Does the Host Country’s Foreign Direct Investment (FDI) Restrictiveness Inhibit the Export Sophistication of the Home Country? Evidence from China’s Manufacturing Data

Abstract

Since the going-global approach of Chinese enterprises has accelerated, the host country’s foreign direct investments (FDI) restrictiveness index has dramatically influenced the upgrading of China’s trade structure. This study investigates the relationship between the host country’s FDI restrictiveness index and the export sophistication of the home country. Using two-way fixed-impact models and firm-based microcosmic data, it verifies the impacts of reverse technology spillover (RTS) by the intermediary model. The empirical outcomes illustrate that the host country’s FDI restrictiveness index significantly inhibits the export sophistication of the home country. In particular, overseas equity restrictions, selection and endorsement requirements, and additional operational limitations hold more substantial influence. However, the limits on key foreign experts have promoted the export sophistication of the home country. Seemingly, host countries’ FDI restrictiveness has inhibited export sophistication in the textile industry and the processing of the resource industry but promoted the same in the mechanical and electronic industries. Likewise, the host country’s FDI restrictiveness impacts the export sophistication of the home nation through resource allocation. Manufacturing enterprises increased export sophistication by guiding resource allocation, and export trade models were changed from the previous quantitative competition to quality competition.

1. Introduction

Export trade is one of the prime influencing factors in China’s increasing economic growth [1]. Since participating in the World Trade Organization (WTO), China’s import and export trade has witnessed rapid growth [2]. Consequently, China has emerged as the world’s largest export-oriented trading economy. However, with China’s total factor costs rising daily, the participation of other developing countries in global competition, and the escalation of global trade protectionism, China’s traditional low-cost advantage is gradually shrinking [3]. As China’s low-price export strategy becomes unsustainable, the focus on trade is shifting from expanding the export scale to increasing the technological sophistication of products [4]. China is actively pursuing the “going global” approach to change its export trade status. Indeed, FDI increased from USD 6.9 billion to USD 1451.9 billion in 2021, with a progressive yearly growth rate of 16.45% [5]. In the process, China has overtaken the United States with the most FDI outflow since 2020. The development of China’s manufacturing industries has become more obvious [6,7], thereby demonstrating a massive fundamental transformation [6].

Interestingly, the fourth industrial revolution (Industry 4.0) radically alters how corporations produce, enhance, and market their goods [7]. The Internet of Things (IoT), cloud computing, manufacturing and analytics, artificial intelligence, and computational modeling are emerging technologies that companies now incorporate into their manufacturing processes and other aspects of their business procedures [8,9]. Slow industrialization and distribution of Industry 4.0 in underdeveloped nations’ manufacturing sectors might create global inequality, as in past technology transitions [10]. Moreover, intelligent manufacturing is becoming a new engine of global economic development and the core driving force of industrial system reform [11]. Developing countries cannot afford to miss this new wave of technological change [12].

Previous studies have pointed out that Industry 4.0 can achieve significant increases in enterprise productivity and automation, which can not only reduce costs, improve market efficiency, and integrate the supply chain [13,14] but also significantly improve the quality of the enterprise’s products, leading to higher profits [15]. Many scholars also point to the advances in big data and analytics, artificial intelligence, and machine learning generated by Industry 4.0 [16,17], which means that manufacturers can choose from hundreds of potential solutions and technology applications to improve the way they work, potentially significantly improving labor productivity and manufacturers’ operations [18,19]. It is obvious that the phenomenon of Industry 4.0 has also brought many social changes. Andrius et al. [20] point out that the new jobs created under Industry 4.0 are mostly skill-intensive and can lead to severe skill mismatches, especially among the aging working class, leading to extreme job disruptions and unemployment and causing social problems.

Seemingly, various existing studies also point out that although Industry 4.0 enables the precise control of energy consumption and waste disposal in companies, the technologies supporting Industry 4.0 consume resources and energy and also have negative environmental impacts [21,22]. Aligning with the trends in Industry 4.0, the competent emerging nations are tending to restructure their industrial foundations over time, substituting lower value-chain services and simple export products with more significant operations and increasingly sophisticated items [23,24]. It will not only help in the exploration of the path to improving the manufacturing industry’s green innovation performance but also help investigate the deep-seated reasons why China’s manufacturing industry is “large but not strong,” which will facilitate the transformation of green innovation and promote China’s manufacturing industry from the low-end to the middle and high-end global value chain [25].

The existing research on the impacts of FDI on export sophistication mainly focuses on whether enterprises invest abroad or expand the scale of foreign investment. However, many macroeconomic indicators can also explain this phenomenon, such as the level of economic and technological development of the host country, the level of economic development of the home country, and the export and import costs of the home country. The study of export economic impacts of outward foreign direct investment (OFDI) is a common occurrence in the literature, although focusing mainly on the level of investment volume. In this respect, Kogut and Chang [26] were the first to examine the home-country technology spillover from FDI. Potterie and Lichtenberg [27] analyzed the technology spillover impacts of trade, FDI, and OFDI using a sample of industries in 13 countries and regions from 1971 to 1990. A significant positive spillover impact on home country productivity was found in investing countries and areas with high R&D intensity. Driffied and Love [28] argued that the pursuit of ownership is not a motivation for OFDI but rather for the acquisition of advanced technology in the host country through investment. The reverse technology spillover impact of OFDI on the domestic manufacturing sector is influenced by the spatial concentration of industries, which is only significant in R&D-intensive industries [29,30]. Masso et al. [31] established that firms have access to advanced foreign technologies through OFDI. By analyzing the OFDI activities of firms in nine new European countries, Damijan et al. [32] found that OFDI substantially improved parent companies’ productivity, which is different in different sectors and countries. According to Anderson et al. [33], the OFDI process passes technology investment, and OFDI is incorporated into the production process as a noncompetitive investment.

The discussion above gives rise to the following questions: (i) does the host country’s FDI restrictiveness inhibits the export sophistication of the home country? (ii) What are the possible mechanisms for restricting FDI? (iii) What are the prime channels by which the host country sets FDI restrictiveness? Those have become the primary concerns of academics and industry. In contrast to previous literature, this study intends to answer those research questions by evaluating the impacts of various sorts of FDI restrictiveness on export sophistication in different sectors. The main contributions of the study are as follows: (i) Existing research on the export impacts of FDI focuses on whether enterprises invest abroad or expand the scale of foreign investment. This research will be one of the first attempts to investigate the impact of the host country’s FDI restrictiveness on the home country’s export sophistication. (ii) The previous literature on the export impacts of FDI mainly focuses on macro-level studies. In this study, the research perspective is specific to various manufacturing enterprises. (iii) The article explores the impacts of different host countries’ FDI restrictiveness on the export sophistication in different industries and provides targeted analysis of and elaboration on the differences. It also verifies that investment constraints in the host country affect the export sophistication of manufacturing enterprises through the resource allocation impact and reverse technology spillover. We used combined data from the China Industrial Enterprise Database and the China Customs Trade Database to assess the causal impact of the host country’s FDI restrictiveness on the export sophistication of the home country.

