Firm’s outward foreign direct investment and efficiency loss of factor price distortion: Evidence from Chinese firms

International Review of Economics and Finance 67 (2020) 176–188

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Firm’s outward foreign direct investment and efficiency loss of factor price distortion: Evidence from Chinese firms Hua Cheng a, 1, Ziqi Wang b, 1, Dan Peng c, 1, Qunxi Kong c, *, 1 a b c

School of Economics, Renmin University of China, Beijing, China School of Business, Nanjing University, Nanjing, China School of Industrial Development, Nanjing University of Finance & Economics, Nanjing, China

A R T I C L E I N F O

A B S T R A C T

Keywords: Outward foreign direct investment Factor price distortion Total factor productivity Propensity score matching method

This paper investigates whether China’s outward foreign direct investment (OFDI) can solve the problem of low productivity caused by factor price distortions and the marketization of China’s factor market. For this analysis industrial enterprise data, covering a period of 5 years from 2003 to 2007, is used. To reveal the relationship between efficiency loss caused by factor price distortion and OFDI the trend score matching method is applied. The results of the analysis are threefold. First of all, the price distortion of elements inhibits the increase of total factor productivity, and labor price or capital price distortion has a significant negative impact on firm productivity. Second, foreign direct investments can mitigate the negative impact of factor price distortions on total factor productivity. Finally, from the perspective of different investment liberalization, there is a heterogeneous impact on the relationship between foreign direct investments and factor price distortions affecting the total factor productivity of enterprises.

1. Introduction The labor-intensive development mode, which resulted in a never before seen speed of growth, has reached a plateau in China. The Chinese government is now seeking a high-quality and more technology-driven mode of development to counteract the decrease in growth. Quality, efficiency, and improvement of the total factor productivity are gradually becoming the major focuses. Meanwhile, the actions taken to improve the total factor productivity are making the factor market more open, thereby, solving the liquidity and optimization problems. Nevertheless, there are a series of concerns regarding this restructuring process, such as defective market systems, historical problems still remaining as a result of the planned economy during the last century, and the factor price distortion caused by the lag between the marketization of factor and product market. This distortion further increases the efficiency loss in the

* Corresponding author. E-mail address: [email protected] (Q. Kong). 1 We would like to thank editors and anonymous referees for useful comments and suggestions. We also thank Professor Sushanta Mallick and Professor Samuel Vigne for their valuable comments to improve the earlier versions of this paper. We acknowledge financial support from the National Natural Science Foundation of China (NO. 71303105): The Study of the Regional Innovation System Centered on Knowledge-intensive Service Enterprises——based on the Perspective of Spatial Agglomeration, and National Natural Science Foundation of China (NO. 71773047): The Research of Human Capital Heterogeneity, Innovation and Producer Service Productivity: Influence and Approach, and National Social Science Foundation (NO. 19FJYB039): Research on the Relationship Between Outward Foreign Direct Investment and China’s Economic Growth in High Quality Development Stage, and Jiangsu Practical Innovation Project (NO. SJKY19_1235): The Impact of Foreign Direct Investment on the Quality of China’s Economic Growth: Theoretical Mechanism and Realization Path. https://doi.org/10.1016/j.iref.2020.01.008 Received 8 August 2019; Received in revised form 5 January 2020; Accepted 24 January 2020 Available online 29 January 2020 1059-0560/© 2020 Elsevier Inc. All rights reserved.

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economic development. An innovative OFDI strategy was adopted to modify the marketization process, ameliorate the liquidity of the factor market, and pursue high-quality development based on more openness. This plan was outlined in the nineteenth economic report of the Communist Party of China. According to the statistics of China’s Ministry of Commerce, the country is ranked third in the world in terms of OFDI. The only countries exceeding China in this regard are the United States and Japan. In 2018, newest data showed that Chinese firms had invested 120.5 billion US dollars in non-financial sectors overseas, which is an increase of 0.3 per cent compared to the prior year. Because of this durable growth in OFDI, a steady and healthy growth trend can be seen. As a consequence, the role of OFDI becomes increasingly important in the national strategy. Furthermore, due to the possibility of achieving high-quality economic development by implementing OFDI strategies, the question of how to make use of OFDIs to solve the low efficiency led by the factor price distortion has received increasing attention in academia. Previous studies have drawn the attention to OFDI in developing countries due to their unique characteristics which differentiate them from developed countries in terms of the market systems and experience in OFDI. Although Mathews (2002) as well as Moon and Roehl (2001) express concerns regarding the applicability of traditional OFDI theories in developing countries, a series of theories are nevertheless assumed to be applicable when taking the characteristics of developing countries into account (Li & Yu, 2009). These theories include the oligopolistic reaction (Knickerbocker, 1973), the internalization approach (Buckley & Casson, 2003), and the international production (Dunning, 2013), which are based on general economic development theories. Incentives for developing countries to engage in OFDI are usually analysed either from the perspective of the destinations or the investing countries. Regarding the investment destinations, the main goal for Chinese companies is to explore new markets and resources (Zhang et al., 2019). In addition, strategic assets such as advanced technologies and management experience are other incentives for Chinese firms to invest abroad (Deng, 2004). From the view of the investing countries, the establishment of policies by emerging market governments, to promote the development of OFDIs implemented are economically imperative and institutionally complementary to offsetting competitive disadvantages of emerging market enterprises in global competition (Luo, Xue, & Han, 2010; Zhang & Guo, 2019). To assess the ability of OFDI to counteract productivity loss, we conduct a theoretical and empirical analysis of the influence of OFDIs on the factor price distortion, based on the requirement of improving the total factor productivity to promote high-quality development. Our paper contributes to the existing literature by, firstly, adding to the current studies on the theories explaining the mechanism behind the factor price distortion, and providing new insights into how OFDI alleviates the efficiency loss due to the factor price distortion. Secondly, we develop a series of policy suggestions that are based on our further analysis of the heterogeneity of investment liberalization. Furthermore, based on our economic explanations, we also provide solid theorical support for regional policy decision making. 2. Theoretical framework and mechanism analysis 2.1. Factor price distortion and total factor productivity We analyze the relationship between factor price distortion and enterprise total factor productivity based on Melitz and Ottaviano (2008) monopolistically competitive model of trade with firm heterogeneity. First, suppose there is an economy with L consumers, and each of them can provide one unit of capital. Consumer preferences are defined by continuous heterogeneous products yi and a fixed number of standardized homogeneous products y0 . Thus, the customers’ utility function can be defined as:
Z u¼ α

n

i¼0

yi di 

β 2

Z

n

ðyi Þ2 di 

i¼0

Z n 2  θ þ y0 ; yi di 2 i¼0

where n is the number of heterogeneous products, and these products are perfect substitutes; α and θ denote the trend of substitution between heterogeneous and homogeneous products; β is the degree of differentiation among heterogeneous products. Given the diminishing marginal utility and y0 > 0, the market demand function can be derived as follows: Yi ¼ Lyi ¼

