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ABSTRACT
In recent years, India has emerged as a leading foreign direct investment (FDI) player, featuring prominently as both an origin and a destination of FDI. This study takes a firm-level perspective to empirically address the relationship between inward FDI, outward FDI, and firm-level performance in India. Using the Orbis database, our estimates reveal that Indian firms that have at least one foreign shareholder and/or one foreign subsidiary outperform those that do not. Controlling for endogeneity through propensity score matching and difference-in-difference techniques, we show that the deeper the FDI involvement, the larger the performance differentials. Moreover, compared with investing abroad, receiving foreign capital can contribute more toward enhancing the performance of Indian firms.
Acknowledgments
The authors are grateful to Gianmaria Martini, Lauretta Rubini and participants to the XIX SIEPI Workshop for suggestions and insightful discussions and to Giorgia Sali for assistance in data collection. The helpful comments by two anonymous Reviewers and the Editor greatly improved the quality and readability of the paper. Valeria Gattai acknowledges financial support from the Università degli Studi di Milano-Bicocca and the Italian Ministry of Education, University and Research.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1. FDI is denoted as an investment in a foreign company in which the investor owns at least 10% of the ordinary shares and the investor undertakes the company with the objective of establishing a lasting interest in the country, a long-term relationship, and a significant influence on the firm’s management (OECD Citation2008).
2. WORLDBOX AG is the data source for India.
3. The Statistical Classification of Economic Activities in the European Community, commonly referred to as NACE (for the French term ”nomenclature statistique des activités économiques dans la Communauté européenne”), is the industry standard classification system used in the European Union. The current version is revision 2 and was established by Regulation (EC) No 1893/2006. Information regarding the conversion from the Indian National Industry Classification (NIC) to NACE are available from the Orbis database upon request.
4. Companies in Orbis are considered “very large” when they match at least one of the following conditions: a) operating revenues are larger than or equal 130 million USD; b) total assets are larger than or equal 260 million USD; c) employees are larger than or equal 1000; d) they are listed.
5. According to the OECD (2008) definition, FDI implies an equity participation above 10%. Therefore, when we downloaded the data, we used the 10% threshold as a filter. This means that all subsidiaries and shareholders in our dataset are characterized by an equity participation above 10%. Then, we distinguished between domestic versus foreign subsidiaries and shareholders by comparing their country ISO code with that of the Indian parent company.
6. The small number of FDI starters compared with the overall number of FDI firms is consistent with previous results from Goldar and Banga (Citation2020).
7. A better proxy would be the number of employees. Unfortunately, our database is missing many values in this variable, therefore, we follow Goldar, Banga, and Renganathan (Citation2004), Petkova (Citation2013), and Goldar and Banga (Citation2020) and measure labor with the cost of employees.
8. In this paper, we pool firms belonging to the manufacturing and the service industry together and account for potential differences through industry controls. An alternative approach would be to split the overall sample in two sub-samples by manufacturing versus service industry.
9. The results from the first stage logit model are available from the authors upon request.
10. NACE 1-digit industry dummies are considered at this stage. IA is not considered depending on the limited number of observations.
11. If a treated unit forward and backward matches happen to be equally good, the routine randomly draws either the forward or backward matches. The ATT is computed by averaging over the unit-level treatment effects of the treated where the control(s) matched to a treated observation is/are those observations in the control group that have the closest propensity score. If there are multiple nearest neighbors, the average outcome of those controls is used.
12. NNM1 is performed. Based on the distance of propensity scores between the treated and control units, one control unit with the smallest distance to its matched treated unit is selected. For treated units that are matched to multiple controls, we use weights to balance the sample. Our choice of NNM1 is intended to keep the sample as balanced as possible in terms of the number of treated and control units, considering the limited number of treated units.
13. The list of NACE 1-digit industries is not complete in . Our firms in the treated group belong only to the reported industries.