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Research Article - Journal of Finance and Marketing (2019) Volume 3, Issue 4

Stock market indicators and human development index in Sub-Saharan Africa: The case of Mauritius, Nigeria and South Africa (1997 - 2017).

Ugherughe JE1*, MaryAnn NI2

1Department of Accounting, Banking and Finance, Faculty of Management Sciences, Delta State University, Asaba Campus, Nigeria

2Department of Banking and Finance, Faculty of Management Sciences, Chukwemeka Odumegwe Ojukwu University, Igbariam Campus, Nigeria

*Corresponding Author:
Ugherughe Joseph Ediri
Department of Accounting, Banking and Finance
Faculty of Management Sciences
Delta State University
Asaba Campus, Nigeria
Tel: +08036918967
E-mail: [email protected]

Accepted on May 21, 2019

Citation: Ugherughe JE, MaryAnn NI. Stock market indicators and human development index in Sub-Saharan Africa: The case of Mauritius, Nigeria and South Africa (1997 – 2017). J Fin Mark. 2019;3(3):28-39.

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Abstract

This study examined the impact of stock market indicators on the human development index of three Sub-Saharan Africa (SSA) countries of Mauritius, Nigeria, and South Africa. The specific objectives of this study are to examine the impact of stock market size and liquidity on the human development index of SSA countries. Data for the study were collected from the World Bank Database and the United Nations Development Programme Database for the period 1997 to 2017. Human Development Index (HDI) is the dependent variable while the independent variables are stock market capitalization (MCAP) and a stock market turnover ratio (TOR) was used as a proxy for stock market indicators. A measure of central tendency was used to describe the characteristics of the variables; Augmented Dickey-Fuller unit root test was used for stationary testing; Johansen Co-integration test was used for the long-run relationship; Ordinary Least Square (OLS) technique was used to test the hypotheses. The results reveal that: stock market size was positive in the three countries and significant in Mauritius and South Africa and insignificant in Nigeria, and Stock market liquidity was positive and significant in Mauritius and South Africa but insignificant Nigeria. The study concludes that the stock market is the most accessible market for long-term fund geared towards improving HDI in the SSA countries studied. The study recommended that the government should encourage the most profitable companies to enlist on the stock exchange and seek more long-term funds to increase the size of the market and liquidity of the stock market.

Keywords

Stock market size, Stock market liquidity, Human development index, Sub-Saharan Africa.

Introduction

Stock markets are established to channel the wealth of savers to those who can put it to long-term productive use, such as companies or governments making long-term investments for economic growth vis a vis economic development. It was in line with this that most countries in the SSA, starting from the 1980s shifted away from the Structural Adjustment Programme (SAP), to the mobilization of resources, mostly financial resources and how to allocate them for national development. It is against this background that they have established financial markets, which serve as a mechanism for the mobilization of financial resources (short-term and longterm) for their economic development. The more developed these markets (money and capital) for short-term or long-term funds; the most efficient they mobilize and distribute funds for developmental purposes [1]. For these SSA countries to have a well mapped out financial markets, an arrangement was put in place for the savings surplus units to transfer their resources to the borrowing deficit units [2]. The arrangement is the stock market where long-term instruments with tenor exceeding a year are traded. Ibenta defines the stock market as a market for the supply of long-term capital to firms [3]. Al-Fake describes the stock market as a network of specialized institutions, a series of mechanisms, processes, and infrastructure of various ways that bring suppliers of long-term funds to invest in socio-economic development projects [4]. Instruments traded in the market include equities, government stocks, industrial loan stocks etc.

As such, a stock market is a financial market in which longterm debt (over a year) or equity-backed securities are bought and sold. In Sub-Saharan Africa (SSA) financial regulators like the apex bank, a commission like the Securities and Exchange Commission (SEC), and the Federal Ministry of Finance oversee stock markets to protect investors against fraud, among other duties.

The stock market, therefore, is the hub for the efficient mobilization and allocation of resources [5]. Thus, making the stock market a vital element in the mobilization and allocation of resources in any modern economy. As a result of this, attention to the relationship between the stock market and economic development have become a focal point of most SSA countries. It is as a result of this that the World Bank Economic Review dedicated its main group report to the support of stock market development [6].

Some of the perceived benefits from the relationship between stock market indicators of market capitalization (MCAP), turnover ratio of stock traded (TOR), value of stock traded (TVT), listed companies equities (LCE), etc and economic development in an emerging economy include savings mobilization, risk diversification and management, facilitating the exchange of goods and services, and ensuring corporate governance and control.

Being mindful of these perceived fundamental roles played by the stock market in an emerging economy, the government of many SSA countries established committees on how to establish a stock market and recommendations of such committees were implemented fully. This led to the establishment of various exchanges in the SSA countries. The efforts of the SSA countries’ government towards growing their economies by establishing the exchanges have actually added value and played significant roles in the financing of industries in the region that has shown strong outlook in economic development. Despite this strong outlook, stock markets in SSA remain immature, except South Africa and Mauritius that have approximately 322% and 62% market capitalization of listed domestic companies as a percentage of GDP respectively (World Bank Development Report- WBDR).

