69 Finance, Accounting and Business Analysis Volume 7 Issue 1, 2025 http://faba.bg/ ISSN 2603-5324 DOI: https://doi.org/10.37075/FABA.2025.1.06 Asymmetric Impact of Interest Rate on Economic Growth in Kenya Talknice Saungweme1* , Glenda Maluleke2 , Nicholas M. Odhiambo3 Department of Economics, University of South Africa, South Africa1 Department of Economics, University of South Africa, South Africa2 Department of Economics, University of South Africa, South Africa3 * Corresponding author Info Articles Abstract History Article: Submitted 30 January 2025 Revised 17 April 2025 Accepted 28 April 2025 Purpose: This study re-examines the relationship between interest rates and economic growth, focusing on the asymmetric effects of lending interest rates on Kenya's economic performance. Design/Methodology/Approach: The study applied the nonlinear autoregressive distributed lag (NARDL) model to ascertain the distinct impacts of positive and negative interest rate shocks on economic growth in both the short and long run. It uses yearly time series data spanning the years 1980-2021. Findings: The results of the cointegration tests found evidence supporting the existence of an asymmetric long-run relationship, while the Wald test results show that there is a long-run and short-run asymmetry link between interest rates and economic growth in Kenya. On average, positive changes in lending interest rates have no significant impact on economic growth in Kenya, both in the short and long run. However, negative interest rate shocks spur economic growth in the short run but impede growth in the long run. Research Limitations/Implication: The study is limited to the Kenyan context and the dataset range of 1980–2021. Future research could explore thresholds for optimal interest rate levels and include a broader range of countries for comparative analysis. Originality/Value: This study uniquely applies the NARDL framework to Kenya, providing new insights into the asymmetric impact of interest rates on economic growth. Paper Type: Research Paper. Keywords: Economic growth, interest rates, Kenya, NARDL JEL: C32, E43, O42 * Address Correspondence: E-mail: talknice2009@gmail.com1 malulg@unisa.ac.za2 odhianm@unisa.ac.za3 http://faba.bg/ mailto:talknice2009@gmail.com1 mailto:odhianm@unisa.ac.za https://orcid.org/0000-0003-1255-7837 https://orcid.org/0000-0002-5234-3115 https://orcid.org/0000-0003-4988-0259 T. Saungweme, G. Maluleke, N. Odhiambo / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 70 INTRODUCTION Introduction, theoretical and empirical literature synthesis Beginning with the Asian Tiger economies and shifting to newly industrialised economies, such as China, South Africa, India, Russia and Brazil, the most powerful and successful strategy for poverty reduction is through sustained economic growth emanating from aggressive and rapid innovation and industrialisation (Chatterjee and Naka 2022; United Nations Industrial Development Organisation 2020). These two groups of countries pursued both export-oriented and import substitution strategies, while advancing innovative production technologies (Liu et al. 2023). While both monetary and nonmonetary factors can be drivers of economic growth, nonmonetary factors have received most of the attention in research studies. This insight serves as the motivation for this paper, which has two primary goals. The first is to thoroughly review and document the relationship between lending interest rates and economic growth in Kenya. Lending interest rates can have a significant impact on the rate and trajectory of economic growth by influencing the magnitude and return of investment, as well as the scope and composition of both saving and consumption. Infrastructure development, industrialisation, institutional investors, mutual funds, and the corporate sector are all exposed to risks stemming from interest rate volatility (Olasehinde-Williams et al. 2024). The second is to bridge the gap in interest rate modelling by applying a model that can be used to quantify and comprehend the nature of the link between interest rates and growth in Kenya. Specifically, this study investigates the asymmetric impact of interest rates on economic growth in Kenya using time series data from 1980 to 2021. The nonlinear ARDL method captures the positive and negative changes asymmetrically and the short- and long-run dynamics of interest rates on economic growth in Kenya, enabling a more precise analysis across different economic conditions (see Saungweme et al. 2024; Shin et al. 2014). To the best of our knowledge, this is the first analysis of its kind conducted in Kenya, and it is unique since it employs an advanced estimation procedure that takes into account the asymmetrical characteristics of lending interest rates. Therefore, the primary goals of this study are to complement previous growth research on Kenya and to support ongoing reforms in the areas of monetary, economic, and financial policy. These reforms are essential to preserving macroeconomic stability, preserving debt sustainability, strengthening market confidence, and enhancing the achievement of Kenya's medium-term growth prospects (International Monetary Fund/IMF 2024; Odhiambo and Saungweme 2023a; Saungweme and Odhiambo 2021). In addition, Kenya’s economy faces unique structural challenges and external shocks, such as fluctuating global interest rates and capital flows (IMF 2024). Therefore, understanding how interest rate fluctuations affect growth can provide insights into optimal policy decisions for sustainable development. From a theoretical standpoint, there are multiple opposing hypotheses about the relationship between interest rates and economic growth. The first is a cogent explanation of the boom-bust pattern offered by the Austrian school of economic thought. That is, low interest rates from the central bank would encourage investment bubbles, which would then lead to a burst in asset prices, a financial crisis, and a severe recession (Foldvary 2015). The rate of interest is interpreted by the Austrian school as reflecting a methodical discounting of future values. The Austrian hypothesis states that the relationship between interest rates and economic growth typically revolves around the time preference issue. For example, increased productivity could encourage people to invest more now, making present-day investing more preferred over future investment (Holmes 2011). Furthermore, the market for loanable funds—funds that are accessible for borrowing—determines the interest rate (Foldvary 2015). Borrowers will be able to access more