Effect of Financial Deepening on Private Investment in Kenya

Effect of Financial Deepening on Private Investment in Kenya

Brendah Chepkorir¹, Onesmus Mbaabu^2, Lenity Maugu^3, Miriam Thuo⁴

¹Chuka University, P.O BOX 109, CHUKA

²South Eastern Kenya University, P.O BOX,170-90200, KITUI

^3,4Chuka University, P.O BOX 109, CHUKA

DOI: https://dx.doi.org/10.47772/IJRISS.2024.8090218

Received: 24 September 2024; Accepted: 30 September 2024; Published: 16 October 2024

ABSTRACT

Private investment remains an important sector in the growth of the economy and sustainable development in Kenya. It is envisioned to create job opportunities, distribute income and alleviate poverty. The growth rate of private investments witnessed in the past years in Kenya is below the expected growth rate of 24%.  Since 1980, the highest growth has been achieved at 15.2% in 2014. It then declined to 12.96% in 2021, 13.34% and 13.40% in 2022 and 2023 respectively, which are much below the projected growth rate.  Existing studies in Kenya lack substantial inquiry on the relationship between financial deepening and private investment and have dealt with them separately without linking the two in a dynamic framework. Additionally, most of those studies tend to rely on a limited set of indicators of financial deepening, while employing different methods for analysis that may not be appropriate for unbiased results. The current study intended to determine the effect of financial deepening on private investment.  A disaggregated analysis of three aspects of financial deepening was done: private sector credit, broad money and bank deposits. Data was obtained from KNBS and World Development Indicators (WDI) websites for the period between 1980-2021. STATA software was used in the analysis of the collected data. ARDL model was employed and the ECM was estimated since there was cointegration. The regression results indicated that Private sector credit, Broad money and Bank deposits were statistically significant at a 1% level in the short run. An increase in 1 percent of private sector credit, Broad money and Bank deposits cause private investment to increase by 0.32, 0.39 and 0.49 percent respectively. The findings show that financial deepening has a positive effect, while broad money and bank deposits have negative effect on private investment in the long run. The three models were good predictors of private investment as shown by R-squared values of 0.96, 0.94 and 0.94 respectively. The study concluded that financial deepening is important for growth of private investment in Kenya. Therefore, Financial intermediation theory is applicable in Kenya. The study recommends policymakers to conceptualize the policies that aim at facilitating financial access and inclusion for the marginalized groups in remote and rural areas. Further, the government needs to support financial sector through enhancing the growth of microfinance institutions. This will facilitate provision of credit and savings services to individuals earning low-income and owning small businesses.

Keywords: Private investment, financial deepening, Private sector credit, Broad Money, Bank Deposits, Public investment.

INTRODUCTION

Policymakers, researchers and economists recognize private investment as an important driver of short-term and long-term output growth (Bussiere, Ferrara & Milovich, 2015; Canh & Phong, 2018). Private investment remains an important sector in the growth of the economy and sustainable development in Kenya. Investment in Kenya’s private sector is considered to employ a large number of Kenyans across all sectors and contribute a higher percentage to the country’s total GDP. The total employment in Kenya grew from 17.4 million in 2020 to 18.3 million people in 2021, representing a 5.5% growth rate of employment in the country.  The number of people found in the informal sector which is part of the private investment sector was 15.3 million. This represents 83 per cent of the total employment (KNBS, 2022). Generally, the private sector constitutes an important part of the Kenyan economy, being related to employment creation, production, and income generation (Amutabi & Wambugu, 2020).

Although private investment has proved to be important in Kenya’s economy, it has faced a number of challenges that limit its contribution. Macroeconomic uncertainty and business recession during elections have led to the weakening of private-sector investments (Njuru et al., 2013; Mmeri & Ndolo, 2023). In every election period, there is low investor confidence and reduced investment activities, which affects the overall performance of investments in the country. Other challenges include an unfriendly business environment and weakening shilling against major currencies, which have affected the expansion of investments. Since 1980, the highest growth of Private investment that has been achieved is 15.20% in 2014 which declined to 13.03% in 2020 due to the COVID-19 pandemic which worsened the state of the economy across the globe (KNBS, 2022). To secure the dream of increasing output growth, the country was expected to grow its private investment annually by at least 24% from 2020 towards 2030 (The Government of Kenya, 2007). However, the performance was 12.96% in 2021, 13.34% and 13.40% in 2022 and 2023 respectively, which are far much below the expected performance. This shows that, there is a critical need to focus on policies that drive private investments to reach the projected potential.

