Impact of Economic Freedom Distance on India’s Inbound Cross-Border Acquisition Volume: Moderating Role of Economic Distance

Abstract

Cross-border acquisitions (CBA) are a form of foreign direct investments and have been dramatically increasing over the last three decades. India has been one of the top CBA destinations among emerging economies, making it interesting to explore the determinants. Even though the CBA research is voluminous, the role of economic freedom is understudied. In this background, by extending the knowledge of distance measures impacting cross-border acquisition (CBA) activities, we examine the impact of economic freedom distance on India’s inbound CBA volume and the moderating role of economic distance. We used a sample of 979 observations by collecting the CBA data from Thomson’s EIKON Mergers and Acquisitions database for our study period covering 1990 to 2020. We show that economic freedom distance negatively impacts India’s inbound CBA volume. Moreover, economic distance significantly moderated their effect. These results indicate that India should strengthen its economic freedom and grow steadily to attract more CBA volume inflow. These findings have important theoretical and practical implications for multinational firms and policymakers in making emerging economies like India an attractive destination for CBA activities.

1. Introduction

Globalization has paved the way for multinational enterprises (MNEs) to enter new markets for their operations. Cross-border acquisitions (CBA), a form of foreign direct investments (Slangen 2006), is one of such entry modes that has increased globally over the last three decades. The CBA sales were $728 billion in 2021, which grew by 53% from 2020. India has been a favourite host country for MNEs as it stood at eighth position globally and third among the emerging markets with USD 45 billion of FDI inflows. Similarly, India is the largest recipient of CBA among the South-Asian firms and received USD 27,211 million of CBA volume in 2020 (UNCTAD 2022), which has been growing consistently since 2005, as shown in Figure 1. Thus, India is among the top attractive host countries for CBA activities and exploring what makes it an attractive destination is fascinating. India’s market potential for economic growth, improving the institutional strength, and various other country-level characteristics are responsible for making it a favourite destination for MNEs’ CBA investments. Therefore, this study explores the role of institutional distance, which is measured as economic freedom distance between the home countries and India, in making India an attractive destination for increasing CBA volume from MNEs.

Various country–character distance measures impact the CBA volume, including cultural, administrative, political, geographical, and economic (CAGE), as suggested by (Pankaj 2001). Various studies have explored these distance measures to broaden and deepen the literature. Berry et al. (2010) proposed a nine-distance measure framework by extending the CAGE and adding financial, demographical, knowledge, and global connectivity distance measures. Similarly, we propose to explore the crucial dimensions of institutional and economic distance measures to analyze their impact on CBA volume received by India to understand whether these factors make India an attractive destination.

An essential institutional factor, economic freedom (EF), conceptualized by the Fraser Institute, has five major dimensions, including the size of government, property rights, monetary policy, international trade, and regulation, which measure the level of freedom that the legal identities have in the system to work, produce, consume, and invest. The index of EF is associated with a healthier society, cleaner environment, country’s wealth, and investments. The existing literature has not well explored the impact of EF on emerging markets’ CBA activities (Zhang et al. 2017). We focus on EF in our study because the gap among the countries in terms of EF has been reducing in recent decades, as shown in Figure 2. Moreover, it is an essential institutional characteristic that gives an understanding of the freedom a country provides to individuals and businesses to make their own economic decisions. Furthermore, EF influences business performance, FDI, and economic growth (Anokhin and Wincent 2012; de Haan and Sturm 2000; Singh and Gal 2020). Therefore, it is interesting to understand how this EF distance impacts the CBA volume received by an emerging economy (EE) such as India.

Although EF is found to have a major role in CBA activities, we have less knowledge from EE’s point of view (Zhang et al. 2017). Specifically, we do not know how these variables impact India’s inbound CBA volume. In this background, we aim to explore the impact of EF emission distance on the CBA volume that India receives, covering the study period from 1990 to 2020. Furthermore, the nexus between economic growth, EF, and FDI flow is commonly found in the existing literature. Economic growth influences EF (de Haan and Sturm 2000) and FDI inflow (Aller et al. 2021). Moreover, economic distance plays a major role in CBA activities (Buckley et al. 2007; Deng and Yang 2015; Dikova et al. 2019; Gaffney et al. 2016; Liou and Rao-Nicholson 2019; Xie et al. 2017). Hence, we also aim to find the moderating effect of economic distance in our study.

Our study fills five crucial knowledge gaps that exist in the literature. First, (Pankaj 2001) provides different distance measures, including the cultural, institutional, geographical, and economic distance (CAGE) that matter in exploring international opportunities, and various authors have examined all these dimensions in detail. We provide evidence to show that distance measures in terms of EF play a role in CBA activities. Second, various articles provide evidence on determinants of India’s outbound CBA activities; however, research on what makes India an attractive destination for CBA activities is sparse. We are one of few to provide evidence from India’s inbound CBA activities. Third, the research on determinants of CBA volume is voluminous; however, how EF distance measures impact them is scarce. This helps provide policy implications to regulatory bodies, governments, and policymakers. Finally, we add value to the existing knowledge by applying the country-specific advantage theory (Dunning 1980, 1998) and institutional framework theory from the EF logic on CBA volume. These contributions make this study unique and one of the few to provide empirical evidence; hence it is vital to undertake this study.

