourages market competition, which is beneficial to productivity growth. For instance, FDI could bring in new planting techniques, farm management and expand the agricultural export market. An indirect channel of technology transfer is through the system of International Agricultural Research Centres including the centres under the support of the Consultative Group on International Agricultural Research (CGIAR) (Pardey et al., 2006). In Thailand context, there have been a small number of studies examining the impact of FDI on agricultural productivity. Previous research has mainly focused on the FDI impact on total factor productivity (TFP) as TFP growth is often viewed as a measure of technological change that can help sustain long-term growth. For example, Kaipornsak (1995) studied agricultural growth in five major crops (soybeans, maize, paddy, sugarcane and cassava) during The study found a declining trend in the use of inputs with the highest TFP growth per annum in the production of soybeans and the lowest in the production of sugarcane and cassava. In analysing TFP decomposition, two major sets of factors are investigated. These are technological advance, represented by R&D and FDI, and competitive environment, represented by the effective rate of protection, exports, concentration ratio and managerial structure. The results show public R&D spending and FDI were major determinants of TFP growth. However, the study assumes there is no lag involved and ignores the roles of research spillovers and extension services. In recent studies, Suphannachart and Warr (2009) measured TFP and examined factors affecting the measured TFP in the overall agricultural sector of Thailand during FDI, measured as net FDI flows, was also included as one potential determinant. Lags and previously ignored factors potentially affecting the TFP, for example, international research spillovers, agricultural extension and infrastructure factors, were also included. It was argued that foreign partners played a major role in choosing processing techniques that suit foreign demand, notably in processed agricultural product like chicken, pineapples and tiger prawns. Foreign companies also brought in seeds and animal breeds that were adapted with local conditions and benefited Thai agriculture. However, there was no statistical evidence to support the hypothesis that FDI significantly affects agricultural TFP (Suphannachart and Warr, 2009). 3. Research Method Characteristics of FDI in the agricultural sector of Thailand are analyzed using descriptive method. Since there is no detailed data on FDI in the agricultural sector available, the project-level data on FDI relating to agricultural production are compiled and analyzed over the period Then the time series data of FDI are used as one of the explanatory variables in the TFP determinants model, other relevant explanatory variables are also included. The model is estimated using the error correction modeling (ECM) procedure of Hendry (1995). The analysis of the FDI impact on productivity is based on a conceptual model that is widely used in explaining the relationship between productivity and technology-related factors (Evenson, 2001, Evenson and Pray, 1991) in which the determinants of TFP include technology factors such as agricultural research, extension services and FDI, infrastructure such as road and irrigation, education that can raise farmers skills, and climate factors. Trade openness is also included as it helps achieve

4 Foreign Direct Investment in the Agricultural Sector of Thailand and Its Effect on Productivity 396 economies of scale by expanding market size through export bringing about real cost reductions, thereby increasing productivity (Coelli et al., 2005). Lags are also incorporated, as discussed below. Other explanatory variables are explored in accordance with their potential connections with TFP in the Thai agriculture context (Suphannachart and Warr, 2012). In stylized form, the model is: TFP f ( FDI, R, E, I, Edu, TO, W ), (1) where TFP = total factor productivity, FDI = foreign direct investment, R = agricultural research expenditure, E = agricultural extension expenditure, I = infrastructure (rural roads and irrigation), Edu = education level of farmers, TO = trade openness, W = weather or climate factor, The ECM technique is employed in examining the linkage between FDI and TFP because it can investigate both short-run and long-run determinants of TFP while allowing dynamic and flexible form of lags. Another reason for using this approach is that it does not require that the variables under consideration have the same order of integration. This approach minimizes the possibility of estimating spurious relationships while retaining long-run information without arbitrarily restricting the lag structure (Hendry, 1995). The ECM also provides estimates with valid t-statistics even in the presence of endogenous explanatory variables (Inder, 1993). Suphannachart and Warr (2012, 2011) also used this method in examining the factors affecting agricultural TFP in Thailand. The estimation procedure begins with an autoregressive distributed lag (ADL) specification of an appropriate lag order. m Y AY B X t i t i i t i t i 1 i 0 m (2) where is a vector of constants, Y t is a ( n 1) vector of endogenous variables, X t is a ( k 1) vector of explanatory variables, and A i and B i are ( n n) and ( n k ) matrices of parameters. The general ADL allows the initial lag length on all variables at two periods, except for the research variable where the lag length extends to four