Financial deepening involves the expansion in the supply of monetary resources in an economy by providing an extensive choice of financial services by financial markets and institutions (Levine, 2001; Beck, Demirgüç-Kunt & Honohan, 2009). Financial deepening in Kenya has been facilitated by the dynamic technological transformations which has resulted in the emergence of new digital financial products such as ‘FULIZA’; which is an overdraft service that allows Customers using Safaricom to make transactions without enough funds in their M-PESA accounts (CBK,2022). Banks in Kenya have advanced through technological innovations that include branchless banking, electronic payment systems, internet banking and mobile banking, enabling efficient operations (FinAccess,2021). Banks have also witnessed increased deposits through digital financial accounts and extend their financial credit access to individuals in remote areas since they can open and manage their accounts through mobile phones. For instance, by the end of the year 2022, the banking sector in Kenya registered a total net asset of Ksh.6.6 trillion, where loans and advances amounted to Ksh.3.6 trillion. Further, a total of Ksh.5.0 trillion was deposited by customers (Central Bank of Kenya, 2023).

Prominent researchers posit that financial deepening plays a critical role in boosting private investments (Samargandi, Fidrmuc & Ghosh, 2015; Imoagwu & Ezeanyeji, 2019). A well-developed financial system with sufficient liquidity, a robust savings base, and access to credit fosters an environment conducive to private investment, leading to economic growth and development (World Bank, 2019; Tyson, 2021). The success of private investment is influenced by financial deepening where financial markets and institutions provide a wide range of financial services, with an enabling environment for exchanging goods and services, effective savings and investment (IMF, 2012). Although the Kenyan government has tried to enhance financial deepening, the financial sector is faced with several challenges such as macroeconomic uncertainty, lack of access to financial services by the poor and the existence of a huge portion of the unbanked population (World Bank Group, 2013; Thiong’o, 2022). These challenges faced in the Kenyan financial sector may limit the effectiveness of financial deepening in driving investments in Kenya.

Notably, efforts to spur the growth of private investments and motivate private investors in Kenya remain the government’s top priority. The government of Kenya has been reviewing, developing and adopting policies to ensure an enabling environment for investments (Kiremu, Scrimgeour, Mutegi, & Mumo, 2022).  For instance, a controlled regime where the government sought to cushion the local investors from unhealthy competition by foreign investors. Additionally, the Structural Adjustment Programme (SAP) included the minimization of the government fiscal interventions in the economy, liberalization of interest rates and removal of credit controls to motivate private investors (Ahmed & Wahid, 2011; Gitonga, 2020). The government has shown its efforts in the development of transport infrastructure and support of innovative and fast-developing ICT industry that facilitates the efficiency of financial services (Africa Development Bank Report, 2017). Following the recent worldwide pandemic of COVID 19, towards the end of 2020, the government of Kenya was forced to intervene through the National treasury to roll out Credit Guarantee Scheme (CGS) to support small investors and to increase financial services access and inclusivity (Republic of Kenya, 2022). In addition, the recently enacted privatization bill by the current government aimed at attracting more investors hence contributing to the private sector growth (The Republic of Kenya, 2023).  Given these interventions, it is important to establish whether financial deepening has a significant effect on the level of private investment.

Empirical evidence in this area of research has greatly overlooked the connection between financial deepening and private investment, especially in Kenya. Previous researchers have mostly looked at the effects of financial deepening on economic growth (Kiriga, Chacha, & Omany 2020; Murunga, 2018; Nyasha and Odhiambo, 2017; Bakang, 2015; Mukundi 2013; Odhiambo, 2009). Some have also focused on determinants of private investment, where; though financial deepening has been seen to influence private investment positively, a limited set of financial deepening indicators especially under financial institutions have been utilized (Birundi, 2014; Byegon, 2009; Mbaye, 2014; Murunga, 2018). There is a lack of substantial inquiry into the relationship in Kenya, particularly with recent data in the post-Covid era (Mose and Jepchumba, 2020; Macharia & Mungai 2021). The period lapsed between the reviewed studies and the current is huge and the previous findings may have little application at the present due to dynamic demands in the economy. The limited indicators of financial deepening used in the Kenyan context provided a need to conduct a disaggregated analysis of various aspects of financial deepening and their effect on private investment. This study sought to fill the aforementioned gaps by employing the ARDL technique to find out the effect of private sector credit, bank deposits and money supply on private investment in the Kenyan context using recent data. This study contributes to the existing literature in various ways. First, the study tested the relationship between financial deepening and private investment, which fills the contextual gap in Kenya using recent data. Second, the study provided a disaggregated analysis of various forms of financial deepening. This aided in finding out the specific areas of financial deepening that influence private investments in Kenya. Third, in the context of Kenya, the study used the autoregressive distributed lag (ARDL) model, which is a robust method that led to reliable results. Lastly, the study controlled for the effects of economic shocks to ensure the results were not biased.

