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Abstract

This paper examines how financial development affects technological progress of 55 developing countries over the 2003 to 2016 period, with particular attention to the interaction between R&D spillovers and financial development. We find that financial development induces total factor productivity improvement in developing countries both directly and indirectly. While there has been a profound literature on the direct effect of financial development on total factor productivity improvement, the evidence of an indirect effect is relatively new. Specifically, the indirect effect takes place through international R&D spillovers from developed countries to developing countries. Between the two components of financial development, the financial institutional aspects exert a more significant effect on total factor productivity than that of their financial market counterparts. As this paper also re-examines the effectiveness of the North-South R&D spillovers, it conveys important implications for policymakers whose objectives are to promote technological development and economic performance.

Keywords

financial development R&D spillovers trade total factor productivity

Introduction

Technological change and other related factors that favor the creation of new knowledge are at the root of economic development (Aghion & Howitt, 2008; Çalışkan, 2015; Hwang & Shin, 2017; Le, 2008b; Le & Le Van, 2016, 2018). This is because technology is fundamentally associated with all aspects of economic performance ranging from output level to product quality, employment and wages. Thanks to the important feature of technology that lies in its non-rival characteristics, investment in R&D not only benefits its own investors but also other firms since technological products contribute to the knowledge pool that is available for public access. Economists refer to this as technological diffusion or R&D spillovers (Coe & Helpman, 1995; Le, 2022; Romer, 1990). This means that technological progress is both a product of purposeful R&D investments and a by-product of technological diffusion. While R&D activity is mostly conducted in developed countries, developing countries, with limited resources, will also enjoy the benefit of this activity thanks to technological diffusion. By enabling a dynamic accumulation process of physical and human capital, the technological diffusion can contribute to the improvement of labor productivity, total factor productivity (TFP) and economic growth of countries (Crepon et al., 1998; Maryam & Jehan, 2018; X. Wang et al., 2021). However, while innovation and technological diffusion are important for national economic development, nurturing innovation and absorbing diffusion is not always an easy task as this process is time-consuming and full of uncertainty (Holmstrom, 1989). Schumpeter (1911) once argues that the development of financial markets is the key for innovation and technological progress of any country as it provides the means for innovation and knowledge absorption to take place. Surprisingly, not much has been done to test the link between financial development and technological change. It is even scarcer when it comes to the aspect of R&D spillovers of this process.

The main purpose of this paper is to examine the impact of financial development on the real economy from the perspective of technological progress. Specifically, we explore the differential impacts of financial institutional development and stock market development on technological change in developing countries’ context. In doing so, we focus on trade-related R&D spillovers from industrialized countries to developing countries, which functions as a potential mechanism through which the impacts occur.

Our identification strategies is to use panel-based fixed effects cointegration method to identify the ways financial development induces technological change in developing countries. We adopt TFP as the proxy for the evolution of technology since this factor accounts for much of differences in GDP per capita across countries over the last century (Caselli, 2005; Hall & Jones, 1999; Le et al., 2022; 2023). On the one hand, we consider the direct effect of financial development on TFP. On the other hand, we investigate if financial development also affects TFP indirectly, particularly via the channels and the related mechanisms that promote international R&D spillovers, which in turn enhance TFP. Panel cointegration method is deemed appropriate for this empirical analysis since it captures both time series and cross-sectional dynamics of interested variables and, hence, allows us to establish the long-run relationship between financial development, international technological diffusion and TFP.

Unlike existing studies that consider financial development as a whole, in this paper, we delve more deeply into the financial development, R&D spillovers and TFP nexus by disentangling financial development into the functions of financial institutions and financial markets. First, we consider how the evolution of different aspects of financial institutions, such as bank credit to private sector as percentage of GDP, number of ATMs per 100,000 adults, net interest rate margin and return on assets, affects this nexus. Second, we examine the impact from the development of financial markets (i.e., the functioning of the financial markets) in angles such as stock market capitalization as percentage of GDP or stock market turnover. By differentiating the effects of these two different macro factors, we are able to compare and contrast them to see which one is more important in driving technological change within the context of a developing country.

To prepare for our empirical investigation, we first collect data on R&D expenditure for 18 OECD countries that serve as technological sources from which technological knowledge diffuses to developing countries. The specific channel of diffusion is international trade in which by importing goods and services from industrialized countries, developing countries can gain substantial knowledge on production methods, technical designs and operations. We then collect data capturing import intensity, technological change, financial development and human capital for 55 developing countries to explore the potential economic interactions between the two country blocs, with particular attention to the potential benefit to the developing countries.

