## Abstract

This paper investigates whether the determinants of mergers and acquisitions (M&A) inflows are different from those of outflows in emerging Asia. We use an augmented gravity model with bilateral cross-border M&A data from 2000 to 2015 for 13 emerging Asian countries. We find that the stock market size of the source country matters for both M&A inflows and outflows. In addition, the motives of firms seeking foreign markets, natural resources, and lower labor costs drive both M&A inflows and outflows. Finally, both the bank credit and the stock market liquidity of the source country play important roles in M&A inflows only, not in M&A outflows.

## 1.  Introduction

The volume of world cross-border mergers and acquisitions (M&A) has been increasing significantly. The value of M&A increased nine-fold, from US$98 billion in 1990 to US$ 959 billion in 2000. Subsequently, with global shocks like the high-tech bubble collapse in 2000, the September 11 terrorist attacks, and the Afghanistan war, cross-border M&A showed a downward trend and reached its lowest point in 2003. Despite the mild revival of the world economy in between, M&A activities slowed again during the 2008–09 global financial crisis (GFC), after which, M&A flows steadily grew from 2014 to 2016. Although developed economies dominate as both sources and destinations of M&A transactions, it is interesting to observe that their shares in the M&A market are decreasing. The M&A sales of developed economies decreased from nearly 90 percent in 1990 to about 74 percent in 2010; meanwhile, M&A purchases declined from almost 88 percent in 1990 to just less than 64 percent by 2010. This decline was largely reflected in a rise in share of emerging countries, especially emerging Asian countries. The share of emerging Asia has increased from 6.2 (0.54) percent of global M&A purchases (sales) in 1990 to 16.68 (6.79) percent in 2015.1

Our paper relates to a surging strand of literature investigating the determinants of cross-border M&A deals. Di Giovanni (2005) estimates the impacts of macroeconomic determinants, particularly financial drivers, on M&A flows. The source country's financial market size is positively associated with M&A outflows, especially for countries with a market-based financial system. Erel, Liao, and Weisbach (2012) find that, apart from geographic distance, quality of accounting disclosure, bilateral trade, and valuation also play important roles in stimulating mergers. The relative appreciation of currencies, relative higher stock market returns, and relative higher market-to-book ratio tend to motivate M&A outflows.

In addition to financial factors, trade and institutional factors are studied in detail as well. Hijzen, Gorg, and Manchin (2008) investigate the different roles of trade costs in explaining the values of horizontal M&A (international M&A activities involving mergers between firms in the same industry) and non-horizontal M&A (international M&A activities involving mergers between firms in different industries). They find that whereas trade costs can negatively affect both horizontal and non-horizontal M&A, they have less influence on horizontal M&A. Rossi and Volpin (2004) study the influence of laws and regulations on M&A. Using accounting standards, anti-director rights, and common law dummy variables as proxies, they find that investor protection is significantly positively related to both domestic and cross-border aggregate M&A volume. Additionally, targets are more likely to be the firms in countries with poor investor protection.

Despite the rising importance of Asian countries in the M&A market, the literature has remained rather limited. Among these few, Jongwanich, Brooks, and Kohpaiboon (2013) use a sample of nine emerging Asian economies to analyze the role of financial variables in promoting bilateral cross-border M&A flows. They find the importance of the size equity market in facilitating M&A flows.

With a clear focus on Asian M&A flows, we contribute to the literature in the following ways: First, we conduct a comprehensive analysis on the determinants of M&A flows by including macroeconomic and financial variables in both host and source countries; second, we systematically study the different determinants of M&A inflows and outflows, emphasizing the different roles when Asia is the host or source of M&A flows. To the best of our knowledge, there is no literature that studies how the determinants of M&A inflows differ from those of outflows in emerging Asian countries. We try to fill this gap.

The remainder of this paper is organized as follows: The next section presents some stylized facts about M&A inflows and outflows in emerging Asia; Section 3 provides empirical analysis; Section 4 conducts robustness tests. The final section concludes.

## 2.  Stylized facts about cross-border M&A in emerging Asia

M&A inflows of emerging Asia2 have experienced a steady increase from 1990 to 1996. Such activities rose sharply in value, from US$5.7 billion in 1996 to US$ 52.77 billion in 2000 (Fig. 1). This spike was triggered by financial liberalization and the huge amount of undervalued assets in most Asian countries following the 1997–98 Asian financial crisis (AFC). After 2000, the M&A inflows in emerging Asia dropped dramatically, and kept at low level until 2004. Before the decline due to the GFC, Asian M&A inflows experienced an upturn from 2004 to 2008.

Figure 1.

M&A inflows in emerging Asia, 1990–2016

Figure 1.

M&A inflows in emerging Asia, 1990–2016

Similar to M&A inflows of emerging Asia, M&A outflows of emerging Asian entities rose steadily from 1997 to 1999, and then experienced a short-lived surge during 2000 (Fig. 2). After that, from 2003 to 2008, M&A outflows increased about eight times from US$9.1 billion to US$ 102 billion. In 2009, there was a downturn because of the GFC. M&A outflows recovered in 2014 and achieved a record high level.

Figure 2.

M&A outflows in emerging Asia, 1990–2010

Figure 2.

M&A outflows in emerging Asia, 1990–2010

### 2.1  The structure of M&A outflows in emerging Asia3

Figure 3 shows the destinations of M&A originating from emerging Asia. Interestingly, we find that 47.32 percent of the M&A outflows are to other emerging Asian economies, compared with 24.66 percent to Europe and Central Asia and 19.13 percent to North America. From the perspective of income levels, emerging Asian countries apparently mainly merge and acquire firms in high-income countries, whose share account for 64.21 percent, with only 6.58 percent of the host economies belong to the low middle-income category.

Figure 3.

Emerging Asian M&A outflows (regions)

Figure 3.

Emerging Asian M&A outflows (regions)

When further analyzing the individual countries, we find that, in total, Singapore has the highest M&A outflows, amounting to US$356.76 billion and accounting for 31.43 percent of whole outflows accomplished by emerging Asian countries during this period. Hong Kong follows Singapore and China, and has the third highest proportion (21.79 percent). Concentrating on the destinations of M&A outflows of emerging Asia, we find that, in total, China attracts most of the M&A from emerging Asian countries. M&A outflows from the rest of the emerging Asian countries to China are around US$ 198 billion and account for 17.45 percent of the whole M&A outflows from emerging Asia.

### 2.2  The structure of M&A inflows in emerging Asia

Figure 4 shows the regional breakdown of foreign purchasers of emerging Asian firms. Among all source countries aiming at Asian companies, a leading proportion (50.50 percent) are other emerging Asian countries. Meanwhile, 24.96 percent of the purchasers come from Europe and Central Asia. North America, taking 22.67 percent, is the other main region targeting Asian firms. From the perspective of income levels, we find that, not surprisingly, around 90 percent of the M&As are from high-income countries.

Figure 4.

Emerging Asian M&A inflows (regions)

Figure 4.

Emerging Asian M&A inflows (regions)

As for M&A destinations, China is involved with a large number of cross-border M&A inflows. Other emerging Asian countries buy US$409.32 billion worth of assets from China through M&A. This amount accounts for 33.45 percent of total M&A inflows in this region. India has the second highest M&A inflow, reaching US$ 205.16 billion or 16.77 percent. As for the source countries, we find that the United States and Hong Kong are the two countries that are most interested in acquiring Asian entities, taking the share of 21 percent and 16 percent of the total emerging Asia M&A inflows, respectively.

## 3.  Data and models

### 3.1  Data

The bilateral cross-border M&A data are from the Zephyr database, which is compiled by Bureau Van Dijk Electronic Publishing. The panel data contain the bilateral M&A data from 2000 to 2015 of 60 countries, of which 13 are emerging Asian economies. The GDP growth rate, GDP in constant 2010 U.S. dollars, real GDP per capita in constant 2010 U.S. dollars, U.S. Consumer Price Index (CPI), official exchange rate of local currency with respect to U.S. dollars, patent applications of residents and nonresidents, and fuel exports of merchandise exports are all obtained from World Bank's World Development Indicators.

