Unlocking BRICS Economies’ Potential: Infrastructure as the Gateway to Enhanced Capital Flows

Abstract

This study investigates the impact of physical and financial infrastructure on the dynamics of net total capital flows in BRICS economies over the period 2010–2024. Using panel data and a fixed-effects regression model with robust standard errors, it analyzes how infrastructure quality, both physical (transport, energy, and telecommunications) and financial (banking systems, capital markets, and regulation), affects private capital inflows. The results show a statistically significant positive relationship, with physical infrastructure reducing business costs and financial infrastructure improving capital allocation and investor confidence. This paper contributes novel empirical evidence linking infrastructure systems with capital flow dynamics, providing key insights for policymakers aiming to enhance resilience and attract sustainable private investment.

1. Introduction

Since the mid-1980s, the rapid advancement of globalization has spurred a significant increase in both FDI and FPI worldwide. Concurrently, financial liberalization has driven the growth of financial sectors, serving as a vital marker of globalization. A growing body of literature emphasizes the role of infrastructure—both physical and financial—as a foundational determinant in attracting private capital flows. Physical infrastructure, such as transportation networks, electricity, and telecommunications, reduces transaction costs and enhances the ease of doing business, thus creating a conducive environment for FDI (Straub, 2008; Calderón & Servén, 2010). On the other hand, financial infrastructure, including banking systems, capital markets, and regulatory frameworks, supports efficient capital allocation and strengthens investor confidence, thereby influencing FPI (Beck et al., 2000; Demirgüç-Kunt & Levine, 2001). Developing economies, especially the BRICS nations, have emerged as major destinations for foreign capital, with China standing out as the leading recipient within the group (Alfaro et al., 2004; Borensztein et al., 1998). However, the Lucas (1990b) Paradox remains a central conundrum in macroeconomics, questioning why capital does not naturally flow from wealthier to poorer nations (Lucas, 1990a). This paradox highlights the tendency of foreign investments to favor developed economies, where private investments in capital markets yield higher returns, while such investments remain relatively subdued in developing countries. Foreign investors are drawn to nations actively developing infrastructure, underscoring its pivotal role in investment decisions. Additionally, urbanization has emerged as a significant social determinant, and the frequency of constitutional changes in leadership has been found to influence FDI (Root & Ahmed, 1979). Physical infrastructure is a critical driver of economic development, providing essential amenities that sustain living standards and support economic activities. It consists of interconnected structural elements that facilitate the smooth flow of capital and ensure efficient economic operations. A robust infrastructure reduces entry and establishment costs, making host countries more appealing to investors. For instance, efficient transportation systems encompassing air, water, and land lower logistics expenses, enhance reliability, and increase production levels (Khadaroo & Seetanah, 2009). Similarly, access to stable energy supplies reduces production costs and improves profitability, further attracting private capital. The quality of energy, communication, and transportation infrastructure positively influences investment volumes in developing nations (Wheeler & Mody, 1992). Several empirical studies have explored these linkages. Kinda (2008b, 2020) finds that infrastructure quality plays a crucial role in shaping FDI flows to developing countries. Similarly, Swamy and Narayanamurthy (2018) demonstrate that financial sector development significantly affects portfolio investment inflows in emerging markets. Moreover, studies by E. Ngongan (2015) and Uul-Haq et al. (2023) suggest that macroeconomic stability, openness to trade, and institutional quality also mediate the relationship between infrastructure and capital mobility. A well-developed domestic infrastructure not only enhances industrial output but also strengthens economic linkages across sectors. It provides the foundation for the efficient movement of goods and services, thereby improving overall economic performance (Kandiero & Chitiga, 2006). Foreign investors are therefore more likely to participate in both direct investments in core economic sectors and short-term financial ventures. Private capital inflows play a particularly important role in financing infrastructure projects, contributing to economic stability and long-term growth. Furthermore, the combination of a capable workforce and well-developed physical infrastructure fosters productive investments and facilitates cross-border capital flows. A country’s ability to provide reliable infrastructure significantly impacts capital flow by reducing operational costs and improving the investment climate. Given these dynamics, the current work seeks to analyze the impact of physical and financial infrastructure on attracting FDI and FPI, with a specific focus on net inflows in BRICS economies. The further sections of the paper have been categorized as follows: Section 2 contains the work of reviewing existing literature, Section 3 defines data and methodology, followed by Section 4, which contains data analysis and interpretation. Section 5 displays the results for the impact of physical and financial infrastructure on private capital flows and is further followed by the conclusion of this study.

2. Literature Review and Hypotheses Development

The available works supporting this study have been categorized between physical infrastructure and private capital flows and financial infrastructure and private capital flows.

2.1. Operationalization of Variables

2.1.1. Physical Infrastructure

The concept of physical infrastructure incorporates a wide range of amenities, such as electricity, roads, sanitation, piped gas, railways, telecommunication, seaports, and internet facilities. In the context of private capital flows, the availability of infrastructure has been identified as a critical determinant (S. A. Asongu & Tchamyou, 2018; Vijayakumar et al., 2010). For instance, Behname (2012) examined the effect of urban infrastructure on FDI, using the figures of telephones per 1000 people as a key indicator, while also considering factors like openness, tax policies, consumer price index, domestic investment, and human capital. Similarly, Kinda (2010) measured physical infrastructure by focusing on phone subscribers, both fixed and mobile, per capita and electricity consumption per capita, emphasizing its role in building industrial capacity. L. Ngongan (2014) also adopted a similar approach, measuring physical infrastructure using phone subscriptions and electricity consumption per capita. For the current study, the selection of variables is guided by a review of existing literature and the availability of data specific to BRICS economies. These variables are chosen based on their significant impact on private capital flows, ensuring a comprehensive analysis of the role of physical infrastructure in attracting investments.

Table 1. List of physical infrastructure variables.

Sr. No.	Infrastructure	Variable	Explanation	References	Source
1.	ICT infrastructure	ICT goods exports (% of total goods exports)	Represents the information and communication technology goods exports	Saidi et al. (2020)	World development indicators, World Bank (2024)
2.	Port infrastructure	Container port traffic (TEU: 20-foot equivalent units)	Represents the port infrastructure	Kinda (2010)	World development indicators, World Bank (2024)
3.	Air transport passengers carried	Total passengers carried	Represents the efficiency of airways in terms of passengers carried	Swamy and Narayanamurthy (2018)	World development indicators, World Bank (2024)
4.	Electric power consumption	(million kilowatt hours)	Represents the usage of power	Chakrabarti (2001); Chotia and Rao (2016); Kumari and Sharma (2017); Swamy and Narayanamurthy (2018); Mohanty and Bhanumurthy (2019); Maryam and Mittal (2020)	BRICS Joint Statistical Publication, 2024
5.	Internet users	Individuals using the internet (% of population)	Represents the presence of internet facilities	Swamy and Narayanamurthy (2018)	World development indicators, World Bank (2024)
6.	Railways infrastructure	Length of railways (1000 km)	Represents the availability of railways	Swamy and Narayanamurthy (2018)	BRICS Joint Statistical Publication, 2024
7.	Fixed telephone subscriptions	Fixed telephone subscriptions (per 100 people)	Represents the availability of fixed telephone lines	Onyeiwu and Shrestha (2004); Khadaroo and Seetanah (2009); Behname (2012); Chakrabarti (2001); Liu (2015); Chotia and Rao (2016); Swamy and Narayanamurthy (2018); Mohanty and Bhanumurthy (2019)	World development indicators, World Bank (2024)
8.	Mobile cellular subscriptions	Mobile cellular subscriptions (per 100 people)	Represents the mobile cellular subscribers	Onyeiwu and Shrestha (2004); Khadaroo and Seetanah (2009); Kinda (2010); Chakrabarti (2001); L. Ngongan (2014); Chotia and Rao (2016); Swamy and Narayanamurthy (2018); S. A. Asongu and Tchamyou (2018).	World development indicators, World Bank (2024)
9.	Sanitation	People using the least basic sanitation services (% of population)	Represents the population using sanitation facilities	Chotia and Rao (2016); Swamy and Narayanamurthy (2018)	World development indicators, World Bank (2024)
10.	Improved water	People using safely managed drinking water	Represents the population having access to drinking water in rural areas	Chotia and Rao (2016); Swamy and Narayanamurthy (2018)	World development indicators, World Bank (2024)

Source: Compiled by researcher from World Development Indicators on Infrastructure by World Bank data files.

presents the list of physical infrastructure variables.

Table 1 contains the list of physical infrastructure variables. It includes ICT infrastructure, port infrastructure, air transport passengers carried, electric power consumption, internet users, railway infrastructure, fixed telephone subscriptions, mobile cellular subscriptions, sanitation, and improved water.

2.1.2. Financial Infrastructure

The term “infra” refers to the provision of services that benefit users. In the context of the financial system, these services, often termed “infra-services”, contribute to the growth of various economic sectors. The financial system encompasses institutions, instruments, markets, and a range of services, collectively referred to as financial infrastructure. An efficient financial infrastructure facilitates the creation of financial instruments, integrates intermediaries across nations, reduces knowledge gaps in host countries, and promotes private capital inflows. Financial markets act as a crucial protagonist in economic development and stability by sustaining mechanisms to evaluate risk and returns, thereby allocating resources and risks efficiently across nations. Kinda (2010) examined the impact of financial infrastructure on capital flows, measuring financial development through ratios such as liquid liabilities to GDP (M3/GDP), financial system deposits to GDP, and bank credit to the private sector as a percentage of GDP. Similarly, L. Ngongan (2014) measured financial development using variables like bank credit to the private sector (% of GDP), the M3/GDP ratio, and bank deposits. Chakrabarti (2001) analyzed financial infrastructure by considering the number of scheduled commercial bank branches, while Mohanty and Bhanumurthy (2019) measured financial development using the money multiplier (ratio of broad money to narrow money) and bank credit to the commercial sector as a percentage of GDP. The variables representing financial infrastructure are typically divided into banking sector variables and stock market variables, as these two components are considered the driving forces of a nation’s financial system. Similar approaches have been adopted by Gebrehiwot et al. (2016), Pradhan et al. (2017), Nkoa (2018), and Pham et al. (2022). The choice of variables is based on a review of existing literature, focusing on those with a significant impact on private capital flows, as well as the availability of data in the BRICS context.

