VOLUME 6 ISSUE 3, 2021 263 Effects of Monetary Sector Financial Liberalization on Domestic Savings in Selected ECOWAS Countries Ihedioha, Victor N.*1 Chris U. Kalu1 Ogbonnaya, Joshua I.2 1Department of Economics, Nnamdi Azikiwe University, Awka 2Department of Religion and Human Relations, Nnamdi Azikiwe University, Awka *Corresponding Author Email: Gentlevic23@gmail.com Abstract There has been rapid decline in the rate of saving in the ECOWAS countries. More so, monetary sector financial liberalization in the region has not yielded a fruitful outcome as the countries have suffered widened disparity of lending and deposit rates, high inflation and continuous decline in economic growth. The study examined the effects of monetary sector financial liberalization in the ECOWAS countries of Benin, Cote d’ Ivoire, Ghana, Liberia and Nigeria from the period 1981-2019. The study anchored on the frameworks of the life cycle and the financial liberalization hypotheses employed the panel Vector auto regression (VAR) estimation technique. The variables of the study are household saving (dependent) and domestic credit provided by the financial sector, growth of GDP, interest rate spread, broad money supply and financial liberalization dummy, and gross fixed investment as independent and control variables. The data were sourced from World Bank Development Indicator (WDI, 2020) and the African Development Bank Database (2020). The study showed that domestic credit has a significant positive effect on domestic saving while gross fixed capital formation has a negative effect on domestic saving. The study further revealed that labour force and money supply are negatively related to household saving in the ECOWAS countries within the reviewing period. From the empirical evidence, the study recommended among others, the need for the Government to implement restrictive monetary policy measures that will curtail excessive money supply in order to reduce inflation spiral and improve household saving in the ECOWAS countries. Keywords: Domestic saving, ECOWAS countries, financial liberalization, monetary sector, panel VAR, JEL Classification: D14, B26, E50, E58, C23 Introduction Saving is defined as that part of disposable income which is not spent on consumption (Bime & Mbanasor, 2011). Saving involves sacrificing the current consumption in order to increase the living standard and fulfilling the daily requirements in the future. Domestic saving therefore becomes that part of the household income not consumed. The vitality of savings to the economy has well-being espoused in the economic development literature. It could be used for investment to earn profit (interest) or be used to purchase assets such as building, machinery and infrastructure. Investment from saving contributes to growth in aggregate wealth. But the investment cannot increase without increasing the amount of saving. Thus, saving performs a major role in providing the national capacity for investment and production, which will affect the potentials of economic growth. In general terms, increasing aggregate saving contributes to higher investment and this leads to higher economic growth Domestic Savings (GDP) both in the long and short-runs. It means that the higher saving rate leads to less consumption, which could also result in larger amount of capital investment and finally a higher rate of economic growth. Furthermore, saving creates capital formation and leads to technical innovation and progress this helps with economies of large scale production and increase specialization. This also helps to accelerate the productivity of labour. Thus, saving leads to fuller utilization of available scarce resources in an efficient way, as it increases the size of national output, income and employment, thereby solving the problems of inflation, unemployment and balance of payments deficits, poverty, inequality and making the economy free from the burden of foreign debt and better welfare of the citizenry. The monetary sector includes the Central Banks and banking financial institutions and units (monetary agency) and certain operations that are usually attributed to the central bank but in some cases, are carried out by other government institutions (example, commercial/deposit money banks). The monetary aggregates include the totality of currency outside the banks. This includes narrow money (M1) and broad money (M2) supplies. The currency outside the central banks includes cash issued by the central bank for circulation, but with the exclusion of cash in the vaults of Deposit Money Banks (DBMs).Narrow money (M1) includes currency in circulation plus transferable deposits held by all deposit money banks. Broad money (M2)is the combination of M1 and deposits (in national currency) plus money market instruments. This may include time deposits of all maturities, or only those deposits with maturities that do not exceed a specified maximum term. The main objective of monetary reform as pointed out by the monetary authority of the ECOWAS countries include: Removal of controls on interest rate to increase the level of savings and improve efficient allocation of domestic credit in the economy; elimination of non-price rationing of credit to reduce misdirected credit and increase competition; adoption of indirect monetary management in place of the imposition of credit ceiling on individual banks; enhancement of institutional structure and supervision, strengthening the money market through policy changes and distress resolution measures and improving the linkage between formal and informal sectors(CBN, 2007).Thus, the idea behind monetary sector liberalization is summarized in two folds: First, to quantity effects through generating higher saving and investment in the economy, and second, to quality effects by efficiently allocating capital to profitable investment (Ahmad &Premaratna, 2020). Monetary sector liberalization serves as a panacea to money market constraints in a financially repressed economy and under the financial repression region. Monetary sector financial liberalization promotes the attraction of foreign investment, availability of credit facilities to the investors and allocation of capital towards the most productive projects, and it also facilitates financial development which in turn could positively affect productivity in the economy (Ikeora, Igbadika& Jessie, 2016).Since the focus of the study is on monetary sector financial liberalization, unlike the entire financial system liberalization with multifaceted characteristics, and to avoid the problems of data measurement, our measures of monetary sector financial liberalization includes: Domestic credit provided by the financial sector in percentage of GDP, interest rate spread, which is the different between the lending rate and deposit rate and a constructed financial liberalization dummy, to account for the various monetary sector financial liberalization regimes in the ECOWAS countries. ECOWAS countries researchers such as Akpan (2008) and Emenuga (2005) in their separate studies concluded that financial liberalization is critical to savings mobilization. Udegbunam (1995) found out that financial liberalization has provided great incentives for the expansion of banking institutions. Similarly, Bakare(2011) found out that financial liberalization has impacted negatively on domestic saving in Nigeria. Other related studies are Bosede (2013); 265 Owusu and Odhiambo (2016); Adewuyi, Bankole & Damilola (2010), Abu, Modh and Mukhriz (2013) and Adebanyo, Awonusi, Ahmed, Ewunaga and Yemisi (2017). However, there is no unanimous agreement on the nature of financial liberalization effects