I. Introduction

Internationalization is a process by which firms get into the global market through the channels of exports and outward foreign direct investment (OFDI). It helps firms in the creation of better networks and technological competitiveness (Barkema & Drogendijk, 2007). Firms face various hurdles in the internationalization process of which availability of finances is a critical factor (Buch et al., 2014; Buckley et al., 2016; Sasidharan & Padmaja, 2018). Firms incur costs in the form of both fixed and variable costs for the overseas expansion of firm activities. Thus, internationalization requires large investments which, in turn, depend on the availability of internal as well as external funds. Firms can use internal financing–cash flows, or external financing–borrowings from banks and other financial institutions (Chawla, 2019). The importance of access to finance is a well-established fact in the literature of firm growth and performance (Rajan & Zingales, 1998). The situation of limited internal funds forces firms to depend on external funds for financing their investments. Thus, firms which are financially constrained are less likely to raise external funds and undertake overseas expansion due to the difficulties involved in accessing finance.

The pecking order theory by Myers and Majluf (1984) advocates that firms prefer internal financing over external financing due to higher costs of external financing arising from information asymmetries. Firms prefer debt financing over equity financing while raising external funds. A set of empirical studies on pecking order theory supports the preference for internal financing over external financing by firms (Almeida & Campello, 2007) whereas an alternative set of studies finds evidence for the preference of external financing over internal financing by firms (Frank & Goyal, 2003). Similarly, OFDI, another mode of internationalization is often hampered by the availability of external finance (Welch et al., 2008). Most of the studies on financing of OFDI firms offer descriptive discussions on host country determinants of financing and the role of parent firm characteristics in determining the financing strategy (Tripathi & Thukral, 2016, 2018). There is lack of empirical evidence that focuses on the link between access to external finance and its role in firms’ OFDI with regard to emerging market firms. ECB is a policy that supports public and private sector firms to raise funds externally. ECB refers to commercial loans in the form of bank loans, buyers’ credit, suppliers’ credit, securitised instruments (e.g., floating rate notes and fixed rate bonds) availed from non-resident lenders with minimum average maturity of 3 years. India’s total ECB was US$21,110 (million) in 2011-2012 and has increased to US$28,714 (million) in 2021-2022 indicating a 36 % increase over the years (Report on India’s External Debt, 2021-2022, Department of Economic Affairs), signifying an increased dependence on ECB by Indian firms.

Indian firms have increased their share in total global OFDI flows to 1.5% in 2020 from 0.05% in 2000 (UNCTAD, 2021). However, it lags other BRICS countries such as Russia (3.7%) and China (8.5%) (UNCTAD, 2021). Thus, this study attempts to examine the role of ECB in facilitating OFDI by Indian manufacturing firms. Even though there are studies examining the macro-economic and firm-level implications of the increasing role of ECB on firm exports, studies that analyze the effect of ECB on firm investments are scarce (Bose et al., 2017). This study adds to the scarce literature by employing an appropriate method which controls for the possible selection bias and endogeneity issues in estimation. To the best of our knowledge, no study has examined the role of ECB in firm OFDI of Indian manufacturing firms.

II. Data and Methodology

A. Description of data and variables

We use firm level data from the PROWESS database which is a corporate sector database used in previous empirical studies (Roy & Narayanan, 2019). The OFDI firms are obtained from the Reserve Bank of India’s (RBI) firm-level data on OFDI published from 2007. The OFDI firms in the RBI’s database are matched with the firms in the PROWESS database. The RBI data suffers from certain limitations. Primarily, it is a provisional data; it is updated as and when the authorized dealers report the transactions. Also, it does not provide investor-wise guarantee invoked data, although it provides investor-wise guarantee issued data (Joseph, 2019). Therefore, we use the field ‘investment outside India’ from PROWESS to capture the final OFDI made by the firms. This study focuses exclusively on Indian manufacturing firms based on the 3-digit NIC industry classification. Firms operating under NIC codes from 101 to 321 are grouped under twelve industry categories based on similarities in their activity. The sample is further filtered based on the following criteria. First, firms with more than four years of continuous missing observations are dropped. Second, only firms with positive sales and fixed assets are included. Third, OFDI firms that have invested only once during the sample period are dropped as these are likely to reflect short-term transactions. The final sample contains a total of 1497 firms with 393 OFDI firms. The sample period of the study is 2008-2020.