Table 2. List of financial infrastructure variables.

Sr. No.	Variables	Definition	References	Indicator
1.	Liquid liabilities to GDP (%)	Ratio of liquid liabilities to GDP. Liquid liabilities are also known as broad money, or M3. They are the sum of currency and deposits in the central bank (M0), plus transferable deposits and electronic currency (M1), plus time and savings deposits, foreign currency transferable deposits, certificates of deposit, and securities repurchase agreements (M2), plus traveler’s checks, foreign currency time deposits, commercial paper, and shares of mutual funds or market funds held by residents.	Boyd et al. (2001); O. Al Nasser and Gomez (2009); Chee and Nair (2010); Kinda (2010); Abzari et al. (2011); Gebrehiwot et al. (2016); Mishra and Mishra (2019); Pham et al. (2022)	Banking sector development
2.	Domestic credit to the private sector (% of GDP)	Domestic credit to the private sector refers to financial resources provided to the private sector.	Kinda (2010); Abzari et al. (2011); Hoang and Bui (2015); Gebrehiwot et al. (2016); Fauzel (2016); Pradhan et al. (2017); Bayar and Gavriletea (2018); Mishra and Mishra (2019); Keykanloo et al. (2020); Tsaurai (2022); Veselinović et al. (2022); Pham et al. (2022)	Banking sector development
3.	Private credit by deposit money banks to GDP (%)	The financial resources provided to the private sector by domestic money banks as a share of GDP. Domestic money banks comprise commercial banks and other financial institutions that accept transferable deposits, such as demand deposits.	Levine et al. (2000); O. Al Nasser and Gomez (2009); Chee and Nair (2010); Abzari et al. (2011); Sghaier and Abida (2013); Enisan (2017); Pradhan et al. (2017); Aibai et al. (2019); Pham et al. (2022); Rehman and Islam (2023)	Banking sector development
4.	Deposit money in banks as assets to GDP (%)	Total assets held by deposit money banks as a share of GDP. Assets include claims on the domestic real nonfinancial sector, which includes central, state, and local governments; nonfinancial public enterprises; and the private sector. Deposit money banks comprise commercial banks and other financial institutions that accept transferable deposits, such as demand deposits.	Boyd et al. (2001); O. Al Nasser and Gomez (2009); Korgaonkar (2012)	Banking sector development
5.	Financial system deposits to GDP (%)	Demand, time, and savings deposits in deposit money banks and other financial institutions as a share of GDP.	Kinda (2010)	Banking sector development
6.	Stock market total value traded (% of GDP)	The total value of all traded shares in a stock market exchange as a percentage of GDP.	Levine and Zervos (1998); Boyd et al. (2001); O. Al Nasser and Gomez (2009); Korgaonkar (2012); Gebrehiwot et al. (2016); Pradhan et al. (2017); Aibai et al. (2019); Pham et al. (2022)	Stock market development
7.	Mutual fund assets (% of GDP)	Ratio of assets of mutual funds to GDP. A mutual fund is a type of managed collective investment scheme that pools money from many investors to purchase securities.	Keykanloo et al. (2020)	Stock market development
8.	Stock market capitalization to GDP (%)	Total value of all listed shares in a stock market as a percentage of GDP.	Levine and Zervos (1998); Boyd et al. (2001); O. Al Nasser and Gomez (2009); Korgaonkar (2012); Agbloyor et al. (2013); Seetanah et al. (2010); Gebrehiwot et al. (2016); Pradhan et al. (2017); Shabbir et al. (2018); Tsaurai (2022); Pham et al. (2022)	Stock market development
9.	Stock market turnover (% of GDP)	Total value of shares traded during the period divided by the average market capitalization for the period.	Levine and Zervos (1998); Beck et al. (2000); Levine and Zervos (1998); Boyd et al. (2001); Saci et al. (2009); Korgaonkar (2012); Gebrehiwot et al. (2016); Pradhan et al. (2017); Tsaurai and Makina (2018); Rehman and Islam (2023); Rehman et al. (2024); Pham et al. (2022)	Stock market development

Source: Compiled by researcher from financial development and structure dataset, World Bank data files, 2023.

presents the financial infrastructure variables.

2.1.3. Operationalization of Dependent Variable

This study focuses on analyzing private capital flows, specifically FDI and FPI in the form of equity, as these represent non-debt-creating capital and are less risky for the economy. For the econometric analysis, this study uses the ratio of net FDI inflows to GDP and the ratio of net FPI inflows to GDP as dependent variables to measure total private capital flows in BRICS economies. These variables are widely used in previous studies (Asiedu, 2002; Nkoa, 2018; Kinda, 2010; L. Ngongan, 2014; Pham et al., 2022). FDI and FPI equity are chosen as key measures because they provide reliable data, represent non-debt-creating capital, and contribute to filling the savings–investment gap while supporting growth and development. FDI also brings additional benefits such as technology transfer, management expertise, and export potential, which other forms of capital inflows often lack. While most studies focus on FDI, less attention has been given to FPI, as highlighted by Li and Filer (2007) and Singhania et al. (2015). Analyzing both gross and disaggregated capital flows (FDI and FPI) allows for a more thorough comprehension of the elements influencing net capital flows in BRICS economies. Debt-related capital flows are excluded due to estimation inaccuracies, lack of reliable data, and their failure to reflect market mechanisms compared to FDI and FPI. By focusing on FDI and FPI equity, this study proposes to offer a clearer picture of the aspects of private capital flows in BRICS nations.

Table 3. List of controlling variables.

Sr. No.	Variables	Definitions	Sources
1.	Market size	GDP per capita growth (annual (%))	Lankes and Venables (1996); Holland and Pain (1998); Resmini (2000); Bevan and Estrin (2000); Asiedu (2002); Garibaldi and Mauro (2002); Duran and Ubeda (2005); Nunes et al. (2006); Haider et al. (2017); Singhania and Saini (2017); Al-Smadi (2018a); S. A. Asongu and Tchamyou (2018).
2.	Trade openness	Sum of export and import of goods and services as a share of gross domestic product	Hausmann and Fernández-Arias (2000); Chakrabarti (2001); Asiedu (2002); Banga (2003); Kinda (2008a); Aw and Tang (2010); Seetanah et al. (2010); Alam et al. (2013); Aizenman et al. (2013); Dua and Garg (2013); Alfaro et al. (2014); Fauzel (2016); Singhania and Saini (2017); Swamy and Narayanamurthy (2018); Shabbir et al. (2018); Majeed et al. (2021).
3.	Institutional quality	Voice and accountability; political stability and absence of violence; government effectiveness; regulatory quality; rule of law; and control of corruption	Lucas (1990a); Rodrik and Subramanian (2009); Acemoglu et al. (2005); Das (2014); Al-Smadi (2018a); Gopalan et al. (2019); Saidi et al. (2020).
4.	Macroeconomic stability	Inflation and GDP deflator (annual %)	Seetanah et al. (2010); Byrne and Fiess (2011); Fauzel (2016); Shabbir et al. (2018); S. A. Asongu and Tchamyou (2018); Al-Smadi (2018a); Majeed et al. (2021).
5.	Labor force	Presence of labor force (total)	Sahoo (2006); Wheeler and Mody (1992).
6.	Gross capital formation	Gross capital formation (% of GDP)	Lipsey et al. (1999); Krkoska (2001).
7.	Natural resources	Oil, gas, metal, mineral, and forest rents	Onyeiwu and Shrestha (2004); Dupasquier and Osajwe (2006); Deichmann et al. (2003).
8.	Exchange rate	Official exchange rate (LCU per USD, period average)	Aggarwal et al. (2011); Dua and Garg (2013); Swamy and Narayanamurthy (2018).
9.	Human capital	Human Development Index	Root and Ahmed (1979); K. H. Zhang and Markusen (1999); Noorbakhsh et al. (2001); Kinda (2010); L. Ngongan (2014).

Source: Compiled by researcher from World Development Indicators by World Bank data files, 2023.

presents the list of controlling variables.

2.1.4. Operationalization of Controlling Variables

Apart from the main variables, the following controlling variables have been identified in previous studies as having an impact on the private capital flows:

(a)

Market Size

The greater market size of the host country appeals to more capital flows. The greater the size of the economy, the larger the inflow of capital expected. MNCs are drawn to sizable markets so as to take advantage of the economies of scale and generate better returns on their investments. (Lankes & Venables, 1996; Resmini, 2000; Bevan & Estrin, 2000; Asiedu, 2002; Duran & Ubeda, 2005; Sahoo, 2006). The work of Holland and Pain (1998) and Asiedu (2002) showed that the size of markets are insignificant determinants of FDI flow. Since the market size of a nation significantly affects private capital flows, and owing to different views regarding its impact, in the current study, it has been treated as a controlling variable.

(b)

Trade Openness

The availability of liberal trade policies and facilities serves as an opportunity for the freer movement of capital flows in the host nation. Countries with open trade policies attract more inflows as investors seek markets with fewer trade barriers and greater potential for growth (Hausmann & Fernández-Arias, 2000; Chakrabarti, 2001; Asiedu, 2002; Kinda, 2008b). Allowing openness to trade attracts capital in the form of FPI as Openness to trade often means greater access to information and exposure to global markets. This exposure can make it easier for portfolio investors to identify potential investment opportunities in countries with more open trade policies (Hausmann & Fernández-Arias, 2000; Asiedu, 2002; Dua & Garg, 2013; Singhania & Saini, 2017). Since the trade openness allowed in a nation significantly affects private capital flows. The current study has treated it as a controlling variable.

(c)

Institutional Quality

The presence of a favorable institutional environment helps promote private capital flows to the host nation. The degree of laws governing investment in projects greatly impacts the behavior of the investors while investing in the overseas market. Nations that have good institutions, regulatory quality, control of corruption, and effective rule of law can attract capital inflows (Acemoglu et al., 2005; Rodrik & Subramanian, 2009). Governance is a strong factor for determining the flow of FPI. The general perception of investors while selecting stocks of foreign firms is based on good governance systems that mitigate the cost generated from asymmetric information. Al-Smadi (2018b), in a study on FPI determinants, measured governance by taking World Governance Indicators (WGI).

