Absolute institutions and FDI

Institutions are the ‘rules of the game’ that are ‘humanly devised constraints that structure human interaction’ (North, Reference North1990: 3) and affect the underlying transaction costs of economic activity. By determining the economic and political environment of a country, institutions provide, amongst other things, potential foreign investors with assurances regarding the protection of property rights, and the necessary legal and regulatory frameworks, and are fundamental to discerning between different possible host country investment options. A favorable institutional setting inspires investor confidence and lowers the risk of doing business there by creating a more reliable business climate.

Dunning (Reference Dunning1998) reignited interest in the locational dimensions of MNE activities. A direct connection exists between the importance of institutions and the location-based advantages in the eclectic OLI (ownership, location, internalization advantages) associated with FDI. Institutions may change the parameters of firm behavior that are feasible in a locale (Dunning and Lundan, Reference Dunning and Lundan2008). Furthermore, within the eclectic paradigm the interaction between location advantages and ownership advantages are of paramount importance (Cantwell, Reference Cantwell2009). Hence, the motivation to internationalize depends upon the existence or non-existence of location advantages, as the MNE seeks to exploit its firm specific advantages in combination with location specific assets (Cuervo-Cazurra and Narula, Reference Cuervo-Cazurra and Narula2015). When making FDI related decisions, MNEs consider the costs and the risks associated with investing in possible host countries and the institutional environment is a critical element of this consideration (Fedderke et al., Reference Fedderke, Luiz and Barnard2023).

Understanding how MNEs make FDI decisions requires an appreciation of both the motives for internationalization as well as the factors that make a host country attractive, including its institutional milieu (Gupta, Reference Gupta2017; Kim and Aguilera, Reference Kim and Aguilera2016; Nielsen et al., Reference Nielsen, Asmussen and Weatherall2017). The absolute view of institutions evaluates the quality of institutions relative to some predetermined criteria and standards. Within empirical economics often the quality of institutions is assessed against these preset benchmarks and institutions in one country are juxtaposed to that of another country e.g., comparing Albania's institutions to that of Switzerland. This comparison usually relies on institutional measures that capture the ease of doing business in a country in terms of costs and procedures, or the World Governance Indicators (WGI) (both produced by the World Bank), or the Heritage Foundation's measures of economic freedom, or other similar global competitiveness measures (Mullings, Reference Mullings2018). The premise is that better quality institutions make the rules of the game more transparent and consistent and thereby mitigate potential risks and lower the transaction costs of doing business in the host locations, whilst weaker institutions would see the reverse (Subramanian, Reference Subramanian2007). In a similar vein, Higgs (Reference Higgs1997) makes the point that investors dislike institutional environments that result in the presence of ‘regime uncertainty’. He argues that unpredictable policy environments and government actions (threats) that weaken private property rights and adversely affect the expected return on investments will reduce MNEs' direct investments in those locations (Higgs, Reference Higgs1997; Krol, Reference Krol2018).

Various empirical studies have confirmed that good quality, predictable institutions are attractive to foreign investors (Bahoo et al., Reference Bahoo, Alon, Floreani and Cucculelli2023; Bailey, Reference Bailey2018; Blonigen, Reference Blonigen2005; Contractor et al., Reference Contractor, Dangol, Nuruzzaman and Raghunath2020). We would therefore expect better absolute institutions (institutional quality) to be more desirable in terms of foreign investment locations and in terms of the scale of the investment that these locales attract. We therefore posit the following hypotheses:

Relative institutions and FDI

Given that MNE activity is defined by the conduct of business across borders, it has been stated that ‘essentially, international management is management of distance’ (Zaheer et al., Reference Zaheer, Schomaker and Nachum2012: 19). Whilst distance has various dimensions including cultural, geographic, and economic, our focus, in line with the objectives of this study, is limited to the institutional aspects. Adopting a relative approach to institutions sees countries analyzed in terms of differences or distances between the home and host institutional environments. MNEs are contextually embedded in institutional environments and, as such, doing business across borders exposes these firms to manifold and distinct institutional settings that present unique complexities and risks (Kostova et al., Reference Kostova, Beugelsdijk, Scott, Kunst, Chua and van Essen2020).