2. Literature Review and Hypothesis Development

Various country-level factors impact CBA volume, including economic indicators such as economic condition, economic growth, and size of the market (Di Guardo et al. 2016; Herger et al. 2008), international trade (Hu et al. 2020), growing financial markets (Ferreira et al. 2009), inflation (Boateng et al. 2017), money circulation, (Uddin and Boateng 2011), corporate tax (McCann 2001), investment treaties (Hyun and Kim 2010), the exchange rate (Erel et al. 2012), resources including natural and technical (Deng and Yang 2015), and human development (Owen and Yawson 2010). Similarly, institutional factors such as its quality (Owen and Yawson 2010), regulation quality (Buch and DeLong 2004), political factors (Basuil and Datta 2019), level of corruption (Demirbag et al. 2007), investor protection (Choi et al. 2016), and disclosure quality (Rossi and Volpin 2004) impact CBA volume. Moreover, cultural distance (Ahern et al. 2012), cultural imports (Li and Yang 2020), language (Buckley et al. 2016) and psychic factors (Gulamhussen et al. 2016), religion (Maung et al. 2020), religiosity and religious freedom (Prasadh and Thenmozhi 2018), and cultural diversity (Gulamhussen et al. 2016) impact CBA volume.

Furthermore, it is common to find scholars exploring how distance measures (difference between the home and host countries) impact CBA activities (Buckley et al. 2007; Deng and Yang 2015; Dikova et al. 2019; Gaffney et al. 2016; Liou and Rao-Nicholson 2019; Xie et al. 2017). Pankaj (2001) suggested four important distance measures to examine the importance of cultural, administrative and political, geographical, and economic distance using the CAGE framework. Similarly, Berry et al. (2010) suggested an institutional perspective by recommending nine distance measures, including economic, financial, political, administrative, cultural, demographical, knowledge, network, and geographical, to understand the importance of distance measures in international business research. Therefore, exploring distance measures in international business and CBA, in particular, has been an exciting area of research.

From the locational advantage theory of Dunning’s eclectic hypothesis (Dunning 1980, 1998), market, resource, knowledge, and efficiency distance between home and host countries persuade MNEs to undertake CBA activities (Buckley et al. 2007; Deng and Yang 2015; Dikova et al. 2019; Gaffney et al. 2016). Through these measures, the authors try to provide evidence of how Dunning’s locational advantage theory plays a vital role in CBA decisions and outcomes. This evidence indicates that country-specific advantages augment competitiveness and motivate firms to internationalize their business through CBA activities. Additionally, from the institutional theory, we can understand that institutional distance impacts the CBA decisions of MNEs (Kedia and Bilgili 2015) and influence them to prefer a partial stake (Lahiri et al. 2013). However, when the target firm is located in an EE, MNEs prefer a full equity stake (Malhotra et al. 2016) if the institutional distance is high, as it leads to the increased risk caused due to the unfamiliar institutional setup (Contractor et al. 2014). Moreover, institutional distance and quality impact CBA completion (Dikova et al. 2010). Similarly, EF is a vital institutional factor influencing FDI flows and fuelling economic growth (Bengoa and Sanchez-Robles 2003; Dkhili and Dhiab 2018; Singh and Gal 2020). Moreover, EF influences CBA activities as it can create or damage the locational advantage benefits for MNEs in the host country. Countries with a high level of EF enjoy more freedom to own properties, boost competitiveness, and encourage innovations (Gwartney et al. 2004), while low-level EF means less encouragement to business opportunities (Aybar and Ficici 2009). The existing studies show that EF impacts FDI (Singh and Gal 2020) and CBA (Zhang et al. 2017). Moreover, younger firms undertaking CBA activities in more developed economies create shareholder value (Liou and Rao-Nicholson 2019). However, it is not clear whether economic freedom distance matters in the CBA activities of an EE.