CEPII4 provides the data of the geographical distance between a source country and a host country. It also provides a common language dummy and colony dummy. The common language dummy takes the value of one if the source and host country share a common official language and zero otherwise. The colony dummy equals one if one of the related countries is a colonizer of the other for a relatively long period and zero otherwise. The data of common religion is from CIA's World Factbook. Data on the ratios of stock market capitalization to GDP, credit to private sector from deposit money banks and other financial institutions to GDP, and stock market turnover ratio are taken from World Bank's Financial Structure data set. The export from source country to host country is from The United Nations Commodity Trade Statistics Database. For financial openness, we use the Chinn-Ito index, which is based on four binary dummy variables, namely, does the country have multiple exchange rates, current account restrictions, capital account restrictions, and requirements of the surrender of export proceeds. The LIBOR rate and T-bill rate are from Economic Research Federal Reserve Bank of St. Louis.5

Institutional quality data is taken from the World Bank. It provides six indices (voice and accountability, political stability and absence of violence/terrorism, government effectiveness, regulatory quality, rule of law, and control of corruption) to measure a country's institutional quality. All the indices range from approximately 0 (weak governance performance) to 1 (strong governance performance). We take the average of these six indices to measure the overall institutional quality of the country.

The bilateral real exchange rate appreciation/deprecation is calculated as follows: First, we deflate the annually official exchange rate of the local currency with respect to the U.S. dollar by CPI; second, we divide the source country's real exchange rate with respect to the U.S. dollar by that of the host country's to obtain the bilateral real exchange rate between the two countries; finally, we take the log difference of the bilateral real exchange rate in periods t and t−1.

### 3.2  Empirical models

Based on the relevant literature, a number of macroeconomic factors are identified as the determinants of M&A flows. We summarize these variables and classify them into several broad types: financial factors, motivation factors, and other factors. Based on these factors, we develop our models of the determinants of M&A inflows and outflows in emerging Asia.

#### 3.2.1  Financial factors

Following Di Giovanni (2005), we use the stock market capitalization and the credit provided by the banking system and other financial institutions to the private sector (private credit) to capture the importance of financial depth. Also, we add the stock market turnover ratio to measure stock market liquidity, because firms can reach financing more easily from the primary market if there is a liquid secondary market.

#### 3.2.2  Motivation of market seeking

Firms that intend to protect their existing foreign markets or to promote new foreign markets may undertake market-seeking mergers. According to Dunning and Lundan (2008), acquiring firms in target markets or in adjacent regions to target markets can benefit firms in the following ways. First, firms may avoid tariffs, transaction costs, and other cost-raising barriers. Second, they can fulfill the taste of local markets more properly. Third, they can carry out their global marketing strategies.

Usually, countries with higher GDP and GDP growth rates have a larger demand for goods and services, and are more likely to be potential foreign markets. Consequently, they tend to attract market-seeking M&A flows. Thus, we expect both GDP and GDP growth rates of the host country to have positive influences on M&A inflows and outflows. Emerging Asian countries, experiencing high economic growth in recent years, are attractive to foreign investors that search for markets. Meanwhile, Asian economies would like to acquire firms in countries with large market size and high market growth rate as well.

#### 3.2.3  Motivation of labor cost saving

Increasing labor cost is a common factor forcing firms to engage in overseas M&A. Firms in countries with lower real wages can be good targets for companies aimed at reducing cost. Given the fact that labor cost in developing Asian countries is fairly low, we expect that multinationals that invest in Asian countries tend to take advantage of the low labor cost. For now, Asian companies who invest abroad may also be driven by such motivation. We, therefore, expect that both M&A inflows and outflows of emerging Asia are positively related to wage difference.

#### 3.2.4  Motivation of seeking natural resources

Firms have incentives to undertake M&A activities, targeting firms in resource-abundant countries. The stylized facts in Section 2 show that corporations in emerging Asia would like to merge enterprises in countries such as Australia, Canada, and Indonesia. As we know, Australia has abundant mineral resources like ore and fuel, the major industrial raw materials. Canada is among the few developed countries that are net exporters of oil. It owns the world's third largest reserves of oil, after Saudi Arabia and Venezuela. Rich with natural reserves, Indonesia is a leading commodities exporter in a number of resources, including crude oil, natural gas, and coal. Similarly, when emerging Asian countries are destinations, countries with affluent resources, such as Indonesia and Malaysia, are attractive to foreign investors. These may be evidence that both M&A inflows and outflows of emerging Asia are driven by natural resource seeking motivation. We use the ratio of fuel exports over merchandise exports as an approximation of the host countries’ resource abundance level. We expect to find that this ratio is positively related to emerging Asian countries’ M&A inflows and outflows.

#### 3.2.5  Motivation of seeking technology

M&A motivated by seeking technology can help acquirers gain strategic assets, and enhance their competitiveness in the long run. Most of technology seeking M&A is involved with intangible assets such as new patents. Obviously, firms with more patent applications appeal to technology-seeking M&A flows. Among the destinations of emerging Asia M&A outflows, the most favorable ones are the United States, United Kingdom, Hong Kong, and Australia, all famous for their technology sophistication. This indicates that M&A outflows of emerging Asian economies may be motivated by technology-seeking purposes. In this paper, we use patent applications to capture technology-seeking motivation.

Technology-seeking motivation is one of the drivers of mergers from developing countries to developed countries but not a driver of mergers from industrialized economies to emerging economies. Therefore, we do not expect that Asian M&A inflows are driven by this purpose.

#### 3.2.6  Other factors

Communication becomes more difficult when the distance between countries increases. This may cause more information asymmetry and result in more risks to M&As. In addition, physical distance can increase the costs of combining firms (Rose 2000), and therefore reduce the probability that two firms in different countries merge. Thus, we expect that physical distance hampers emerging Asian M&A inflows and M&A outflows.

Sharing a common language and religion can reduce the information asymmetry between investors in different countries. Therefore, firms may gain benefits from sharing a common language and religion, which may result in a better understanding of each other. We expect that sharing of a common language and religion between source country and host country advances bilateral M&A flows.

The relationship between trade and foreign investment can be either substitutive or complementary. The substitution relationship usually exists in horizontal investment, which refers to multinational firms producing the same goods in multiple plants in different countries (Brainard 1997; Head and Ries 2001). The complementarity between foreign investment and exports is usually associated with vertical FDI, which refers to multinational enterprises geographically fragmenting their production by stages (Clausing 2000; Bajo-Rubio and Montero-Munoz 2001). Therefore, the relationship between bilateral trade and M&A is determined by the relative importance of the substitution effect and the complementary effect. If horizontal M&As dominate vertical ones, a negative relationship is expected. If vertical investments dominate horizontal ones, however, a positive relationship is expected.

There is ample empirical evidence to show that the bilateral exchange rate can affect cross-border M&As. Erel, Liao, and Weisbach (2012) argue that both temporary and permanent depreciation of local currency can promote M&A inflows. When the depreciation of local currency is temporary, the arbitrage opportunity encourages M&A to activate. When the exchange rate difference is permanent, the attractiveness of acquisitions increases because of the lower cost of capital. Thus we expect that the appreciation of local currency increases M&A outflows but reduces M&A inflows.6

For host countries, better institutional quality means better protections and lower risks because of a fine economic environment and well-established market structure. Therefore, institutional quality in the host country is important in stimulating M&A inflows. High-risk countries, however, may also offer high returns for foreign firms. For companies that are skillful at risk management, low institutional quality is attractive. In addition, targets are usually from countries with poor protection (Rossi and Volpin 2004). Considering the fact that companies in developed countries are usually more skilled at risk management and corporate governance than companies in developing countries, we expect that Asian countries tend to invest in countries with better institutional quality, but Asian countries with a high level of institutional quality may not be attractive to foreign investors.

The relationship between financial liberalization and international capital flow always attracts policymakers’ attention. After the AFC, most Asian countries introduced various measures to liberalize foreign investment. In this paper, we use the capital account openness index developed by Chinn and Ito (2008) to test whether the increase of financial openness can advance cross-border M&As. Intuitively, with less capital control, domestic firms can more easily merge and acquire companies in foreign countries. Also, the more open a country is to international investment, the more likely it is to attract foreign acquirers.

When the global economy is booming, companies are optimistic toward the future and are more likely to conduct M&A; when the world economy is in recession, firms are more hesitant to invest in foreign countries because of the potential high risks. We use the TED spread, the difference between the LIBOR rate and the T-bill rate, to capture the global economic situation. Most importantly, this indictor can be used to control the influence of the GFC. We expect this variable to be negatively related to the bilateral M&A flows.