on domestic saving from these studies and papers. The relationship between financial liberalization and domestic saving is complex not only because there are short and long-run effects involved but because financial liberalization is a process with many dimensions. These studies were deficient on the measurement of financial liberalization. Again, existing empirical studies focusing on the effect of financial liberalization on saving in sub-Saharan Africa (SSA) have employed the real rate of interest (Oshikoya, 1992; Seck& El Nil, 1993; Azam, 1996; Matsheka, 1998), and measures of financial deepening such as the broad money ratio (Mwega, 1997; Elbadawi & Mwega, 2002) and the ratio of bank credit (Elbadawi & Mwega, 2000; Kelly &Mavrotas, 2002) as proxies for financial liberalization. However, such variables are inadequate measures of financial liberalization because they fail to explicitly account for different liberalization measures. As a result, the study attempts to contribute to the literature on the effects of monetary sector financial liberalization on domestic saving in the ECOWAS countries of Benin, Ghana, Cote d’Ivoire, Liberia and Nigeria, relying on monetary sector variables only other than measuring financial liberalization with either a variable of the money market or capital market. Such measurements lead to estimation bias. The study employed the panel vector autoregressive approach (PVAR) to investigate the effects of monetary sector financial liberalization on domestic saving in ECOWAS countries from the period 1981 to 2019. The research questions that formed the focus of discussion in the paper is as follow:  What is the effect of monetary sector financial liberalization on domestic saving ECOWAS countries?  What is the effect of economic growth on domestic saving in ECOWAS countries?  What is the effect of monetary sector liberalization policy regimes/reforms on domestic saving in the ECOWAS countries? The overall objective of the study is to examine the effect of monetary sector financial liberalization on domestic savings in Benin, Cote d’Ivoire, Ghana, Liberia and Nigeria. Specifically, the study objectives are: To determine the effects of domestic credit provided by the financial sector (% of GDP), interest rate spread and broad money supply on household savings in Benin, Cote d’Ivoire, Ghana, Liberia and Nigeria; To investigate the effect of annual growth rate on household savings in Benin, Cote d’Ivoire, Ghana, Liberia and Nigeria and; To empirically evaluate the effect of monetary sector financial liberalization policy regimes on household saving in the ECOWAS countries of Benin, Ghana, Cote d’Ivoire, Liberia and Nigeria. Theoretical Framework, Model Specification and Data Sources Theoretical Framework The Life Cycle Hypothesis (LCH) and the financial liberalization thesis form the theoretical frameworks of the study. The LCH was first theorized by Modigliani and Brumberg (1954) to establish a positive relationship between the saving ratio and output growth. Within the theory of LCH, the individual objective is to enhance consumption over the life time. Savings are therefore determined by total life time earnings and not by the level of current income. The theoretical arguments for monetary sector financial liberalization are centered mainly on the need for a more laissez faire banking policy, especially the domestic financial market that Domestic Savings is determined by the market forces. It will ensure that interest rate captures the actual scarcity of capital in less developed countries. McKinnon (1973) and Shaw (1973), the proponents of the financial liberalization thesis produced a theoretical basis for financial development that has been formalized and extended to show how some financial controls that produce financial repression effects could make the financial sector stifle rather than promote a country’s development. The McKinnon-Shaw analysis is anchored on the fact that interest rate ceilings stagnate savings and reduce the quality of investments. Moreover, it implies that an end to interest rate ceilings and other government regulations responsible for slow competitive operations in the market for funds will be beneficial to developing countries. Higher interest rates will result in increased savings and investment, which in turn contribute to economic growth and investment. Efficient financial system will lead to appropriate channeling of financial resources provided that the financial system is efficient and well-functioning. This means that firms could grow their enterprises through the opportunity of borrowing at lower interest rates. More so, financial intermediaries will enable investors direct their funds to more rewarding projects. This main critique of the financial liberalization theory emanates from the imperfect information hypothesis. That school of thought assesses the problem of financial development within the context of information asymmetry and costly information resulting from credit rationing. The frameworks are adopted following the relevance to the study Empirical Model Specification On the basis of the theoretical frameworks presented in the foregoing, household domestic saving proxy by domestic saving function is specified in a Panel VAR model form to enable estimation to be carried out for the selected ECOWAS Countries. The model estimation follows: Adewuyi, Bankole and Arawomo (2010), whose model is specified as thus: GDStk =b0 + b1GRGDPtk + b2TOTkt +b3GDPtk + b4BRMOtk + b5GDPPCtk + b6INFtk + b7INTtk + b8DCPRrtk + b9LFEtk + b10LAPtk +Utk.(3.1) Where GDS is gross domestic saving (measured as gross domestic saving as a percentage of GDP.GRGDP is growth rate of gross domestic product, TOT is Terms of Trade, GBP is Government Budget position (Fiscal deflect or surplus as a percentage of GDP), BRMO is the degree of financial depth (measure as broad money supply as a percentage of GDP), GDPPC is gross domestic product per capital (GDP as a ratio), DEPR is Dependency Ratio, LFE is life expectancy ratio, LAP in labour participation rate. Since the objective of the current study is to investigate the effect of monetary sector financial liberalization domestic of savings in the selected ECOWAS countries, equation (3.1) is re-specified with adjustment as follows: GDStk= b0 + b1DESCREtk + b2M2/GDPtk + b3INTSPtk + b4GDPPCtk +b5GFCFtk+b6POGROW b7FINLDUMtk + utk(3.2) Where GDS is Gross Domestic Saving; DESCRE, is domestic credit allocated to the private sector by the banking sector; M2/GDP, is broad money supply as a percentage of GDP; INTSP, interest rate spread previously defined; GDPPC, growth per capita; GFCF, aggregate investment as gross fixed capital formation and FINLDUM, liberalization dummy representing the different policy regimes of reforms/liberalization in the economies of the ECOWAS countries over the reviewing period and U, the error term. K represents countries heterogeneity and t is the time frame. The study is focusing on the monetary sector financial liberalization because the complexity of financial liberalization itself. This becomes expedient so as to avoid the problem of multicollinearity and data biasness resulting from the 267 construction of an index variable to measure financial liberalization. Measurement error may occur during the factor analysis. Domestic credit provided by financial sector (in percentage of GDP): One of the explanatory variables used in measuring monetary sector financial liberalization. Domestic credit provided by the financial sector includes all credit to various sectors on a gross basis, with the exception of credit to the central government, which is net. The financial