Abstract
Background: The Mundell-Fleming model (MFM) predicts that in an environment with freely floating exchange rates, a drop in interest rates will lead to capital flight. In other words, if the domestic interest rate is higher than the world interest rate, capital flows in. The model plays a key role in anticipating the link between interest rates and capital flows.
Aim: The core objective of this study was to test the applicability of the MFM in emerging market economies. Despite its importance, no study has examined the applicability of the MFM model in emerging market economies, as far as this study is aware.
Methods: A quantitative approach using panel annual data over the period of 2000 to 2017 for five emerging countries was carried out. Brazil, Malaysia, China, India and South Africa were the countries considered because of data availability. The dynamic ordinary least square (DOLS) and fully modified ordinary least square (FMOLS) were used to analyse the data.
Results: The study confirmed the applicability of the MFM, given a positive relationship between interest rate and portfolio investment.
Conclusion: The study recommended that to ease the threat of currency appreciation, the central banks in emerging market economies must ensure that the domestic interest rate aligns with the world interest rate. This will promote exchange rate stability, and whenever there is an appreciation or depreciation, the central banks must use interest rates to bring the exchange rate back to the desired rate.
Contribution: The results from the study can be used by the Central Banks to make effective monetary policy. The Central Banks in emerging market economies have been in a policy blind regarding how they should react to the changes in US interest rates. Understanding the applicability of Mundell-Fleming model in emerging market economies can be very powerful in helping the Central Banks and the government to make informed, educated, and unemotional foreign exchange decisions.
Keywords: capital flows; interest rates; exchange rates; GDP; Mundell-Fleming; portfolio investment.
Introduction
Fleming (1962) and Mundell (1963), in their major works, found an adverse link between the degree of global capital mobility and the effectiveness of fiscal policy as measured by its effect on total output. According to the Mundell-Fleming model (MFM), when two large, interdependent economies coexist, capital mobility results in a crowding-out effect caused by exchange rates. The MFM predicts that in an environment with a free-floating exchange rate, a drop in interest rates would lead to capital flight, which in turn lowers the exchange rate and raises net exports. Weeks (2008) argues that when the movement of capital is elastic and a floating exchange rate system is used, the application of monetary policy yields better results than fiscal policy. Numerous emerging market economies have found that luring in foreign capital has a number of consequences, including macroeconomic imbalances and associated financial crises. The spike in capital inflows frequently results in a sizable portion going into foreign exchange reserves, which has an effect on the imbalances. A gain in output brought on by an increase in the money supply will result in a trade imbalance that will be addressed by a fall in the exchange rate (Shweta, Vanitha & Ramesh 2021).
The dynamic evolution of international capital flows has been a key issue in the economic debate since the 1990s financial crises, which revealed the fragility of emerging market economies in the face of the volatile movements of cross-border capital. However, recently economists have paid attention to the destabilising impact of uncontrolled capital flows on developing economies, in particular on financial instability, currency crises and macroeconomic imbalances (Derick 2018). For example, a sharp reversal of capital inflows in the 2013 Taper Tantrum led to significant exchange rate depreciation and capital flight in countries such as India and Brazil (Eichengreen & Gupta 2016), confirming the risks associated with unregulated financial integration.
International capital flows have become crucial to the global economy in the era of economic globalisation and trade liberalisation (Li et al. 2025). Even though net capital flows are going to the developed countries, there are some situations where the indirect effect may outweigh the direct effect and increase output in both developed and developing countries (Botta, Yajima & Porcile 2023; Hernandez-Vega 2019). Some emerging economies, such as Turkey, have been the main beneficiaries of financial flows from the well-to-do economies such as the United States and Europe to the developing world. These massive outflows appear to be caused by a variety of sources. These flows have been negatively impacted by several factors including the dramatic drop in commodity prices, concerns over China’s economic downturn and the normalisation of the monetary policy of the Federal Reserve (International Monetary Fund 2018). Several special shocks of a political and/or geopolitical nature have also contributed, in addition to the economic shocks that have affected most of the emerging countries.