Our results obtained from panel cointegration regressions indicate that financial development positively and significantly affects TFP. In addition to a direct effect of financial development on TFP, there is an indirect effect of financial development as well which functions via the R&D spillover channel. While the direct effect of financial development on TFP has been well documented in the existing literature, the finding of an indirect effect is novel. In particular, financial development promotes R&D spillovers from developed to developing countries, which in turn enhance the latter’s TFP. Between the two components of financial development, the financial institutional aspects are found to exert a more significant effect on TFP than that of their financial market counterparts.

This paper makes several contributions to different strands of literature. To the best of our knowledge, this study is among the pioneering work that links financial development with international R&D spillovers. By doing so, our paper complements an extensive body of literature on the potential benefits of financial development to TFP and economic growth. It is also well posited within the literature on the role of financial development in promoting innovation.

Our paper proceeds as follows. In Section 2, we provide a brief review of the related literature. In Section 3, we discuss our data collection and empirical strategies. In Section 4, we present empirical results and related discussion. Finally, we conclude this paper in Section 5.

Literature Review

International R&D Spillovers

R&D spillovers are mostly defined as externalities, where firms are unable to reap all benefits from their own R&D activities in full. According to Grossman and Helpman (1991), R&D spillovers mean either firms can acquire information innovated by others without having to pay for that information or the current owners of the information have no effective recourse in case other firms also utilize that information. In a broader sense, R&D spillovers refer to the forced “leakage” as well as the voluntary exchange of useful technological information (Steurs, 1997). At macro levels, Coe and Helpman (1995), Coe et al. (1997, 2009), Engelbrecht (1997), Le (2022) and Luintel et al. (2014) show that knowledge spreads not only within a country’s borders, but also internationally.

International knowledge diffusion across countries occurs when the knowledge originating from one country contributes to the innovation process of others (Cincera & van Pottelsberghe de la Potterie, 2001). Knowledge spillovers are generally characterized by the international transfer of technology that may take place via different channels such as trade (Caselli & Coleman, 2001; Coe & Helpman, 1995; Coe et al., 2009; Engelbrecht, 1997; Keller, 1998, 2000, 2002; Le, 2022), inflows and outflows of foreign direct investment (Driffield et al., 2010; G. Lee, 2006; Singh, 2007; van Pottelsberghe de la Potterie & Lichtenberg, 2001; Veugelers & Cassiman, 2004), migration of scientists and highly skilled workforce (Hakkala & Sembenelli, 2018; Le, 2008a, 2010, 2012, 2022; Park, 2004; Trippl, 2013) or business visits of entrepreneurs (Hovhannisyan & Keller, 2015; Piva et al., 2018). International knowledge spillovers have enormous effects on economic development, even for developed countries. For example, Griffith et al. (2006) suggest that the increase in the US R&D was associated with a 5%-higher level of TFP for the UK firms, with the majority of the benefits accruing to firms having innovative presence in the US. As a matter of fact, because there is only a handful of developed countries that account for the majority of the world’s new technology creation, for most other countries, especially developing ones, the domestic productivity growth is mainly brought by international sources of technology (Keller, 2004; Zhang, 2017). This source of foreign technology is critical for the technological progress of developing countries whose technological knowledge and human capital is somewhat limited due to the lack of financial resources for R&D investment and human capital accumulation.

Financial Development and Economic Growth

The financial system, including banks and financial intermediaries, as well as financial markets help gather surplus funds from smaller savers, allocate those funds to borrowers with most important uses, and monitor to ensure that the funds are effectively utilized. During this process, the financial system transfers, pools and reduces risk, increases liquidity, and conveys information (Stiglitz, 1998). Therefore, a well-functioning financial system helps select the most productive recipients that can utilize the resources effectively and efficiently to ensure the highest possible return, which in turn will have an impact on economic growth and total factor productivity in many aspects. In particular, a well-developed financial system plays an important role in enhancing economic growth by helping to reduce market friction as well as solving the issue of asymmetric information on investment opportunities (Levine, 1997). In line with this, Greenwood and Jovanovic (1990) find that a well-developed financial market helps reduce information asymmetry, thereby, leading to more efficient capital allocation as well as mitigating the cost of corporate governance (Bencivenga & Smith, 1993). Additionally, Levine and Zervos (1996) argue that well-developed stock markets are able to offer different kinds of financial services besides those provided by the banking system, hence, provide different kinds of support to investment and growth. Furthermore, Levine (1997) states that financial systems not only facilitate transactions of goods and services but also provide means for individuals and organizations to trade, diversify and mitigate risks. Another important benefit that a well-monitored financial system can bring is to help enhance the technological innovation by prioritizing funding for entrepreneurship development (King & Levine, 1993). For instance, credit supply through financial intermediaries can be utilized to finance working capital and capital expenditures that can help to enhance productivity in the real sector (Das & Guha-Khasnobis, 2008). Numerous empirical studies find that the development of financial system has a positive impact on economic growth (Asteriou & Spanos, 2019; Christopoulos & Tsionas, 2004; King & Levine, 1993; Pradhan et al., 2014, 2018; Wu et al., 2010). However, there are also other studies that suggest that there is no association between financial development and economic growth (Bhanumurthy & Singh, 2013; Cheng et al., 2021; Ductor & Grechyna, 2015; Grassa & Gazdar, 2014).