αL L θNL  pi þ p: θN þ β β βðθN þ βÞ

Secondly, we assume that each firm will only invest one factor unit and produce one kind of the heterogeneous product. Consequently, this means that a firm’s production function is linear. This relationship can be represented by the formula yi ¼ Ai ki , where Ai is the productivity of the firm and ki the capital input of the firm. In addition, we use the “iceberg” cost τ to indicate the factor price distortion that firms encounter. In general, the larger the value of τ, the greater the factor price distortion. ri in this equation denotes the capital price. The profit function of a firm is determined as π ¼ pi yi  τri ki . Assuming yi ¼ ki is known, combined with the market demand function, the following functions can be derived for the firms’ optimal price and output: pi ¼

  1 αβ þ θN τri pþ ; 2 θN þ β Ai

yi ¼

  1 αβ þ θN τri p : 2β θN þ β Ai

(2.1)

Substituting equation (2.1) into the profit formula results in the following equation:

π¼

  1 αβ þ θN τri 2 p 4β θN þ β Ai

(2.2)

βþθN p, a From equations (2.1) and (2.2), with the factor price distortion, firms’ marginal cost of the product is MC ¼ τArii . When MC > αθNþβ

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βþθN firm invests and produces; When MC < αθNþβ p, a firm does not invest in production. Therefore, the break-even condition for the firms to

βþθN p ¼ τArii . It implies that the firm will invest in capital and produce heterogenous products only when its total invest in production is αθNþβ

¼ Amin . factor productivity satisfies Ai ¼ τri ðαθNþβ βþθNÞp Finally, we define the equilibrium condition of market clearing as zero expected profits. Following Wang and Zhu (2018), we assume Ai and ri follows a joint probability function f ðA; ri Þ. Ai 2 ½A; A, ri 2 ½0; r. Let fE denote a firm’s opportunity cost of investment and production. Thus, the zero expected profit condition at equilibrium is

πe ¼

Z

r

0

Z A

A

  1 αβ þ θN τ ri 2 p f ðA; ri ÞdAi dri  fE ¼ 0 4β θN þ β Ai

(2.3)

βþθN p, then by equation (2.3), we can derive the relationship between firms’ total factor productivity and where A ¼ Amin . Let MC* ¼ αθNþβ dπ =dτ dAi factor price distortion: dMC dτ ¼  dπ e =dMC* > 0 ⇒ dτ < 0. We can see there is a negative relationship between the two: the more distorted *

e

the factor prices are, the lower the total factor productivity is. Therefore, we propose the following hypothesis: Hypothesis 1.

The distortion of factor prices suppresses the improvement of firms’ total factor productivity.

2.2. Outward foreign direct investment and total factor productivity Based on the analysis above, we conclude that, in the context of China’s progressive marketization, the factor price distortion caused by the underdeveloped factor market has a restraining effect on firms’ productivity. China will therefore need to adopt policies to further promote the international trade and investment to drive the reform of opening up the markets for OFDIs. It is important to understand how investment liberalization policies, mainly OFDI, influence the suppression effect of factor price distortion on total factor productivity. To be concrete, we are interested in whether this effect is dampened or amplified by these polices. First of all, the spillover effects of reverse innovation and the flow of talent created by OFDI will intensify the competition in the domestic market. Consequently, domestic firms will be forced to undertake technological transformations and equipment upgrades to remain competitive (Smarzynska Javorcik, 2004; Shi & Xian, 2012; Zhang et al., 2020). Therefore, domestic firms will gradually shift the source of profit from a short-sighted rent seeking strategy to a long-term development oriented strategy that focuses on R&D investments and productivity enhancement. Additionally, factor price distortion creates a competitive price advantage for firms engaging in OFDI, which bring high-quality production factors such as advanced technology, management experience, and highly skilled personnel from the international market back to the domestic base through the “parent-branch” structure of the firms. Furthermore, this mechanism can also positively impact the total factor productivity (Dunning & Lundan, 2008; Driffield & Love, 2003). In conclusion, OFDI can be assumed to directly improve firms total factor productivity and mitigate the negative impact of factor price distortion on the total factor productivity. Based on these arguments, we propose two further hypotheses: Hypothesis 2.

Firms’ OFDI helps to increase their total factor productivity.

Hypothesis 3.

Firms’ OFDI helps to alleviate the efficiency loss caused by factor price distortions.

3. Data, variables, and estimation strategy 3.1. Model specification After the detailed theoretical discussion, this part explains the construction of the empirical models used to analyze the relationship between the factor price distortion and OFDI. Furthermore, it describes, how the effects of OFDI on the efficiency loss, due to the factor price distortion, were analysed. The process of how the regression models were established, including the baseline as well as the extended models, is outlined below. First, we established an interactive model to discuss the impacts on the total factor productivity from the factor price distortion and OFDI, with special emphasis on the effect of the factor price distortion on OFDI. The existence of the factor price distortion hinders the correct reflection of a resource’s rarity on the factor price. This in turn results in the price mechanism being ineffective in the resource allocation. The connection between the total factor productivity and the factor price distortion has always been a focus in academia, and first studies already show that the factor price distortion can significantly lower the total factor productivity (Brandt, Tombe, & Zhu, 2013; Vollrath, 2009). Meanwhile, OFDI has been shown to be able to significantly improve the total factor productivity of a firm (Zhao, Liu, & Zhao, 2010; Herzer, 2011). With the expanding scale of Chinese OFDI, these money outflowing activities have been taking on an increasingly important role in the economic development. Thus, in this paper, we consider both factor price distortion and OFDI, and introduce an interaction term to discuss whether OFDI can mitigate the negative impacts stemming from the factor price distortion on the total factor productivity. The model for the analysis is as follows:   TFPijkt ¼ λ0 þ λ1 Dijkt þ λ2 OFDIijkt þ λ3 Dijkt ⋅ OFDIijkt þ θ1 Xijkt þ νj þ νk þ νt þ εijkt

(3.1)

where i, j, k and t represent firm, industry, and time. TFPijkt is the dependent variable, representing the total factor productivity measured by the LP method. Dijkt is the factor price distortion rate, which is indexed based on the capital and labor price distortion. We 178