Following Di Giovanni (2005), we estimate an augmented gravity model that contains macroeconomic variables to understand the main drivers of Asian-related M&A. A large share of our dependent variable takes the value of zero because there is no M&A deal between many countries in some years. The ordinary least squares method, which assumes that the dependent variable is normally distributed, is inappropriate in this case. To correct this sample selection problem, we pool the data and apply the tobit method, censoring at zero.

Our baseline specification is outlined as follows:
logMAi,j,t is the log value of M&A volume (deflated by the U.S. CPI) from source country i to host country j; llogStmktcapi,t is the first lag of the log value of the ratio of stock market capitalization to GDP of the source country; llogPcrdi,t is the first lag of the log value of the ratio of credit to the private sector from deposit money banks and other financial institutions to GDP of source country; llogStmkttrovi,t is the first lag of the log value of stock market turnover ratio of the source country; GDPGj,t is the GDP growth rate of the host country; logPGDPd,t is the log difference of GDP per capita between source country and host country, which is used to proxy the wage difference between source country and host country; logFuelexj,t is the log value of the ratio of fuel exports to merchandise exports of the host country; logPatentj,t is the log value of total (resident and nonresident) annual patent applications of the host country; logDist shows the log value of the geographic distance between the source country and the host country; logGDPi,t is the log value of gross domestic production of the source country; logGDPj,t is the log value of gross domestic of production of host country; Comlangi,j is a dummy variable, which takes the value of one when the source country and host country share a common official language, zero otherwise; Creligi,j is also a dummy variable that takes the value of one when the source country and host country share a common major religion, zero otherwise; Colonyi,j is a dummy variable that equals one if one of the related country is colonizer of the other for a relatively long period into the model and zero otherwise; Exporti,j,t is the log value of export from the source country to the host country; Birexi,j,t is the appreciation/depreciation of the bilateral real exchange rate of the source country with respect to the host country; IQj,t is the institutional quality of the host country; KAi,t is the increase of financial openness of the source country; KAj,t is the increase of financial openness of the host country; logTEDt, which measures the global investment environment, is the log difference between the LIBOR rate and the T-bill rate.

To control for the influences of China, two dummies, Chinai and Chinaj, are included in the model. Chinai equals one if the source country is China and zero otherwise. Chinaj equals one if the host country is China and zero otherwise. In addition, Finceni, a dummy variable that equals one if the source country is a financial center and zero otherwise, is added into the model to see whether financial centers provide more supports to M&A.7 The variables in host countries are suffixed by “_h” and the variables in source countries are suffixed by “_s.” The summary of data statistics is presented in Table 1.

Table 1.
Summary of statistics
Variables(1) N(2) Mean(3) SD(4) min(5) max
logma 9,904 11.38 2.779 −3.270 20.04
llogpcrd_s 51,625 4.012 0.835 1.416 5.390
llogstmktcap_s 48,085 3.718 1.083 −0.892 6.991
llogstturnover_s 48,675 3.644 1.161 −1.386 6.203
logpgdp_d 54,520 1.703 −4.675 4.675
logpatent_h 56,640 7.764 2.781 13.91
logfuelexpt_h 56,640 1.963 1.586 −5.087 4.602
gdpgrowth_h 55,578 3.567 3.976 −14.80 34.50
loggdp 55,578 26.70 1.255 23.30 30.44
logdist 52,896 8.609 0.924 4.088 9.892
logex 49,825 19.37 2.766 26.74
kaopen 55,696 0.965 1.493 −1.904 2.374
comlang 52,896 0.0889 0.285
colony 56,640 0.0260 0.159
crelig 56,640 0.192 0.394
iq 52,215 0.434 0.955 −1.421 1.985
birex 31,786 1.013 0.186 0.236 4.246
china 56,640 0.0167 0.128
fincen 56,640 0.133 0.340
logted 56,640 1.260 1.121 0.118 2.841
Variables(1) N(2) Mean(3) SD(4) min(5) max
logma 9,904 11.38 2.779 −3.270 20.04
llogpcrd_s 51,625 4.012 0.835 1.416 5.390
llogstmktcap_s 48,085 3.718 1.083 −0.892 6.991
llogstturnover_s 48,675 3.644 1.161 −1.386 6.203
logpgdp_d 54,520 1.703 −4.675 4.675
logpatent_h 56,640 7.764 2.781 13.91
logfuelexpt_h 56,640 1.963 1.586 −5.087 4.602
gdpgrowth_h 55,578 3.567 3.976 −14.80 34.50
loggdp 55,578 26.70 1.255 23.30 30.44
logdist 52,896 8.609 0.924 4.088 9.892
logex 49,825 19.37 2.766 26.74
kaopen 55,696 0.965 1.493 −1.904 2.374
comlang 52,896 0.0889 0.285
colony 56,640 0.0260 0.159
crelig 56,640 0.192 0.394
iq 52,215 0.434 0.955 −1.421 1.985
birex 31,786 1.013 0.186 0.236 4.246
china 56,640 0.0167 0.128
fincen 56,640 0.133 0.340
logted 56,640 1.260 1.121 0.118 2.841

Note: The variables in host countries are suffixed by “_h” and the variables in source countries are suffixed by “_s.”

## 4.  Results

### 4.1  M&A outflows of emerging Asia

Table 2 presents the results of estimations of transactions with emerging Asian countries as acquirers (M&A outflows). We start with a baseline model that only contains basic gravity variables and other factors (model 1). Then we add financial factors (model 2) and motivation factors (model 3) separately. After that we estimate the full model with all financial factors and motivations factors (model 4).

Table 2.
M&A outflows of emerging Asia
(1)(2)(3)(4)
llogstturnover_s  −0.732  −0.225
(0.470)  (0.473)
llogstmktcap_s  2.488***  2.364***
(0.368)  (0.383)
llogpcrd_s  0.471  −1.084
(0.646)  (0.674)
gdpg_h   0.106 0.152*
(0.0820) (0.0811)
logpgdp_d   3.602*** 3.202***
(0.329) (0.349)
logfuelexpt_h   1.271*** 1.214***
(0.175) (0.173)
logpatent_h   0.318 0.392
(0.273) (0.272)
logdist −2.503*** −2.827*** −2.818*** −2.835***
(0.383) (0.392) (0.385) (0.390)
loggdp_s 1.520*** 2.010*** 2.800*** 2.697***
(0.344) (0.433) (0.374) (0.446)
loggdp_h 0.787*** 1.070*** 1.399*** 1.310***
(0.251) (0.261) (0.392) (0.391)
comlang 5.935*** 5.286*** 6.251*** 5.347***
(0.553) (0.586) (0.555) (0.587)
crelig 3.292*** 4.036*** 2.766*** 3.295***
(0.749) (0.748) (0.769) (0.768)
colony 4.258*** 4.322*** 3.902*** 4.111***
(1.245) (1.245) (1.218) (1.219)
export 3.384*** 3.060*** 2.469*** 2.450***
(0.217) (0.232) (0.229) (0.242)
birex 2.668 0.592 0.686 −0.772
(2.489) (2.464) (2.516) (2.507)
iq_h 2.793*** 3.003*** 6.304*** 6.045***
(0.390) (0.388) (0.525) (0.534)
china_s −2.777*** −3.083** −4.979*** −3.628***
(1.013) (1.221) (1.045) (1.219)
china_h −1.841 −1.564 0.384 0.224
(1.317) (1.310) (1.387) (1.383)
fincen_s 1.564* 1.268 −4.882*** −3.906***
(0.858) (1.087) (1.021) (1.193)
kaopen_s 1.875*** 0.593 1.322*** 0.303
(0.322) (0.386) (0.321) (0.381)
kaopen_h −0.579** −0.640*** 0.382 0.272
(0.248) (0.246) (0.253) (0.253)
logted −0.120 −0.618*** −0.404* −0.665***
(0.206) (0.219) (0.215) (0.226)
Constant −124.9*** −142.7*** −154.7*** −152.8***
(10.57) (12.89) (12.38) (13.81)
Observations 6,439 6,352 5,970 5,892
(1)(2)(3)(4)
llogstturnover_s  −0.732  −0.225
(0.470)  (0.473)
llogstmktcap_s  2.488***  2.364***
(0.368)  (0.383)
llogpcrd_s  0.471  −1.084
(0.646)  (0.674)
gdpg_h   0.106 0.152*
(0.0820) (0.0811)
logpgdp_d   3.602*** 3.202***
(0.329) (0.349)
logfuelexpt_h   1.271*** 1.214***
(0.175) (0.173)
logpatent_h   0.318 0.392
(0.273) (0.272)
logdist −2.503*** −2.827*** −2.818*** −2.835***
(0.383) (0.392) (0.385) (0.390)
loggdp_s 1.520*** 2.010*** 2.800*** 2.697***
(0.344) (0.433) (0.374) (0.446)
loggdp_h 0.787*** 1.070*** 1.399*** 1.310***
(0.251) (0.261) (0.392) (0.391)
comlang 5.935*** 5.286*** 6.251*** 5.347***
(0.553) (0.586) (0.555) (0.587)
crelig 3.292*** 4.036*** 2.766*** 3.295***
(0.749) (0.748) (0.769) (0.768)
colony 4.258*** 4.322*** 3.902*** 4.111***
(1.245) (1.245) (1.218) (1.219)
export 3.384*** 3.060*** 2.469*** 2.450***
(0.217) (0.232) (0.229) (0.242)
birex 2.668 0.592 0.686 −0.772
(2.489) (2.464) (2.516) (2.507)
iq_h 2.793*** 3.003*** 6.304*** 6.045***
(0.390) (0.388) (0.525) (0.534)
china_s −2.777*** −3.083** −4.979*** −3.628***
(1.013) (1.221) (1.045) (1.219)
china_h −1.841 −1.564 0.384 0.224
(1.317) (1.310) (1.387) (1.383)
fincen_s 1.564* 1.268 −4.882*** −3.906***
(0.858) (1.087) (1.021) (1.193)
kaopen_s 1.875*** 0.593 1.322*** 0.303
(0.322) (0.386) (0.321) (0.381)
kaopen_h −0.579** −0.640*** 0.382 0.272
(0.248) (0.246) (0.253) (0.253)
logted −0.120 −0.618*** −0.404* −0.665***
(0.206) (0.219) (0.215) (0.226)
Constant −124.9*** −142.7*** −154.7*** −152.8***
(10.57) (12.89) (12.38) (13.81)
Observations 6,439 6,352 5,970 5,892