The core premise of institutional distance is that it leads to higher costs of doing international business because the institutional differences impose additional challenges. Therefore, when MNEs enter host countries with different institutional environments, they must make significant adaptations to their strategies in response to different legal systems, political and governmental frameworks, tax laws, and other regulations, which entails greater uncertainty, risks and higher transaction costs (Cezar and Escobar, Reference Cezar and Escobar2015). Based upon this, it can be argued that institutional distance deters FDI – both the decision to invest in the distant host countries as well as the scale of that investment. Hence, we hypothesize:

Empirical context

We focus on India for two reasons. First, given that our aim is to disentangle the effect of institutional quality and institutional distance on OFDI, we needed to select a country from the middle of the institutional profile distribution (as per Van Hoorn and Maseland, Reference Van Hoorn and Maseland2016). India satisfies this criterion based on the World Governance Indicators (WGI) published by the World Bank for over 200 countries from 1996 onwards. For 133 countries, for which data on all six institutional indicators (detailed later) are available for the time period of this study, the average of the scores show that India sits in the middle quantile—at the 61st position (five spots below the median)—thereby providing an appropriate empirical context.

Second, owing to the rapid growth of Indian economy and improvements in institutional quality over the past two decades, the country has become an important receiver as well as source of global FDI flows (Nayyar and Mukherjee, Reference Nayyar and Mukherjee2020; Wagner and Delios, Reference Wagner and Delios2023). The Indian government started a process of economic liberalization in 1991, which included the initiation of pro-market reforms that aimed at encouraging private sector participation through easing restrictions on doing business in India. Since then, the cost of doing business in the country has declined and the institutional environment has become more supportive/business friendly. For example, improvements in institutional quality (absolute institutions) in India can be ascertained from its average score on the WGI that rose from −0.32 to −0.11 (on a possible range between −2.5 and 2.5 with higher values indicating better institutions) between 2002 and 2020. The initial reform framework has been reinforced by the subsequent governments. From 2014 onwards, India has witnessed a slew of big and bold economic reforms in various areas, including the formalization of the economy, indirect taxation, digitization, a better policy environment, and fiscal incentives to attract investments in the manufacturing sector. In the past five years according to the World Bank's ease of doing indicators, India saw its ranking improve from 142 to 63.

A consequence of these institutional and policy improvements is that the Indian economy has grown from being the 11th largest economy to the 5th largest between 2012 and 2022 (World Bank, 2023). Indian MNEs have become important global investors and India's OFDI flows have risen at an unprecedented pace and shown signs of resilience even during the global financial and eurozone crises (Iqbal et al., Reference Iqbal, Rahman and Yusuf2018), and more recently the COVID pandemic. Between 2008 and 2020, Indian MNEs invested USD350 billion across 164 countriesFootnote ¹. Mirroring their contribution to the country's GDP, the services sector contributed the majority of India's OFDI (51%), followed by manufacturing (40%), and the primary sector (9%). Drilling down further into the sectoral data, reveals that firms in the high technology and knowledge intensive industries such as information technology, pharmaceuticals, and automobiles, played a particularly prominent role in these investment flows. North America and Western and Northern Europe accounted for 24% and 37%, respectively of Indian OFDI, followed by Asia at 21%, and Africa at 6%. Despite the growing importance of the Indian economy and its OFDI, India's OFDI flows remain relatively understudied in the literature (Zhu et al., Reference Zhu, Cantwell, Jia and Wang2022a, Reference Zhu, Sardana and Tang2022b), further supporting our choice of country.

Sampling

The initial sample for the study consisted of 164 countries which attracted Indian OFDI flows between 2008 and 2020. In order to control for the sample selection bias, we followed previous studies and extended our sample to include 35 more countries which did not attract Indian OFDI and for which data on all institutional dimensions from 2008 to 2020 are available. (e.g., Buckley et al., Reference Buckley, Yu, Liu, Munjal and Tao2016; Munjal et al., Reference Munjal, Varma and Bhatnagar2022).

We then excluded offshore financial centers as FDI flows to and from these countries are affected by round tripping (Aykut et al., Reference Aykut, Sanghi and Kosmidou2017), and excluded those countries for which data are missing. Consequently, our final sample consists of 131 countries and 1478 country-year observations. Of these 131 countries, 113 countries received India's OFDI.

Variables and measures

Dependent variable: We constructed two dependent variables capturing two stages in the FDI decision: location choice and scale of investment. The location choice (OFDI_ct) is a dichotomous variable that takes value 1 if OFDI from India went to country ‘c’ in year ‘t’, 0 otherwise. The scale of investment (VOFDI_ct) is measured as an aggregate of equity and loan investment (in USD millions) made by India in host country ‘c’ in year ‘t’.

