BỘ GIÁO DỤC VÀ ĐÀO TẠO TRƯỜNG ĐẠI HỌC KINH TẾ TP HCM KHƯƠNG THỊ PHƯƠNG THẢO NHÂN TỐ ẢNH HƯỞNG ĐẾN QUYẾT ĐỊNH THỰC HIỆN ĐẦU TƯ FDI VÀO ASEAN CỦA CÁC DOANH NGHIỆP VIỆT NAM LUẬN VĂN THẠC SĨ KINH TẾ Tp Hồ Chí Minh - Năm 2016 BỘ GIÁO DỤC VÀ ĐÀO TẠO TRƯỜNG ĐẠI HỌC KINH TẾ TP HCM KHƯƠNG THỊ PHƯƠNG THẢO NHÂN TỐ ẢNH HƯỞNG ĐẾN QUYẾT ĐỊNH THỰC HIỆN ĐẦU TƯ FDI VÀO ASEAN CỦA CÁC DOANH NGHIỆP VIỆT NAM CHUYÊN NGÀNH: TÀI CHÍNH – NGÂN HÀNG MÃ SỐ: 60340201 LUẬN VĂN THẠC SĨ KINH TẾ NGƯỜI HƯỚNG DẪN KHOA HỌC: PGS.TS LÊ THỊ LANH Tp Hồ Chí Minh - Năm 2016 LỜI CAM ĐOAN Tôi xin cam đoan luận văn “ Nhân tố ảnh hưởng đến định thực đầu tư FDI vào ASEAN doanh nghiệp Việt Nam” công trình nghiên cứu tác giả, thực hướng dẫn khoa học PGS.TS Lê Thị Lanh Các số liệu kết nghiên cứu trình bày luận văn hoàn toàn trung thực chưa công bố công trình nghiên cứu khác Thành phố Hồ Chí Minh, tháng 10 năm 2016 Tác giả luận văn Khương Thị Phương Thảo MỤC LỤC TRANG BÌA LỜI CAM ĐOAN MỤC LỤC DANH MỤC TỪ VIẾT TẮT DANH MỤC BẢNG TÓM TẮT CHƯƠNG 1: GIỚI THIỆU 1.1 Lý chọn đề tài nghiên cứu 1.2 Mục tiêu nghiên cứu 1.3 Đối tượng nghiên cứu 1.4 Phạm vi nghiên cứu .2 1.5 Phương pháp nghiên cứu .3 1.6 Đóng góp luận văn 1.7 Cấu trúc nội dung nghiên cứu CHƯƠNG 2: CƠ SỞ LÝ THUYẾT VÀ TỔNG QUAN CÁC NGHIÊN CỨU TRƯỚC ĐÂY .4 2.1 Khái niệm hình thức FDI 2.2 Vai trò FDI 2.3 Đặc điểm FDI 2.3.1 Chênh lệch suất cận biên vốn nước 2.3.2 Chu kỳ sản phẩm 2.3.3 Ảnh hưởng FDI làm mở rộng vốn đầu tư quốc gia .9 2.3.4 Ảnh hưởng FDI tăng suất sản xuất thông qua đầu tư vốn theo chiều sâu .10 2.4 Khung nghiên cứu lý thuyết 11 2.4.1 Lý thuyết chiết trung ( Eclectic), Dunning 12 2.4.2 Học thuyết lợi độc quyền, Stephen Hymer .13 2.4.3 Học thuyết nội hoá (Internalization), Buckley Casson 13 2.4.4 Lý thuyết bước phát triển đầu tư (Investment Development Path - IDP), Dunning 14 2.5 Các nghiên cứu trước 18 2.5.1 Nghiên cứu nước .18 2.5.2 Nghiên cứu nước .24 CHƯƠNG 3: PHƯƠNG PHÁP NGHIÊN CỨU VÀ MÔ HÌNH NGHIÊN CỨU 26 3.1 Mô hình nghiên cứu 26 3.2 Phương pháp thu thập xử lý liệu .33 3.2.1 Phương pháp thu thập liệu 33 3.2.2 Phương pháp xử lý liệu .34 CHƯƠNG 4: KẾT QUẢ NGHIÊN CỨU 39 4.1 Thống kê tổng quát 39 4.2 Kiểm định kết hồi quy .35 4.2.1 Nhóm phương trình hồi quy xem xét yếu tố ảnh hưởng tới định đầu tư doanh nghiệp Việt Nam vào nước Asean 35 4.2.2 Nhóm phương trình hồi quy xem xét ảnh hưởng riêng lợi nhuận doanh nghiệp Việt Nam mối liên hệ với biến động tỷ giá chéo quốc gia tới định định đầu tư doanh nghiệp Việt Nam vào nước Asean 47 4.2.3 Nhóm phương trình hồi quy xem xét ảnh hưởng riêng biến động giá chứng khoán doanh nghiệp Việt Nam mối liên hệ với định định đầu tư vào nước Asean 54 CHƯƠNG 5: KẾT LUẬN 58 5.1 Kết luận 58 5.2 Hạn chế đề tài .60 5.3 Nghiên cứu đề xuất .61 DANH MỤC TỪ VIẾT TẮT ASEAN: Hiệp hội quốc gia Đông Nam Á (Association of Southeast Asian Nations) SUR: Phương trình hồi quy không liên quan ( Seemingly Unrelated Regressions) FDI: Đầu tư trực tiếp nước ( Foreign Direct Investment) M&A: Mua bán sáp nhập (Mergers and Acquisitions ) TTCK: Thị trường chứng khoán MNC: Công ty đa quốc gia( Multinational Corporation) DN: Doanh nghiệp DANH MỤC BẢNG Bảng 3.1 Danh sách 29 doanh nghiệp Việt Nam thu thập số liệu 31 Bảng 3.2 Danh sách quốc gia khu vực ASEAN thu thập số liệu 32 Bảng 3.3 Tóm tắt biến 35 Bảng 4.1 Thống kê tổng quát nhóm biến vĩ mô quốc gia ASEAN 39 Bảng 4.2 Thống kê tổng quát nhóm biến liên quan DN Việt Nam định đầu tư FDI hay M&A 40 Bảng 4.3 Thống kê tổng quát nhóm biến M&A, đầu tư, mục đích kinh nghiệm41 Bảng 4.4 Tương quan biến 33 Bảng 4.5 Kết phương trinh hồi quy 35 Bảng 4.6 Kết phương trinh hồi quy 38 Bảng 4.7 Kết phương trinh hồi quy 40 Bảng 4.8 Kết phương trinh hồi quy 43 Bảng 4.9 Kết phương trinh hồi quy 45 Bảng 4.10 Kết phương trinh hồi quy 47 Bảng 4.11 Kết phương trinh hồi quy 49 Bảng 4.12 Kết phương trinh hồi quy 52 Bảng 4.13 Kết phương trinh hồi quy 54 Bảng 4.14 Kết phương trinh hồi quy 10 56 MỞ ĐẦU Với mục tiêu nghiên cứu tác động nhân tố: Corruption, Business, IPRS, SHRS, GDP, Population, Bilateral, Tax, RDI, Firms, MTB, MAP, Forex, Profits, Regional, Purpose đến định thực đầu tư trực tiếp nước công ty niêm yết TTCK Việt Nam thông qua kiểm định mô hình SUR Tác giả tiến hành đánh giá mức độ ảnh hưởng biến mô hình từ quốc gia ASEAN nhận đầu tư Tác giả lấy liệu nghiên giai đoạn 2008 – 2015 công ty Việt Nam niêm yết ASEAN Exchanges kết hợp số liệu sàn chứng khoán nước Kết nghiên cứu cho thấy nhân tố Corruption, Business, Regional Purpose giải thích tốt tác động lên định lựa chọn đầu tư trực tiếp nước doanh nghiệp Việt Nam Từ khóa: FDI, đầu tư nước ngoài, đầu tư khu vực ASEAN CHƯƠNG 1: GIỚI THIỆU 1.1 Lý chọn đề tài nghiên cứu Trong xu phát triển mạnh mẽ kinh tế toàn cầu, hoạt động đầu tư không bị giới hạn lãnh thổ quốc gia mà mở rộng thị trường giới thông qua thương vụ đầu tư nước như: M&A, đầu tư trực tiếp FDI mới,… Theo số liệu thông kê Worldbank, tổng vốn đầu tư trực tiếp nước FDI năm 2009 1.361 nghìn tỷ đô la Mỹ, năm 2015 2.04 nghìn tỷ đô la Mỹ.Đối với tổng giá trị thương vụ M&A toàn cầu, năm 2008 đạt xấp xỉ nghìn tỷ đô la Mỹ, năm 2015 tổng giá trị tăng lên đáng kể đạt mức 4.31 nghìn tỷ đô la Mỹ Những số liệu nêu phần phản ánh tình hình phát triển với tốc độ nhanh hoạt động đầu tư xuyên quốc gia giới Theo Hội nghị quốc gia thương mại phát triển, đầu tư trực tiếp nước (FDI) toàn cầu vượt 1.5 nghìn tỷ đô la Mỹ năm 2011, với mức ý nghĩa tăng trưởng kinh tế có tăng trưởng mạnh Xu hướng FDI thể phát triển kinh tế toàn cầu dẫn dắt sách thương mại đầu tư tự Chính phủ kết hợp với phát triển kinh tế Sự đa dạng hóa hoạt động thị trường nước chiến lược cần thiết công ty để tối đa hóa lợi nhuận thời đại toàn cầu hóa nay, việc lựa chọn thực M&A hay đầu tư trực tiếp FDI để mở rộng kinh doanh định quan trọng nhà quản trị Việt Nam đánh giá là kinh tế có tốc độ phát triển nhanh, xu hướng đầu tư giới tác động lớn đến kinh tế Việt Nam Năm 2008, Việt Nam đầu tư trực tiếp nước 104 dự án, ứng với tổng vốn đăng ký 3,147 triệu đô la Mỹ Tổng số dự án đầu tư trực tiếp có tăng lên đạt 118 dự án năm 2015, tổng vốn đăng ký lại giảm 774 triệu đô la Mỹ Xu hướng đầu tư hội nhập với kinh tế giới đặt nhiều thách thức cho doanh nghiệp Việt Nam Thấy cần thiết việc tìm mô hình phù hợp nhằm giúp tổ chức 104 M Nagano / Emerging Markets Review 16 (2013) 100–118 In Eqs (1) and (2), we assume that firm i has 12 potential countries from the Asia and Oceania region in which to invest Thus, when firm i chooses to invest in one of these 12 countries for its FDI project A, the chosen country's dependent variable equals one and those for the other 11 countries automatically equal zero The independent variables are the same across all 12 countries XcM refers to the variables that represent the macroeconomic factors of host country c that may affect FDI inflows XcL refers to the variables that denote the level of the development of the legal environment concerning IPR and SHR protection in country c Xi refers to the variables pertaining to the firm-specific factors of firm i For XcM, we include per capita GDP (per capita GDP), population size (Population), corporate tax rate (Corporate Tax), and the bilateral trade between home and host countries (Bilateral Trade) in order to capture the country-specific effects of host country c Per capita GDP represents the level of household income in the host country Population represents the host-country size Corporate tax represents the cost of taxation to local operations in the host country Bilateral trade denotes the export values from Japan to host country c divided by the total international trade values between Japan and the 12 sample countries This variable proxies for the level of international trade development between Japan and host