Table 1.Descriptive statistics
Variable Explanation Mean Std Dev Min Max
Size Log of sales 7.943 1.722 0.101 15.458
TFP Total Factor Productivity 0.317 0.482 0.000018 2.652
Age Age of the firm 33.536 18.717 1 141
OFDI Intensity OFDI/Sales 0.264 0.635 0 2.003
Export Intensity Exports/sales 0.162 0.248 0 6.336
Profit Intensity Profit/Sales 0.045 0.051 -0.036 0.135
R&D Intensity R&D Expenditure/Sales 0.006 0.049 0 5.126
Access to Finance Long-term borrowings from banks/ Total assets 0.048 0.107 0 2.289
Current Ratio Current Assets/ Current Liabilities 1.634 2.094 0.01 83.21
External Commercial Borrowings (ECB) ECB/Total Assets 0.008 0.042 0 1.631
Dummy Variable Explanation Mean % of observations (=1) Min Max
OFDI dummy =1 if firm invests =0 Otherwise 0.230 23.08 0 1
Group =1 if firm belongs to a Group =0 Otherwise 0.395 39.55 0 1
Foreign =1 if firm is owned by Foreign Promoter =0 Otherwise 0.097 9.75 0 1
Listing Dummy =1 if firm is listed in NSE/BSE
=0 otherwise
0.570 57.07 0 1
Total Observations 19461

Note: This table reports descriptive statistics of all variables used in this study.

Following Manova (2015), the access to finance variable, used to capture the role of external finance, is constructed as the ratio of Long-Term Borrowings to Banks to Total Assets. Current ratio represents the internal fund availability whereas access to finance and ECB are used as proxies for external finance.

B. Empirical model

We examine the impact of ECB on firm OFDI intensity using the Heckman two-step procedure (1979). Selection bias due to self-selection by firms can be applicable in the current context, since few firms may self-select to undertake OFDI. Using Ordinary Least Squares regression results in biased estimates in such contexts. The Heckman two-step procedure helps in solving the sample-selection bias and the endogeneity issues in estimation. The advantage of the Heckman model is that it is able to model factors influencing the decision to undertake OFDI and OFDI intensity in a single framework while simultaneously correcting for any possible sample selection bias. The method involves two steps: i) the first stage estimates a model that addresses the likelihood of OFDI participation using a probit regression to derive the Inverse Mills’ Ratio (IMR) ii) the second step estimates the relationship between the explanatory variables and OFDI intensity using OLS (including IMR as an additional control variable), while correcting for potential sample selection bias. A significant result of IMR (λ) indicates the presence of sample selection bias.

To analyze the effect of ECB on firm OFDI, we use the following model:

\small{\begin{aligned} {OFDIdummy}_{it} &= \alpha_{0} + \alpha_{1}{Age}_{it - 1} + \alpha_{2}{Size}_{it - 1}\\ & \quad + \alpha_{3}{Accfinance}_{it - 1}\\ & \quad + {\alpha}_{4}{Currentratio}_{it - 1}\\ & \quad + \alpha_{5}{ECB}_{it - 1} + \alpha_{6}{Exportint}_{it - 1}\\ & \quad + \alpha_{7}{R\& Dint}_{it - 1} + \alpha_{8}{Profitint}_{it - 1}\\ & \quad + \alpha_{9}{TFP}_{it - 1} + {\alpha}_{10}{Group}_{it - 1}\\ & \quad + {\alpha}_{11}{Foreign}_{it - 1} + \alpha_{12}{Listdummy}_{it - 1}\\ & \quad + Inddummies + Timedummies + µ_{it} \end{aligned}\tag{1}}

The outcome equation with OFDI intensity is as follows:

\small{\begin{aligned} {OFDIIntensity}_{it} &= \alpha_{0} + \alpha_{1}{Age}_{it - 1} + \alpha_{2}{Size}_{it - 1}\\ & \quad + \alpha_{3}{Accfinance}_{it - 1}\\ & \quad + \alpha_{4}{Currentratio}_{it - 1}\\ & \quad + {\alpha}_{5}{ECB}_{it - 1} + {\alpha}_{6}{Exportint}_{it - 1}\\ & \quad + \alpha_{7}{R\& Dint}_{it - 1} + \alpha_{8}{Profitint}_{it - 1}\\ & \quad + {\alpha}_{9}{TFP}_{it - 1} + \alpha_{10}{Group}_{it - 1}\\ & \quad + \alpha_{11}{Foreign}_{it - 1} + Inddummies\\ & \quad + Timedummies + µ_{it} \end{aligned}\tag{2}}

where OFDI dummy in equation (1) represents the firm OFDI status and OFDI intensity in Equation (2) represents the amount of OFDI. The covariates included are lagged values of age, size, access to finance, current ratio, ECB, export intensity, R&D intensity, profit intensity and total factor productivity (TFP)[1] in the selection equation (Equation 1). We also control for group affiliation, foreign ownership, industry and time using binary variables. The outcome equation includes all variables except listing status of firms in stock market, which is an additional control for selection equation (Bhat & Narayanan, 2011).