We extend this research by including EF distance while enriching the existing knowledge from a distance measure perspective. We argue that the home-based or host-based perspective provides single-sided knowledge as it only suggests either home country EF or host country EF impacting CBA activities. When MNEs undertake CBA activities, home and host country characters affect their CBA performance. Moreover, MNEs can enjoy more when there is a locational advantage. Hence, to extend the knowledge of location advantage, we prefer to consider the EF distance measure. The existing literature has not well explored the impact of EF on emerging markets’ CBA activities (Zhang et al. 2017). The Heritage Foundation defines EF as the absence of restrictions or compulsion from the government on the economic activities of an economy. The DE provides better EF to business firms compared to emerging economies (Meyer et al. 2009). EF influences business performance, FDI, and economic growth (Anokhin and Wincent 2012; de Haan and Sturm 2000; Singh and Gal 2020). Therefore, it is interesting to understand how this EF distance impacts the CBA volume received by an emerging economy (EE) such as India. EF impacts not only FDI (Singh and Gal 2020) but also CBA (Zhang et al. 2017). The findings of Zhang et al. (2017) show that EF distance negatively impacts the CBA completion rate. However, the difference is that Zhang et al. (2017) have not explored the EE as a host country; instead, their study shows the impact of EF distance on ten EE’s outbound CBA activities. Moreover, we have no evidence to validate EF distance impact on CBA volume received by India. The economic theory argues that EF does not provide freedom in economic activities and negatively impacts business performance, production, and resource utilization. EF impacts FDI (Singh and Gal 2020) and CBA (Zhang et al. 2017). Moreover, younger firms undertaking CBA activities in more developed economies create shareholder value (Liou and Rao-Nicholson 2019). Hence, we can find evidence to support that better EF in host countries encourages MNEs to undertake more CBA activities. In this background, we argue that EF distance can impede MNEs from acquiring new knowledge and applying existing knowledge in the host country, as institutional distance measures negatively influence CBA activities (Dikova et al. 2010). Moreover, a mere EF distance does not imply anything unless it is compared to some business decisions to understand its impact. The majority of India’s inbound CBA flow is from DE, which enjoys a high EF compared to that of India. Such a distance measure can be positive when India has less EF score than home countries and vice versa. In other words, a positive EF distance means home countries have better EF than India, meaning MNEs acquiring target firms have less freedom in business and properties in India; hence, MNEs invest less in it. Based on this argument, we believe that the EF distance measure directly impacts CBA volume; more specifically, we expect it to negatively impact India’s inbound CBA volume. Therefore, when the EF distance between the home countries and the host country increases, it leads to less CBA volume flowing into India.

H1. 

When the economic freedom distance between home countries and India increases, India receives a lesser volume of CBA inflow.

Economic distance refers to the economic development in home and host countries, associated with the market size and growth rate availability of resources, infrastructural facilities, and information and communication networks (Dong et al. 2019). A larger distance leads to a high trade and information cost, resulting in fewer advantages for MNEs undertaking CBA activities. The difference in economic conditions can reduce the chances of success of CBA activities (Lim and Lee 2017) and hence lesser CBA activities with smaller volumes. DE have larger markets (Malhotra et al. 2009), and thus EE firms undertake CBA activities in such DE to access better markets, infrastructure/resources, and technology (Deng and Yang 2015). Economic distance, which indicates the disparity in economic conditions between the host and home countries, is a two-edged sword for cross-border mergers and acquisitions, including in India. When MNEs undertake CBA activities in less developed economies (compared to the home countries), they can gain through resource exploitation. Similarly, when such CBA activities are undertaken in more developed countries, they can gain through resource exploration (Makino et al. 2002). Hence, it can be understood that there is a proportionate relationship between increasing economic distance and CBA activities. Hence, with the increasing economic distance, MNEs gain, and more CBA volume flows. On the contrary, with lesser economic distance, MNEs do not gain, and hence, less CBA volume flows.

On the other hand, MNEs may be misled by the economic distance of host countries with the consumer preferences, and hence, their CBA activities may fail (Pankaj 2001). Moreover, when MNEs are from less developed countries, there will be the transfer of inferior goods, which results in liabilities of foreignness (Liou and Rao-Nicholson 2017). Furthermore, MNEs lose value when they acquire targets in less developed countries and gain when their targets are located in more-developed countries (Aybar and Ficici 2009), and economic distance makes integration challenging (Cuervo-Cazurra et al. 2018). We argue that MNEs prefer to invest in India as India is the fastest-growing economy, and growth potential has a positive impact on the CBA volume (Di Guardo et al. 2016; Hyun and Kim 2010). When the host country shows market potential to grow, it attracts MNEs to undertake CBA activities and pays a higher premium as its growing markets offer new opportunities and future net present income (Kiymaz 2004). Usually, DE MNEs prefer to invest in EE, such as India (Rossi and Volpin 2004). Even though the economic distance between home countries and India can positively impact the CBA volume India receives, it impacts negatively when interacting with EF distance. As discussed before, EF distance does not offer a conducive business environment as with the increasing distance, MNEs have to adapt to a new institutional setup in the host country, which adds more cost. MNEs entering India seek market growth potential; however, to explore it, they should have a conducive institutional environment, specifically economic freedom. When economic distance and economic freedom distance increase, it becomes much more challenging to gain benefits from CBA activities (Liou and Rao-Nicholson 2019). Hence, we expect the economic distance to impact CBA volume positively; however, market potential can be exploited by MNEs only when EF exists to own and trade with properties. When MNEs find no freedom to do business in the host country, economic distance can add additional costs to trade and information gathering.

H2. 

Economic distance significantly moderates the impact of economic freedom distance on India’s inbound CBA volume.

3. Data and Methods

3.1. Study Design

We designed the study to answer our research questions—whether the EF distance measure impacts the CBA volume received by India and whether economic distance significantly moderates the impact of EF distance on the CBA volume received by India. The study is empirical research and relies on secondary data. The CBA deal level data was gathered from the Thomson Reuters Eikon database (Jain et al. 2019; Otto et al. 2020) from 1990 to 2020. We collected 5641 aggregate deal level data where India was a target nation, and acquirers were worldwide. Since India globalized and liberalized the entrance and trade policies for MNEs in 1991, we chose our study period from 1990. Initially, our gross sample consisted of 81 acquiring countries targeting firms based in India. Following Ahern et al. (2012) and Prasadh and Thenmozhi (2018), we defined our dependent variable as the sum of the dollar value of CBA activities between an acquiring home country and India (host country) at a particular period, a year. To obtain this dependent variable, we converted the deal level data into country–year-paired observations, as our study focuses on the amount of volume India receives from various acquiring countries. By the end of this process, we obtained 1597 observations depicting India’s inbound CBA volume. Therefore, this entire process helped us define, clean, and arrange our dependent variable, the volume of CBA.