2. Measurements of Indicators

2.1. Measurement of FDI Restrictiveness Index of Enterprises

Based on OECD’s scoring criteria [34], we obtained the foreign equity limits (${\mathit{Fdx}}^{\mathit{eq}}$), screening and approval requirements (${\mathit{Fdx}}^{\mathit{sr}}$), restrictions on key foreign experts (${\mathit{Fdx}}^{\mathit{key}}$), and other operational restrictions (${\mathit{Fdx}}^{\mathit{ot}}$). The FDI regulatory restrictiveness index (FDI Index) measures statutory restrictions on foreign direct investment in 22 economic sectors across 69 countries, including all OECD and G20 countries. The FDI restrictiveness index (Fdx) is the sum of the previous four items. Where c denotes the country or region, and j refers to the economic sector. The FDI restrictiveness index was excerpted from the OECD database [35]. The scoring of the FDI restrictiveness is presented in

Table 1. Scoring of FDI restrictions indexes.

Items	Scores
i. Foreign equity limits (${\mathit{Fdx}}^{\mathit{eq}}$)
No foreign equity allowed	1
Foreign equity < 50% of total equity	0.5
Foreign equity > 50% but <100% of total equity	0.25
Foreign equity prohibited	0.5
Foreign equity < 50% of total equity	0.25
Foreign equity > 50% but <100% of total equity	0.125
ii. Screening and approval (${\mathit{Fdx}}^{\mathit{sr}}$)
Approval required for new FDIs/acquisitions of <$100 mn or if corresponding to <50% of total equity	0.2
Approval required for new FDIs/acquisitions above $100 mn or if corresponding to >50% of total equity	0.1
Notification with discretionary elements	0.025
iii. Restrictions on key foreign experts
Foreign key experts prohibited	0.1
Economic needs test for employment of foreign key experts	0.05
Time-bound limit on employment of foreign key experts	0.025
Nationality/residence requirements for the board of directors
The majority must be nationals	0.075
At least one must be national	0.02
iv. Other operational restrictions
Establishment of branches prohibited/local incorporation required	0.05
Reciprocity requirements	0.1
Restrictions on profit/capital repatriation	1–0.1
Access to local finance	0.05
Acquisition of land for business purposes	0.1
Land ownership is prohibited, but a lease is possible	0.05–0.01

Adopted from Sabharwal and Singh [36].

, and the nomenclature and symbols in the equations are portrayed in Nomenclature part.

Referring to the studies of Sabharwal and Singh [36] and Dai and Xu [37] to construct the indicator methodology, the equation of the FDI restrictiveness index (${\mathrm{Fdx}}_{\mathit{it}}$) is presented as follows:

$${\mathrm{Fdx}}_{\mathit{it}}={{\displaystyle \prod }}_{c=1}^n {\left({\mathrm{Fdx}}_{\mathit{cj}}\right)}^{w_{\mathit{ict}}}$$

(1)

where i, t, and c represent the enterprise, time, and country or region, respectively. Next, $w_{\mathit{ict}}$, denoting trade weights, is the ratio of the goods exported to a country or region during the period to the total enterprise exports in that year. We thus have ${{\displaystyle \sum }}_{c=1}^n{w}_{\mathit{ict}}=1$.

The trends in the host country’s FDI restrictiveness faced by China’s manufacturing enterprises are shown in

Table 2. Trends of FDI restrictiveness index from 2010–2013.

Year	Classification of FDI Restrictiveness					The Industry Classification of Enterprises
	FDI Restrictiveness	Foreign Equity Limits	Screening and Approval Requirements	Restrictions on Vital Foreign Experts	Other Operational Restrictions	Light Textile Industry	Resource Processing Industry	Mechanical and Electronic Industry
2010	2.5331	0.4150	1.1006	0.7433	1.3212	2.2648	2.7781	2.6125
2011	2.6766	0.4675	1.0968	0.7999	1.4520	2.4390	2.9225	2.7028
2012	2.9554	0.4890	1.2256	0.8542	1.5921	2.7240	3.2145	2.9640
2013	2.7412	0.4665	1.1116	0.7714	1.5225	2.5836	2.8697	2.7689
2010–2013	2.7399	0.4621	1.1362	0.7945	1.4826	2.5153	2.9492	2.7766

. An overall upward trend is seen in various investment constraints. Taking 2010 as the base period, the FDI restrictiveness index, the foreign equity limits, the screening and approval measures, and other operational restrictions grew by 8.22%, 12.40%, 3.78%, and 15.24%, from 2010 to 2013, respectively. The investment restrictions faced by Chinese companies in the process of going global are increasingly tightened over time. In particular, China is subject to increasing foreign equity limits and other operational restrictions [38].

The resource processing sector faces the most significant FDI restrictions from the host nation, followed by machinery and electronics. The light textile industry faces the least stringent FDI restrictiveness. During the period from 2010 to 2013, the average host country’s FDI restrictiveness index against the resource processing industry was 2.9492, which is 0.1726 higher than that of the machinery and electronics industry (2.7766) and 0.4339 higher than that of the light textile industry (2.5153). In contrast, host countries’ FDI restrictiveness indexes in sundry industries present an upward trend [39]. Taking 2010 as the base period, the FDI restrictiveness indexes of the light textile industry, resource processing industry, and mechanical and electronic industry grew by 14.08%, 3.30%, and 5.99%, respectively. Chinese companies engaged in the light textile industry face increasingly stringent host country controls on their FDI, which could be associated with the increasing development of China’s light textile industry [40].

2.2. Measurement of FDI Restrictiveness Index of Enterprises

Based on OECD’s scoring criteria [34], we obtained the foreign equity limits (${\mathit{Fdx}}^{\mathit{eq}}$), screening and approval requirements (${\mathit{Fdx}}^{\mathit{sr}}$), restrictions on key foreign experts (${\mathit{Fdx}}^{\mathit{key}}$), and other operational restrictions (${\mathit{Fdx}}^{\mathit{ot}}$). The FDI restrictiveness index ($\mathit{Fdx}$) is the sum of the previous four items.

2.3. Measurement of Export Sophistication Index of Enterprises

Referring to Hausmann et al. [41] and combining the data from the China Industry Business Performance Database and the China Customs Trade Database, we measured the export sophistication index of enterprises. The equation of export sophistication of industry is presented as follows:

$${\mathrm{PRODY}}_j={{\displaystyle \sum }}_c\frac{(x_{\mathit{cj}}/X_c)}{{{\displaystyle \sum }}_c(x_{\mathit{cj}}/X_c)}\times {\mathit{pgdp}}_c$$

(2)

where c denotes each county or region, $x_{\mathit{ck}}/X_c$ represents the share of the country or region’s exports in that industry, $x_{\mathit{ck}}$ refers to the export value of the industry in a country or region, $X_c$ is the export value in a country or region, and ${\mathit{pgdp}}_c$ means the real GDP per capita of the country or region c. The equation of enterprise export sophistication is presented as follows:

$${\mathit{ESI}}_i={{\displaystyle \sum }}_k(\frac{x_{\mathit{ij}}}{X_i})\times {\mathrm{PRODY}}_j$$

(3)

where ${\mathit{ESI}}_i$ denotes the export sophistication of enterprise i; $X_i$ represents the enterprise export value; $x_{\mathit{ik}}$ is the industrial export value of enterprises; and $x_{\mathit{ik}}/X_i$ means the share of enterprise exports in that industry.

