key: cord-0915969-uyvpdq83 authors: Wong, Zoey; Chen, Afei; Peng, Dan; Kong, Qunxi title: Does technology-seeking OFDI improve the productivity of Chinese firms under the COVID-19 pandemic? date: 2021-09-25 journal: Global Finance Journal DOI: 10.1016/j.gfj.2021.100675 sha: 6d57c67a17371f5e984840fe581008fea3f119cf doc_id: 915969 cord_uid: uyvpdq83 This paper empirically investigates the impact of technology-seeking outward foreign direct investment (OFDI) on firms' productivity under the influence of negative external shocks, taking as a sample the investment data of Chinese firms before and during COVID-19. The results show that technology-seeking OFDI improves productivity, but not under negative external shocks. The dampening effect of such shocks is more significant when the host country is a developed country and in firms with multiple branches. Technology-seeking OFDI particularly improves the productivity of research and development and processing firms, and (among the productivity measures tested) most prominently affects total factor productivity. economic and social environments will bring serious exogenous shocks to OFDI (Kong, Peng, Ruijia, & Wong, 2021; Zhang, Guo, Wang, & Chen, 2020) . The sudden COVID-19 epidemic in 2020 has had a huge impact on both China and the global economy, weakening the global economy, complicating the trade environment, and causing an economic downturn. So, against the backdrop of both the demand for high-quality domestic development and the spread of a global epidemic, is technology-seeking OFDI conducive to improving Chinese enterprises' production efficiency? Even if a country has become a major foreign investment country, OFDI will still have a profound and lasting impact on the economic development of the home country as well as on the development of enterprises in the host country (Chou, Chen, & Mai, 2011; Friedman & Gerlowski, 1996; Kong, Guo, Wang, Sui, & Zhou, 2020; Zhang, Tong, & Li, 2020) . Existing studies have focused on the impact of OFDI on the economic growth of home countries. The most influential theory is the investment acquire advanced technologies and standards from overseas and improve innovation efficiency (Yu, 2013; Zhang Mohsin, Rasheed, Chang, & Taghizadeh-Hesary, 2021) . Technology-seeking OFDI can give home country firms proximity to the rich research and development (R&D) resources of the host country and thus acquire the reverse technology spillover from the host country (Pottelsberghe & Lichtenberg, 2001; Wang & Yao, 2017; Yao et al., 2016) to better promote industrial optimization and economic efficiency in the home country (Kong, Shen, Sun, & Shao, 2020) . In particular, the real growth trajectory of the integrated emerging economies shows that their rise has benefited to a large extent from technology-seeking OFDI (Goh, Wong, & Tham, 2013) . Emerging economies and developing countries have become the main forces driving the strong recovery of the global economy. China is the developing country most involved in overseas investment, and promoting technological progress and improving production efficiency of domestic enterprises is one of the important objectives of China's continuous opening-up (Kong, Tong, Peng, Wong, & Chen, 2021) . However, number of foreign branches and scope of operations vary across firms, so the effects of technology-seeking OFDI on firm productivity may also vary and need to be tested empirically. policies. Few studies discuss the impact of infectious disease, though the economic costs of mass infectious diseases are enormous. Researchers generally agree that the short-term impact of epidemics is negative, but their long-term effects are not well understood. Epidemics not only directly cause economic losses but also affect the behavioral decisions of economic agents, which produce long-term effects on the economy (Adda & Jérôme, 2016; Kong, Shen, Peng, & Wong, 2021) . In addition, the impact of epidemics on the economy is complex and markedly heterogeneous. The mechanisms of epidemics' effects on the economy differ from one historical period to another and from one specific condition to another, mainly because modern medicine may greatly reduce long-term economic and social impacts. At the same time, the various measures taken to control an epidemic as well as panic and other behaviors can affect short-term economic performance (Noy & Shields, 2019) . And significant differences between epidemics in mode of transmission, transmission capacity, incubation period, and lethality give each epidemic a unique impact on the economy (Barro, Ursúa, & Weng, 2020) . This paper attempts to clarify the effect of technology-seeking OFDI on Chinese enterprises' productivity. This is undoubtedly of great theoretical and practical value in promoting the quality of China's economic growth. We argue that such research must include exogenous shocks in the analytical framework; doing so can enrich the existing research results and predict the impact of COVID-19 on China's foreign J o u r n a l P r e -p r o o f Journal Pre-proof economy. The rest of this paper is organized as follows. The second section introduces the measurement model, estimation methods, variable construction, and data sources. The third section examines whether the impact of technology-seeking OFDI on Chinese firms' productivity differs along four dimensions: firm attributes, investment target countries, number of foreign branches, and business scope. The fourth section summarizes the research in this paper and makes recommendations to promote technology-seeking OFDI to drive Chinese enterprises to high-quality development. To study the relationship between firm productivity and the size of technology-seeking OFDI as rationally and comprehensively as possible, we build a model at the micro level through the following steps. First, we study the