The impact of OFDI on the performance of Chinese firms along the ‘Belt and Road’

ABSTRACT

Using feasible generalized least squares (FGLS) and ordinary least square (OLS) estimations on a dataset of 1208 outward Foreign Direct Investment (OFDI) events by Chinese-listed firms from 2004 to 2015, this paper investigated the impact of OFDI on the performance of Chinese firms, from which it was found that Chinese firms that had invested in Belt and Road Initiative (BRI) countries were more productive than those that had invested in non-BRI countries. However, OFDI by both state-owned enterprises (SOE) and non-SOE were on average found to be negatively related to productivity and profitability, with state-owned enterprises (SOEs) having worse performance in terms of total factor productivity (TFP) than non-SOEs. A further subsample analysis found that Chinese firms that were investing in developing economies were performing better than those that had invested in developed ones; firms investing in sub-regions like Middle East and South Africa, East Asia and the Pacific, Latin America and the Caribbean experienced a positive post-OFDI TFP but investment in other regions had either insignificant or negatively significant coefficients, indicating that firms in general had poor post-OFDI performances. The findings in this paper are informative for developing going-global strategies for both firms and government authorities.

KEYWORDS:

• OFDI
• SOE
• Chinese multinationals
• Belt and Road

JEL CLASSIFICATION:

• F40
• F23

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

¹ t= 0 initiated OFDI, t﹤0 pre OFDI period, t﹥0 post OFDI period for all the figures.

² The following equation was used as TFP calculation by the Levinsohn–Petrin method:

$Ln{Y}_{it}=lnA+\alpha ln{K}_{it}+\beta ln{L}_{it}+{\mu }_{it}$

Where Y_it is the measure of output for firm i at time period t. L_it is the value of free variable, which is labor. K_it is capital input which is termed as state variables. TFP is the residual of this function. Output is measured as revenue; to measure the capital of the firm the proxy for total assets has been taken, labour has been measured as the total number of employees. L-P method of TFP estimation is a refined form of the Olley and Pakes approach which is rational and unbiased as compare to simple OLS technique of productivity estimation (Mollisi& Rovigatti, 2017; Satpathy & Mahakud, 2017).

³ Due to page limit, this paper did not report the pairwise correlation results table. It can be provided under request.

⁴ Industries are classified using WIND standards. See appendix Table 2 for details.

⁵ The World Bank classifies the world’s economies into seven regions: East Asia and Pacific, Europe and Central Asia, Sub-Saharan Africa, North America, South Asia, Latin America and the Caribbean, and the Middle East and North Africa. This paper refers to the World Bank’s fiscal year 2018 classification of world by region at this website.: http://datatopics.worldbank.org/world-development-indicators/the-world-by-income-and-region.html.

⁶ We did robustness check by using OLS regressions. The results are consistent with FGLS results. For page limits, we did not report the robustness check results. They are available by request.

Additional information

Funding

This work was supported by the The National Social Science Fund [19BJY100];The Department of Education Research Fund[17YJC790094]; The Fundamental Research Funds for the Central Universities[2018skqy_pt160].