Financial Development and Innovation

As pointed out by Solow (1957), innovation plays a vital role in ensuring a country’s long-term economic growth and strengthening its competitive advantage. However, motivating and nurturing innovation is challenging because the nature of the whole innovation process is not only long, idiosyncratic, and unpredictable, but also involves a very high probability of failure (Holmstrom, 1989; Hsu et al., 2014; Le & Tang, 2015). Therefore, promoting innovation effectively requires well-functioning financial systems that help reducing financing costs, allocating scarce resources, evaluating innovative projects, managing risk, and monitoring managers (Hsu et al., 2014).

Motivated by Schumpeter (1934) work, many studies find evidence supporting the argument that financial development encourages innovative activities (Aghion et al., 2005; Allen & Gale, 1999; Baloch et al., 2021; King & Levine, 1993; Law et al., 2018; Levine, 1997; Rajan & Zingales, 1998; Zang & Kim, 2007). On the one hand, these studies suggest that banks promote technological innovation by allocating financial resources to entrepreneurs with the most feasible and promising new opportunities, such as inventions of new products and production know-how. This finding implies that by influencing and monitoring the resource allocation process, the financial sector of a country can have a positive impact on innovation by providing useful financial services including information acquisition and risk management, thus facilitating investment in innovative business segment (Levine, 1997). On the other hand, among activities of a firm, innovation is the most susceptible one to adverse selection and moral hazard in which the innovator is likely to have much more information about the success possibility than the lenders or investors (Sharma, 2007). To avoid market failure as a consequence of this information asymmetry, a well-developed financial system can help by reducing the cost of screening and monitoring activities, which subsequently diminishes asymmetric problems and, hence, inspires firms to engage in innovation-related activities. In line with this, Zang and Kim (2007) suggest that by reducing agency cost, a well-developed financial system helps enhancing funding to the R&D sector, hence, increasing the pace of innovation, which in turn constitutes an engine of growth for a country. Furthermore, the development of financial markets, especially equity (stock) markets, can help nurture the technological innovation by providing a rich set of risk management tools, encouraging investors to shift their portfolios toward innovative projects with higher risk and higher expected returns. The offering of higher stock prices to innovative firms will, therefore, encourage innovation (Hsu et al., 2014).

The above literature review reveals that technological advancement not only comes from purposeful R&D investments but also spillover effects. Financial development is vital for economic growth and potentially beneficial for innovation. However, not much has been done to examine the link between financial development and technological development. This is even scarcer for the nexus between R&D spillovers and financial development and on how this nexus influences TFP. The study in this paper, therefore, is an important attempt to fill these literature gaps.

Data and Empirical Approach

As discussed in our introduction, we aim to examine the impact of financial development on TFP. Specifically, we focus on the indirect effect in which international R&D spillovers is the proposed channel of the impact. Our baseline regression equations are as follows:

\log (TF P_{i, t}) = α_{i} + α_{1} \log (S_{i, t - 2}^{m}) + X_{i, t - 1} Δ + γ_{t} + ε_{i, t},

(1)

\log (TF P_{i, t}) = α_{i} + β_{1} \log (F D_{i, t - 1}) + X_{i, t - 1} Δ + γ_{t} + ε_{i, t},

(2)

\begin{matrix} \log (TF P_{i, t}) = α_{i} + α_{2} \log (S_{i, t - 2}^{m}) + β_{3} \log (F D_{i, t - 1}) \\ + X_{i, t - 1} Δ + γ_{t} + ε_{i, t}, \end{matrix}

(3)

\log (S_{i, t}^{m}) = α_{i} + β_{2} \log (F D_{i, t - 1}) + γ_{t} + ε_{i, t},

(4)

where $\log (TF P_{i, t})$ is natural logarithm of total factor productivity (TFP) of country $i$ at time $t$ ; $S_{i, t}^{m}$ is import-embodied foreign R&D capital stock; $F D_{i, t}$ is level of financial development; $X_{it}$ is a vector of other covariates that affect TFP; $α_{i}$ and $γ_{t}$ are a country-specific fixed effect and a time-specific fixed effect respectively, and $ε_{i, t}$ is an error term. The vector of control variables, $X_{it}$ , in the first instance, includes log of human capital stock, $\log (H C_{i, t}$ ). Ideally, it should also include domestic R&D capital stock, $\log (S_{i, t}^{d})$ . However, given that data on domestic R&D of developing countries are either non-existent or negligibly small, we follow Coe et al. (2009), and many subsequent others, to exclude this term from our regression. To allow for the time it takes for the all changes in foreign technology to have an impact on TFP, we lag the import-embodied foreign R&D capital stock by 2 years, and financial development and human capital by 1 year. Cross-sectional and time fixed effects are used to account for unobservable characteristics that may have effects on TFP.