Note: Standard errors in parentheses with ***p < 0.01, **p < 0.05, *p < 0.1.

Regarding the financial factors, we find that the increase of stock market size stimulates M&A outflows of emerging Asia whereas the increase of bank credit has no significant effect on outward M&A. After the AFC, especially during the period 2002–07, the stock markets of emerging Asia developed rapidly. As a result, they can provide funds for companies that plan to engage in M&A activities. The banking sector does not play an important role in facilitating M&A outflows, however, although most of emerging Asian countries adopt bank-based financial systems. It may reflect the low-risk attitude of the banks in emerging Asia regarding cross-border M&A. Also, the results show that stock market liquidity of Asian countries cannot promote cross-border M&A outflows.

When it comes to motivation factors, the results show that the coefficients of both host country GDP and GDP growth rate are positive and statistically significant, suggesting that market-seeking motivation is important for Asian M&A outflows. The coefficient of GDP per capita difference is positive and significant in models 3 and 4. It seems that M&A outflows from emerging Asia do seek lower labor costs as one of their motivations to go abroad. As expected, the coefficient of fuel export of the host country is significantly positively related to M&A activities, revealing the importance of the fuel resource-seeking motivation of M&A outflows.

Contrary to our expectation, the coefficient of patent application is insignificant, implying that emerging Asia is not technology-seeking. It may be because emerging Asia countries are mainly focused on the manufacturing industry, which does not depend so much on technology.

We now discuss the results of other factors. As expected, the coefficient of distance between two countries is negative and significant in all of the models, implying that physical distance causes information gaps and hampers M&A flows. The coefficient of the common language dummy, the common religion dummy, and the colony dummy are positive and significant, revealing that sharing a common official language and religion can stimulate the M&A flows. The coefficient of export is positive and significant, implying that vertical investments dominate horizontal ones. The coefficient of institutional quality of the host country is significant and positive, suggesting that a fine economic environment and a well-established market structure in the host country attract overseas investment and stimulate M&A outflows. The financial openness of emerging Asian countries can stimulate M&A outflows. But the financial openness of the host country does not matter. In addition, the TED spread is negatively related to the bilateral M&A flow, implying that outward M&As of Asian countries also follow a cyclical pattern because of limitation of funds during a financial crisis.

### 4.2  M&A inflows of emerging Asia

Table 3 presents the results of estimations of M&A deals with Asian companies as targets (M&A inflows). A major finding is that when Asian countries are destinations, both the stock market capitalization and private credit play a pivotal role in supporting foreign investments. Stock market liquidity also promotes cross-border M&As.

Table 3.
M&A inflows of emerging Asia
(1)(2)(3)(4)
llogstturnover_s  0.513*  0.334
(0.285)  (0.283)
llogstmktcap_s  2.245***  2.180***
(0.295)  (0.300)
llogpcrd_s  2.654***  1.735***
(0.410)  (0.429)
gdpg_h   0.116 0.195**
(0.0769) (0.0767)
logpgdp_d   2.387*** 1.587***
(0.244) (0.263)
logfuelexpt_h   1.038*** 1.001***
(0.182) (0.176)
logpatent_h   −0.0728 −0.431
(0.332) (0.338)
logdist −2.127*** −2.306*** −2.657*** −2.616***
(0.333) (0.349) (0.334) (0.350)
loggdp_s 1.307*** 1.387*** 1.273*** 1.427***
(0.203) (0.245) (0.203) (0.246)
loggdp_h 1.246*** 1.629*** 0.891* 1.573***
(0.237) (0.242) (0.488) (0.497)
comlang 3.769*** 2.656*** 3.329*** 2.427***
(0.447) (0.460) (0.453) (0.470)
crelig 2.998*** 3.216*** 3.828*** 3.902***
(0.613) (0.621) (0.617) (0.627)
colony 1.474 1.923* 2.863*** 3.154***
(0.983) (1.007) (0.971) (1.004)
export 3.281*** 2.554*** 2.978*** 2.429***
(0.191) (0.200) (0.191) (0.201)
birex 0.0257 0.994 −0.138 1.188
(1.768) (1.826) (1.739) (1.784)
iq_h −0.0909 0.738* 3.776*** 3.669***
(0.414) (0.417) (0.637) (0.652)
china_s −3.524*** −4.386*** −2.380** −2.899***
(1.051) (1.047) (1.045) (1.056)
china_h −2.948*** −1.959** 2.173** 2.186**
(0.791) (0.800) (1.044) (1.056)
fincen_s 4.163*** 1.801*** 3.538*** 1.764***
(0.545) (0.583) (0.541) (0.581)
kaopen_s 1.786*** 0.882*** 0.362* 0.113
(0.169) (0.183) (0.217) (0.225)
kaopen_h 0.380 0.247 0.584** 0.328
(0.245) (0.244) (0.259) (0.258)
logted −0.983*** −1.088*** −0.764*** −0.874***
(0.164) (0.167) (0.177) (0.180)
Constant −126.5*** −144.3*** −108.9*** −134.6***
(7.414) (7.905) (11.29) (11.88)
Observations 6,658 6,053 6,411 5,847
(1)(2)(3)(4)
llogstturnover_s  0.513*  0.334
(0.285)  (0.283)
llogstmktcap_s  2.245***  2.180***
(0.295)  (0.300)
llogpcrd_s  2.654***  1.735***
(0.410)  (0.429)
gdpg_h   0.116 0.195**
(0.0769) (0.0767)
logpgdp_d   2.387*** 1.587***
(0.244) (0.263)
logfuelexpt_h   1.038*** 1.001***
(0.182) (0.176)
logpatent_h   −0.0728 −0.431
(0.332) (0.338)
logdist −2.127*** −2.306*** −2.657*** −2.616***
(0.333) (0.349) (0.334) (0.350)
loggdp_s 1.307*** 1.387*** 1.273*** 1.427***
(0.203) (0.245) (0.203) (0.246)
loggdp_h 1.246*** 1.629*** 0.891* 1.573***
(0.237) (0.242) (0.488) (0.497)
comlang 3.769*** 2.656*** 3.329*** 2.427***
(0.447) (0.460) (0.453) (0.470)
crelig 2.998*** 3.216*** 3.828*** 3.902***
(0.613) (0.621) (0.617) (0.627)
colony 1.474 1.923* 2.863*** 3.154***
(0.983) (1.007) (0.971) (1.004)
export 3.281*** 2.554*** 2.978*** 2.429***
(0.191) (0.200) (0.191) (0.201)
birex 0.0257 0.994 −0.138 1.188
(1.768) (1.826) (1.739) (1.784)
iq_h −0.0909 0.738* 3.776*** 3.669***
(0.414) (0.417) (0.637) (0.652)
china_s −3.524*** −4.386*** −2.380** −2.899***
(1.051) (1.047) (1.045) (1.056)
china_h −2.948*** −1.959** 2.173** 2.186**
(0.791) (0.800) (1.044) (1.056)
fincen_s 4.163*** 1.801*** 3.538*** 1.764***
(0.545) (0.583) (0.541) (0.581)
kaopen_s 1.786*** 0.882*** 0.362* 0.113
(0.169) (0.183) (0.217) (0.225)
kaopen_h 0.380 0.247 0.584** 0.328
(0.245) (0.244) (0.259) (0.258)
logted −0.983*** −1.088*** −0.764*** −0.874***
(0.164) (0.167) (0.177) (0.180)
Constant −126.5*** −144.3*** −108.9*** −134.6***
(7.414) (7.905) (11.29) (11.88)
Observations 6,658 6,053 6,411 5,847