funding for consumption and investment at reduced interest rates. In general, Austrian economics holds that a central bank's manipulation of money and interest rates is what causes recessions; the best way to prevent these controls is to let the money supply and interest rates be determined by free market forces in money and banking. Keynesian theory comes second. Keynes' approach to interest rate dynamics is in contradistinction with loanable funds theory. For Keynes, interest rate dynamics is based on his conception of ontological uncertainty, liquidity preference, investors’ expectations and animal spirits, financial institutions, financial markets, and institutional practices (Akram 2021). According to Keynes, the short-term interest rate is determined by the central bank's policy rate, which then affects the long-term interest rate (Akram 2021). These long-term interest rates then influence investment, saving and consumption decisions in the economy. The McKinnon-Shaw hypothesis comes in third. In their original individual works, they contended that financial policies in developing and emerging economies, including low and restricted interest rates and restrictive credit management, among other financial repression acts, result in a decrease in savings, investment and economic growth (Wilson and Odhiambo 2023). McKinnon (1973) studied an economy in which the vast majority of investors had very limited access to external financing. In his view, savers may find it more convenient to build up their money in financial assets until they have sufficient funds to invest T. Saungweme, G. Maluleke, N. Odhiambo / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 71 in higher-yielding physical assets (Leshoro and Wabiga 2023). Thus, deposits can act as a channel for the accumulation of capital, rendering deposits and capital complementary assets. The availability of deposits with positive real rates of return may thus encourage both saving and capital accumulation. In contrast to McKinnon, Shaw (1973) focused more on external rather than internal financing options as a fundamental constraint to capital formation. Shaw also underlined the significance of positive real deposit rates as an incentive to save in financially depressed economies. Shaw (1973) emphasised that high deposit rates might encourage investment spending by enabling the credit supply to grow in accordance with the financing requirements of the economy's productive sectors (Iddrisu and Alagidede 2020). Thus, McKinnon (1973) and Shaw (1973) suggest that low interest rates do not really increase investment and economic growth (Owusu 2023). After analysing the McKinnon-Shaw arguments, Mohlo (1986) came to the conclusion that deposits and physical capital complement each other in an intertemporal fashion, with current deposits being used to fund future investments. This link suggests that higher deposit rates inhibit investment in the short run but will eventually boost it in the long run. There is currently little but growing empirical research on the link between interest rates and economic growth (Leshoro and Wabiga 2023; Adabor 2022). First, Leshoro and Wabiga (2023) looked at how both positive and negative interest rate shocks affect private investment in South Africa. The study employed annual time series data from 1971 to 2019 and a nonlinear autoregressive distributed lag technique. The results indicate that interest rates and private investment exhibit short-run and long-run asymmetric relationships, with private investment responding differently to negative and positive shocks in interest rates. Second, by applying the NARDL approach, Adabor (2022) tested the asymmetric impact of lending interest rates on economic growth in Ghana using yearly time series data covering the period of 1970 to 2019. The study found evidence of long-run and short-run asymmetrical effects of lending on economic growth in Ghana. The findings further show that positive changes in lending rates generate a decrease of nearly 0.2% in economic growth while negative changes lead to an increase of about 0.7% in economic growth. Other non-asymmetric studies conducted on the interest rate-growth linkage include Lee and Werner (2023), Shaukat et al. (2019), Awad and Al Karaki (2019). Lee and Werner (2023) analysed the impact of interest rates on economic growth in 19 industrialised and emerging economies. The analysis used a time- varying dynamic conditional correlation in a GARCH model and further tested the direction of causation between the two variables in the studied economies. The results provide evidence consistent with the conclusion that lowering interest rates is counterproductive when trying to stimulate the economy. Awad and Al Karaki (2019) examined the impact of bank lending on economic growth in Palestine using quarterly time series data for the period from 1996 to 2015. The study employed the vector autoregressive model and vector error correction model, as well as the Granger causality test to test the underlying relationships. The study found that there was a statistically insignificant relationship between bank lending and economic growth. Additionally, there is evidence of unidirectional causality that runs from economic growth to bank lending. Shaukat et al. (2019) studied the mechanism by which the real interest rate establishes a negative effect on economic growth in 38 transitory economies. The study applied a dynamic panel data technique based on the Generalised Method of Moments for the period 1996-2015. The study found that during the transition period of developing economies, a high real interest rate restricts the economy's potential to grow. Considering the aforementioned theoretical stances and empirical evidence, the goal of this study is to empirically test the interest rate-growth relationship tailored to the Kenyan context. The remainder of the research is arranged as follows: Section 2 provides an overview of interest rates and economic growth trends in Kenya. Data, methodology, and estimation techniques are provided in Section 3. The empirical analysis is presented in Section 4, and the main conclusions and policy implications are summed up in Section 5. Overview of interest rate and economic growth trends in Kenya Kenya experienced a general balance of payments surplus from 1964 to 1972, with the exception of 1964, 1967, and 1971 (World Bank 2022). Interest rate policy in Kenya was largely dormant throughout this time. The government managed interest rates by setting minimum savings for all deposit-taking institutions and minimum lending rates for