The above regression equations provide a layout of Aiken et al.’s (1991) four-step procedure that has been used by some recent studies, including Le et al. (2022). Specifically, in Equation 1, we examine the effect of technological diffusion on TFP without considering financial development. In Equation 2, we examine the effect of financial development on TFP without including R&D spillovers. In Equation 3, we explore the effects of both variables on TFP simultaneously. In the last step, we look at how financial development promotes international knowledge spillovers by considering Equation 4. The purpose as well as the advantage of this procedure is that it allows us to disentangle the effect of financial development on TFP. In particular, the total effect is captured by $β_{1}$ in Equation 2 and the direct effect is captured by $β_{3}$ in Equation 3. Meanwhile, the indirect effect $β_{indirect}$ is determined together by Equations 3 and 4 as follows:

β_{indirect} = α_{2} * β_{2} .

(5)

This indirect effect coefficient follows a normal distribution as per Sobel (1982) of which the test statistic is given by:

t = \frac{α_{2} * β_{2}}{\sqrt{α_{2}^{2} * Var (β_{2}) + β_{2}^{2} * Var (α_{2})}},

(6)

where $Var (α_{2})$ and $Var (β_{2})$ denote the variances of $α_{2}$ and $β_{2}$ , respectively.

Log of TFP, our dependent variable, for 55 developing countries over the years of 2003 to 2016, is calculated as the difference between log of GDP and the weighted average of logs of labor and capital inputs. The weights used in this formula are obtained from the simple regression of GDP on labor employment and capital formation. We use data from World Development Indicators Database to execute these calculations.

The import-embodied foreign R&D capital stock variable is meant to capture R&D spillovers through import channel. It is equivalent to the one originally used in Coe and Helpman (1995), however, computed using the method initially suggested by Lichtenberg and van Pottelsberghe de la Potterie (1998). This method offers several advantages over the one by Coe and Helpman (1995) including a regression bias (see Lichtenberg and van Pottelsberghe de la Potterie (1998) for a detailed discussion on this issue). Hence, the import-embodied foreign R&D capital stock variable is computed as follows:

S_{i, t}^{m} = \sum_{j} \frac{m_{ij, t}}{y_{j, t}} . S_{j, t}^{d} .

In this formulation, $m_{ij, t}$ captures developing country $i$ ’s value of imported goods and services from developed country $j$ at time $t$ whose GDP is $y_{j, t}$ and total domestic R&D capital stock is $S_{j, t}^{d}$ . This variable is constructed following the notion that knowledge is absorbed through a learning by importing process. By importing more products from an industrial country $j$ , developing country $i$ can potentially obtain more technological knowledge deployed in country $j$ . We obtain data on total R&D expenditure (which are used to calculate total domestic R&D capital stock following the method proposed by Coe and Helpman (1995)) and GDP for 18 OECD over the 2003 to 2016 period from OECD Statistical Database. Data on bilateral import flows are sourced from United Nations Comtrade Database.

Our measure on financial development, $F D_{i, t}$ , comes from the Financial Development Index Database constructed by the International Monetary Fund. The dataset contains nine indices of financial development ranging from the overall development to the distinctive development of either financial institutions or financial markets. Each of these groups can be further broken down into different indicators such as depth (size and liquidity), access (ability to access financial services) and efficiency (ability to provide service at low cost and sustainable revenue). The dataset covers annual data for more than 180 countries from 1980 onward. For our purposes of comparing the relative importance of financial institutions and financial markets in affecting technological diffusion across borders, we consider two broad components of the aggregate financial development index $F D_{i, t}$ , which are Financial Institutions index ( $F I_{i, t}$ ) and the Financial Markets index ( $F M_{i, t}$ ), besides this aggregate index itself.

Data on stock of human capital, used to construct the human capital variable $H C_{i, t}$ , the main control variable, are extracted from Penn World Table (version 9.0). This is an index of human capital per person, which is computed based on information on average years of schooling and return to education. It is available on an annual basis rather than in 5-year averages as per Barro and Lee (2013). Thus, it is more advantageous and fits better with our study purposes than the two latter datasets.