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implemented a dummy variable OFDIijkt to represent whether the firm engages in OFDI activities. We also used a control variable matrix Xijkt to capture other factors that can influence the TFP. Lastly, we have included νj , νk , and νt as surrogates for industry, region, and time fixed effects, and εijkt as the white noise into the equation. The Greek letters represent the coefficients in this estimation, in which λ1 is the direct effects of the factor price distortion on the total factor productivity, thereby, λ1 < 0 indicates a negative impact from the factor price distortion to the total factor productivity. λ2 measures the effects of the OFDI to the total factor productivity, where a value of λ2 > 0 means that OFDI improves the total factor productivity. The coefficient λ3 of the interaction term comprises both the factor price distortion as well as OFDI and is the main coefficient we focus on. If λ3 > 0, OFDI can alleviate the negative impacts of the factor price distortion on total factor productivity. Furthermore, we can also adjust the regression to reflect both labor and capital factor price distortion. Additionally, to account for the quality of economic development, we used the intermediate input and final output to test the influence of the OFDI on factor price distortion. In fact, efficiency is the core part of the economic development target, where the total factor productivity can be a surrogate to measure the quality of the economic development (Bekaert, Harvey, & Lundblad, 2011). However, a general interpretation of the input-output relationship is limited in this analysis since the LP method used as a measurement of the total factor productivity ignores the allocation of resources. In recent literature, the quality of economic development was measured by applying the intermediate input-output efficiency and the value-added ratio (Jones, 2011). Thus, in this part, we use the intermediate input and value added to measure the total factor productivity:   ðTFP RÞijkt ¼ β0 þ βDijkt þ β2 OFDIijkt þ β3 Dijkt ⋅ OFDIijkt þ θ2 Xijkt þ νj þ νk þ νt þ εijkt

(3.2)

  ðTFP VÞijkt ¼ γ 0 þ γ 1 Dijkt þ γ 2 OFDIijkt þ γ 3 Dijkt ⋅ OFDIijkt þ θ3 Xijkt þ νj þ νk þ νt þ εijkt

(3.3)

where ðTFP RÞijkt and ðTFP VÞijkt denote the intermediate input-output efficiency and the value-added ratio, respectively. The other variables applied in this equation have the same meaning as before. In addition, based on the liberalization of the investment market, we analyze the effect of foreign direct investment on the relationship between price distortions and total factor productivity. Since OFDI can be incorporated by companies for different reasons, there are different forms of OFDI, aligned to the actual requirements of the firm. These different forms of OFDI, however, could have different effects on the company itself. For example, Li et al. (2016) analysed data of various Chinese provinces and found that OFDI has a significant positive impact on domestic innovation. You and Solomon (2015) believe that domestic investment has a positive effect on China’s foreign direct investment. Following the ideas above, considering the diversities of capital flows, the sources of the investment, and the expansion of the investment destinations, we categories OFDI into different groups. We classify OFDI as OFDI in developed countries and OFDI in developing countries, single-branch OFDI and multi-branch OFDI, and R&D processing type OFDI and capital type OFDI. For example, we construct OFDI in developing countries and OFDI in developed countries models as follows:  down TFPijkt ¼ η0 þ η1 Dijkt þ η2 OFDI down þ θ4 Xijkt þ νj þ νk þ νt þ εijkt ijkt þ η3 Dijkt ⋅ OFDI ijkt

(3.4)

 up TFPijkt ¼ τ0 þ τ1 Dijkt þ τ2 OFDI up ijkt þ τ 3 Djikt ⋅ OFDI ijkt þ θ5 Xijkt þ νj þ νk þ νt þ εijkt

(3.5)

Where η2 stands for the effect of downgradient OFDI on the total factor productivity, and η3 represents whether downgradient OFDI can alleviate the negative impact from the factor price distortion on the total factor productivity. τ2 and τ3 have same definitions but for upgradient OFDI. Similarly, we can revise equations (3.4) and (3.5) to get other versions of OFDI testing models.

3.2. Variables explanation The dependent variables in this paper are the total factor productivity (TFP), intermediate input-output ratio (TFP_R) and added value ratio (TFP_V). The key explanatory variables compromise the index of the factor price distortion (D), the index of the labor factor price distortion (Li), the index of the capital factor price distortion (Ki), and those used for robustness tests including the index of the factor misallocation (dist), the index of the labor factor misallocation (distL), the index of the capital misallocation (distK). Control variables are firm wage level (wage), corporate capital intensity (ci), and firm age (age). The measurement methods are shown below: 3.2.1. Dependent variables First, we applied the LP method to measure the total factor productivity. The first step of this method is to log-linearize the high degree polynomial function of the capital and intermediate input to get an approximation. The formula is yit ¼ βt þ βl lit þ βm mit þ βk kit þ ωit þ εit ,thereby, yit lit mit , kit represent the output, labor force, intermediate input and capital of an enterprise, after taking the natural logarithm, respectively. The formula also includes the total factor productivity ωit and a random error term εit . After setting up the equation, we executed the OLS regression to obtain the coefficient of labor. Furthermore, based on the results from the first step, we also got the coefficients of capital and intermediate input. The third step was to estimate the total factor productivity. The reason why we applied the LP method instead of the OP method to estimate TFP, was based on the proxy variables which are being used for these methods. While the intermediate input is used to proxy for TFP in the LP method, investments are taken as a proxy variable in the OP method. However, the observations of investment are not complete in our dataset. Thus, taking the LP instead of OP method for our 179

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Table 1 Matching results. 2003 Matching method

Firm scale Labor productivity Profit rate 2007 Matching method

Firm scale Labor productivity Profit rate

Pre-matching

Post-matching

OFDI

Non-OFDI

Prob.

OFDI

Non-OFDI

Prob.

11.940 9.646 0.151

9.908 8.151 0.004

0.000 0.000 0.047

11.940 9.646 0.051

11.944 9.636 0.043

0.989 0.947 0.427

Pre-matching

Post-matching

OFDI

Non-OFDI

Prob.

OFDI

Non-OFDI

Prob.

