Task

The Impact of Financial Development on OFDI - Based on GMM Estimates

Introduction

Abstract: In this study, we employ panel data from 2010 to 2019, encompassing a sample of 64 countries representing a mix of developing and developed economies. Our Research delves into the influence of financial development on outward foreign direct investment (OFDI) through the application of four distinct estimation methodologies: namely, pooled ordinary least squares (OLS), fixed effects estimation, random effects estimation, and systematic generalized method of moments (GMM).

OFDI pertains to the cross-border flow of direct investments from a given nation, manifesting as investments orchestrated and conducted directly within foreign countries by investors from the source nation. We gauge financial development through a triad of indicators: the scale of economic growth, the structural composition of financial products, and the efficiency of financial development. Our findings reveal a consistent positive association between all dimensions of financial development and OFDI.

Additionally, we introduce country-specific dummy variables to stratify the analysis between developing and developed nations. Our empirical results underscore the statistical significance of these dummy variables, signifying discernible distinctions in OFDI patterns between the two categories of countries. On this basis, this paper examines developing and developed countries separately and comes up with different results.

Keywords: financial development, OFDI, system GMM, developing countries, developed countries

1. Introduction

OFDI, an acronym for "Outward Foreign Direct Investment," denotes the cross-border deployment of international direct investments from a nation. Specifically, it entails investments orchestrated by investors directly organizing and overseeing business enterprises in foreign jurisdictions (Chen, 2023; Yang, 2021; Christofi et al., 2022).

Investors opt for OFDI as a strategic approach to establishing foreign subsidiaries, facilitating production, operational expansion, and profit maximization. Consequently, the capital deployed for

OFDI is substantial (Xu, 2020; Fahad et al.). OFDI represents a pivotal strategy for nations seeking to venture beyond their borders, leveraging dual resources and markets, bypassing international trade obstacles, assimilating advanced foreign technology and management expertise, and staying abreast of external information (Cui, 2017; Li & Che, 2021; Jia, 2015; Guo et al., 2020).

Within the realm of OFDI, the designation "home country" pertains to the country of origin where a company is incorporated initially and its primary operational entity is domiciled.

Conversely, the "host country" designates the jurisdiction where the multinational corporation's business expansion activities are situated. OFDI manifests through two primary modalities: firstly, through greenfield investments, wherein the parent company seeks total control of a subsidiary by establishing an independent office or branch.

Secondly, it materializes through acquisitions or mergers conducted within the host country. Given the relative clarity and precision of the OFDI concept, this Study posits a refined definition: OFDI embodies the direct investment activities in production or services undertaken by enterprises operating within a nation's jurisdiction into foreign countries and regions beyond the nation's borders.

While the intuitive perception of OFDI may suggest a depletion of capital resources within the home country, potentially impeding its economic growth, a nuanced examination reveals multifaceted implications. OFDI, as undertaken by multinational enterprises, serves as a conduit for enriching human capital and technological prowess.

This enrichment is evident in the augmentation of productivity, achieved through enhancing individuals' knowledge, management capabilities, and technological competencies, as well as the broadening of international perspectives. Furthermore, domestic enterprises from the home country engaged in OFDI possess the capacity to steer the evolution of their industrial structure towards higher-end sectors.

Such a strategic shift is conducive to securing stable reservoirs of natural resources and market opportunities, thereby ameliorating the domestic business landscape. Consequently, these indirect mechanisms not only facilitate an influx of foreign direct investment (FDI) but also contribute to the overall economic advancement of the home country (Paul, 2016).

The upsurge in OFDI emanating from the home country holds the potential to catalyze increased levels of domestic investment. This phenomenon is rooted in the practice where foreign subsidiaries operating within the host country incorporate resources and inputs from the home country into their production processes (Desai et al., 2005; Yu & Yang, 2014; He, 2019).

Notably, multinational corporations (MNCs) are adept at amalgamating domestic and foreign production activities, leading to cost efficiencies. These cost efficiencies, in turn, enhance the profitability of domestic production initiatives, thus fortifying domestic investment (Herzer & Schrooten, 2008). Furthermore, internationalization can catalyze domestic Research and Development (R&D) endeavours, particularly within high-tech and low-tech industries. Empirical support for the favourable impact of a firm's OFDI on its domestic R&D expenditures is underscored by a panel data study involving Taiwanese manufacturing firms (Chen, 2013).

At the same time, there is growing evidence that OFDI can foster innovation and technological progress. OFDI can acquire knowledge and technology that does not exist in the domestic market (Amann & Virmani, 2014). These knowledge effects can benefit the outward investing firms and domestic firms in the home economy (Mani, 2013).

OFDI has the potential to instigate a reverse technology transfer dynamic, thereby catalyzing advancements in technological capabilities within the home country (Ye et al., 2017; Li et al., 2017; Hao et al., 2020). Notably, Hong et al. (2019) employed the technology gap theory and discerned that OFDI activities substantially enhanced provincial innovation performance within China.

Based on portfolio theory, Clegg et al. (2016) confirm that fast-growing OFDI patterns can generate reverse technology transfer and that firms' multinationality strategy significantly impacts the relationship between OFDI and performance. Nair et al. (2016) revealed the positive effect of absorptive capacity on reverse knowledge transfer through social learning theory.