commercial banks, non-bank financial institutions, and building societies (Baynham 1989). A variety of internal and external causes, notably inflation brought on by a significant rise in oil prices and the consequences of a severe national drought in 1973, had a negative impact on the economy between 1972 and 1981 (IMF 1985). Following the shocks, the government re-examined its regulatory structure, which resulted in the progressive escalation of restrictions on imports, exports, interest rates, and domestic pricing. Between 1974 and 1989, the monetary authorities in Kenya used an administered interest rate framework. The employment of statutory credit ceilings and minimum savings deposit rates was a crucial T. Saungweme, G. Maluleke, N. Odhiambo / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 72 tool for managing market liquidity (IMF 1991a). A revision to the credit ceiling policy between 1989 and 1991 resulted in the maximum rate being split between short-term and long-term lending (IMF 1991a). The major challenge with controlling interest rates was a widening gap between administered and effective bank lending rates. For this reason, the monetary authorities have been unable to sell enough quantities of government securities to limit the growth of the money supply (IMF 1991b). The government then started a series of economic adjustment programs with the World Bank and IMF in 1991 and continued them until 1996. The overarching policy framework placed a strong emphasis on trade liberalisation, interest rate deregulation, and macroeconomic stabilisation (Obrien and Ryan 1999). In 1991, this framework resulted in a comprehensive liberalisation policy ideology. This philosophy is supported by the theoretical expectations of the McKinnon-Shaw hypothesis, which postulates that easing regulatory restrictions on interest rates will increase the volume of funds in deposit-taking institutions (McKinnon 1973; Shaw 1973). Regarding economic growth, the years 1963 and 1973 can be regarded as a decade of exceptional growth, with an average annual real growth rate of 6.7% (World Bank 2022). The favourable weather and trade conditions for Kenya's commodity exports, along with the successful execution of the import- substitution program, all contributed to the country's impressive economic performance (Odhiambo and Saungweme 2023b). Between 1974 and 1979, the country underwent an economic recession marked by declining terms of trade and rising oil prices (IMF 2022). The period 1980-85 can be described as an era of macroeconomic imbalance and stabilisation, with low growth. Economic and monetary policy interventions between 1985 and 1989, which were further enhanced by the 1986 coffee boom, led to an economic rebound. For the first half of this period, real GDP increased by an average of 5.3% yearly (World Bank 2022). Figure 1 shows interest rates and GDP per capita growth rates for the period 1980 to 2022. Source: Authors’ compilation using World Bank (2022) data Figure 1. Interest rate and economic growth trends in Kenya (1980-2022) Figure 1 largely describes a negative correlation between interest rates and annual growth of GDP per capita in Kenya. Interest rates rose sharply during times of economic instability, such as 1980–1985, 1989–1992, 1995–1996, 2008, and 2019. Despite the series of economic reforms and monetary reforms, interest rates in Kenya remained high over the period under review, 1980-2022. These high interest rates on loans from the banking sector have been perceived by some policymakers as an obstacle to greater investment, financial inclusion, and economic growth (IMF 2019). As a result, the monetary authorities in Kenya were compelled to revert to managing interest rates in 2016. As a result, the observable flip in interest rates from 2016 is consistent with the capping of interest rates in Kenya, which went into effect that same year (Central Bank of Kenya/CBK 2018). According to the new rule, the maximum lending rate cannot be more than 4% over the base rate set by the central bank (CBK 2018). However, the interest rate caps on commercial loans were lifted in 2019, and this is shown by an upturn in interest rates in Figure 1 (IMF 2021). The goal of eliminating interest rate ceilings in 2019 was to facilitate greater credit expansion and to stimulate private investment (IMF 2021). Despite the repeal, lending rates charged by banks have not increased significantly over the prior cap rate. In 2023, as part of an ongoing set of reforms, the Kenyan central bank established a new interest rate corridor to guide the -10 -5 0 5 10 15 20 25 30 35 40 1980 1985 1990 1995 2000 2005 2010 2015 2020 A n n u a l % Interest rates GDP per capita growth T. Saungweme, G. Maluleke, N. Odhiambo / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 73 overnight interbank rate and reduce the premium for its discount (IMF 2024). The initiative was undertaken to further strengthen the monetary policy implementation framework. From the economic growth front, the notable downward spike in GDP per capita between 1990 and 1992 can be linked to the suspension of the balance of payments assistance from bilateral donors, while the downward spike in the 2019-20 period is attributable to the COVID-19 pandemic shock and ongoing volatility in the global financial system (see also IMF 2022). METHODOLOGY Data description The study used annual time-series data from 1980 to 2021. The availability of reliable and consistent time-series data for each of the model variables for the study country influenced the selection of the data range. The data used in this study were obtained from the World Bank's online database. Table 1 presents the definition of variables, measurements, and expected signs. Table 1. Definitions of variables and data sources Variables Definitions of variables (Measurements) A priori expectation Economic growth (Y) GDP per capita (constant 2015 US$) Dependent variable Interest rate (INT) Lending interest rate (%) +/- Investment (INV) Gross fixed capital formation (% of GDP) + Financial development (CRED) Domestic credit to private sector (% of GDP) + Trade openness (TO) Trade (% of GDP) +/- Exchange rate (EXC) Official exchange rate (LCU per US$, period average) +/- Model specification The baseline model used in this study is defined as follows: Yt = f(INT, INV, CRED, TO, EXC) (1) Where Table 1 defines each variable. In order to obtain elasticity coefficients on the variables and minimise the impact of outliers, the variables are converted to logarithms. Therefore, equation (1) is specified as follows: lYt = α0 + β1lINTt + β2lINVt + β3lCREDt + β4lTOt + β5lEXCt + μt (2) There is a vast array of literature that attempts to theorise the numerous causes of economic growth. The core tenet of the Harrod-Domar (H-D) model of economic growth is that increased production levels at the micro and macro levels are the result of a progressive accumulation of additional physical capital financed by savings and investments (Nguyen 2023). Subsequent to the H-D model is the Solow-Swan model, which emphasises the smooth substitution between capital and labour (Nguyen 2023). Early in the 20th century, financial depth and trade openness were recognised as crucial components of economic growth (Odhiambo and Saungweme 2023a). Nyasha et al. (2021), use GDP per capita as the dependent variable to facilitate cross-country comparisons of different population sizes. In other words, GDP per capita adjusts nominal GDP for changes in price levels and population growth. The lending interest rate is the primary independent variable in this study. Theoretically, interest rates are regarded as the costs of borrowing investment and consumption funds from financial institutions. Bank lending rates are expected to either have a positive or negative relationship with economic growth. A decrease in lending rates is expected to induce borrowing for investment and consumption, hence leading to economic growth (Adabor 2022; Foldvary 2015). However, an increase in lending interest rates can lead to a decline in economic growth as it increases the cost of borrowing, which might discourage borrowing for investment and consumption, which is needed to grow the economy. The incorporated control variables in model 1, namely investment, financial development, trade T. Saungweme, G. Maluleke, N. Odhiambo / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 74 openness, and exchange rate, are buttressed by both theory and prior empirical evidence; hence, it is anticipated that their coefficients will be statistically significant. The Solow-Swan model states that investment variations have an impact on total production up until steady-state per capita income is reached. This suggests that while investment has a significant role in determining growth in the short term, its impact on national output is essentially neutral over the long term. Empirical studies consistent with a positive relationship between investment and economic growth include Odhiambo and Saungweme (2023b) and Ibrahimov et al. (2023). The inclusion of financial development in the model is due to the perceived positive spillover effects in an economy reliant on knowledge and technology, which ultimately leads to economic growth (Giri et al. 2023). Furthermore, the underlying theory supporting the inclusion of trade openness in the baseline model argues that trade openness either amplifies or attenuates the effects of globalisation, which in turn encourages technology transfer and foreign direct investment, which eventually influences economic growth (Chen et al. 2020). However, Baliamoune-Lutz and Ndikumana (2007), in their panel data analysis from 39 African countries covering the period 1975-2001, found evidence in support of a negative relationship between trade openness and economic growth. This relationship was caused by weak institutions in the studied economies. The role of exchange rates in influencing economic growth cannot be understated, particularly in an analysis of a commodity-exporting country, such as Kenya. Overvalued currencies can be linked to macroeconomic instability, rent-seeking and corruption, unsustainable high current account deficits, foreign exchange shortages, and balance of payments crises (Rodrik 2008). All these factors are detrimental to economic growth. Estimation techniques This study applies a nonlinear autoregressive distributed lag (NARDL) model. The NARDL model explicitly captures the short-run and long-run asymmetries (positive and negative shocks) in lending interest rates on economic growth—a major advantage over linear vector autoregressive models. The cumulative dynamic multipliers of the NARDL model further explain the speed with which economic growth returns to equilibrium following a negative or positive shock in lending interest rates (Shin et al. 2014). The superiority of NARDL over other competing techniques is its ability to give reliable coefficients even in small samples, account for short-run volatilities and structural break problems in the data, account for endogeneity among all the variables, and its applicability to data with mixed orders of integration of at most one (Shin et al. 2014). Thus, following Shin et al. (2014), interest rates can be decomposed into partial sums of positive changes and negative changes, making it possible to examine the marginal impact of the two components on economic growth in Kenya. This gives the following expression: lYt = η+lINTt + + η−lINTt − + Zt + ξ1t (3) where: 𝑙𝐼𝑁𝑇𝑡 + = ∑ ∆𝑙𝐼𝑁𝑇𝑡 + 𝑡 𝑘=1 = ∑ max(∆𝑙𝐼𝑁𝑇𝑘 ; 0) 𝑡 𝑘=1 (4) 𝑙𝐼𝑁𝑇𝑡 − = ∑ ∆𝑙𝐼𝑁𝑇𝑡 − 𝑡 𝑘=1 = ∑ min(∆𝑙𝐼𝑁𝑇𝑘 ; 0) 𝑡 𝑘=1 (5) Where ∆ change, Z is a set of control variables, 𝜉1𝑡 is white noise error term. Using equations (4) and (5), the NARDL framework as defined by Shin et al. (2014) is specified as: ∆𝑙𝑌𝑡 = 𝜅0 + ∑ 𝜆1𝑖∆𝑙𝑌𝑡−𝑖 + 𝜌 𝑖=1 ∑ 𝜆2𝑖 + ∆𝑙𝐼𝑁𝑇𝑡−𝑖 + + 𝜐1 𝑖=0 ∑ 𝜆3𝑖 − ∆𝑙𝐼𝑁𝑇𝑡−𝑖 − + 𝜐2 𝑖=0 ∑ 𝜆4𝑖∆𝑙𝐼𝑁𝑉𝑡−𝑖 𝜐3 𝑖=0 + ∑ 𝜆5𝑖 𝜐4 𝑖=0 ∆𝑙𝐶𝑅𝐸𝐷𝑡−𝑖 + ∑ 𝜆6𝑖∆𝑙𝑇𝑂𝑡−𝑖 𝜐5 𝑖=0 + ∑ 𝜆7𝑖∆𝑙𝐸𝑋𝐶𝑡−𝑖 𝜐6 𝑖=0 + 𝜓1𝑙𝑌𝑡−1 + 𝜓2 +𝑙𝐼𝑁𝑇𝑡−1 + + 𝜓3 −𝑙𝐼𝑁𝑇𝑡−1 − + 𝜓4𝑙𝐼𝑁𝑉𝑡−1 + 𝜓5𝑙𝐶𝑅𝐸𝐷𝑡−1 + 𝜓6𝑙𝑇𝑂𝑡−1 + 𝜓7𝑙𝐸𝑋𝐶𝑡−1 + 𝜉2𝑡 (6) Where 𝜌; 𝜐1 − 𝜐6 is optimal lag order, 𝜅0 is constant, 𝜆1, 𝜆4, 𝜆5, 𝜆6, 𝜆7 𝑎𝑛𝑑 𝜆8 are short-run T. Saungweme, G. Maluleke, N. Odhiambo / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 75 coefficients, 𝜆2𝑖 + 𝑎𝑛𝑑 𝜆2𝑖 − are short-run asymmetric distributed lag parameters, 𝜓1, 𝜓4, 𝜓5, 𝜓6, 𝜓7𝑎𝑛𝑑 𝜓8 are long-run coefficients, 𝜓2 + 𝑎𝑛𝑑 𝜓2 − are long-run asymmetric distributed lag parameters, 𝜉2𝑡 is white noise error term, t is time period and l is natural logarithmic transformation. All other variables are as defined in Table 1. Cointegration in a NARDL model setting is ascertained by comparing the computed F-statistic to the upper and lower critical bounds from Pesaran et al. (2001) critical values. The asymmetric impact of lending interest rates on economic growth in Kenya is ascertained if 𝜓2 + ≠ 𝜓3 −. If this condition holds, then the following error correction model (ECM) is specified: 𝑙𝑌𝑡 = 𝜅0 + ∑ 𝜆1𝑖∆𝑙𝑌𝑡−𝑖 + 𝜌 𝑖=1 ∑ 𝜆2𝑖 + ∆𝑙𝐼𝑁𝑇𝑡−𝑖 + + 𝜐1 𝑖=0 ∑ 𝜆3𝑖 − ∆𝑙𝐼𝑁𝑇𝑡−𝑖 − + 𝜐2 𝑖=0 ∑ 𝜆4𝑖∆𝑙𝐼𝑁𝑉𝑡−𝑖 𝜐3 𝑖=0 + ∑ 𝜆5𝑖 𝜐4 𝑖=0 ∆𝑙𝐶𝑅𝐸𝐷𝑡−𝑖 + ∑ 𝜆6𝑖∆𝑙𝑇𝑂𝑡−𝑖 𝜐5 𝑖=0 + ∑ 𝜆7𝑖∆𝑙𝐸𝑋𝐶𝑡−𝑖 + 𝜙𝐸𝐶𝑀𝑡−1 + 𝜉3𝑡 𝜐7 𝑖=0 (7) Where 𝜙 is coefficient of the error