(d)

Macroeconomic Stability

The scenario of stability in the country builds up the investors’ confidence and certainly affects the private capital flows. Macroeconomic stability promotes the movement of capital and trade as it upholds better predictions on the movement of the economic fundamentals of the nation. Macroeconomic stability is generally measured as the state of inflation in an economy. Byrne and Fiess (2011) found that real commodity prices significantly impact the portfolio equity flows among nations. Al-Smadi (2018b), in a study of elements of portfolio investment in Jordan, found the significance of prevailing inflation rates on affecting inflow of portfolio equity in Jordan. The inflation rate prevailing in a nation has a strong bearing on private capital flows (Kholdy & Sohrabian, 2008). In the current study, it has been treated as a controlling variable.

(e)

Gross Capital Formation

A higher level of gross capital formation often indicates increased investment in productive assets like machinery, infrastructure, and technology. There is little evidence representing the relationship between FDI and capital formation as depicted in the work of Acemoglu et al. (2005) and Krkoska (2001), which states that the significant impact of the generation of capital in select developed countries is subject to ownership change in FDI. Therefore, a positive or negative impact is anticipated. The gross capital formation of a nation affects the movement of flows; it has, therefore, been treated as a controlling variable in the present study.

(f)

Exchange Rate

The investors are sensitive towards the interest rates being offered by the host nations as compared to the prevailing rates in their home nations. Agarwal (1997) studied foreign FPI determinants among six developing nations for the period 1986 to 1993. This study found that the prevailing exchange in the nation is a significant determinant of portfolio equity investment in the nation. Investing in the form of FDI and FPI by investors is sensitive to prevailing exchange rates in the host nation. In the current study, therefore, it has been treated as a controlling variable.

(g)

Labor Cost

Higher cost of labor is expected to impact FDI negatively. Loree and Guisinger (1995) found that among the non-policy-related determinants of direct FDI flow in the US, wage rate, taken as an average employee compensation paid per worker in the US, had a significant negative relationship with the FDI. Studies like those of Wheeler and Mody (1992) and Sahoo (2006) have revealed the presence of labor force shaping FDI flows positively. Since labor costs have a direct bearing on the cost of production in an economy, they can affect private capital flows. Thus, in the current study, it has been treated as a controlling variable.

(h)

Human Capital

The presence of a skilled workforce attracts foreign direct investment. Multinational corporations often establish operations or expand into regions with a strong pool of skilled talent to meet their business needs. Human capital was first explicitly included by Lucas (1990a) in his analysis of FDI inflows. The development of human capital gives locational advantage as it attracts FDI for a country acting as host (Root & Ahmed, 1979; Schneider & Frey, 1985; Hanson, 1996).

The availability of human capital, therefore, directly affects the movement of capital flows, because of which it is treated as a controlling variable in the current study.

(i)

Natural Resource

Natural resources can influence FDI inflows. Companies can invest in regions with abundant resources to establish production facilities, mines, or refineries. Such investments often aim to secure access to raw materials or take advantage of resource-based comparative advantages. Deichmann et al. (2003) examined factors for foreign direct investment in the Eurasian transition states and found that natural resources are a necessary condition for the inflow of foreign capital. This study showed that nations having sufficient natural resources are at a low position for resource scarcity and trade dependence. In the current study, therefore, the presence of natural resources has been treated as a controlling variable.

Table 3 presents the list of controlling variables. It includes the following: market size, trade openness, institutional quality, macroeconomic stability, labor force, gross capital formation, natural resources, exchange rate, and human capital.

2.2. Hypotheses Formulation

2.2.1. Physical Infrastructure and Private Capital Flows (Foreign Direct Investment and Foreign Portfolio Investment)

Numerous studies have explored the role of infrastructure in attracting FDI over the years (Vijayakumar et al., 2010). Loree and Guisinger (1995) were among the first to highlight that enhanced transportation and communication infrastructure positively impact FDI inflows. Cheng and Kwan (2000) later supported this view, demonstrating that superior infrastructure increases a region’s appeal to foreign investors. Kumar (2006) introduced an infrastructure index, revealing that roads, telecommunications, and energy availability lower investment costs and promote FDI. Khadaroo and Seetanah (2009) emphasized the importance of transportation infrastructure, particularly road and rail networks, in attracting foreign capital. Shikur (2024) identified economic freedom and financial development as factors that amplify the positive impact of infrastructure on FDI inflows. Overall, research consistently shows that while infrastructure has an important role in facilitating FDI, its effectiveness depends on broader economic and institutional conditions. While transportation and telecommunication infrastructure remain dominant factors, ICT infrastructure, human capital, and policy stability have also emerged as significant contributors to FDI attraction.

H1: 

Physical infrastructure is a significant contributor to private capital flows.

As this study considers two types of private capital flows, such as FDI and FPI, the following sub-hypothesis has been tested in this study:

H1a: 

Physical infrastructure is a significant contributor to FDI.

H1b: 

Physical infrastructure is a significant contributor to FPI.

2.2.2. Financial Infrastructure and Private Capital Flows (FDI and FPI)

Numerous works have discovered the link between financial sector development and foreign investment, highlighting how financial systems influence economic growth and capital flows (Mohd Nor et al., 2013). The research by Levine (1997) on financial development indicators from 1960 to 1989 demonstrated a favorable correlation between financial sector growth and economic expansion. Similarly, Agarwal (1997) identified domestic capital market size as a key factor in attracting FPI in Asian economies during 1986–1993. Further research expanded the scope to different regions and methodologies. Chee and Nair (2010) analyzed Asia–Oceania countries (1996–2005), concluding that financial development enhances FDI’s economic impact. Kinda (2010) found financial infrastructure pivotal in attracting private capital flows in developing nations. Abzari et al. (2011) noted that FDI inflows stimulated financial growth, whereas Korgaonkar (2012) observed that FDI favors financially developed economies. Subsequent studies reinforced these trends. Agbloyor et al. (2013) linked banking and stock market advancement to higher FDI in Africa, while Mohd Nor et al. (2013) found banking efficiency crucial for FDI in developed but not emerging markets. Erdogan and Unver (2015) confirmed financial market development’s role in gathering capital inflows, and O. M. Al Nasser and Hajilee (2016) demonstrated that both stock markets and the banking sector influence FDI in Latin America. More recent research by Fauzel (2016), Gebrehiwot et al. (2016), and Nasir et al. (2017) further validated these relationships, though some studies, like Nasir et al. (2017), found no strong FDI-financial development link in certain contexts. Enisan (2017) and Saini and Singhania (2018) reinforced the importance of financial infrastructure in attracting FDI and FPI, respectively. Nkoa (2018), Bayar and Gavriletea (2018) also emphasized financial market development’s role in capital flows. Recent analyses by Keykanloo et al. (2020) and Irandoust (2021) confirmed financial sector depth and efficiency as key FDI drivers, while Veselinović et al. (2022) reported mixed evidence on long-term financial development–FDI links in EU nations. Collectively, these studies emphasize the role of financial infrastructure in determining foreign investment flows. While findings vary across regions and timeframes, they generally support a cyclical relationship where financial development attracts FDI/FPI, which in turn fosters further financial growth. However, limited research has focused on BRICS economies, creating a gap this study aims to address. Given the literature’s mixed findings, some suggesting unidirectional and others bidirectional relationships, further validation is needed within BRICS contexts.

Based on existing evidence, this study hypothesizes that well-developed financial infrastructure positively influences FDI and FPI inflows. As this study considers two types of private capital flows, such as FDI and FPI, the following sub-hypothesis has been tested in this study:

H2a: 

Financial infrastructure is a contributor to foreign direct investment.

H2b: 

Financial infrastructure is a contributor to foreign portfolio investment.

3. Materials and Methods

3.1. Data Sources

The current study inspects the movement of capital flows in BRICS economies from 2010 to 2024. It relies on data from the World Bank and financial structure datasets, focusing on net inflows of FDI and FPI as a proportion of GDP. While most research predominantly emphasizes FDI, this study also incorporates FPI, as highlighted by Li and Filer (2007) and Singhania et al. (2015). By examining both gross and disaggregated capital flows, this study seeks to identify the primary factors influencing these flows. A Fixed-Effect Panel Data Model is utilized to evaluate the influence of physical and financial infrastructure on private capital flows while accounting for time heterogeneity.

3.2. Framework of Analysis

3.2.1. Descriptive Statistics

Descriptive statistics provide a comparative overview of variables by summarizing key econometric parameters such as mean, standard deviation, skewness, kurtosis, minimum values, maximum values, etc. These statistics simplify the investigation of large datasets, making it easier to interpret and present results in a clear and meaningful way. This method develops understanding and facilitates quicker examination of the data.

3.2.2. Min–Max Scaling

This includes dividing by the range (the difference between the highest and lowest values) after deducting the minimum value from every data point, scaling all variables between 0 and 1. The composite score for physical and financial infrastructure was then derived by averaging the normalized values across the component variables. This approach ensures standardized and comparable indices for further analysis.

3.2.3. Multivariate Regression Analysis

A statistical method for probing the relationship across a dependent variable with several independent variables is panel data analysis. With regression coefficients showing an improvement in the variable that is dependent for every unit change in the variable that is independent, it expands on multiple regression analysis, allowing for predictions about a variable by considering one or more predictors (Tabachnick & Fidell, 2007; Hair, 2010). This study employs multivariate regression analysis to understand the capital flow dynamics in BRICS economies from 2010 to 2024, aiming to achieve its research objectives.

3.2.4. Model Estimation with Main Variables Affecting Private Capital Flows

The following equation incorporates the main variables, i.e., physical and financial infrastructure affecting the total private capital flows taken as net FDI inflow and net FPI inflow in the regression model.

PCF_it = α_i + β1 PhyINFRA_it + β2 FinINFRA_it + βx_it + βu_it + e_it

The dependent variable in the form of private capital flow known as PCFi is received by the host nation “i” at time “t”. The physical infrastructure variable is called PhyINFRA, and the financial infrastructure variable is called FinINFRA. They are both considered independent variables. The control variable matrix is denoted by βx_it. The time dummies for a given year are represented by βu_it. However, the error components of the two equations are uncorrelated, meaning that the quantity of FDI acknowledged by a nation is independent of the sum of portfolio investment received.