country c All country-specific variables are converted into their natural logarithm values For the XcL variables, we employ quantitative scores published by International Management Development (IMD), which evaluates the legal environments of various countries in terms of IPR protection (IP Rights Score) and SHR protection (SH Rights Score) To support the first hypothesis, the signs of the coefficients of IP Rights Score would be positive in Eq (2) but not in Eq (1), while those of SH Rights Score would be positive in Eq (1) but not in Eq (2) The empirical model also employs IPR-sensitive (IPR Sensitive) and SHR-sensitive (SHR Sensitive) dummy variables IPR Sensitive equals one when the firm belongs to any of the following sectors and zero otherwise: pharmaceuticals, chemicals, machinery, electronic machinery, or precision machinery We follow Mansfield's (1995) argument that the above sectors are IPR-sensitive SHR Sensitive equals one when the firm belongs to any of the following sectors and zero otherwise: food, paper and pulp, transportation equipment, and precision machinery These sectors comprise the most cross-border M&A experiences in Hong Kong, Australia, Singapore, and South Korea, which have the four largest capital market capitalizations to nominal GDP in the studied region Xi represents the firm-specific variables of home-country firms We employ Regional Networks as a proxy of the degree of regional network development of firm i based on the intersection of Bilateral Trade and Regional Experience Regional Experience equals one when firm i already has local bases in country c at the time of investment We also employ the variable Entry Purpose to examine whether the purpose of the foreign operation influences the choice of FDI The variable equals one when the purpose of firm i's FDI is to establish sales distribution channels and zero when the purpose is to enhance production capability or R&D activities We also employ the cumulative values of R&D expenditure at the time of firm i's investment divided by total annual sales (R&D Intensity), total assets (Firm Size), and market-to-book ratio (Market to Book) as control variables All these variables are converted into their natural logarithm values and first-differentiated in order to eliminate any firm-specific effects that may influence the empirical results Regional Experience and Entry Purpose are not first-differenced data We also add year, country, and industry dummy variables The industry dummies are based on the U.S two-digit Standard Industrial Classification We simultaneously estimate the above two empirical equations using a seemingly unrelated regression (SUR) We employ a SUR estimation because the empirical results of the two equations are statistically comparable, since both employ a common sample dataset In other words, we can objectively compare two coefficients each from Eqs (1) and (2) in order to find out which variables have a larger impact on either investment decision 2.2.2 Home-country firm's stock price and FDI To further examine the relationship between a home-country firm's individual characteristics and its FDI choice, we estimate a second set of empirical equations Whereas the base case empirical equations employ the market-to-book ratio (Market to Book) as a proxy of the firm's growth opportunities, the second set of empirical equations estimates several variables using daily and monthly stock price data The purpose of this second empirical analysis is to verify the hypothesis that a home-country firm's stock price is correlated to both cross-border M&A and greenfield FDI, but that the correlation differs between these two types of FDI M Nagano / Emerging Markets Review 16 (2013) 100–118 105 Table Definition of variables CBMA GFFDI IP Rights Score SH Rights Score IPR Sensitive SHR Sensitive Per capita GDP Population Bilateral Trade Corporate Tax R&D Intensity Firm Size Market to Book Market Adj Price Forex Adj Price Definition Expected sign of coefficient Source This variable equals one when firm i has invested in country c through cross-border M&A and zero otherwise For each firm, the number of observations is equal to the number of possible host countries in the sample This variable equals one when firm i has invested in country c through greenfield FDI and zero otherwise For each firm, the number of observations is equal to the number of possible host countries in the sample The natural logarithm value of IMD's score for the legal environment concerning IPR protection laws based on the results of its survey on whether IPR are adequately enforced or not The natural logarithm value of IMD's score for the legal environment concerning SHR protection laws based on the results of its survey on whether SHR are sufficiently protected or not This variable equals one when the firm belongs to the pharmaceutical, chemicals, machinery, electronic machinery, or precision machinery sectors and zero otherwise This variable equals one when the firm belongs to the food, paper and pulp, transportation equipment, or precision machinery sectors, which have the most number of cross-border M&A experiences in Hong Kong, Australia, Singapore, and South Korea The natural logarithm value of the per capita GDP of country c The natural logarithm value of country c's population The natural logarithm value of exports from Japan to host country c divided by total trade values between Japan and the 12 sample countries The natural logarithm value of country c's corporate tax rate The ratio of the cumulative values of R&D expenditure at the time firm i invests in country c to firm i's total sales, converted into the natural logarithm value The natural logarithm value of firm i's total assets The natural logarithm value of firm i's book value of liability plus the market value of capital divided by the book value of total assets The prior three-month (60-operating day) average return of firm i's stock price minus the prior three-month (60-operating day) average home-country market index return The prior three-month (60-operating day) average of firm i's stock price return minus the prior three-month (60-operating day) average market index return minus the prior three-month (60-operating day) average foreign exchange rate return Dependent variable Thomson Reuters, Thomson Bank One Dependent variable Toyo Keizai Shimpo Sha, Overseas Business Operating Firm Databook CBMA GFFDI+ IMD, The World Competitiveness Yearbook CBMA+ GFFDI IMD, The World Competitiveness Yearbook CBMA GFFDI+ Toyo Keizai Shimpo Sha, Overseas Business Operating Firm Databook CBMA+ GFFDI Thomson Reuters, Thomson Bank One CBMA+/− GFFDI+/− CBMA+ GFFDI+ CBMA GFFDI+ United Nations Statistical Division, National Accounts Section United Nations Population Division, Population Information Network Japanese Ministry of Finance, International Trade Statistics CBMA− GFFDI− CBMA+ GFFDI− World Bank, http://data.worldbank.org/ indicator/GB.TAX.CMAR.ZS/countries Thomson Reuters, Thomson Bank One CBMA+ GFFDI− CBMA GFFDI+ Thomson Reuters, Thomson Bank One Bloomberg CBMA GFFDI+ Bloomberg CBMA GFFDI+ Bloomberg (continued on next page) 106 M Nagano / Emerging Markets Review 16 (2013) 100–118 Table (continued) CAR1m CAR2m Regional Experience Regional Networks Entry Purpose Definition Expected sign of coefficient Source Firm i's cumulative abnormal stock price return one month (20 operating days) before and after investment Firm i's cumulative abnormal stock price return three months (40 operating days) before and after investment This variable equals one when firm i already has local operations in country c at the time of its investment Intersection of Bilateral Trade and Regional