The movement of capital can either be beneficial or have a negative impact on output. According to conventional wisdom, capital inflows will redirect investments towards increasingly lucrative industries, perhaps increase innovation and efficiency, and increase output creation and favourable economic development (Bukovšak, Lukinić Čardić & Pavić 2020; Su & Makoto 2023). However, there are fears about the potentially disruptive impact of the movement of capital. Lartey (2008) and End (2024) assert that an increase in capital inflows will eventually cause non-tradable demand to grow, increase non-tradable yield as well as raise the price of non-tradables. A rise in the price of non-tradables causes an appreciation of the exchange rate, suggesting a loss of international competitiveness that is disruptive to the tradable sector and is referred to as the ‘Dutch sickness’. Furthermore, this effect is particularly pronounced for open small economies that are highly dependent on trade. Johnson et al. (2001) and Mansour (2025) also claim that economic harm can result from an increase in value followed by significant capital inflows. All things considered, the question of whether the movement of capital is beneficial for the development of an economy or not is one that is far from being resolved.
There have been some fluctuations in the movement of capital to emerging market economies (EMEs) during the past 10 years in line with fluctuations in US interest rates (US monetary policy) (Clark et al. 2019). These fluctuations in capital flows have influenced exchange rates and the overall stability of the financial markets and economies of emerging countries. For emerging market economies, managing capital flows and their effects on exchange rates is difficult as they fuel extreme borrowing, increase the risk of asset price bubbles and result in unintended changes in the exchange rate (Shi & Phillips 2022). According to the IMF (2018), the possibility of changes in net inflows on the capital account complicates the work of macroeconomic policymakers tasked with preventing inflation and creating the conditions for real development. Factors such as the domestic interest rate have an impact on both local and foreign capital flows, and shocks are potentially ever-present. Strong inflows of foreign capital have led to macroeconomic imbalances and severe financial vulnerability in several target nations (Clark et al. 2019; Schroth 2023). Some of the nations that make up the developing markets have gone through this. Recurrent current account deficits in Brazil, India and South Africa have impacted those countries’ exchange rates (Shi & Phillips 2022). Examining the dynamics of interest rates, financial flows and currency rates in these economies becomes necessary as a result. Testing the MFM, which depicts the short-term link between an economy’s nominal exchange rate, interest rate and output, can be used to do this. However, this study only looks at the first stage of the MFM. The first stage predicts that interest rates attract capital flows. The second stage looks at how the increase in capital flows influences demand for the domestic currency leading to an appreciation of the domestic currency. The study focuses only on the first stage of the MFM to isolate and clearly assess the direct relationship between interest rates and capital flows without the added complexity of exchange rate dynamics.
The study focuses only on the first stage of the MFM because no previous research has conducted a panel study on emerging economies to specifically examine the direct relationship between interest rates and capital flows. By isolating the first stage, the study fills an important gap in the literature, providing new insights into how interest rates influence capital movements in emerging markets. By concentrating on this initial link, the study ensures a more precise understanding of how interest rate changes independently attract or deter capital movements. Emerging countries such as South Africa have been experiencing problems when advanced economies such as the US adjust their interest rates.
Literature review
Theoretical framework
Mundell-Fleming model
The model is founded on an extremely restrictive premise. It considers a small open economy with perfect movement of capital. This implies that the economy may freely acquire loans from the international financial markets at the current interest rate because the local interest rate is governed by the global interest rate. Therefore, the interest rate is not considered while formulating policy in the small economy under consideration. This essentially implies that the only variables that can affect macroeconomic adjustment are changes in the exchange rate. The changes in exchange rates on foreign currency markets account for most of the adjustment required to maintain the officially specified exchange rate. In response to changing economic conditions, the central bank permits currency rate changes (Meyer & Sack 2014).
According to Muhammad et al. 2017, the MFM offers a structure for evaluating monetary and fiscal policy as well as describing the operation of a small economy that is open to international trade in products and financial assets. A static, non-micro-founded model that extends the Keynesian model of investment, savings and liquidity preference makes up the fundamental framework (IS-LM). In fact, the MFM and IS-LM models have similar philosophical and methodological approaches as well as similar fundamental characteristics: the model is linear, and the primary presumption is that consumer prices remain constant (Piazzesi 2005). With a specific parameterisation, the MFM nests the IS-LM model as a special instance. The open macro application of the typical IS-LM study is the MFM. It is an IS-LM analysis with commerce and global capital mobility, to be more precise.
Neo-classical economic efficiency theory
The neoclassical microeconomic theory of efficiency is concerned with both stocks and flows. It is argued that capital movements enhance portfolio investing on the equity side. This is accomplished by diversifying investment possibilities which leads to an increase in the returns available to people who are saving (Rey 2015). This improves the optimality of portfolios, which, in turn, boosts growth. The second and most important argument for the efficiency of capital movement is that it enhances national saving and investment, hence promoting capital accumulation and economic progress. According to the loanable funds theory, the interest rate is controlled by supply and demand in the money market.