LITERATURE REVIEW

Generally, private investments involve purchasing ownership stakes in companies, providing capital in exchange for shares or equity ownership, and lending money to individuals, businesses, or governments in return for fixed interest payments (Kumar, 2022). Additionally, it involves investing in Real Estate with the aim of generating rental income (Baker et al., 2021). On the other hand, financial deepening can be taken as a process of widening the supply of financial services by availing a variety of those services as well as increasing the number of individuals who benefit from such services (IMF, 2015; World Bank, 2016).

The financial liberalization theory and the theory of financial intermediation provide a foundation for the relationship between financial deepening and private investment. The former was proposed by Shaw (1973), who noted that a repressed financial sector hinders economic development and is succinctly characterised by low and even negative real interest rates, low savings and low investments. McKinnon’s model (1973) suggests that, investments in emerging economies are generally self-funding and therefore require enough savings accumulated through bank deposits. Financial liberalization can as well cause financial deepening through the redistribution of savings and investment among different sectors, as well as creating more openings for speculation. According to the theory of financial intermediation, financial intermediaries ensure distribution of financial resources among depositors and investors while acting as liquidity providers to ensure adequate money supply (broad money) thus influencing the investment.

There are various arguments in the literature connecting financial deepening and private investments. According to FSDK (2011), financial deepening catalyses rapid economic growth by accelerating investment level. An all-inclusive and broad financial system boost income equality and enhance sustainable economic growth (World Bank, 2019). It also facilitates the technological advancement, innovation and crucial funding for entrepreneurs, enabling them to implement their ideas and initiate successful businesses (Adusei, 2016; Ho, et al., 2017). A well-developed financial system with sufficient liquidity, a robust savings base, and access to credit fosters an environment conducive to private investment, leading to economic growth and development (World Bank, 2019; Tyson, 2021).

Empirical studies done in Sub-Saharan Africa show evidence of a positive link between financial deepening and private investment. Misati, & Nyamongo (2011) assessed panel data from 18 countries in Sub-Saharan Africa, while using credit to private sector and interest rate as independent variables. It was displayed that, private sector credit influence private investment. Similarly, Asongu & De Moor (2017) utilized Generalized Method of Moments to examine the impact of financial globalization on financial deepening and private investment in 53 African countries for the period 2000–2011. The findings revealed that, financial deepening positively influence private investment, contributing to the economic growth in the region.

Several researchers have also conducted empirical research in specific African countries. While examining financial deepening and its effect on investment in Nigeria, Obafemi et al. (2016) employed the Gregor-Hansen Endogenous methodology and found a positive significant effect. Further, there was a unidirectional causality, of which financial deepening causes investment. In the same context, Nwaolisa, & Cyril (2019) while utilizing OLS also found that, financial deepening (private sector credit and money supply) significantly affects private investment. Similar findings were attained by Muyambiri (2017) in a study conducted in Botswana. Sakyi et al., (2016) conducted a study in Ghana and found a positive link between financial sector development and private investment. Other African studies show contradicting findings. For instance, Brima and Brima (2017) find a positive link between bank deposits and private investment while Duramany-Lakkoh et al. (2022) found that bank deposits do not have a significant effect on the economy.