Piperopoulos et al. (2018) substantiated the assertion that OFDI exerts a beneficial influence on the innovation performance of Chinese subsidiaries, with this effect being more pronounced when the focus of OFDI is directed toward developed nations as opposed to emerging economies. 

OFDI exhibits distinct characteristics, entailing substantial initial capital outlays and protracted capital return timelines. Consequently, countries embarking on foreign investments must grapple with financing constraints and potential losses stemming from investment risks.

This juncture underscores the pertinence of contemplating the significance of financial development, a phenomenon distinguished by the amplification of financial transaction volumes and the systematic refinement of the financial industry. This refinement catalyzes sustained enhancements in financial efficiency, concretized through the eradication of financial repression and the evolution of financial structures.

The latter is marked by innovations in financial instruments and the diversification of financial institutions, aligning them with the requisites of economic development (Huang & Huang, 2023; Behera et al., 2020; Wu, 2017; Yang, 2021). It is worth noting that the influence of financial development on economic growth is notably pronounced in developing nations. This observation stems from the dual role financial development plays in these contexts.

On the one hand, the improvement of the financial system holds equal significance to the expansion of the real economy. On the other hand, financial development serves as a catalyst propelling the advancement of the real economy (Batool et al., 2022). In light of these considerations, this Study defines financial development as the enhancement of financial efficiency resulting from the concurrent expansion of financial scale and the optimization of financial structure.

The current academic Research on the relationship between financial development and OFDI has achieved some revealing results. However, there is still some disagreement on whether financial development promotes or inhibits OFDI, and most empirical methods have neglected the dynamic development between economic variables.

Based on the triple perspective of financial scale, structure and efficiency, this paper establishes a dynamic panel model to comprehensively examine the relationship between financial development and OFDI to make up for the shortcomings of the existing Research to a certain extent. Financial development can affect OFDI in many ways.

For example, firstly, expanding a country's financial scale (bank credit scale, stock market scale and bond market scale) can provide OFDI with the required investment capital and alleviate financial constraints. The expansion of bank credit provides enterprises with more indirect financing opportunities, especially in many developing countries.

The financing channels mainly rely on bank credit, so the bank credit scale plays a more significant role in easing the financing constraints in the process of OFDI;. In contrast, the expansion of stock market scale and bond issuance scale provides enterprises with more direct financing opportunities. At the same time, the expansion of financial scale will reduce the financing cost in the process of OFDI, because the expansion of financial scale will bring about the effect of economies of scale and reduce the financing cost of enterprises.

Secondly, the enlargement of the financial scale plays a pivotal role in enhancing investment efficiency and mitigating the inherent risks associated with OFDI. As the financial development scale expands, the heightened competition among financial institutions drives improvements in their capacity to discern and assess project-related risks, resulting in reinforced risk oversight.

This, in turn, mitigates the risks stemming from information asymmetry, thereby optimizing capital allocation. In the fiercely competitive landscape, it guides capital flows towards high-quality projects characterized by moderate risks, ultimately elevating investment efficiency.

Furthermore, the expansion of the financial development scale, coupled with advancements in developmental levels, particularly the growth of insurance financial institutions, has engendered a proliferation of richer and more robust insurance and guarantee products within the financial marketplace. These products, in turn, furnish robust risk-mitigation mechanisms, serving to safeguard OFDI activities.

The optimization of financial structure improves the efficiency of OFDI. The external financing channels of enterprises mainly include direct financing methods mainly based on stocks and bonds, and indirect financing methods mainly based on bank credit. With the optimization of financial structure, the proportion of direct financing methods such as stocks and bonds will gradually increase.

The bank credit method raises the financing cost because the supply and demand of funds cannot be communicated directly, the information between the two sides is asymmetric, and the investor relies entirely on the bank's risk identification and supervisory ability.

The communication and supervision costs of direct financing methods such as stocks and bonds are lower than those of indirect financing methods, because the supply and demand of funds directly establish a relationship based on the information held by each other, and the financier needs to be supervised by the investor, thus prompting the investor to conduct a rigorous examination of the investment project, choose high-quality investment projects, and continuously innovate in the process of foreign investment to improve the efficiency of OFDI.

Improvements in financial efficiency have reduced the cost of indirect financing for enterprises. As the level of financial development rises, the scale of banks and other financial institutions continues to expand, costs are lowered, and the efficiency of financial institutions in converting savings into enterprise investment is improved, reducing the circulation of funds within the financial system and making it easier for funds to enter the real sector. The increase in the savings-to-loan ratio has therefore made it easier for enterprises to obtain credit financing on the one hand, and reduced the cost of loans to enterprises on the other, thereby promoting OFDI.

Literature review and hypothesis development

2.1 Literature review

Nguyen et.al. (2018) study examines the impact of institutional quality, FDI, trade openness and their interactions on the equilibrium of financial development by two systems -GMM estimators for 33 emerging economies over the period of 2002 to 2015. The Study shows that inward FDI has a booming effect, while trade openness exhibits a crowding-out effect. Yin and Zhang (2016) analyzed the reverse technology spillover effect of China's OFDI by using China's inter-provincial panel data from 2003 to 2012, with the help of GMM estimation, and the results show that the positive reverse spillover effect has not yet appeared at this stage.