In the next phase of our research design, we collected various independent variables, including control variables, as listed in

Table 1. Measurement units and sources of all independent variables.

Independent Variable	Definition	Source	Reference
Economic Freedom Distance	The difference in economic freedom index between the home country and India during an acquisition year	Fraser Institution	(Majeed et al. 2021)
Economic Distance	Log form of the difference in the gross domestic product (GDP) per capita of the home country and India in an acquisition year	World Bank	(Buckley and Munjal 2017; Dikova et al. 2019)
Geographical Distance	Log form of distance between capital cities of home country and India (a time-invariant variable)	Time and Data	(Cuypers et al. 2015; Jongwanich et al. 2013)
Institutional Distance	Differences in the world governance index between the home country and India in an acquisition year; proxies are calculated following (Kogut and Singh 1988); this measure proxies for overall administrative and political distance	(Kaufmann et al. 2010)	(Yoon et al. 2020)
Cultural Distance	Differences in Hofstede’s cultural dimensions (a time-invariant measure) between the home country and India; measured values are calculated following (Kogut and Singh 1988) methodology	Hofstede-insights.com	(Yoon et al. 2020)
Tax Burden Index of India	Tax burden score of India during the acquisition year to proxy financial development	Heritage Foundation	(Prasadh and Thenmozhi 2018)
The inflation rate in India	It is measured as the consumer price index in the acquisition year.	World Bank	(Ibrahim and Raji 2018)
Financial depth of the home country	Measured as the ratio of domestic private credit to GDP in the acquisition year	World Bank	(Erel et al. 2012; Liang et al. 2018)
Exchange Rate Growth	Measured as exchange rate change between the home country and India	World Bank	(Erel et al. 2012)

. The major independent variables of our study are, first, the EF index collected from the Fraser Institute following Majeed et al. (2021). The index includes five major areas covering government size, legal system and protection of property rights, monetary policy, international trade policy, and regulation. Within these five areas, there are twenty-six components in this index and, in total, forty-four variables. Each component is measured on a scale of 0 to 10. A high EF index indicates more freedom and vice versa. The score suggests freedom concerning personal choices, voluntary market exchange, entering and competing in markets, and owning and protecting properties. The index indicates the extent to which the country’s institution provides freedom in the above-said matters to individuals and firms. Our moderating variable is the economic distance, measured GDP per capita, considered a proxy for measuring the market size and potential, collected from the World Bank database following Dikova et al. (2019). We considered the distance measures as guided by the CAGE framework (Pankaj 2001), and hence, all three major independent variables were measured as a difference in values between home countries and India for a particular period, an acquisition year. We also planned and collected various control variables based on the existing literature review support, as shown in Table 1. To this end, due to missing values of independent variables, our sample was further reduced to 979 observations, where there were 47 acquiring/home countries targeting India as their host country.

3.2. Econometric Model

The dependent variable is CBA volume, which is continuous data; hence, we have used a log form to standardize and smooth the data. The CBA volume is censored data, and there were no values recorded between an acquiring country and India as there was no CBA activity between them. Therefore, there are zero value issues in the dependent variable. To address the censored data, the Tobit regression model has been applied in all similar studies literature (Ahern et al. 2012; Hu et al. 2020; Li and Yang 2020; Maung et al. 2020), as it is the best statistical tool to address the censoring bias, and hence, we also decided to use it. Tobin propounded the Tobit regression model in 1958 to measure censored data. The Tobit model removes the bias due to the ad hoc approach, assigning arbitrary values to censored cases. Least square regression models are not a good fit for censored data (Osgood et al. 2002); hence, Tobit regression is applied in our study.

Mathematically, the Tobit regression model can be written as follows:

$$Y_j=\max \left(Y_j^{*},0\right)$$

(1)

where $Y_j^{*}$ is the latent variable from the classic linear regression model:

$$Y_j^{*}=\beta X_j+{{\displaystyle \cup }}_j$$

(2)

and

$$Y_j=\{\begin{array}{l}0, Y_j^{*}\le 0\\ Y_j^{*},0<Y_j<1\\ 1, Y_j \ge 1\end{array}$$

(3)

where $X_j$ represents the independent variable; in our case, we have $X_1$ and $X_2$ indicating EF distance and economic distance, respectively. We will also have $X_1\ast X_2$ to show the interaction effects in our Tobit regression model. ${{\displaystyle \cup }}_j$ indicates the error term. ${{\displaystyle \cup }}_j$ is assumed to be independent and normally distributed, which will have a mean value of 0 with a constant variance ${{\displaystyle \cup }}_j~ N (0, {\sigma }^2$) (Martey et al. 2012; Osgood et al. 2002; Wulff and Villadsen 2020).