Note: Standard errors in parentheses with ***p < 0.01, **p < 0.05, *p < 0.1.

Consistent with market-seeking motivation, the coefficient of the GDP of the host country is significant and a high GDP growth rate also helps emerging Asian countries attract M&A inflows. For the labor cost saving motivation, we notice that the coefficient of GDP per capita difference is positive and significant, which means that the low labor cost of emerging Asian countries is one of the primary factors that attracts inward M&A. The coefficient of fuel export is quite significant. Interestingly, patent applications are also insignificant at the 10 percent confidence level, which is contrary to the conventional wisdom that the high technology level of emerging Asia can help attract foreign investors.

For other variables, the coefficient of distance is significant and negative, indicating that physical distance is negatively related to emerging Asia's M&A inflows; common language, common religion, and colony dummies are positively associated with M&A sales; the bilateral real exchange rate is insignificant; the coefficient of institutional quality of the host country and bilateral trade are still positive and significant; the financial openness of emerging Asian countries does play a significant role in promoting M&A inflows, and the financial openness of source country also stimulates bilateral mergers and acquisitions; the TED spread is negatively related to the bilateral M&A flows, implying that inward M&As of Asian countries follows a cyclical pattern because of the limitation of funds during a financial crisis.

From the results we can see that China plays a special role in attracting M&As, but it does not play a positive role in acquiring firms in other countries when controlling for all financial factors and motivations factors. Financial centers can provide more supports for firms that invest abroad.

### 4.3  Further analysis

In our analysis investigating M&A outflows of emerging Asia, we include both Asian and non-Asian host countries. Similarly, we include both Asian and non-Asian sources when we study M&A inflows of emerging Asia. Nevertheless, we may expect the intra-Asia transactions to be different from those between Asian companies and non-Asian companies. Some Asian companies are motivated to acquire firms in other Asia countries on the basis of personal relations, ethnicity, and social connections rather than the determinants we investigated earlier. These kinds of motivations are difficult to measure but play significant roles in promoting M&A among mainland China, Hong Kong, Taiwan, and even Singapore. Additionally, home bias may affect intra-Asia M&A transactions. Investors in Asia tend to merge firms in other Asian countries. Therefore, we split our sample to compare the different determinants of M&A flows between Asian countries and between Asian and non-Asian countries.

Table 4 presents the results of estimations of subsamples. Columns (1) and (2) show the results of Asian M&A outflows and Asian M&A inflows respectively, copied from model 4 in Table 2 and model 4 in Table 3, for comparison. Column (3) is the estimation of M&As from Asian countries to non-Asia countries. Column (4) is the result based on intra-Asia transactions. Column (5) investigates the determinants of cross-border M&A from non-Asian countries to Asian countries.

Table 4.
Regional differences
(1)(2)(3)(4)(5)
llogstturnover_s −0.225 0.334 −0.722 0.147 −0.000626
(0.473) (0.283) (0.738) (0.574) (0.413)
llogstmktcap_s 2.364*** 2.180*** 2.882*** 2.131*** 3.510***
(0.383) (0.300) (0.601) (0.453) (0.580)
llogpcrd_s −1.084 1.735*** −0.0886 −0.877 0.922
(0.674) (0.429) (1.117) (0.804) (0.668)
gdpg_h 0.152* 0.195** 0.157 0.196* 0.110
(0.0811) (0.0767) (0.121) (0.116) (0.102)
logpgdp_d 3.202*** 1.587*** 2.992*** 1.980*** 3.449***
(0.349) (0.263) (0.696) (0.488) (0.442)
logfuelexpt_h 1.214*** 1.001*** 1.686*** 0.811*** 1.427***
(0.173) (0.176) (0.265) (0.247) (0.256)
logpatent_h 0.392 −0.431 1.182*** −0.914* 0.0919
(0.272) (0.338) (0.437) (0.500) (0.461)
logdist −2.835*** −2.616*** −2.722*** −0.0800 −2.665***
(0.390) (0.350) (0.792) (0.555) (0.599)
loggdp_s 2.697*** 1.427*** 5.118*** −0.440 2.523***
(0.446) (0.246) (0.699) (0.614) (0.426)
loggdp_h 1.310*** 1.573*** 1.854** 1.491** 1.549**
(0.391) (0.497) (0.724) (0.716) (0.675)
comlang 5.347*** 2.427*** 8.168*** 1.464** 2.631***
(0.587) (0.470) (1.030) (0.732) (0.688)
crelig 3.295*** 3.902*** 1.840 2.623*** 4.678***
(0.768) (0.627) (1.295) (0.930) (0.929)
colony 4.111*** 3.154*** 4.379***  3.330***
(1.219) (1.004) (1.508)  (1.074)
export 2.450*** 2.429*** 1.388*** 3.413*** 1.383***
(0.242) (0.201) (0.393) (0.390) (0.279)
birex −0.772 1.188 −1.431 1.519 2.819
(2.507) (1.784) (3.483) (3.938) (2.112)
iq_h 6.045*** 3.669*** 7.101*** 3.734*** 7.110***
(0.534) (0.652) (0.814) (1.106) (0.953)
china_s −3.628*** −2.899*** −4.059** −1.105
(1.219) (1.056) (1.825) (1.599)
china_h 0.224 2.186**  0.345 6.816***
(1.383) (1.056)  (1.860) (1.440)
fincen_s −3.906*** 1.764*** −3.750* −1.037 2.156**
(1.193) (0.581) (2.011) (1.493) (0.888)
kaopen_s 0.303 0.113 0.416 0.0744 0.341
(0.381) (0.225) (0.601) (0.463) (0.363)
kaopen_h 0.272 0.328 0.164 0.682* 0.210
(0.253) (0.258) (0.370) (0.381) (0.356)
logted −0.665*** −0.874*** −0.559 −0.702** −0.498**
(0.226) (0.180) (0.353) (0.283) (0.248)
Constant −152.8*** −134.6*** −227.7*** −105.0*** −155.3***
(13.81) (11.88) (24.57) (20.35) (17.57)
Observations 5,892 5,847 4,354 1,538 4,309
(1)(2)(3)(4)(5)
llogstturnover_s −0.225 0.334 −0.722 0.147 −0.000626
(0.473) (0.283) (0.738) (0.574) (0.413)
llogstmktcap_s 2.364*** 2.180*** 2.882*** 2.131*** 3.510***
(0.383) (0.300) (0.601) (0.453) (0.580)
llogpcrd_s −1.084 1.735*** −0.0886 −0.877 0.922
(0.674) (0.429) (1.117) (0.804) (0.668)
gdpg_h 0.152* 0.195** 0.157 0.196* 0.110
(0.0811) (0.0767) (0.121) (0.116) (0.102)
logpgdp_d 3.202*** 1.587*** 2.992*** 1.980*** 3.449***
(0.349) (0.263) (0.696) (0.488) (0.442)
logfuelexpt_h 1.214*** 1.001*** 1.686*** 0.811*** 1.427***
(0.173) (0.176) (0.265) (0.247) (0.256)
logpatent_h 0.392 −0.431 1.182*** −0.914* 0.0919
(0.272) (0.338) (0.437) (0.500) (0.461)
logdist −2.835*** −2.616*** −2.722*** −0.0800 −2.665***
(0.390) (0.350) (0.792) (0.555) (0.599)
loggdp_s 2.697*** 1.427*** 5.118*** −0.440 2.523***
(0.446) (0.246) (0.699) (0.614) (0.426)
loggdp_h 1.310*** 1.573*** 1.854** 1.491** 1.549**
(0.391) (0.497) (0.724) (0.716) (0.675)
comlang 5.347*** 2.427*** 8.168*** 1.464** 2.631***
(0.587) (0.470) (1.030) (0.732) (0.688)
crelig 3.295*** 3.902*** 1.840 2.623*** 4.678***
(0.768) (0.627) (1.295) (0.930) (0.929)
colony 4.111*** 3.154*** 4.379***  3.330***
(1.219) (1.004) (1.508)  (1.074)
export 2.450*** 2.429*** 1.388*** 3.413*** 1.383***
(0.242) (0.201) (0.393) (0.390) (0.279)
birex −0.772 1.188 −1.431 1.519 2.819
(2.507) (1.784) (3.483) (3.938) (2.112)
iq_h 6.045*** 3.669*** 7.101*** 3.734*** 7.110***
(0.534) (0.652) (0.814) (1.106) (0.953)
china_s −3.628*** −2.899*** −4.059** −1.105
(1.219) (1.056) (1.825) (1.599)
china_h 0.224 2.186**  0.345 6.816***
(1.383) (1.056)  (1.860) (1.440)
fincen_s −3.906*** 1.764*** −3.750* −1.037 2.156**
(1.193) (0.581) (2.011) (1.493) (0.888)
kaopen_s 0.303 0.113 0.416 0.0744 0.341
(0.381) (0.225) (0.601) (0.463) (0.363)
kaopen_h 0.272 0.328 0.164 0.682* 0.210
(0.253) (0.258) (0.370) (0.381) (0.356)
logted −0.665*** −0.874*** −0.559 −0.702** −0.498**
(0.226) (0.180) (0.353) (0.283) (0.248)
Constant −152.8*** −134.6*** −227.7*** −105.0*** −155.3***
(13.81) (11.88) (24.57) (20.35) (17.57)
Observations 5,892 5,847 4,354 1,538 4,309