Empirical Results

Financial Development, R&D Spillover and Technological Change

We aim to explore long-run relationships between the interested variables. As such, we employ a panel cointegration method. This method has been widely used within the R&D spillovers literature (e.g., Coe & Helpman, 1995; Coe et al., 2009; Le, 2022). Pioneering contributions to the development of panel cointegration methods are Banerjee (1999) and Kao and Chiang (2000) among others. Besides the R&D spillovers literature, the method has also been used in studies examining long-run money demand (e.g., Mark & Sul, 2003), growth and convergence (e.g., K. Lee et al., 1997) and purchasing power parity (e.g., Pedroni, 2001). As an important step in conducting the panel cointegration method, we need to check if our interested variables are non-stationary or not. For that purpose, we first perform a unit root test put forward by Hadri (2000). This test starts with the null hypothesis of stationarity on the variable. After that, we proceed with another test suggested by Im et al. (2003) where the null hypothesis positing that the variable contains an individual unit root process. For each test, we adopt a 5% level of significance. While the former is suited with large panels, the latter works well with small-sized panels. Obtained results recorded in Table 1 indicate the overall non-stationarity for all variables.

Table 1.

Panel Unit Root Tests (at 5% Level of Significance, 55 Countries, 2003–2016).

Variable	Hadri’s (2000) test		Im et al.’s (2003) test		Decision
Variable	Statistics	Implication	Statistics	Implication	Decision
$\log (TFP)$	3.095 (.001)	$I (1)$	1.566 (.059)	$I (1)$	$I (1)$
$\log (S^{m})$	16.148 (.000)	$I (1)$	1.614 (.053)	$I (1)$	$I (1)$
$\log (FD)$	13.774 (.000)	$I (1)$	0.343 (.366)	$I (1)$	$I (1)$
$\log (HC)$	20.126 (.000)	$I (1)$	9.333 (1.000)	$I (1)$	$I (1)$

Note. $\log (X)$ is log of $X$ . $TFP, S^{m}, FD$ , and $HC$ are TFP, import-weighted foreign R&D capital stock and human capital, respectively. p-values are in parentheses. $I (1)$ indicates the existence of a unit root.

We next examine if the variables exhibit any co-integrating relationship. To this end, we report results from two panel cointegration tests suggested by Pedroni (1999) at 5% level of significance in Table 2. The null hypothesis of these tests is that there exists no cointegrating relationship between considered variables. Obtained results show the existence of a cointegrating and, hence, long-term relationship between the variables of interest. This means that associated regressions involved these variables are not spurious and can be estimated with either pool estimation technique or group mean estimation technique.

Table 2.

Panel Cointegration Tests, Based on Pedroni (1999) (at 5% Level of Significance, 55 Countries, 2003–2016).

Variables	Panel $t$ -statistic	Group $t$ -statistics	Decision
$\log (TFP), \log (S^{m}), \log (HC)$	−6.135 (.000)	−5.547 (.000)	$CI$
$\log (TFP), \log (FD), \log (HC)$	−4.871 (.000)	−3.060 (.001)	$CI$
$\log (TFP), \log (S^{m}), \log (FD), \log (HC)$	−5.937 (.000)	−3.862 (.000)	$CI$

Note. $\log (X)$ is log of $X$ . $TFP, S^{m}, FD$ , and $HC$ are TFP, import-weighted foreign R&D capital stock and human capital, respectively. p-values are in parentheses. $CI$ indicates the existence of a cointegrating relationship.

We will start our empirical exploration with panel least squares regressions augmented by cross-sectional and time fixed effects (OLS). To allow for the time it takes for the all changes in either foreign technology, financial development or human capital to have an impact on TFP, we regress log of TFP on the lagged values of log of these variables. Given that OLS results may suffer from a second-order asymptotic bias due to the potential endogeneity problem of the regressors (Kao et al., 1999; Tsionas, 2019), we additionally apply the dynamic OLS technique (DOLS) proposed by Kao and Chiang (2000) to our data. An advantage of this technique lies in its superior small sample properties in which the potential bias is adjusted by the leads and lags of the differenced regressors.

In Table 3, we report regression results first obtained by using fixed effect augmented OLS method then obtained by using the fixed effect augmented DOLS technique. It should be noted that in running the DOLS regressions, due to relatively short-time horizon of our data, we adopt one lead and one lag for the cointegrating regressors. All equations include unreported country- and time-specific fixed effects.

Table 3.

The Effects of Foreign R&D Capital Stock and Financial Development on TFP (Two-Way Fixed Effects, 55 Countries, 2003–2016).