Empirical literature
The Mundell-Fleming model assumes that in case of high capital mobility, the influence of monetary policy on output is determined by the exchange rate regime. In contrast to the model’s presuppositions, it has been recently found that the emerging market economies (EMEs) with floating exchange rates are still subject to capital flow volatility.
Wang Guosong et al. (2006) took CF1 (the sum of the increase in official reserves and the difference between imports and exports) and CF2 (the sum of the net investment income, as well as the difference between imports and exports and the increase in official reserves) as explanatory variables. Respectively, price levels, interest rate spread and nominal exchange rate expectations were used as explanatory variables. The empirical results during the entire sample period of 1982–2004 show that the main factor affecting the flows of capital in China was the RMB exchange rate expectation and none of the other factors passed the test. However, the test for the years 1982–1993 was not ideal and the results of the test for 1994–2004 were ideal except for the interest rate spread.
Wang (2006a) study measured capital flows in China by merging missing accounts and the net error in capital and financial accounts. He chose the difference in interest rates, the exchange rate and prices between China and the United States and at the same time introduced two new variables: trade openness and exchange rate system reform. He explained the capital flows from 1985 to 2003 in China and the test results showed that the exchange rate, policy variables and trade openness had a strong explanatory power regarding the Chinese capital flow ability; however, the effect of interest rate spreads was very weak, and the price difference did not pass the statistical test.
Griffith-Jones and Gallagher (2011) expressed concerns about the abrupt cessation of capital flows soon after the financial crisis, which they believed exposed emerging economies to the worst growth outcomes and which capital controls may have prevented. Ng’ambi (2015) investigated how fluctuations in exchange rates affected capital flows in South Africa from 2000: Q1 to 2014: Q3. He looked at the effects that exchange rate volatility had on various types of capital flows. To calculate exchange rate volatility from a trade-weighted exchange rate, the conditional variance Generalised Autoregressive Conditional Heteroskedasticity (GARCH) model was used. The results of the multiple regression analysis showed that the aggregated capital flows to South Africa were negatively affected by exchange rate volatility in a statistically significant way. The results employing bivariate vector autoregressions (VARs), the Granger-causality test, impulse response and variance decomposition showed that there was a dynamic link between exchange rate volatility and both aggregated and disaggregated capital flows. The results of the VAR specifications also demonstrated that portfolio movements account for a sizable portion of innovations in exchange rate volatility and have a large bidirectional causal link with exchange rate volatility.
Ayanou (2016) investigated how foreign capital inflows affected mortgage interest rates in the United States. According to the study’s findings, foreign capital movements and 30-year mortgage interest rates were considerably and adversely associated. The nature and size of the capital inflows affect the evidence for how responsive the interest rates were to foreign capital inflows. Raza and Zoega (2019) looked into the effects of inflows of capital on the real exchange rate as well as the growth of output, consumption and investment using data for Iceland from the first quarter of 1997 to the last quarter of 2018. They discovered that rising capital inflows into a currency area increased investment, consumption and output. Considering the above, higher domestic interest rates may have an expansionary impact in an environment of unrestricted capital mobility by promoting capital inflows that raise real exchange rates, output and private spending.
Olaberria (2014) studied the association between US long-term interest rates and cycles of capital flows to EMEs. It finds that, indeed, cycles in capital flows to EMEs were linked to global conditions, including global risk aversion and long-term interest rates in the United States. In particular, higher US long-term interest rates were associated with lower levels of gross capital flows to EMEs and to a higher probability of observing sharp reversals in those flows. Ibarra and Tellez-Leon (2020) analysed the impact and persistence of shocks to global (push) and domestic (pull) factors on each component of the financial account for the Mexican Balance of Payments, at the highest degree of disaggregation, including investment by foreign residents in Mexican public and private sector securities as well as investment by domestic residents in foreign securities. To this end, we estimate impulse response functions from vector autoregressive models for the period 1995–2015. We find that an increase in the foreign interest rate led to lower portfolio investment. An increase in global risk generated lower portfolio investment, particularly in private sector securities. Foreign investors responded to a greater extent to foreign interest rate and liquidity shocks compared to domestic investors.