Building upon this foundation, this Study undertakes a more comprehensive analysis of the factors delineating absorptive capacity and their influence on the reverse technology spillover effect. It discerns that domestic investment in R&D, the accumulation of human capital, the level of economic development, the degree of international openness, infrastructural development, and the magnitude of financial development all exhibit a positive propensity to facilitate the realization of the reverse technology spillover effect emanating from OFDI.

Nevertheless, when subjected to subregional scrutiny, notable disparities emerge. Within the eastern region, domestic R&D investments, the degree of international openness, infrastructure development, and the scale of financial development emerge as pivotal factors propelling the realization of OFDI reverse technology spillovers. In contrast, the central region's capacity to promote such reverse spillovers is more limited, primarily associated with the degree of international openness, infrastructural enhancements, and the scale of financial development. Notably, the western region demonstrates a unique profile, with positive reverse technology spillovers being primarily attributed to the scale and efficiency of financial development, constituting distinctive enablers in this context.

Ji (2017) utilized the macro, regional, and industrial perspectives to study the financial development and technological innovation-related data for econometric analysis and found that financial development and technological innovation will indeed play a role in promoting OFDI. However, it is imperative to recognize that distinct dimensions of financial development yield varying effects on OFDI within the Chinese context.

Specifically, financial expansion exerts a notable and positively facilitating impact on OFDI, while financial deepening demonstrates only a modest and weakly positive facilitative effect on OFDI. This divergence in effects can be primarily attributed to the prevailing challenges in China's financial system, which is predominantly characterized by the dominance of state-owned banks.

Research by Kergroach (2019) illuminates that the externalities stemming from FDI, commonly referred to as FDI spillovers, can assume diverse orientations, including positive, neutral, or negative outcomes. These dynamics are moderated by several factors, including the host country's institutional absorptive capacity, the stock of R&D capital, and the state of its financial markets, among others.

Empirical findings by Wei (2017) underscore that, at the present stage, OFDI has not exhibited a significant reverse technology spillover effect on China's technological advancement and innovation capacity. Nevertheless, it is noteworthy that the inclusion of financial markets can enhance China's absorptive capacity, augmenting the impact of OFDI on reverse technology spillovers to a certain extent.

Syamala and Wadhwa (2019) conducted an inquiry into the impact of foreign institutional investors (FIIs) trading activities on the enhancement of information dissemination within India's financial market. Notably, in recent years, the Indian market has emerged as the preferred investment destination for global investors within the spectrum of emerging markets.

Employing an extensive dataset encompassing individual trades spanning the period from 2003 to 2016, and employing four distinct delay measures, this Research undertakes an in-depth examination of the role played by FIIs in facilitating information dissemination within the Indian stock market. The outcomes of this investigation reveal that the correlation analysis unequivocally demonstrates a negative relationship between FII trading activities and the measured delays. This observed phenomenon may be attributed, at least in part, to the idiosyncratic characteristics of individual firms operating within the market.

Meng and Li ( 2016) found that the OFDI of developing countries has a facilitating effect on the economic growth of their home countries, but there are lags and regional differences in the economic growth effect of their home countries, and the results of the threshold test show that the level of financial development is one of the important factors affecting the effect of economic growth of their home countries.

Cheng and Wang (2017) empirically tested the existence of the threshold effect of financial development on the impact of international capital flows and concluded that the financial scale can ultimately realize the nonlinear impact on the economic growth process by nonlinearly affecting OFDI. Zheng (2017) empirically tested the threshold effect of OFDI on the home country's total factor productivity (TFP) impact based on China's inter-provincial panel data from 2003-2014 and constructed a panel threshold model with the financial development composite index as the threshold variable.

The results of the Study show that: the impact of OFDI on the home country's TFP has a significant double-threshold effect based on the level of financial development of the country, i.e., when the level of financial development does not cross the first threshold, OFDI has a significant negative effect on TFP; when the level of financial development is located in the middle of the two thresholds, the negative effect of OFDI on TFP is weakened; when the level of financial development crosses the second threshold, OFDI significantly contributes to TFP; when the level of financial development crosses the second threshold, OFDI significantly contributes to TFP.

When the level of financial development crosses the second threshold, OFDI significantly promotes TFP growth. The current level of China's overall financial development has not yet reached the level that prompts OFDI to generate positive productivity spillovers, and regions in China with a higher degree of coupling and coordination between OFDI and financial development during the sample period tend to have higher TFP. Chen and Zhu(2018) utilize China's inter-provincial data from 2007-2014, combined with the generalized least squares method, to study the impact of OFDI on TFP based on the theory of optimal financial structure theory to study the relationship among the three variables of OFDI, financial structure and total factor productivity.

The results show that: (1) the impact of reverse technology spillovers formed by OFDI in China on TFP is not significant overall, and the impact of OFDI on TFP is affected by financial structure, i.e., the financial structure can significantly improve the impact effect of OFDI on TFP; (2) the promotion of China's financial structure on TFP is significant. By region, the financial structure in central and western China has a significant effect on TFP in each region, while the effect is not significant in the relatively economically developed eastern region.