periods. The two-year lag is the established practice in modeling with annual data (Athukorala and Tsai, 2003). Equation (2) can be rearranged by subtracting Yt 1from both sides, yielding the explanatory variables in terms of differences, representing the short-run multipliers, and the lagged levels of both the dependent and explanatory variables, capturing the long-run multipliers of the system. m 1 m 1 * * t i t 1 i t i 0 t m 1 t m t i 1 i 0 (3) Y A Y B X C Y C X m where C 0 I Ai, m C1 Bi, I is the identity matrix and the long-run multipliers of the i 1 i 0 1 system are given by C 0 C1. Equation (3) is known as the error correction mechanism (ECM) representation of the model. Under the ECM, the long-run relationship is embedded within a sufficiently detailed dynamic specification, including both lagged dependent and independent variables, which helps minimize the possibility of estimating a spurious regression. The ECM can be estimated by OLS and the short- and long-run parameters can be separately identified. 4. Data There are two main sources of foreign direct investment (FDI) statistics in Thailand; the Bank of Thailand (BOT) and the Board of Investment (BOI). Both are employed in this study. The BOT s FDI statistics cover overall FDI flowing into the Thai economy while the BOI s FDI partially cover the FDI

5 397 Waleerat Suphannachart that receive BOI s promotion packages. It is important to note that not all FDI projects apply for BOI s promotion and the two data sources are compiled on a different basis. Nonetheless, both data sets complement each other. BOT s FDI data represent actual flows of FDI into Thailand while BOI s data indicate trends of FDI. The BOT s FDI depict the overall picture of FDI at an aggregate level while BOI s FDI allows us to investigate the role of foreign companies at a project level. FDI data collected by the Bank of Thailand follows the International Monetary Fund (IMF) Balance of Payments Manual, which is an international standard for collecting FDI statistics. The BOT s FDI statistics comprise three components; equity capital with at least 10% of foreign shareholding, loans from affiliates, and reinvested earnings (Bank of Thailand, 2010). Since the data definitions are in accordance with the international standard they are comparable among countries and widely used in the analysis of FDI. The BOT s statistics represent the entire streams of investment and often reported as net FDI flows (inflows minus outflows). Only the FDI inflows are considered in this study as it represents the actual interests of foreign investment in the sector of interest. FDI data collected by the BOI refer to projects with foreign capital of at least 10%. The BOI s FDI definition does not strictly comply with the IMF s direct investment standard therefore the data is often called foreign investment instead of foreign direct investment. The BOI s foreign investment data cover only projects applied for or received approval for BOI promotion. There are seven sectors under the BOI promotion; i) agriculture and agricultural products ii) mining, ceramics and basic metals iii) light industry iv) metal products, machinery and transport equipment v) electronic industry and electrical appliance vi) chemicals, paper and plastics vii) services and public utilities. This study focuses only the promoted projects on agricultural production under the first sector. Project-level data allows the data to be re-grouped so that they match with the scope of this study. Agricultural products and food-processing are excluded from the FDI data series. For the investigation of the FDI impact on productivity, agricultural total factor productivity (TFP) is used as a dependent variable. TFP is an index of output over a set of inputs. An increase in TFP means an output can be raised using fewer inputs thereby output growth can be sustained using fewer resources. The TFP is measured as a residual of output growth that cannot be explained by major factor inputs using the Solow-type growth accounting. The TFP growth is converted into index to capture the long-term information. 1 Both BOT and BOI data are tested in the TFP determinants model, individually. Besides the FDI, other potential determinants of the TFP are also included, that is, public agricultural research and extension, education, infrastructure (irrigation), trade openness and rainfall. All the data are time-series covering the period of as summarized in Table1. Table 1: Summary of variables and sources of data Variables Definitions Data Sources 1) inflows of FDI in the agricultural sector 1) Bank of Thailand 2) foreign investment projects in the agricultural sector, 2) Office of the Board of received investment promotion privileges Investment Residual of output growth that cannot be explained by primary input growth (including land, labour and capital). The TFP growth is converted to an index using 1970 as a base year Foreign direct investment (FDI) Agricultural total factor productivity (TFP) Public agricultural research (R) Agricultural extension services (E) Real public agricultural research budget (covering mainly crops and livestock), adjusted to real terms using implicit GDP deflator. Real public agricultural extension budget (covering only crops), adjusted to real terms using implicit GDP deflator. Author calculation based on the growth accounting method (Suphannachart and Warr, 2012) Bureau of the Budget, Office of Prime Minister Bureau of the Budget, Office of Prime Minister 1 See detailed explanations on the TFP measurement in Suphannachart and Warr (2012).