Jabbor and Awan (2014) evaluated the factors of capital influx in a sample of developing nations with a particular emphasis on Pakistan. The study’s focus variable was internal factors which included the current account, foreign reserve and domestic interest rate, and the study’s analytical technique was panel data. The study’s findings indicate, among other things, that foreign reserves and current account balances are significant determinants of capital inflows in the studied nations. Makhetha-Kosi, Mishi and Ngonyama (2016) explored how international capital flows respond to interest rate differentials in South Africa from 1990 to 2013. The study examined the impulse response of capital flows to interest rate differentials using Vector Error Correction Model (VECM) analysis. It concluded that, while the country has a positive interest rate differential, it does not necessarily result in an increase in capital inflow. Additionally, the study discovered that various components of capital flows behave differently in response to interest rate differentials.
Odionye, Ojiaku and Uba (2023) investigated the effects of interest rate differential, exchange rate changes and political stability on the inflow of foreign capital into Nigeria between 1981 and 2021. To investigate the impact of the factors on foreign capital inflow in regimes with low and high interest rate differentials, the study used a discrete threshold regression model (DTRM). The study discovered an interest rate differential threshold value of 3.68%, indicating that an interest rate differential at or above the predefined threshold promotes a favourable and considerable inflow of foreign capital into the nation. Additionally, the country’s capital inflow is influenced by political stability and exchange rate fluctuations. Mansur (2024) tested capital flow volatility regimes and the monetary policy dilemma. Mundell–Fleming’s trilemma underlines trade-offs among three things: stable exchange rates, independent monetary policy and free capital mobility. Not only does the amount of capital flows matter, but their volatility also causes substantial impacts.
Nwosa and Adeleke (2017) used the Exponential Generalised Autoregressive Conditional Heteroskedasticity (EGARCH) model to examine the factors that contribute to the volatility of foreign direct investment (FDI) and foreign portfolio investment (FPI) in Nigeria from 1986 to 2016. Their analysis concentrated on one aspect of foreign exchange flows: private investment. Their study examined local interest rates, stock values, trade openness and global economic revenues. They discovered that the domestic interest rate, trade openness and stock valuation are the primary drivers of private investment. Goldberg and Krogstrup (2024) tested international capital flow pressures and global factors. The risk sensitivity of international capital flow pressures is explored using a new Exchange Market Pressure index that combines pressures observed in exchange rate adjustments with model-based estimates of incipient pressures masked by foreign exchange interventions and policy rate adjustments. The sensitivity of capital flow pressures to risk sentiment evolves over time, varies significantly across countries, and differs between normal times and extreme stress events.
Gelos et al. (2024) tested the impact of US dollar on capital flows to EMEs. Over the past two decades, emerging market economies (EMEs) have shifted their external financing sources away from foreign currency bank lending to local currency bonds and equities. They found that the strength of the US dollar against advanced economy currencies is a key driver of local currency bond and equity flows as it affects global investors’ risk appetite. Moreover, its importance has risen over the past decade, in part driven by a growing role of mutual funds among foreign investors. These findings may help EMEs achieve more stable external financing. Matschke, Alice and Sai (2024) examined the three most recent US policy tightening cycles to analyse when and why central banks in emerging markets raised their own policy rates. They find that while emerging markets sometimes raised rates in response to capital outflows or a depreciation of their currency resulting from US monetary policy, they more frequently raised rates in response to domestic inflationary pressures. Their findings provide new descriptive evidence on the conduct of monetary policy in emerging markets.
Research methods and design
Data sources
The study used panel annual data for the period 1995 to 2019 to achieve its goals. This study period was chosen because of data availability constraints. Five countries, namely Brazil, Malaysia, China, India and South Africa, were selected because they had reliable and consistent data patterns. Data were sourced from the World Bank.
Model specification
To test the MFM, the study adopted the approach of Warnock and Warnock (2009) who tested US interest rates and international capital flows. The modified model (Equation 1) is as follows:
where gross portfolio investment (GPI) is capital flows in the form of portfolio investments, INT is nominal interest rate, INF is the inflation rate, GDPG is the gross domestic product growth rate, CRD is credit to the private sector, GS is gross savings in country i at time t, µ_1 is the error term. The full definition of the variables is as follows:
Gross portfolio investment
Capital flows involve the movement of assets like capital, money and investments across international borders. These flows can include FPI. This study used FPI. Foreign portfolio investment involves an investor purchasing foreign financial assets. The transaction of foreign securities generally occurs on an organised formal securities exchange or through an over-the-counter market transaction. This is the dependent variable of the study.