impact of technology-seeking OFDI on productivity at the firm level through propensity matching. China's research on technology-seeking OFDI started late, with Du and Zhu enterprises into a treatment group (a set of technology-seeking OFDI enterprises) and a control group (a set of non-technology-seeking OFDI enterprises) for paired tests, and establish a dummy variable, = {0,1}, that takes a value of one if firm i is a technology-seeking OFDI firm, or zero if firm i is a non-technology-seeking OFDI firm. To proxy firm productivity we use input-output ratio ( ), value-added ratio ( ), labor productivity ( ), and total factor productivity ( ). Following Heckman, Ichimura, and Todd (1997) , we represent the difference between the input-output ratios of firm i in the presence and in the absence of technology-seeking OFDI by the following model. Suppose a reasonable adjustment is made to Eq. (1), so that we can obtain difference models for the value-added ratio, labor productivity, and total factor productivity both with and without technology-seeking OFDI. In Equation (1) Greenaway, and Kneller (2004) , we suggest that if there are firms in the sample period whose technology-seeking OFDI behavior is consistently unobservable, then their input-output ratio can be used to proxy the input-output ratio of firms that are not engaged in technology-seeking OFDI. That is, , which leads to Eq. (1). To increase the robustness of the results, we use nearest-neighbor matching to select the nearest control sample for technology-seeking OFDI firms. We follow Helpman, Melitz, and Yeaple (2004) and Hijzen, Jean, and Mayer (2011) to select matching criteria that importantly influence firms' technology-seeking OFDI: capital intensity (the ratio of net fixed assets to the number of employees), firm size, number of employees, total profit, and total assets. The matching ratio is set at 1:3. Table 1 shows no significant difference between the treatment and control groups in any of these five matching indicators, confirming that a suitable control group has been selected. Next, we construct a binary variable, = {0,1}, where = 0 indicates the period before technology-seeking OFDI and = 1 indicates the period after it. We then build a model to measure the differences in input-output ratio between firms with and without technology-seeking OFDI. where k and t denote region and year, respectively. The subscript s represents the base J o u r n a l P r e -p r o o f Journal Pre-proof period of the technology-seeking OFDI, so where > , t denotes a year after the investment is made. An value of one indicates that firm i has conducted a technology-seeking OFDI in year s. The coefficient 1 of × represents the result of the matched multiplier estimation of the effect of the firm's technology-seeking OFDI on economic growth. If 1 is obtained, it means that the input-output ratio changes more in the treatment group than in the control group from before to after the investment, meaning that the technology-seeking OFDI contributes to the firm's productivity growth. The term is a control variable (see subsection 2.2 for details of the measurement), is the time fixed effect, is the firm fixed effect, and is a random perturbation term. To examine the impact of technology-seeking OFDI on the input-output ratio of Chinese firms under COVID-19, in Equation (3) we add an exogenous shock (I) as a moderating variable: Finally, we divide OFDI into OFDI in developed versus developing countries, multibranch versus single-branch OFDI, and R&D versus trade sales OFDI, and build a model to test for differences-a slight variation of Equation (3), which is omitted here. J o u r n a l P r e -p r o o f Journal Pre-proof The variable to be explained is firm productivity. The academic community has not yet come to a consensus on how to measure productivity at the firm level; There are two methods of measuring total factor productivity (TFP). The OP J o u r n a l P r e -p r o o f Journal Pre-proof method (Olley & Pakes, 1996) takes the natural logarithm of both sides of a production function of C-D form to obtain a linear equation. According to Solow's residual method, the linear equation's error term are the productivity. The error term is then decomposed into total factor productivity (TFP) and the true error term. The TFP is then obtained by using investment as a proxy variable for productivity and using methods such as polynomial estimation method and Probit model. Whereas the OP method uses the amount of investment as its proxy variable, the LP method (Levinsohn & Petrin, 2003) enterprises' current operating incomes are declining. Therefore, this paper focuses on the negative shock perspective. The advantage of this approach is twofold: it takes into account firm differences, and it allows us to infer industry characteristics from consistent responses to a certain type of shock across a group of firms. In the propensity matching process, we use the following firm-level control industry fixed effects. The odd column results show that, with controls for other variables, all of the coefficient estimates * are significant at the 5% level or better, except for the rate of value added by firms. This indicates that in general, OFDI promotes firm productivity measured by input-output rate, labor productivity, and total factor productivity. The even column results show that the coefficient estimates of the core explanatory variable, * * , are significantly negative for all four indicators of firm productivity after we account for negative exogenous shocks. In absolute terms, the strongest dampening effect of the shocks is that on value added. In other words, under negative exogenous shocks, technology-seeking OFDI significantly impairs firm productivity, with the negative effect on firm value-added rates being most prominent. Among the control