Experience This variable equals one when firm i's foreign market entry purpose in country c is to establish sales distribution channels and zero otherwise CBMA+ GFFDI Bloomberg CBMA+ GFFDI Bloomberg CBMA GFFDI Toyo Keizai Shimpo Sha, Overseas Business Operating Firm Databook CBMA GFFDI+ CBMA+ GFFDI Toyo Keizai Shimpo Sha, Overseas Business Operating Firm Databook Toyo Keizai Shimpo Sha, Overseas Business Operating Firm Databook Note: The independent variables are categorized as legal environment variables (XL), host-country variables (Xc), and home-country firm variables (Xi) The legal environment variables are converted into dummy variables and employed as dependent variables in the second set of empirical equations in the probit model with sample selection in Section 2.3 The stock prices used in this examination are the prior 60-business operating day average return of the home-country firm's stock price minus the prior 60-operating day average home-country market index return (Market Adj Price) The second stock price variable we employ is the prior 60-operating day average return of the home-country firm's stock price minus the prior 60-operating day average market index return minus the 60-operating day monthly average foreign exchange rate return (Forex Adj Price) We use the daily data from Thomson Reuters's cross-border M&A deals and Bloomberg's firm stock prices We calculate the above variables based on the announcement dates of the cross-border M&A deals indicated in the Thomson Reuters data Since Toyo Keizai Shimpo Sha data only provide the dates of FDI on a monthly basis, we calculate these variables by using the daily stock price and foreign exchange rate returns of the last 60 operating days The third and fourth stock price variables are represented by the cumulative abnormal returns of the home-country firm's stock prices one month and three months before and after the deal (CAR1, CAR2m) To calculate cumulative abnormal returns, we employ daily data for the cross-border M&A empirical equation, but monthly data for the greenfield FDI empirical equation, because of the limitations of Toyo Keizai Shimpo Sha data The normal stock price return is computed using an OLS market model with each firm's stock price return and the return on the NIKKEI 225 index We approximate 20 and 40 operating days as one and two months and assume that 41 and 81 operating days are adequate windows for the purposes of our study We obtain two point estimators for each independent variable since we estimate two equations, that is, Eqs (1) and (2) Then, we statistically compare these two coefficients for the described variables and test the mean and median value differences for each pair of coefficients 2.2.3 Re-examination The third analysis additionally investigates the similarities and differences between the determinants of cross-border M&A and greenfield FDI using another empirical methodology in order to confirm that the results are consistent with those of the empirical equations in Sections 2.2.1 and 2.2.2 We re-examine our hypotheses using the following empirical methodology: F F F F FDIic ¼ X L δ1 þ X i δ2 þ χ ic   P^ ðFDIÞ ¼ Φ Z c;t M Z c;t ¼ X c θM þ εc : ð3Þ ð4Þ M Nagano / Emerging Markets Review 16 (2013) 100–118 107 In contrast to Eqs (1) and (2), Eqs (3) and (4) are individually but simultaneously estimated for both F cross-border M&A and greenfield FDI In Eq (3), the dependent variable, FDIic , equals one when a firm engages in cross-border M&A (greenfield FDI) and zero otherwise The independent variables of Eq (3) are the legal environment variables, namely the IPR protection law and SHR protection law scores, and the firm-specific variables, namely Regional Networks, Entry Purpose, R&D Intensity, Firm Size, ROA, and the several proxies for stock price The dependent variable of Eq (4), P^ ðFDIÞ, equals one when the firm carries out either type of FDI in host country c and zero otherwise The independent variables of Eq (4) are the country-level variables, namely per capita GDP, Growth, Population, and Bilateral Trade The definition of Growth is indicated in Table Thus, Eq (3) confirms whether the FDI choice of the home-country firm is determined by the host-country legal environment and firm-specific factors, while Eq (4) confirms whether the host-country macroeconomic variables increase both types of FDI Eqs (3) and (4) are estimated simultaneously by using a probit model with a sample selection by type of FDI 2.3 Data Our dataset consists of data on emerging host countries from Asia and Oceania and Japanese firms as home-country firms during the period 1999–2009 Home-country Japanese firms are all publicly listed firms We exclude non-East Asian emerging countries from the sample to eliminate any possible geographical biases that may influence firm i's investment decisions We obtain data on Japanese firm FDI outflows from the Overseas Business Operating Firm Databook published by Toyo Keizai Shimpo Sha This databook is published annually and it contains all overseas business operating information on Japanese firms, including their overseas branches and subsidiaries as well as corresponding information such as addresses, year and month of establishment, number of employees, financial statement summaries, missions, and percentage shares held by parent Japanese firms We define the month and year of establishment of the overseas operation as the month and year the FDI is carried out However, since the original data not indicate which type of FDI is pursued, we match the cross-border M&A individual deal data from Thomson Reuters's Thomson Bank One with the comprehensive FDI data from Tokyo Keizai Shimpo Sha and define unmatched FDI deals as greenfield FDI We obtain data for the country-level variables, Xc, from the United Nations, World Bank, and Japanese Ministry of Finance: per capita GDP and Population from the United Nations; Corporate Tax from the World Bank; and Bilateral Trade from the Japanese Ministry of Finance For the host-country legal environmental data for XL, we employ two datasets that represent the evaluation of the IPR and SHR protection laws We obtain these legal environmental data from The World Competitiveness Yearbook published by IMD IMD has been publishing scores for the legal environment concerning IPR and SHR by country since 1996 Those scores are calculated by the objective formula below based on IMD's annual survey research in each country The World Competiveness Yearbook covers 45 countries throughout 1996–2009 and publishes 331 scores, including IPR and SHR protection scores Survey respondents only provide answers on the country in which they have worked and resided during the past year The number of respondents was 4985 in 2009; although this number varies from year to year, it consistently falls within 4000 and 5000 during the sample period Respondents assess the levels of IPR and SHR protection offered by the country's legal system by answering questions on a scale of 1–6, with indicating the most negative perception and the most positive IMD staff calculates the average values for each country, and the data are converted from a 1–6 scale into a 0–10 scale using the formula below Finally, survey responses are transformed into their standard deviation values, from which rankings are calculated vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  u  u∑ Q−Q t Q−Q S¼ ðSTD⋅ValueÞi ¼ S N Q = original value, Q = average value of sample countries, N = number of economies, and S = standard deviation Existing empirical studies frequently use the indices developed by Rapp and Rozek (1990) and Ginarte and Park (1997) to evaluate IPR protection and those