There are significant regional differences in the impact of financial structure on TFP; (3) China's adoption of the internationalization path of OFDI has effectively promoted the development of the domestic financial structure and catalyzed the transformation of the financial structure from "bank-led" to "market-led".

2.2 Hypothesis

In this paper, financial development is measured by financial development scale, financial development structure and financial development efficiency (Jiang et al.,2020). Jiang et al. (2020) conducted a study based on the panel data of 31 provinces in China (2008- 2016).

The results show that financial deepening is important for OFDI and its spillover effects. Zhong et al.(2018) use China's inter-provincial panel data from 2007-2015 to go to explore the impact of financial development on OFDI. The results show that financial development has a significant positive driving effect on regional OFDI.

The continuous forward development of finance mainly refers to the continuous development and increasing maturity of the capital market and credit market.

Under a higher level of financial development, it helps the concentration of capital, thus realizing large-scale production and operation and economies of scale; it is conducive to the rational allocation of resources and the improvement of the utilization efficiency, thus realizing higher social and economic efficiency; it is conducive to optimizing the rational allocation of credit funds between the state-owned and private sectors; and it is conducive to the formation of a better economic structure.

It is also conducive to optimizing the rational distribution of credit funds between the state-owned sector and the private sector, thus forming a better economic structure. Therefore, the level of financial development will have an impact on OFDI. This paper combines the "Mckinnon's Indicator" in R.L. Mckinnon's theory of financial inhibition to measure the core explanatory variables of financial development in terms of the scale of financial development, the structure of financial development, and the efficiency of financial development (Jiang et al., 2020).

Hypothesis 1: The financial development scale has a positive impact on OFDI.
Hypothesis 2: Financial development structure has a positive impact on OFDI.
Hypothesis 3: Financial development efficiency has a positive effect on OFDI.

3. Research Design

3.1 Model setting

This Research investigates the influence of financial development on OFDI, utilizing macroeconomic indicators. Given the nature of the research data, a quantitative analysis is conducted employing secondary data sources. Panel data regressions are chosen as the appropriate analytical tool for this Study due to their ability to capture the intertemporal dynamic behaviour inherent in the dataset.

They offer a fundamental framework for addressing issues related to time-invariant variables, which may entail omitted or latent factors in the regression model, thereby allowing for the mitigation of heterogeneity bias. These methodological strengths enhance the robustness of our conclusions, differentiating them from analyses solely reliant on static cross-sectional data or time-series comparisons.

Our Research employs a diverse array of estimation techniques to comprehensively investigate the phenomenon. Specifically, for the static version of the model, we apply pooled ordinary least squares (OLS), fixed-effects estimation, and random-effects estimation methods.


To delve into the dynamic dimension of the analysis, we embrace the system generalized method of moments (GMM) estimation method. GMM is favoured due to its capacity to address potential concerns related to endogeneity and individual heterogeneity. Specifically, the GMM approach provides parameter estimates that exhibit consistency (Arellano & Bond, 1991). The Research encompasses a total sample of 64 countries, comprising 53 developing nations and 11 developed ones. Consequently, to underscore the outcomes of developing countries, this Study incorporates a country-specific dummy variable, differentiating between the two categories of countries.

The rationale for employing alternative estimation methods in this context is predicated on the potential inaccuracies that may arise when relying solely on OLS estimates, particularly due to concerns related to endogeneity and heterogeneity. While both fixed effects and random effects estimation methods offer the advantage of addressing heterogeneity issues, they do not fully resolve endogeneity concerns, especially in situations where the model incorporates lagged first-order OFDI.

Hence, it is imperative to exercise prudence and scepticism when interpreting the findings emanating from these three estimation methodologies. Their outcomes may exhibit a degree of imprecision when confronted with endogeneity issues. The principal rationale behind the inclusion of these three estimation techniques is to employ them as comparative benchmarks, accentuating the resilience and accuracy of the system GMM approach.

Before embarking on the estimation process, it becomes imperative to undertake a comprehensive set of diagnostic assessments. Among these, the autocorrelation test emerges as a pivotal statistical analysis, serving as a means to determine the presence of correlations between observations of a variable at distinct time points.

Autocorrelation tests find widespread application across a multitude of domains, encompassing fields such as finance, economics, and meteorology. A widely employed autocorrelation test involves the scrutiny of the null hypothesis, as exemplified by the Wooldridge test, to appraise the existence of an autocorrelation phenomenon.

Another essential diagnostic test relates to heteroskedasticity, which stands in contrast to homoskedasticity, wherein the random error terms exhibit varying variances. The verification of a heteroskedasticity problem is often accomplished through White's test.

Furthermore, multicollinearity, characterized by intricate correlations or high interrelationships between explanatory variables within a linear regression model, can distort or impede accurate model estimation.

Consequently, it is imperative to employ a series of econometric tests to identify and rectify such issues, ensuring the veracity and reliability of the Study's results. In cases where econometric problems are detected within the sample, a robustness test becomes indispensable to validate the fairness and dependability of the Study's findings.

3.2 Data processing 

This Research employs a quantitative approach, utilizing secondary data drawn from both developing and developed countries. The choice to focus on developing countries stems from the notable surge in OFDI originating from these regions in recent decades. For instance, in the year 1995, OFDI originating from developing nations represented a modest 15% share of the global OFDI flows.