13.004 10.411 0.162

10.940 9.385 0.061

0.000 0.000 0.046

12.991 10.400 0.062

12.986 10.332 0.059

0.953 0.430 0.757

Treatment group

Control group

Matching control group

257 257 257

241944 241944 241944

760 760 760

Treatment group

Control group

Matching control group

696 696 696

104719 104719 104719

1990 1990 1990

Note: (1) the null hypothesis for the T test is that the mean values of the treated group and control group are the same. (2) Firm scale is the log of total assets; Labor productivity is the log of the ratio of gross industrial output to total assets; Profit rate is the ratio of gross profit to main business income. And the data is from Chinese Industry Business Performance Database. (3) We use a 1:3 ratio to match. (4) Nevertheless, we drop some unqualified observations so that the final results are close but not 1:3.

analysis prevents the loss of a large number of observations in our sample. Moreover, for the measurement in our analysis we used added values instead of total values for various reasons. First, added values are better to reflect firms’ production abilities, excluding the intermediate input Furthermore, the total output is highly correlated with the intermediate input. Thus, value added values do not only better reflect a firm’s production abilities, but also reduce the elasticity between capital and labor. Second, we calculated the intermediate input-output ratio (TFP_R) as. TFP R ¼ VAD=MINP ¼ ðVAD =LÞ=ðMINP =LÞ Where VAD is the added value and MINP is the intermediate input. Thus, VAD=L and MINP=L are added values and intermediate input per capita. Third, we derived the added value by deducting the total output by the intermediate input. Thus, the added value ratio (TFP_V) is the weight of the difference between the total output and the intermediate input in the total output. 3.2.2. Explanatory variables First, to calculate the factor price distortion index (D), labor price distortion index (Li), and capital price distortion index (Ki), most studies use the production function method, stochastic frontier analysis, shadow price method, and marketization index method. Among them, the production function method can directly measure the marginal output of the production factor. Thus, it can objectively reflect the true meaning of the factor price distortion. Based on this method, we applied a translog production function to measure the factor price distortion index. We used the added value, yearly average fixed asset net value, and the number of employees to represent the total output (Y), the capital input (K), and labor input (L). The capital price (r) is indexed by the ratio of interest expense to total debt. The labor price (w) is captured by the quotient of the total salary payment divided by the number of employees. The elasticities of the factors, α , and β, are obtained from the regressions. Thus, the variables are calculated as follows: Li ¼ MPL =w, Ki ¼ MPk =r, and D ¼ ðLiÞα=ðαþβÞ ðKiÞβ=ðαþβÞ . If the index is larger than 1, the factor price exhibits negative distortion, and vice versa. Second, we obtained the index of the factor misallocation (dist), the index of the labor factor misallocation (distL) and the index of the capital misallocation (distK) by calculating distL ¼ 1 þ Li, distK ¼ 1 þ Ki, and dist ¼ ðdistLÞα=ðαþβÞ ðdistKÞα=ðαþβÞ . 3.2.3. Control variables We applied five control variables: wage level (wage), capital intensity (ci), firm age (age), financial constraints (finance), and firm scale (scale). They are measured by the quotient of the total salary payment divided by the number of employees, fixed assets price minus accumulated depreciation and divided by the number of employees, the current year minus the year of establishment plus one, the ratio of the account receivable and total sales, and the total assets. 3.3. Data Our paper covers the period from 2003 to 2007. The data sources include the Chinese Industry Business Performance Database, Outward Foreign Direct Investment Firms List, and the China statistical Yearbook. The micro data including corporate performance and OFDI data are collected from the Chinese Industry Business Performance Database and the Outward Foreign Direct Investment Firms List. The export product price index and consumer product price index are both from the China statistical Yearbook. In order to minimize biases and avoid the influence of unrelated variables, we matched the Chinese Industry Business Performance Database and the Outward Foreign Direct Investment Firms List according to the names of the firms. In detail, we selected the firms with OFDI activities in the Chinese Industry Business Performance Database by using the firm information from the Outward Foreign Direct Investment Firms List. Then we dropped observations which either exhibited abnormal values or did not comply satisfactory with the accounting standards. In total we obtained an OFDI sample comprising 376 firms. To mitigate biases, we used the PSM matching method with a 1:3 ratio, obtaining a sample of 1083 firms with no OFDI activities. A more detailed explanation of the execution of the PSM method will take 180

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Table 2 Baseline regression results. Variables

(1)

(2)

D Li

0.021 (0.24)

0.143 (0.26)

(3)

(4)

0.385****** (3.79)

0.968****** (3.11)

Ki OFDI D**OFDI Li**OFDI Ki**OFDI wage

0.440****** (11.83) 1.023**** (2.07)

0.046** (1.90)

0.198 (0.99)

4.579 (0.68)

0.395**** (2.16)

6.196****** (409.41) Yes

0.284****** (25.74) 0.176****** (7.57) 0.106****** (6.84) 0.262****** (39.99) 0.226****** (30.12) 1.904****** (25.40) Yes

6.214****** (398.05) Yes

0.334****** (21.37) 0.190****** (8.64) 0.117****** (7.41) 0.258****** (39.57) 0.229****** (30.12) 1.753****** (21.07) Yes

6.195****** (411.08) Yes

0.379****** (3.50) 0.284****** (25.72) 0.154****** (7.56) 0.206****** (6.82) 0.262****** (39.99) 0.226****** (30.11) 1.906****** (25.41) Yes

0.018 7295

0.450 7295

0.029 7295

0.460 7295

0.019 7295

0.450 7295

finance scale

industry, region, and year effects adj_R2 Observations

0.024****** (11.50) 0.339****** (11.87)

0.499****** (16.14)

age

constant

(6)

0.466****** (7.24)

ci

0.518****** (20.41) 2.035****** (3.24)

(5)

0.518****** (20.43)

Note: Standard errors in parentheses ***p < 0.01, **p < 0.05, *p < 0.1.

place in the following section. 4. Empirical test results and analysis 4.1. Propensity score matching In order to eliminate self-selection bias, we used the minimum domain method, following previous literature (Helpman, Melitz, & Yeaple, 2003; Greenaway, Guariglia, & Kneller, 2007). Specifically, we selected enterprise size, labor productivity, and corporate profitability as the matching variables and conducted a 1 to 3 ratio proportion propensity score match. We divided the samples according to years, taking into account that matching indicators vary in time. We illustrated the method using data from 2003 to 2007. The results of the matching variables are shown in Table 1. From the results it can be seen, that firm scale, labor productivity, and profit ratio for firms incorporating OFDI in the pre-treatment group are significantly higher than those without OFDI. This is because firms in the treatment group usually undertake OFDI, when they are in a high-efficiency state, which leads to a “self-selection effect” (Helpmen.et al., 2003). These firms will continuously strengthen themselves due to the choice inertia. While the null hypothesis of the T-test that “the mean values of the treatment group and the control group are equal”, can be rejected for all variables of the pre-matching group, the null hypothesis fails to be rejected for all variables in the post-matching group. Consequently, the mean values of the match treated and control enterprises do not exhibit significant differences in terms of firm size, labor productivity and profit ratio. This implies that the individual differences between the two groups have been partially eliminated. This result demonstrates that the propensity score matching method was capable of finding the closest match of OFDI firms in the control group. The matching makes the estimation possible and eliminates the self-selection effect of OFDI. 4.2. Regression results 4.2.1. Baseline regression results We conducted a regression analysis to investigate the impact of factor price distortion on the total factor productivity, thereby, controlling for industry, region and year effects. The results are summarized in Table 2. The odd-numbered columns in Table 2 are the regression results of the total factor price distortion, the labor price distortion, and the capital price distortion. Furthermore, it depicts the results of the control variables. The even-numbered columns are the regression results without controlling for the effects mentioned above. We can observe that when the control variables are not included, the factor price distortion, the labor price distortion and the capital price distortion have a significant negative impact on the productivity improvement. There are several possible reasons for this. First, the low cost of labor force in China highlights the relatively high cost of capital. The distortion of the relative prices for labor and capital incurs the high capital investment and the high-quality labor shortage issues. Second, the income gap and the factor price distortion directly cause the lack of domestic demand. The “low-end” structure of consumer demands restricts the development of 181