3. Materials and Methods

3.1. Measurement of Export Sophistication Index of Enterprises

To examine the influence of the host country’s FDI restrictions on China’s export sophistication, we used the following benchmark equation:

$${\mathit{ESI}}_{\mathit{it}}={\alpha }_0+{\alpha }_1{\mathit{Fdx}}_{\mathit{it}}+{{\displaystyle \sum }}_m{\alpha }_m{X}_{\mathit{it}, m}+v_i+v_t+{\epsilon }_{\mathit{it}}$$

(4)

where i, t, $v_i$, $v_t$ represent the enterprise, time, enterprise fixed impacts, and year fixed impacts, respectively. Additionally, ${\epsilon }_{\mathit{it}}$ denotes random perturbation terms and ${\epsilon }_{\mathit{it}}~N\left(0,{\sigma }^2\right), {\mathit{ESI}}_{\mathit{it}}$ as an explained variable represents the export sophistication of enterprise i, and ${\mathit{Fdx}}_{\mathit{it}}$ refers to a nuclear explanatory variable representing the host country’s FDI restrictiveness faced by enterprise i in period t.

In addition, $X_{\mathit{it},m}$ represents the mth control variable that may affect the technical complexity of the enterprise. Control variables specifically include (i) age, which signifies the years that the enterprise has been in operation and is calculated as the variations between the current year and the year the business was opened; (ii) size, i.e., the enterprise size measured as the logarithm of enterprise sales. More enterprise sales always mean the bringing of bigger enterprise size; (iii) “fin” represents financing constraints and is calculated as the proportion of interest expense to fixed assets. A higher value specifies that the enterprise is facing fewer financing constraints; (iv) “klr” represents the investment intensity of the enterprise and is calculated by the proportion of the yearly aggregate equilibrium of net fixed assets to the number of employees; (v) considering the different numbers of exporting host countries and export product industries of enterprises, the model considered “conm” and “indm”. Among others, “conm” represents the number of countries the enterprise exports, which is used to control the impact of the degree of fragmentation of product export countries on the technical complexity. At the same time, “Indm” denotes the number of export product industries and is used to control the impact of the degree of fragmentation of industries to the technical complexity; (vi) large and small represent large and small business dummy variables. According to the sales, enterprises were categorized into small, medium, and large enterprises. When enterprise i is a large firm, “large” will be assigned the value of 1, otherwise 0. When enterprise i is a small firm, “small” will be designated the value of 1, otherwise 0.

3.2. Data Descriptions

The FDI restrictiveness data utilized in this article are compiled from the OECD database. Enterprise data from China Industrial Enterprise Database and China Customs Trade Database. China Industrial Enterprise Database updated to 2013. Therefore, we select 2013 as the latest data period in the study. The scoring criteria for FDI restrictiveness were revised in 2010. Therefore, the data period was chosen from 2010 to 2013. Referring to the method of Upward et al. [42], we matched the 2010–2013 China Industry Business Performance Database and China Customs Trade database to obtain enterprise micro-level data. According to methods advocated by Gary et al. [43], the data were processed to acquire a sample of 229,066 observations from 94,098 manufacturing enterprises.

4. Results

4.1. Analysis of the Impact of the Host Country’s FDI Restrictiveness on Export Sophistication

Columns (1) to (2) of

Table 3. Impact of host country’s FDI restrictiveness on export sophistication.

	(1)	(2)	(3)	(4)	(5)	(6)
Fdx	−0.0193 *** (0.00194)	−0.0562 *** (0.00591)	-	-	-	-
${\mathit{Fdx}}^{\mathit{Eq}}$	-	-	−0.0540 ***(0.0111)	-	-	-
${\mathit{Fdx}}^{\mathit{Sr}}$	-	-	-	−0.173 ***(0.00922)	-
${\mathit{Fdx}}^{\mathit{Key}}$	-	-	-	-	0.199 ***(0.00892)	-
${\mathit{Fdx}}^{\mathit{Ot}}$	-	-	-	-	-	−0.213 ***(0.00760)
Age	-	−0.0129 *** (0.00099)	−0.0130 *** (0.00099)	−0.0129 *** (0.00099)	−0.0134 *** (0.00099)	−0.0127 *** (0.00099)
Size	-	−0.272 *** (0.00596)	−0.274 *** (0.00595)	−0.274 *** (0.00595)	−0.285 *** (0.00596)	−0.266 *** (0.00596)
Fin	-	0.417 *** (0.0321)	0.417 *** (0.0321)	0.414 *** (0.0321)	0.418 *** (0.0322)	0.428 *** (0.0320)
Klr	-	−0.430 *** (0.0263)	−0.429 *** (0.0262)	−0.434 *** (0.0262)	−0.439 *** (0.0262)	−0.419 *** (0.0263)
Conm	-	0.0282 *** (0.00252)	0.00839 *** (0.00107)	0.0321 *** (0.00165)	−0.0167 *** (0.00136)	0.0555 *** (0.00198)
Indm	-	−0.705 *** (0.00780)	−0.703 *** (0.00779)	−0.700 *** (0.00780)	−0.698 *** (0.00779)	−0.707 *** (0.00780)
Large	-	0.335 *** (0.0172)	0.329 *** (0.0172)	0.349 *** (0.0172)	0.323 *** (0.0172)	0.328 *** (0.0172)
Small	-	−0.558 *** (0.0171)	−0.561 *** (0.0171)	−0.565 *** (0.0171)	−0.551 *** (0.0171)	−0.546 *** (0.0170)
Year	Control
Enterprise	Control
Constant	29.53 *** (0.0160)	33.41 *** (0.0604)	33.46 *** (0.0600)	33.44 *** (0.0600)	33.59 *** (0.0601)	33.28 *** (0.0604)
R-squared	0.018	0.072	0.071	0.073	0.073	0.075
N	229,066	229,066	229,066	229,066	229,066	229,066

Note: t-values are in parentheses. *** coefficients are significant at the 1% level.

depict the regression outcomes of the host country’s FDI restrictiveness on the export sophistication of China’s manufacturing industries. The results show that the probable coefficient of FDI restrictiveness is significantly aversive. This suggests that the host country’s FDI restrictiveness prevents Chinese manufacturers from exporting technological sophistication. In column (1), only the host country’s FDI restrictiveness and export sophistication are considered. The model in column (2) considers the control for other influences. The investment constraint remains substantially negative at the 1% level and suggests that the host country’s FDI restrictiveness inhibits the export sophistication of China’s manufacturing.

According to the outcomes in column (3) of

Table 4. Impact of host country’s FDI restrictiveness on export sophistication in different industries.

	(1)	(2)	(3)
	Light Textile Industry	Resource Processing Industry	Mechanical and Electronic Industry
Fdx	−0.257 *** (0.00605)	−0.212 *** (0.0133)	0.0873 *** (0.00700)
age	0.00304 ** (0.00120)	−0.0359 *** (0.00210)	0.00309 *** (0.000933)
size	−0.0316 *** (0.00704)	−0.258 *** (0.0133)	−0.0851 *** (0.00642)
fin	−0.0447 (0.0285)	0.135 * (0.0759)	0.237 *** (0.0422)
klr	−0.289 *** (0.0389)	−0.311 *** (0.0470)	0.203 *** (0.0275)
conm	0.127 *** (0.00257)	0.143 *** (0.00577)	−0.0476 *** (0.00301)
indm	−0.747 *** (0.0166)	−0.101 *** (0.0165)	0.174 *** (0.00870)
large	0.0984 ***(0.0162)	−0.138 *** (0.0396)	−0.192 *** (0.0215)
small	−0.0830 *** (0.0192)	−0.00595 (0.0368)	0.0391 ** (0.0195)
Year	control	control	control
Enterprise	control	control	control
Constant	32.73 *** (0.0720)	30.97 *** (0.141)	28.81 *** (0.0684)
R-squared	0.093	0.049	0.065
N	76,653	67,297	85,116

Note: t-values are in parentheses. *, **, and *** coefficients are significant at the 10%, 5%, and 1% levels, respectively.