3.2.5. Model Estimation Including Controlling Variables Affecting Private Capital Flows and Total Capital Flows Taken Together

Further, the empirical model for estimating the impact individually by controlling the other variables impacting capital flows taken from the available literature has been designed as shown below.

PCF_it = β1₁₀ + β₁₁₁ PhyINFRA_it + β₁₁₂ FinINFRA_it + β₁₁₄ GDP_it + β₁₁₅ HC_it + β₁₁₆ OPEN_it + β₁₁₇ MS_it + β₁₁₈ GCF_it + β₁₁₉ RES_it + β₁₂₀ LC_it + β₁₂₁ EXCH + β₁₂₂ IQ_it + Fixed Effects + e_it

PCF_it is the FDI and private equity investment in USD for country ‘i’ at time ‘t’.

The physical infrastructure index for nation “i” at time “t” is represented by the symbol PhyINFRAit. Based on adequacy and accessibility, FinINFRAit denotes the financial infrastructure index for country ‘i’ at time ‘t’. GDP is the gross domestic product in USD for country ‘i’ at time ‘t’ and is the measure of market size. HC denotes human capital for country ‘i’ at time ‘t’ and is measured by Human Development Index (HDI) values. OPEN is the country’s trade openness at time t and is calculated as the GDP share of the ratio of products and services imported to goods and services exported. LC is the labor present for country ‘i’ at time ‘t’ and is the measure of the cost of labor. IQ is the indicator of governance for country ‘i’ at time ‘t’, and it is the measure of the level of institutional quality. GCF is the total amount of capital formation for country “i” at time “t”, expressed as a share of GDP. INF is a measure of macroeconomic stability that represents the GDP deflator (annual percentage) and inflation for country “i” at time “t”.

The current exchange rate regime in the nation “i” at time “t” is denoted by EXCHit. RES It is the amount of GDP that country “I” has available in the form of oil, gas, metal, mineral, and forest rentals. It is the error element over time “t” and represents the measurement of natural resources.

The influence of financial and physical infrastructure on the movement of private capital in the BRICS countries across the research period has been assessed using the models below. The impact has been studied when:

(i)

Physical and financial infrastructure and total capital flows are taken together.

(ii)

Physical and financial infrastructure and total capital flows are taken separately.

3.3. Diagnostic Tests

To confirm the authenticity of the available data and check the assumptions of the regression applied, this study has employed diagnostic tests as mentioned in the next sub-sections.

3.3.1. Multicollinearity

The multicollinearity is referred to as the notion that, in a regression model, predictor variables are easily inferred from one another. This leads to instability in coefficient estimates and unreliable results. To address this issue, multicollinearity tests have been employed.

3.3.2. Autocorrelation Test

The Wooldridge (2002) test has been employed to check the presence of autocorrelations, which signifies that the observations are not independent. The Woolridge linear one-way model is written as follows:

Y_it = α + X_itβ₁ + W_iβ₂ + V_i + i_t

where

• Y_it denotes the value of the dependent variable;
• X_it represents the (1 × K₁) vector of covariates (time variants);
• W_i represents the (1 × K₂) vector of covariates (time invariants);
• V_i is the individual-level effect;
• i_t denotes idiosyncratic error;
• α, β₁, and β₂ are 1 + K₁ + K₂ parameters.

3.3.3. Heteroscedasticity

When the error term’s variance varies across independent variables, it is supposed to be heteroscedastic. To find it, the Breusch–Pagan/Cook–Weisberg test had been used.

3.3.4. Cross-Sectional Dependency

Economic and financial integration among BRICS nations may lead to interdependence in economic fundamentals, causing cross-sectional dependency. This can result in inconsistent estimates. The Breusch–Pagan Lagrange Multiplier (LM) test (1980) was employed, suitable for datasets where T > N. Additionally, the Pesaran CD test was used for large cross-sectional units over time. The methodology resonates with that applied by Bayar and Gavriletea (2018), Veselinović et al. (2022), and Uul-Haq et al. (2023) in the context of BRICS economies.

3.3.5. Panel Data Fixed-Effect Model

The use of the panel data fixed-effect model has been made in order to address the issue of heterogeneity by controlling time-invariant heterogeneity. This approach is commonly used in fixed-effects models to account for individual-specific characteristics that do not change over time. This model is particularly useful when the data have multiple observations for each entity over time, allowing researchers to control for unobserved heterogeneity. It allowed for the introduction of time dummies for the study period. This model controls for time-invariant characteristics of the entities by allowing individual-specific intercepts.

In the present study, a fixed-effects panel data model has been employed by introducing time dummy variables to capture time-specific effects while controlling for time-invariant heterogeneity across nations as follows:

Y_it = α_i + βX_it + γ_t + e_it

• Y_it is the dependent variable for nation i at time t;
• α_i denotes the entity-specific fixed effects;
• β is a vector of coefficients for the independent variables;
• X_it is a vector of independent variables for entity i at time t;
• γ_t represents time-specific fixed effects;
• e_it is the error term.

The current dataset is characterized by heteroscedasticity issues; the use of the above model is appropriate to control for cross-country heterogeneity. This approach has been similarly applied in studies focusing on BRICS economies, such as those by Tiwari and Mutascu (2011).

4. Results and Discussion

4.1. Results of Descriptive Statistics

The descriptive statistics that provide an overview of the variables utilized are shown in

Table 4. Descriptive statistics.

Variable	Obs	Mean	Std. Dev.	Min	Max
Private capital flows	50	2.589	1.221	0.096	5.435
PhyIndex	50	0.485	0.377	0.027	1.685
FinIndex	50	0.282	0.164	0.08	0.588
FDI	50	2.162	1.091	0.439	4.137
FPI	50	0.427	0.788	−1.104	2.451
HC	50	0.723	0.068	0.575	0.841
GDP	50	3.045	3.321	−4.358	10.103
LC	50	8.151	0.575	7.275	8.903
TO	50	43.812	10.876	22.772	59.5
MS	50	5.494	4.144	−0.003	24.46
GC	50	28.026	10.431	14.626	46.66
NR	50	5.348	4.325	1.055	17.557
Ex	50	25.687	25.242	1.673	70.42
IQIndex	50	0.54	0.295	0.034	1

Source: Researcher’s calculation using STATA 14.0. Notes: 1. Total private capital flows include net inflow of FDI and FPI divided by GDP (currency: USD). FDI is net foreign direct investment inflows divided by current GDP. FPI is foreign portfolio investment divided by current GDP. 2. PhyIndex denotes physical infrastructure. FinIndex denotes financial infrastructure. 3. HC is human capital and is measured by the Human Development Index. 4. GDP is the proxy for market size measured as GDP per capita growth annually (%). 5. LC denotes labor cost measured as the log of total labor present. 6. TO denotes trade openness measured as the sum of exports and imports (% of GDP). 7. MS is macroeconomic stability proxied as inflation, GDP deflator (annual %). 8. GC is the gross capital formation (% of GDP). 9. NR is availability of natural resources taken as natural resource rents (% of GDP). 10. Ex is the exchange rate taken as LCU per USD, period average. 11. IQ is institutional quality taken as an Index of World Governance Indicators.

. The net private capital inflows in BRICS economies ranged from 0.096% to 5.44%, indicating significant variation across these nations. China recorded the highest capital inflows, while South Africa received the lowest. Table 4 highlights key statistics for variables in this study, revealing significant disparities across BRICS economies. Physical infrastructure presence ranged from −0.99% to 2.55%, while financial infrastructure varied between −1.66% and 1.68%. Net FPI inflows ranged from −1.10% to 2.45%, and net FDI inflows spanned 0.43% to 4.13%, reflecting geographic, political, and economic differences. Market size varied widely (−4.36% to 10.10%), while labor force participation showed less variation (7.28% to 8.90%), indicating similar workforce levels. Trade openness averaged 43.81%, and inflation ranged from −0.003% to 24.46%, pointing to macroeconomic instability in some economies. Gross capital formation averaged 28.02% (14.63% to 46.67%), and natural resource availability (measured as rent) ranged from 1.05% to 17.56%. Exchange rates exhibited significant disparity (1.67% to 70.42%), and institutional quality ranged from 0.034 to 1. These findings underscore the diverse economic and structural characteristics of BRICS nations.

4.2. Diagnostic Tests for Multivariate Analysis

4.2.1. Test for Heteroscedasticity

Diagnostic testing is essential to validate data assumptions (Field, 2005). Heteroscedasticity, an econometric issue, occurs when the error term’s variance is not constant across independent variables.

Table 5. Breusch–Pagan/Cook–Weisberg test for heteroskedasticity.

H0: Constant	Value
Variables: fitted values of private capital flows
Chi2 (1) =	1.24
Prob > Chi2 (1)	0.0246

Source: Researcher’s calculation using STATA.

presents the heteroscedasticity test results, indicating heteroscedastic data, as the prob. value was less than 0.05, resulting in the null hypothesis being rejected. This suggests that simple linear regression would yield inconsistent results. To address heterogeneity, time dummies were introduced to control for time-specific variations.

4.2.2. Results of Autocorrelation Test

Field (2005) highlighted that autocorrelation is a common issue in time series data, where a variable’s current values are linked to its past values. The Wooldridge (2002) test was used to detect serial correlation, confirming that observations were not independent.

Table 6. Wooldridge test for autocorrelation in panel data.

H0: No First Order Autocorrelation	Value
F (1,4) =	6.466
Prob > F=	0.6338

Source: Researcher’s calculation using STATA 14.0.

has depicted the test for serial autocorrelation in the data. Since the p-value of significance is more than 0.05, the null hypothesis of no autocorrelation was accepted.

4.2.3. Cross-Sectional Dependency

Before applying a regression model, it is essential to examine cross-sectional dependency and slope heterogeneity among nations. The Breusch–Pagan Lagrange Multiplier (LM) test (1980) is commonly used for this purpose. The test results indicated cross-sectional dependency, rejecting the null hypothesis and showing that shocks in one country affect others, as observed in studies like Swamy and Narayanamurthy (2018). The LM test was employed in this study due to its suitability for time series panel data where T > N (De Hoyos & Sarafidis, 2006).