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independent innovation. Thus, it reduces the production efficiency of firms. Therefore, the results of this regression confirm Hypothesis 1. In addition, the coefficient of OFDI is positive and significant at the 1% confidence level. Consequently, this means that a company’s OFDI increases its productivity. This is possibly caused by the expanding exports and the “economies of scale effect” achieved due to the investment. Thereby, the average costs of a product decreases and ultimately, the productivity increases (Cozza, Rabellotti, & Sanfilippo, 2015). Thus, according to our analysis Hypothesis 2 has been verified. Adding the control variables into the regression still yields significant negative coefficients for factor price distortion, labor and capital price distortions. However, the coefficients change their sign and take on positive values when the interaction terms with OFDI are included. This implies that firm’s OFDI can alleviate the restraining effects that factor price distortion exerts on the total factor productivity. Consequently, Hypothesis 3 has also been verified in our analysis. The combination of the market liberalization and the increasing significance of OFDI, which constitutes an important mean of cross-border movement of production factors and therefore, contributes to the optimal allocation of production factors on a global scale. Hence, this investment type is conducive to the improvement of resource allocation efficiency. Moreover, the coefficients of labor price distortion and the OFDI interaction term are greater than the coefficients of capital price distortion and the OFDI interaction term. Another immediate observation from the regression results is that OFDI has a more significant effect on reducing the negative impact of labor price distortion on total factor productivity than that of capital price distortion. The regressions including control variables exhibit robust results. This means that our empirical model is correct to a certain extent. While the coefficients of the variables wage level and scale of the firms are significantly positive, the coefficient of capital intensity is significantly negative. This indicates that larger firms which offer higher wages to their employees demonstrate greater potential for productivity improvements. However, high capital intensity is unfavorable for productivity improvement. Similarly, the negative coefficient of firm age implies that the productivity might be negatively influenced by the loss of innovation as firms grow older and become more experienced (Miaojie, 2010). Also, a firm’s financial constraints have a significant negative effect on a firm’s productivity (Zhang et al., 2019). The reason therefore, could be that the improvement of productivity encounters more obstacles when external financing dependencies are higher and external constraints are tighter. 4.2.2. A discussion on endogeneity There might be measurement errors that could lead to endogeneity when measuring factor price distortion. We used the idea of Lewbel (1997) to construct D_iv¼(D-D mean)3 as an instrument variable for the factor market distortion. We conducted estimations using 2SLS. Columns (1), (3), and (5) in Table 3 are the IV estimation results without the control variables, and columns (2), (4), and (6) are the IV estimation results with the control variables. First, the coefficients of the key explanatory variables D, Li, and Ki are all negative and the coefficient of OFDI is significantly positive. This means, while factor price distortions inhibit the improvement of productivity, OFDI improves the productivity. Therefore, these results are consistent with the conclusions above. Keeping all other variables constant, the coefficients of all interaction terms are positive, indicating that OFDI mitigates factor price distortion, which is therefore also consistent with the previous observations. Second, the coefficients of other covariates do not change substantially in either the sign or the significance, compared to the results above. Finally, to investigate whether the key variables are endogenous, we conducted a Hausman endogeneity test with the null hypothesis that “the explanatory variables are exogenous”. The test results indicate that these variables do not exhibit endogeneity. This can also be Table 3 Endogenous tests. Variables

(1)

(2)

D Li

0.059 (0.44)

0.167 (0.33)

(3)

(4)

0.186**** (2.01)

0.880****** (4.18)

Ki OFDI

0.304**** (2.01)

0.422**** (2.25)

D**OFDI

0.134****** (6.12)

0.571****** (4.26)

Li**OFDI Ki**OFDI wage ci age finance scale constant Hausman endogenous tests industry, region, and year effects adj_R2 Observations

5.627****** (5.47) 2.72

1.029 (0.56) 0.764 (0.44) 0.202 (0.03) 1.001 (0.01) 1.049 (0.09) 5.166 (0.67)

3.496****** (2.66)

2.334**** (2.05)

6.568 (1.08)

1.659**** (2.27)

(5)

(6)

0.010****** (10.40) 0.593**** (2.16)

0.023****** (3.37) 0.305**** (2.39)

0.104 (0.13) 1.174 (0.04) 0.104 (0.26) 0.219 (1.03) 0.198** (1.78) 2.096 (0.21) 5.046 (0.48)

4.855 (1.60)

0.503**** (2.29) 0.530** (1.75) 0.382 (0.78) 0.213 (0.35) 0.131 (0.60) 2.161 (0.83) 3.728 (1.30)

9.04

3.85

6.30

6.40

6.075****** (18.99) 6.92

Yes

Yes

Yes

Yes

Yes

Yes

0.071 7295

0.030 7295

0.136 7295

0.315 7295

0.370 7295

0.625 7295

Note: Standard errors in parentheses ***p < 0.01, **p < 0.05, *p < 0.1. 182

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Table 4 Robustness tests. Variables

Factor distortion index (1)

(2)

Mahalanobis matching (3)

(4)

(5)

183

finance scale constant industry, region, and year effects adj_R2 Observations

0.025****** (4.02)

0.550****** (13.55) 2.726****** (6.30)

0.363****** (6.55)

0.024****** (6.44) 0.623****** (21.51)

0.681****** (24.48) 1.001**** (2.13)

2.008**** (2.27)

0.671****** (16.87)

0.672****** (24.19)

0.167****** (3.31) 1.024****** (4.92)

0.400**** (2.38)

0.381****** (36.96) 0.170****** (8.27) 0.235****** (6.00) 0.175****** (26.84) 0.208****** (33.77) 1.748****** (27.87) Yes

0.357****** (14.69) 0.113****** (8.08) 0.536****** (6.53) 0.240****** (23.36) 0.220****** (28.61) 1.965****** (27.00) Yes

1.024****** (6.01) 0.243****** (22.32) 0.128****** (2.78) 0.455****** (6.15) 0.223****** (32.06) 0.236****** (33.04) 1.655****** (21.44) Yes

0.627 7295

0.502 7295

0.513 7295

Note: Standard errors in parentheses ***p < 0.01, **p < 0.05, *p < 0.1.