Interest rate
The interest rate at which depository institutions trade funds (balances held at Federal Reserve Banks) with each other overnight. Various studies have shown that interest rates influence capital flows. Moreover, the Mundel-Flemming model predicts a relationship between interest rates and capital flows. In the MFM, interest rates play a crucial role in determining capital flows and consequently exchange rates. Higher domestic interest rates relative to foreign interest rates attract foreign capital, leading to an appreciation of the domestic currency and potentially a surplus in the balance of payments (Carvalho & Fidora 2015).
Inflation
A consumer price index is a statistical estimate of the level of prices of goods and services bought for consumption purposes by households. It is calculated as the weighted average price of a market basket of consumer goods and services. Inflation and capital flows are interconnected, with high inflation potentially leading to capital outflows as investors seek more stable returns elsewhere (Rey 2015). Conversely, capital inflows can sometimes contribute to inflationary pressures, especially if they are not managed carefully.
Gross domestic product
Annual percentage growth rate of gross domestic product (GDP) at market prices is based on constant local currency. Aggregates are based on constant 2015 prices, expressed in US dollars. Gross domestic product is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. Gross domestic product significantly influences capital flows by acting as a proxy for a country’s economic strength and potential for growth, thereby attracting or deterring investment (Ikpesu 2019). Generally, a higher GDP suggests a more robust economy, leading to increased capital inflows, while a lower GDP may signal economic weakness and potentially prompt capital outflows
Credit to private sector
Domestic credit to private sector refers to financial resources provided to the private sector by financial corporations, such as through loans, purchases of non-equity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries, these claims include credit to public enterprises. Limited domestic credit can restrict economic growth, potentially leading to lower consumption and investment, which could in turn lead to a decrease in demand for foreign goods and a potential increase in capital inflows (Fanelli & Straub 2021).
Savings
Gross savings are calculated as gross national income less total consumption, plus net transfers. When domestic savings are high, there is potentially less need for borrowing from abroad to finance domestic investment, leading to a net outflow of capital (a current account surplus) (Hata & Sekine 2019). Conversely, if domestic investment exceeds domestic savings, a country might need to borrow from abroad, resulting in a net inflow of capital (a current account deficit).
Estimation techniques
Unit root and cointegration
Given that the variables must be integrated in the same order, it is necessary to first examine the data’s unit root properties before testing the panel cointegration among the variables. LM-Pesaran-Shin (Pesaran et al. 2003), Levin, Lin and Chu (2002), Fisher ADF and Fisher PP tests are the four-unit roots methods applied in the research. The null hypothesis of all these panel unit roots tests consistently taken non-stationary data.
Cointegration tests were conducted once the unit root had been confirmed in the study. Three different types of panel cointegration tests were used in the investigation. Pedroni (1999) introduced one form of test, Kao (1999) introduced a second type and Fisher introduced a third type of test that included the Johansen test. It is crucial to ascertain whether there is a cointegrating relationship between the variables if a unit root is found in the variables.
Panel autoregressive distributed lag (Pooled mean group)
The Panel Autoregressive Distributed Lag (ARDL) model, often referred to as the Pooled Mean Group (PMG) estimator, is a dynamic panel data model widely used for analysing long-term relationships among variables in panel data settings. This model is particularly advantageous because it allows for the estimation of both short-term and long-term coefficients while accounting for heterogeneity across cross-sectional units, the PMG estimator pools the long-run parameters across cross-sectional units while allowing short-run parameters and error variances to differ. This approach is especially useful when dealing with panels that include heterogeneous units, as it provides a more flexible framework for capturing the dynamics of economic relationships.
Results
Descriptive statistics
In this section, the summary statistics for the key variables under investigation are presented. The summary statistics are shown in Table 1 in terms of the mean, standard deviation, lowest and maximum values and the number of observations.
Table 1 shows that there is no normal distribution for all the series employed. The GS variable was the only variable that was normally distributed. This is shown by the Jarque-Bera test which had a probability value of 0.241999.
Cointegration
The study performed a Pedroni cointegration test and results are displayed in Table 2.