6 Foreign Direct Investment in the Agricultural Sector of Thailand and Its Effect on Productivity 398 Table 1: Summary of variables and sources of data - continued Irrigation (I) Farmers education (Edu) percentage share of irrigated area in total agricultural land area Agricultural labour with upper secondary education level as percentage share in total agricultural labour force Office of Agricultural Economics Labour force survey, National Statistical Office Office of Agricultural Economics and Agricultural export and import as percentage Trade openness (TO) National Social and Economic share in total agricultural GDP Development Board Rainfall (Rain) Amount of average rainfall in millimeter Office of Agricultural Economics 5. Results 5.1. Characteristics of FDI in the Agricultural Sector: A Review Over the Past Forty Years This section discusses the characteristics of FDI in the agricultural sector of Thailand over the period of It begins with the BOT FDI data as they represent the overall FDI that flow into the agricultural sector, recorded through the balance of payments. Then, the BOI FDI data are discussed as they represent the FDI that received the promotion privileges. Since BOI data are collected at project level they provide more details on the characteristics of FDI, hence, this study focuses more on the BOI data. However, as noted earlier the two sources of FDI data are compiled on a different basis they are not comparable. In overall, there had been an increasing trend of FDI inflows in the agricultural sector as shown in Table 2. On average, FDI inflows in the agricultural sector is million Baht (8.17 US dollar), increasing at an average annual growth rate of 10.54%. There was a sharp drop during mainly due to the 1997 financial crisis. There was an upward pick up afterwards leading to a positive growth rate during Sources of these FDI are from Japan, U.S. and Taiwan. The agricultural FDI shares in total FDI inflows and agricultural GDP are very small, accounting for 0.41% of the total FDI and 0.07% of GDP, respectively. The agricultural sector has been the smallest recipient of FDI compared with other sectors. This is largely because most of primary agricultural production is done by the locals. The presence of FDI in Thai agriculture is restricted by the Foreign Business Act (1999), in which the majority of shareholdings must be Thais. 2 Most agricultural activities are reserved for Thai natives, namely rice farming, farming or gardening, animal husbandry, forestry, and fishery for marine animals. Although there are still rooms for FDI with 10-49% foreign shareholdings FDI mainly concentrates in the manufacture of agricultural products rather than primary agriculture (Suphannachart and Thirawat, 2012). The patterns of FDI involvement in each subsector are shown more clearly with the BOI data, discussed below. Table 2: FDI Inflows in the agricultural sector of Thailand, Period Total FDI inflows (million Baht) 3, , , , , FDI inflows in Agriculture (million Baht) Growth rate (%) Share in total FDI (%) Share in AgGDP (%) Source: Bank of Thailand (BOT). Note that all numbers are annual average. AgGDP is agricultural GDP at current prices. The GDP data are taken from the National Economic and Social Development Board. Since the establishment of the Office of the Board of Investment in 1966, agriculture and agroindustry has been one of the seven eligible activities Thai government tries to induce more investment 2 Foreign investment in Thailand is governed by the Foreign Business Act (1999), Alien Employment Act (1978) and Investment Promotion Act (1977).