Note: Standard errors in parentheses with ***p < 0.01, **p < 0.05, *p < 0.1. Model (1) and (2) represent overall Asian M&A outflows and inflows, model (3) represents M&A flows from Asia to non-Asia, model (4) from Asia to Asia, and model (5) from non-Asia to Asia.

Comparing column (1) with columns (3) and (4), we find that in some aspects, the determinants of M&A from emerging Asia to other regions is different from those of M&A between Asian countries. Starting with the financial factors, we find that in these subsamples, the stock market capitalization of the source country can promote cross-border M&A transactions whereas private credit cannot, implying that the limitation of external financing from bank system is not a constraint of Asian outward M&A.

Column (3) shows that Asian companies are motivated by foreign market size. Column (4) shows that Asian companies are motivated by foreign market growth. The fuel resource endowment of the host country and cheap labor cost are positively related to the volume of M&A in both columns (3) and (4). The natural resource seeking motivation and labor cost saving motivation hold both for M&A from Asian countries to countries in other regions and for M&A from Asian countries to other Asian countries.

For other factors, sharing a common official language and religion are positively related to M&A from Asian countries to countries in other regions. The coefficients of financial openness of the source country are both insignificant in the subsample with transactions between Asian countries and non-Asian countries and with transactions between Asian countries. The coefficients of institutional quality are all positive and significant in columns (1), (3), and (4), but the coefficient of institutional quality in column (3) is much larger than the rest. Asian companies pay more attention to a fine economic environment when merging firms in other regions.

The results for M&A transactions between Asian countries and transactions from non-Asian countries to Asian countries are presented in column (4) and (5) of Table 4, respectively. Stock market stimulates M&A transactions from other regions to Asia and intra-Asia. The coefficients of private credit are both insignificant in column (4) and (5), but the coefficient of private credit is positive when purchasers come from non-Asia and negative when purchasers come from Asia. The stock market turnover ratio becomes insignificant in both sub-samples. When it comes to the motivation factors in column (4) and (5), we find that market-seeking is one of the purposes of M&A from other regions to Asia. The coefficients of GDP per capita difference are positive and significant in two sub-samples. Labor cost saving is also one of the purposes of M&A. The coefficient of fuel export is positive and significant, indicating that the M&A transactions from both Asian and non-Asian countries are motivated by fuel resource seeking motivation. The coefficient of patent applications is negative and significant in sub-sample of transactions in intra-Asia. Column (5) shows that the GDP of the source country, common language and common religion promote the mergers and acquisitions. High institutional quality of host country promotes M&As. China plays a special role in attracting M&As from other regions. Financial centers can provide more supports for firms who engage in foreign investment.

We also break the sample into subsamples according to the development stage of the target country (Asian M&A outflows) and acquiring country (Asian M&A inflows). Table 5 presents the results of estimations of M&A transactions from emerging Asia to the whole world, from the world to emerging Asia, from emerging Asia to developed countries, from emerging Asia to developing countries, from developed countries to emerging Asia, and from developing economies to emerging Asia (column (1) to column (6), respectively).