term and ECM is error correction term. To confirm convergence to long-run equilibrium following a shock or short-term disequilibrium, the coefficient of the error correction term (𝜙) is anticipated to be negative and statistically significant, lying between 0 and 1. The current paper makes use of time series data, so it is necessary to pre-test each variable for unit root in order to prevent spurious regressions and to determine the order of integration for each variable. According to Pesaran et al. (2001) and Shin et al. (2014), the NARDL model requires that no variable be integrated of an order higher than one. To distinctly determine the order of integration, the paper uses three techniques, namely, the Dickey-Fuller Generalised Least Square (DF-GLS), Phillips-Perron (PP) and Zivot- Andrews (ZAURoot) techniques. The paper incorporates the ZAURoot technique so as to correct for structural breaks and, therefore, correctly determine the order of integration among the variables. After ascertaining the order of integration for each variable, the paper then conducts a cointegration test to ascertain the applicability of the NARDL process. The paper also undertakes nonlinearity tests in the series using the BDS test. The null hypothesis of linearity, under various BDS dimensions (m = 2, 3, 4, 5, 6), is put to the test. The Wald test is also included in this paper to check for both short-run and long-run asymmetries. After undertaking these preliminary checks, the paper proceeds to estimate the asymmetrical long- and short-run effects of lending interest rates on economic growth using equations 5 and 6. Finally, the paper carries out post-diagnostic tests, including the recursive CUSUM and CUSUMSQ tests, to check the null hypothesis that the parameters are unstable. The dynamic multiplier tests further show graphically the rate of response of economic growth to positive and negative variations in interest rates (see Shin et al., 2014). RESULTS AND DISCUSSION Nonlinearity and stationarity results The study first determines whether the series has a nonlinear relationship. The findings are shown in Table 2. There is evidence indicating the existence of a nonlinear relationship between the series in Table 2. This result is confirmed by the BDS test statistics for each variable, which were found to be statistically significant at 1% across all dimensions. Panels A, B, and C of Table 3 provide a summary of the three stationarity test results. The results of the DF-GLS test show that investment is stationary at all levels [I(0)], while GDP per capita, trade openness, and exchange rate are all conclusively stationary after first differencing [I(1)]. According to the findings of the PP and ZAURoot tests, all series are conclusively integrated of order one (1). Overall, the stationarity results indicate that the order of integration of the variables is a mixture of not more than 1. This attests to the appropriateness of using the bounds test to investigate the long-run relationship between interest rates and GDP per capita in Kenya. T. Saungweme, G. Maluleke, N. Odhiambo / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 76 Table 2. BDS Test results for nonlinearity Variables BDS Statistic Dimension 2 Dimension 3 Dimension 4 Dimension 5 Dimension 6 BDS Statistic P- value BDS Statistic P- value BDS Statistic P- value BDS Statistic P- value BDS Statistic P- value lY 0.153*** 0.000 0.239*** 0.000 0.274*** 0.000 0.279*** 0.000 0.251*** 0.000 lINT 0.146*** 0.000 0.237*** 0.000 0.290*** 0.000 0.318*** 0.000 0.326*** 0.000 lINV 0.070*** 0.000 0.100*** 0.000 0.110*** 0.000 0.107*** 0.000 0.093*** 0.000 lCRED 0.105*** 0.000 0.153*** 0.000 0.180*** 0.000 0.184*** 0.000 0.170*** 0.010 lTO 0.132*** 0.000 0.193*** 0.000 0.201*** 0.000 0.169*** 0.000 0.113*** 0.000 lEXC 0.203*** 0.000 0.343*** 0.000 0.442*** 0.000 0.510*** 0.000 0.558*** 0.000 Notes: *** denote statistical significance at 1% leve1. Table 3. Stationarity results Panel A: Dickey-Fuller Generalised Least Square (DF-GLS) Variable Level First Difference Without Trend With Trend Without Trend With Trend lY 1.488 -0.434 -4.021*** -4.814*** lINT -1.217 -1.425 -4.591*** -5.611*** lINV -2.774*** -2.980* ___ ___ lCRED -1.196 -3.094* -7.389*** -7.882*** lTO -0.605 -1.974 -6.602*** -6.608*** lEXC 0.086 -1.073 -4.368*** -5.705*** Panel B: Phillips-Perron (PP) Level First Difference Without Trend With Trend Without Trend With Trend lY 1.419 -0.323 -3.949*** -4.710*** lINT -1.862 -2.061 -5.432*** -5.606*** lINV -2.732* -2.890 -11.123*** -11.332*** lCRED -1.102 -3.521* -8.038*** -7.958*** lTO -0.888 -1.848 -6.605*** -6.655*** lEXC -3.207** -1.772 -5.039*** -5.615*** Panel C: Zivot-Andrews (ZAURoot) Level First Difference Without Trend Break With Trend Break Without Trend Break With Trend Break lY -2.466 1992 -3.339 2000 -5.441*** 1991 -5.730*** 1990 lINT -2.936 1989 -3.177 1999 -6.836*** 1995 -7.043*** 1995 lINV -4.198 1996 -4.261 1996 -6.443*** 2000 -6.415*** 2000 lCRED -3.765 2006 -4.018 2006 -6.464*** 1991 -7.196*** 2013 lTO -3.635 2015 3.814 2010 -6.906*** 1988 -7.147*** 1995 lEXC -4.254 1991 -6.761*** 1993 -6.276*** 1995 -6.571*** 1994 Notes: ** and *** denotes statistical significance at 5% and 1% level. Cointegration and asymmetric test results Table 4 displays the outcomes of the cointegration tests carried out utilising the NARDL bounds testing methodology. The results show that the F-statistic value for the NARDL model is 4.215 and is statistically significant at the 5% level. This suggests that the variables in the nonlinear model have a cointegrating correlation. The Wald test results presented in Table 5 firmly reject the null hypothesis of long- run and short-run symmetry. This is confirmed by the associated long-run and short-run p-value of the Wald F-statistic (WLR), which is statistically significant at 1%, and WSR, which is significant at the 10% level. This finding implies that interest rates have a distinct long-run and short-run asymmetric effect on economic growth in Kenya. T. Saungweme, G. Maluleke, N. Odhiambo / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 77 Table 4. Bounds F-test results for cointegration – NARDL F-Statistic Cointegration Status 4.215** Cointegrated Pesaran et al. (2001), p.300, Table CI(iii) Case III Asymptotic critical values for 10% 5% 1% I(0) I(1) I(0) I(1) I(0) I(1) 2.53 3.59 2.87 4 3.6 4.9 Notes: **denotes statistical significance at 5% level. Table 5. Wald test results Test F-statistic P-value Decision WLR 9.201*** 0.007 Asymmetric WSR 4.046* 0.056 Asymmetric Notes: WLR is long-run asymmetric test; WSR is short-run asymmetric test; *** and * signifies significance at 1% and 10% level. Long-run and short-run NARDL results Table 6, panels A and B, presents the long-run and short-run NARDL results, respectively. Table 6. NARDL results - long-run and short-run coefficients Dependent Variable is Y Panel A: Long-Run Results Regressor Coefficient T-ratio [p-value] lINT+ -0.086 -0.980 [0.339] lINT− 0.461*** 4.399 [0.000] lINV -0.043 -0.269 [0.791] lCRED 0.066 0.570 [0.575] lTO 0.056 0.480 [0.637] lEXC -0.322*** -4.174 [0.001] Panel B: Short-Run Results Regressor Coefficient T-ratio [p-value] C 4.229*** 6.225 [0.000] @Trend 0.031*** 6.335 [0.000] ∆lINT+ 0.030 0.721 [0.480] ∆lINT− 0.041 1.278 [0.217] ∆lINT−(−1) -0.178*** -3.989 [0.001] ∆lINT−(−2) -0.120*** -3.297 [0.004] ∆lINV 0.057* 1.817 [0.085] ∆lINV(−1) 0.131*** 4.327 [0.000] ∆lCRED -0.089*** -3.097 [0.006] ∆lCRED(−1) -0.076** 2.408 [0.026] ∆lTO 0.056* 2.052 [0.054] ∆lTO(−1) -0.061** -2.254 [0.036] ∆lEXC -0.183*** -3.972 [0.001] ECM(−1) -0.567*** -5.904 [0.000] Panel C: Test statistics R- Squared R-Bar-Squared F-Statistic [Prob] Normality Serial Correlation Heteroscedasticity Functional Form 0.793 0.693 7.959 [0.000] 0.779 [0.677] 0.969 [0.400] 1.007 [0.493] 0.879 [0.361] Notes: *, ** and *** denote statistical significance at 10%, 5% and 1% levels, respectively “+” and “-” denotes positive and negative shocks. T. Saungweme, G. Maluleke, N. Odhiambo / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 78 The NARDL results, which are reported in Table 6, indicate that positive changes in interest rates (𝑙𝐼𝑁𝑇+) have no significant impact on economic growth, irrespective of whether the analysis is conducted in the short run or in the long run. The results also indicate that negative changes in interest rates (𝑙𝐼𝑁𝑇−) and economic growth move in the same direction, as evidenced by the positive and statistically significant coefficient of the negative partial sum of interest rates. This suggests that, other things being equal, negative shocks to interest rates are likely to be accompanied by a corresponding decline in economic growth in the long run. One reason for this could be that when the central bank lowers interest rates, economic agents save less because they will not be earning higher returns on their savings. The low saving rate can lead to a lower level of investment, which could contribute to a decrease in economic growth. The results also indicate that there is an inverse relationship between negative changes in lending interest rates from the preceding period and economic growth in the short run. This is supported by the coefficient of the partial negative sum of interest rates, which has been found to be negative and statistically significant. This indicates that decreases in interest rates have the potential to spur economic growth in the short run, as lower rates can incentivise borrowing for both investment and consumption. The major findings of this study indicate that negative changes in interest rates have an asymmetrical impact on economic growth in Kenya, depending on the direction of change and the time scale taken into account. Overall, based on these conclusions, the study concludes that negative interest rate changes play a significant role in defining the country's possibilities for future prosperity. The results of control variables reported in panels A and B show that investment has a significant positive impact on economic growth in Kenya, only in the short run. This finding is consistent with the principles of H-D and Solow-Swan models presented in earlier sections. The findings of financial development point to detrimental effects on economic growth exclusively in the short run, while it is statistically insignificant in the long run. Trade openness has been shown to promote economic growth in the short run, but the results also indicate that trade openness from previous periods causes economic decline. While the adverse effects of financial development may indicate a small financial sector vulnerable to adverse financial developments in international markets, Baliamoune-Lutz and Ndikumana (2007) contend that weak institutions to facilitate reciprocal trade benefits are the root cause of the negative correlation between trade openness and economic growth (see also, IMF, 2024). Furthermore, it was shown that changes in exchange rates have a negative impact on economic growth in the long and short run. This implies that a depreciation of the exchange rate in Kenya is likely to boost exports and increase economic growth. The dynamic multiplier graph presented in Figure 2 validates the presence of an asymmetric relationship between interest rates and economic growth. Explicitly, the black dotted line indicates the non- linear adjustment of economic growth to negative shocks, while the solid black line portrays the adjustment of economic growth to positive shocks. Overall, the dynamic multiplier reported in Figure 2 shows that the effects of positive shocks to interest rates are more pronounced than those of negative shocks in the long run. The CUSUM and CUSUMSQ graphs presented in Figure 3 are within the bounds at a 5% significance level, implying that the estimated model passes the stability test, confirming the consistency and reliability of the coefficients. Figure 2. Dynamic multiplier graph -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 1 3 5 7 9 11 13 15 Multiplier for LINT(+) Multiplier for LINT(-) Asymmetry Plot (with C.I.) T. Saungweme, G. Maluleke, N. Odhiambo / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 79 Figure 3a. CUSUM graph Figure 3b. CUSUMSQ graph CONCLUSION AND RECOMMENDATIONS The economic and monetary policies of Kenya underwent significant evolution from 1964 to 2023. Initially characterised by dormant interest rate policies managed through statutory controls, the landscape shifted by 1991 as internal and external shocks prompted economic adjustments emphasizing trade liberalisation and interest rate deregulation. Therefore, this study extended the investigation of the asymmetric impact of lending interest rates on economic growth in Kenya using a nonlinear ARDL model and annual time-series data spanning the years 1980-2021. Unlike some of the previous studies, which used linear models, this research employed the NARDL framework to examine the short- and long-run asymmetric impact of lending interest rates on economic growth in Kenya, providing an intricate understanding of the interest rate-growth nexus. The findings underscore the existence of an asymmetric long-run relationship, as evidenced by cointegration and dynamic asymmetry tests. Specifically, while