developed by Gompers et al (2003) for SHR protection These indices, however, have some limitations The indices presented by Rapp and Rozek (1990) only cover 108 M Nagano / Emerging Markets Review 16 (2013) 100–118 five countries, while those by Ginarte and Park (1997) use quinquennial data rather than annual data, thereby assuming that the legal environment remains constant throughout each five-year period Further, the indices proposed by Gompers et al (2003) not necessarily represent the legal environment in terms of SHR protection objectively, since they evaluate the corporate governance structure of the existing legal system Based on these limitations, this study employs IMD's annual quantitative data Further, we obtain data on the firm-specific variables, Xi, including R&D Intensity, Firm Size, ROA, and Market to Book from Thomson Reuters's Thomson Bank One We define Regional Networks as Regional Experience multiplied by Bilateral Trade We obtain information regarding Regional Experience to input the quantitative dummy variable data from the Overseas Business Operating Firm Databook of Toyo Keizai Shimpo Sha The missions of Japanese firms' local bases are also indicated in the Overseas Business Operating Firm Databook; these missions or activities are often categorized as “sales distribution,” “manufacturing,” and/or “R&D.” The variable Entry Purpose equals one when the local base's purpose is “sales distribution” and zero otherwise However, since a local base may have more than one purpose, the variable also equals one when the local base has other purposes in addition to “sales distribution” and zero otherwise (Table 4) We repeatedly estimated several empirical equations, but we only report the pertinent results herein Finally, as demonstrated by Svensson (2005) and Olken and Barron (2009), the level of host-country corruption is another important factor that may influence FDI inflows We therefore employ the Corruption Perception Index of Transparency International as a proxy for the level of host-country corruption for Eqs (1) and (2), but we not report the empirical results since the coefficients are not significant Empirical results 3.1 Similarities in the determinants between these two types of FDI Our first empirical results are presented in Table As for the country-level macroeconomic variables, the coefficients of per capita GDP, Population, and Corporate Tax are significant The signs of the coefficients of per Table Descriptive statistics IP_Rights Score Cross-border M&A (N = 205) Greenfield FDI (N = 1956) No FDI firms (N = 20,367) Cross-border M&A (N = 205) Greenfield FDI (N = 1956) No FDI firms (N = 20,367) Mean sd Mean sd Mean sd Mean sd Mean sd Mean sd 1.603 0.290 1.656 0.210 1.724 0.295 Greenfield FDI (N = 1956) No FDI firms (N = 20,121) Mean sd Mean sd Mean sd 1.800 0.185 1.718 0.195 1.845 0.181 Per capita GDP Population 8.659 1.459 7.826 1.145 8.724 1.461 18.116 1.845 19.434 1.931 17.477 1.753 Corporate Tax Bilateral Trade 3.327 0.228 3.405 0.175 3.320 0.230 −2.652 0.981 −2.002 0.789 −2.916 0.961 R&D Intensity Market to Book Firm Size ROA Regional Networks Entry Purpose −5.644 4.859 −4.178 4.715 −4.231 4.884 0.170 0.306 0.137 0.301 0.139 0.307 13.667 1.607 12.417 1.589 12.472 1.611 −4.146 0.977 −3.856 0.967 −3.865 0.979 0.029 0.104 0.136 0.343 0.000 0.010 0.359 0.480 0.051 0.220 N.A N.A Forex Adj Price CAR1m CAR2m Market Adj Price Cross-border M&A (N = 205) SH_Rights Score 0.012 0.031 0.010 0.022 0.008 0.019 0.013 0.032 0.009 0.024 0.007 0.021 0.248 0.232 0.248 0.371 0.251 0.265 0.430 0.303 0.485 0.374 0.423 0.466 Note: The figures in this table indicate the mean and standard deviation of the variables employed in this paper The variables are categorized by FDI type (i.e., cross-border M&A firm, greenfield FDI firm, and firms without FDI) M Nagano / Emerging Markets Review 16 (2013) 100–118 109 Table Determinants of cross-border M&A and greenfield FDI decisions: SUR empirical results Independent variables Dependent variable Model A Panel A IP_Rights Score SH_Rights Score IP Rights Score ∗ IPR Sensitive SH Rights Score ∗ SHR Sensitive IP Rights Score^2 SH Rights Score^2 Corporate Tax Model B (A) CBMA (B) GFFDI (A) CBMA (B) GFFDI 0.098*** (4.390) 0.123*** (3.110) −0.003*** (−3.180) −0.122 (−1.160) −0.042*** (−6.440) −0.034*** (−2.870) −0.022*** (−3.620) 0.143** (2.300) 0.021 (0.100) 0.002 (0.550) 1.955** (5.300) −0.061** (−2.380) −0.001 (−0.010) −0.075*** (−3.930) 0.095*** (4.310) 0.142*** (2.790) −0.002 (−0.030) 0.150** (2.460) −0.008 (−0.040) 0.024* (1.750) −0.017*** (−3.650) 0.159*** (5.160) 0.006* (1.760) −6.34E−04 (−0.550) 1.14E−03*** (4.330) 1.61E−04 (0.110) −0.002 (−1.200) 0.014*** (3.330) −3.283*** (−4.990) Yes Yes Yes 264.4*** 0.013 −0.041*** (−2.960) 0.465*** (5.290) 0.064*** (7.020) 5.57E−05 (0.180) −0.006*** (−8.730) 3.72E−04 (0.100) 0.857*** (157.110) −0.006 (−0.120) −9.432*** (−5.170) Yes Yes Yes 46,448.2*** 0.624 22,528 −0.043*** (−6.100) −0.038*** (−2.760) −0.010*** (−2.760) −0.005 (−0.610) −0.002 (−0.640) −0.015*** (−3.650) 0.158*** (4.850) 0.006* (1.630) −1.46E−04* (−1.890) 1.04E−03*** (4.340) 1.73E−04 (0.130) −0.002 (−1.270) 0.015*** (3.280) −3.242*** (−5.010) Yes Yes Yes 261.5*** 0.012 −0.041** (−2.370) −0.002 (−0.030) −0.077*** (−3.790) −0.004* (−1.690) 0.003 (0.170) −0.043*** (−2.965) 0.459*** (5.140) 0.067*** (6.900) −8.19E−05 (−0.390) −0.006*** (−8.700) 4.13E−04 (0.110) 0.867*** (157.090) −0.005 (−0.100) −9.466** (−5.160) Yes Yes Yes 46,474.3*** 0.675 22,528 −0.045 0.102* 0.052*** −2.12E−04 −0.860*** 0.020*** (−0.700) (1.770) (4.780) (−0.060) (−5.970) (3.440) −0.054 0.150** 0.067*** −2.41E−04 −0.870*** 0.020*** (−0.840) (1.970) (4.450) (−0.060) (−5.990) (3.420) IPR Score ∗ IPR Sensitive ∗ Corporate Tax SHR Score ∗ SHR Sensitive ∗ Corporate Tax Per capita GDP Population Bilateral Trade R&D_Intensity Firm Size Market-to-Book Regional Networks Entry Purpose Const Year dummy Industrial dummy (2 digits) Country dummy Chi2 Pseudo R2 Observations Panel B IP_Rights Score SH_Rights Score Corporate Tax Market-to-Book Regional Networks Entry Purpose Note 1: In this estimation, the dependent variables are the cross-border M&A and greenfield FDI dummies The independent variables are the legal environment variables, host-country macroeconomic variables, and home-country firm-specific variables The estimation results for Models A and B are indicated in Panel A and those for the test of the differences for each pair of coefficients are indicated in Panel B Panel C indicates the predicted probability of each empirical equation Note 2: ***, **, and * denote significance at 1, 5, and 10%, respectively The dependent variable is equal to one if firm i invests in country c and zero otherwise The year and industry dummies are included in all the equations 110 M Nagano / Emerging Markets Review 16 (2013) 100–118 Table Effects of firm stock return on cross-border M&A and greenfield FDI decisions: SUR empirical results Independent variables Dependent variable Model A Panel A IP_Rights Score SH_Rights Score IP Rights Score ∗ IPR Sensitive SH Rights Score ∗ SHR Sensitive IP Rights Score^2 SH Rights Score^2 Per capita GDP Population Bilateral Trade Corporate Tax R&D_Intensity Firm Size Regional Networks Entry Purpose Market Adj Price Model B (A) CBMA (B) GFFDI (A) CBMA (B) GFFDI 0.096*** (4.380) 0.118*** (2.970) −0.003*** (−3.830) 0.737*** (5.660) −0.054*** (−6.360) −0.033*** (−2.800) −0.018*** (−3.690) 0.157*** (5.110) 0.005* (1.680) −0.023*** (−3.360) −1.42E−04* (−1.870) 0.001*** (4.550) −0.002 (−1.260) 0.012*** (3.290) −0.007 (−0.190) 0.142** (2.260) 0.034 (0.310) −0.001 (−0.350) −0.058* (−1.910) −0.049** (−2.540) −0.013 (−0.390) −0.041*** (−2.960) 