7 399 Waleerat Suphannachart from both local and foreign companies. Investment projects under the agriculture and agricultural products sector are also classified as priority activities receiving the maximum tax privileges. At the beginning there was no foreign investment in the agriculture and agricultural products sector. Later in the mid-1970s, foreign investors had shown their interests in this sector and brought in technology to invest in food ingredients projects. The projects used local agricultural outputs such as palm, cassava, and rubber as raw materials and added value to their products (BOI, 2006). Since then, foreign investors confidence has improved as shown by their continuous increased investments in this sector up to the present. Over the period , foreign investment in the agricultural sector promoted by the BOI, although has increased markedly, has a relatively small share in total foreign investment compared with other sectors. FDI in the agriculture and agricultural products has concentrated in export-oriented activities particularly in food processing and agro-industry. They have largely operated in the form of joint-venture. Major investing countries have come from Japan, U.S., Malaysia and Taiwan (Suphannachart and Thirawat, 2012). As shown in Table 3, the value of foreign investment in the agriculture and agricultural products during is totaled at 324,341 million Baht; accounting for 5.4 percent of the total foreign investment approved by the Board of Investment. The number of approved projects in this sector is 1,805 projects, accounting for 11.3 percent of the total number of approved foreign projects. As for the primary agricultural production, there are altogether 189 projects, accounting for 10.5% of the promoted projects in the agriculture and agricultural product sector. Its investment value is 35,233 million Baht, accounting for 10.9% of the investment value in the agriculture and agricultural products sector. Considering at sub-periods, the value of investment in primary agriculture had increased over time. However, its share in the sector had declined during and but had picked up during , showing an increasing interest in the agricultural production. Table 3: Promoted FDI in the Agricultural Sector, No. of Investment No. of Investment No. of Investment No. of Investment No. of Investment project (M.Baht) project (M.Baht) project (M.Baht) project (M.Baht) project (M.Baht) Total FDI (1) 97 7,562 2, ,597 4,583 2,305,732 8,694 3,198,585 16,030 6,065,477 FDI in Ag&Pro (2) 24 1, , , ,074 1, ,341 share in (1) FDI in Agriculture , , , ,233 share in (2) Source: International Affairs Bureau, BOI. Note: 1) Promoted FDI projects refer to projects with foreign capital of at least 10%. 2) Ag&Pro refers to the agriculture and agricultural products sector include eligible activities in primary production, food processing, manufacturing and services relating to agriculture and agricultural products. Disaggregating the BOI data, Table 4 shows that foreign investment in the primary agricultural production (including crops, livestock, fisheries and forestry) accounts for only 10.5% of the sector s investment value whereas the majority is concentrated in the manufacturing of agricultural products (including food-processing) and agricultural services. This is in line with the fact that Thailand has become industrialized with more emphasis on agro-industry. Within the primary agriculture, livestock had occupied the largest share, both in terms of number of project and investment value, followed by crops, fisheries and forestry. The total value of foreign investment in livestock during is 29,399 million Baht, generating employment for 31,736 Thai workers. Most of the livestock projects comprise livestock breeding and husbandry, mainly in the broiler chicken and swine production, producing mainly for export markets. Crop projects include plant propagation and development and hydroponics cultivation. They are predominated by vegetables, fruits and field crops production. Fishery projects involve aquatic husbandry and deep sea fisheries, mainly in prawn aquaculture. Foreign investment in forestry came mainly from a few forest plantation projects (teakwood, sandalwood and argarwood). Approved projects in crops, livestock and fisheries have taken place since the mid-1970s while that of forestry has just begun since 2004.