, foreign equity limits inhibit the export sophistication of China’s manufacturing industry. Based on the results in column (4), screening and approval requirements inhibit the export sophistication of China’s manufacturing. Column (5) examines the impacts of restrictions on crucial foreign experts on export sophistication. The results support those limitations on key foreign experts to enhance the export sophistication of China’s manufacturing enterprises. Per the outcome presented in column (6) of Table 4, other operational restrictions inhibit the export sophistication of China’s manufacturing.

4.2. Analysis of the Impact of the Host Country’s FDI Restrictiveness on Export Sophistication Based on the Industry Perspective

As shown in the outcomes in column (1) of

Table 5. Robustness test results.

	(1)	(2)	(3)	(4)	(5)
Fdx	−1.429 *** (0.0763)	-	-	-	-
${\mathit{Fdx}}^{\mathit{eq}}$	-	−4.103 *** (0.213)	-	-	-
${\mathit{Fdx}}^{\mathit{sr}}$	-		−10.22 *** (0.932)	-	-
${\mathit{Fdx}}^{\mathit{key}}$	-	-	-	7.361 *** (0.448)	-
${\mathit{Fdx}}^{\mathit{ot}}$	-	-	-	-	−1.963 *** (0.103)
age	−0.0106 *** (0.00141)	−0.0117 *** (0.00127)	−0.00262 (0.00363)	−0.0286 *** (0.00191)	−0.00995 *** (0.00132)
size	−0.0145 (0.0188)	−0.0954 *** (0.0141)	0.324 *** (0.0632)	−0.610 *** (0.0201)	−0.0502 *** (0.0165)
fin	0.687 *** (0.0476)	0.741 *** (0.0478)	0.844 *** (0.119)	0.528 *** (0.0622)	0.702 *** (0.0448)
klr	−0.608 *** (0.0426)	−0.109 *** (0.0396)	−1.209 *** (0.121)	−0.592 *** (0.0469)	−0.538 *** (0.0384)
conm	3.008 ***	1.376 ***	8.626 ***	−0.808 ***	2.305 ***
	(0.160)	(0.0714)	(0.785)	(0.0493)	(0.120)
indm	−0.172 ***	−0.631 ***	0.979 ***	−0.594 ***	−0.248 ***
	(0.0321)	(0.0128)	(0.160)	(0.0190)	(0.0273)
large	2.242 ***	1.071 ***	6.116 ***	0.123 ***	1.759 ***
	(0.101)	(0.0420)	(0.521)	(0.0387)	(0.0746)
small	−0.721 *** (0.0227)	−0.725 *** (0.0223)	−1.104 *** (0.0686)	−0.295 *** (0.0321)	−0.660 *** (0.0208)
Year	control	control	control	control	control
Enterprise	control	control	control	control	control
Constant	17.76 *** (0.858)	25.82 *** (0.419)	−9.307 ** (3.930)	37.47 *** (0.249)	20.90 *** (0.677)
N	229,069	229,069	229,069	229,069	229,069

Note: t-values are in parentheses. **, and *** coefficients are significant at the 5%, and 1% levels, respectively.

, the host country’s FDI restrictiveness inhibits export sophistication of light textile exports. It achieves a reduction in the portion of main industry products in China’s exports and an increase in the share of products with high export sophistication. It is possible for the endowments and levels of technological innovation present in any given industry to influence the effect that FDI restrictions have on the export sophistication of industry businesses. This is because textile enterprises invest in the host country to reduce production costs by rationing resources based on geographical advantages, transportation costs, and labor distribution. Therefore, the manufacturing industry was subdivided into light textile (GB/T Industry Code 13–24), resource processing (GB/T Industry Code 25–34), and machinery and electronics (GB/T Industry Code 35–41) [44].

According to the outcomes in column (2), the regression coefficient of the impact of the host country’s FDI restrictiveness on export sophistication is −0.212, which is significant at the 1% level in resource processing industries. This demonstrates that FDI restrictiveness affects the export sophistication of the resource processing industry. Easing investment constraints helps enterprises to make greenfield investments or local acquisitions. The resource-based primary products with low export sophistication can be mined and processed nearby. Then, the share of resource-based main products in exports decreases, and export sophistication increases. Based on the results in column (3), FDI restrictiveness furthers export sophistication in the mechanical and electronic industry, possibly because that industry is the core of China’s manufacturing sector. When the host country makes any decisions on investment and technology embargoes, enterprises will increase innovation investment for national security and independent industrial development. The increase in the technological level of products leads to upgrading export sophistication.

4.3. Robustness Test

The host country’s FDI restrictiveness policy-making is largely exogenous to individual Chinese companies. In this case, it is difficult for respective Chinese companies to influence the FDI restrictiveness policy-making of the host country; instead, they can only passively accept it. Based on robustness, the country area of the host country was selected as the instrumental variable for the investment constraint, and 2SLS estimation was used to perform the endogenous test. At the same time, “area” represents the country area. The smaller the country, the stronger the demand for external investment and the less FDI restrictiveness. ${\mathit{area}}_{\mathit{it}}$ denotes the area of the host country faced by the exports of enterprise i in period t. The equation is provided as follows:

$${\mathit{area}}_{\mathit{it}}={{\displaystyle \prod }}_{c=1}^n{\left({\mathit{area}}_c\right)}^{w_{\mathit{ict}}}$$

(5)

where i, t, and c represent enterprise, time, and country, respectively; $w_{\mathit{ict}}$ denotes trade weights, and ${{\displaystyle \sum }}_{c=1}^n{w}_{\mathit{ict}}=1$. This data was excerpted from the World Bank Database.

The regression results for model 2SLS are reported. According to the results, the coefficients of foreign equity limit screening, approval requirements, other operational restrictions, and foreign equity constraints are significantly negative. The factor of restrictions on vital foreign experts is substantially positive. This result is parallel with the benchmark regression outcomes, suggesting that the conclusions of this article remain robust and do not possess any issues of endogeneity.

4.4. Mechanism Analysis

Some studies have explored the reverse technology spillover of OFDI to home-country enterprises. Since the progress in science and technology, the technological gap between developing and developed countries is continuously decreasing, and the transfer of sophisticated technology has become more prominent. Therefore, reverse technology can act as a compelling method of increasing technology transfer capacity for transferring complex technologies [45]. The process is to directly master foreign enterprises’ advanced technologies through mergers and acquisitions or acquire local talents, patents, and other specific production factors through greenfield investments. Therefore, enterprises can generate reverse technology spillover to home countries through personnel mobility and re-export. Reverse technology spillovers help improve home country enterprises’ technology level, thus enhancing the technological sophistication of home country exports. Still, OFDI reverse technology spillovers have significant regional differences and threshold effects [46] and even inhibit domestic skill-biased technological progress [47]. With the upgrading of industrial structure, a country will gradually lose its comparative advantage in the form of OFDI to move overseas [48].

