Impact of Outward Foreign Direct Investments on Patent Activity of Greenfield Multinationals
We study the impact of outward greenfield foreign direct investment (OGFDI) expansion of multinational enterprises (MNEs) on their innovation activity, proxied by the internal generation of patents. By combining three databases: Orbis by Bureau van Dijk, FDIMarkets by Financial Times, and PATSTAT by The European Patent Office, we obtain a sample of more than 20,000 firms for the period 2008–2015. The MNE firms originate in 175 countries, which span the entire spectrum of economic development- from low to high-income countries. The industrial distribution of the firms covers 72 industries at the 2-digit ISIC level. We construct a model, which allows us to analyze the impact of OGFDI capital investment, as well as OGFDI-created employment, on the number of patents of the companies engaging in the GFDI. We control for various firm characteristics, such as performance measures and size of the corporate group. We also control for the country of origin of the firms, stratifying the sample into emerging market MNEs and developed countries MNEs. We use linear estimation techniques to analyze the relationship between the variables and test how the results change across industries with different patent intensities. We find that both capital investment and jobs created through OGFDI boost the innovation activity of MNEs, and these effects are bigger for industries with higher patent intensities, such as the production of chemicals, computers, and motor vehicles. For sectors that rely less on patents (oil and electricity sectors) the impact of GFDI on patenting activity is smaller and for the case of building constructions, we even find a negative impact of investments on innovation. We further find that the effects are larger for EMNEs than DMNEs and discuss appropriate policies.
Valacchi, G., Doytch, N., & Yonzan, N. (2021). Impact of outward foreign direct investments on patent activity of Greenfield multinationals. Technological Forecasting and Social Change, 173, 121168. https://doi.org/10.1016/j.techfore.2021.121168