8 Foreign Direct Investment in the Agricultural Sector of Thailand and Its Effect on Productivity 400 Table 4: Promoted FDI in the Agriculture and Agricultural Products Sector, Agriculture and Agricultural Products Share in total (%) Subsectors No. of Investment Employment No. of Investment Employment project (million Baht) (person) project (million Baht) (person) Crops 65 4,529 11, Livestock ,399 31, Fisheries Forestry Agriculture ,233 43, Manufacture of Ag products 1, , , Others (services) ,098 27, Total 1, , , Source: Authors calculation based on the data from the International Affairs Bureau, BOI. There has been a changing investment structure within the primary agricultural production activities, as shown in Table 5. During the early periods ( ), crops were the major recipient of foreign investment, accounting for almost 80% of the total investment value in primary agriculture. Livestock and fisheries received moderate investment while none for forestry. The crop projects that were approved by the BOI in early days were fast grown tree cultivation and pineapple cultivation projects. In more recent years, investment has shifted to the production of hybrid corn seeds, mushroom, and hydroponic vegetables. Since the 1980, crops have received less investment while livestock and fisheries have gained more foreign investment. FDI in the livestock subsector had expanded significantly during , receiving 82% share, and remained the largest recipient of FDI in the agricultural sector up to the recent period of Table 5: Promoted FDI in the Agricultural Sector Classified by subsectors, Sub-sectors No. of Investment No. of Investment No. of Investment No. of Investment No. of Investment project (M.Baht) project (M.Baht) project (M.Baht) project (M.Baht) project (M.Baht) Crops , , ,529 share in Ag Livestock , , , ,399 share in Ag Fisheries share in Ag Forestry share in Ag Agriculture , , , ,233 Source: Authors calculation based on the data from the International Affairs Bureau, BOI Examining the Effects of FDI on Agricultural Productivity: Results from the ECM Method From the estimation of the TFP determinant model using the ECM method developed by Hendry (1995), the TFP determinant equations are statistically significant at the 1% level in terms of the standard F test and perform well in terms of standard diagnostic tests for serial correlation, functional form specification, heteroskedasticity and stationarity of the residuals. The final parsimonious equations are shown in Table 6. The choice of dropping or keeping variables in the final models was statistical acceptance in terms of the joint variable deletion tests against the maintained hypothesis. Since all variables are measured in logarithms, the regression coefficients can be interpreted as elasticities and the size of the coefficients also indicate the magnitude of their relative influence. The results are divided into two models using the overall FDI inflows (BOT data) and the promoted foreign investment (BOI data).

9 401 Waleerat Suphannachart Table 6: TFP Determinants in the Agricultural Sector of Thailand Dependent variables = ln TFP t Model1: FDI = Inflows of FDI (BOT FDI) Model2: FDI = Promoted FDI (BOI FDI) Coefficient Coefficient Long-run elasticity (t-statistics) (t-statistics) Long-run elasticity constant (-0.129) (-0.119) ln R P t (0.821) (1.024) ln E t (-0.708) (-0.304) ln FDIt 1 (0.045) (1.799)* ln R t 2 (1.267) (0.768) lntot (1.986)* (2.063)** ln Edut (2.900)*** (1.909)* ln Raint (2.137)** (1.774)* lntfpt 1 (-6.421)*** (-5.197)*** No. of observation Adjusted R-squared F-statistics (p-value = 0.000)*** (p-value = 0.003)*** Notes: ***, **, * mean significant at the levels of 1%, 5% and 10%, respectively. The models pass standard diagnostic tests: Breusch-Godfrey serial correlation LM test, Ramsey test for functional form misspecification, Breusch- Pagan-Godfrey Heteroskedasticity test, and Augmented Dickey Fuller test for residual stationarity. All variables are expressed in natural logarithm. Variables expressed in delta terms represent short-term impact. Long-run elasticity is computed by dividing the estimated coefficients of the level term with the positive value of the lagged dependent variable (TFP t-1 ). The results indicate that most of the variables have a long-run impact on TFP, as shown by the significance of the estimated coefficients expressed in the level rather than the change terms. Searching for the determinants of TFP using only the first differenced or growth rate can miss out these level relationships. The ECM results do not show quite significant evidence on the linkage between FDI and agricultural productivity. As for the model using FDI inflows there is no statistical evidence that the overall FDI that flows into the agricultural sector has a significant impact on the agricultural TFP. The foreign investment promoted by the Board of Investment has a positive and significant impact (at 10% level) on the TFP. The computed long-run elasticity of the promoted FDI variable is indicating quite a small impact relative to other significant factors. The significant impact can only be observed in the promoted FDI suggest that the FDI impact on productivity is observed more clearly in the promoted group, receiving the BOI incentives, than the overall FDI. The BOI has listed all projects investing in the agricultural sector as priority activities in which the maximum tax privileges are granted. The significant impact, though