Using the proposed variables and based on the aforesaid understanding, we developed a Tobit regression model as given below:

$$\begin{gathered} \mathrm{Ln}\left(\mathrm{CBA}{\mathrm{Volume}}_{\mathrm{ij},\mathrm{t}}\right)={\mathsf{\beta }}_1(\mathrm{EF}\_\mathrm{Distance})+{\mathsf{\beta }}_2(\mathrm{Economic}\_\mathrm{Distance})+{\mathsf{\beta }}_3(\mathrm{EF}\_\mathrm{Distance}\times \\ \mathrm{Economic}\_\mathrm{Distance})+{\mathsf{\beta }}_4(\mathrm{Controls})+\mathrm{Acquiring}\mathrm{country}\mathrm{dummies}+\mathrm{Year}\mathrm{dummies}+\mathrm{Constants}+{{\displaystyle \cup }}_{\mathrm{ij},\mathrm{t}} \end{gathered}$$

where i represents home countries; j represents India, a host country; t represents the year of acquisition.

We introduced year dummies as we wanted to capture the time shocks and acquiring country dummies to capture the time invariable effect following Prasadh and Thenmozhi (2018). Dikova et al. (2019) suggest using one-year lag values to avoid possible endogeneity issues; hence, we have taken lagged values of all the independent variables.

4. Results and Discussions

Table 2. Summary statistics of all the variables under the study.

Variable	Obs.	Mean	Std. Dev.
Volume of CBA	979	1.756	2.570
Geographical Distance	979	8.362	1.377
Institutional Distance	979	0.003	0.004
Cultural Distance	979	0.002	0.002
Tax burden_Host	979	75.533	3.657
Inflation_Host	979	6.658	2.918
Financial Depth_Home	979	80.911	46.264
Exchange Rate Growth	979	0.131	5.956
Economic Distance	979	2.834	1.221
Economic Freedom Distance	979	14.311	8.535

provides the summary statistics of all the variables of interest. There are 979 observations, as discussed before. The dependent variable, the volume of CBA, has a mean value of 1.756 with a standard deviation of 2.570. Our major independent variable is EF distance, and a moderating variable is economic distance, which has a mean (standard deviation) of 14.311 (8.535) and 2.834 (1.221), respectively. The positive mean value of EF distance indicates that, on average, the home countries have better EF than India. In short, home countries have better EF than India; hence, as expected, it may negatively impact CBA volume. Moreover, the moderating variable is the economic distance which has a positive mean value indicating home countries have larger markets than India. However, the potential to grow in India is assumed to be much faster and larger; hence, we expect the economic distance to have a positive impact.

Table 3. Correlation matrix of all the variables.

Variables	1	2	3	4	5	6	7	8	9	10
Volume of CBA (1)	1
Geographical Distance (2)	−0.215	1
Institutional Distance (3)	−0.017	0.161	1
Cultural Distance (4)	−0.029	0.079	0.165	1
Tax burden_Host (5)	0.159	0.013	0.129	−0.016	1
Inflation_Host (6)	−0.023	−0.006	−0.111	0.005	−0.445	1
Financial Depth_Home (7)	0.188	0.173	−0.171	0.075	0.108	0.045	1
Exchange Rate Growth (8)	−0.017	0.002	0.019	−0.002	−0.073	0.055	−0.011	1
Economic Distance (9)	0.147	0.414	−0.297	−0.209	−0.048	−0.008	0.417	−0.056	1
Economic Freedom Distance (10)	0.221	0.317	−0.211	0.039	−0.067	−0.065	0.447	−0.004	0.732	1

provides the correlation matrix of all the variables of interest. The correlation coefficient of EF distance and CBA volume seems to have a weak positive relationship. However, we expected it to have a negative impact. Furthermore, we expected economic distance to have a positive relationship with CBA volume, and we found the correlation coefficient to be positively related. Additionally, none of the variables has a very strong association, as none of the correlation coefficients is greater than 0.8. Therefore, the initial multicollinearity test has given a positive signal to run the empirical regression model. Furthermore, we also estimated the variance inflation factor (VIF) to confirm no multicollinearity in the models shown in

Table 4. VIF values for all the models used in the Tobit regression.

Variable	Model 1	Model 2	Model 3
Geographical Distance	1.08	1.39	1.39
Institutional Distance	1.14	1.26	1.26
Cultural Distance	1.04	1.13	1.21
Tax burden_Host	1.3	1.32	1.34
Inflation_Host	1.26	1.28	1.31
Financial Depth_Home	1.12	1.31	1.38
Exchange Rate Growth	1.01	1.01	1.01
Economic Distance		1.8	3.06
Economic Freedom Distance			2.52
Mean VIF	1.13	1.31	1.61

. None of the models has any variables with more than 4 VIF values, indicating no multicollinearity in the models.

Table 5. Tobit regression models presenting the impact of independent variables on India’s inbound CBA volume and the control variables.