sector includes monetary authorities and deposit money banks, as well as other financial institutions. Elom- Obed, Odo, Udude and Okonkwo(2016) revealed a unidirectional causality from domestic credit (DCPS%GDP) to domestic private saving, indicating a positive significant relationship between the two variables. Our study hypothesizes a positive effect of domestic credit on household saving, as such, α1>0. Economic Growth: Economic growth, proxy by GDPPC measured in annual percentage rate entered into the model as a control variable, in the sense that growth affects domestic saving outside the explanatory variables.From a theoretical point of view, a positive relationship between domestic savings and economic growth is expected, due mainly to the fact that an increase in savings would positively stimulate economic growth, and economic growth could in turn stimulate the growth of domestic saving via investment and employment. Misztal (2011) found the existence of one-way causal relationship between gross domestic savings and gross domestic product in the case of developed countries as well as in developing countries. In this study, our study expects a positive significant relationship between both variables. Our study therefore hypothesizes a positive effect of economic growth on household saving, as such, α2>0. Interestrate Spread: This is another explanatory variable of the study. Interest-rate spread is defined as the difference between the lending rate and the deposit rate. The life cycle theory introduced that the net effect of the real interest rate increases the current price of consumption relative to the future price, thus affecting savings positively. The income effect indicates that if the household is a net lender, an increase in the interest rate will have a positive effect on savings ratio only when the substitution effect dominates the income effect. In developing countries, like the ECOWAS countries, where financial markets are still not developed, substitution effect is expected to be much greater than income effect, and thus the real interest rate is likely to have a net positive effect on domestic savings. Hence, α3 >0. Broad Money Supply (M2/GDP) The broad money supply is among the measures of financial deepening of the money market reforms. The broad money supply involves currency in circulation and all reserve balances/deposits held by money bank financial institutions in ECOWAS countries. It is the overall money held by the ECOWAS countries country in the form of liquid instruments for a specified time. The broad money supply also includes balances held in cheques and savings accounts, cash and coins. Brookin (2001); Narayan & Siyabi (2005), concludes on the inverse connection with aggregate savings. Conversely, money supply growth has positive effect on gross domestic savings (Khan, Teng, Khan, Jadron&Rehan, 2017). The study hypothesizes a positive relationship between broad money supply and gross domestic savings. A positive relationship is expected such that α4>0.Population growth entered into the model as a control variable and is measured by labour force rate. This refers to the increase in the number of individuals in a population. The effect of population growth, say, family size on household saving can be negative, negligible or positive. For example, a positive effect of children on saving can result if their presence increases family income more than their effect on Domestic Savings consumption. Population growth could lead to increase in saving through the growth effect or a decrease in saving through the dependency effect. Financial Liberalization Dummy (FINLDUM: A control variable in the model. A dummy for monetary sector financial reforms, it captures the liberalization policy regimes in the ECOWAS countries. These reforms periods should, in particular, include policies that should induce higher growth thereby generating savings and investment. In relevant studies, Akinsola, Odhiambo & McMillan (2017) found a negative result for low-income countries. On the other hand, (Kunt & Demigrunt, 1998) found out that the different policies are strongly and positively correlated with the indicators of measurement. It is expected that these policy regimes have positive effect on household saving. Results, Analysis and Discussion of Findings Summary of Descriptive Statistics Table 1: Summary of Descriptive Statistics HHS BMS DCR IRS GGDPGCFCFLIB Mean 4.242608 27.47 115.4958 8.66 3.36266 4.34 0.764103 Median 11.59121 26.38 24.56837 8.7 4.000000 5.79 1.000000 Maximum 39.31757 101.8 3170.321 16.2 106.2798 123 1.000000 Minimum -152.5373 8.89 -26.65229 0.31 -51.03086 0.45 0.000000 Std. Dev. 31.89767 10.2 350.5593 3.13 11.36628 12.4 0.425651 Skewness -3.093106 2.3 5.488374 -0.14 2.813243 11.0 -1.244128 Kurtosis 12.68144 16.1 38.23734 2.6 40.22144 57.2 2.547855 Tarque-Bera 1072.495 1645 11067.54 0.34 11513.88 65.1 51.96633 Probability 0.000000 0.0 0.000000 1689 0.000000 75.2 0.000000 Sum 827.3085 5357 22521.67 1903 655.7394 438.4 149.0000 Sum Sq.Dev. 19737.5 20305 23841.0070.234 2.5063.30 1867 35.14872 Observation 195 195 195 195 195 195 195 Note: HHS: Household savings, domestic credit, interest rate spread, broad money supply), GGDP, economic growth, FLIB, Financial liberalization dummy).Significance level; 5% Source: Authors Computation using Econometric View 11.0 Table 1. present the descriptive statistic of the model variables for household savings (% of GDP), financial index of domestic credit (% of GDP, interest rate spread (lending-deposit rate), and broad money supply (% of GDP), growth of GDP in annual percentage (GGDP) and financial liberalization dummy (FLIB). The summary statistics indicate the existence of wide variations in the variables. For instance, the average household savings rate for the 1981 to 2019 periods was 4.21 percent compared to 115.4, 3.36 and 0.76 percentage points for financial index variables, growth rate and the financial liberalization dummy. The maximum for household savings was 39.32, 3170 for the index 106.3 for the growth rate and 1.0 for the liberalization dummy. Similarly, the minimum ranges from -152.5 to 0.00 for household savings, the financial liberalization index, growth rate and liberalization dummy. The skewness statistics showed that with the exception of household savings and liberalization dummy with negative values, the others, financial index and growth were positive. The Kurtosis statistics showed that the values of the data ranges from 12.6 to 2.54 suggesting that the variables are leptokurtic, i.e, the distribution is peaked relative to normal distribution. Finally, the Jarque-Bera statistics values of 1072 to 51 rejected the null hypothesis of normal distribution for the variables at the 5% critical value. 