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The purpose of this paper is to study the spillover effects of foreign direct investment (FDI) on skilled–unskilled wage inequality in the Indian manufacturing industries.

The authors show theoretically with a model of spillover that if foreign firms (receiving FDI) have a negative spillover effect on domestic firms (not receiving FDI), then the level of capital and skilled workers in the domestic firms falls down. Consequently, the authors conduct an empirical analysis by using system GMM estimation technique on the firm-level data of the Indian organised manufacturing sector.

The authors show that wage inequality worsens when there is negative spillover effects like competition spillover or skill spillover effect of FDI in India.

To the best of the authors’ knowledge, this is the first attempt to measure the various spillover effects of FDI on the wage inequality in the Indian manufacturing industries by using firm-level data.

This paper aims to examine the impact of six possible foreign direct investment (FDI) spillover channels on the total factor productivity (TFP) of Chinese agricultural enterprises and investigate the moderating role of absorptive capacity (technological acumen) on TFP spillover effects.

Based on data from 118 agricultural and related Chinese industries, the authors employ a multithreshold regression model to empirically analyze the impact of FDI on the TFP of agricultural enterprises and the threshold effect of absorptive capacity. To overcome potential endogeneity problems, the authors select the FDI stock of corresponding USA industries and the industrial access policy index as instrumental variables and re-estimate the model.

The results suggest foreign-invested agricultural enterprises are more likely to benefit from FDI, while the “aggregate” FDI spillover effect is negative for domestic agricultural enterprises. However, once threshold effects are introduced, the authors find firms “close to” (“far from”) the technological frontier experience statistically significant positive (negative) spillover effects. Similar results are obtained for virtually all FDI spillover channels for firms in both upstream and downstream industries. FDI spillovers, when they occur, can be a two-edged sword – benefiting some firms at the expense of others.

The authors introduce six FDI spillover channels to examine the impact of FDI on the productivity of foreign-invested and domestic agricultural enterprises. Moreover, the authors analyze the threshold effect of firms' absorptive capacity. These findings can help formulate foreign investment introduction policies based on the characteristics of agricultural enterprises with different ownership structures. These results are also beneficial for agricultural enterprises to better exploit FDI spillover effects and improve their productivity.

This paper aims to examine spillover effects of heterogenous foreign direct investment (FDI) enterprises (domestic vs. export-oriented) through horizontal and vertical linkages and absorptive capacity effect on domestic firms' total factor productivity (TFP). It clarifies the spillover effect on domestic firms in accordance with industrial zones, business size, technology sector and geographical agglomeration, respectively.

The dataset used is based on Vietnamese manufacturing firms during 2011–2014, input–output (I–O) Table 2012. This paper is conducted in two steps: (1) TFP is estimated by using a semi-parametric approach developed by Levinsohn and Petrin (2003); (2) Regression with panel data for domestic firms, applying the fixed effect method.

In terms of domestic-oriented FDI (DFDI) enterprise group: TFP spillover through horizontal linkages is found negative for domestic firms but positive for those participating in export. Additionally, backward linkages have a negative impact on TFP for most domestic enterprises, except for those operating in the high-tech sector. In terms of export-oriented FDI (EFDI) enterprise group, horizontal linkages have a negative impact on domestic firms' TFP including domestic ones participating in export whereas backward linkage is an important channel with positive effects. Absorptive capacity enables firms to improve productivity through linkages with EFDI and DFDI enterprises. Exporters located in industrial zones or regions with numerous exporters can receive better impacts through backward linkages EFDI.

Comprehensively, this is the first paper to detect FDI heterogeneity in their behavior when entering a developing country like Vietnam. The added value in this study comes from the export ability of local firms which is in line with Melitz (2003) theory that they can excel in absorping the TFP spillover from competing with DFDI competitors or from supplying to EFDI enterprises. Moreover, the role of small and medium-sized enterprises (SMEs), low technology, high technology and learning by regions affecting the impact through both horizontal and vertical linkages are included for analysis.

The purpose of this paper is to develop a double mechanism model to separate two foreign direct investment (FDI) intra-industry spillovers mechanisms: spillovers by FDI intensity and by FDI efficiency. This paper seeks to illustrate the potential use of the double mechanism model rather than provide precise estimates of spillovers. The evidence on the links between technology and the nature, size and mechanisms of FDI spillovers effects in economically developing countries is mixed.

A model is developed and tested, in principle. Empirical testing was conducted in two steps. In the first step, the authors examined the effect of each influencing factor to FDI spillovers separately. To complete this step, the authors divided the whole sample industry into sub-groups and tested them with the double-mechanism using ordinary least squares regression. This study applies Chinese National Bureau of Statistics manufacturing industry level data, for the years 2000, 2001 and 2002, including the food industry, beverage industry, textile industry, textiles and garments, chemicals and chemical products industry, overall manufacturing equipment, special equipment, computer and other electronic equipment manufacturing industries.