	Model 1	Model 2	Model 3	Model 6
	β	β	β	β
Control Variables
Geographical Distance	31.820 *** (0.000)	28.270 *** (0.000)	28.177 *** (0.000)	29.187 *** (0.000)
Institutional Distance	9.826 (0.844)	18.154 (0.719)	20.678 (0.681)	23.061 (0.646)
Cultural Distance	−2.713 *** (0.000)	−2.495 *** (0.000)	−2.448 *** (0.000)	−2.408 *** (0.000)
Tax burden_Host	1.714 * (0.075)	1.688 * (0.083)	1.721 * (0.076)	1.700 * (0.078)
Inflation_Host	3.821 (0.211)	3.346 (0.279)	3.481 (0.258)	3.325 (0.277)
Financial Depth_Home	−0.002 (0.973)	0.007 (0.913)	0.010 (0.889)	0.007 (0.929)
Exchange Rate Growth	0.001 (0.886)	−0.002 (0.788)	0.001 (0.922)	0.000 (0.962)
Moderating Variable
Economic Distance		2.270 *** (0.000)	2.392 *** (0.000)	1.370 * (0.091)
Independent Variable (H1)
Economic Freedom Distance			−0.079 * (0.098)	−0.083 * (0.080)
Interaction Effect (H2)
Economic Freedom Distance * Economic Distance				−1.273 * (0.064)
Observations	979	979	979	979
Chi-Square	781.89 *** (0.000)	795.37 *** (0.000)	798.13 *** (0.000)	801.58 *** (0.000)
Pseudo R-square	0.246	0.250	0.251	0.252
Log Likelihood	−1201.325	−1194.584	−1193.209	−1191.479
Constants	−383.21 *** (0.000)	−358.50 *** (0.000)	−360.33 *** (0.000)	−367.62 *** (0.000)
Country dummy	Yes	Yes	Yes	Yes
Time dummy	Yes	Yes	Yes	Yes

Note: * and *** represent significance at 10% and 1% levels, respectively.

provides the results of Tobit regression models examining the impact of our independent variables on India’s inbound CBA volume involving 979 observations. Model 1 shows the impact of all the control variables. We found geographical distance has a positive and statistically significant impact (p < 0.01), indicating that with the increasing geographical distance between home countries and India, India’s inbound CBA volume increases by 31.82%. Institutional distance has a positive coefficient; with the increasing institutional distance, India’s inbound CBA volume increases by 9.826%, but its impact is statistically insignificant (p > 0.1). Cultural distance has a substantial negative impact, indicating that India’s inbound CBA volume increases by 2.713% with the reduction of cultural distance, which is statistically significant (p < 0.01). The tax burden of the host country, representing the financial development, has a positive impact, indicating that with the increasing tax burden of the host country, India’s inbound CBA volume increases by 1.741% and is statistically significant (p < 0.1). The inflation rate of the host country has a positive impact, indicating that with an increase in the rate, India’s CBA volume increases by 3.821%, which is statistically insignificant (p > 0.1). The financial depth of the home countries impacts negatively; with the increasing depth, India’s inbound CBA volume decreases by 0.002%, which is statistically insignificant (p > 0.1). Exchange rate growth positively impacts India’s CBA volume as the CBA volume increases by 0.001%, which is statistically insignificant (p > 0.1). The impact of all these control variables is approximately similar to all other models.

Model 2, shown in Table 5, provides the results of the Tobit model as we introduce our moderating variable in the regression along with the control variables. We found economic distance to have a positive impact, indicating that an increasing economic distance leads to a 2.27% increase in India’s inbound CBA volume which is statistically significant (p < 0.01). Models 3 and 4 introduced our major independent variables along with the interaction effect to test Hypotheses 1 and 2. Model 3 results show that EF distance negatively impacts the CBA volume, which is statistically significant (p < 0.1), supporting Hypothesis 1. When the EF distance between the home countries and India increases, India’s inbound CBA volume decreases by 0.079%. Model 4 shows the interaction effect of EF and economic distance on India’s inbound CBA volume (Hypothesis 2). We supported Hypothesis 2; with the economic distance, the impact of EF distance is intensified as the interaction effect is statistically significant (p < 0.1). With the economic distance, when the EF distance increases, it leads to a 1.356% (0.083 + 1.273) decrease in India’s inbound CBA volume. Therefore, we found support for all our hypotheses.

Robustness of the Results

We divided the sample into high-economic freedom distance and low-economic freedom distance groups to check the robustness of the volume of CBA activities. The data was divided into less economic freedom distance and more economic freedom distance based on the median value of the economic freedom distance from the entire sample.

Table 6. Tobit regression model results presenting the impact of independent variables on India’s inbound CBA volume and the control variables for less economic freedom distance and more economic freedom distance groups of countries.