269 Correlation Matrix In furtherance to the descriptive statistics, the correlation matrix test was carried out to show the movement and pattern of the data used in the model estimation, -1 indicates a perfectly negative linear correlation between two variables, 0 indicates no linear correlation between the two variables and 1 indicates a perfectly positive linear correlation between two variables. Table 4.2 shows the correlation matrix of the data between the periods 1981 to 2019. Table 2: Correlation Matrix Correlation Probability HHS DCR BMS IRS GGDP GCFC FLIB Observations HHS 1.000000 195 DCR,BMS,IRS -0.073111 0.3098 195 1.000000 - 195 GGDP -0.068799 0.3392 195 -0.097927 0.1732 195 1.000000 - 195 FLIB -0.080515 0.2632 195 0.101383 0.1585 195 1.000000 Source: Authors’ Computation using E-View 11.0 The correlation matrix presented in Table 2 shows that the coefficients of DCR, BMS IRS, GGDP and FLIB were all negative. The coefficient of HHS was positively signed showing perfect correlation. Meanwhile, DCR, BMS and is negatively related with HHS, the same with GGDP with HHS. Liberalization dummy was also negatively related to household savings. The results show that the coefficient is free from multicollinearity. Panel Unit Root and Co integration Results The first step of the analysis is to look at the data properties. Two classes of tests allow the investigation of the presence of the unit root: the first generation panel unit-root tests (including Hadri (2000) and Im et al., (2003), were developed on the assumption of cross- sectional independence among panel units (except for common time effects), and may be at odds with economic theory and empirical results. On the other hand, second generation tests (Smith et al., (2004); Pesaran, 2007) relax the assumption of cross-sectional independence, allowing for a variety of dependence across the different units. We employ four different types of panel unit roof tests; Im, Pesaran and Shin, Levin, Lin Chu, ADF (augmented Dickey-Fuller and Philip-PerronFisher Chi-Square). The tests are constructed with a unit roof under the null hypothesis and heterogeneous autoregressive roots under the alternative, which means that a rejection should be taken as evidence in favour of staionarity for a least one country Domestic Savings Table 3: Panel Unit Root Test Variables Levin, Lin & Chu t* First Differen ce Im, Pesaran & Shin W-stat Test Differen ce ADF- Fisher Chi- Square First Differen ce PP Fishe r Chi- Squar e First Differ ence GGDP -0.95 -7.136 1.37 -7.619 2.49 71.7759 0.02 -71.69 DCR, BMS, IRS -2.63 -11.4087 0.02 -10.8997 0.99 108.80 1.18 148.37 8 HHS 1.37 -3.05 -3.90582 -17.31 1.18 35.299 -2.07 35.510 2 FLIB -4.99 -17.97 -3.98 -11.46 2.09 -5.59 -0.70 -23.22 Note: The statistic test is the cross-sectionally Augmented Dickey Fuller of Pesaran (2007). The test has the null hypothesis of presence of unit roof. Source: Researchers’ Computation using E-view 11.0 Table 3 reports the results of the first second generation unit roof test of Persan (2007), Levin, Lin & Chu, Im, Pesaran and Shin W-Stat, ADF-Fisher chi-square and PP-fisher chi- square. At conventional levels of significance, the results show that most of the variables are not stationary in levels but stationary in first difference. Form the report, the variable GGDP was not significant at its levels except in its first difference the same with the financial liberalization index (DCR, IMs, IRs). The household savings variable was the same. The financial liberalization dummy representing financial regime shifts in the selected ECOWAS countries were significant in its levels and first difference. Due to the existence of mixed levels of integration among the series, we proceed to apply the panel co-integration of Johansen fisher panel co-integration test presented in Table 4. Table 4: Johansen Fisher Panel Cointegration Test Sample (adjusted): 1981-2019 Series: HHS DCRBMS IRS FLIB GGDP Lags interval (in first difference): 1-3 Unrestricted Co-integration Rank Test Hypothesized No. of CE (s) Fisher Stat (Trace test) Prob. Fisher staft (Max-Eyen Staf) Prob. None 305.3 0.0000 148.9 0.0000 At Most 1 209.1 0.0000 118.9 0.0000 At Most 2 122.4 0.0000 80.83 0.0000 At Most 3 55.90 0.0000 41.86 0.0000 At Most 4 ` 25.18 0.0050 19.41 0.0353 At Most 5 23.62 0…87 23.62 0.0087 Note: Probabilities are computed using asymptotic, Chi-square distribution. 5% critical values are: 82.49; 59.46; 39.89; 24.31; 3.84 1% critical values are: 90.45; 66.52; 45.52; 45.58; 79.75; 6.51 Source: Researchers’ Computation using E-view 11.0. 271 The Johansen Fisher Panel co integration test for the selected countries in ECOWAS show that the variables are all the variables are co integrated at both 1 percent and 5 percent significance levels. In all the co integration results implies that there exists a long-run relationship between financial liberalization and domestic savings in the selected ECOWAS countries of Benin, Cote d’ Ivoire, Ghana, Liberia and Nigeria. Table 4b. which is also cross section of the Johansen Fisher Panel co integration test further supported the existence of co integration or long-run relationship between financial liberalization and domestic savings in the selected ECOWAS countries. Table 4b: Johansen Fisher Panel Cointegration Test Result. Cross Section Trace Test Statistics Probability Max-Eyen Test Statistics Probability Hypothesis of no cointegration Benin 240.6289 0.0000 83.6522 0.0000 Cote d’Ivoire 282.4478 0.0000 115.6547 0.0000 Ghana 253.9848 0.0000 119.3316 0.0000 Liberia 239.3161 0.0000 124.3513 0.0000 Nigeria 225.6890 0.0000 81.5542 0.0000 Hypothesis of at most 1 cointegration relationship Benin 156.9767 0.0000 61.2830 0.0000 Cote d’Ivoire 166.7931 0.0000 98.0344 0.0000 Ghana 134.6532 0.0000 50.6129 0.0002 Liberia 114.9649 0.0000 48.8971 0.0004 Nigeria 144.1348 0.0000 55.9884 0.0000 Hypothesis of at most 2 cointegration relationship Benin 95.6937 0.0000 50.0168 0.0000 Cote d’Ivoire 68.7588 0.0002 30.3448 0.0215 Ghana 84.0402 0.0000 47.9136 0.0000 Liberia 66.0617 0.0004 27.6856 0.0485 Nigeria 88.1463 0.0000 54.1357 0.0000 Hypothesis of at most 3 cintegration relationship Benin 45.6769 0.0004 28.8479 0.0034 Cote d’Ivoire 38.4140 0.0040 26.0449 0.0094 Ghana 36.1266 0.0082 25.3588 0.0120 Liberia 38.3821 0.0040 24.6588 0.0153 Nigeria 34.0107 0.0154 17.7138 0.1409 Hypothesis of at most 4 cointegration relationship Benin 16.8289 0.0313 15.1998 0.0355 Cote d’Ivoire 12.3691 0.1401 11.4872 0.1314 Ghana 10.7678 0.2262 7.9923 0.3795 Liberia 13.7233 0.0908 11.5914 0.1270 Nigeria 7.1109 0.0077 7.1109 0.0077 ** Mackinnin-Haug-Michelis (1999) P-values Source: Researchers’ Computation using E-view 11.0 Domestic Savings The cointegration test of the cross section of the countries involved showed that there are existence of long-run relationship between financial liberalization and domestic saving in the selected ECOWAS countries. There is a hypothesis of at most five cointegration relationships at both the 1 percent and 5 percent significant values. Panel VAR Lag Order Selection Criteria Test Panel VAR analysis is predicated upon choosing the optimal lag order in both panel VAR specification and moment condition. Andrews and Lu (2001) proposed MMSC for GMM models based on Hansen’s (1982) J statistic of over identifying restrictions. Their proposed MMSC are analogous to various commonly used maximum likelihood-based model selection criteria, namely, the Akaike Information Criteria (AIC) (Akaike 1969), the Bayesian Information Criteria (BIC) (Schwarz, 1978; Rissanen, 1978, Akaike 1977), and the Hannan- Quinn Information Criteria (HQIC) (Hannan& Quinn, 1979). Correct lag-length selection is critical for PVAR since excessively short lags may fail to capture the system’s dynamics, lead to omitted variables, bias the remaining coefficients, and likely produce serially correlated errors. Meanwhile too many long a lag leads to a rapid loss of degrees of freedom and to over parameterization. Given that the number of variables included in PVAR and the time dimension of the time series, the system cannot be tested for a lag length more than three (IMF, 2000). Table 5: PVAR Lag Selection Criteria Lag Logl LR FPE AIC Sc HQ 0 -3934.303 NA 1.45e+12 45.03204 45.14054 45.07605 1 -3190.267 1428.549 4.45e+08 36.94020 37.69975 37.24821 2 -3101.846 69.51928 4.38e+08 36.92249 38.33308 37.49467 3 -3152.718 90.6972 3.71e+084 36.75253 38.81416 37.58879 4 -3078.530 39.97040 4.31e+08 36.89749 39.61016 37.99783 Note: *indicates lag order selected by the criterion LR: Sequential modified LR test statistic (test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schewarz information criterion HQ: Hannan-Quinn information criterion Source: Researchers’ Computation using E-View 11.0 Using the sample period by taking into account the number of lost observation with each additional lag, the lowest value of the criteria for the same four countries attested that a uniform lag-length of three as shown in Table 5 should be selected. A lag-length of one was not sufficient to yield a white noise residual in a number of cases. Impulse Response Function (IRFs) Test The generalized IRFs, traces out the responsiveness of the dependent variable domestic saving in selected ECOWAS countries to shocks of each of the variables: domestic credit, broad money supply and interest rate spread) financial liberalization dummy and growth rate( not presented here). For each equation, a unit shock is applied to the error, and the effects upon the system over 10 horizons are noted. Since the study has six variables, a total of 36 impulses could be generated. However, since our objective is to examine the effect of the explanatory variables on the dependent variable (HHS-Housing Saving), we only trace out 273 the responsiveness of the independent variables on the dependent variable. Sims (1980) proposed the Choleskydecomposition of Ʃ to impose a recursive structure on a Panel VAR. The decomposition, however, is not unique but depends on the ordering of variables in Ʃ. Figure 5.1 present the response to Cholesky One S.D (d.f adjusted) innovations -2.5.E. The impulse responses for the recursive VAR, in response to the Cholesky One S.D. innovations are plotted. The first row show the effect of an unexpected one percentage point increase in household savings on the financial liberalization variables (DCR, BMS, and IRS), financial liberalization dummy and growth rate, as it works through the recursive VAR systems with the coefficients estimated from actual data. The second, third and fourth rows shows the effect of an unexpected increase of one percentage point in the financial liberalization index to household savings. The fifth row shows the effect of an unexpected increase of one percentage point in the financial liberalization dummy to household savings, while the sixth row shows the effect of an unexpected increase of one percentage point in the growth rate to household savings in the selected ECOWAS countries. Also plotted are ±1 standard error bands, which yield an approximate 66% confidence interval for each of the impulse responses. These estimated impulse responses show patterns of persistent common variation. For example, an unexpected rise in financial liberalization index (DCR, BMS, IRS) financial liberalization dummy and economic growth slowly fades over the 10 quarters, and is associated with a persistent increase/decrease in household savings in the countries of Benin, Coe d’ Ivoire, Ghana. Variance Decomposition (Forecast Error Decomposition) Test The forecast error decomposition is the percentage of the variance of the error made in forecasting a variable (e.g. household saving), due to a specific shock (e.g. the error term in the financial liberalization and domestic savings equation) at a given horizon. Thus, the forecast error decomposition is like a partial R2 for the forecast error, by forecast horizon. These are shown in Table 4. for the recursive VAR. Table 6: Variance Decomposition for the Recursive VAR Ordered as HHS, DCR, BMS, IRS, FLIB, and GGDP (a) Variance Decomposition of HHS Forecast Horizon Forecast Standard Error Variance Decomposition (Percentage Point) HHS DCR BMS IRS FLIB GGDP 1 14.83 100.0 0.00 0.00 0.00 0.00 0.00 4 24.10 93.19 0.23 1.15 1.40 0.08 3.85 7 28.35 90.35 0.44 3.55 1.12 0.14 4.41 10 30.66 88.02 0.38 5.98 1.44 0.16 4.01 (b) Variable Decomposition of FINLINDEX DCR Forecast Horizon Forecast Standard Error Variance Decomposition (Percentage Point) HHS DCR BMS IRS FLIB GGDP 1 182.64 2.13 97.87 0.00 0.00 0.00 0.00 4 283.41 5.29 48.23 0.897 0.75 0.97 43.86 7 338.33 6.19 36.57 2.20 1.10 1.49 52.50 10 357.38 6.98 33.88 4.10 1.25 1.73 52.05 Domestic Savings (c) Variance Decomposition of BMS Forecast Horizon Forecast Standard Error Variance Decomposition (Percentage Point) HHS DCR BMS IRS FLIB GGDP 1 6.24 0.00 72.05 27.94 0.00 0.00` 0.00 4 9.20 0.10 35.74 29.50 0.37 1.10 33.17 7 10.31 1.24 29.54 26.04 0.50 2.31 41.35 10 10.57 3.76 27.26 25.01 0.52 3.03 40.41 (d) Variance Decomposition of FINLINDEX IRS Forecast Horizon Forecast Standard Error Variance Decomposition (Percentage Point) HHS DCR BMS IRS FLIB GGDP 1 1.04 1.82 0.14 5.174 92.87 0.00 0.00 4 1.61 1.10 2.69 2.62 89.50 0.19 3.90 7 2.01 0.88 5.88 3.01 85.799 0.19 4.22 10 2.32 1.11 8.61 4.77 81.72 0.27 3.52 (e) Variance Decomposition of FLIB Forecast Horizon Forecast Standard Error Variance Decomposition (Percentage Point) HHS DCR BMS IRS FLIB GGDP 1 0.16 8.69E.05 0.24 0.05 0.69 99.01 0.00 4 0.27 0.03 0.52 0.21 3.15 95.73 0.36 7 0.30 0.06 0.43 0.24 3.04 95.92 0.31 10 0.31 0.17 0.40 0.25 2.94 90.95 0.29 (f) Variance Decomposition of GGDP Forecast Horizon Forecast Standard Error Variance Decomposition (Percentage Point) HHS DCR BMS IRS FLIB GGDP 1 10.25 1.12 8.29 0.00 3.83 1.24 85.51 4 12.10 3.12 6.32 0.21 3.22 1.37 85.74 7 12.15 3.18 6.34 0.41 3.26 1.42 85.37 10 12.21 3.20 6.30 0.44 3.26 1.46 85.33 Source: (a-f) was computed using E-view 11.0 Table 6. (a-f) suggest considerable interaction among the variables. The variance decomposition indicates that household saving changes in the selected ECOWAS countries explained about 100 percent of the shocks to itself in the first quarter. It inclined to about 88 percent in the 10th quarter. Domestic credit, one of the indicators of the financial liberalization index accounted for about 2 percent shock to household savings in the first quarter. This increased to 7 percent in the 10th quarter. This explains that domestic credit account for about 7 percent changes in household saving in the ECOWAS selected countries 275 over the reviewing period. Broad money, the second indicator of the financial liberalization index accounted for about 0 percent shock in household savings, this increased to 4 percent in the 10th quarter. Interest rate spread accounted for 1.8 percent of the shocks to household saving in the first quarter, however, declined to 1.1 percent in the 10th quarter, while liberalization dummy accounted for 0.2 percent in the 10th quarter. Growth rate of output accounted for 1.12 percent of the changes in household saving in the selected ECOWAS countries. From the results presented, growth rate caused the greatest shock to household savings. The reason could be that increase in growth promotes employment which in turn stimulates household savings. In order words, inclusive growth is a necessary condition for household welfare and the attendant improvement in household income, investment and savings. Vector Autoregression Estimates (Summary Statistics) Table 7 Presents the Summary Statistics of the VAR HHS DCR BMS IRS FLIB GGDP R-squared 0.818 0.77 0.67 0.90 0.83 0.30 Adj-R-squared 0.80 0.71 0.63 0.89 0.82 0.22 Sum square resid 35424.3 5370464 6281.45 174.64 4.15 16906.52 S. E. equation 14.833 182.63 81.848 46.35 3.85 18.39 F-statistics 40.21 30.56 18.39 81.845 46.35 3.85 Log likelihood -730.81 -1182.7 -575.12 -252.69 83.86 -664.23 Akaike AIC 8.33 13.35 6.94 3.018 -0.72 7.59 Schwarz Sc 8.67 13.69 6.94 3.36 -0.38 7.92 Mean dependent 3.796 119.18 27.92 8.79 0.83 3.82 S. D dependent 32.97 364.03 10.35 3.15 0.37 11.6 Source: Researchers’ Computation using E-View 11.0 Discussion of Findings Because VARs involve current and lagged values of multiple time series, they capture co movements that cannot be