The Pedroni Test, which has eight weighted subtests including panel-v, panel-rho, panel Phillips–Perron and panel Augmented Dickey–Fuller statistics, is represented by the first panel in the table. These tests are based on pooling the regression residuals along the panel’s internal dimension. The second panel in the table contains the second type of the Pedroni Test with three sub-tests, i.e., group rho, group PP and group ADF statistics. These tests, however, are based on pooling the regression residuals along the panel’s cross-sectional dimension. All in all, there are 11 tests (8 under the common Autoregressive coefficients and 3 under the individual Autoregressive coefficients) in the Pedroni Test. The same null hypothesis of no cointegration applies to seven of the subtests from each of the two types. About 8 of the 11 statistics are significant, according to the results. According to Lee & Chang 2010, the null hypothesis that cointegration does not exist can be rejected if a minimum of four statistics are significant, and as a result, cointegration is present. This means that the study had to adopt some panel cointegration regressors.
Long run estimates (autoregressive distributed lag)
After detecting the presence of cointegration, the study estimated the long-run relationship using the ARDL technique and dynamic ordinary least square (DOLS). Results are shown in Table 3 and Table 4.
According to the results, there is a positive relationship between INT and GPI. This is what the MFM predicts. This result means that when there is an increase in interest rates, gross portfolio capital flows increase. This implies that increases in interest rates are accompanied by increases in capital inflows. This shows that the interest rate is a powerful tool that affects the flow of foreign capital in emerging markets. The implication of this is that when interest rates in emerging markets are lower, there will be capital outflows. Conversely, when the interest rates are higher, there will be capital inflows in emerging markets. An open economy lacks capital regulations and because there are no restrictions on the movement of financial assets, people are attracted to assets with higher interest rates. When industrialised and developing countries have different interest rates, the latter may experience spikes in capital inflows and periods of currency appreciation (Acharya & Bengui 2016; Zoega 2016). There is considerable disagreement in the literature on this topic. For instance, Grubel (1968) showed that even if the disparity is zero or negative, capital can still flow, suggesting that the interest rate level may not have an impact on capital flows. Verma and Prakash (2011), Dua and Garg (2013) and Nier et al. (2014), on the other hand, argue that there is a causal link between capital movement and interest rate differentials, with the latter proving tenacity in the effect of interest rate differentials on capital movements relative to other factors, particularly during stressful times.
The outcomes also indicate that INF and GPI have a negative relationship. The negative relationship means that when inflation is rising, capital flows decrease. This is understandable given that economic activities do not benefit from inflation. Every economic participant, including central banks, government policymakers, producers and consumers, is impacted by inflation, which is a crucial and cyclical macro indicator (Koepke 2015). The implication of this is that emerging economies must keep inflation under control. Higher levels of inflation might discourage capital inflows, and this may hurt investment. Inflation is a major consideration when it comes to the performance of investment portfolios. Investors often buy fixed-income assets like bonds and treasuries because they want a consistent flow of interest income. However, because the interest rate on the majority of fixed-income assets remains constant until maturity, the purchasing power of interest payments declines as inflation increases. The amount of foreign currency reserves rises as a result of capital inflows. As these reserves are used to purchase local currency, the local monetary base grows without a corresponding increase in production. This leads to a situation in which too much money starts chasing too few available goods and services (IMF 2021).
The results show a negative relationship between GS and GPI. This makes sense because when domestic savings are low, capital is supposed to flow into the country. This is so because investors would see an opportunity to save their financial resources. Feldstein and Horioka (1980) established the connection between savings and investment for the first time as a quantity-based criterion to evaluate global capital mobility. In their view, domestic savings would seek out the maximum returns in the fully linked capital markets in a world where capital was totally mobile and should be unrelated to domestic investment demand. By the same token, domestic investment requirements would be met by global capital markets independent of domestic savings availability. They therefore put out the hypothesis that the less mobile the international capital is, or, to put it another way, the less integrated the global capital markets are, the higher the positive connection between savings and investment. The assumption of small countries is a key factor in testing the savings-investment relationship. The Feldstein and Horioka (1980) hypothesis is limited to small open economies. Even if the foreign capital market operated flawlessly, domestic savings and investment may move together for a large open economy such as the United States. This is because a substantial economy might have an impact on the real interest rate on the global economy. A decline in domestic savings could result in higher domestic and global interest rates, which would discourage investment worldwide, for a nation large enough to have an impact on the state of the global financial markets (Blanchard et al. 2016). It would be incorrect to infer from this situation that poor capital mobility follows from a high connection between savings and investments.