In Kenya, a number of studies find a strong link between financial deepening and economic performance (Kiriga et al., 2020; Mukundi, 2013; Nzomoi et al., 2012; Odhiambo, 2009).  Kenyan studies focusing on the nexus between financial deepening and private investment have used various methodologies and indicators of financial deepening. Birundi (2014) utilized flexible accelerator model and found a positive link between private sector credit and investment. Kiriga et al. (2020) explored on the influence of private-sector credit on investment in Kenya and established a positive effect of private sector credit. Similarly, Mose and Jepchumba (2020) found that in three East African countries, Kenya included, credit availability causes a positive and statistically significant impact on private investment growth. In Kenya, Byegon (2009), Mbaye (2014) and Similarly, Nyasha and Odhiambo (2017). Further, Olweny & Chiluwe (2012) and Lidiema (2017) found a positive link between bank deposits and private investment. Byegon (2009) and Mbaye (2014) found a strong link between money supply and private-sector investment. Some of these studies majored on the determinants of private investment, which shows that there was no specific attention to financial deepening and private investment. Murunga (2018) observed that bank deposits significantly influence economic expansion. Macharia & Mungai (2021) established that bank deposits and bank credit positively and significantly affect financial performance.

METHODOLOGY

Data

The study utilized secondary data where annual time series data on financial deepening indicators and the Private investment variables were obtained from KNBS and WDI for the period 1980-2021. Download requests were made to the websites to get the required time series data. According to the World Bank (2016) and IMF (2017), Private Sector Credit to GDP, Financial Institutions’ assets to GDP, M2 to GDP, Deposits to GDP, and Gross value added of the financial sector to GDP are the financial deepening indicators under the financial institution umbrella. In the context of Kenya, the use of Bank deposits (savings), private sector credit and broad money supply (M2) as the fundamental indicators of financial deepening can best capture the financial system liquidity, intermediation, and ability to provide credit to the private investments as well as providing important understanding on the country’s financial depth (Central Bank of Kenya, 2020). Additionally, they are in support of the fact that Kenya’s financial system is dominated by the banking sector which contribute a higher percentage of lending (Nyasha & Odhiambo, 2017; Chen, Hongo, Ssali, Nyaranga & Nderitu, 2020). Finally, the indicators best agree with Kenya’s economic development plan, Vision 2030.

Model Specification

The ARDL approach to co-integration was employed, as per the proposed methodology by Pesaran et al. (2001), in building the model. The methodology is mainly preferred over other co-integration procedures due to its advantages. For example, the methodology is applicable regardless of the state of variables. It can also accommodate a greater number of variables. Further, if variables are cointegrated, the ARDL Model can be reparametrized into the ECM model to infer the long-run relationship of the variables (Nkoro &Uko, 2016).

i) The model for testing the effect of Private sector credit on private investment is given as:

\[
\Delta PI_t = \beta_0 + \sum_{i=1}^{p} \beta_1 \Delta PI_{(t-1)} + \sum_{i=1}^{q} \beta_2 \Delta PSC_{(t-1)} + \sum_{i=1}^{q} \beta_3 \Delta RIR_{(t-1)} + \sum_{i=1}^{q} \beta_4 \Delta PU_{(t-1)} + \Delta GDP_{(t-1)} + \beta_5 DUM_1 + \lambda ECT_{(t-1)} + \epsilon_t \tag{6}
\]

ii) The model to test the effect of Broad Money on Private Investment is specified as:

\[
\Delta PI_t = \beta_0 + \sum_{i=1}^{p} \beta_1 \Delta PI_{(t-1)} + \sum_{i=1}^{q} \beta_2 \Delta M2_{(t-1)} + \sum_{i=1}^{q} \beta_3 \Delta RIR_{(t-1)} + \sum_{i=1}^{q} \beta_4 \Delta PU_{(t-1)} + \Delta GDP_{(t-1)} + \beta_5 DUM_1 + \lambda ECT_{(1-t)} + \epsilon_t \tag{7}
\]

iii) The model for testing the effect of Bank Deposits on Private Investment is specified as:

\[
\Delta PI_t = \beta_0 + \sum_{i=1}^{p} \beta_1 \Delta PI_{(t-1)} + \sum_{i=1}^{q} \beta_2 \Delta BD_{(t-1)} + \sum_{i=1}^{q} \beta_3 \Delta RIR_{(t-1)} + \sum_{i=1}^{q} \beta_4 \Delta PU_{(t-1)} + \Delta GDP_{(t-1)} + \beta_5 DUM_1 + \lambda ECT_{(1-t)} + \epsilon_t \tag{8}
\]

Where:

\(\beta_0\) represents Private Investment levels that are independent of financial deepening

\(\beta_1\) represents the elasticity parameter of private investment

\(\beta_2\) represents the parameter estimates for financial deepening indicators (corresponding to the objective)