Table 5.
Development differences
(1)(2)(3)(4)(5)(6)
llogstturnover_s −0.225 0.334 −0.358 −0.191 0.196 1.123***
(0.473) (0.283) (0.836) (0.573) (0.456) (0.432)
llogstmktcap_s 2.364*** 2.180*** 2.944*** 2.197*** 1.611** 2.180***
(0.383) (0.300) (0.691) (0.461) (0.649) (0.416)
llogpcrd_s −1.084 1.735*** −0.0930 −1.044 1.798* 0.510
(0.674) (0.429) (1.287) (0.807) (1.062) (0.648)
gdpg_h 0.152* 0.195** 0.0115 0.104 0.186* 0.163
(0.0811) (0.0767) (0.178) (0.0937) (0.105) (0.107)
logpgdp_d 3.202*** 1.587*** 2.809*** 3.321*** 2.395*** 1.793***
(0.349) (0.263) (0.845) (0.410) (0.515) (0.346)
logfuelexpt_h 1.214*** 1.001*** 0.782** 1.158*** 1.027*** 0.871***
(0.173) (0.176) (0.376) (0.227) (0.243) (0.243)
logpatent_h 0.392 −0.431 0.280 0.129 0.825* −1.088**
(0.272) (0.338) (0.524) (0.362) (0.481) (0.470)
logdist −2.835*** −2.616*** 0.404 −3.043*** −0.829 −2.038***
(0.390) (0.350) (1.126) (0.485) (0.725) (0.471)
loggdp_s 2.697*** 1.427*** 4.694*** 2.149*** 2.344*** −0.316
(0.446) (0.246) (0.763) (0.585) (0.456) (0.494)
loggdp_h 1.310*** 1.573*** 2.652*** 1.467*** 1.283* 1.648**
(0.391) (0.497) (0.854) (0.550) (0.699) (0.680)
comlang 5.347*** 2.427*** 6.625*** 3.481*** 1.025 3.014***
(0.587) (0.470) (1.208) (0.732) (0.730) (0.660)
crelig 3.295*** 3.902*** −0.455 3.134*** 2.470** 2.837***
(0.768) (0.627) (1.821) (0.885) (1.188) (0.816)
colony 4.111*** 3.154*** 4.955***  3.561***
(1.219) (1.004) (1.480)  (0.952)
export 2.450*** 2.429*** 1.252** 2.315*** 0.835** 3.615***
(0.242) (0.201) (0.548) (0.316) (0.347) (0.301)
birex −0.772 1.188 −8.689** 3.268 −0.527 2.368
(2.507) (1.784) (4.420) (3.070) (2.984) (2.091)
iq_h 6.045*** 3.669*** 3.233** 7.354*** 5.762*** 3.239***
(0.534) (0.652) (1.267) (0.757) (1.024) (0.900)
china_s −3.628*** −2.899*** −4.110** −2.520*  −2.104
(1.219) (1.056) (2.062) (1.514)  (1.446)
china_h 0.224 2.186**  1.640 3.817** 0.573
(1.383) (1.056)  (1.725) (1.527) (1.493)
fincen_s −3.906*** 1.764*** −1.617 −3.583** 4.367*** −1.305
(1.193) (0.581) (2.407) (1.434) (0.967) (0.886)
kaopen_s 0.303 0.113 −0.300 0.369 0.223 0.263
(0.381) (0.225) (0.681) (0.460) (0.818) (0.314)
kaopen_h 0.272 0.328 2.303*** 0.0215 −0.122 0.728**
(0.253) (0.258) (0.822) (0.288) (0.363) (0.354)
logted −0.665*** −0.874*** −0.840** −0.659** −0.340 −0.961***
(0.226) (0.180) (0.402) (0.275) (0.262) (0.261)
Constant −152.8*** −134.6*** −246.3*** −138.1*** −145.2*** −112.4***
(13.81) (11.88) (28.08) (18.04) (17.66) (18.56)
Observations 5,892 5,847 2,039 3,853 2,060 3,787
(1)(2)(3)(4)(5)(6)
llogstturnover_s −0.225 0.334 −0.358 −0.191 0.196 1.123***
(0.473) (0.283) (0.836) (0.573) (0.456) (0.432)
llogstmktcap_s 2.364*** 2.180*** 2.944*** 2.197*** 1.611** 2.180***
(0.383) (0.300) (0.691) (0.461) (0.649) (0.416)
llogpcrd_s −1.084 1.735*** −0.0930 −1.044 1.798* 0.510
(0.674) (0.429) (1.287) (0.807) (1.062) (0.648)
gdpg_h 0.152* 0.195** 0.0115 0.104 0.186* 0.163
(0.0811) (0.0767) (0.178) (0.0937) (0.105) (0.107)
logpgdp_d 3.202*** 1.587*** 2.809*** 3.321*** 2.395*** 1.793***
(0.349) (0.263) (0.845) (0.410) (0.515) (0.346)
logfuelexpt_h 1.214*** 1.001*** 0.782** 1.158*** 1.027*** 0.871***
(0.173) (0.176) (0.376) (0.227) (0.243) (0.243)
logpatent_h 0.392 −0.431 0.280 0.129 0.825* −1.088**
(0.272) (0.338) (0.524) (0.362) (0.481) (0.470)
logdist −2.835*** −2.616*** 0.404 −3.043*** −0.829 −2.038***
(0.390) (0.350) (1.126) (0.485) (0.725) (0.471)
loggdp_s 2.697*** 1.427*** 4.694*** 2.149*** 2.344*** −0.316
(0.446) (0.246) (0.763) (0.585) (0.456) (0.494)
loggdp_h 1.310*** 1.573*** 2.652*** 1.467*** 1.283* 1.648**
(0.391) (0.497) (0.854) (0.550) (0.699) (0.680)
comlang 5.347*** 2.427*** 6.625*** 3.481*** 1.025 3.014***
(0.587) (0.470) (1.208) (0.732) (0.730) (0.660)
crelig 3.295*** 3.902*** −0.455 3.134*** 2.470** 2.837***
(0.768) (0.627) (1.821) (0.885) (1.188) (0.816)
colony 4.111*** 3.154*** 4.955***  3.561***
(1.219) (1.004) (1.480)  (0.952)
export 2.450*** 2.429*** 1.252** 2.315*** 0.835** 3.615***
(0.242) (0.201) (0.548) (0.316) (0.347) (0.301)
birex −0.772 1.188 −8.689** 3.268 −0.527 2.368
(2.507) (1.784) (4.420) (3.070) (2.984) (2.091)
iq_h 6.045*** 3.669*** 3.233** 7.354*** 5.762*** 3.239***
(0.534) (0.652) (1.267) (0.757) (1.024) (0.900)
china_s −3.628*** −2.899*** −4.110** −2.520*  −2.104
(1.219) (1.056) (2.062) (1.514)  (1.446)
china_h 0.224 2.186**  1.640 3.817** 0.573
(1.383) (1.056)  (1.725) (1.527) (1.493)
fincen_s −3.906*** 1.764*** −1.617 −3.583** 4.367*** −1.305
(1.193) (0.581) (2.407) (1.434) (0.967) (0.886)
kaopen_s 0.303 0.113 −0.300 0.369 0.223 0.263
(0.381) (0.225) (0.681) (0.460) (0.818) (0.314)
kaopen_h 0.272 0.328 2.303*** 0.0215 −0.122 0.728**
(0.253) (0.258) (0.822) (0.288) (0.363) (0.354)
logted −0.665*** −0.874*** −0.840** −0.659** −0.340 −0.961***
(0.226) (0.180) (0.402) (0.275) (0.262) (0.261)
Constant −152.8*** −134.6*** −246.3*** −138.1*** −145.2*** −112.4***
(13.81) (11.88) (28.08) (18.04) (17.66) (18.56)
Observations 5,892 5,847 2,039 3,853 2,060 3,787

Note: Standard errors in parentheses with ***p < 0.01, **p < 0.05, *p < 0.1. Column (1) presents the results of estimations of M&A transactions from emerging Asia to the whole world, column (2) from the world to emerging Asia, column (3) from emerging Asia to developed countries, column (4) from emerging Asia to developing countries, column (5) from developed countries to emerging Asia, and column (6) from developing economies to emerging Asia.

In column (3), we see that stock market size matters. The market size, labor cost saving, and fuel resource endowment of the host country are all significant determinants of M&A from emerging Asia to industrialized countries, indicating that investors from emerging Asia prefer developed countries with large markets, cheap labor, and abundant fuel resources. Also significant are the common language dummy, the colony dummy, and the capital openness of the host country. These three variables appear to capture the tendency for companies from emerging Asia to invest in industrialized countries with the same official language, colony history, and that are more open to foreign investment. The positive and significant coefficient of the market size of the source country indicates that large Asian economies are more likely to merge firms in developed economies. The TED spread is significant and negative, which is consistent with the result in the whole sample.

The results in column (4) show that the development of the stock market of an emerging Asian country can significantly support Asian companies’ acquisition of firms in developing countries. Because the coefficient of the GDP of the host country is positive, market seeking is also a main driver for M&As from emerging Asian countries to developing countries. Labor cost saving motivation and fuel source seeking are highly important for M&A transactions from emerging Asia to developing countries.

Based on the results in column (5), we find that M&A flows from industrialized countries to emerging Asian countries are determined by some conventional factors. Both stock market capitalization and private credit promote cross-border M&As. Market seeking motivation is also important. Countries with large market size and high economic growth, such as China, attract multinational firms from developed countries. In addition, fuel resource seeking, labor cost saving, and technology seeking motivations are all at play. Regarding control variables, geographical distance, the depreciation of source country currency, and capital openness of host and source country are all insignificant. Common religion, colony, export, and institutional quality promote cross-border M&A from developed countries to Asian countries.

The results of the column (6) show that M&A decisions of developing countries are not only influenced by the impact of the size of the stock market, but also affected by the impact of stock market liquidity. Similarly, the search for markets, resources, and cheap labor play a key role in developing countries’ M&A in Asian countries.

Table 6 8 presents the results of breaking up the sample into subsamples by different time periods. The main message we can draw is that the motivation of seeking cheap labor plays a growing role in Asian outward M&A decisions over years. The coefficient of GDP per capita difference (logpgdp_d) is insignificant from 2000 to 2005. As is well known, the domestic labor cost of most emerging Asian countries is low in this period. It is probable that searching for low wages abroad is not one of the primary goals of enterprises in emerging Asia. In the subsamples from 2006–10 and from 2011–15, however, the coefficient of GDP per capita difference becomes significant. The real GDP per capita from main M&A outflow sources—Singapore, China, and Hong Kong, accounting for 79.85 percent of M&A outflows of total emerging Asia—has increased sharply and searching cheap labors abroad becomes important for enterprises in emerging Asia.