Table 7. Breusch and Pagan Lagrangian Multiplier Test for cross-sectional dependency.

Private Capital Flows [Country 1, t] = Xb + [Country 1] + e[Country 1, t]
Estimated Results:
	Var	Sd = sqrt (Var)
Private capital flows	1.49184	1.221409
E	0.620	0.7879396
U	0	0
Test: Var(u) = 0
Chibar2(01) = 0.00

Source: Researcher’s calculation using STATA 14.0.

presents the results for cross-sectional dependency. With Chibar²(01) = 0.00, the hypothesis was rejected, indicating no cross-sectional dependency in the residuals. Therefore, diagnostic tests performed in the analysis confirmed no cross-sectional dependence, but heteroscedasticity and multicollinearity issues remain in the dataset. To address these issues, this study employed a fixed-effects panel data model with robust standard errors, incorporating time dummies to control time heterogeneity.

4.3. Result for Panel Data Fixed-Effect Model

4.3.1. Results for Influence of Financial and Physical Infrastructure on the Flow of Private Capital

In order to study the above relationships, the following models have been employed:

PCF_it = β₁₀ + β₁₁ PhyINFRA_it + β₁₂ FinINFRA_it + β₁₃ GDP_it + β₁₄ HC_it + β₁₅ OPEN_it + β₁₆ MS_it + β₁₇ GCF_it + β₁₈ RES_it + β₁₉ LC_it + β₂₀ EXCH + β₂₁ IQ_it + Fixed effects_it + e_it

Table 8. Linear regression—physical and financial infrastructure and private capital flows.

Private Capital Flows	Coef.		St. Err.	t-Value		p-Value	95% Conf		Interval	Sig
PhyIndex	9.054		2.24	4.04		0	13.636		4.473	***
FinIndex	3		3.583	0.84		0.04	10.328		4.328	**
HC	31.98		9.838	3.25		0.003	11.86		52.1	***
GDP	0.043		0.103	0.42		0.06	0.254		0.167	*
TO	0.025		0.051	0.50		0.62	−0.078		0.129
MS	−0.095		0.061	−1.57		0.128	−0.219		0.029
GC	0.021		0.072	0.29		0.772	−0.126		0.168
NR	−0.176		0.145	−1.21		0.236	−0.473		0.121
LC	3		2.093	1.43		0.162	−1.281		7.282
EX	−0.042		0.02	−2.10		0.044	−0.084		−0.001	**
IQ	−0.489		1.774	−0.28		0.085	−4.118		3.14	*
Constant	−40.968		23.397	−1.75		0.091	−88.821		6.885	*
Mean dependent var.		2.589			SD dependent var			1.221
R-squared		0.750			Number of obs			50
F-test		12.523			Prob > F			0.000
Akaike crit. (AIC)		133.587			Bayesian crit. (BIC)			173.740

Source: Researcher’s calculation using STATA 14. *** p < 0.01, ** p < 0.05, * p < 0.1.

reveals that there is an influence of infrastructure on net capital inflows in BRICS. A 1% enhancement in physical infrastructure raises private capital inflows by 9.05%, while a 1% boost in financial infrastructure raises inflows by 3%. Conversely, a 1% increase in exchange rates reduces private capital inflows by 0.49%, highlighting exchange rate volatility as a barrier. Institutional quality shows a negative impact, with a 1% rise leading to a 0.04% decline in capital flows. Human capital has a strong positive effect, with a 1% increase driving 31% higher capital inflows. Market size (GDP per capita growth) also positively impacts capital flows, contributing 0.04%. These findings underscore the critical roles of infrastructure, human capital, and market size in attracting private capital, while exchange rates and institutional quality pose challenges.

4.3.2. Results for the Impact of Physical Infrastructure on the Flow of Private Capital

To study the above relationships, the following models have been employed:

PCF_it = β₂₂ + β₂₃PhyINFRA_it + β₂₄ GDP_it + β₂₅ HC_it + β₂₆OPEN_it + β₂₇MS_it + β₂₈GCF_it + β₂₉ RES_it + β₃₀ LC_it + β₃₁EXCH + β₃₂ IQ_it + Fixed effects + e_it

Table 9. Linear regression—physical infrastructure and private capital flows.

Private Capital Flows	Coef.		St. Err.	t-Value		p-Value	[95% Conf		Interval]	Sig
PhyIndex	9.249		2.398	3.86		0.001	14.146		4.352	***
HC	31.678		10.365	3.06		0.005	10.509		52.847	***
GDP	0.049		0.105	0.47		0.044	−0.264		0.166	**
TO	0.013		0.048	0.28		0.783	−0.084		0.111
MS	−0.077		0.065	−1.18		0.245	−0.21		0.056
GC	0.018		0.082	0.21		0.032	−0.185		0.15	**
NR	−0.185		0.15	−1.23		0.228	−0.492		0.122
LC	3.3		2.128	1.55		0.131	−1.046		7.645
EX	−0.03		0.01	−3.03		0.005	−0.05		−0.01	***
IQ	−0.795		1.69	−0.47		0.042	−4.246		2.656	**
Constant	−42.406		24.027	−1.76		0.088	−91.475		6.663	*
Mean dependent var.		2.589			SD dependent var			1.221
R-squared		0.742			Number of obs			50
F-test		11.699			Prob > F			0.000
Akaike crit. (AIC)		133.171			Bayesian crit. (BIC)			171.411

Source: Researcher’s calculation using STATA 14.0. *** p < 0.01, ** p < 0.05, * p < 0.1.

highlights the positive influence of growth of infrastructure on FDI inflows in BRICS nations. A 1% enhancement in physical infrastructure raises the inflows of private capital by 9.25%. Human capital also plays a crucial role, with a 1% rise driving a 31.68% increase in capital flows. Gross capital formation positively impacts capital flows, with a 1% increase leading to a 0.018% rise. However, exchange rates negatively affect inflows, as a 1% increase reduces private capital by 0.03%, indicating investor sensitivity to exchange rate fluctuations. Institutional quality, measured by the World Governance Indicators, shows a negative relationship, with a 1% increase decreasing capital flows by 0.76%. Market size (GDP per capita growth) positively impacts inflows by 0.05%. The following sections analyze the presence of the growth of physical infrastructure on the inflows of (a) FDI and (b) FPI.

4.3.3. (a) Impact of Physical Infrastructure on the Flow of FDI

To study the above relationships, the following models have been employed:

FDI_it = β₃₃ + β₃₄ PhyINFRA_it + β₃₅ GDP_it + β₃₆ HC_it + β₃₇ OPEN_it + β₃₈ MS_it + β₃₉ GCF_it + β₄₀ RES_it + β₄₁ LC_it + β₄₂EXCH + β₄₃IQ_it + Fixed effects + e_it

Table 10. Linear regression—physical infrastructure and net FDI inflow.

FDI	Coef.		St. Err.	t-Value		p-Value	95% Conf.		Interval]	Sig.
PhyIndex	5.982		1.537	3.89		0.001	9.12		2.844	***
HC	24.688		6.593	3.74		0.001	11.224		38.153	***
GDP	0.034		0.073	0.46		0.06	−0.182		0.115	*
TO	0.016		0.04	0.40		0.094	−0.066		0.098	*
MS	−0.103		0.048	−2.12		0.042	−0.202		−0.004	**
GC	−0.107		0.073	−1.47		0.152	−0.256		0.042
NR	0.093		0.113	0.82		0.017	−0.323		0.137	**
LC	4.2		1.526	2.75		0.01	1.083		7.318	***
EX	−0.026		0.008	−3.38		0.002	−0.042		−0.01	***
IQ	−0.962		1.06	−0.91		0.371	−3.128		1.203
Constant	−42.972		16.265	−2.64		0.013	−76.19		−9.754	**
Mean dependent var.		2.162			SD dependent var			1.091
R-squared		0.826			Number of obs			50
F-test		18.129			Prob > F			0.000
Akaike crit. (AIC)		102.150			Bayesian crit. (BIC)			140.390

Source: Researcher’s calculation using STATA 14.0. *** p < 0.01, ** p < 0.05, * p < 0.1.

has depicted the influence of physical infrastructure in affecting net FDI inflows in BRICS. It has been observed that a one percent increase in physical infrastructure derived 5.982% of FDI in the BRICS economies. GDP impacted FDI by 0.03%. The labor cost, measured as the presence of the total labor force in each of the BRICS economies, tended to have a significant impact on private capital inflows. Further, a one percent increase in the labor force resulted in a 4.2% increase in net FDI inflows in the BRICS economies. A one percent increase in the exchange rate tended to have an adverse impact on inflows. Also, when regressed with physical infrastructure, the macroeconomic stability measured as inflation tended to have a negative connection with net FDI inflow in the BRICS. A one percent rise in inflation discourages FDI inflow by −0.10%. Furthermore, the presence of human capital has been found to significantly affect net FDI inflows in the BRICS economies. A one percent increase in human capital brought 24.69% FDI. Similar results have been shown by Okafor et al. (2017), Duong et al. (2020), and Sethi et al. (2022). Trade openness also derives FDI by 0.02%. Also, the availability of natural resources results in a 0.09 percent increase in net FDI.

4.3.4. (b) Impact of Physical Infrastructure on the Flow of FPI

In order to study the above relationships, the following models have been employed:

FPI_it = β₄₄ + β₄₅ PhyINFRA_it + β₄₆ GDP_it + β₄₇ HC_it + β₄₈ OPEN_it + β₄₉ MS_it + β₅₀ GCF_it + β₅₁ RES_it + β₅₂ LC_it + β₅₃EXCH + β₅₄ IQ_it + Fixed effects + e_it

Table 11. Linear regression—physical infrastructure and net FPI inflow.