	Dependent variable: $\log (TFP)$						Dependent variable: $\log (S^{m})$
	(3.1)	(3.2)	(3.3)	(3.4)	(3.5)	(3.6)	(3.7)	(3.8)
	OLS	DOLS	OLS	DOLS	OLS	DOLS	OLS	DOLS
$\log (S^{m})$	0.005^** (0.002)	0.011^*** (0.002)			0.004^* (0.002)	0.011^*** (0.002)
$\log (FD)$			0.004^** (0.002)	0.012^*** (0.004)	0.003^* (0.002)	0.010^** (0.005)	0.400^*** (0.033)	0.531^*** (0.076)
$\log (HC)$	0.047^** (0.020)	0.109^*** (0.011)	0.039^** (0.016)	0.086^*** (0.023)	0.046^** (0.020)	0.116^*** (0.011)
$R^{2}$	0.273	0.302	0.266	0.278	0.275	0.313	0.985	0.987
$Adj . R^{2}$	0.191	0.198	0.189	0.180	0.191	0.205	0.983	0.986
Observations	660	550	715	605	660	550	770	605

Note. $\log (X)$ is log of $X$ . $TFP, S^{m}, FD$ , and $HC$ are TFP, import-weighted foreign R&D capital stock and human capital respectively. Robust standard errors are in parentheses. *, **, *** indicate parameters that are significant at 10%, 5%, and 1% levels of significance, respectively. All regressions include unreported country-specific and time-specific constants as well as one lead and one lag of the first differenced independent variables.

The results show that all variables have an expected sign. In comparison, DOLS regressions generally yield larger and more significant estimates than the OLS counterparts do. Specifically, the coefficient estimate of $\log (S^{m})$ is positive and statistically significant across the regressions. This is consistent with the findings of Coe and Helpman (1995), Le (2008a, 2022) and G. Lee (2006) about the effective role of trade in transferring knowledge across national borders. Similarly, the coefficient of $\log (HC)$ is always positive and statistically significant. This aligns with Coe et al. (1997), Engelbrecht (1997) and Le (2022) among others that confirm the importance of human capital for technological improvement.

Columns (3.3) and (3.4) yield a positive and significant coefficient of $\log (FD)$ . This means that financial development exerts a positive and significant total effect on TFP. In columns (3.5) and (3.6), the coefficient of the variable becomes smaller although it is still positive and significant. This coefficient captures the direct effect of financial development on TFP.

The estimates in columns (3.7) and (3.8) indicate that financial development strongly influences technological diffusion. This is because the coefficient of $\log (FD)$ is positive and significant in both OLS and DOLS regressions. This allows us to move on to the calculation of the indirect effect of financial development on TFP based on Sobel’s (1982) method as described in Equation 7. In particular, using OLS results, we have $α_{2} = 0.004, Var (α_{2}) = 0 . 002^{2}, β_{2} = 0.400$ and $Var (β_{2}) = 0 . 033^{2}$ respectively. This gives $β_{indirect}^{OLS} = 0.004 * 0.400 = 0.0016$ and its corresponding statistic $t = 1.969$ , which is significant at 5% level of significance as $t$ follows a normal distribution.

In the same manner, if we use DOLS results instead then $α_{2} = 0.011, Var (α_{2}) = 0 . 002^{2}, β_{2} = 0.531$ and $Var (β_{2}) = 0 . 076^{2}$ . Thus, $β_{indirect}^{DOLS} = 0.011 * 0.531 = 0.0058$ and $t = 4.322$ , which is significant at 1% level of significance.

Overall, the results confirm that financial development positively and significantly affects TFP. On the one hand, financial development directly promotes innovation, which in turn boosts up technological progress. On the other hand, financial development enhances the domestic absorption of technological knowledge deployed in a foreign base. Both of these effects are beneficial for economic development path of developing countries.

Financial Institutions Versus Financial Markets

By construction, the Financial Development index ( $FD$ ) developed by the International Monetary Fund is an aggregation of the Financial Institutions index ( $FI$ ) and the Financial Markets index ( $FM$ ). In essence, $FI$ captures the development of national financial institutions in terms of depth, accessibility, and efficiency. This is reflected through a wide range of aspects such as bank credit to private sector as percentage of GDP, number of bank branches per 100,000 adults and return on assets. Meanwhile, $FM$ is more concerned with the evolution of the stock market such as stock market capitalization to GDP, stocks traded to GDP and stock market turnover. It will be interesting to know which of these components of financial development has more profound impact on TFP and the knowledge diffusion process in a developing country’s context. To that end, we regress TFP on these components and consider their coefficient estimates. Obtained results are provided in Tables 4 and 5.

Table 4.

The Effects of Foreign R&D Capital Stock and Financial Development Components on TFP (Two-Way Fixed Effects, 55 Countries, 2003–2016).