In contrast, other studies have outlined the OFDI resource allocation perspective. Optimal resource allocation helps increase firms’ motivation to innovate, increase R&D investment, and promote technological progress in the industry, which will boost the knowledge of technical product complexity [49]. The production factors released through marginal industrial transfer are transferred to other sectors to promote the reallocation of resources and improve the efficiency of resource allocation [50]. In addition, profit capital repatriation and local financial services constraints can affect firms’ financing ability, and firms’ external creditors can influence corporate decisions and affect the efficiency of resource allocation through debt camera governance [51].

We propose a framework that suggests that restrictive investment policies affect the technological sophistication of home country manufacturing exports through reverse technology spillovers and resource allocation effects. We will verify the reverse technology spillover effect from four restrictive investment policies: foreign equity access constraint, approved equity access constraint, key foreigner and executive access constraint, and other capital access constraint in the host country through a mediation model and verify the resource allocation effect using the change in the dispersion of export technology complexity. The next step is to validate the reverse technology spillover impacts through the intermediary model. The resource allocation impact is verified using changes in the dispersion of export sophistication.

4.4.1. Reverse Technology Spillover Impact

Reverse technology spillover is the focus of research on the outward investment of enterprises. The reverse technology spillover indicator of the enterprise was selected as the mediating variable. The intermediary equations are presented as follows:

$${\mathit{ESI}}_{\mathit{it}}={\alpha }_0+{\alpha }_1{\mathit{Fdx}}_{\mathit{it}}+{{\displaystyle \sum }}_m{\alpha }_m{X}_{\mathit{it}, m}+v_i+v_t+{\epsilon }_{\mathit{it}}$$

(6)

$${\mathit{DSP}}_{\mathit{it}}={\beta }_0+{\beta }_{1}Fd{\mathit{Fdx}}_{\mathit{it}}+{{\displaystyle \sum }}_m{\beta }_m{X}_{\mathit{it}, m}+v_i+v_t+{\epsilon }_{it}$$

(7)

$${\mathit{ESI}}_{\mathit{it}}={\gamma }_0+{\gamma }_1{\mathit{Fdx}}_{\mathit{it}}+{\gamma }_2{\mathit{DSP}}_{\mathit{i}}+{{\displaystyle \sum }}_m{\gamma }_m{X}_{\mathit{it}, m}+v_i+v_t+{\epsilon }_{\mathit{it}}$$

(8)

where ${\mathit{DSP}}_{\mathit{it}}$ denotes the RTS of enterprise i in year t. Referring to Braconie et al. [52], the reverse technology spillover was measured as China’s stock of research and development (R&D) investment from the host country. ${\mathit{DSP}}_{\mathit{ct}}$ represents the reverse technology spillover of country c. The equation is presented as follows:

$${\mathit{DSP}}_{\mathit{ct}}=\frac{{\mathit{ofdi}}_{\mathit{ct}}}{{\mathit{fixk}}_{\mathit{ct}}}{S}_{\mathit{ct}}$$

(9)

where ${\mathit{ofdi}}_{\mathit{ct}}$ is the stock of China’s FDI in country c in year t; ${\mathit{fixk}}_{\mathit{ct}}$ denotes the amount of fixed investment formation in country c in year t; and $S_{\mathit{ct}}$ is the stock of research and development (R&D) investment in the host country c in year t. The equation using the perpetual inventory method is presented as follows:

$$S_{\mathit{ct}}=\frac{{\mathit{RD}}_{\mathit{ct}}}{g_{\mathit{ct}}}+\left(1-\delta \right)S_{c t-1}$$

(10)

where ${\mathit{RD}}_{\mathit{ct}}$ denotes the amount of R&D investment expenditure in country c in year t, which was calculated from R&D expenses as a share of GDP, and g refers to the country’s GDP deflator. Data for RD and g were excerpted from the World Bank Database. $\delta$ denotes the depreciation rate of R&D investment. Referring to Coe and Helpman [44], $\delta$ was assigned 5%. Taking 2010 as the base year in this paper, the equation of R&D investment stock in host countries in 2010 is presented as follows:

$$S_{c2010}=\frac{{\mathit{RD}}_{c2010}}{\delta +r_c}$$

(11)

where $r_c$ is the average annual growth rate of R&D investment spending in country c from 2010–2013. Finally, the equation of ${\mathit{DSP}}_{\mathit{it}}$ is presented as follows concerning the construction method above:

$${\mathit{DSP}}_{\mathit{it}}={{\displaystyle \prod }}_{c=1}^n{\left({\mathit{DSP}}_{\mathit{cj}}\right)}^{w_{\mathit{ict}}}$$

(12)

where $w_{\mathit{ict}}$ is trade weights measured by the ratio of goods exported by enterprise i to country or region c in period t to the total export value of the enterprise, and we have ${{\displaystyle \sum }}_{c=1}^n{w}_{\mathit{ict}}=1$.

Columns (1) to (3) refer to the test results for the intermediary impact of reverse technology spillover in foreign equity limits. According to the results in column (1), foreign equity limits inhibit the export sophistication of China’s manufacturing. Based on column (2), foreign equity limits inhibit reverse technology spillover from enterprises. In column (3), the projected coefficient of reverse technology spillover is significantly positive, and the projected coefficient of foreign equity limits is substantially negative. These findings indicate an intermediary impact of reverse technology spillover between foreign equity limits and export sophistication. Columns (4) to (6) of

Table 6. Reverse technology spillover impacts of foreign equity limits and screening and approval requirements.

	(1)	(2)	(3)	(4)	(5)	(6)
	ESI	DSP	ESI	ESI	DSP	ESI
${\mathit{Fdx}}^{\mathit{eq}}$	−0.0806 *** (0.0118)	−0.0261 ** (0.0120)	−0.0798 *** (0.0118)
${\mathit{Fdx}}^{\mathit{sr}}$	-	-	-	−0.0821 *** (0.00754)	−0.142 *** (0.00776)	−0.0782 *** (0.00753)
DSP	-	-	0.0340 *** (0.00209)	-	-	0.0276 *** (0.00219)
age	−0.0150 *** (0.00113)	0.00245 ** (0.00114)	−0.0151 *** (0.00113)	−0.0149 *** (0.00113)	0.00397 *** (0.00108)	−0.0150 *** (0.00113)
size	−0.265 *** (0.00684)	0.117 *** (0.00782)	−0.270 *** (0.00684)	−0.261 *** (0.00682)	−0.000352 (0.00709)	−0.262 *** (0.00682)
fin	0.359 *** (0.0344)	−0.856 *** (0.0403)	0.388 *** (0.0345)	0.358 *** (0.0344)	−0.663 *** (0.0385)	0.377 *** (0.0345)
klr	−0.0011 *** (4.64−05)	−0.00042 *** (4.65−05)	−0.0011 *** (4.64−05)	−0.0011 *** (4.65−05)	−0.00023 *** (4.32−05)	−0.0011 *** (4.65−05)
conm	0.00247 ** (0.00117)	−0.378 *** (0.00152)	0.0153 *** (0.00142)	0.00756** (0.00366)	−1.061 *** (0.00376)	0.0369 *** (0.00436)
indm	−0.749 *** (0.00877)	0.236 *** (0.0106)	−0.757 *** (0.00877)	−0.729 *** (0.00879)	0.149 *** (0.00994)	−0.733 *** (0.00878)
large	0.386 *** (0.0188)	1.549 *** (0.0206)	0.333 *** (0.0191)	0.430 *** (0.0188)	1.245 *** (0.0197)	0.396 *** (0.0190)
small	−0.537 *** (0.0195)	0.422 *** (0.0214)	−0.551 *** (0.0195)	−0.561 *** (0.0201)	−0.547 *** (0.0203)	−0.546 *** (0.0201)
Year	control	control	control	control	control	control
Enterprise	control	control	control	control	control	control
Constant	33.61 *** (0.0679)	13.10 *** (0.0800)	33.16 *** (0.0729)	33.56 *** (0.0703)	18.35 *** (0.0734)	33.06 *** (0.0807)
R-squared	0.085	0.402	0.087	0.086	0.459	0.087
n	183,641	183,560	183,560	183,641	183,560	183,560