variables, capital intensity is positively associated with firm input-output rate, value-added rate, labor productivity, and total factor productivity at the 1% significance level, reflecting the importance of R&D innovation generated by capital intensity in improving firm productivity. Whether a firm is state-owned is significantly and positively correlated with total factor productivity, while it is significantly and negatively correlated with input-output rate, value-added rate, and labor productivity. Cross-sectional comparison of the regression results for the four indicators of firm productivity, some of which pass the 1% significance level test and J o u r n a l P r e -p r o o f Journal Pre-proof some of which are significant at only the 5% level, generally indicates that the results are robust. Table 3 shows that under negative exogenous shocks, compared with investment in developing countries, OFDI in developed countries depresses firm productivity, especially labor productivity and total factor productivity. Moreover, the test results for all four indicators of firm productivity are similar, indicating that the results are robust. A possible reason is that investment in frontier technology in developed countries is more dependent on the high-end international market and therefore carries greater inherent uncertainty and risk. Further, exogenous negative shocks hit local enterprises harder, impairing input-output efficiency in the home country and thus the high-quality development of enterprises. Negative shocks also inhibit the effect of OFDI in developing countries on firm productivity, but not significantly. Since the technology spillover that firms can obtain from technology-seeking OFDI may be limited by the depth of the investment, we separate our sample into firms with multibranch OFDI and those with single-branch OFDI. As Table 4 shows, the regression coefficients for the effect of * * * _ on the _ on the labor productivity of enterprises is positive but insignificant. In contrast, the regression coefficients for the effect * * * _ on firms' input-output rate, labor productivity, and total factor productivity are significantly positive, while the coefficient for the effect on firms' value-added rate is significantly negative. These results indicates that under an exogenous negative shock, investing in a larger number of branches abroad may reduce the firm's productivity. The reason may be that multibranch OFDI deepens the investment and thus connects home firms more closely to the host country's economic situation. If the host country is more deeply hurt by exogenous shocks, the fluctuations in the host country to some extent squeeze out capital that the home country firm could have invested in R&D and inhibit that firm's productivity growth. Small, single-branch investments may be more prudent because they conduce to effective management by the parent firm under negative shocks and thus tend to increase productivity. To a certain extent, this phenomenon inspires Chinese enterprises' cross-border investment. Cross-border investment decisions should be made rationally while paying attention to and maintaining R&D at home headquarters, as expanding overseas investments may not always be a good thing. Next, we divide OFDIs into R&D-processing OFDIs and trade-sales OFDIs (see Table 5 ). Under negative exogenous shocks, R&D processing-based OFDI increases J o u r n a l P r e -p r o o f Journal Pre-proof the firm's input-output rate, labor productivity, and total factor productivity and reduces its value-added rate-all at significance levels of 5% or lower. In contrast, trade-sales OFDI increases the firm's input-output and value-added rates, but decreases labor productivity and total factor productivity, although only the coefficient on input-output rate passed the significance test. We conclude that when faced with negative external shocks, R&D-processing OFDI enterprises are less dependent on the flow of human and material resources, and with the support of Internet technology, they can still maintain their original scales of development. In the face of common negative shocks, cooperation among countries can help enterprises acquire rigorous local management modes and absorb R&D capabilities (Zhang & Vigne, 2021) . For example, there is a certain reverse technology spillover effect in the healthcare industry, promoting the sustainable and stable development of OFDI enterprises. However, OFDI firms tend to expand their international market share and trade by establishing overseas branch structures. This is not conducive to the China's capacity to control the scale of the negative impact, resulting in barriers to Chinese enterprises, making it difficult to achieve tangible results in the short term and impeding their development. Our results show that technology-seeking OFDI improves firm productivity growth when negative shocks are not considered-but not during the pandemic. In J o u r n a l P r e -p r o o f general, negative external shocks inhibit the effect of technology-seeking OFDI on firm productivity, especially when the investment host country is a developed country or the investing firm has set up multiple branches abroad. Technology-seeking OFDI improves the productivity of R&D processing firms as a whole and especially improves total factor productivity. Notes: Values in parentheses are t-statistics corrected for heteroskedasticity. ***, **, and * indicate significance levels of 1%, 5%, and 10%, respectively. Notes: Values in parentheses are t-statistics corrected for heteroskedasticity. ***, **, and * indicate significance levels of 1%, 5%, and 10%, respectively. Notes: Values in parentheses are t-statistics corrected for heteroskedasticity. ***, **, and * indicate 1%, 5%, and 10% significance levels, respectively. 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