Table 6.
Period differences of M&A outflows
Period
(1) 2000–15(2) 2000–05(3) 2006–10(4) 2011–15
llogstturnover_s 0.0402 −1.034 −0.0227 −0.478
(0.238) (1.948) (0.562) (0.427)
llogstmktcap_s 0.745*** 0.617 −0.0641 1.057***
(0.178) (1.771) (0.243) (0.294)
llogpcrd_s −0.546* −2.611 0.490 −0.714
(0.312) (2.734) (0.647) (0.473)
gdpg_h 0.0761** −0.00347 0.128** 0.0423
(0.0376) (0.261) (0.0513) (0.0705)
logpgdp_d 1.097*** 3.440 0.718** 0.786**
(0.266) (2.225) (0.356) (0.371)
logfuelexpt_h 0.567*** 1.104** 0.466*** 0.492***
(0.0903) (0.560) (0.152) (0.121)
logpatent_h 0.0827 1.434 0.179* −0.00556
(0.0591) (1.084) (0.102) (0.0710)
logdist −1.431*** −2.429 −1.098*** −1.246***
(0.297) (1.582) (0.412) (0.393)
loggdp_s 1.271*** 3.855 1.468** 0.952**
(0.307) (2.725) (0.689) (0.371)
loggdp_h 1.113*** 1.087 0.860*** 1.003***
(0.194) (0.887) (0.272) (0.261)
comlang 1.830*** 4.055 1.877*** 1.425***
(0.368) (2.483) (0.623) (0.427)
crelig 1.532*** 3.541* 0.731 1.488***
(0.364) (1.960) (0.546) (0.490)
colony 0.638 2.764 0.504 0.0495
(0.470) (2.324) (0.709) (0.633)
export 0.199 2.823 −0.965 0.0723
(0.399) (2.012) (0.726) (0.591)
birex −0.302 3.676 −1.924 3.479*
(1.085) (5.361) (1.626) (2.010)
iq_h 1.945*** 3.628 1.880*** 1.299**
(0.454) (2.874) (0.613) (0.630)
china_s 0.899 −2.435 −0.111 2.332***
(0.614) (5.099) (1.254) (0.893)
china_h 1.471*** 2.557 0.439 1.282
(0.547) (2.514) (0.828) (0.825)
fincen_s −0.890 −6.929 −0.278 1.197
(0.657) (4.848) (0.893) (1.343)
kaopen_s 0.490*** 1.885 0.701** −0.0658
(0.187) (1.792) (0.285) (0.377)
kaopen_h 0.319*** 1.247 0.0286 0.409**
(0.111) (0.816) (0.178) (0.160)
logted −0.0551 −3.274 −0.0661 −0.703
(0.0996) (6.302) (0.166) (0.807)
Constant −49.77*** −116.0 −49.81** −39.06***
(11.59) (75.20) (22.07) (14.22)
Observations 5,645 1,089 2,044 2,512
Period
(1) 2000–15(2) 2000–05(3) 2006–10(4) 2011–15
llogstturnover_s 0.0402 −1.034 −0.0227 −0.478
(0.238) (1.948) (0.562) (0.427)
llogstmktcap_s 0.745*** 0.617 −0.0641 1.057***
(0.178) (1.771) (0.243) (0.294)
llogpcrd_s −0.546* −2.611 0.490 −0.714
(0.312) (2.734) (0.647) (0.473)
gdpg_h 0.0761** −0.00347 0.128** 0.0423
(0.0376) (0.261) (0.0513) (0.0705)
logpgdp_d 1.097*** 3.440 0.718** 0.786**
(0.266) (2.225) (0.356) (0.371)
logfuelexpt_h 0.567*** 1.104** 0.466*** 0.492***
(0.0903) (0.560) (0.152) (0.121)
logpatent_h 0.0827 1.434 0.179* −0.00556
(0.0591) (1.084) (0.102) (0.0710)
logdist −1.431*** −2.429 −1.098*** −1.246***
(0.297) (1.582) (0.412) (0.393)
loggdp_s 1.271*** 3.855 1.468** 0.952**
(0.307) (2.725) (0.689) (0.371)
loggdp_h 1.113*** 1.087 0.860*** 1.003***
(0.194) (0.887) (0.272) (0.261)
comlang 1.830*** 4.055 1.877*** 1.425***
(0.368) (2.483) (0.623) (0.427)
crelig 1.532*** 3.541* 0.731 1.488***
(0.364) (1.960) (0.546) (0.490)
colony 0.638 2.764 0.504 0.0495
(0.470) (2.324) (0.709) (0.633)
export 0.199 2.823 −0.965 0.0723
(0.399) (2.012) (0.726) (0.591)
birex −0.302 3.676 −1.924 3.479*
(1.085) (5.361) (1.626) (2.010)
iq_h 1.945*** 3.628 1.880*** 1.299**
(0.454) (2.874) (0.613) (0.630)
china_s 0.899 −2.435 −0.111 2.332***
(0.614) (5.099) (1.254) (0.893)
china_h 1.471*** 2.557 0.439 1.282
(0.547) (2.514) (0.828) (0.825)
fincen_s −0.890 −6.929 −0.278 1.197
(0.657) (4.848) (0.893) (1.343)
kaopen_s 0.490*** 1.885 0.701** −0.0658
(0.187) (1.792) (0.285) (0.377)
kaopen_h 0.319*** 1.247 0.0286 0.409**
(0.111) (0.816) (0.178) (0.160)
logted −0.0551 −3.274 −0.0661 −0.703
(0.0996) (6.302) (0.166) (0.807)
Constant −49.77*** −116.0 −49.81** −39.06***
(11.59) (75.20) (22.07) (14.22)
Observations 5,645 1,089 2,044 2,512

Note: Standard errors in parentheses with ***p < 0.01, **p < 0.05, *p < 0.1.

## 5.  Conclusions

This paper investigates the extent to which the mainstream theory, explaining the determinants of locations of multinational firms, is applicable to two categories of Asia-related M&As—transactions with Asian companies as targets (inflows) and transactions with Asian firms as acquirers (outflows). In particular, we intend to answer how the determinants of M&A inflows are different from those of outflows. We use an augmented gravity-type model framework with bilateral cross-border M&A data from 2000 to 2015 for 60 countries, of which 13 are emerging Asian countries.

Regarding financial variables, the size of the stock market (measured by stock market capitalization) plays a crucial role for both emerging Asian M&A outflows and inflows. It seems that the stock market is an effective channel to mobilize funds used in emerging Asian M&A. Interestingly, the size of bank lending is only important in M&A inflows, not outflows. This may reflect the low risk attitude of emerging Asian banks, whereas they are reluctant to supply funds for Asian firms to engage in cross-border M&A purchases (outflows).

The motives of seeking foreign markets and lower foreign labor costs seem to drive emerging Asian M&A inflows as well as outflows. In addition, the latter motive has been playing a more and more important role in outward M&A decisions over time. In the most recent years, the labor costs in emerging Asia increase significantly and lower labor costs become a major concern for Asian firms trying to acquire foreign firms.

Additionally, both M&A inflows and outflows seem to be focused on the natural resource endowment of host countries. Although there are indications that emerging Asian countries have become increasingly acquisitive in recent years, however, we do not find evidence of technology-seeking motivation for Asian M&A outflows. Possible reasons may be that emerging Asian countries mainly focus on products with low technology and they may lack the ability to apply high technology gained from developed countries to their own production.

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## Notes

*

Financial support from the China National Social Science Fund (grant 16BJY167) is acknowledged. All errors remain ours.

1

2

We define emerging Asia as East and South Asia, excluding Japan, following the regional classification from the World Bank. The emerging Asian economies in our sample include China, Hong Kong, India, Indonesia, Malaysia, Pakistan, the Philippines, Singapore, South Korea, Sri Lanka, Taiwan, Thailand, and Vietnam.

3

The data source of this section is the Zephyr database (Bureau Van Dijk, Amsterdam).

6

When a firm makes the decision to merge another firm, it considers the real return instead of nominal return of the investment. In addition, most of the previous literature, such as Di Giovanni (2005) and Erel, Liao, and Weisbach (2012), use real exchange rate. Therefore, we focus on bilateral real exchange rates in the following analysis.

7

Here we consider one country as a financial center if one of its cities is in the top ten centers of the Global Financial Centers Index (March 2013). These top ten centers are in the following eight countries: Singapore, Hong Kong, United Kingdom, United States, Japan, Germany, Switzerland, and South Korea.

8

In Table 6, we choose the Heckman selection model as a robustness test.