	Less Economic Freedom Distance Group		More Economic Freedom Distance Group
	Model 1	Model 2	Model 3	Model 4
	Β	β	Β	β
Control Variables
Geographical Distance	−1.966 (0.999)	−1.861 (0.999)	29.093 *** (0.000)	29.675 *** (0.000)
Institutitonal Distance	14.489 (0.804)	22.053 (0.708)	−77.863 (0.578)	−78.078 (0.578)
Cultural Distance	−1.399 (1.000)	−3.342 (1.000)	−24.488 *** (0.000)	−24.947 *** (0.000)
Tax burden_Host	−0.128 (0.770)	−0.091 (0.835)	1.600 *** (0.043)	1.601 *** (0.043)
Inflation_Host	−0.884 (0.224)	−0.929 (0.200)	3.424 (0.192)	3.456 (0.188)
Financial Depth_Home	0.007 (0.690)	0.012 (0.505)	0.003 (0.625)	0.003 (0.605)
Exchange Rate Growth	−0.005 (0.948)	−0.010 (0.903)	0.201 (0.457)	0.188 (0.486)
Moderating Variable
Economic Distance	4.428 *** (0.000)	3.676 *** (0.001)	1.023 *** (0.049)	1.617 *** (0.039)
Independent Variables
Economic Freedom Distance	−0.129 * (0.094)	−0.048 * (0.088)	−0.219 *** (0.004)	−0.086 * (0.060)
Interaction Effect
Economic Freedom Distance * Economic Distance		−0.095 * (0.082)		−0.033 * (0.091)
Observations	486	486	493	493
Chi-Square	298.377 *** (0.000)	298.865 *** (0.000)	474.140 *** (0.000)	474.300 *** (0.000)
Pseudo R-sqaure	0.263	0.262	0.255	0.255
Log Likelihood	−460.877	−460.019	−694.411	−694.333
Constants	19.283 *** (0.000)	19.313 *** (0.000)	−350.035 *** (0.000)	−357.208 *** (0.000)
Country dummy	Yes	Yes	Yes	Yes
Time dummy	Yes	Yes	Yes	Yes

Note: * and *** represent significance at 10% and 1% levels, respectively.

provides the Tobit regression model results for less economic freedom distance and more economic freedom distance groups of countries. Model 1 and 4 shows that economic distance positively impacts CBA volume that flows from less economic freedom distance and more economic freedom distance countries. The results show that with increasing ED economic freedom distance, countries invest more volume of CBA into India, which is statistically significant (p < 0.01). With every unit increase in the ED, they invest 4.428% more in India through CBA activities. Similarly, more economic freedom distance countries also consider ED as an opportunity, and with every unit increase in the ED, they invest 1.023% more volume of CBA in India, which is statistically significant (p < 0.05). However, ED matters more for less economic freedom distance countries than more economic freedom distance countries. Furthermore, economic freedom distance negatively impacts the CBA volume received by India from both less ECONOMIC FREEDOM DISTANCE and more economic freedom distance countries. The results show that with every unit increase in the economic freedom distance, less economic freedom distance countries invest 0.129% less volume of CBA in India, which is statistically significant (p < 0.1). Similarly, with every unit increase in the economic freedom distance, more economic freedom distance countries invest 0.219% lesser volume of CBA in India. Hence, it indicates that economic freedom distance matter more for more economic freedom distance countries. It is evident that less economic freedom distance countries find India more attractive compared to more economic freedom distance countries in terms of EF that they get in India. Since more economic freedom distance countries have better EF in their home countries, the impact of EF on CBA volume is larger compared to less economic freedom distance countries.

Model 2 and 4 shows that the interaction of economic freedom distance and ED has a negative impact on both less economic freedom distance countries and more economic freedom distance countries. With the presence of ED, every unit increase in economic freedom distance leads to a 0.143% (0.048% + 0.095%) lesser volume of CBA that flows from less economic freedom distance countries. Similarly, with the presence of ED, every unit increase in economic freedom distance leads to a 0.119% (0.086% + 0.033%) lesser volume of CBA that flows from more economic freedom distance countries. Therefore, the interaction effect of ED and economic freedom distance is also more on CBA volume received from less economic freedom distance countries. To this end, we understand that economic freedom distance matters to the CBA volume received by India.

5. Discussion

We aimed to find the impact of EF distance on India’s inbound CBA volume and the moderating role of economic distance. We sought to find the answers to our research questions by using a sample of 979 observations of 47 home countries targeting India as a host country. We gathered the CBA data from Thomson’s EIKON Mergers and Acquisitions database for our study period of the last three decades, i.e., from 1990 to 2020. As our dependent variable, CBA volume, was censored, we applied the Tobit regression model to test our hypothesis empirically. We provided evidence to show that the EF distance negatively impacts India’s inbound CBA volume, while economic distance impacts it positively. Furthermore, we found that economic distance significantly moderated the impact of our major independent variable on CBA volume. We used an alternative model to robust our findings of this study and found similar results even though the effect size differs.

Pankaj (2001) suggested four necessary distance measures to examine the importance of cultural, administrative and political, geographical, and economic distance using the CAGE framework in international business. We have been inspired by this work and the research work of Berry et al. (2010); we have added two scientific values to this framework. First, we extended the model by adding our contribution by bringing a different dimension of institutional variable, economic freedom distance, and found that when the host country is an EE, it negatively impacts the CBA volume. This is similar to the findings of Zhang et al. (2017), who show that EF distance negatively impacts the CBA completion rate. However, the difference is that Zhang et al. (2017) have not explored the EE as a host country; instead, their study shows the impact of EF distance on ten EE’s outbound CBA activities. Our results are unique and show that India received more volume of CBA when the EF distance between the home and host countries is smaller. This indicates that India is attracting those countries to invest more who have similar economic freedom. On the other hand, when the EF distance is large, India receives less CBA volume, indicating MNEs from a distance EF have to spend time learning and experiencing to gain knowledge of new institutional setup, which is different from their home countries and hence may not be interested in investing more in such situation.