Nevertheless, this percentage exhibited a remarkable surge, reaching 34.6% by the year 2014 and further expanding to a substantial 52.3% by 2020. This pronounced escalation in cross-border investment activity, encompassing a diverse array of industries in developing countries, stands as a notable phenomenon that warrants in-depth empirical examination (Khan, 2012; Nayyar & Mukherjee, 2020; Sauvant et al., 2010).

The Study encompasses a ten-year timeframe, spanning from 2010 to 2019. Macroeconomic data for each country were meticulously collated from reputable sources such as the World Bank and UNCTAD. Rigorous efforts were made to exclude outliers and instances of missing country-specific data from the analytical framework, thus ensuring the integrity of the dataset.

3.3. Variable description 

Dependent Variables: In this Study, the focal point of analysis centres on OFDI, making it imperative to establish a scientifically precise definition of OFDI to ensure the robustness of empirical findings. To align with the protracted trajectory of industrial restructuring and circumvent transient fluctuations within the data, the variable representing OFDI is defined in terms of its flow value. Furthermore, a logarithmic transformation is applied to the OFDI variable, a technique known to effectively mitigate the impact of anomalous values in individual years, as demonstrated by Song (2020).

Independent Variables. The scale of financial development (FD) measures the overall level of development. Financial development is, first and foremost, the deepening of monetization. The main internationally used indicator is the McIntosh indicator, which measures the degree of monetization of a country through the ratio of the money stock to GDP. It was first used by McKinnon in measuring the level of financial development.

The level of financial development of a country can be reflected through McIlroy's indicator. A country with a higher monetization rate indicator means a higher level of financial development. Therefore, the first basic indicator to measure financial development should be the monetization ratio.

In this paper, the ratio of broad money M2 to GDP is used to measure the scale of financial development (Qamruzzaman & Wei, 2020). Financial development structure (FDS) measures the trend of financial development and the direction of allocation of available resources.

The flow of financial resources is divided into two main directions: the private sector and the state sector. An increase in the share of private sector credit reflects the increased marketization of the financial sector and is an indicator of the optimization of the FDS structure.

Private sector credit excludes credit to the public sector, such as the government, and therefore reflects to some extent the efficiency of a country's financial system in the allocation of funds (Desbordes & Wei, 2017; Wang, 2018). To eliminate the effect of market size, a logarithmic form is adopted. Financial development efficiency (FDE) measures the speed of financial development. In this paper, we consider the efficiency of financial institutions in converting deposits into loans.

One of the most important functions of financial markets is to finance capital and accelerate capital flows. It also attracts savings and increases investment, so the ratio of deposits to loans can be a good measure of the allocation efficiency of funds (Zang, 2018; Jiang, 2014; Dai, 2020; Jiang et al., 2020)

However, the shortcoming is that the indicator cannot reflect the efficiency of other ways of allocating funds in the financial market, such as allocating funds through stocks and bonds. Although stock markets in developing countries are well developed, a large amount of idle capital still exists in the form of savings, and firms that want to obtain capital prefer to borrow from banks.

Therefore, it is feasible to use the deposit-to-loan ratio indicator to reflect the efficiency of financial development. Because of the serious lack of data on deposit-to-loan ratios in some developing and developed countries, this paper uses the deposit-to-loan spread to measure the efficiency of financial development (Aizenman et al.,2015; Raifu, 2019).

Control Variables: The theory about economic development cycles posits a substantial association between the level of OFDI emanating from a specific country or region and the corresponding degree of economic development. In quantifying the extent of economic development (LGDP), this Study adopts GDP per capita as the chosen metric, aligning with the approach outlined by Qamruzzaman & and Wei (2020).

Labor-abundant（LAB） countries tend to have a large pool of available workers, which can result in lower labour costs than in other countries. This advantage attracts foreign investors who want to reduce production costs. Foreign investors grab the space of domestic companies and, at the same time, raise workers' wages.

So to reduce costs and make more profits, the home country will choose to use OFDI to invest in foreign countries and develop new markets(Peng, 2021； Cieślik & Tran, 2019). Urbanization Level (UP) corresponds to the process of urbanization, characterized by the gradual migration of the agricultural population to urban centres.

This migration triggers the agglomeration of capital, labour, and other production factors in urban locales. Population concentration, in a reciprocal manner, draws in top-tier talent whose engagement in economic pursuits propels technological progress congruent with the local development needs, consequently augmenting the level of OFDI.

In this investigation, the urban population's ratio to the total population serves as the selected gauge, in concurrence with the methodologies proposed by Zhang (2021) and Song (2020).

3.4 Regression modelling

First and foremost, my Research encompasses a diverse sample comprising both developing and developed nations. To facilitate empirical differentiation between these categories, a dummy variable has been incorporated into this Study. Specifically, designating a value of 1 to denote developing countries and 0 to signify developed countries.

D={(1-Developing countries
  0-Developed countries )

This Study examines three independent variables: financial development scale, financial development structure, and financial development efficiency. As such, the analysis in this Study is structured around three distinct models.