positive shocks in lending interest rates exhibit no significant impact on economic growth, negative interest rate changes are shown to spur growth in the short run but impede it in the long run. Short-run benefits of lower interest rates stem from increased borrowing for productive investments, enhanced consumption, and the stimulation of export-driven industries such as agriculture and manufacturing, owing to currency depreciation. Conversely, prolonged interest rate reductions may deter savings and compromise long-term investment, posing challenges to sustained economic growth. Investment, financial development, trade openness, and exchange rates were found to play critical roles in moderating the growth trajectory, though their effects varied across time horizons. Policy suggestions from this paper are: (1) Policymakers should carefully balance short-term stimulus policies with long-term sustainability by avoiding extreme interest rate fluctuations. Short-term measures should focus on reducing lending rates during economic slowdowns to encourage borrowing for investment, entrepreneurship, and job creation. (2) Given that investment positively affects economic growth in the short run, particularly with a lag, policymakers should implement short-term measures to stimulate both private and public investments, such as subsidies and credit facilities. Since investment also contributes to long-term economic growth, authorities should establish sustainable financing mechanisms, such as public-private partnerships for infrastructure projects. (3) As credit to the private sector has a negative impact on economic growth in the short run, financial regulators should closely monitor excessive lending to ensure that credit allocation supports productive sectors rather than speculative activities. Authorities should promote responsible lending through macro-prudential policies while also developing robust credit risk management frameworks and fostering financial literacy to enhance the positive long-run effects of financial intermediation. (4) Since exchange rate depreciation negatively affects economic growth in both the short and long run, the central bank should continuously monitor exchange rate fluctuations and, when necessary, implement appropriate monetary policies - within the bounds of its existing free-floating exchange rate system - to smooth out extreme and undesirable movements. (5) Export diversification strategies should be encouraged to reduce vulnerability to external shocks and enhance economic resilience. Future studies on the subject should extend the analysis to estimate the threshold point of lending interest rates that would set the country on an optimal growth path. Additionally, the scope of analysis should be broadened to include other African economies to uncover regional trends and policy implications. -15 -10 -5 0 5 10 15 2004 2006 2008 2010 2012 2014 2016 2018 2020 CUSUM 5% Significance -0.4 0.0 0.4 0.8 1.2 1.6 2004 2006 2008 2010 2012 2014 2016 2018 2020 CUSUM of Squares 5% Significance T. Saungweme, G. Maluleke, N. Odhiambo / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 80 REFERENCES Akram, T. 2021. A Keynesian approach to modelling the long-term interest rate. Levy Economics Institute. Working Paper No. 988. Awad, I. M., and M. S. Karaki. 2019. The impact of bank lending on Palestine economic growth: an econometric analysis of time series data. Financial Innovation, 5(14): 1-21. https://doi.org/10.1186/s40854-019-0130-8 Baliamoune-Lutz, M., and L. Ndikumana. 2007. The growth effects of openness to trade and the role of institutions: New evidence from African countries. Economics Department Working Paper Series. 38. https://doi.org/10.7275/1068933 Baynham, S. 1989. The Kenyan economy: An overview. Africa Insight, 19(4). https://journals.co.za/doi/pdf/10.10520/AJA02562804_392 Central Bank of Kenya/CBK. 2018. Central Bank of Kenya bank supervision annual report 2018. https://www.centralbank.go.ke/uploads/banking_sector_annual_reports/1174296311_2018%20 Annual%20Report.pdf Chatterjee, M., and I. Naka. 2022. Twenty years of BRICS: political and economic transformations through the lens of land. Oxford Development Studies, 50(1): 2-13. https://doi.org/10.1080/13600818.2022.2033191 Chen, H., D. O. Hongo, M. W. Ssali, M. S. Nyaranga, and C. W. Nderitu 2020. The asymmetric influence of financial development on economic growth in Kenya: evidence from NARDL. Sage Open, 1-17. https://doi.org/10.1177/2158244019894071 Foldvary, F. E. 2015. The Austrian theory of the business cycle. The American Journal of Economics and Sociology, 74(2): 278-297. Giri, A. K., G. Mohapatra and B. Debata. 2023. Technological development, financial development, and economic growth in India: Is there a non-linear and asymmetric relationship? Journal of Economic and Administrative Sciences, 39(1): 117-133. DOI:10.1108/JEAS-03-2021-0060 Holmes, M. J., J. Otero and T. Panagiotidis. 2011. The term structure of interest rates, the expectations hypothesis and international financial integration: Evidence from Asian economies. International Review of Economics and Finance, 20: 679-689. DOI: 10.1016/j.iref.2010.11.021 Ibrahimov, Z., S. Hajiyeva, I. Seyfullayev, U. Mehdiyev and Z. Aliyeva. 2023. The impact of infrastructure investments on the country’s economic growth. Problems and Perspectives in Management, 21(2): 415- 425. Doi:10.21511/ppm.21(2).2023.39 Iddrisu, A. A., and I. Alagidede. 2020. Is the interest rate setting behaviour of the Bank of Ghana constrained by high debt levels? African Development Review, 32(3): 459-471. International Monetary Fund/IMF. 1985. Kenya: The general setting: Financial policy workshops. International Monetary Fund, 9-23. https://www.elibrary.imf.org/downloadpdf/book/9780939934379/ch02.pdf International Monetary Fund/IMF. 1991a. 23 Kenya’s Transition from direct to indirect instruments of monetary policy, in, The Evolving Role of Central Banks, eds, Downes, P., and Vaez-Zadeh, R. https://doi.org/10.5089/9781557751850.071 International Monetary Fund/IMF. 1991b. Coordination of financial policy, in, Determinants and Systemic Consequences of International Capital Flows. IMF. Washington, DC. International Monetary Fund/IMF. 2019. Do interest rate controls work? Evidence from Kenya. IMF Working paper. WP/19/119. International Monetary Fund/IMF. 2021. Kenya 2021 article IV consultation; second reviews under the extended arrangement under the extended fund facility and under the arrangement under the extended credit facility. IMF country report no. 21/275. International Monetary Fund/IMF. 2022. Fourth reviews under the extended arrangement under the extended fund facility and under the arrangement under the extended credit facility. IMF Country Report No. 22/382. International Monetary Fund/IMF. 2024. Kenya 2023 Article IV consultation, sixth reviews under the extended fund facility and extended credit facility arrangements. IMF Country Report No. 24/13. Lee, K.S., and R.A. Werner. 