0.459*** (5.360) 0.063*** (6.960) −0.069*** (−3.670) −4.05E−05 (−0.170) −0.001*** (−8.420) 0.857*** (157.370) −0.004 (−0.110) 0.172* (1.750) 0.095*** (4.360) 0.117*** (2.960) −0.003*** (−3.740) −0.011 (−1.070) −0.043*** (−6.160) −0.034*** (−2.780) −0.018*** (−3.630) 0.157*** (5.080) 0.005* (1.660) −0.025*** (−3.390) −1.42E−04* (−1.850) 0.001*** (4.570) −0.003 (−1.300) 0.012*** (3.290) 0.144*** (2.310) 0.032 (0.290) −0.001 (−0.400) −0.058* (−1.930) −0.050** (−2.570) −0.012 (−0.370) −0.041*** (−3.000) 0.470*** (5.360) 0.063*** (6.990) −0.071*** (−3.730) −5.56E−05 (−0.260) −0.056*** (−8.450) 0.857*** (157.290) −0.004 (−0.110) 0.009 (0.260) 0.141* (1.690) −3.230*** (−5.200) Yes Yes Yes 263.5* 0.012 −9.534*** (−5.240) Yes Yes Yes 46,738.0*** 0.675 22,528 −0.049 0.085* 0.046*** (−0.710) (1.720) (4.580) −0.131* −0.859*** 0.016*** (−1.710) (−5.950) (3.440) Forex Adj Price IP Rights Score ∗ IPR Sensitive ∗ Market Adj Price SH Rights Score ∗ SHR Sensitive ∗ Market Adj Price IP Rights Score ∗ IPR Sensitive ∗ Forex Adj Price SH Rights Score ∗ SHR Sensitive ∗ Forex Adj Price Const Year dummy Industrial dummy (2 digits) Country dummy Chi2 Pseudo R2 Observations Panel B IP_Rights Score SH_Rights Score Corporate Tax Market Adj Price Forex Adj Price Regional Networks Entry Purpose −0.007 (−0.500) 0.909 (1.260) −0.010 (−0.740) 0.885 (1.420) −3.269*** (−5.000) Yes Yes Yes 261.4* 0.013 0.031*** (2.120) −0.033 (−0.110) 0.076*** (2.460) −0.066 (−0.080) −9.508*** (−5.240) Yes Yes Yes 46,760*** 0.685 22,528 −0.046 0.084* 0.046*** −0.179* (−0.670) (1.710) (4.530) (−1.700) −0.859*** 0.016*** (−5.930) (3.420) M Nagano / Emerging Markets Review 16 (2013) 100–118 111 capita GDP and Corporate Tax are negative and that of Population is positive These results fully support our first hypothesis that these macroeconomic variables are common determinants of both types of FDI in the host country The positive sign of the parameter for Population suggests that both cross-border M&A and greenfield FDI are encouraged when the host country has a large population Javorcik (2004) also shows a positive and significant sign of the parameter for this variable in the case of 6707 Eastern European host-country cases The negative sign of per capita GDP is also debatable in the literature Generally, the parameter is expected to be positive when a firm increases FDI to a high-growth economy However, per capita income and unit labor cost are positively correlated in emerging countries Accordingly, it is plausible that a home-country firm has an incentive to invest in a low unit labor cost country and, consequently, the sign of the per capita GDP parameter would be negative A negative relation between per capita income and FDI is also reported in Javorcik (2004) and Huizinga and Voget (2009) The negative sign of the Corporate tax parameter suggests that a home-country firm is likely to invest in a low corporate tax country This result agrees with the view of Huizinga and Voget (2009), who also report a negative sign for the parameter for corporate tax rate in relation to cross-border M&A for 8042 European M&A deal observations Panel B of Table reports the statistical test results of the differences in each pair of independent variable coefficients for the CBMA (cross-border M&A) and GFFDI (greenfield FDI) equations, as specified in Models A and B The z-values of the tests for CBMA coefficient − GFFDI coefficient > are indicated beside the difference in the coefficients enclosed within parentheses Panel B also reports that the absolute value of the Corporate Tax coefficient for GFFDI is statistically larger than that of CBMA in both models 3.2 Impact of host-country legal environment on FDI The empirical results for Model A show that the coefficients of IP Rights Score are significantly positive in the case of both dependent variables (i.e., CBMA and GFFDI) Meanwhile, the coefficient of SH Rights Score is significantly positive for CBMA but not significant for GFFDI These results suggest that an aspect of our second hypothesis, namely that IPR and SHR protection laws influence either type of FDI but not both, is not always supported, as the results show that IPR protection law reform attracts both types of FDI to the host country These positive signs of the parameter for the IPR protection law results are consistent with the papers of Lee and Mansfield (1996) and Maskus (1998), which examine U.S home-country firms and South American host countries Meanwhile, the results are consistent with the other aspect of our second hypothesis, namely that SHR protection law reform increases inward cross-border M&A but not greenfield FDI In Model B, we employ the intersection of the variables of IP Rights Score and Corporate Tax, and of SH Rights Score and Corporate Tax These intersections are also multiplied by IPR Sensitive and SHR Sensitive, respectively The empirical results show that the coefficient of the intersected variable IP Rights Score × IPR Sensitive × Corporate Tax is significantly negative in the case of GFFDI However, the coefficients of the intersected variable SH Rights Score × SHR Sensitive × Corporate Tax are not significant both for CBMA and for GFFDI These results suggest that in combination with taxation reform, IPR protection legal reform influences greenfield FDI but not cross-border M&A However, the results also indicate that although the coefficient of the IPR protection law variable positively influences both types of FDI, in combination with a macroeconomic variable, it only influences greenfield FDI Judging from the above two empirical results, we regard Hypothesis as empirically supported The IP Rights Score coefficient of the CBMA equation is not always significantly larger than that of the GFFDI equation in the case of Model A The coefficient difference test of CBMA coefficient − GFFDI coefficient > in Model B is also not significant However, the results show that the SH Rights Score coefficient of the CBMA equation is significantly larger than that of the GFFDI equation in both Models A and B These results have two Notes to Table Note 1: In this estimation, the dependent variables are the cross−border M&A and greenfield FDI dummies The independent variables are the legal environment variables, host-country macroeconomic variables, and home-country firm-specific variables The estimation results for Models A and B are indicated in Panel A and those for the test of the differences for each pair of coefficients are indicated in Panel B Panel C indicates the predicted probability of each empirical equation Note 2: ***, **, and * denote significance at 1, 5, and 10%, respectively The dependent variable is equal to one if firm i invests in country c and zero otherwise The year and industry dummies are included in all the equations 112 M Nagano / Emerging Markets Review 16 (2013) 100–118 implications First, IPR protection law reform influences both types of FDI, but it is statistically impossible to determine which type of FDI is more significantly influenced Second, the impact of SHR law reform on cross-border M&A is clearly statistically larger than that on greenfield FDI 3.3 Impact of home-country firm FDI experience on FDI choice The empirical results that explain the relationship between individual firm managerial environment concerning past foreign market entry and the FDI decision are described next The coefficient of Regional Networks is not significant for CBMA but is significantly positive for GFFDI The coefficient of Entry Purpose is significantly positive for CBMA but is not significant for GFFDI These results support Hypothesis and indicate that firm-level factors concerning past FDI experience and investment