0.148**** (2.29)

0.144****** (4.27)

1.350**** (2.04) 0.341** (1.69)

0.121****** (9.79) 0.142****** (3.45) 0.324**** (2.04) 0.246****** (30.61) 0.134****** (15.99) 1.990****** (23.64) Yes

0.140****** (11.93) 0.162****** (5.51) 0.113****** (3.89) 0.246****** (33.69) 0.133****** (16.73) 2.071****** (26.17) Yes

0.120****** (9.91) 0.143****** (3.54) 0.302**** (2.28) 0.248****** (31.31) 0.135****** (16.08) 1.995****** (23.82) Yes

0.317 6535

0.316 6535

0.304 6535

0.001**** (2.09) 0.466****** (3.37) 0.194 (1.55)

0.440****** (4.15) 0.172 (1.39)

0.456****** (3.28) 0.191 (1.50)

0.228**** (2.41) 0.211****** (3.35) 0.207**** (2.17)

0.227****** (2.96) 0.210****** (3.48) 0.205**** (2.26)

0.149**** (2.34) 0.215****** (3.32) 0.204**** (2.12)

2.633****** (2.81) Yes

2.725****** (3.31) Yes

2.656****** (2.83) Yes

0.376 4620

0.401 4620

0.369 4620

International Review of Economics and Finance 67 (2020) 176–188

age

0.370**** (2.30)

0.202** (1.78)

Li**OFDI Ki**OFDI

ci

(9)

0.015****** (14.03)

distL distK

wage

(8)

0.031**** (2.52)

Ki

D**OFDI

(7) 0.165****** (10.91)

0.206****** (5.39)

Li

OFDI

(6)

0.215****** (10.80)

D

dist

Lag effects

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seen in the calculations above. 4.2.3. Robustness tests In order to test the robustness of the relationship between factor price distortion and low production efficiency, we alter the calculation of the key explanatory variables, change the sample matching method, and take possible time lag effects into account. Specifically, next to propensity score matching, Mahalanobis distance matching is also a commonly used matching method that is based on non-playback. By calculating the size of Mahalanobis distance between different groups of individuals to match, the method is capable of eliminating the interference stemming from correlations between variables. The estimated results are shown in Table 4. Columns (1)–(3) are the regression results of the total factor mismatch index, labor mismatch index and capital mismatch index as proxy variables; columns (4)–(6) are the results of the sample processed using Mahalanobis distance method with a 1 to 3 matching ratio; columns (7)–(9) are 2SLS estimation results in which the factor price distortion in the model is replaced by a one period lag term. It can be observed that the estimation results of the first three columns in Table 4 are basically consistent with those of the corresponding key explanatory variables in Table 2. They are both significantly negative, verifying the effects of the total price distortion, labor price distortion and capital price distortion on the productivity. Similarly, the results from the sample processed using Mahalanobis distance method and the estimation results involving lag terms are also largely consistent with their counterparts in Table 2. In addition, the regression results of other variables in the model are also approximately the same as those in Table 2. We are not repeating them in this section. To summarize, this should be sufficient evidence to prove that the results above are robust. 4.3. Further discussions: a perspective of investment liberalization 4.3.1. Factor price distortion and resource allocation efficiency In this section, we used the LP method to calculate TFP. The goal is to further examine the relationship between factor price distortion and resource allocation efficiency. We used the intermediate input-output ratio (TFP_R) and the value-added rate (TFP_V), used in the LP method, as indicators for the resource allocation efficiency. The regression results are shown in Table 5. Columns (1)–(3) present the estimated results when using the intermediate input-output ratio as the dependent variable, and columns (4)–(6) are the estimations with the added value rate as the dependent variable. The following two conclusions can be derived from the results of the key explanatory variables. First, the coefficients of factor price distortion are negative. Specifically, the labor factor price distortion significantly suppresses the intermediate input-output ratio. The coefficient of OFDI is significantly positive, indicating that OFDI can have a positive effect on the improvement of productivity, which is consistent with previous conclusions of this analysis. Second, the coefficients of the interaction terms exhibit significant positive values. Thereby, it is worth noticing that the coefficient of the interaction term between labor price distortion and OFDI is greater than the coefficient of the interaction term between capital price distortion and OFDI. This outcome also corresponds with our previous findings. It shows that technological advances in foreign direct investment had a biased impact on factors of production. On the one hand, Table 5 Resource allocation efficiency. Variables

TFP_R (1)

D Li Ki OFDI D**OFDI

TFP_v (2)

(3)

(4)

0.115 (0.24) 0.101**** (2.06) 0.109****** (6.34) 1.156****** (2.95)

Li**OFDI Ki**OFDI

0.151****** (5.94)

0.234 (1.59) 0.108****** (6.28)

0.029**** (2.45)

0.039****** (2.86)

0.226 (0.80) 0.028**** (2.35)

1.130****** (2.74) 1.374**** (2.26)

1.348** (1.74)

age finance

0.119****** (3.17) 0.117****** (2.88) 1.012 (0.10) 0.521 (0.16)

0.118****** (2.60) 0.115**** (2.56) 1.103 (0.09) 0.422 (0.28)

scale constant

0.219 (1.12) 0.073 (1.54)

0.102 (0.50) 0.096** (1.90)

0.225 (1.08) 0.074 (1.56)

industry, region, and year effects adj_R2 Observations

Yes

Yes

0.014 7295

0.016 7295

ci

(6)

0.030 (0.69)

1.121****** (5.34) 0.112****** (3.18) 0.117****** (2.85) 1.028 (0.11) 0.108 (0.18)

wage

(5)

0.113 (1.39)

1.132**** (2.17) 0.206 (1.35)

0.209** (1.70)

0.155 (1.32)

0.037 (1.61)

0.056 (1.29)

0.124 (1.57)