There is a substantial and expanding body of research on the topic of whether domestic or foreign (or “push”) forces are responsible for the capital flows into emerging market countries. Some claim that when global financial restrictions loosen, capital flows to developing market economies grow. The external and internal determinants are affecting capital flows to a few highly linked developing Asian economies (Adegboye et al. 2020). The Tobit model was used to investigate the factors that influence portfolio and other investment flows, and a gravity model was created to estimate the factors that influence foreign direct investment. Their findings demonstrate that while domestic factors, including per capita income, labour costs, trade and financial openness draw FDI inflows, growth in advanced economies has a major impact on the flow of bank loans and portfolio investments into developing Asia. The short-run results are displayed in Table 4.
The error correction term that was obtained from the model was −0.977. The probability of the error correction term was 0.000, and this shows that it is significant. It can thus be said that the model has an 89% speed of adjustment to the equilibrium. This shows that there is a strong adjustment. The shortrun results show that inflation has a negative relationship with capital flows. It can be said that when inflation increases, capital flows decrease. The short-run results show an insignificant relationship between interest rates (INT) and capital flows. This shows that interest rates do not affect capital flows in the short run. Results also show a positive relationship between savings and capital flows. This shows that the savings levels affect capital flows. Domestic credit is also seen to be insignificant. This shows that the level of domestic credit does not affect capital flows. The results show a positive relationship between GDP and capital flows. Gross domestic product is seen to be positively influencing capital flows in the short run. This means that when GDP increases, capital flows also increase.
Conclusions and recommendations
The study used panel cointegration methods: the ARDL and DOLS. These techniques were used because there was cointegration among the variables. Results showed that there is a positive relationship between interest rate and portfolio investment. This relationship is found in both the ARDL and the DOLS. This is what the MFM predicts. This result means that when there is an increase in interest rates, capital flows increase. An open economy has little capital restrictions, and when there are no restrictions on the movement of financial assets, individuals will be attracted to assets with higher real interest rates. This is what is predicted by the MFM. This means that the MFM holds in emerging market economies. The findings also highlighted that there is a negative relationship between INF and PI. The negative relationship means that when inflation is rising, capital flows decrease.
Based on the findings, the following recommendations can be made. Emerging market economies can use the MFM to predict the interest rate-exchange rate and output relationship. The central banks, in emerging market economies, must understand that the relationship between interest rate, capital flows and exchange rate is not straightforward, and needs to be scrutinised to grow the economy without disturbing other macroeconomic factors. For example, raising interest rates to attract capital flows can be beneficial in raising capital flows, but this may hurt domestic consumption and investment. Furthermore, when the exchange rate is appreciating or depreciating because of capital flows, there needs to be a strong mechanism that can regulate the rate of appreciation or depreciation. In emerging market economies, the reserve bank must employ what is referred to as the ‘sterilisation’ of capital flows to lessen the threat of currency appreciation. The local component of the monetary base (bank reserves plus currency) is decreased in a successful sterilisation operation to counteract the reserve influx, at least temporarily. A central bank uses sterilisation to lessen the effect of capital inflows and outflows on the supply of money. Sterilisation often comprises a central bank buying or selling financial assets in order to offset the effects of foreign exchange intervention.
Acknowledgements
First author would like to express heartfelt gratitude to all those who contributed to the completion of this research. Special thanks go to Dr Courage Mlambo and Dr Kin Sibanda for their invaluable insights. Authors are deeply grateful to Nelson Mandela University for providing the resources and support needed to carry out this study. This article is partially based on the author’s thesis entitled ‘An application of the Mundell Fleming model in emerging market economies’ towards the degree of (PhD) Economics in the Faculty of Economic Sciences, Nelson Mandela University, South Africa, December 2023, with supervisor Prof S. Mishi. It is available here: http://hdl.handle.net/10948/66039
Competing interests
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
Authors’ contributions
M.T. conceptualised and designed the study, conducted data collection and analysis, and wrote the manuscript. S.M. provided guidance throughout the research process, contributed to the interpretation of results and critically revised the manuscript for intellectual content. Both authors approved the final version of the manuscript for submission.
Ethical considerations
Ethical clearance to conduct this study was obtained from Nelson Mandela University, Ethics Committee of the Faculty of Business and Economic Science (H21-BES-ECO-025).
Funding information
This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
Data availability
The data that support the findings of this study are available upon request from the corresponding author.
Disclaimer
The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors.
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