detected in invariance or bivariate models standard VARs summary statistics (impulse response functions and variance decompositions) are well accepted and widely used methods for portraying these movements. These summary statistics are useful because they provide targets for the model and were the focus of the discussions. From the impulse response function, an unexpected rise in domestic credit, broad money supply and interest rate spread is associated with a persistent decrease in household savings in the countries of Benin, Cote d’Ivoire, Ghana, Liberia and Nigeria. The finding is in tandem with previous related study. For example, Elom et al., (2016) found a positive significant relationship between interest rate and domestic savings in Nigeria in the long-run and insignificant influence of interest rate on domestic saving in the short-run. For broad money supply, the findings of Ogbokor and Samalivya (2017) for Benin supports the result of the current study and concluded that deposit rate and financial deepening (M2/GDP) have no significant effects on domestic savings. Aggregate investment has also significant level of shock on the selected ECOWAS economies, while the labour force rate has less shock on the household saving over the horizons. From the variance decomposition result, domestic credit accounted for about 7 percent changes, positive or negative changes in domestic saving in the selected ECOWAS countries, while broad money supply accounted for 4 percent shock to domestic savings in the 10th quarter. Interest rate spread accounted for 2 percentage shocks to household savings in the first quarter, however, declined to 1 percent in the 10th quarter, this implies that economic growth may not be a major shock to household savings in the selected and this reinforces the Domestic Savings need for inclusive growth in the ECOWAS countries. Inclusive growth promotes employment generation, which in turn promotes household savings for poverty reduction. Policy Implication of Findings The policy implication of the empirical results is germane for policy input and implementation. The following are some of the deduced implications: 1. Even though domestic credit was found to have a positive significant effect on domestic savings in the selected ECOWAS countries, more policy effect in needed to strengthen the liberalization of countries the credit market and ECOWAS in general for easy access by households. 2. Broad money supply is related to the monetary base of the selected ECOWAS countries Central Banks through the money multiplier. Given the increased importance of the behaviour of banks in a deregulated environment for the determination of the shock of money supply, the Central Banks of the selected ECOWAS have a major role to play in strengthening the monetary variables of the interest rate, broad money supply and availability of domestic credit. This is to avoid repression of the selected ECOWAS and the rest of the countries in the region. 3. Infrastructure no doubt promotes financial development. From the evidence, aggregate investment measured by the gross fixed capital formation has negative relations with domestic saving. Therefore, the Government and policy makers of the ECOWAS countries must ensure that infrastructure is adequately provide. 4. Labour force ratio is also negatively related to household saving. This implies the absence of employment opportunities in these countries. Without infrastructure, the aims of reform/liberalization will not be fully optimized. Conclusion, Policy Recommendation and Agenda for Future Research Conclusion This paper examined the effect of monetary sector financial liberalization on domestic saving in five selected ECOWAS countries of Benin, Cote d’Ivoire, Ghana, Liberia and Nigeria between the the period 1981 to 2019 and using the panel VAR approach The key findings of the study are re-represented and these indicates to the fact that are negative and positive significant effects of monetary sector financial liberalization on household saving in the ECOWAS countries. . The implication of the findings had been espoused and it points to the fact that there is likelihood of potential negative effects of domestic credit, interest rate spread and broad money supply to GDP after a shock has occurred, especially a negative shock like financial crisis and the covi-19 pandemic. The decrease in domestic credit to the household affects productivity which is turn affects employment of productivity and saving. The widening gap between interest rate and deposit rates affects households which influences negatively on household saving. Again, increase in broad money supply has a reactionary effect on inflation which in the long-run affects savings. Decrease in economic growth resulting from lack of investment induces unemployment and this in turn affect household saving. Government policy directions in the ECOWAS countries may bring the necessary changes so as to promote household saving. Policy Recommendations In the light of the empirical evidence, the following are recommended for policy considerations. 277 i. Considering the fact that domestic credit was found to have a significant effect on domestic saving during the 10 quarter horizon in the selected ECOWAS countries, there is need for the Government and policy makers of the ECOWAS region to formulate monetary policy will lower the interest rate so as to deepen the credit market and to enhance investments, employment and ultimately household saving propensity. ii. In order not to trigger inflationary pressure, the policy makers and the Central Bankers of the selected ECOWAS countries should implement restrictive monetary policy measures that will curtail excessive money supply in order to reduce inflation spiral and household purchasing power. iii. Monetary sector financial liberalization was found to have contributed insignificantly to domestic savings in the selected ECOWAS countries. This calls for re-examination of the reform programmes with the aim of strengthening the reform measures to improve domestic savings. iv. The growth rate of the impulse response function shows a declining relationship with household savings over the 10 quarters horizons. This necessitates policy action on the part the ECOWAS government for improving and sustaining the growth potentials of the economies. v. Following (iv) above, the Government and policy-makers of the ECOWAS countries must institute growth enhancing mechanisms through product diversity and inclusion in the growth process. This policy stimulates employment through investment and enhancement of domestic saving rate. Agenda for Further Studies The analysis of the paper using Panel VAR no doubt is limited by data and by method. Therefore, further empirical attempts should incorporate: Structural breaks to capture the break-points in the policy regimes and construction of an index to measure the three segments of liberalization as well expanding and the number of ECOWAS countries in the analysis. Declaration of Interest: None Funding: None Authors Contribution: All were involved in every stage of the paper. References Abrigo, M.R.A. & Love, I. (2015). Estimation of panel vector autoregression in Stata: A package of programs. Retrieved from http://paneldataconference2015.ceu.hu/program/Micheal-Abrigo.pdf. Abu, N., Mohd, Z.A.K. & Mukhriz, I.A.A.