The analysis suggests significant differences between types of spillovers: export orientation of domestic firms mainly influences FDI spillovers by intensity; the capability gap between local and foreign firms influences spillovers by efficiency; and the growth of local firms influences both types of spillovers. This paper develops existing models of FDI and suggests that disaggregating spillovers types may provide important theoretical and policy insights.

This study has found, first, that compared with the classic single mechanism model, the double mechanism model is more appropriate for testing FDI intra-industry spillovers, as it is able to separate spillovers by intensity and spillovers by efficiency, which are shown as two distinct mechanisms for FDI spillovers. This allows a deeper analysis into each mechanism and the identification of relevant influencing factors.

Using panel data of 11324 firms in China from 1996 to 1999, the study finds that FDI tends to generate positive technology spillovers to domestic firms within the same industry, but adversely affect productivity of domestic firms in other industries. It is also found that both the positive and the adverse effects are more significant at the local than the national level. Evidence from China thus suggests that FDI technology spillovers are in favor of domestic firms within the same industry rather than domestic firms in other industries, and are most likely to affect domestic firms within the same locality. The finding has significant implications for the study of the interaction between MNEs and local firms in emerging markets.

For the developing countries involving in the Belt and Road Initiative (BRI) with China as the main source of foreign development investment (FDI) and development as the top priority, it appears to attract more and more attention on how to make the best use of China’s outward foreign development investment. However, the contradictory evidence in the previous studies of FDI spillover effect and the remarkable time-lag feature of spillovers motivate us to analyze the mechanism of FDI spillover effect. The paper aims to discuss this issue.

The mechanism of FDI spillovers and the unavoidable lag effect in this process are empirically analyzed. Based on the panel data from the Belt and Road developing countries (BRDCs) and China’s direct investments (CDIs) from 2003 to 2017, the authors establish a panel vector autoregressive model, employing impulse response function and variance decomposition analysis, together with Granger causality test.

Results suggest a dynamic interactive causality mechanism. First, CDI promotes the economic growth of BRDCs through technical efficiency, human capital and institutional transition with combined lags of five, nine and eight years. Second, improvements in the technical efficiency and institutional quality promote economic growth by facilitating the human capital with integrated delays of six and eight years. Third, China’s investment directly affects the economic growth of BRDCs, with a time lag of six years. The average time lag is about eight years.

Based on the analysis on the mechanism and time lag of FDI spillovers, the authors have shown that many previous articles using one-year lagged FDI to examine the spillover effect have systematic biases, which contributes to the research on the FDI spillover mechanism. It provides new views for host countries on how to make more effective use of FDI, especially for BRDCs using CDIs.

When spillovers are measured at a national level, the regional benefits may not be identified if they are too small. Very few studies that examined how FDI impacts the regional productivity of the host nations have shown mixed results. The evidence is still scarce and little is known about how the regional penetration of FDI affects the regional productivity performance. The trajectory of regional productivity growth in India has been a subject of scrutiny and intense debates and remains less systematically investigated. The purpose of this paper is to fill this lacuna by investigating the effect of FDI spillover on regional productivity in Indian states.

Using data supplied by the Central Statistical Organization, National Statistical Organization, National Sample Survey Office, and National Accounts Statistics, Government of India at the Indian Ministry of Statistics and Programme Implementation, first, the study employs stochastic frontier model to explore the extent to which FDI spillover contributes to the regional productivity from panel data of 28 Indian states over 1993-2013. Second, the study examines the roles of absorptive ability and technology gap on productivity effect of FDI. Third, by adopting SFA, we measure productivity growth of Indian states in terms of Malmquist productivity index. Fourth, India’s development is imbalanced. To analyze the imbalance due to skewed distribution of FDI among Indian states, Indian states are divided into three regions, and the spillover effects of FDI on TFP in these regions are explored.

The results on the effects of FDI spillover on regional productivity in India using stochastic frontier and panel data from 28 states over 1993-2013 show that R&D, technology import, human capital, and various specifications of FDI have a significant impact on the regional productivity in India except technology gap. Study does not find support for the resource curse hypothesis in Indian states. Productivity growth for India using the Malmquist TFP index based on the stochastic frontier shows positive impact. The TFP growth in the three regions of India is turned to be differently attributed by the FDI spillover.

Little is known about how the regional penetration of FDI affects the regional productivity performance. This research aims to fill this lacuna by investigating the effect of FDI spillover on regional productivity in Indian states which has been a subject of scrutiny and intense debates.

Previous studies have reached inconsistent conclusions on foreign direct investment (FDI) technology spillovers and corporate innovation. The main purpose of this paper is to explore the technological spillover effects of FDI from the microperspective of firm linkages induced by geographic distance. Further analysis is conducted on the impact and mechanism of this spillover on the innovation quality of Chinese enterprises. The conclusions drawn from this paper can guide Chinese enterprises' foreign capital utilization and innovation strategy choices.