\(\beta_3\) represents the parameter estimates for the Real Interest Rate

\(\beta_4\) represents the parameter estimates for public investment

\(\beta_5\) represents the parameter estimates for dummy variables

\(PSC_{t-1}\) is the Private Sector Credit

\(M2_{t-1}\) is the lagged value of Broad Money

\(BD_{t-1}\) is the lagged value of Bank Deposits

\(RIR_{t-1}\) is the lagged Real Interest Rate

\(PU_{it}\) is public investment at the time \(t\)

\(GDP_{it}\) is GDP per capita at time \(t\)

\(DUM_1\) – Dummy variables for economic structural breaks in Kenya, represented by 1 and 0 to indicate years before and after structural breaks, respectively

\(\lambda\) denotes the elasticity parameter of the Error Correction Term

\(\Delta\) denotes the first difference operator of the variables

\(ECT_{t-1}\) represents the Error Correction Term

Estimation Techniques

The stationarity of the variables was determined by employing the Philips-Perron (PP) unit root tests (Nkoro & Uko, 2016). The differencing of the data was done to render the data stationary. To see if there exists a long-run equilibrium between the variables, bounds test cointegration was used. Akaike information criterion (AIC) and Schwartz information criterion (SIC) were used to determine the lag length of the model. Finally, the regression was subjected to diagnostics tests such as multicollinearity, autocorrelation, and heteroscedasticity to see if the ordinary least squares assumptions were met. Following Ditzen, et.al, (2021),the study used a dummy variable (D1) to account for the structural breaks in Kenya. The years when there were structural breaks were represented by 1, and the years when there were no structural breaks had the value 0.

RESULTS AND DISCUSSION

Descriptive Statistics and Normality Test

Descriptive statistical analysis involves summarizing the collected data in the form of the mean, standard deviation, and minimum and maximum values. The mean provides a base for making the general view regarding the distribution of the data. The values of mean and standard deviation within the close range portray data to be statistically fit.

Table 1: Summary Statistics

Based on the results presented in Table 1, the private investment (PI) variable recorded a mean of 9.21 percent with a standard deviation of 3.91 percent. The maximum value of 15.20 percent was recorded in the year 2014 and the minimum of 2.93 percent in 2005. During the study period, the mean is below the threshold of 24 percent envisioned in Vision 2030. This implies that in Kenya, private investment has been so low within the study period. Private sector credit (PSC) registered the highest percentage of 36.64 in 2015 and a minimum percentage of 18.42 in 1987. The average percentage recorded during the period was 24.66 and the standard deviation was 5.06 implying high variability in the rate at which private sector access credit in Kenya. Further, Broad Money (M2) recorded a mean of 35.34 percent with a standard deviation of 4.46. The maximum percentage of 42.81 and minimum of 26.68 were observed during 2015 and 1985 respectively. The average percentage for bank deposit (BD) was 30.65 with a standard deviation of 4.76. The variable also registered a highest percentage of 39.22 in 2015 and a lowest of 23.01 in 1985.

Unit Root Test

To check for the non-stationarity of the individual time series, the study utilized the Phillips-Perron test. When the PP test is more than the critical value at a 5% significant level, the null hypothesis of non-stationarity is accepted, implying the non-stationarity of the variables in the series. The results were summarized in Table 4.

Table 2: Unit Root test for Variables

The PP results in Table 2 indicate that all variables were stationary at the first differencing except the Real interest rate which was stationary at level, I (0). This is because their PP test statistics were less than their critical values at 5% and 10% respectively. This indicates that the variables are stationary at the first difference, I (1).

Bounds Cointegration Test

The study employed the Bounds test for cointegration analysis to test for the long run relationship between the variables. This was due to a mixed combination of variables that are stationary at level I (0) and those that are stationary at the first difference, I (1). The test involved estimating the parameters using the ARDL technique and determining if the resulting residuals were cointegrated. The decision rule was; to reject the null hypothesis of no cointegration if the F-statistic was greater than the upper bound critical values at 10%, 5%, 2.5% and 1% significance levels.

Table 3: Cointegration test

Tables 3, show the existence of cointegration between private investment and independent variables, which emphasizes the relevance of the long-run concept. The F-statistic values of models 1,2 and 3 are 6.174, 18.914 and 6.174 respectively. These values are greater than all upper bound critical values at 10%, 5%, 2.5% and 1% significance levels respectively in the three models. This confirms the presence of co-integration between private Investment and independent variables; hence, the null hypothesis of no co-integration is rejected. Since there was a long-run relationship between the private investment and independent variables, the ECM estimation technique was utilized to estimate the short and long-run parameters.