OFDIit = β0 + β1OFDIit-1 + β2FDit + β3LGDPit +β4LABit + β5UPit + β6D + εit   
Model 1
OFDIit = β0 + β1OFDIit-1 + β2FDSit + β3LGDPit +β4LABit + β5UPit + β6D +εit  
Model 2
OFDIit = β0 + β1OFDIit-1 + β2FDEit +β3LGDPit + β4LABit + β5UPit + β6D + εit  
Model 3
Where，
                      OFDIit: OFDI stocks from country i in year t.
                      OFDIit-1: the first-order lagged term of OFDI.
FDit: the financial development scale of the country i in year t.
FDSit: the financial development structure of country i in year t.
FDEit: the financial development efficiency of country i in year t.
LGDPit: the level of economic development of country i in year t.
LABit: labour abundance in country i in year t.
                      UPit: urbanization percentage in country i in year t.  
                      D: dummy variables.
β0: constant term.
                      β1, β2, β3, β4, β5, β6 : estimated coefficients.
                      εit: error term for country i in year t.

4. Empirical results and discussion

4.1 Descriptive analysis

Descriptive statistical analysis is the statistical description of the data related to all variables of the survey as a whole, which simply means that a series of complex data sets are described by a few representative data, which in turn can intuitively explain the changes in the data to have an in-depth understanding of the nature of the variables. Table 1 presents the outcomes of a descriptive analysis encompassing all variables under consideration.

Within the subset of developing countries, the mean value of OFDI stands at 1.496. Furthermore, the average value of the financial development scale (FD) registers at 0.645, while the mean value of the financial development structure (FDS) is 0.521, and the mean value of financial development efficiency (FDE) equals 0.340. The level of economic development (LGDP) demonstrates an average value of 0.901, while the level of urbanization (UP) averages 0.589, and the labour force level (LAB) averages 6.913. Notably, the standard deviations exhibit considerable dispersion around the mean values, signifying the elasticity of these variables in the cross-sectional dataset, indicating their capacity for variations.

In the context of developed countries, a distinct set of statistical parameters emerges. Specifically, the mean value of OFDI stands at 4.260. Additionally, the financial development scale (FD) records an average value of 1.079, while the financial development structure (FDS) exhibits an average value of 1.287, and the financial development efficiency (FDE) maintains a mean value of 0.415. Furthermore, the level of economic development (LGDP) presents an average value of 4.623, whereas the level of urbanization (UP) attains an average of 0.841, and the labour force level (LAB) achieves an average of 7.461. Notably, the scale of financial development (FD), the structure of financial development (FDS), and the efficiency of financial development (FDE) all manifest notably higher values in developed countries in comparison to their counterparts in developing nations. This observation may be attributed to the intrinsic discrepancy in the level of financial development between developed and developing countries.

Table 1  Descriptive statistics

 

4.2 Diagnostic checking

Table 2 provides the results of a comprehensive diagnostic assessment, including the autocorrelation test, heteroskedasticity test, and multiple covariance test. The initial hypothesis of the Wooldridge test is employed to detect potential autocorrelation problems, and the findings unequivocally confirm the presence of autocorrelation issues across all models. Likewise, the original hypothesis of White's test is utilized to scrutinize heteroskedasticity problems, with the results unambiguously indicating the existence of heteroskedasticity issues in all models. Furthermore, an examination of the Variance Inflation Factor (VIF) values reveals that the majority of them remain below the threshold of 5. Hence, this analysis suggests that there is no significant covariance problem evident within this dataset.

Table 2 Diagnostic checking report

Note: The Wooldridge test is used to test the existence of an autocorrelation problem. The White test is used to identify the existence of a heteroskedasticity problem, and VIF is a diagnostic of collinearity.
***Significant at 0.01 confidence level, **Significant at 0.05 confidence level, *Significant at 0.1 confidence level, p-value are in parentheses.

4.3 Result

Tables 3, 4, 5, and 6 provide the results obtained through a diverse array of estimation methodologies, encompassing pooled OLS, fixed effects, random effects, and system GMM techniques, respectively. Beginning with Table 3, which presents the findings from the pooled OLS estimation, the results elucidate that the scale of financial development (FD), the structure of financial development (FDS), and the efficiency of financial development (FDE) all demonstrate a positive and statistically significant impact on OFDI. In all three models, the level of economic development (LGDP), the urbanization rate (UP), and labour abundance (LAB) also exhibit statistically significant and positive effects on OFDI.

Table 3 Regression results using the pooled OLS estimation

***Significant at 0.01 confidence level, **Significant at 0.05 confidence level, *Significant at 0.1 confidence level

Table 4 presents the results derived from the fixed effects estimation approach. The Study's findings reveal that the scale of financial development (FD), the structure of financial development (FDS), and the efficiency of financial development (FDE) exert positive and statistically significant effects on OFDI at the 1% significance level. However, it is worth noting that the level of economic development (LGDP), the degree of urbanization (UP), and the size of the labour force (LAB) exhibit adverse effects, which diverge from the prevailing consensus in the existing literature. Most previous studies have indicated a positive impact of these variables on OFDI (Sun & Liu, 2020; Tian et al., 2021; Wang, 2022).

Table 4 Regression results using the Fixed Effect Estimation. 
 