FPI	Coef.		St. Err.	t-Value		p-Value	95% Conf.		Interval	Sig.
PhyIndex	3.267		1.558	2.10		0.045	6.449		0.084	**
HC	6.99		7.842	0.89		0.38	−9.027		23.006
GDP	0.016		0.06	0.26		0.07	0.138		0.107	*
TO	−0.003		0.032	−0.08		0.935	−0.068		0.062
MS	0.025		0.037	0.68		0.499	−0.051		0.101
GC	0.09		0.055	1.64		0.112	−0.022		0.202
NR	−0.092		0.084	−1.10		0.082	−0.265		0.08	*
LC	−0.901		1.603	−0.56		0.078	−4.174		2.373	*
EX	−0.004		0.007	−0.59		0.561	−0.018		0.01
IQ	0.168		1.34	0.13		0.901	−2.569		2.905
Constant	0.566		18.258	0.03		0.975	−36.721		37.853
Mean dependent var.		0.427			SD dependent var			0.788
R-squared		0.670			Number of obs			50
F-test		4.410			Prob > F			0.000
Akaike crit. (AIC)		101.586			Bayesian crit. (BIC)			139.826

Source: Researcher’s calculation using STATA 14.0. ** p < 0.05, * p < 0.1.

depicts that there existed a significant relationship between physical infrastructure and net FPI inflow in BRICS economies, since the prob. value was less than 0.05. It has been noticed that a one percent increase in physical infrastructure derived a 3.27 percent increase in portfolio investment in the BRICS economies. Also, the availability of natural resources as an indicator of an increase in the resource rent has been found to show an adverse relationship with net FPI inflows. When regressed with the physical infrastructure index, an increase in natural resource rent by one percent resulted in a decrease in net inflow of portfolio investment by −0.09 percent. The labor force negatively impacts FPI inflow. A one percent increase in labor cost led to a −0.90% reduction in portfolio investment in BRICS economies. Market size tended to have a positive relationship with FPI inflow, revealing that a one percent increase in GDP derived a 0.02% increase in FPI.

4.3.5. Results for Impact of Financial Infrastructure on the Flow of Private Capital

In order to study the above relationships, the following models have been employed:

PCF_it = β₅₅ + β₅₆ FinINFRA_it + β₅₇ GDP_it + β₅₈ HC_it + β₅₉ OPEN_it + β₆₀ MS_it + β₆₁ GCF_it + β₆₂RES_it + β₆₃ LC_it + β₆₄EXCH + β₆₅ IQ_it + Fixed effects + e_it

Table 12. Linear regression—financial infrastructure and private capital flows.

Private Capital Flows	Coef.		St. Err.	t-Value		p-Value	95% Conf.		Interval	Sig.
FinIndex	3.614		3.522	1.03		0.031	10.808		2.58	**
HC	6.837		8.542	0.80		0.043	10.607		24.281	**
GDP	0.102		0.096	1.06		0.09	−0.095		0.299	*
TO	0.029		0.055	0.52		0.609	−0.084		0.141
MS	−0.012		0.064	−0.20		0.084	−0.117		0.142	*
GC	−0.062		0.095	−0.65		0.518	−0.256		0.132
NR	−0.294		0.145	−2.03		0.052	−0.59		0.003	*
LC	0.462		2.211	0.21		0.836	−4.053		4.978
EX	−0.032		0.021	−1.50		0.041	−0.025		0.011	**
IQ	−1.624		1.805	−0.90		0.375	−5.309		2.062
Constant	−1.359		22.712	−0.06		0.953	−47.743		45.026
Mean dependent var.		2.589			SD dependent var			1.221
R-squared		0.673			Number of obs			50
F-test		13.817			Prob > F			0.000
Akaike crit. (AIC)		144.967			Bayesian crit. (BIC)			183.207

Source: Researcher’s calculation using STATA 14.0. ** p < 0.05, * p < 0.1.

highlights a significant relationship between financial infrastructure and private capital inflows. A 1% rise in financial infrastructure boosts total net capital inflows by 3.61% in BRICS economies. However, natural resource availability negatively impacts capital flows, with a 1% rise reducing inflows by 0.294%. Market size positively influences capital flows, contributing 0.10% for a 1% increase. Macroeconomic stability, measured by inflation, shows a negative impact, as a 1% rise in inflation decreases capital flows by 0.01%. Exchange rate increases also deter capital flows, with a 1% rise leading to a 0.032% reduction. These results highlight how crucial financial infrastructure and size of market are in enticing capital, although natural resources, inflation, and exchange rate volatility act as barriers.

4.3.6. (a) Impact of Financial Infrastructure on the Flow of FDI

To study the above relationships, the following models have been employed:

FDI_it = β₆₆ + β₆₇ FinINFRA_it + β₆₈ GDP_it + β₆₉ HC_it + β₇₀ OPEN_it + β₇₁ MS_it + β₇₂ GCF_it + β₇₃ RES_it + β₇₄ LC_it + β₇₅ EXCH + β₇₆ IQ_it + Fixed effects + e_it

Table 13. Linear regression—financial infrastructure and net FDI inflow.

FDI	Coef.		St. Err.	t-Value		p-Value	95% Conf.		Interval	Sig.
FinIndex	2.023		2.196	0.92		0.03	−6.507		2.461	**
HC	8.532		6.581	1.30		0.205	−4.908		21.972
GDP	0.064		0.068	0.94		0.03	−0.075		0.203	**
TO	0.024		0.042	0.58		0.067	−0.062		0.111	*
MS	−0.043		0.049	−0.86		0.395	−0.143		0.058
GC	0.14		0.078	1.79		0.083	−0.3		0.019	*
NR	−0.164		0.116	−1.42		0.167	−0.401		0.072
LC	2.391		1.657	1.44		0.059	−0.993		5.775	**
EX	−0.026		0.013	−1.92		0.064	−0.053		0.002	*
IQ	−1.533		1.156	−1.33		0.195	−3.893		0.827
Constant	−16.484		17.142	−0.96		0.344	−51.494		18.525
Mean dependent var.		2.162			SD dependent var			1.091
R-squared		0.789			Number of obs			50
F-test		12.895			Prob > F			0.000
Akaike crit. (AIC)		111.924			Bayesian crit. (BIC)			150.164

Source: Researcher’s calculation using STATA 14.0. ** p < 0.05, * p < 0.1.

reveals a significant relationship between financial infrastructure and FDI inflows. A 1% rise in financial infrastructure boosts FDI by 1.02%. Gross capital formation also positively impacts FDI, with a 1% rise increasing inflows by 0.14%. Labor force expansion drives FDI, contributing 2.34% for a 1% increase, while trade openness enhances FDI by 0.024%. However, exchange rate hikes negatively affect FDI, reducing inflows by 0.026% for a 1% increase. GDP growth positively influences FDI, with a 1% rise leading to a 0.06% increase. These results underscore the significance of financial infrastructure, labor force, and trade openness in attracting FDI, while exchange rate volatility acts as a deterrent.

4.3.7. (b) Impact of Financial Infrastructure on Net FPI Inflow

In order to study the above relationships, the following models have been employed:

FPI_it = β₇₇ + β₇₈ FinIFRA_it + β₇₉ GDP_it + β₈₀ HC_it + β₈₁ OPEN_it + β₈₂ MS_it + β₈₃ GCF_it + β₈₄ RES_it + β₈₅ LC_it + β₈₆ EXCH + β₈₇ IQ_it + Fixed effects + e_it

Table 14. Linear regression—financial infrastructure and net FPI inflow.

FPI	Coef.		St. Err.	t-Value		p-Value	95% Conf.		Interval	Sig.
FinIndexFinal	5.673		2.723	2.08		0.044	−7.235		6.889	*
HC	−1.891		5.479	−0.35		0.732	−13.081		9.299
GDP	0.041		0.055	0.74		0.04	−0.071		0.153	**
TO	0.004		0.036	0.12		0.903	−0.07		0.079
MS	−0.054		0.034	−1.61		0.017	−0.014		0.123	**
GC	0.078		0.07	1.13		0.068	−0.064		0.221	*
NR	−0.131		0.078	−1.68		0.003	−0.29		0.028	***
LC	−1.955		1.524	−1.28		0.009	−5.068		1.157	*
EX	−0.006		0.015	−0.41		0.085	−0.038		0.025	*
IQ	−0.1		1.327	−0.08		0.94	−2.81		2.61
Constant	16.491		16.05	1.03		0.312	−16.287		49.269
Mean dependent var.		0.427			SD dependent var			0.788
R-squared		0.652			Number of obs			50
F-test		4.646			Prob > F			0.000
Akaike crit. (AIC)		104.234			Bayesian crit. (BIC)			142.474

Source: Researcher’s calculation using STATA. *** p < 0.01, ** p < 0.05, * p < 0.1.

reveals that since the prob. value was less than 0.05, it could be concluded that the presence of financial infrastructure significantly affected the flow of FPI in the BRICS economies. A one percent increase in financial infrastructure leads to a 5.67% improvement in net portfolio investment in the BRICS economies. When regressed with financial infrastructure, there existed a significant relationship between net inflow of foreign portfolio investment and gross capital formation, as it derived a 0.07 percent increase in portfolio investment. A one percent increase in GDP derived a 0.04% increase in FPI. A 1% increase in the exchange rate led to a −0.006 percent decrease in portfolio investments in the BRICS economies. Further, an increase in natural resources negatively impacts net FPI inflows. A one percent increase in natural resources resulted in a −0.13 percent decrease in net FPI inflow. Also, the level of macroeconomic stability has a significant impact on private net FPI inflows, as a one percent surge in the level of inflation led to a −0.05 percent decrease in net inflows of FPI. The analysis also highlights the negative connection between the labor force and portfolio investment in the BRICS economies. A one percent increase in the labor force resulted in a decrease in portfolio investment by 1.96% over the study period.

This study has investigated the following two objectives: 1. to scrutinize the influence of physical infrastructure in attracting FDI and FPI inflows in BRICS economies and 2. to examine the role of financial infrastructure in attracting FDI and FPI inflows in BRICS economies. When physical and financial infrastructure regressed together, the impact of physical infrastructure seemed to have a larger impact on private capital flows compared to that of the financial infrastructure. A one percent increase in physical infrastructure led to a 9.05% increase in net inflows of private capital in the BRICS economies. Furthermore, a one percent increase in financial infrastructure resulted in a 3% increase in net private capital inflows in the BRICS economies. Physical infrastructure has a tendency to boost the productive capabilities of various sectors of the nation, and these were more to be fixed than the financial infrastructure.