Diagnostic Tests

Diagnostic tests to check the presence of autocorrelation, heteroscedasticity and multicollinearity were performed before the findings from the model were reported. Table 4 shows the summary of the tests that were conducted.

Table 4.  Summary of diagnostic tests

Correlation Analysis

Having done all the diagnostic tests, the model was found fit for regressions.  The correlation results are presented in Table 5.

Table 5: The Correlation Matrix

The results in Table 5 indicate that the correlation coefficient for PSC and PI was 0.1928 implying a positive relationship. M2 and PI have a negative but weak correlation shown by (-0.1836). The findings also display a negative correlation between BD and private investment (PI).  Further, M2 and PSC, BD and M2 have positive and strong correlations as shown by their coefficients, 0.8066   and 0.9678   respectively, implying strong and positive causal effects. PU and RIR also have a negative correlation with private investment while GDP shows a positive correlation with a coefficient of 0.4587.

Regression Results

Table 6: Effect of Private Sector Credit on Private Investment in Kenya.

Note: *significant at 10%, **significant at 5%, ***significant at 1%

The short-run dynamics results in Table 6 report that, the speed of adjustment coefficient is negative (-1.321122) and highly significant with the probability value (0.000). The negative value implies that Private Investment in Kenya is in an unstable state, and the independent variables are trying to correct this state with time. The adjustment is to the backward direction to stabilize the Private Investment over time, hence the corrective mechanism. The magnitude of the ECM coefficient (1.321122) suggests a rapid adjustment process.

The R-squared is 0.9626 and the adjusted R-square is 0.9291 implying that 92.91 per cent of private investment is explained by the independent variables in the model. The constant coefficient of -0.83559 was not statistically significant, it represents the value of private investment when other predictors are held constant. The p-value was 0.0026< 0.05, showing that the model is fit. The coefficients for Private sector credit, real interest rate, public investment, GDP and a dummy for structural breaks were 0.3227, 0.03976, -0.7371, 0.00329, and -7.587 in the short run respectively.

The private sector credit was statistically significant with a positive coefficient of 0.3227 at a 1 percent significance level in the short run. However, in the long run, it was statistically insignificant with a coefficient of 3.6914. This implies that a 1 percent increase in private sector credit leads to a 0.32 percent increase in private investment in the short run. The positive effect can be due to a rise in credit access by private investors. This enables them to finance new business ideas, expand their existing ventures and invest in innovations, hence contributing to an increase in private investment. However, the excess borrowing in the short run can lead to a financial crisis for private investors as they are obliged to repay. The positive short-run results agree with Okorie (2019) and Ayeni (2020) who found that private-sector credit positively affects private investment. The current findings contradict with submission by Mbaye (2014), who stated that private-sector negatively associates affect domestic private investment in Kenya.

Public investment has a negative coefficient and is statistically insignificant in the long run. However, it is statistically significant at a 1% significance level with a coefficient of -0.7371 in the short run. Though not significant in the long run, it still displays a negative effect on private investment. In the short run, it implies that a 1 percent increase in public investment leads to a 0.74 percent decrease in private investment. This suggests a possibility of crowding out effect, where the government outcompete the private sector over financial and physical resources. As the government borrow from domestic financial institutions, the loanable funds left for the private sectors reduce while interest rate is increased. The findings are consistent with Nyang’aya (2017) from Kenya who found a negative effect. However, they contradict with Mbaye (2014) who found a positive effect, attributing to the government investment on infrastructure hence boosting private investment.

GDP was not statistically significant and had a positive coefficient of 0.0032862 in the short run. However, in the long run, it has a negative coefficient (-0.0149637) which is against the expected positive influence on private investment. The insignificant effect of GDP per capita implies that other factors are more influential in determining private investment decisions. The findings coincide with the submission by Nyang’aya (2017) who found an insignificant effect of GDP on private investment in Kenya. The short-run results also support that of Makuyana and Odhiambo (2018) who reported positive and insignificant effects. Real interest rate was found to be insignificant but displayed negative effect (-0.1810244) on private investment in the long run. This implies that an increase in real interest rates causes the cost of borrowing to be expensive, discourages private investors from accessing credit and consequently reduces private investment.