	Dependent variable: $\log (TFP)$
	(4.1)	(4.2)	(4.3)	(4.4)	(4.5)	(4.6)	(4.7)	(4.8)	(4.9)	(4.10)
	OLS	DOLS	OLS	DOLS	OLS	DOLS	OLS	DOLS	OLS	DOLS
$\log (S^{m})$					0.004^** (0.002)	0.010^*** (0.002)	0.004^** (0.002)	0.012^*** (0.002)	0.004^* (0.002)	0.011^*** (0.002)
$\log (FI)$	0.002 (0.003)	0.013^** (0.005)			0.002 (0.004)	0.011^** (0.005)			0.002 (0.006)	0.011^** (0.005)
$\log (FM)$			0.011^** (0.006)	0.023^* (0.012)			0.009 (0.006)	0.019 (0.016)	0.009 (0.006)	0.016 (0.016)
$\log (HC)$	0.041^** (0.017)	0.088^*** (0.024)	0.038^** (0.016)	0.088^*** (0.023)	0.048^** (0.020)	0.115^*** (0.013)	0.045^** (0.020)	0.118^*** (0.011)	0.046^** (0.020)	0.124^*** (0.014)
$R^{2}$	0.265	0.275	0.266	0.276	0.274	0.309	0.274	0.316	0.274	0.322
$Adj . R^{2}$	0.188	0.176	0.189	0.178	0.190	0.200	0.191	0.207	0.190	0.208
Observations	715	605	715	605	660	550	660	550	660	550

Note. $\log (X)$ is log of $X .$ $TFP, S^{m}, FI, FM$ and $HC$ are TFP, import-weighted foreign R&D capital stock, financial institution index, financial market index and human capital respectively. Robust standard errors are in parentheses. *, **, *** indicate parameters that are significant at 10%, 5%, and 1% levels of significance, respectively. All regressions include unreported country-specific and time-specific constants as well as one lead and one lag of the first differenced independent variables.

Table 5.

The Effects of Financial Development Components on Foreign R&D Capital Stock (Two-Way Fixed Effects, 55 Countries, 2003–2016).

	Dependent variable: $\log (S^{m})$
	(5.1)	(5.2)	(5.3)	(5.4)
	OLS	DOLS	OLS	DOLS
$\log (FI)$	0.437^*** (0.053)	0.509^*** (0.118)
$\log (FM)$			0.847^*** (0.186)	1.256^*** (0.278)
$R^{2}$	0.985	0.987	0.984	0.987
$Adj . R^{2}$	0.983	0.985	0.983	0.985
Observations	770	605	770	605

Note. $\log (X)$ is log of $X$ . $S^{m}, FI, FM$ and $HC$ are import-weighted foreign R&D capital stock, financial institution index, financial market index and human capital respectively. Robust standard errors are in parentheses. *, **, *** indicate parameters that are significant at 10%, 5%, and 1% levels of significance, respectively. All regressions include unreported country-specific and time-specific constants as well as one lead and one lag of the first differenced independent variables.

In Table 4, we report results obtained from regressing TFP on each component of financial development index separately before including them together in the same regressions. In doing so, we follow the steps put out in Equations 1 to 3. Each time, we run the regressions in a pair of OLS and DOLS. In general, they yield coefficients of the same sign. However, those generated by DOLS regressions are more significant and have a larger magnitude than those generated by OLS regressions.

The results indicate a positive and significant total and direct effect of $FI$ on TFP in DOLS regressions of (4.2) and (4.6) respectively. While there is significant evidence of a positive total effect of $FM$ on TFP as in (4.3) and (4.4), no significant evidence of the direct effect of $FM$ is found, as in (4.7) and (4.8). Similarly, when both $FI$ and $FM$ are included in (4.10), only the direct effect on TFP of $FI$ is found significant, but not that of $FM$ . This result reflects a distinctive feature of developing countries where stock market is rather underdeveloped which limits its direct contribution to TFP. As such, the direct effect of the financial system on TFP, at the very most, mainly works through the credit channel that is captured by the $FI$ index.

Regarding other variables, import-weighted foreign R&D has a positive and significant impact on TFP. Human capital also positively and significantly technological progress in developing countries. These are evidenced by positive and significant estimates on the coefficients of these variables across different regressions.

Given that the total effects of both $FI$ and $FM$ on TFP are both positive and significant, in Table 5, we explore the possibility that these variables may also exert their influence on TFP via international R&D spillovers. To that goal, we regress $\log (S^{m})$ on $\log (FI)$ and $\log (FM)$ respectively. Again, we also run regressions in pair, with OLS regression first and followed by DOLS regression. Results reported in Table 5 show that both $FI$ and $FM$ significantly promote technological diffusion through international trade. By importing goods and services from industrialized countries, a developing country can benefit from a learning-by-importing process. The development of the financial system in both credit channel and stock market capitalization will provide a big push to that learning process.

