Local Institutions, Foreign Direct Investment and Productivity of Domestic Firms

Abstract

This paper examines the linkage between foreign direct investment (FDI) and the productivity of domestic firms, paying particular attention to local institutions. Using Chinese manufactures from 1998 to 2007, we find strong evidence that FDI negatively affects the productivity of domestic firms. Firm in regions with better institutions suffers more from foreign presence owing to production contraction, labor cost increases and innovation deterrence.

Foreign Capital and Domestic Productivity in Developing Countries: An Empirical Analysis

“…With his other group members, Ausloos, Eskandary, Kaur, & Dhesi, (2019); Hong, Sun, & Huang, (2016) explored the effect of FDI on the TFP of Chinese product industries. In their study, they examined three areas of the forest industry: The timber manufacturing industry, the paper manufacturing industry and its products, and furniture manufacturing.…”

Multinational enterprises and structural transformation in emerging and developing countries: A survey of the literature

“…Others identified a negative impact of MNCs' presence on TFP growth, often due to competition effects, and that these tended to outweigh any gains in allocative efficiency (Bwalya, 2006;Lo, Hong and Li, 2016;Hong, Sun and Huang, 2016).…”

Governance, Information Spillovers, and Productivity of Local Firms: Toward an Integrated Approach to Foreign Direct Investment and Global Value Chains

“…Rather than, or in addition to, estimating equation directly, some studies estimate TFP first and regress it on FDI spillovers and other control variables in equation . Representative examples of this are: Javorcik and Spatareanu (, 2011); Liu (); Blalock and Gertler (); Keller and Yeaple (); Barrios, Görg, and Strobl (); Fernandes and Paunov (); Merlevede, Schoors, and Spatareanu (); Newman et al (); Fatima (); Hong, Sun, and Huang (); Thang, Pham, and Barnes (); Choi and Pyun (); Lu, Tao, and Zhu (); Ebghaei and Wigley (); and Njikam and Leudjou (), as presented in Table . Note that they employ either level or first difference.…”

“…Fifth, different industries have the same production function parameters; that is, β K and β L are identical. However, some studies use the two‐stage estimation method—such as Todo and Miyamoto (); Javorcik and Spatareanu (, ); Liu (); Blalock and Gertler (); Fernandes and Paunov (); Merlevede, Schoors, and Spatareanu (); Newman et al (); Fatima (); Hong, Sun, and Huang (); Thang, Pham, and Barnes (); and Lu, Tao, and Zhu ()—separately estimate the production function in each industry in the first stage (Table ). In summary, these restrictive assumptions are likely to lead to biased or imprecise estimations of the regression parameters.…”

“…In contrast, many studies that estimate equation (1) or its modified forms in developing countries find negative intra-industry spillover effects. The major examples are: Aitken and Harrison (1999) for Venezuela; Bwalya (2006) for Zambia; Javorcik and Spatareanu (2008) for Romania; Fatima (2016) for Turkey; Hong, Sun, and Huang (2016) for China; Kinuthia (2016) for Kenya; and Lu, Tao, and Zhu (2017) for China (Tables 2 and 3).…”