The dummy variable (Dum1) which proxies the structural break is statistically significant at a 1% significant level with a negative coefficient of -7.587 in the short run but does not have a significant impact in the long run. This means that, the structural break due to the economic shocks negatively affects the private investment in the short run. However, in the long run, it does not have any impact since the disequilibrium have been corrected.

Table 7: Effects of Broad Money on Private Investment

Note: *significant at 10%, **significant at 5%, ***significant at 1%

Table 7 results revealed that in the short run, the lagged private investment is statistically significant at a 1% significance level with a negative coefficient of -0.4846937. It implies that there is a short-run convergence of the variables. Errors from the previous period were corrected at a rate of 48.46 percent, indicating a fair rate of adjustments. From the long-run results, the Error Correction Term was fairly high with a rate of 79.41 percent implying the speed of adjustment at which private sector investment, converges towards long-run equilibrium after economic shocks experienced during the short-run period.

The R-squared is 0.9434 and the adjusted R-square is 0.9003 implying that, 90.03 percent of private investment is explained by the independent variables in the model leaving around 9.07 percent for other predictors. The p-value was 0.0000< 0.05, showing that the model is fit. In the short run, the coefficients for Broad money, real interest rate, public investment, GDP and dummy for structural breaks were 0.3816, 0.01936, -0.21813, 0.002413and -4.6024 and in the long run, the coefficients for Broad money, public investment, real interest rate, GDP and dummy for structural breaks were-0.71378, -0.71659, 0.0383, 0.00394 and 7.301 respectively.

Broad money was found to be statistically significant at a 5% significance level with a negative effect in the long run and a positive effect during the short run at a 1% significance level. The results depict that a 1 percent increase in broad money causes private investment to rise by 0.397 percent in the short run and reduce by 0.714 percent in the long run. The money supply in Kenya is very crucial as it facilitates financial intermediation by boosting liquidity in the financial system This avails more funds for investment due to low interest rates and private investors can borrow at a low-priced rate. However, over time, a persistent increase in broad money that exceeds the economy’s productive capacity can cause inflation. Higher inflation leads to currency depreciation, increased risks and reduced real returns on investment which discourage long-term investment. The findings were in support of findings by Mbaye (2014) from Kenya who found that broad money affects private investment positively. Similar results were obtained by Obafemi (2016) and Okorie (2019) from Nigeria.

The public investment was statistically significant at 1% significant level and a negative coefficient in the short run, it indicates 1 percent increase in public investment leads to 0.717 percent reduction in private investment. In the long run, it leads to decrease of private investment by 0.218 percent at 5% significance level. This suggests the existence of a crowding-out effect in the short run and the long run. They are consistent with the findings by Makuyana & Odhiambo (2018) from South Africa who observed the crowding out effect of public investment during the short run period. However, it was not consistent with the results by Nyang’aya (2017) from Kenya who found a positive effect attributing it to the impact of public and private partnerships in Kenya. Similar results were reported by Nguyen and Trinh (2018) from Vietnam.

Real interest rates and Gross domestic product (GDP) were positive but statistically insignificant in both the long run and short run. However, their positive coefficients of 0.038352 and 0.0039446 respectively, suggest that they have a positive relation with private investment. The results are consistent with that of Mose (2017) who found a positive relationship between GDP per capita growth and private investment in the East African Community. The dummy variable representing the structural breaks is statistically significant in the short run at a 1% significance level, with the negative effect which is in line with the prior expectation. However, in the long run, it has a positive coefficient at a 5% significance level. This depicts a healthy and stable economy that is favourable for private investment.

Table 8: Effect of Bank Deposit on Private Investment

The results revealed that in the long run and short run the lagged private investment is statistically significant at a 1% significance level with a negative coefficient of -0.7573055 and -0.4883706 respectively. It implies that there is long-run and short-run convergence of the variables. Errors from the previous period were corrected at the rate of 75.7 percent and 48.8 percent in the current period, indicating a fairly high rate of adjustment in the long run. In the short run, the coefficients for Bank deposits, real interest rate, public investment, GDP and a dummy for structural breaks were 0.4875377, 0.0178037, -0.2293223 0.0033599 and-4.539712.

The R-squared showed that the model is a good fit with 0.9441 (94.41%) changes in private investment accounted for by the change in independent variables. This implies that 94.41 percent private investment is explained by total changes in independent variables. The adjusted R² is 0.9015, this shows that 90.15 percent of the changes in private investment are accounted for by the variations in the independent variables. The dummy variable was statistically significant in both the long run and short run, with a 5% and 1% significance level respectively.