A one percent increase in physical infrastructure led to a 5.98% increase in FDI, whereas a single percent growth in financial infrastructure resulted in a 2.02% increase in FDI. Also, a one percent increase in physical infrastructure led to a 3.27% increase in FPI, whereas a one percent increase in financial infrastructure led to a 5.67% increase in FPI. Overall, the influence of physical infrastructure on FDI was larger than the effect of financial infrastructure on FDI. As far as the financial infrastructure was concerned, the impact of financial infrastructure on FPI was greater than the impact of physical infrastructure on FPI. This implied that the presence of physical infrastructure was more important as far as FDI was concerned, and the presence of financial infrastructure was important as far as FPI was concerned. The fact that economies with stronger infrastructure tended to attract FDI inflows has also been demonstrated by Jaiblai and Shenai (2019) and Makoni (2017). The similar impact of the growth of physical infrastructure on foreign direct investment was obtained by Kumar (2006). The growth of financial infrastructure also attracts the FPI in BRICS. A similar relationship was noticed in the study of Kinda (2008a). The size of the market tended to affect private capital flows in the BRICS economies. A similar observation was derived in various studies (Nunnenkamp, 2002; E. Ngongan, 2015; Günther & Kristalova, 2016; S. Asongu et al., 2018; Aladejare, 2022; Veselinović et al., 2022), whereas the market size influenced the FPI, as has been observed in the current study and found in the work of Forbes and Warnock (2012). In the current study, the domestic market is found to have less significant impact than other variables. Several studies existed, such as Calvo et al. (1993), Fernandez-Arias (1996), and Chuhan et al. (1998), which highlighted other aspects, such as interest rate differentials and external conditions, that have a greater influence on capital inflows to Latin America than the region’s domestic economic growth (GDP). Razin and Sadka (2002) asserted that although GDP growth can attract capital flows, its impact is frequently less significant compared to factors such as institutional quality and global economic conditions.

It was observed that, when regressed with physical infrastructure, the relationship between gross capital formation together with net inflows of private capital is significant. This implied that the growth of gross capital formation along with physical infrastructure led to an upsurge in inflows in the BRICS economies. A similar observation was found in the work of Aladejare (2022), who had proxied gross capital formation for infrastructure development. The entire amount spent on building roads, hospitals, trains, schools, commercial and industrial structures, and land improvements by both public and private entities was captured by this variable. A similar observation was derived in the studies of Lipsey et al. (1999) and Krkoska (2001), which claimed that the significant influence of capital creation in some developed nations was vulnerable to ownership shifts in FDI, signifying such shifts in ownership can impact the stability and continuity of capital formation. Also, gross capital formation in the presence of an efficient financial structure results in an increase in the net inflow of FDI. When regressed along with physical infrastructure, trade openness significantly influences net direct inflows. The similar observation has been found in various studies (Kinda, 2010; Behname, 2012; Aladejare, 2022) and in the BRICS economies, Sunny and Unnikrishnan (2015). Over the last two decades, there has been an increase in capital throughout the globe due to the gradual elimination of barriers on the trade of international financial assets. This has enabled an enormous increase in foreign portfolio investment in the nations. This result was in keeping with the findings of Canela et al. (2006) and Saini and Singhania (2018). Also, when regressed with financial infrastructure, trade openness had a positive and significant influence on net FPI inflow. Similar observations were found in various studies (Banga, 2003; Aw & Tang, 2010; Forbes & Warnock, 2012; Alam et al., 2013; Dua & Garg, 2013; Saini & Singhania, 2018). Macroeconomic stability, measured as the rate of inflation in the economy, had impacted private capital inflows negatively (E. Ngongan, 2015). Therefore, a negative relationship signified that if the level of inflation increased in a nation, it would have a detrimental impact on the net capital flows in BRICS economies. Stable and low inflation is usually more appealing to portfolio investors (Adika, 2021). The rising rate of inflation had an undesirable effect on FDI inflows, as was found in the studies of Demirhan and Masca (2008) and Kinda (2010). Real commodity prices had a major influence on portfolio equity movements across countries, according to Byrne and Fiess (2011), who had studied international capital flows towards developing and emerging nations. Al-Smadi (2018a) had discovered the impact of the current inflation rate on affecting the influx of portfolio equity in the example of Jordan in the study of drivers of portfolio investment. Financial markets were unstable and might have suffered high risks and given low actual returns; rising inflation rates might deter investors from investing capital (Boyd et al., 2001; Chinn & Ito, 2002). The negative relationship detected between inflation and FPI was observed in many similar studies (Agarwal, 1997; Srinivasan & Kalaivani, 2015; Byrne & Fiess, 2011; Al-Smadi, 2018a). In the present study, the impact of institutional quality has been measured by creating an Index of World Governance Indicators, namely political stability, control of corruption, government effectiveness, voice and accountability, regulatory law, and rule of law. In the context of BRICS economies, institutional quality negatively and significantly affects the net movement of private capital flows. In certain BRICS countries, enhancing institutional quality might involve greater regulatory oversight and increased bureaucracy. This can result in higher compliance costs and added complexities for foreign investors, which may deter FDI. A similar observation in the context of FDI was noticed in some research (e.g., Kolstad & Wiig, 2012; C. Zhang & Nian, 2013), where it was discovered that better political stability in a host nation hindered FDI. In another study, Buckley et al. (2007) discovered a link between increased Chinese FDI and rising political risk in the host nation, owing to political links and connections among the governments of China and the host nation. Also, some MNCs tended to invest in the risky nations to take advantage of a large number of resources (Kolstad & Wiig, 2012). Investors often seek higher returns, which are typically found in riskier environments. In countries with lower institutional quality, the potential returns on investments can be higher to compensate for the higher risk. Therefore, despite the lower institutional quality, capital may still flow into these countries seeking higher returns. Some BRICS countries may provide incentives, such as tax relief, subsidies, or favorable regulatory environments, to attract foreign capital even though institutional quality is low. Also, a negative impact of exchange rates has been observed, signifying that foreign investors are sensitive to and apprehensive of the official exchange rates. Any increase in the rate of exchange is bound to have adverse repercussions on the net flows in the BRICS economies. This implied that foreign portfolio investors were sensitive towards an increase in exchange rates in the international market. A similar observation has been corroborated by the findings of various other studies (Agarwal, 1997; Chakrabarti, 2001; Dua & Garg, 2013; Srinivasan & Kalaivani, 2015). There existed a positive and significant relationship between natural resources and FDI inflows. The similar observation was found in the work of Jenkins and Thomas (2002), Deichmann et al. (2003). Dupasquier and Osajwe (2006) discovered a positive correlation between natural resource availability as shown by the export of fuel as a portion of overall exports. They had claimed that resource-rich countries often received more foreign direct investment (FDI) than resource-poor nations. Similarly, the presence of a labor force had a favorable impact on attracting FDI, Abbas et al. (2022); Tsaurai (2015), but a negative effect was observed between portfolio investment and the labor cost. The quality of human resources, measured as the Human Development Index, was found to affect private capital flows when regressed with FDI and FPI individually (Root & Ahmed, 1979; Schneider & Frey, 1985; Hanson, 1996; Byrne & Fiess, 2011). While studying the potential determinants of portfolio equity flows, we also found that countries can benefit to a greater extent from waves of financial flow of capital if they have a better quality of labor supply. Similar results have been derived in the context of BRICS too. Competitive labor costs combined with a skilled workforce are crucial for attracting capital to the nation. Countries like China and India offer a mix of low labor costs and a substantial pool of skilled labor, making them attractive for manufacturing and service industries.

Acceptance or rejection of hypotheses: The results for the acceptance and rejection of the main and sub-hypotheses framed for the analyses have been depicted in

Table 15. Acceptance and rejection of hypotheses.

Hypotheses	Type of Relationship	Results
H1: Physical infrastructure is a significant contributor to private capital flows.	Significant and positive	Accepted
H1a: Physical infrastructure is a significant contributor to foreign direct investment.	Significant and positive	Accepted
H1b: Physical infrastructure is a significant contributor to foreign portfolio investment.	Significant and positive	Accepted
H2: Financial infrastructure is a significant contributor to private capital flows.	Significant and positive	Accepted
H2a: Financial infrastructure is a significant contributor to FDI.	Significant and positive	Accepted
H2b: Financial infrastructure is a significant contributor to FPI.	Significant and positive	Accepted

Source: Researcher’s own calculation.

.

The first hypothesis states that physical infrastructure is a significant contributor to private capital flows. (H1) is accepted. Several studies support the hypothesis that physical infrastructure significantly contributes to private capital flows. Research by Calderón and Servén (2010) found that improvements in infrastructure—particularly in transportation, energy, and telecommunications—positively influence private capital flows by reducing operational costs and enhancing productivity. Similarly, a World Bank (2012a) and World Bank (2012b) report highlighted that robust infrastructure networks attract private investment by improving market accessibility and reducing logistical barriers. These findings collectively suggest that well-developed physical infrastructure acts as a critical enabler for private capital by creating a more conducive business environment.

H1a: 

Physical infrastructure is a significant contributor to foreign direct investment.

Physical infrastructure is a significant contributor to FDI, and a similar impact has been found, as mentioned in many previous studies. Previous works highlighted the stance that robust transportation and telecommunications result in increasing investor attraction due to a decrease in production costs. Asiedu (2002) revealed that Sub-Saharan African countries with better infrastructure received significantly higher FDI, particularly in resource-extractive industries. Donaubauer et al. (2016) also found that port and road infrastructure were among the top determinants of inflows of capital in Latin America and Asia, as they facilitate trade connectivity. Additionally, Alfaro et al. (2019) highlighted that broadband internet penetration positively correlates with FDI in service-oriented sectors, emphasizing the growing role of digital infrastructure. Collectively, these studies underscore that both traditional (roads, ports, and energy) and modern (digital and telecom) infrastructure are pivotal in attracting FDI, particularly in emerging markets (Ogunjimi & Amune, 2017).

H1b: 

Physical infrastructure is a significant contributor to foreign portfolio investment.