decision making influence the firm's FDI choice A positive relation between regional production network and FDI is also reported by Kimura and Kiyota (2006) and the other results are also consistent with their 22,182-participant study However, a variable that represents the purpose of establishing the firm production network is not employed in any literature to our best knowledge Therefore, our positive sign of the parameter for CBMA is a unique result obtained in this study The coefficient difference of Regional Networks is significantly negative in both Models A and B In other words, the coefficient of the GFFDI model is significantly larger than that of the CBMA model in both models The coefficient difference of Entry Purpose is significantly positive for both models As shown in Panel B of Tables and 6, in the CBMA model, the value of the coefficient of Entry Purpose is statistically larger than that in the GFFDI model, while in the GFFDI model, the value of the coefficient of Regional Networks is larger than that in the CBMA model These results suggest that the foreign market entry purpose influences the choice of cross-border M&A and that having a regional network influences the choice of greenfield FDI 3.4 Impact of home-country stock price on FDI choice The empirical results that examine the relationship between the home-country firm's stock price and choice of FDI are shown in Tables and In the analysis of Model A, the coefficient of the employed stock price variable (i.e., Market Adj Price) is not significant for CBMA but is significantly positive for GFFDI In the analysis of Model B, the coefficient of the employed foreign exchange adjusted stock price variable (i.e., Forex Adj Price) is not significant for CBMA but is significant for GFFDI These results suggest that a home-country firm's prior stock price does not influence its choosing of cross-border M&A but influences its choosing of greenfield FDI Both Chari et al (2010) and Erel et al (2012) show a positive relationship between the firm's cross-border M&A investment decision and the cumulative abnormal stock price return of the mother country firm Our results are thus consistent with these studies, but we also show a positive relationship between the firm's greenfield FDI decision and its growth opportunity, which is a novel finding As shown in Panel B, the statistical differences in the Market Adj Price coefficients between equations (A) and (B) and Forex Adj Price coefficients between equations (A) and (B) are highly significant In the GFFDI model, the values of the coefficients of Market Adj Price and Forex Adj Price are larger than those in the CBMA model These results imply that the home-country firm's stock price prior to the investment and having a regional network influence its choosing of greenfield FDI Table presents the empirical results of the relationship between the home-country firm's stock price and FDI choice when we employ cumulative abnormal returns as proxies of firm stock price In Model C, the coefficient of the one-month cumulative abnormal stock price return (CAR1m) is significantly positive for CBMA but not significant for GFFDI The empirical result is also similar when we employ the three-month cumulative abnormal stock price return (CAR2m) The coefficient is significantly positive for CBMA but not significant for GFFDI Panel B shows the statistical test results of the difference between the coefficients of CAR1m and CAR2m These results show that the coefficients of CAR1m and CAR2m in the CBMA model are larger than those in the GFFDI model, suggesting that a home-country firm's stock price increases immediately after it chooses cross-border M&A but not immediately after it chooses greenfield FDI This relationship between the firm's cumulative abnormal returns and choice of cross-border M&A contrasts with that between the firm's stock price prior to investment and choice of greenfield FDI M Nagano / Emerging Markets Review 16 (2013) 100–118 113 Table Effects of firm cumulative abnormal returns on cross-border M&A and greenfield FDI decisions: SUR empirical results Independent variables Dependent variables Model C Panel A IP_Rights Score SH_Rights Score IP Rights Score ∗ IPR Sensitive SH Rights Score ∗ SHR Sensitive IP Rights Score^2 SH Rights Score^2 Per capita GDP Population Bilateral Trade Corporate Tax R&D_Intensity Firm Size Regional Networks Entry Purpose CAR1m Model D (A) CBMA (B) GFFDI (A) CBMA (B) GFFDI 0.096*** (4.360) 0.118*** (2.980) −0.003*** (−3.770) −0.011 (−0.990) −0.044*** (−6.090) −0.032*** (−2.890) −0.018*** (−3.630) 0.158*** (5.020) 0.005 (1.490) −0.023*** (−3.350) −1.55E−04** (−1.980) 0.001*** (4.450) −0.001 (−0.320) 0.015*** (3.320) 0.020* (1.660) 0.148** (2.380) 0.022 (0.200) −0.001 (−0.460) 2.068*** (5.550) −0.052*** (−2.660) −0.009 (−0.320) −0.046*** (−3.250) 0.488*** (5.510) 0.064*** (7.030) −0.067*** (−3.530) −7.76E−06 (−0.030) −0.006*** (−8.560) 0.857*** (156.890) −0.005 (−0.120) −0.015 (−0.320) 0.098*** (4.360) 0.117*** (2.980) −0.003*** (−3.950) 0.734*** (5.550) −0.043*** (−6.180) −0.033*** (−2.800) −0.017*** (−3.610) 0.156*** (5.000) 0.005 (1.510) −0.025*** (−3.370) −1.49E−04* (−1.890) 0.001*** (4.440) −0.002 (−1.210) 0.015*** (3.300) 0.145** (2.330) 0.020 (0.180) −0.001 (−0.417) −0.063** (−2.080) −0.051** (−2.570) −0.008 (−0.250) −0.045*** (−3.280) 0.490*** (5.550) 0.066*** (7.240) −0.071*** (−3.700) −6.16E−05 (−0.280) −0.006*** (−8.470) 0.857*** (157.070) −0.005 (−0.110) 0.043* (1.690) −0.006 (−0.540) −3.225*** (−4.900) Yes Yes Yes 263.4*** 0.017 −9.887*** (−4.950) Yes Yes Yes 46,627*** 0.676 22,282 −0.047 0.097* 0.046*** (−0.770) (1.780) (4.380) 0.049* −0.859*** 0.020*** (1.890) (−5.990) (3.450) CAR3m IP Rights Score ∗ IPR Sensitive ∗ CAR1m SH Rights Score ∗ SHR Sensitive ∗ CAR1m Const Year dummy Industrial dummy (2 digits) Country dummy Chi2 Pseudo R2 Observations Panel B IP_Rights Score SH_Rights Score Corporate Tax CAR1m CAR3m Regional Networks Entry Purpose 0.003 (0.440) 0.744* (1.760) −3.236*** (−4.950) Yes Yes Yes 261.6*** 0.012 −0.012 (−0.010) −0.004 (−0.660) −9.236*** (−4.930) Yes Yes Yes 46,725*** 0.676 22,282 −0.053 0.095* 0.044*** 0.035* (−0.720) (1.820) (4.550) (1.660) −0.858*** 0.020*** (−6.190) (3.460) Note 1: In this estimation, the dependent variables are the cross-border M&A and greenfield FDI dummies The independent variables are the legal environment variables, host-country macroeconomic variables, and home-country firm-specific variables The estimation results for Models A and B are indicated in Panel A and those for the test of the differences for each pair of coefficients are indicated in Panel B Note 2: ***, **, and * denote significance at 1, 5, and 10%, respectively The dependent variable is equal to one if firm i invests in country c and zero otherwise The year and industry dummies are included in all the equations 114 M Nagano / Emerging Markets Review 16 (2013) 100–118 3.5 Re-examination with a probit model with sample selection Table presents the empirical results of the re-examination of the similarities and differences in the FDI determinants of cross-border M&A and greenfield FDI The results indicate that the coefficient of CAR1m in Model A is positively related to cross-border M&A decisions, consistent with the empirical results shown in Table Further, the coefficient of Market Adj Price in Model B is positively related to greenfield FDI decisions, consistent with the results in Table Table also presents the empirical results for the other variable coefficients The results show that Regional Networks and greenfield FDI decisions are positively related in Model B and that the coefficient of Regional Networks is not significant