The study reveals that bank deposits are statistically significant at a 5 percent significance level with a negative coefficient in the long run. However, during the short run period it has a positive coefficient and is statistically significant at 1% significance level. This implies that in the long run, a 1 percent increase in bank deposits leads to a reduction in private investment by 0.88 percent. During the short run, a 1 per cent increase in bank deposits leads to an increase in private investment by 0.49 per cent. An increase in Bank deposits positively influences private investment in Kenya since it enables banks to have more funds available for lending to private investors to finance their investments. Further, increased deposits improve general liquidity in the financial system, easing the process of financial transactions and investments. However, failure to correctly allocate resources, where banks lend to private investments that are not productive, results in the discouragement of long-term investment. The results were consistent with the study by Okoroafor, et al. (2018) in the short run where it revealed a positive correlation between bank deposits and private investment.

There is a statistical significance between public investment and private investment at 5 percent significance level both in the short run and in the long run. The coefficients in the long run and short run are negative. It implies that a 1 percent increase in public investment leads to a reduction in private investment by 0.63 in the long run and 0.23 percent in the short run. The study is consistent with submission by Machagua & Naikumi (2023) who established that public investment has a negative effect on private investment. Phiri & Ngeendepi (2021) noted that public investment by the government crowds out private investment, especially within low-income economies. However, it contradicts with findings by Ouédraogo, et al. (2019) who noted that in sub-Saharan Africa public investment crowds in private investment.  A study by Mukui, et al. (2023) in Kenya also supports the findings of a positive effect of public investment.

The results show that gross domestic product and the real interest rate are statistically insignificant both in the short run and long run and. However, their positive coefficients display a positive influence on private investments. This implies that increase in real interest and GDP per capita, encourage investors to save, availing funds for financing investments. Mose & Jepchumba (2020) found similar results of a positive effect of GDP per capita on private investment in Kenya. The dummy variable representing the structural breaks is statistically significant at a 1 percent significance level with a negative effect in the short run which is in line with the prior expectation. However, in the long run, it had a positive coefficient at a 5% significance level. This depicts a healthy and stable economy that is favourable for private investment.

CONCLUSION

This study aimed to determine the effect of financial deepening on private investment in Kenya. Time series data was collected over the period. All the data were found to be stationary at first difference except the real interest rate which was found to be stationary at level. The ARDL bounds cointegration test was done to establish whether there is a long-run relation between the variables. There were cointegrations among the variables in the three models and the Error Correction model was estimated.

Based on the findings, there was a positive and significant relationship between private sector credit and private investment in the short run and an insignificant relationship in the long run. This implies that private-sector credit is important for the growth of private investment in Kenya. The positive effect of broad money in the short run shows that the money supply in Kenya is very crucial as it facilitates financial intermediation by boosting liquidity in the financial system. The study concludes that broad money is important but there is a need for close monitoring and regulation on inflation that may arise in the long run. An increase in Bank deposits positively influences private investment in Kenya since it enables banks to have more funds available for lending to private investors to finance their investments. The study’s overall conclusion is that financial deepening proxied by private sector credit, broad money and bank deposits influence the growth of private investment in Kenya. Therefore, Financial intermediation theory is applicable in Kenya.

The government and policymakers should conceptualize policies facilitating financial access and inclusion for marginalized groups in remote and rural areas. This involves creating awareness about the digital finance transformation where mobile phones are used to open and manage financial accounts.  Based on the positive effect of broad money and bank deposits in the short run, the government should ensure flexible policies and measures are set up to mitigate dynamic forces in the economy including inflation and currency depreciation. For the negative impact in the long run, regular review and adjustments of monetary and fiscal policies are necessary to curb their negative impact in the long term. The government should continue to support the financial sectors by creating a stable economic and political environment. In addition, enhancing the growth of microfinance institutions to facilitate the availability of credit and savings services to individuals earning low-income and small business owners, is vital.

The current study focused on the effect of financial deepening(depth)on private investment, there is a need to investigate how financial stability as other measures for financial development also influence private investment in Kenya. Future researchers can explore and compare the different sectors of the economy in Kenya that benefit from financial deepening. This will guide the financial institutions to channel their funds to the productive sectors.

Conflict of Interest: The authors declare no conflict of interest.

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