Note: T-values are in parentheses. **, and *** coefficients are significant at the 5%, and 1% levels, respectively.

demonstrate the outcomes of the test for the intermediary impact of reverse technology spillover in screening and approval requirements. In column (3), the projected coefficient of reverse technology spillover is significantly optimistic. According to the results in Column (4), screening and approval requirements inhibit the export sophistication of China’s manufacturing. Based on the outcome portrayed in Column (5), screening and approval requirements inhibit reverse technology spillover from enterprises. The projected coefficient of screening and approval requirements is substantially negative. Our findings reveal an intermediary impact of reverse technology spillover between screening and approval conditions and export sophistication.

Columns (1) to (3) of

Table 7. Reverse technology spillover impacts of restrictions on vital foreign experts and other operational restrictions.

	(1)	(2)	(3)	(4)	(5)	(6)
	ESI	DSP	ESI	ESI	DSP	ESI
${\mathit{Fdx}}^{\mathit{key}}$	0.200 *** (0.00973)	0.489 *** (0.0118)	0.185 *** (0.00977)
${\mathit{Fdx}}^{\mathit{oth}}$	-	-	-	−0.0845 *** (0.00578)	−0.110 *** (0.00575)	−0.0813 *** (0.00579)
DSP	-	-	0.0302 *** (0.00210)	-	-	0.0287 *** (0.00223)
age	−0.0155 ***(0.00114)	0.00144(0.00113)	−0.0156 ***(0.00114)	−0.0149 ***(0.00113)	0.00419 ***(0.00106)	−0.0150 ***(0.00113)
size	−0.278 *** (0.00684)	0.0930 *** (0.00778)	−0.281 *** (0.00684)	−0.257 *** (0.00685)	0.0293 *** (0.00700)	−0.258 *** (0.00685)
fin	0.359 *** (0.0345)	−0.847 *** (0.0401)	0.384 *** (0.0345)	0.362 *** (0.0344)	−0.646 *** (0.0378)	0.380 *** (0.0344)
klr	−0.0011 *** (4.64−05)	−0.00046 *** (4.63−05)	−0.0011 *** (4.64−05)	−0.0011 *** (4.65−05)	−0.00031 *** (4.25−05)	−0.0011 *** (4.65−05)
conm	−0.0244 *** (0.00149)	−0.434 *** (0.00186)	−0.0113 *** (0.00175)	0.0173 *** (0.00266)	−0.707 *** (0.00270)	0.0376 *** (0.00308)
indm	−0.743 *** (0.00877)	0.243 *** (0.0105)	−0.750 *** (0.00877)	−0.730 *** (0.00878)	0.214 *** (0.00972)	−0.736 *** (0.00878)
large	0.379 *** (0.0188)	1.534 *** (0.0204)	0.333 *** (0.0191)	0.425 *** (0.0187)	1.471 *** (0.0193)	0.383 *** (0.0190)
small	−0.525 *** (0.0195)	0.442 *** (0.0213)	−0.538 *** (0.0195)	−0.544 *** (0.0200)	−0.462 *** (0.0201)	−0.530 *** (0.0200)
Year	control	control	control	control	control	control
Enterprise	control	control	control	control	control	control
Constant	33.75 ***	13.38 ***	33.35 ***	33.46 ***	16.12 ***	33.00 ***
	(0.0679)	(0.0797)	(0.0732)	(0.0698)	(0.0719)	(0.0780)
R-squared	0.087	0.408	0.088	0.087	0.478	0.087
N	183,641	183,560	183,560	183,641	183,560	183,560

Note: t-values are in parentheses. *** coefficients are significant at the 1% level.

represent the test results for the intermediary impacts of reverse technology spillover restrictions on vital foreign experts. According to the column (1) results, restrictions on key foreign experts further the export sophistication of China’s manufacturing, and based on column (2), restrictions on key foreign experts advance the reverse technology spillover from enterprises. In column (3), the projected coefficient of reverse technology spillover is significantly positive, and the projected coefficient of restrictions on vital foreign experts is positive. In column (3), the projected coefficient of reverse technology spillover is significantly positive, and the projected coefficient of other functional limitations is substantially negative. This indicates an intermediary impact of reverse technology spillover between restrictions on key foreign experts and export sophistication. Columns (4) to (6) of Table 7 depict the test results for the intermediary impact of reverse technology spillover in other operational restrictions.

Our findings indicate an intermediary impact of reverse technology spillover between other functional limitations and export sophistication. According to the results in column (4), different functional conditions inhibit the export sophistication of China’s manufacturing. Based on column (5), other operational restrictions inhibit reverse technology spillover from enterprises. In summary, RTS plays a significant intermediary role between the host country’s FDI restrictiveness and the export sophistication of China’s manufacturing. The transmission mechanism is presented as follows: host country’s FDI restrictiveness$\to$reverse technology spillover$\to$export sophistication of home country, which validates the analysis of the reverse technology spillover mechanism in the second part of the paper.

4.4.2. Resource Allocation Impact

Further research was conducted from the perspective of export sophistication distribution to explore the relationship between export sophistication and the host country’s FDI restrictiveness. Referring to Syverson [53], the dispersion of export sophistication is presented as follows:

$${\mathit{ESI}}_{\mathit{kt}}^{75-25}=\frac{{\mathit{ESI}}_{\mathit{kt}}^{75}-{\mathit{ESI}}_{\mathit{kt}}^{25}}{{\mathit{ESI}}_{\mathit{kt}}^{50}}$$

(13)

where k denotes GBT industry; ${\mathit{ESI}}_{\mathit{kt}}^{75}$, ${\mathit{ESI}}_{\mathit{kt}}^{25}$, and ${\mathit{ESI}}_{\mathit{kt}}^{50}$ represent the export sophistication of the 25th quartile, 75th quartile, and median enterprise in industry k in year t, respectively. However, Equation (14) illustrates how FDI restrictions influence manufacturing export sophistication:

$${\mathit{Dis}}_{\mathit{kt}}^{25-75}={\alpha }_0+{\alpha }_1{\mathit{Fdx}}_{\mathit{kt}}+{{\displaystyle \sum }}_m{\alpha }_m{R}_{\mathit{kt}t,m}+v_k+v_t+{\epsilon }_{\mathit{kt}}$$