Using the data of China's A-share listed companies from 2009 to 2019, this paper explores the role of FDI technology spillover in enterprise innovation quality through a two-way fixed-effect model. The robustness of the results is proven by substituting variables, adding industry fixed effects and excluding high-profit groups, and further using the two-stage least squares (2SLS) method to alleviate the empirical endogeneity problem.

These findings indicate that FDI technology spillover based on geographic proximity has a positive impact on the innovation quality of Chinese enterprises. However, there are different impacts for different types of enterprises. FDI technology spillover has a positive impact on the innovation quality of non-state-owned enterprises (non-SOEs) and small- and medium-sized enterprises (SMEs), while it has no effect on state-owned enterprises (SOEs) and large enterprises. The authors also find that the degree of financing constraints and R&D investment are important transmission mechanisms between FDI technology spillover and enterprise innovation quality.

This study ignores industry characteristics when considering foreign enterprises around Chinese enterprises. In fact, technology spillover effects differ across industries. When the authors matched microdata to regions, only the provincial level was considered. Therefore, there is still room for further research. In future research, the authors should consider industry characteristics and group foreign enterprises and Chinese enterprises in the same industry and in different industries to explore industry differences in technology spillover. In addition, when matching corporate data to regions, the authors can match to the city level and draw city-level conclusions.

This study is different from previous studies that focus on the quantity of enterprise innovation or innovation output. The authors focus on the role of technological spillovers in the quality of Chinese enterprise innovation, enriching research in the field of enterprise innovation quality. In addition, the current FDI technology spillover indicators are technically difficult to measure at the micro level. The authors draw inspiration from the theory of the geographical structure of financial supply and combine the creation methods of macro and micro indicators in existing articles in other fields. The authors ingeniously construct a new FDI technical spillover indicator. This indicator combines the commonly used regional FDI technology spillover with the geographic proximity of enterprises at the microlevel by constructing an interaction term between the two. This indicator not only alleviates the endogeneity problem to a certain extent but also has implications for future research in the field of FDI technology spillovers at the micro level.

(1) FDI technology spillovers are an effective way to improve the innovation quality of local enterprises, especially for non-SOEs and SMEs. Therefore, The authors suggest that in the context of dual circulation, the Chinese government should continue to open wider to the outside world and encourage foreign enterprises to invest in China. (2) In future development, managers of SOEs and large enterprises should create an innovation incentive mechanism. Moreover, they should change their vertical management structure and make full use of their policy advantages and budget advantages to increase innovation activities. In the process of acquiring technology spillovers, enterprises need to solve their own financing constraints.

First, this study solves a technical problem. It is technically difficult to measure the current FDI technical spillover indicators at the micro level. This study innovatively constructs a new FDI technology spillover indicator that combines regional FDI technology spillovers with the microperspective of the geographical proximity of enterprises. This approach not only alleviates certain endogeneity problems in the empirical evidence but also enriches relevant research in the field of technology spillover. In addition, this study focuses on the impact and mechanism of this spillover, which addresses the current research gap among previous studies that mainly focus on innovation quantity and ignore innovation quality.

This study aims to examine the complex effects of foreign direct investment (FDI) on China’s agricultural total factor productivity (TFP) from 2005 to 2020. It also explores the role of absorptive capacity as a moderating factor during this period.

Employing provincial panel data from China, this research measures agricultural TFP using the Stochastic Frontier Approach (SFA)-Malmquist method. The impact of FDI on agricultural productivity is further analyzed using a nondynamic panel threshold model.

The results highlight technological progress as the main driver of agricultural TFP growth in China. Agricultural FDI (AFDI) seems to impede TFP development, whereas nonagricultural FDI (NAFDI) shows a distinct positive spillover effect. The study reveals a threshold in absorptive capacity that affects both the direct and spillover impacts of FDI. Provinces with higher absorptive capacity are less negatively impacted by AFDI and more likely to benefit from FDI spillovers (FDISs).

This study provides new insights into the intricate relationship between FDI, absorptive capacity and agricultural productivity. It underscores the importance of optimizing technological progress and research and development (R&D) to enhance agricultural productivity in China.

This paper aims to study the role of foreign direct investment (FDI) channels in improving local firms' productivity. Two transmission channels of knowledge spillovers are empirically investigated. The study focuses on the role of high-growth firms (HGFs) that are assumed to have a higher absorptive capacity.

A threshold regression model that considers country and sector fixed effects is applied to investigate 8525 firms across 50 sectors in 12 developing countries in the East Asia and Pacific (EAP) region.

The author's findings indicate that first, larger firms with external market linkages are more productive. Second, high-growth enterprises are powerful engines of job creation; however, the firms do not outperform other firms in terms of capacity in absorbing FDI spillovers and do not have higher productivity.

The findings highlight the necessity of rethinking public policy priorities to support firm growth. Policies to maximize the gains from FDI spillovers are discussed.

This is the first study to investigate the strength of FDI spillover channels across different sectors, and the channels' impact on the productivity of local enterprises in the EAP region. This study also explores the potential role of high-growth firms (HGFs) in this interaction via job creation and improving output growth rate.