Yes

0.209 (0.73) 0.512****** (4.94) 0.023****** (2.70) 0.365****** (10.23) Yes

0.141 (0.80) 0.436****** (5.07) 0.211****** (3.00) 0.381****** (10.47) Yes

0.183 (0.75) 0.213****** (4.98) 0.210****** (2.77) 0.366****** (10.24) Yes

0.023 7295

0.064 7295

0.024 7295

0.053 7295

Note: Standard errors in parentheses ***p < 0.01, **p < 0.05, *p < 0.1. 184

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Table 6 Multi-directional flow of capital in the OFDI regression results. Variables

Down-gradient OFDI (1)

D Li Ki OFDI D**OFDI Li**OFDI Ki**OFDI Controls industry, region, and year effects adj_R2 Observations Variables

(4)

0.021 (0.88) 2.290**** (2.46)

(5)

(6)

0.105** (1.71) 0.094** (1.94) 0.201 (0.23)

0.013 (0.54) 0.221 (1.09) 0.022 (0.91)

0.017 (0.30) 2.183**** (2.08)

0.699 (0.74)

0.122 (0.10)

0.208 (0.92) 0.078 (0.44)

2.321**** (2.02)

Yes Yes

Yes Yes

1.001****** (5.52) Yes Yes

0.038 7295

0.023 7295

0.022 7295

0.045 7295

0.124 7295

0.073 7295

1.193****** (2.90)

(2)

(3)

(4)

(5)

(6)

Yes Yes

Yes Yes

Yes Yes

Up-gradient OFDI

1.042**** (2.14)

1.021****** (2.97) 0.197**** (1.98)

Li Ki OFDI

0.141**** (7.58)

D**OFDI Li**OFDI

1.034**** (2.14)

Ki**OFDI Controls industry, region, and year effects adj_R2 Observations

(3)

0.152 (0.15)

(1) D

(2)

0.178****** (6.89)

0.120**** (2.44) 1.011**** (2.46) 0.140****** (7.52)

0.048****** (3.63)

0.037****** (2.88)

1.009****** (2.97) 0.038****** (3.00)

1.005****** (3.33) 1.391**** (2.06)

1.023**** (2.32)

Yes Yes

Yes Yes

1.052**** (2.47) Yes Yes

Yes Yes

Yes Yes

1.074****** (2.96) Yes Yes

0.116 7295

0.023 7295

0.138 7295

0.135 7295

0.035 7295

0.154 7295

Note: Standard errors in parentheses ***p < 0.01, **p < 0.05, *p < 0.1.

there is a “price effect”, which is biased towards scarcity. On the other hand, there is a “scale effect.” Technological advances tend to favor relatively cheap factors (Acemoglu, 2012). Therefore, if the “scale effect” is greater than the “price effect”, technological progress will increase the price of labor factors and consequently, reduce the price distortion of labor factors. The test results are slightly different when the intermediate input-output rate and the value-added rate are used as the explanatory variables, respectively. Specifically, there is a significant negative correlation between capital intensity and intermediate input-output ratio, but there is no significant relationship between capital intensity and value-added rate. Corporate financing constraints generally have an inhibitory effect on total production factor. The existence of financing constraints will not only decrease the amount of OFDI but also reduce a firm’s innovations. Therefore, it is not conducive to the improvement of TFP. The wage level and the size of the firms still have a significant positive impact on the overall resource allocation efficiency. The results mentioned above are relatively robust. 4.3.2. A perspective of multi-directional flow of capital Based on the “duality” of OFDI, this type of investment can be divided into two categories. These two categories are downgradient OFDI towards developing countries and upgradient OFDI towards developed countries. In this section, we further examine the relationship between the price distortions and productivity. The results are depicted in Table 6. From the results above, two conclusions can be drawn. First, in contrast to the upgradient OFDI, the impact of the downgradient OFDI on the intermediate input-output rate and value-added ratio is not significant. The probable reason of this phenomenon is that the establishment of foreign direct investments should entail access to new resources and markets. However, downgradient OFDI does not bring innovation or simulation learning to Chinese companies. In contrast, by increasing investments in innovation and absorbing local advanced technologies, OFDI can improve the productivity of the enterprises located in the home country. Second, while the interaction between factor price distortions and upgradient OFDI are positive, the interaction between upward-sloping foreign direct investment and labor and capital price distortions exhibits different results. Because of upgradient OFDI, Chinese firms can absorb technologies from developed countries. The technological progress in those countries is mainly focused on the improvement of capital productivity, which leads to a decrease in labor factor price. As a result, the mitigation effect of upgradient OFDI on the capital price distortion is more prominent. It is possible that the negative impact of labor price distortion could even be enhanced. 185

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Table 7 Investment entity diversification regression results. Variables

Single-branch OFDI (1)

D

D**OFDI

(3)

(4)

0.112 (0.30)

0.108****** (6.00) 1.125****** (3.40)

Li**OFDI

0.164****** (5.40)

0.123 (0.47) 0.142 (1.55) 0.107****** (5.91)

Controls industry, region, and year effects adj_R2 Observations

Yes Yes

Yes Yes

0.413 7295

0.117 7295

0.362 7295

Variables

Multi-branch OFDI (2)

(3)

0.326 (0.44)

Li**OFDI Ki**OFDI

0.136 (0.82) 0.028**** (2.32)

Yes Yes

Yes Yes

1.039****** (3.18) Yes Yes

0.216 7295

0.139 7295

0.163 7295

(4)

(5)

(6)

0.404 (1.49) 0.189** (1.82)

0.210 (0.35) 2.242****** (3.14)

0.030**** (2.35)

1.034****** (2.67) 1.141****** (5.30) Yes Yes

Controls industry, region, and year effects adj_R2 Observations

0.042****** (3.27)

1.888**** (2.28)

(1)

(6)

1.102****** (4.10)

Ki**OFDI

D Li Ki OFDI D**OFDI

(5)

0.124****** (3.60) 0.106**** (2.13)

Li Ki OFDI

(2)

0.193 (0.19)

0.241 (0.52) 0.345 (0.23) 0.125 (0.84)

0.247 (0.25) 1.142****** (4.29)

1.970** (1.82)

0.158 (0.26)

0.352 (1.17) 0.111 (0.27)

2.120** (1.78)

Yes Yes

Yes Yes

2.257****** (3.12) Yes Yes

0.408 7295

0.238 7295

0.124 7295

Yes Yes

Yes Yes

1.234****** (3.28) Yes Yes

0.413 7295

0.345 7295

0.205 7295

Note: Standard errors in parentheses ***p < 0.01, **p < 0.05, *p < 0.1.