(14)

where ${\mathit{Fdx}}_{\mathit{kt}}$ represents the host country’s FDI restrictiveness faced by the industry; $v_j$ and $v_t$ are industry fixed impacts and year fixed impacts, respectively; ${\epsilon }_{\mathit{it}}$ is a random perturbation term; and ${\epsilon }_{\mathit{kt}}~N\left(0,{\sigma }^2\right)$. $R_{\mathit{jt}t,m}$ represents the m-th control variable that may affect the technical complexity of the industry. Control variables specifically include: (1) ${\mathrm{age}}_k$, which signifies the number of years that the industry has been operating; (2) ${\mathrm{size}}_k,$ which denotes the industry’s size and is calculated as the logarithm of industry sales; (3) ${\mathrm{fin}}_k,$ which symbolizes the financing constraints of the industry and is measured as the ratio of industry interest expense to fixed assets; (4) ${\mathrm{klr}}_k$, which represents the investment intensity of the industry, is measured as the ratio of the average yearly balance of the sector and net fixed assets to the number of employees; and (5) ${\mathrm{conm}}_k{ \mathrm{and} \mathrm{indm}}_k$ represent the number of countries and industries that the industry exports and the number of export product industries, respectively.

Column (1) of

Table 8. Resource allocation impacts of foreign equity limits and screening and approval requirements.

	(1)	(2)	(3)	(4)	(5)	(6)
	Foreign Equity Limits			Screening and Approval Requirements
	${\mathit{Dis}}^{75-25}$	${\mathit{Dis}}^{90-10}$	${\mathit{Dis}}^{95-5}$	${\mathit{Dis}}^{75-25}$	${\mathit{Dis}}^{90-10}$	${\mathit{Dis}}^{95-5}$
${\mathit{Fdx}}_k^{\mathit{eq}}$	0.0893 *** (0.0106)	0.159 *** (0.0168)	0.162 *** (0.0185)	-	-	-
${\mathit{Fdx}}_k^{\mathit{sr}}$	-	-	-	0.0456 *** (0.0141)	0.0849 *** (0.0206)	0.106 *** (0.0233)
${\mathrm{age}}_k$	−0.00184 ** (0.00072)	−0.00192 * (0.0011)	−0.00190 * (0.00114)	−0.00213 *** (0.00075)	−0.00243 ** (0.0011)	−0.00234** (0.0012)
${\mathrm{size}}_k$	0.00153(0.00375)	0.00704(0.00580)	0.00805(0.00646)	0.00702 * (0.00386)	0.0168 *** (0.00610)	0.0177 *** (0.00676)
${\mathrm{fin}}_k$	−0.0792 *** (0.0247)	−0.146 *** (0.0367)	−0.189 *** (0.0409)	−0.0833 *** (0.0257)	−0.153 *** (0.0379)	−0.194 *** (0.0422)
${\mathrm{klr}}_k$	0.00213 (0.00484)	0.00564 (0.00645)	0.00487 (0.00765)	0.00181 (0.00487)	0.00500 (0.00671)	0.00387 (0.00789)
${\mathrm{conm}}_k$	−0.0112 *** (0.00085)	−0.0179 *** (0.0014)	−0.0164 *** (0.0015)	−0.0138 *** (0.0023)	−0.0230 *** (0.0034)	−0.0249 *** (0.0039)
${\mathrm{indm}}_k$	0.0663 *** (0.00642)	0.106 *** (0.0105)	0.109 *** (0.0119)	0.0510 *** (0.00585)	0.0789 *** (0.00948)	0.0818 *** (0.0109)
Year	control	control	control	control	control	control
Industry	control	control	control	control	control	control
Constant	0.00548 (0.0377)	−0.00437 (0.0594)	0.0181 (0.0665)	−0.0161 (0.0386)	−0.0421 (0.0624)	−0.0171 (0.0693)
R-squared	0.148	0.190	0.170	0.091	0.110	0.105
N	1426	1426	1426	1426	1426	1426

Note: t-values are in parentheses. *, **, and *** coefficients are significant at the 10%, 5%, and 1% levels, respectively.

reports the results of the foreign equity limits in Equation (14). The projected coefficient of foreign equity limits is considerably optimistic. The finding represents that foreign equity limits intensify the dispersion of export sophistication, possibly because foreign equity limits are not conducive to resource allocation in the global market based on origin, sales, raw materials, transportation costs, etc. It reduces the efficiency of enterprises’ resource allocation and inhibits the export sophistication of home-country manufacturing. Column (4) reports the screening and approval requirements results in Equation (14). The projected coefficient of screening and approval requirements is significantly positive, indicating that screening and approval requirements increase the dispersion of export sophistication.

Column (1) of

Table 9. Resource allocation impacts of restrictions on vital foreign experts and other operational restrictions.

	(1)	(2)	(3)	(4)	(5)	(6)
	Restrictions on Vital Foreign Experts			Other Operational Restrictions
	${\mathit{Dis}}^{75-25}$	${\mathit{Dis}}^{90-10}$	${\mathit{Dis}}^{95-5}$	${\mathit{Dis}}^{75-25}$	${\mathit{Dis}}^{90-10}$	${\mathit{Dis}}^{95-5}$
${\mathit{Fdx}}_k^{\mathit{key}}$	−0.0168 * (0.0101)	−0.0329 ** (0.0148)	−0.0348 ** (0.0163)
${\mathit{Fdx}}_k^{\mathit{oth}}$	-	-	-	0.0530 *** (0.00973)	0.0962 *** (0.0142)	0.100 *** (0.0157)
${\mathrm{age}}_k$	−0.00239 *** (0.000663)	−0.00273 ** (0.00107)	−0.00272 ** (0.00118)	−0.00226 *** (0.000740)	−0.00266 ** (0.00106)	−0.00266 ** (0.00118)
${\mathrm{size}}_k$	0.00701** (0.00351)	0.0190 *** (0.00625)	0.0203 *** (0.00694)	0.00220 (0.00364)	0.00804 (0.00592)	0.00885 (0.00664)
${\mathrm{fin}}_k$	−0.0855 *** (0.0229)	−0.163 *** (0.0379)	−0.207 *** (0.0422)	−0.0862 *** (0.0245)	−0.158 *** (0.0360)	−0.202 *** (0.0401)
${\mathrm{klr}}_k$	0.00279 (0.00439)	0.00632 (0.00687)	0.00556 (0.00818)	0.00217 (0.00485)	0.00569 (0.00674)	0.00491 (0.00806)
${\mathrm{conm}}_k$	−0.00476 *** (0.00117)	−0.00590 *** (0.00176)	−0.00409 ** (0.00195)	−0.0181 *** (0.00218)	−0.0306 *** (0.00320)	−0.0299 *** (0.00358)
${\mathrm{indm}}_k$	0.0518 *** (0.00542)	0.0770 *** (0.00929)	0.0796 *** (0.0107)	0.0508 *** (0.00584)	0.0785 *** (0.00928)	0.0812 *** (0.0106)
Year	control	control	control	control	control	control
Industry	control	control	control	control	control	control
Constant	−0.0208 (0.0352)	−0.0673 (0.0642)	−0.0464 (0.0715)	0.0473 (0.0368)	0.0726 (0.0621)	0.0993 (0.0697)
R-squared	0.095	0.097	0.087	0.107	0.134	0.121
N	1426	1426	1426	1426	1426	1426

Note: t-values are in parentheses. *, **, and *** coefficients are significant at the 10%, 5%, and 1% levels, respectively.

report the results of the restrictions on vital foreign experts in Equation (14). The projected coefficient of restrictions on crucial foreign experts is significantly negative, indicating that restrictions on key foreign experts decrease the dispersion of export sophistication. The possible reason for this is that senior technical staff or senior management being retained in the home country increase the efficiency of resource allocation. Column (4) reports the results of the other operational restrictions in Equation (14). The projected coefficient of other functional limitations is significantly positive, indicating that other operating conditions increase the dispersion of export sophistication. To sum up, resource allocation has a significant intermediary influence between the host country’s FDI restrictiveness and the export sophistication of China’s manufacturing. For the robustness of the findings, the paper took ${\mathit{Dis}}^{90-10}$ and ${\mathit{Dis}}^{95-5}$ as the dependent variables for regression estimation. The projected results are robust and support the above conclusions.