(2013). Low saving rates in the economic community of West African States (ECOWAS): The role of the political instability- income interaction. South East European Journal of Economics and Business 8(2): 53-63. Retrieved from Doi:10.2478/jeb-2013-2018 Adam, A.J. &Agba, A.V. (2006). Conceptual Issue on Savings in Nigeria. Central Bank of Nigeria Bullion, Jan/March, 30 (1): 40-51. Adewuyi, A. Bankola, A. S. &Arawomo, D. F. (2010). What determines saving in the Economic Community of West African States (ECOWAS). West African Journal of Monetary and Economic Integration, 10 (2): 71-99. http://paneldataconference2015.ceu.hu/program/Micheal-Abrigo.pdf Domestic Savings Adewuyi, A., Bankole, A.S. &Dumilola, F.A. (2010). What determines saving in the economic community of West African States (ECOWAS). Journal of Monetary and Economic Integration 10(2): 71-99. AFDB (2020). African Development Bank Data Base. A Publication of the African Development Bank. African Economic Outlook (2021) From debt resolution to growth: The Road ahead of Africa. Africa Development Bank Group Publications. Ahmad, D. &Premaratna, S.P. (2019). Effect of financial liberalization on savings and investment in Nigeria. Colombo Journal of multi-disciplinary Research 4 (1): 1-23. Ahmed, A.V., Awonusi, F., Adebanjo, J.F. &Adebimpe, E.Y. (2017). Financial sector reforms and savings mobilization in Nigeria (1980-2013). CARD International Journal of Management Studies, Business & Entrepreneurial Research 2 (2): 1-21. Ahmed, A.V., Awonusi, F., Adebanjo, J.F. &Ewunuga, Y.A. (2017). Financial sector reforms and saving mobilization in Nigeria (1980-2013) CARD International Journal of Management Studies, Business and Entrepreneurship Research 2(2): 1-21. Retrieved from http://www.casirmediapublishing.com Aizenman, J., Cheung, Y.W. & Ito, H. (2017). The interest rate effect on private saving: Alternative perspective ADBI working paper series, Asian Development Bank Institute. Akinsola, F.A. &Odhiambo, N.M (2017). The impact of financial liberalization on economic growth in sub-Saharan Africa. Cogent Economics & Finance 5: 1338851. Retrieved from https://doi.org/10.1080/23322039.2017.1338857 Akinsola, F.A., Odhiambo, N.M., & McMillan (2017). The impact of financial liberalization on economic growth in sub-Saharan Africa. Department of Economics, University of South Africa, Pretoria. Akpan, D.B. (2008). Financial liberalization and endogenous growth in Nigeria. Economic and Financial Review. 46(2): 1-27. Alade, A.J. (2006). Financial Intermediation and economic growth. Evidence from Nigeria, Journal of Economic Management 5(2): 12-24. Alfaro, L., Chanda A., Kalemli-Ozcan, S. &Sayek, S. (2004). FDI and economic growth: The role of local financial markets. J. Int. Eco. 64(1): 89-112. Andrew, K.W. & Lu, B. (2001). Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models. Journal of Econometrics, 101 (8): 123-164. http://doi.org110.1016/50304-4076(00)00077-4 Anthony, O. (2012). Bank savings and bank credits in Nigeria: Determinants and impact on economic growth. International journal of Economics and Financial issues 2(3): 357- 372. Aryeetey, E. &Udry, C. (2000). Saving in sub-Saharan Africa, CID working Paper 38, Centre for International Development, Harvard University. Cambridge M.A Asongu, S.A. &Odhiambo, M.N. (2019). Challenges to doing business in Africa: a systematic review. J. Afri. Bus. 20(2): 259-268. Athukorapa, P. &Sen, K. (2001). The determinants of the private saving in India, Economic December. Auerbach, P. &Siddiki, J.U. (2004). Financial liberalization and economic development: An Assessment Journal of Economic Survey 18(3): 231-265. Azam, J. (1996). Saving and interest rate: The cast of Kenya.Saving and Development 20 (1): 33-43. Bakare, A.S. (2011). Financial Sector reforms and domestic savings in Nigeria: An econometric assessment. Prime Journals of Business Administration and Management (BAM), 1(6): 198-204. Retrieved fromwww.primejournal.org/bam http://www.casirmediapublishing.com/ https://doi.org/10.1080/23322039.2017.1338857 http://doi.org110.1016/50304-4076(00)00077-4 http://www.primejournal.org/bam 279 Bank of Ghana (2021) Monetary Policy Report. 4 (4): 1-9. Bekaert, G., Harvey, C.R. &Lundblad, C. (2005). Does financial liberalization super growth? Journal of Financial Economics 77:3-55. Bime, M.J. &Mbanasor, J. (2011). Determinants of informal savings amongst vegetable farmers in North West region, Cameroon. Journal of Development and Agricultural Economics. 3(12): 588-592. Bosede, V.K. (2013). Savings and its determinants in West African Countries, Journal of Economics and Sustainable Development 4(18): 107-119. Boubtane, E., Coulibaly, D. &Rault, C. (2012). Immigration, growth and unemployment: Panel VAR Evidence from OECD Countries IZA DP No. 6966. Brookin, M.A. (2001). Saving behavior in low-and middle income developing countries: 38- 71. Central Bank of Liberia (2016) Annual Report. Monwiva, Liberia Central Bank of Liberia (2017) strategic plan 2017-2019, Monwvia, Liberia. Central Bank of Nigeria (2007). Available at http://www.cbn.gov.ng./MonetaryPolicy/Policy.asp. Central Bank of Nigeria (2021). Banking Supervision Annual Report. Abuja, Nigeria: Central Bank of Nigeria Publication. Chete, L. N (1999) Macroeconomic determinants of private saving in Nigeria. NBER Monograph series No. 7. Chong C. K, Yusop Z, &Soo Sc. (2004). Foreign direct investment, economic growth, and financial sector development a comparative analysis. ASEAN Wcon Bull 21(3): 278- 289. Collins, S. M (1991). Saving behavior in ten developing Countries, In B. Douglas Beriheam J.B. Shown, (edc). National Saving and Economic Performance Chicago. NBER PP. 349-373. Collins, S.M. (1991). Saving behavior in ten developing countries. In B. Douglas Bernheim and J.B. Shoven, eds, National Saving and Economic performance. Chicago: NBER. Pp. 349-373. Cote d’IVorie Analytical Report 2017, Financial Inclusion insights, August-tebber 2017. Dahunsi, O. (2020). Effect of interest rate liberalization on domestic savings in Nigeria. Journal of Advanced Research in Economics and Administrative Sciences, 1 (2): 123- 133, http://doi.org/10.47631/jares.v/12.59. Edwards, S, (1989). On the sequencing of structural reforms, OECD Working Paper, No70, September. Elbadawi, I. A &Mwega. F. M (2000). Can Africa’s Saving collapse be reserved? The World Bank Economic Review 14 (3): 415-444. El-Seoud, M. (2014).The effect of interest rate inflation rate and GDP on national saving rate. Global Journal of Commerce and Management Perspective. 3 (3): 1-7. Emenuga, C. (2005). The outcome of financial sector reforms in West Africa International Development Centre: Science for Humanity, Canadan. Fantessi, A.A., &Kiprop, S.K. (2015). Financial development and economic growth in West African Economic and Monetary Union (WAEMU). African Journal of Business Management 9(17): 624-632. Retrieved from http://www.academicjournals.org/AJBM. Fowowe, B. (2009). Does financial liberalization really improve saving? Additional Evidence from sub-Saharan Africa. In: Applied Econometric &Macroeconometric Modelling in Nigeria (Eds). A. Adekinju; Busari, D. and Olofin, S., Ibadan University Press, University of Ibadan. http://www.cbn.gov.ng./MonetaryPolicy/Policy.asp http://doi.org/10.47631/jares.v/12.59 http://www.academicjournals.org/AJBM Domestic Savings Gravin M., Hausman, R. &Talvic; E. (1997). Saving behavior in Latin America. Overview and policy issues. Inter-American Development Bank Working Paper. No. 346 May, Wessington, D.C. Hadri, K. (2009). Testing for stationarity in heterogeneous panel data. Econometric Journal, 3 (2): 14861. Hamitton, J.D. (1994). Time Series Analysis. Princeton: Princeton University Press. Hermes, N. &Lensink, R. (2005). Does financial liberalization influence saving, investment and economic growth? Evidence from 25 emerging market economies, 1973-96, United Nations University Research Paper No. 2005/69. Ikeora, J.J.E., Igbodika, M.N. &Chukwunulu, J.I. (2016). Financial liberalization and economic growth in Nigeria. IOSR Journal of Humanities and Social Sciences (IOSR- JHSS) 21(5): 123-132 www.iosrjournal.org Im, K., Pesaran, M. & Shin, R. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics. 115 (1): 5374. IMF (2015). Financial development in sub-Saharan Africa: Promoting inclusive and sustainable growth. International Monetary Fund. African Department. Jagadeesh, D. (2015). The impact of savings in economic growth: An empirical study based on Botswana. International Journal of Research in Business Studies and Management, 2 (9): 10-21 Kelly, R. &Mavrolas, G. (2003). Savings and financial Sector development panel conintergration evidence from African. WIDER Dissension Paper, Helsinkic Khan, M.I., Teng, J-Z., Khan, M.K., Jadon, A.U. &Rehan, M. (2017). Factor affecting the rate of gross domestic savings in different countries. European Academic Research 5(8): 4261-4291. Kibet, Mutai, Quma&Owuor (2009). Examined the determinant of household saving in rural areas of Kenya Journal of Development and Agriculture Economics 1(7): 137-143. Available at https://waw.academicpuiral.org?JDAE. Kunt, A.D. &Demigrunt, E. (1998). Financial liberalization and financial fragility. IMF Working Paper WP/98/93. Loots, E. (2003). Globalization and economic growth in South Africa: Do we benefit from trade and financial liberalization? South African Journal of Economics and Management Sciences, 6(2): 218-224. Lutkepohl, H. (2005). New introduction to multiple time series analysis. New York: Springer. http://doi.org/10.1007/978-3-540-27752-1. Masson, P. R, Boyommir, T &Samile, H (1998). Saving behavior in industrial and developing countries. Staff studies for the World Economic Outlook. International Monetary Fund. Washington, D. C. Matsheka, T.C. (1998). Interest rates, and the saving-investment process in Botswana.African Review of Money, Finance and Banking, 1-2: 5-23. McKinnon, R. (1973). Money and capital in economic development. The Brooking Institution. Misztal, P. (2011). The relationship between savings and economic growth in countries with different levels of economic development. efinance: Financial Interest Quarterly, 7 (2): 17-29. Modigliani, F. &Brumberg, R. (1954). Utility analysis and the consumption function: an interpretation of cross-section data, in Post-Keynesian Economics K.K. Kurihara (ed.), New Brunswik, NJ: Rufgers, University Press, pp. 388-436. http://www.iosrjournal.org/ https://waw.academicpuiral.org/?JDAE http://doi.org/10.1007/978-3-540-27752-1 281 Montiel, P.J. (1995). Financial Policies and Economic growth: Theory, Evidence and Country-specific experience from Sub-Saharan African. AERC special paper No. 18, Nairobi. Mwega, F.M. (1997). Saving in Sub-Saharan African: A Comparative Analysis, Journal of African Economies, (3): 199-228. Nyaran, P.K. &Siyabi, A.L. (2005). An empirical investigation of the determinants of Oman’s national savings.Economics Bulletin. 3(51): 1-7. Oageng, M., &Boitumelo, M.(2017) Effects of external debt on saving in Botswana. African Journal of Economic Review, 5(1): 69- 83 Obamuyi, T.M. (2009). Government financial liberalization policy and development of private sector in Nigeria: Issues and Challenges, www.growinginclusivemarket.org. Odhiambo, M.M. (2010). Finance-investment growth nexus in South Africa: An ARDL- bounds testing procedure. Econ. Chang. Restrict, 43(3): 205-219. Ogwunike, F.O. &Ofoegbu, D.I. (2012). Financial liberalization and domestic savings in Nigeria. The Social Sciences, 7(4): 632-646. Okpara, G.C. (2010). Relative Potency of financial regression and liberalization on financial development and economic growth: An empirical survey. American Journal of Scientific and Industrial Research, I(3): 643-650. Oluwatoyin, A.M. &Olusegun, O. (2011). The impact of liberalized financial system on savings, investment and growth in Nigeria. Knowledge Management, Information Management, Learning Management 14:109-122. Onwumere, J.U.J. &Ibe, I.G. (2012). Impact of interest rate liberalization on savings and investment evidence from Nigeria. Research Journal of Finance and Accounting, 3(10): 130-136. Onwusu, E.L. &Odhiambo, M.N (2016). Financial Liberalization and economic growth in Ivory Coast: An empirical investigation. Investment Management and Financial Innovation 10(4): 21-32. Oshikoya, T.W. (1992). Interest rate liberalization, saving, investment and growth: the case of Kenya.Savings and Development, 16 (3): 305-320. Owusu, E.L. &Odhiambo, N.M. (2018). Interest rate liberalization in West African countries: Challenges and Implication. EKONOMSKI/PREGLED, 67(6): 557-580. Ozcan, K. M (2000). Determinants of private savings in the Arab Countries, Iran and Turkey, University of Bilkert. February. Pesaran, M.H. (2004). General diagnostic tests for cross-section dependence in panels. Cambridge working papers in Economics no 435 and CE-Sifo working paper series no 1229. University of Cambridge, Cambridge United Kingdom. Pesaran, M.H. (2007). A simple panel unit roof test in the presence of cross-section dependence. Journal of Applied Econometrics 27 (2): 265-312. Roodman, D. (2009). How to do Xtabondz: An Introduction to Difference and System-GMM in Stata. Stata Journal 9 (1): 86-136. Schimdt-Hebbel, K. B, Webb, S.B &Corselti G. (1992). Household saving in developing countries. First Cross-Country Evidence, the World Bank Economic Review, 6(3): 529-547. http://www.growinginclusivemarket.org/ Domestic Savings Schmulker, S.L &Vesperoni, E. (2006). Financial globalization and debt maturity in emerging economics. Journal of Development Economics 79: 183-207. Seck, D. & El Nil, Y.H. (1993). Financial Liberalization in Africa. World Development (II): 1867-1881. Serieux, J. (2018). Deposit rates money and price stabilization under structural adjustment: Theory and evidence from Ghana and Kenya. Draft: University of Toronto Press. Shaw, E.S. (1973). Financial deepening in economic activity. New York: Oxford University Press. Svenssion, E. O (2011). The relation between monetary policy and financial policy. SverigesRsksbanky, Streckholm University, CEPR and NBER. Tchamyou, V.S. (2020). Education, Lifelong learning, inequality and financial access: evidence from African countries contemp. Soc. Sci. 15(1): 7-25. Tchanyon, V.S. &Asongu, S.A. (2017). Information sharing and financial sector development in Africa. Journal of African Business 18(7): 24-49. Tyson, E. (2017) Private sector development in Liberia Financing for economic transformation in a fragile context Supporting Economic Transformation. Udegbunam, R.T. (1995). Financial deregulation and monetary policy in Nigeria: An examination of the new operating techniques: Nigerian Economic and Financial Review, 33 (1): 41-63. Virani, V. (2012). Saving and investment pattern of school teachers- A study with special reference to Rajkot city, Gujrat. Abginav National Referred Journal of Research in Commerce and Management 2(4): 2277-1166. WDI (2020). World Development Indicators. A Publication of the World Bank, Washington DC; United States of America. West African Economic Outlook 2020: Coping with the Covid-19 Pandemic. World Bank (1989) World Development Report, Washington D. C World Bank (1989). Adjustment in Africa: Reforms, Results and the Koad Ahead. World Bank Policy Research Report, Washington D.C: World Bank. World Bank (2021) Benin Economic Update. Macroeconomic and Fiscal Management Washington D.C. Yakubu, Z., Loganathan, N., Mursitama, T.N., Mardimi, Khan, S.Y.R. & Hassan, A.A.G. (2020). Financial liberalization, political stability, and economic determinants of real economic growth in Kenya, Energies, MDPI, 13:3426, doi.10.3390/en13133426. Page 4