***Significant at 0.01 confidence level, **Significant at 0.05 confidence level, *Significant at 0.1 confidence level

Table 5 furnishes the outcomes derived from the random effects estimation approach. These results substantiate the earlier findings presented in Table 3, further underscoring the significant roles played by the dimensions of financial development, including the scale of financial development (FD), the structure of financial development (FDS), and the efficiency of financial development (FDE), in driving OFDI growth.

Additionally, the Study reaffirms that the level of economic development (LGDP), urbanization rate (UP), and labour abundance (LAB) all exert positive and statistically significant effects on OFDI. However, the country dummy variables do not demonstrate significance. It is worth noting that the Hausman test suggests that the fixed effects estimation method may be more appropriate than the random effects estimation for this analysis.

Table 5 Regression results using the Random Effect Estimation. 

As delineated earlier, the three preceding estimation methods do not account for the potential endogeneity inherent in the regression variables. Thus, a degree of caution should be exercised in interpreting these findings. To address this endogeneity concern, the Study resorts to the system GMM approach.

The results of the system GMM estimation are presented in Table 6. The system GMM estimation necessitates an initial validation of instrumental variables and the rationale of the estimation method. In general, two essential conditions must be satisfied: firstly, the absence of autocorrelation among random error terms, which is typically determined by a p-value exceeding 0.05 in the Arellano-Bond AR(2) test, indicating the lack of second-order serial autocorrelation among error terms; secondly, the exogeneity of instrumental variables, confirmed by a p-value exceeding 0.05 in the Hansen test, signifying the validity of the selected instrumental variables.

This Study employs both the Arellano-Bond test and the Hansen test. The Arellano-Bond test scrutinizes whether the residuals of the differenced model exhibit serial correlation. The system GMM allows for first-order autocorrelation among different terms but excludes second-order autocorrelation.

The Hansen test evaluates the potential over-identification of instrumental variables, establishing the validity of these variables. Table 6 showcases the results for all three models. Notably, models (1), (2), and (3) all pass the Hansen test, affirming their validity. Furthermore, first-order autocorrelation is evident, while second-order autocorrelation is absent. The lagged first-order OFDI demonstrates significance at the 1% significance level, indicating a substantial positive influence of prior-period OFDI on the current period.

This suggests that a favourable investment climate in the past enhances confidence and sets higher investment targets, thereby strengthening OFDI in the present. The financial development scale (FD) and financial development structure (FDS) both pass the 1% significance level test, while financial development efficiency (FDE) passes the 5% significance level test, underscoring their significant positive impact on OFDI. Increased financial development offers enterprises enhanced financial support, broader financing avenues, reduced financing costs, diminished information asymmetry risk, and encourages OFDI activities (Li, 2021).

Significantly, the country dummy variable demonstrates significance, indicating distinct patterns of OFDI between developing and developed countries.

Table 6 Regression results using the System GMM Estimation. 
 

 



























***Significant at 0.01 confidence level, **Significant at 0.05 confidence level, *Significant at 0.1 confidence level

In summary, our research outcomes affirm the constructive influence of financial development on OFDI. This affirmation is robustly supported by the amalgamated findings from the pool OLS, random Effects, fixed Effects, and system GMM estimations. It is noteworthy that while the combined OLS, Random Effects, and Fixed Effects estimations may overlook the endogeneity concern, their congruence with the results derived from the system GMM methodology lends additional credence to this conclusion, in line with the work of Muhammad et al. (2015).

In the results of the system GMM, the country dummy variable is significant, indicating that OFDI from developing countries is different to OFDI from developed countries. For a clearer understanding of the differences between developing and developed countries, this Study uses a two-sample t-test to test whether the OFDI means of developing and developed countries are equal. Table 7 shows the results of the two-sample t-test. The result shows that the p-value is 0.000, and the original hypothesis is rejected, which means that there is a difference between OFDI of developing and developed countries. To seek different results, this Study divided the total sample into developing and developed countries and analyzed them separately. Table 8 shows the results of GMM for developing and developed countries.

Table 7 Two-sample t-test results for OFDI in developing and developed countries

Notes: significant at the 0.1 (*), 0.05 (**), 0.01 (***) levels

Table 8 The summary of GMM estimation on the impact of impact of financial development on OFDI from 2010 to 2019 for developing and developed Countries
  

Notes: significant at the 0.1 (*), 0.05 (**), 0.01 (***) levels, the t-values are in parentheses.

For both developing and developed countries, the lagged first order of OFDI is significant and positive. This suggests that the OFDI situation in the previous period contributes to OFDI in the current period. In both developing and developed countries, OFDI is a continuous and dynamic process.

For developing countries, both the scale of financial development and the structure of financial development have a significant positive impact on OFDI, while the efficiency of financial development has a positive impact on OFDI, but not significant. For developed countries, both the scale of financial development and the structure of financial development have a negative but insignificant effect on OFDI.

In contrast, financial development efficiency has a significant positive impact on OFDI. Financial development in developing countries is still in its infancy and the scale is still expanding, which provides more financing options for firms and reduces financing costs (Travkina et al., 2023). The financial development in developed countries has been completed and the scale is saturated, the scale of financial development cannot bring more promotion.