III. Results and Discussion

Table 2 reports the results of the Heckman (1979) two-step procedure. Panel A reports the results obtained from Equation (1). The first-stage probit regression examines the effect of access to finance and ECB on firm OFDI participation (i.e., probability of undertaking OFDI). The positive and significant coefficient of lagged value of ECB indicates that firms with higher ECB are more likely to undertake OFDI, when other factors are kept fixed, whereas current ratio does not report a significant effect on OFDI. Furthermore, other firm characteristics such as size, age, export intensity and profit intensity also significantly improve OFDI participation of firms.

Panel B reports the results of the second stage regression analysis. The second stage regression estimates the effect of access to finance and ECB on firm OFDI intensity. The results indicate that external financing has a significant bearing on firm OFDI intensity as indicated by the coefficients of lagged values of access to finance and ECB, whereas current ratio does not significantly affect the OFDI intensity of firms. This indicates that OFDI firms are dependent on external funds for overseas investment.

Table 2.Heckman two-step procedure results
Variable Coefficient Standard Error
Panel A: Selection Equation with OFDI Dummy
Aget-1 0.001** 0.0007
Size t-1 0.2885*** 0.009
Acc finance t-1 0.1896 0.0125
Current ratio t- 1 0.004 0.007
ECB t-1 1.074** 0.355
Export Intensityt-1 0.7310*** 0.0610
R&D Intensity t-1 0.84118 2.135
Profit Intensityt-1 1.657*** 0.269
TFP t-1 -0.0396 0.0374
Group 0.0138 0.0286
Foreign -0.627*** 0.0492
Constant -3.043*** 0.103
List Dummy 0.3778*** 0.026
Industry Dummies Yes
Time Dummies Yes
Panel B: Outcome Equation with OFDI Intensity
Aget-1 -0.0016** 0.0007
Size t-1 0.013 0.216
Acc finance t-1 0.737** 0.133
Current ratio t- 1 0.001 0.006
ECB t-1 1.954*** 0.290
Export intensity 0.208* 0.076
R&D Intensity t-1 2.579 0.611
Profit Intensityt-1 1.791*** 0.333
TFP t-1 -0.063 0.038
Group 0.116* 0.031
Foreign 0.203** 0.065
Constant 0.914** 0.309
IMR (λ) 0.221**
Rho 0.26
Wald Chi2 394.58
Industry Dummies Yes
Time Dummies Yes
No. of observations 16,002

Notes: This table reports the results of Heckman two-step procedure. ***, **, * represent 1%, 5% and 10% significance respectively.

Furthermore, greater access to finance and larger amounts of ECB helps firms to undertake more OFDI. The coefficient of ECB is the highest among all sources of funds including current ratio and access to finance. This is in line with the observation that 70% of India’s OFDI flows between 2007 and 2021 were either through loans or guarantees issued, as per RBI’s firm-level OFDI data. This indicates that many OFDI firms depends on ECB as an important source of external finance. The significance of access to finance and ECB represents the fact that the smaller the constraints of external financing, the higher the OFDI. These results are consistent with those of studies showing the importance of external finance over internal funds in firm investment decisions (Patnaik et al., 2015). Export intensity, profit intensity, group and foreign ownership are found to have positive and significant effects, whereas R&D intensity, size and TFP do not play a significant role in determining OFDI intensity.

IV. Conclusion

Using Heckman (1979) two-step procedure, we provide evidence in favour of the significant role of ECB in firm OFDI in the context of Indian manufacturing firms. The findings suggest that lowering financial constraints and creating easier access to external finance are important for encouraging firm internationalization. Future research can be extended to examine the role of ECB in alternative firm investments. Additionally, the significant role of external financing calls for the framing of policies to lower firm financial constraints, thereby encouraging higher OFDI by Indian firms.

  1. TFP is calculated following Levinsohn & Petrin (2003). Capital stock, an input variable, is constructed using the Perpetual Inventory Method (Srivastava, 1996).