for CBMA in Model A The results also show that Entry Purpose and cross-border M&A decisions are positively related in Model A and that the coefficient of Entry Purpose is not significant for GFFDI in Model B These results are also consistent with the empirical results shown in Tables to On the other hand, the coefficient of the proxy of the home-country firm's internal funding ability, ROA, is not significant in Models A and B This finding suggests that ROA influences neither greenfield FDI nor cross-border M&A decisions Table Effects of firm-specific factors on FDI choice: probit model with sample selection results Independent variables/dependent variable IP_Rights Score SH_Rights Score R&D_Intensity Firm Size Regional Networks Entry Purpose ROA Market Adj Price Model A Model B (A) CBMA (B) CBMA 0.701* (1.900) 0.153*** (3.340) −0.016 (−0.960) 0.260*** (5.800) 0.170 (0.310) 0.520*** (3.860) 0.043 (0.620) −0.495 (−0.880) 0.694* (1.910) 0.155*** (3.330) −0.018 (−0.880) 0.240*** (5.740) 0.160 (0.300) 0.490*** (3.900) 0.044 (0.590) CAR1m Growth Population Bilateral Trade Year dummy Industrial dummy (2 digits) Country dummy LR Test of rho = 0: Chi^2 Observations (D) GFFDI (E) GFFDI 0.170*** (3.630) 0.153 (0.340) 0.016* (1.900) −0.060*** (−5.800) 1.174*** (6.310) 0.528 (1.390) −0.043 (−0.620) 0.495* (1.880) 0.172*** (3.620) 0.153 (0.310) 0.015* (1.880) −0.062*** (−5.740) 1.176 (6.330) 0.511 (1.400) −0.044 (−0.610) 0.094* (1.880) Per capita GDP Const (C) FDI Dum −4.780*** (−5.360) Yes Yes Yes 21.260*** 22,314 −4.464*** (−5.860) Yes Yes Yes 22.460*** 22,314 (F) FDI Dum 0.041 (0.610) −0.054** (−2.410) −0.159 (−0.370) 0.245*** (14.810) 0.609*** (11.260) −4.029 (−8.110) Yes Yes Yes 4.780*** (5.360) Yes Yes Yes 21.860*** 22,314 4.766*** (5.440) Yes Yes No 21.77 22,314 −0.054** (−2.410) −0.159 (−0.370) 0.245*** (14.810) 0.609*** (11.260) −4.029*** (−8.110) Yes Yes Yes Note 1: In this estimation, the determinants of cross-border M&A and greenfield FDI are examined using a probit model with sample selection to check the robustness of the empirical results in Tables to The dependent variable of Model A is cross-border M&A and that of Model B is greenfield FDI Models A and B are estimated using the second empirical model where the dependent variables are the firm FDI dummies, respectively Note 2: ***, **, and * denote significance at 1, 5, and 10%, respectively The dependent variable is equal to one if firm i invests in country c and zero otherwise The year and industry dummies are included in all the equations M Nagano / Emerging Markets Review 16 (2013) 100–118 115 Discussion and conclusions Based on the empirical evidence presented herein, we can draw the following conclusions Some hostcountry variables promote both types of FDI, but the home-country firm's preference of the type of FDI is determined by the host-country's legal environment and individual firm-level variables The existing literature shows evidence of the determinants that encourage cross-border M&A and greenfield FDI, but this study is the first to present the similarities and differences of such determinants using a common sample As for similarities in cross-border M&A and greenfield FDI determinants, our results regarding the relationship between the country-level variables and FDI (Hypothesis 1) are entirely consistent with the findings of the existing literature Our study's results suggest that per capita GDP is significantly and negatively related to both cross-border M&A and greenfield FDI decisions Although we originally hypothesized that the sign of the per capita GDP coefficient is positive, unit labor cost and household income are in general correlated in emerging host countries, which negatively influence FDI decisions Our empirical results for the country-level variable coefficients also yield valuable insights We find that in addition to per capita GDP, Population, and Corporate Tax all influence both cross-border M&A and greenfield FDI Our first main conclusion, therefore, is that while these country-level variables commonly increase or decrease both types of FDI, they not determine the firm's choice of either investment type In other words, while the country-level variables employed in this study are required conditions to encourage FDI in the host country, they not determine the type of FDI chosen by the home-country firm Regarding differences in these two types of FDI determinants (Hypothesis 2), we find that whether changes in the legal environment influence a home-country firm's FDI decision depends on the aspect of the legal environment as well as on type of FDI For instance, while cross-border M&A is not influenced by an enhancement of the host country's IPR protection laws, greenfield FDI is promoted by such an enhancement; these results are consistent with the conclusions drawn by Lai (1998), Branstetter et al (2006), and Javorcik (2004) In the context of the recent dramatic increases in cross-border M&A, our empirical results suggest that enhancements of SHR protection laws are required Our finding of a positive relationship between the legal environment concerning SHR protection and cross-border M&A supports the conclusions of Giovanni (2005) Indeed, Indonesia and the Philippines scored between four and five points on the IMD SHR scale and thus they belong to the lowest cohort of the 45 sample countries On the other hand, Malaysia, Singapore, and India scored between six and seven points, paralleling the recent cross-border M&A increases in these countries In terms of firm-level factors, we find that the existence of a regional network in the host country and home-country firm stock price are two major determinants in the decision whether to choose either FDI, but not both Our results in Table also show that Bilateral Trade influences both types of FDI, whereas Regional Networks, the variable intersecting Bilateral Trade and Regional Experience, only influence greenfield FDI (Hypothesis 3) This finding occurs because Bilateral Trade is a country-specific variable, whereas Regional Experience, which pertains to the existence of operations in the host country, is a firm-specific variable In other words, our empirical results suggest that firm-specific variables determine the choice of FDI once country-level variables have been converted into firm-specific variables For instance, in 2010–2011, several Japanese brewery firms announced cross-border M&A deals with companies in Australia and Brazil Although Japan, as a country, has significant bilateral export and import relationships with these two countries, the Japanese brewery firms in question not have local distribution channels there Accordingly, their acquisitions were based on their own decisions as individual firms These cases are also consistent with our empirical results that show a positive relationship between Entry Purpose and cross-border M&A The empirical results on the relationship between the home-country firm's stock price and FDI decision (Hypothesis 4) also provide important implications We find that cross-border M&A is not influenced by firm stock price prior to the investment decision Although these two variables are positively related, our results show that the investment decision rather induces the stock price change, not the other way around A possible reason for this outcome is that many cross-border M&A deals by Japanese firms are aimed at Although China also scored 3.0–4.0 SHR points during the sample period, it has substantially more cross-border M&A inflows than outflows In our study, we estimated several empirical models in addition to models (1) and (2) In one of these additional models, we employed the intersection