2023. Are lower interest rates really associated with higher growth? New empirical evidence on the interest rate thesis from 19 countries. International Journal of Finance and Economics, 28(4): 3467-4737. https://doi.org/10.1002/ijfe.263 Leshoro, T.L.A., and P. Wabiga. 2023. The Asymmetric effects of interest rates on private investment in South Africa. Acta Universitatis Danubius. Œconomica, 19(3): 161–182. https://dj.univ- danubius.ro/index.php/AUDOE/article/view/2335 Liu, F., X. Zhang, A. S. Adebayo and A. A. Awosusi. 2022. Asymmetric and moderating role of industrialisation and technological innovation on energy intensity: Evidence from BRICS https://protect.checkpoint.com/v2/___https:/doi.org/10.1186/s40854-019-0130-8___.YzJlOnVuaXNhbW9iaWxlOmM6bzpjNGI5ZjI2NjAzMzZiZjM2ZTA5YzJiYTAzZDgzMDAwNzo2OjQ4MTY6MDNjY2I4MTUwOTdiZjUzYTZjMDEzMjRmNDIwNzAxOWU1YmRhZWYzMmQ4YjM3OWRkNDk3NjA0ZGZlMGMwYzVhNzpwOlQ6Tg https://doi.org/10.7275/1068933 https://journals.co.za/doi/pdf/10.10520/AJA02562804_392 https://www.centralbank.go.ke/uploads/banking_sector_annual_reports/1174296311_2018%20Annual%20Report.pdf https://www.centralbank.go.ke/uploads/banking_sector_annual_reports/1174296311_2018%20Annual%20Report.pdf https://doi.org/10.1080/13600818.2022.2033191 https://protect.checkpoint.com/v2/___https:/doi.org/10.1177/2158244019894071___.YzJlOnVuaXNhbW9iaWxlOmM6bzoxMTdmNzRlZTMzYzk3YmIzZWZjM2RkY2ZkN2I4ZThkMjo2Ojg5MTY6NDdjNGFhMWEwMGFiZDcwZjRlYmRhM2Q0MzlkYmQ5MjgwNDVjNWZlMTQxYWRkZDRiY2JkNTQwNzIxNjRjMTIwMTpwOlQ https://protect.checkpoint.com/v2/___https:/doi.org/10.1108/JEAS-03-2021-0060___.YzJlOnVuaXNhbW9iaWxlOmM6bzpjZDM5ODlhYzcwMWJjZjU0OWNiN2E4OTViNjE2Y2VhOTo2OmVkYTU6Mzg2YTUxZjY3MmFlMzlmMTBjYWEyODYzMTZjZGRhNDlhYzMwY2ExNzdhMWYzMDgyMDJkYzM3NzEyYjFkMGQxYTpwOlQ https://www.elibrary.imf.org/downloadpdf/book/9780939934379/ch02.pdf https://doi.org/10.5089/9781557751850.071 https://protect.checkpoint.com/v2/___https:/doi.org/10.1002/ijfe.263___.YzJlOnVuaXNhbW9iaWxlOmM6bzpjNGI5ZjI2NjAzMzZiZjM2ZTA5YzJiYTAzZDgzMDAwNzo2OjIzYjk6ZDQ0ZmQxMjU3OWM4YjA1MjRlZjAzMDI1NjcyNDdmYTE2ZDQ2NGFlNzIxODI1OTE0MTNlYzczZDExODU3Y2U1YzpwOlQ6Tg https://dj.univ-danubius.ro/index.php/AUDOE/article/view/2335 https://dj.univ-danubius.ro/index.php/AUDOE/article/view/2335 T. Saungweme, G. Maluleke, N. Odhiambo / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 81 economies. Renewable Energy, 198(October), 1364-1372. https://doi.org/10.1016/j.renene.2022.08.099 McKinnon, R. I. 1973. Money and capital in economic development. Washington, DC: The Brookings Institution. Modugu, K. P., and J. Dempere. 2022. Monetary policies and bank lending in developing countries: evidence from Sub-Sahara Africa. Journal of Economics and Development, 24(3): 217- 229. https://doi.org/10.1108/JED-09-2021-0144. Mohlo, L.E. 1986. Interest rates, saving, and investment in developing countries: A re-examination of the McKinnon-Shaw hypotheses. International Monetary Fund. https://doi.org/10.5089/9781451956726.024 Nguyen, Q. K. 2023. Macroeconomic determinants of economic growth in low- and mid-income countries: new evidence using a non-parametric approach. Applied Economics Letters, 1-7. https://doi.org/10.1080/13504851.2023.2283774 Nyasha, S., N. M. Odhiambo and S. A. Asongu. 2021. The impact of tourism development on economic growth in Sub-Saharan Africa. The European Journal of Development Research, 33(6): 1514-1535. O’Brien, F. S., and T. C. I. Ryan. 1999. Aid and reform in Africa: Kenya case study. World Bank. https://policycommons.net/artifacts/1515025/aid-and-reform-in-africa/2189840/ Odhiambo, N. M., and T. Saungweme. 2023a. Financial development and economic growth in Africa, in, Finance for Sustainable Development in Africa. Routledge: Taylor & Francis Group Publishers. Odhiambo, N. M. and T. Saungweme. 2023b. Public investment and economic growth in Kenya, Economics, Management, and Financial Markets, 18(2): 54-76. Olasehinde-Williams, G., R. Omotosho and F. V. Bekun. 2024. Interest rate volatility and economic growth in Nigeria: new insight from the quantile autoregressive distributed lag (QARDL) model. Journal of the Knowledge Economy. https://doi.org/10.1007/s13132-024-01924-x Owusu, E. L. 2023. Interest rate reforms in African countries, in, Finance for Sustainable Development in Africa. Routledge: Taylor & Francis Group Publishers. Pesaran, M., H. Y. Shin, and R. J. Smith. 2001. Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16: 289-326. Saungweme, T and N. M. Odhiambo. 2021. Inflation and economic growth in Kenya: An empirical examination. Advances in Decision Sciences (ADS), 25(3): 1-25. https://doi.org/10.47654/v25y2021i3p1-25 Shaukat., B., Q. Zhu and M. I. Khan. 2019. Real interest rate and economic growth: A statistical exploration for transitory economies. Physica A: Statistical Mechanics and Its Applications, 534: 122193. https://doi.org/10.1016/j.physa.2019.122193 Shaw, E. 1973. Financial deepening in economic development. New York: Oxford University Press. Shin, Y., B. Yu, M. Greenwood-Nimmo. 2014. Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In: Sickles, R., Horrace, W. (eds). Springer, New York, NY. https://doi.org/10.1007/978-1-4899-8008-3_9 United Nations Industrial Development Organisation. 2020. Industrialisation as the driver of sustained prosperity. Vienna. UNIDO. Wilson, M. K., and N. M. Odhiambo. 2023. Financial Reforms and Financial Development in Africa: Evidence from Selected SSA Countries, in, Finance for Sustainable Development in Africa. Routledge: Taylor & Francis Group Publishers. World Bank. 2022. World Bank online database. Washington DC: World Bank. https://doi.org/10.1016/j.renene.2022.08.099 https://protect.checkpoint.com/v2/___https:/doi.org/10.1108/JED-09-2021-0144___.YzJlOnVuaXNhbW9iaWxlOmM6bzpjNGI5ZjI2NjAzMzZiZjM2ZTA5YzJiYTAzZDgzMDAwNzo2OmIxYjM6YmZiZGVkNTkwY2EwNzY2ZjVkNDZmNWQ3MDUyY2E3ZmQ5OTU2OWE1MDBkYzRhYWRjNGY0ODhhZTdhYjU0ZDViNTpwOlQ6Tg https://doi.org/10.5089/9781451956726.024 https://doi.org/10.1080/13504851.2023.2283774 https://policycommons.net/artifacts/1515025/aid-and-reform-in-africa/2189840/ https://doi.org/10.1007/s13132-024-01924-x https://doi.org/10.47654/v25y2021i3p1-25 https://doi.org/10.1016/j.physa.2019.122193 https://doi.org/10.1007/978-1-4899-8008-3_9