In short, we find evidence that improvements in financial institutions in developing countries affect technological progress both directly and indirectly (via enhancing international technological diffusion). Meanwhile, improvements in financial market will only affect TFP indirectly, particularly via the technological diffusion channel.

Discussion and Conclusions

This paper presents cross-country evidence on how financial development affects technological progress in developing countries. Using a large dataset that covers 55 developing countries between 2003 and 2016 and a fixed effects panel cointegration identification strategy, we identify economic mechanisms through which financial development affects TFP of these countries. We showed that besides exerting a direct effect on TFP, financial development in developing countries also enhances their TFP via promoting R&D spillovers from developed countries. Between the two key components of financial development, financial institutions seems to have a more profound impact on TFP than their financial market counterparts. These findings offer new insights into the real effects of financial development on the economy and, hence, suggest important policy implications for governments and policymakers.

The finding on the indirect effect of financial development on TFP is interesting and novel. Specifically, financial development helps developing countries absorb more technological knowledge developed from foreign technological base. This important channel has been largely neglected in the existing literature despite a significant amount of research examining the direct effect of financial development and the beneficial effect of international R&D spillovers separately. This finding implies that although foreign technological knowledge is beneficial, its benefit may not be fully utilized if there is an insufficient level of financial development in the domestic economy.

Since the financial markets in most developing economies are relatively less developed, financial intermediaries, especially banks, play a significant role in financing and promoting innovation. However, due to the high-risk nature of innovations, which is in contrast with the banks’ prudent culture, it is necessary for governments and regulatory authorities to have strategic policies to support banks. These supports can come in various dimensions such as promoting corporations to setting up risk-sharing mechanism and designing a special lending entity for innovative enterprises, to name a few. In addition, governments in developing countries should also have strategic policies to strengthen the functioning of financial markets, such as improving the legal framework and law enforcement, as this will enhance long-term technological progress and economic growth.

Because results of our paper reconfirm the beneficial effect of international R&D spillovers, there are also other important implications for practice. In particular, we suggest that policymakers should actively take steps in promoting the exchange of innovative knowledge. This includes, but is not limited to, increasing the training of high-quality and innovative talents by forming innovative talent training centers, integrating high-quality talent systems in various fields or establishing a talent training information sharing platform (Belazreg & Mtar, 2020; R. Wang & Tan, 2021). In addition, as innovation is the key driver of economic growth in the last century (Le & Le Van, 2018; Romer, 1990), innovation-driven development strategies should also be promoted vigorously. To that aim, long-term mechanisms for technological innovation output transformation should be established. There should also be more provision of research subsidies and investment in R&D to encourage and support innovation. All this will help to ensure that technological innovation can be fully utilized for economic development (R. Wang & Tan, 2021).

Our paper lays important foundation from which several research directions can take place in the future. In this paper, we focused on only one type of international R&D spillovers that are channeled via trade. As discussed in the literature review section, R&D spillovers can also take place via other means such as foreign direct investment or human capital mobility, what remains to be seen is whether financial development can also affect TFP indirectly via these channels. In addition, it will be interesting to delve more deeply into more specific components on financial development, such as access to and efficiency of financial services, to see their differential effects on TFP. All these suggest a fruitful future research agenda.

Footnotes

Appendix

Table A2.

List of Developing Countries in the Sample.

Algeria	Colombia	Gabon	Malaysia	Sri Lanka
Angola	Comoros	Ghana	Mali	Suriname
Argentina	Congo	Guatemala	Nepal	Tanzania
Bangladesh	Costa Rica	Guinea	Nigeria	Thailand
Benin	Cote d’Ivoire	Haiti	Pakistan	Togo
Bolivia	Dem. Rep. Congo	Honduras	Panama	Trinidad and Tobago
Botswana	Dominican Rep.	India	Peru	Tunisia
Brazil	Ecuador	Indonesia	Philippines	Uganda
Burkina Faso	Egypt	Jamaica	Rwanda	Uruguay
Cameroon	El Salvador	Kenya	Senegal	Vietnam
Chile	Ethiopia	Madagascar	South Africa	Zambia

Acknowledgements

The authors would like to thank the editor of this journal and three anonymous reviewers for their constructive comments. The usual disclaimers apply.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Thanh Le

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Financial Development and International R&D Spillovers Through Trade: Evidence From Developing Countries

Abstract

Keywords

Introduction

Literature Review

International R&D Spillovers

Financial Development and Economic Growth

Financial Development and Innovation

Data and Empirical Approach

Empirical Results

Financial Development, R&D Spillover and Technological Change

Financial Institutions Versus Financial Markets

Discussion and Conclusions

Footnotes

Appendix

Acknowledgements

Declaration of Conflicting Interests

Funding

ORCID iD

References