5. Discussion

The prime aim of the study was to comprehensively explore and elaborate on the impact mechanism of FDI restrictiveness of host countries’ and home countries’ technological sophistication. Moreover, we outlined how capital access constraints impact manufacturing firms’ exports using the China Industrial Enterprise Database and the China Customs Database from 2010 to 2013. Our study finds that a strict capital access constraint in the host country can significantly lower the technological sophistication of manufacturing firms’ exports. The findings are well aligned with the various previous studies, which have shown that capital access and capital controls in host countries present strict constraints that significantly increase the external financing costs of home country firms, which in turn affects outward FDI [54,55,56]. Moreover, we found that foreign equity limits, screening and approval requirements, and other operational restrictions have an optimistic impact on shaping manufacturing firms’ export sophistication. The findings are partially supported by Beghin et al. [57] and Chen et al. [58]. In practical terms, facilities and infrastructure support provided by the host countries can significantly reduce the FDI risk, thus helping home country enterprises carry out stable technological inputs [59]. As a result, it is conducive to shifting the production of our primary products to labor-intensive countries and shifting the export market share from low export technical complexity to high export technical complexity with good efficiency [60,61]. However, as the increase in the local constraints in some host countries will significantly assist other countries’ enterprises in carrying out capital investment, it will substantially reduce the technological complexity of manufacturing exports in the home country. Interestingly, by exploring the trends in and determinants of FDI within heavy industries in Sri Lanka, Ravinthirakumaran et al. [62] found similar findings.

In addition, scholars generally agree that foreign investors are relatively more productive producers, are one of the critical drivers of technological development, and play an essential role in exports [63]. Multinational corporations often possess advanced technologies and are subjects of technology spillovers, and FDI inflows can reduce technology development and learning costs, thus contributing to the technological sophistication of exports [64]. However, many regional host countries are subject to control and regulation, or there are differences with multinational enterprises in terms of culture and philosophy, business model, etc. that undoubtedly cause the export of technical complexity to decline [65]. Sun et al. [66] pointed out that developing agreements such as regional trade agreements can reduce capital access constraints in host countries and significantly contribute to the export quality of Chinese manufacturers as exogenous trade costs fall relative to lower average tariff levels. The findings are parallel with our study. In addition, we find that the host country’s capital access constraints have a depressing effect on the technical complexity of exports of firms in the light textile and resource processing industries and a facilitating impact on the technical complexity of the exports of firms in the machinery and electronics industry. The assumptions are consistent with the study of Hu et al. [67] and Cao et al. [68]. In practical terms, capital access constraints in host countries reduce export technological complexity, and many host country access standards also have an impact. For example, implementing cleaner production standards in host countries significantly increases the export technical complexity of Chinese textile firms [69]. Cleaner production standards increase export technological complexity by increasing textile firms’ capital and labor inputs and promoting innovation and productivity improvements [70]. Our current study also outlines similar assumptions.

6. Conclusions

Foreign direct investment has positive and negative attributes for host countries’ perspectives and could be affected by various factors. Establishing an environment that is favorable to FDI and does not engage in discrimination has positive impacts over the long run. There has been some literature exploring the relationship between investment and exports. However, little literature has explored the impacts of the host country’s FDI restrictiveness from a policy perspective. Using the China Industry Business Performance Database and the Chinese Customs Trading Database from 2010 to 2013, this article empirically explores the relationship between the host’s FDI restrictiveness index and the export sophistication of the home country for the first time. In addition, we look into how different FDI constraints affect export sophistication within various circumstances. The prime findings of the study are as follows.

(i) We found that the host country’s significant FDI restrictions significantly impacted the advancement of the home country’s export sophistication, and foreign equity limits, stricter screening and approval requirements, and other operational restrictions were critical factors. In contrast, restrictions on vital foreign experts had a promotional effect on export sophistication. (ii) The impacts of FDI restrictions possessed significant variations within different industries. Among them, light textile and resource-processing enterprises were more adaptive toward the FDI restrictions, thus boosting export sophistication from mechanical and electronic enterprises. (iii) In contrast, export sophistication inhabited reverse technology spillover effects and resource allocation efficiency impacts and enhanced market resource distribution competence. Enterprises with skilled employees found the most effective method of extending the notion of reverse knowledge and technology spillovers and eventually upgraded their technology level. Manufacturing enterprises increased export sophistication by guiding resource allocation. Export trade models changed from the previous quantitative competition to quality competition. Ultimately, upgrading the industrial structure and transforming the economic development mode was achieved.

The study faces some limitations and challenges, which future studies should carefully handle to provide more robust assumptions and estimations. First, our study confirms that FDI in host countries limits the export maturity of traditional manufacturing home countries using data from 2013 and focusing on traditional manufacturing. However, the Industry 4.0 concept was only formally introduced in Germany in 2013 at the Hannover Messe. It generally evolved to emphasize the integration of manufacturing technologies and information and communication technologies (ICT) to overcome the negative impact of higher labor costs on the international competitiveness of manufacturing industries. Interestingly, the study does not examine the impact of Industry 4.0 technologies on the export sophistication of existing firms. Therefore, the effect of the role of Industry 4.0 in accordance with the technology intensity on enterprises’ export complexity should be examined more deeply by future researchers. Industry 4.0 has brought massive changes in how enterprises operate and manufacture, so future research needs to include Industry 4.0 technology intensity in the core research framework to outline more robust outcomes.

Second, our study was mainly based on traditional manufacturing industries, and the applicability of the findings may be greatly weakened in the context of Industry 4.0. Therefore, potential studies should highlight the technologies involved within various manufacturing enterprises and integrate the potentiality of big data, cloud manufacturing and ICT in the research sample to make the research results more representative. Third, the scope of our chosen study is only survey data from the China Industrial Enterprise Database and the China Customs Trade Database from 2010 to 2013, and the research results may be challenging to be universally applied in more regions and areas. Therefore, future studies should extend and use more cutting-edge and up-to-date data and diverse sectors to compile the measurement data. Finally, the study does not include crucial factors such as political circumstances, currency exchange rates, social/consumer behavior, factor endowments (labor, capital, and land), productivity, trade policies, inflation, and demand. Thus, potential studies should explore the impacts of these external factors on facilitating export restrictions by home countries. Moreover, export sophistication can be determined by factor endowments and productivity, trade policy, exchange rates, foreign currency reserves, inflation, and demand. Therefore, potential studies should investigate these internal factors while rectifying export sophistication.