Second, Berry et al. (2010) suggested an institutional perspective by recommending nine distance measures, including economic, financial, political, administrative, cultural, demographical, knowledge, network, and geographical, to understand the importance of distance measures in international business research. Inspired by this work, we explored another dimension of administrative distance, EF, and found that it plays a significant role in India’s inbound CBA volume.

We further show that the economic distance between home countries and India attracts more CBA volume. MNEs prefer to invest in India as it is the fastest-growing economy, and growth potential has a positive impact on the CBA volume (Di Guardo et al. 2016; Hyun and Kim 2010). Therefore, increasing economic distance positively impacts India’s inbound CBA volume. Moreover, we show how economic distance moderates the impact of EF distance on India’s inbound CBA volume. We are the first to provide evidence that EF distance matter attracts inbound CBA volume from an EE perspective. To this end, we add scientific value to the institutional theory, locational advantage theory, CAGE framework, and emerging markets’ CBA activities.

The basic statistics show that home countries have better EF and have larger economies than India. This explains that India should strengthen the EF and grow steadily to attract more CBA volume inflow. Therefore, our findings have important theoretical and practical implications for multinational firms and policymakers in making emerging economies like India an attractive destination for CBA activities. Since EF distance matters, MNEs are advised to compare the location advantage from the EF lens before undertaking CBA activities. On the other hand, Indian policymakers, economists, regulatory authorities, and government should put more effort into improving institutional quality and providing more EF to its people and businesses.

Our study contributes scientific value to the existing body of literature. First, Pankaj (2001) provides a CAGE framework that matters in exploring international business opportunities; we contribute to it by providing evidence that distance measures in terms of EF play a role in CBA activities. Second, we contribute to understanding how selected distance measures play a role in making India a host destination. Various articles provide evidence on determinants of India’s outbound CBA activities; however, research on what makes India an attractive destination for CBA activities is sparse. We are one of few to provide evidence from India’s inbound CBA activities. India’s EF has improved over the last decades due to the prompt efforts of the Government of India in strengthening its institutional setup. This has been reflected in improved EF through more liberalization and widened the scope for economic choices over the last decades. India has been striving hard to match its institutional setup with international standards. India has been reducing its size of government through its reforms and privatization policies to improve economic efficiency and provide more opportunities to private firms. India considers property rights as human rights and not just as fundamental rights. India provides rights to possess, control, exclude, enjoy, and dispose of one’s properties without the interference of the government or any other regulator. India’s efforts are commendable in terms of the effectiveness of its monetary policy, which has been fuelling the growth of the economy, maintaining the exchange rates within predictable ranges, and stabilizing inflation rates and interest rates. India has been heavily reliant on international trade. It has signed bilateral agreements with more than 50 countries, 11 regional trade agreements, and preferential trade agreements with around 20 countries. This shows India’s intention of growth expectations regarding international trade with the rest of the world. Its efforts to strengthen the regulatory institution in terms of economic regulation, regulation in the public interest, and environmental regulations are highly commendable. All these efforts in improving the overall institutional setup have resulted in improved EF and, hence, increased CBA activities, which make the contribution of this study unique and novel. Third, we contribute to the existing CBA literature by adding knowledge on EF distance as determinants from an EE perspective. Finally, we add value to the existing knowledge by applying the country-specific advantage theory (Dunning 1980, 1998) and institutional framework theory from the EF logic on CBA volume. These contributions make this study unique, novel, and one of the few to provide empirical evidence showing distance measures impacting India’s inbound CBA activities.

6. Conclusions

Extending to the knowledge of distance measures impacting CBA activities, we examine the impact of EF distance on India’s inbound CBA volume and the moderating role of economic distance. We use a sample of 979 observations by collecting the CBA data from Thomson’s EIKON Mergers and Acquisitions database covering the period from 1990 to 2020. We provide evidence that the EF distance negatively impacts India’s inbound CBA volume, while economic distance impacts it positively. Furthermore, we found that economic distance significantly moderated the impact of our major independent variables on CBA volume. These results indicate that when the EF distance is smaller, more CBA volume flows into India. Therefore, India should strengthen the institutional setup like it has been doing to improve and reduce the EF distance to attract more CBA volume. These findings have important theoretical and practical implications for multinational firms and policymakers in making emerging economies like India an attractive destination for CBA activities.

Our study has certain limitations, such as not comparing economic freedom as an independent measure of home and host country base; instead, we took distance measures. Hence further research can explore these factors from home and host country-based perspectives and add more dimensions. We have used only India as a host country, and one country’s studies cannot be generalized to all others. However, our study shows the direction to undertake a similar study with larger emerging host markets to generalize and form a basis for a new framework or model. Furthermore, one can compare the same with different income group countries to have a more generalized understanding of the same. In addition, due to data availability, we could not extend our study to examine the effect on other aspects of CBA activities, such as stake acquisitions, completion rate, premium, and post-acquisition performance. Therefore, further study can add these aspects of CBA research and contribute more value to the area.