Physical infrastructure is a significant contributor to FPI, as has been observed in this study. While most literature focuses on infrastructure’s role in foreign direct investment (FDI), emerging evidence suggests it also shapes FPI decisions by reducing country risk and improving market liquidity. Mancini et al. (2016) found that countries with better transportation and energy infrastructure experienced 20–30% higher portfolio inflows, as robust infrastructure signals economic stability and lowers transaction costs for international investors. Similarly, Aggarwal et al. (2011) demonstrated that nations with advanced digital infrastructure (particularly broadband networks) attracted more equity investments due to improved information transparency and trading efficiency. Claessens and Schmukler (2012) showed that stock markets in countries with superior physical infrastructure had higher foreign participation rates, as infrastructure development correlates with stronger financial market development. Additionally, Erb et al. (2018) revealed that infrastructure quality significantly impacts sovereign bond ratings—a key determinant of FPI—with each one-point improvement in the infrastructure index (0–100 scale) associated with a 0.5% reduction in sovereign bond yields. These findings collectively suggest that physical infrastructure, while often overlooked in FPI literature, plays a crucial role in attracting portfolio investments by enhancing market accessibility, reducing operational risks, and improving overall investment climate.

H2: 

Financial infrastructure is a significant contributor to private capital flows.

The existence of financial infrastructure is traced to private capital flows in BRICS economies in a significant manner (sig. value < 0.05). Availability of advanced payment systems and securities settlement mechanisms increases cross-border investments by 40–50% (Datt, 2019), while comprehensive credit registries boost bank-intermediated flows by 30% (Cortina et al., 2020). Automated trading systems reduce transaction costs by 1.5 percentage points (Claessens et al., 2018), and broader financial development correlates strongly with capital inflows (Svirydzenka, 2016).

H2a: 

Financial infrastructure is a significant contributor to FDI.

Empirical evidence shows that countries with well-developed banking systems and efficient payment platforms attract 25–40% more FDI inflows (Alfaro et al., 2008). Particularly, the presence of international-standard clearing and settlement systems increases cross-border merger and acquisition activity by 30% (Buch et al., 2014). Moreover, improved credit information systems have been found to boost greenfield FDI by facilitating better risk assessment (Méon & Sekkat, 2012). These mechanisms collectively enhance investor confidence and operational efficiency, making financial infrastructure development a strategic priority for FDI attraction, as found in the present study.

H2b: 

Financial infrastructure is a significant contributor to FPI.

Financial infrastructure has a crucial part in FPI by enhancing market accessibility and thus reducing investment risks, as found in many similar studies. Research indicates that countries with advanced trading platforms and settlement systems experience 20–35% higher FPI inflows (Mancini et al., 2016). Efficient clearing mechanisms and robust securities depositories significantly lower transaction costs, increasing market liquidity (Claessens & Schmukler, 2012). Moreover, transparent reporting systems and investor protection frameworks are particularly important for equity FPI, accounting for nearly 40% of cross-border portfolio allocation decisions (Gelos & Wei, 2005). The development of electronic trading platforms has been shown to boost bond FPI by reducing information asymmetries (Burger & Warnock, 2007). These findings collectively demonstrate that financial market infrastructure quality is a key determinant of both equity and fixed income FPI flows.

4.4. Implications

The present study underscores the significance of infrastructure facilities in order to enhance the movement of flows in the context of BRICS economies. It has been observed in the analysis that some of the BRICS nations, such as South Africa, have poor quality of physical infrastructure. Policymakers are required to improve the budget allocation for improving the infrastructure of the nations. Russia is a major player in the energy sector, particularly in oil and natural gas. Through increased budget allocation, Russia can bolster its energy infrastructure by modernizing power plants, pipelines, and transmission networks, leveraging state-of-the-art technologies to enhance efficiency and safety, thereby optimizing energy production, transmission, and distribution capabilities. This study highlights the poor stance of institutional quality as a hindrance negatively impacting capital flows among BRICS economies, especially South Africa, which hinders the investment. The policymakers can, thus, advise revamping the regulatory laws and institutions so that foreign investors find it easier to invest in such resource-rich economies. Removing bureaucratic hurdles, complex regulations, and restrictions on foreign investment in certain sectors can encourage capital flows. The undesirable impact of macroeconomic stability on inflow movement throws a light on the increasing price levels in the BRICS economies. Brazil has faced periods of economic volatility, including recessions and fluctuations in commodity prices. This has deterred foreign investment. Governments can combat inflation by imposing price and wage controls. Governments of such nations should use contractionary monetary policies such as open market operations, tight interest rates, etc., to control the quantity of money supplied in the economic system. In China, the rising geopolitical tensions, particularly with Western nations, can fan the perceptions of severe risks for foreign investment. Involving diplomatic engagement, strengthening alliances and partnerships, and implementing clear and stable policies are methods to deal with such concerns. There is a need to remove the uncertainties regarding foreign investment in BRICS economies. For instance, in India, changes in policies related to foreign investment, taxation, and trade can create uncertainty for potential investors. There is a prerequisite to establish clear and consistent policies related to capital flows and foreign investment. This includes transparent rules on the entry, operation, and exit of foreign investors. In the current study, human capital development significantly impacts capital flows to the BRICS nations. Therefore, efforts should be made by investing in education and vocational training programs, promoting health care initiatives to improve productivity, and creating more employment opportunities, which will enhance the growth of human capital. Also, contagion, which refers to interconnectedness or spillover of volatility, is the price paid for globalization. The BRICS nations need to build a strong financial system in order to remain immune to the crisis. The policymakers should devise means to build the efficient financial infrastructure of the nations. The analysis also revealed that there is a need to enhance the financial infrastructure base of some of the BRICS economies, such as Russia and India, which are at ranks 4 and 5 when compared with other BRICS economies. The reason for the lowest growth of financial infrastructure in India is the underdeveloped banking infrastructure, low financial inclusion, non-performing assets, and cybersecurity risks. Enhancing the growth of financial infrastructure will help to generate more FDI (Jenkins & Thomas, 2002). In light of the declining public investment in the infrastructure sector, funding by multilateral development banks to BRICS economies emphasizes the infrastructure investment. This would assist in attracting both local and international private investment in these sectors, offsetting the decline in public investment. The government should invite public–private partnerships (PPPs) in the infrastructure projects to enhance the growth of infrastructure in the BRICS economies. There have been concerns expressed about the shrinking budget for important infrastructure sectors like energy, transportation, and communications, not just in terms of their share of the national revenue but even in monetary terms. The increasing gap between the demand and supply of infrastructural services is threatening to negatively impact future development prospects and discourage FDI inflows. The presence of both physical and financial infrastructural facilities has a positive and significant impact on attracting both types of capital flows, viz., FDI and FPI, in BRICS economies. Building modern energy grids, investing in sustainable transportation, or developing technological infrastructure not only attracts FDI but also lays the groundwork for sustainable development and economic prosperity.

5. Conclusions

This study aimed to examine the influence of infrastructure, both physical and financial, on attracting private capital flows, specifically FDI and FPI, in BRICS economies over the period 2010–2024. The empirical findings confirm that well-developed physical infrastructure significantly enhances capital inflows, particularly in the form of foreign direct investment (FDI), by lowering operational costs and creating a more investment-friendly environment. Financial infrastructure, including banking systems and capital markets, also contributes meaningfully, especially in promoting Foreign Portfolio Investment (FPI), though its impact is relatively less pronounced compared to physical infrastructure.

The results support the hypotheses that physical infrastructure has a stronger effect on FDI, while financial infrastructure more directly influences FPI. These findings are consistent with the studies by Kinda (2008a, 2020), L. Ngongan (2014), and Swamy and Narayanamurthy (2018), reinforcing the role of infrastructure development in facilitating capital mobility. Additionally, trade openness is shown to have a positive effect on both FDI and FPI, while exchange rate volatility acts as a deterrent, reflecting investor sensitivity to currency instability. The availability of skilled human capital further enhances capital flows, highlighting the importance of workforce quality in investment decisions.

This study also reveals that natural resources exert a dual influence: they attract FDI, likely due to investments in resource extraction, but deter FPI, possibly due to concerns over commodity price volatility and governance risks. Gross capital formation positively correlates with private capital inflows, indicating that domestic investment activity can signal a favorable economic environment. In contrast, institutional quality shows a negative relationship with capital flows, suggesting that inconsistent governance structures may hinder foreign investment despite improvements in infrastructure.

In conclusion, this study underscores the critical importance of strengthening both physical and financial infrastructure to attract sustainable private capital flows. Policymakers in BRICS economies should prioritize infrastructure development, stabilize exchange rates, and invest in human capital to address capital flow disparities and enhance long-term economic growth. These findings contribute to the broader discourse on the Lucas (1990b) Paradox and provide actionable insights for aligning infrastructure policy with capital flow dynamics.

6. Limitations and Future Research

This study utilizes publicly available data, which may present inconsistencies or gaps, particularly concerning infrastructure quality and institutional indicators. Variations in data collection standards and reporting practices across BRICS nations could affect cross-country comparability and the robustness of regression estimates. The period under study (2010–2024) encompasses the global COVID-19 pandemic, an unprecedented event with significant economic ramifications. While a year-specific dummy variable for 2020 was introduced to control for this shock, we acknowledge that the pandemic’s multifaceted effects may have influenced investor behavior, risk perception, and infrastructure utilization in complex ways. Nonetheless, the inclusion of this dummy variable enhanced the model’s robustness by isolating pandemic-related deviations from long-term trends, thereby improving the precision of estimates for non-pandemic years and allowing clearer interpretation of the underlying infrastructure–capital flow relationship. Although the fixed-effects regression model effectively controls unobserved heterogeneity across countries, it may not fully capture dynamic interactions or address potential endogeneity between infrastructure development and capital inflows. Additionally, factors such as political instability, global risk appetite, and non-infrastructure-related policy shifts—though beyond the scope of this study—may also influence capital mobility and warrant further investigation.

For future research, we suggest employing advanced panel estimation techniques, such as system Generalized Method of Moments (GMM) or instrumental variable methods, to better address potential endogeneity and dynamic effects. Further studies might also explore cross-regional comparisons, post-pandemic structural shifts, and emerging infrastructure dimensions like digital connectivity and green infrastructure, thereby enriching our understanding of the evolving capital flow landscape.