4.3.3. A perspective of investment entity diversification The magnitude of technological spillovers from OFDI is constrained by the number of investment branches. From the perspective of investment entity diversification, we divide firms with OFDI into multi-branch and single branch OFDI enterprises. The results are shown in Table 7. The estimation results in Table 7 demonstrate that: (1) The direct impact of the factor price distortion on the efficiency of resource allocation is negative, indicating that factor price distortion reduces the efficiency of resource allocation. When factor price distortion is present, the information received by the enterprise is biased, affecting the production decision making and the development of the enterprise. This result corresponds with the results of Ouyang and Sun (2015). (2) The coefficients of interactions between factor price distortions and OFDI are all positive. In particular, the coefficients of the interaction terms for multi-branch OFDI firms are larger than those for single-branch firms. There are three possible reasons that can explain this occurrence. First, multi-branch OFDI companies face more diversified competition than single-branch organizations do. This environment is more conducive to the “competitive effect” and “learning effect” of firms. It will further enhance the independent innovation capability, improve product quality, and promote enterprise competency. Second, multi-branch organizations can utilize market internalization to reduce the market transaction costs of strategic resources and intermediate products. It can also improve the technical complexity of products. Third, multi-branch institutions can take advantage of the bigger market, thereby, extend the industrial chain and supply chain, increase market share, and build up its own sales network. Thus, it can enhance the influence of the firms, and in turn, promote the development of the firms. 4.3.4. A perspective of investment industry expansion From our previous analysis, we are aware that different OFDI scopes have different impacts on a firm’s resource allocation efficiency. To investigate these influences, the ideas of Jones and Wren (2016) were utilized. Therefore, this section divides OFDI into those of a R&D processing type, and those of a trade and sales type. We analyze the impacts of factor price distortion on OFDI of these two types. The results are shown in Table 8. From the estimation results in Table 8, three conclusions can be drawn. First, the direct impact of factor price distortions on the resource allocation efficiency are all negative. This implies that factor price distortions have a constraining effect on the resource 186

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Table 8 Investment industry expansion regression results. Variables

R&D processing type OFDI (1)

D Li Ki OFDI D**OFDI

Variables

0.119 (0.57) 1.102****** (2.91)

D**OFDI

(6)

0.183** (1.83)

0.135 (0.50) 0.148 (0.20) 0.124 (0.74)

0.182**** (2.51) 1.092****** (4.22)

0.164**** (2.26)

0.132 (1.16) 0.154**** (2.34)

0.739**** (2.16)

Yes Yes

Yes Yes

1.303****** (4.96) Yes Yes

0.123 7295

0.119 7295

0.128 7295

0.154 7295

0.112 7295

0.143 7295

Yes Yes

Yes Yes

1.248****** (8.79) Yes Yes

(3)

(4)

(5)

(6)

Trade and sales type OFDI (2)

0.133 (0.19)

0.128****** (3.64) 0.193** (1.92)

0.132****** (7.25) 0.205****** (2.86)

Li**OFDI Ki**OFDI Controls industry, region, and year effects adj_R2 Observations

(5)

0.121** (1.67)

1.890** (1.70)

Li Ki OFDI

(4)

0.192** (1.89)

(1) D

(3)

0.123 (0.45)

Li**OFDI Ki**OFDI Controls industry, region, and year effects adj_R2 Observations

(2)

0.148****** (6.89)

0.127 (0.53) 0.143 (1.31) 0.131****** (7.20)

0.151****** (3.79)

0.141****** (3.16)

0.129 (0.75) 0.135****** (2.80)

0.134****** (4.27) 0.607** (1.72)

0.541**** (2.45)

Yes Yes

Yes Yes

0.103****** (5.80) Yes Yes

0.115 7295

0.123 7295

0.146 7295

Yes Yes

Yes Yes

0.162****** (4.23) Yes Yes

0.154 7295

0.112 7295

0.117 7295

Note: Standard errors in parentheses ***p < 0.01, **p < 0.05, *p < 0.1.

allocation efficiency. Thus, it is not beneficial for the production. However, it is consistent with the previous results. Second, the coefficients of interactions between factor price distortions and OFDI are all positive. Consequently, this means that both types of OFDI alleviate the negative impacts of factor price distortion on the productivity. Third, by comparing the coefficients, we conclude that the OFDI of R&D processing reduces the constraining effect of the factor price distortion on the productivity more than the OFDI of trade and sales type does. The reason for this could be that OFDI of the former type conduct product research and development, production and manufacturing overseas. Thereby, accelerating the learning and upgrading abilities of domestic enterprises due to reverse technology spillover effects. However, OFDI of the latter type solely utilizes trade to satisfy the production and consumption, which is only a small part in the global value chain. The benefits of reverse technology spillover effects are not entirely taken advantage of by this type of OFDI. Therefore, the trade and sales type OFDI are not comparable to the OFDI of R&D processing type in the context of mitigating factor price distortion. 5. Conclusion This paper delved into the impacts of factor price distortion on total factor productivity of firms by using industrial enterprise data from 2003 to 2007. We concluded that, first, the distortion of factor price impedes the improvement of the total factor productivity, from the perspective of labor price distortion and capital price distortion. Second, OFDI can alleviate the negative effects stemming from the distortion of factor price. Based on this analysis we can assume constructive impacts originating from Chinese market liberalization policies, of which positive OFDI policies are an essential part. Third, from the perspective of different investment liberalization processes, OFDI have heterogeneous effects on the impact of factor price distortion on total factor productivity. In particular, downgradient OFDI can mitigate the negative impact on the decline of total factor productivity resulting from factor price distortion. In contrast, labor factors and capital factors have opposite effects on the upgradient OFDI. Additionally, multi-branch OFDI and R&D processing type OFDI tend to play a more essential role in relieving the negative impact on total factor productivity from factor price distortion, than singlebranch OFDI and trade processing type OFDI. Derived from the conclusions of our analysis, a variety of policy implications can be drawn. First of all, it is clear that factor price 187

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distortion is not conducive to improving the total factor productivity of enterprises. From the perspective of the labor factor market or the capital factor market, the marketization process is more important. Second, previous liberalization policies on foreign direct investment provided experience for the marketization of factor markets. In the context of high-quality development, foreign direct investments are needed to improve the factor market. Thereby, further optimizing the allocation of resources around the world, can enhance the usage of scarce resources to invest in high value-added high-tech industries. In this way, factor price distortions as well as the negative impact on total factor productivity can be mitigated. In addition, foreign direct investment activities can transfer advanced management experience and advanced technologies to enterprises. 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