The data on OFDI flows is obtained from the RBI overseas direct investment database. RBI, India's Central Bank, publishes firm-level monthly data on actual OFDI made by Indian firms in the form of equity, loans, and guarantees issued in overseas joint ventures and wholly owned subsidiaries, since July 2007. The database is comprehensive as it covers all investing Indian firms irrespective of their listing status, size, or age, and provides details on the geographical destination of the OFDI flows. For these reasons, the database has been used in prior Indian OFDI studies (e.g., Das and Banik, Reference Das and Banik2015; Munjal et al., Reference Munjal, Varma and Bhatnagar2022).

Independent variables: We have two main independent variables—absolute institutions (i.e., institutional quality) and relative institutions (differences in institutional quality between the host country and India, i.e., institutional distance). Our measure of institutions is based on six World Governance Indicators (WGI) indicators, namely, voice and accountability; political stability and absence of violence/terrorism; government effectiveness; regulatory quality; rule of law; and control of corruption, which are published by the World Bank and have been widely used as a measure of a country's institutions (e.g., Adusei and Sarpong-Danquah, Reference Adusei and Sarpong-Danquah2021; Tang and Buckley, Reference Tang and Buckley2022; Van Hoorn and Maseland, Reference Van Hoorn and Maseland2016). The WGI provides comprehensive coverage of institutional environments based on several hundred variables from over 30 underlying data sources (Kaufmann et al., Reference Kaufmann, Kraay and Mastruzzi2009).

A composite measure of absolute institutions (institutional quality) is constructed through the technique of principal component analysis with varimax rotation on standardized values of the six indicators (Hernández and Nieto, Reference Hernández and Nieto2015). The application of Kaiser's rule led us to retain the first component, which accounts for 85.89% of the variation in the six underlying indicators. All the variables loaded significantly (factor loadings >0.86) on the derived component (Table 1).

Table 1. Principal components analysis

Notes: Extraction method: principal component analysis; rotation method: Varimax with Kaizer normalization.

We measured relative institutions (institutional distance) by subtracting the composite measure for institutional quality of the home country (India) from the composite measure for institutional quality of each host country.

Control variables: We control for various location attributes that could affect FDI inflows in the host countries. This includes gross domestic product (GDP) and GDP growth rate to control for the present and potential market size. We control for the natural resource and strategic assets endowments of the host countries. The natural resources endowment is captured through the World Bank's measure of ‘total natural resources rents (% GDP)’ as the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents. This measure includes not only the resources that are actually exported but also those that are unused or dormant but have an export potential and therefore can be attractive to the foreign direct investors (Munjal et al., Reference Munjal, Varma and Bhatnagar2022). The data for the three variables came from the World Development Indicators (WDI, World Bank). The strategic assets endowment is measured as the total count of patent applications as reported by the filing office, obtained from World Intellectual Property Organization (United Nations).

Bilateral exchange rates between the home (Indian Rupees) and host currency (from the WDI) are also controlled for. There is mixed evidence on the relationship between exchange rates and FDI inflows in the country. Theoretically, a stronger host country exchange rate discourages FDI inflows by making foreign assets and factors of production more expensive. However, it raises MNEs' revenue and profits from foreign subsidiaries and therefore may encourage FDI in the form of reinvested earnings (Phillips and Ahmadi-Esfahani, Reference Phillips and Ahmadi-Esfahani2008). Prior research has shown that trade and FDI have a strong relationship and that FDI usually follow exports (Blomstrom and Kokko, Reference Blomstrom and Kokko1997; Kang and Jiang, Reference Kang and Jiang2012). Therefore, we controlled for home-host trade intensity (proportion of trade volume between India and host countries relative to host's total international trade volume) (Buckley et al., Reference Buckley, Yu, Liu, Munjal and Tao2016). This data came from the International Trade CentreFootnote ². The openness of the host country to FDI flows is an important factor as regards attracting FDI by offering a conducive business environment (Zhou and Lall, Reference Zhou and Lall2005). We measure FDI openness by calculating the proportion of inward FDI stock to GDP, obtained from UNCTAD FDI Statistics database.

We also control for geographic and cultural distances between India and the host countries. Higher geographic distance implies higher transaction costs such that FDI is more likely to go to proximate countries (Buckley et al., Reference Buckley, Yu, Liu, Munjal and Tao2016). The distance between the capital cities of the host countries and New Delhi is taken as a measure of geographic distanceFootnote ³. Similarly, cultural distance is argued to deter FDI flows by increasing adaptation costs for MNEs in the distant countries (Nayyar et al., Reference Nayyar, Mukherjee and Varma2021). Following prior work, we conceptualize cultural distance using a ‘common language’ dummy that takes the value 1 if India and the host country share a common official language, 0 otherwise (e.g., Buckley and Munjal, Reference Buckley and Munjal2017; Munjal et al., Reference Munjal, Varma and Bhatnagar2022). The data for both the distance measures are obtained from the CEPII databaseFootnote ⁴. Lastly, we included time dummies to control for year fixed effects.