of SH Rights Score and per capita GDP and obtained a significantly positive coefficient In the case of China, this suggests that the country's economic growth substitutes for its legal environment 116 M Nagano / Emerging Markets Review 16 (2013) 100–118 acquiring new additional customers and responding to demand in host countries Following such deals, the stock market expects an improvement in the financial performance of the acquiring firm, thereby confirming the positive relationship between stock price and cross-border M&A How should we then understand how the above legal environment and firm-level factors work together to influence home-country firm FDI choice? The interpretation of our conclusions runs as follows In contrast to greenfield FDI, cross-border M&A can dramatically increase a firm's sales network capabilities even in the short run, which can potentially lead to an improvement in its operating profit If this were so, then all firms would have preferred cross-border M&A to greenfield FDI in the past However, in reality, greenfield FDI still significantly outnumbers cross-border M&A, because the probability of completing cross-border M&A deal after an announcement is low For instance, of the 243 cross-border M&A deals we originally obtained for our sample, 97 were terminated prior to completion Ultimately, our dataset only included the 146 completed cross-border M&A deals In short, based on our sample, cross-border M&A has an approximately 40% probability of incompletion It is often thought that cross-border M&A is a ready-made international business strategy, while greenfield FDI is a tailor-made international business strategy for entering new foreign markets In the ready-made strategy, although thorough due diligence is necessary to close a successful deal, such due diligence is not a sufficient condition, since unexpected environmental changes may affect the deal immediately after the transaction In this regard, the tailor-made strategy of entry enables the home-country firm to cope with the host-country market more flexibly and thus have a higher probability of completion The characteristics of cross-border M&A may also influence the firm's choice Specifically, the legal environment concerning SHR protection is one of the factors that may influence this probability The better the enforcement of SHR protection laws, the greater is the probability of firms choosing cross-border M&A because strong SHR protection by law enables the home-country firm to control the target host-country firm in order to achieve its ultimate purpose of market entry, even in the case of the ready-made strategy of entry In the case of greenfield FDI, prior increases in the home-country firm's stock price positively influence a firm's investment decision In other words, as firm value increases in the domestic market, the firm is more likely to enter a neighboring country through greenfield FDI This probability rises if the firm already had local operations in the host country prior to the investment because a firm that is highly competitive in the domestic product or services market generally experiences stock price increases in its home country These prior stock price increases encourage the firm to establish a new production base and sales network overseas An acquiring firm's high market value in its home country affords it time and resources to successfully operate in the host country Through greenfield FDI, the firm avoids the risk of withdrawal or termination that is otherwise present in cross-border M&A Therefore, it does not choose cross-border M&A when it already has local networks in a host country Indeed, market value may even decrease when cross-border M&A market entry is not accepted by capital market participants due to its low probability of completion or view of future poor performance in the post-merger period On the other hand, market value can increase with the announcement of cross-border M&A when the future expected return of the deal makes up for the risk of incompletion In this regard, a lower stock price before the FDI decision may be better than the risk of incompletion In summary, the study contributes to the body of knowledge on this topic in four specific ways First, our study shows evidence that the host country's population size, per capital income, and corporate tax rate are common determinants for inviting inward FDI Further, we also show that the host country's legal environment and home-country individual factors determine either type of FDI, but not both Second, we show that the host country's IPR protection law influences the incoming greenfield FDI but not the cross-border M&A decision of the home-country firm By contrast, the host country's SHR protection law influences the incoming cross-border M&A but not the greenfield FDI decision of the home-country firm Third, whether the home-country firm has past FDI experience in the host country influences the greenfield FDI decision but not the cross-border M&A decision However, when the firm has no past FDI experience in the country, whether the purpose of the new entry is to establish a sales distribution channel is a major determinant of cross-border M&A Fourth, the home-country stock price appears to influence both FDI decisions, but it increases immediately before and after the investment announcement for cross-border M&A, while a high stock price in the pre-investment period encourages the greenfield FDI decision However, the following limitations must also be recognized in our study We employ only two quantitative data to represent the legal environment of the host country due to data limitations In emerging M Nagano / Emerging Markets Review 16 (2013) 100–118 117 countries, especially, obtaining quantitative data on the prevailing legal environment is complex, but other legal environment factors may also influence incoming FDI Whether host-country legal environment factors other than IPR and SHR protection laws influence FDI choice should thus be investigated by future research In addition, we employ data on home-country Japanese firms and host countries in the Asia-Pacific region Individual FDI deal-level data are also difficult to obtain Thus, our result should be re-examined using other regional data as necessary Acknowledgments The author thanks various seminar participants for their helpful comments and suggestions This research is financially supported by KAKENHI, Grant-in-Aid for Scientific Research (C) 24530357 Appendix A Annual distribution of cross-border M&A and greenfield FDI deals of Japanese firms by industry (1999–2009) Food products Tobacco products Textile mill products Apparel & related products Lumber & wood products Furniture & fixtures Paper & allied products Printing & publishing Chemicals & allied products Petroleum refining & related ind Rubber & plastic products Leather & leather products Stone clay & glass products Primary metal industries Fabricated metal products Machinery Electrical equipment Transportation equipment Instruments related products Other manufacturing industries Cross-border M&A Greenfield FDI Total Total 14 0 12 10 13 4 36 26 20 20 18 Purpose of FDI Local production and R&D Sales base establishment 0 7 2 16 14 11 8 0 0 5 2 20 12 12 14 59 36 29 23 284 103 46 125 63 411 323 389 47 Purpose of FDI Local production and R&D Sales base establishment 38 31 20 18 227 82 34 101 54 246 248 332 24 21 57 21 12 24 165 75 57 23 Source: The figures were calculated by the authors using data from Thomson Bank One, Bloomberg, and Toyo Keizai Shimpo Sha References Ahern, Kenneth R., Daminelli, Daniele, Fracassi, Cesare, 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