This Study takes into account the situation of developing countries and uses the share of private credit as an indicator of the structure of financial development. The stock and bond markets in developing countries are still to be perfected and have many drawbacks. The funds needed by enterprises for OFDI are still provided by bank credit, so the optimization of the financial development structure will have a significant impact on developing countries. Developed countries' stock and bond markets are more perfect, and enterprises' financing is more inclined to stock and bond markets (Kumar, 2022), so the role of bank credit in OFDI is not significant.

The efficiency of financial development emphasizes the rational allocation of financial development resources and the rational distribution of idle funds. However, the financial system of developing countries is still in its infancy, the scale of financial development is still expanding, and the structure of financial development has not yet reached the optimal level, so it is not possible to realize the rational allocation of financial development resources (Appiah et al.,2023)

Therefore, the impact of financial development on OFDI is mainly reflected in the scale and structure of financial development. The expansion of the scale and optimization of the structure of financial development in developed countries can only play a small role in promoting.

However, the expansion of scale and optimization of structure leads to the efficiency, specialization and transparency of the financial system, which is more reflected in the improvement of the efficiency of financial development. Therefore, the efficiency of financial development will have a facilitating effect on OFDI.

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5. Conclusion and policy recommendations

This Research encompasses a comprehensive dataset comprising 64 countries, with 53 categorized as developing nations and 11 as developed countries. The dataset spans a decade, from 2010 to 2019, and employs a dynamic panel model to investigate the impact of financial development on OFDI.

The empirical findings consistently affirm that the dimensions of financial development—namely, the scale, structure, and efficiency—all wield a statistically significant and positive influence on the promotion of OFDI. These outcomes align harmoniously with the initially formulated research hypotheses. In the context of control variables, factors such as economic development, the labour force, and urbanization are observed to exert favourable effects on OFDI.

Notably, this Study introduces country-specific dummy variables into the analytical framework, effectively distinguishing between developing and developed countries. These dummy variables emerge as statistically significant, further underscoring the discernible distinctions in OFDI patterns between these two distinct categories of nations.

Based on this, this Study analyzes developing and developed countries separately. For developing countries, both the scale of financial development and the structure of financial development have a significant positive impact. For developed countries, the efficiency of financial development has a significant positive impact.

This paper has four recommendations. First, the implementation of different financial development strategies to the differences in national financial development, the governments of each country by the actual situation of their development, to formulate financial development strategies suitable for their own countries.

Grasp the low level of financial development of the region, focus on promoting the financial development of the low level of financial development of the city to promote the overall level of financial development of the whole country to improve, and better solve the problem of unbalanced development. Developing countries, in particular, should not blindly follow the developed countries, but should adapt to their development situation and formulate reasonable financial development policies.

Furthermore, the augmentation of financial development's scale is poised to foster the advancement of OFDI. However, it's essential to acknowledge that the scale of financial development in developing countries presently stands at a moderate level. Therefore, there exists a compelling necessity to persist in expanding the ambit of financial development. This endeavour could encompass initiatives to incentivize residents to channel their idle funds into savings and amplify the capacity of financial institutions, including banks, to accumulate savings.

Such measures are envisioned to enhance the liquidity of the financial market and optimize its role as a conduit for financing. Concurrently, it becomes imperative to elevate support for market enterprises, stimulating both corporate entities and residents to explore opportunities for securing loans. A persistent strategy of expanding tax incentives and reducing fees should be pursued to alleviate the fiscal burdens on enterprises.

The continued implementation of a prudent monetary policy, marked by judicious reductions in interest rates, would serve to augment the accessibility of loans for enterprises. Furthermore, robust backing should be extended to meet the financial needs of enterprises engaged in R&D. Encouraging enterprises to embark on R&D initiatives via loan mechanisms could, in turn, catalyze technological advancements, bolster profitability, and act as a motivating catalyst for OFDI.

Thirdly, the direct financing market in developing countries is still imperfectly developed, and there is still a very big difference between the direct financing market and that of developed countries. To improve the structure of the financial market, it is necessary not only to better improve the indirect financing market but also to better develop the direct financing market.

We should formulate corresponding policies to promote the benign development of the direct financing market, establish a sound system for listing enterprise financing, and allow more enterprises to raise funds in the stock and bond markets. The expansion of the bond market, the reform of the stock market and the improvement of equity trading can be adopted to promote the development of the direct financing market and provide enterprises with more and more flexible direct financing channels.

Furthermore, heightened efficiency within the sphere of financial development stands as a potent catalyst for the advancement of OFDI. Regrettably, the contemporary landscape of financial efficiency in developing countries remains characterized by notable deficiency and instability.

The enhancement of financial development efficiency holds the promise of further reducing the operational costs of financial markets, thereby fostering a symbiotic relationship between banks, other financial institutions, and enterprises. This pursuit of augmented financial development efficiency holds profound implications for the deepening of market-oriented financial reforms, fortifying the financial system, refining resource allocation efficiency, directing capital flow towards the real economy, and ensuring the provision of substantial credit support and financing mechanisms for enterprises engaged in OFDI endeavours. Consequently, it becomes imperative to proactively facilitate the enhancement of financial development efficiency. Encouraging the transformation of funds from deposits into loans assumes paramount significance in this endeavour. Such an approach maximizes the judicious utilization of dormant funds within society, thereby invigorating market dynamics and augmenting enterprise vitality to its fullest extent.

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