Model specification and estimation techniques

We use Heckman's (Reference Heckman1979) two-stage procedure to test our hypotheses. The procedure allows us to examine OFDI decisions at two stages and to estimate the unbiased results by including those countries which did not receive India's OFDI. In the first stage, we estimate a ‘selection’ model where the decision is made as regards location choice. If the decision is made to invest in a particular host country, the second stage involves deciding on the volume/scale of investment in that country. The second stage model, i.e., the ‘outcome’ model estimates this scale of investment.

Given the dichotomous nature of the first-stage dependent variable, we use a random effects probit regression model to estimate the selection model, which is represented in equation (1) below:

(1)$${\rm OFD}{\rm I}_{ct} = \left\{{\matrix{ {{\rm OFDI}_{ct}^\ast \;\;{\rm if}} \hfill & {{\rm OFDI}_{ct}^\ast \ > 0} \hfill \cr {0\quad \;\quad \,\,\,\,{\rm if}} \hfill & {{\rm OFDI}_{ct}^\ast \;\le 0} \hfill \cr } } \right.$$

The scale of the investment (represented by the latent variable OFDI*_ct) is given by the second stage ‘outcome’ model, which is expressed in equation (2) below:

(2)$${\rm OFD}{\rm I}^\ast _{ct }= \alpha + \beta _1I_{ct-1} + \beta _2x_{ct-1} + \mu _{ct}$$

where t ~ 2008–2020, I _ct−1 are the institutional indicators in the period t – 1; x_{ct −1} is a vector of control variables and μ_ct is the error term that respects the assumptions of normal distribution (0, σ ²).

For the estimation of the second-stage model, we used random-effects generalized least squares regression. The use of random effects panel regression methods was informed by the fact that the country fixed effects of the host country are not correlated with the independent variables (Cameron and Trivedi, Reference Cameron and Trivedi2005). To enable the transformation of the non-linear relationship between dependent and independent variables, we used log–log variable transformation (except for binary and computed variables) to estimate the model (Crown, Reference Crown1998). We also used a one-year lag of time-varying independent variables to reduce endogeneity and allow for the fact that the strategic decision to make an investment at time ‘t’ would depend on the institutional and other location characteristics of the host country at time ‘t – 1’ (Buckley and Munjal, Reference Buckley and Munjal2017).

Since the choice to invest in a host country is a result of self-selection reflected in the volume of investment, its inclusion in our sample is not random, suggesting the presence of a selection bias. The ignorance of this selection bias results in the error terms in the regression to be correlated with explanatory drivers of the investment, leading to biased estimates. Therefore, to correct for the sample induced endogeneity, we exclude a variable in the outcome model (FDI openness) that is more likely to affect OFDI location choices in the first stage than determine the scale of investment in the second (Sartori, Reference Sartori2003), and include the inverse-mills ratio as an additional control variable.

Thus, the second model estimation [E (OFDI_ct|X_{ct −1}, OFDI*_ct > 0)] is represented by equation (3) below:

(3)$$\eqalign{E \,( {{\rm OFD}{\rm I}_{ct}\vert X_{ct}, \;{\rm OFD}{\rm I}^\ast_{ct} > 0} ) & = X^{\prime}_{ct-1}\,{\rm \beta} + E\,( {\mu_{ct} \vert \mu_{ct } \ge X^{\prime}_{ct-1}\,{\rm \beta} } ) \cr & = X^{\prime}_{ct-1}\,{\rm \beta} + \sigma \;\left[{\displaystyle{{\rm {\it \phi} \,( {{\it X}^{\prime}_{{\it ct\; }}\beta /{\rm \sigma }} ) } \over {\;\Phi \;( {{\it X}^{\prime}_{{\it ct\;}}{\rm \beta /\sigma }} ) {\rm \;}}}} \right]\;\ne X^{\prime}_{ct-1}\,{\rm \beta} ; \;} $$

where $[ {\phi \;( {{\it X}^{\prime}_{ct}\, {\rm \beta} /{\rm \sigma }} ) /\;\Phi \;( {{\it X}^{\prime}_{ct{\rm \;}}{\rm \beta /\sigma }} ) } ]$ is the inverse-mills ratio, defined as the ratio of the probability that India will invest in a particular host country to the cumulative probability of the decision to invest.