very small, may also reflect benefits received from the promotional privileges. The determinants of TFP are not confined only to the technology-related factor but also include other economic and non-economic factors. Other major factors significantly influencing the TFP are education (Edu), trade openness (TO), and rainfall (Rain) as shown in Table 2. All these variables have the expected positive signs. The significant positive impact of education implies that it improves farmers ability to process information and select, manage, and operate new technologies. The trade openness expands market size through exports and brings in foreign technology thereby raising agricultural productivity. More rainfall increases agricultural production and hence productivity. Under the first model using FDI inflows data the long-run elasticities calculated from the steady-state solutions for TO, Edu and Rain are 0.08, 0.06 and 0.07,

10 Foreign Direct Investment in the Agricultural Sector of Thailand and Its Effect on Productivity 402 respectively. The elasticities are also of similar size for the second model using the promoted FDI; they are estimated at 0.05, 0.05 and 0.07 for TO, Edu and Rain, respectively. The coefficients of the lagged dependent variable (TFP t-1 ) are statistically significant with the expected negative signs in both equations. They indicate the speed of adjustment of TFP to exogenous shocks. The coefficients corresponding to TFP t-1 is quite large, implying a very high speed of adjustment to dissipate the shock in the absence of policy action. There is no statistical evidence that the coefficients of public research (R) and extension (E) are significant in either the short run or the long run. Other explanatory variables including infrastructure, do not appear to be statistically significant and dropping them improving the explanatory power of the model. It is important to note that the results could reflect data problems. In particular, the public agricultural research and extension variables cover mainly crops and livestock expenditure. Given the partial measures of these variables, it is not safe to infer they are unimportant in explaining the variation of TFP in the agricultural sector that includes crops, livestock, fisheries and forestry. 6. Summary and Concluding Remarks This paper provides an empirical review of the characteristics of FDI in the Thai agricultural sector over the forty year period of The agricultural sector comprises crops, livestock, fisheries and forestry. By compiling the FDI data, both overall FDI inflows and the approved FDI projects under the BOI promotion, the findings are that FDI in the agricultural sector has been very small, accounting for less than 1% of the total inflows of FDI. These FDI account for about 5% of the total value of foreign investment received BOI privileges scheme. Similar to FDI in manufacturing and services, the majority of FDI in agricultural production has concentrated in export-oriented activities and largely operated in the form of joint-venture. There has been a changing industry structure of FDI in the agricultural sector. During the early period ( ) the FDI had concentrated in the crop subsector and then shifted to the livestock, fisheries and forestry in later periods. In overall, the majority of FDI have concentrated in the livestock, mainly in broiler chicken and swine production, with 83% share in the total value of foreign investment in primary agriculture. The promoted FDI projects also generated 43,890 jobs to Thai workers. The study also investigates the impact of FDI on agricultural productivity. Since FDI is widely recognized as an important channel bringing in new technology and know-how agricultural total factor productivity (TFP) is used as a measure of technology-induced productivity. The error correction modelling technique (Hendry, 1995) is employed to investigate both short-term and long-term impact of FDI on the TFP along with other potential determinants of TFP (such as agricultural research, farmers education and trade openness). The results are divided into two models using the overall FDI inflows (BOT data) and the promoted foreign investment (BOI data). The results show that the FDI impact on productivity is observed more clearly when focusing on the promoted foreign investment classified as priority activities under the BOI policies. There is no statistical evidence that the overall FDI inflows had a significant impact on productivity. On the other hand, the foreign investment promoted by the BOI has a positive and significant impact (at 10% level) on the TFP. Despite the very small value of FDI in the agricultural sector, its benefits on productivity can still be partially observed and shall be strengthened by a clear direction and an emphasis of investment policies. Other major factors significantly influencing the TFP are education, trade openness, and rainfall. This suggests policies relating to education and international trade also play an important role in enhancing the agricultural productivity. Climate factor should also be taken into account when planning agricultural production that aims at raising the TFP, which can help sustain the long-term agricultural growth. References [1] Athukorala, P. and P. L. Tsai Determinants of Household Saving in Taiwan: Growth, Demography and Public Policy. The Journal of Development Studies, 39, pp

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