The aim of every government in the world is on how to improve its Human Development Index – HDI. In spite of the tremendous contributions of the stock markets in Sub- Saharan Africa (SSA), HDI for most countries in the region still remain at the low ebb since its introduced by the United Nations Development Programme (UNDP) in 1990 to capture a more realistic human development factors. The computation of HDI includes per capita income; literacy rate; employment rate; and life expectancy. Each part varies from zero (0) to one (1), zero being the lowest level of development and one being the highest and a country’s score is represented by the value in percent that it scores. These variables in the calculation of HDI are used in the literature as a proxy for economic growth or economic development. Ruwaydah and Ushad posit that economic development is aimed at improving the general well-being of the people in an economy. Since the introduction of the HDI, most of the studies seen are from content different from this one on the stock market and HDI of SSA of countries under study [7- 12]. The time frame of these studies was short and also not consistent. Method of study is not robust enough because of the nature of the study. Again, findings from these studies have conflicting conclusions.

Therefore, this study examined the impact of capital market performance indicators on the economic development of SSA using HDI as a proxy for economic development. This study improve on the above shortcomings by using: more current data, panel data analysis, concentrating on three SSA countries, recent development in the econometrics of panel regression analysis. Hence, this constitutes a research gap, which this study intends to fill. The study, therefore, seeks find out if: stock market size and stock market liquidity does not have a significant impact on the economic development of SSA countries. In light of this, the remaining part of the study is divided into four, viz: literature review, research methodology, result and discussions, conclusion and recommendations.

Literature Review

Conceptual economic development

The concept of economic development was given in terms of growth of output over time, but now in terms of per capita income, literacy and life expectancy. The terms growth and development were used interchangeably by some researchers, this is not right. Economic growth is a means to economic development. Economic development is, therefore, a multivariate concept; hence there is no single satisfactory definition of it. Economic development is a process where low-income national economies are transformed into modern industrial economies. It involves qualitative and quantitative improvements in a country’s economy. Political and social transformations are also included in the concept of economic development in addition to economic changes [13]. Therefore, this study concludes that aggregate and per capita real incomes are not sufficient indicators of economic development. Rather, economic development is concerned with economic, social and institutional mechanisms that are necessary for bringing large scale improvements in the levels of living of the citizens.

The impact of the stock market on economic development

The roles of the stock market in the development of the economy according to Aremu, Suberu and Ladipo include provision of opportunities for companies to borrow funds needed for long-term investment purposes, provision of avenue for the marketing of shares and other securities in order to raise fresh funds for expansion of operations leading to increase in output/production, provision of a means of allocating the nations real and financial resources between various industries and companies [13]. Through the capital formation and allocation mechanism the stock market ensures an efficient and effective distribution of the scarce resources for the optimal benefit to the economy, reduction in the overreliance of the corporate sector on short term financing for long term projects and also provision of opportunities for government to finance projects aimed at providing essential amenities for local investors. Also, the stock market can aid the government in its privatization programme by offering her shares in the public enterprises to members of the public through the stock exchange. The stock market offers access to a variety of financial instruments that enable economic agents to pool, price and exchange risk. It encourages savings in financial form. Increased economic growth would, in the long run, lead to economic development. Although economic growth does not by itself guarantee economic development, it makes economic development possible. Economic growth enables improvements or positive changes to take place in various areas of economic activity due to the increased production of goods and services.

Theoretical framework

This study is theoretically underpinned to the Schumpeterian Theory and Endogenous Growth Theory credited to Romer [14]. Helpman argues that Schumpeterian or endogenous growth theory emphasized two significant channels for investment to affect economic growth: Firstly, through the impact of the range of available financial assets, and secondly, through the impact on the stock of new technology accessible for research and development (R&D) [15].

Empirical review

Stock market size and human development index: Ruwaydah study the effects of stock market development on economic growth in SADC countries [7]. They used pooled panel data from 1980 to 2011of real per capita GDP as for economic growth and MCAP, the total value of shares traded ratio and turnover ratio as a proxy for stock market development indicators. Also, domestic credit and liquid liabilities as a proxy of banking development indicators, and inflation, investment rate, and a log of GDP as control variables were introduced. The random effect model was employed to analyze these variables. The result of the study reveals that there is a strong link between stock market development and economic growth in SADC countries.

Khetsi study the impact of capital markets on economic growth in South Africa [8]. They employed a vector error correction model to analyze the time series data from 1971 to 2013. GDP is the dependent variable as a proxy for economic growth while market capitalization, total values of transactions as a proxy for capital market indicators and Exchange rate as a control variable. The result of the study reveals that there is a positive relationship between economic growth and capital markets in South Africa.

Rurangwa study capital market development and economic growth in Rwanda using quarterly data from 2009Q1 to 2016Q4 [9]. They employed GDP as a proxy for economic growth and market capitalization, turnover, and volume of share traded as a proxy for capital market development indicators. The study employed an Ordinary Least Square method and found out that all independent variables positively contributed to economic growth in Rwanda.

Stock market liquidity and human development index: Okoye, Modebe investigate the relationship between capital market development and economic growth using Nigerian data on GDP (a proxy for economic growth), market capitalization ratio, value traded ratio and stock market turnover ratio (proxies for capital market development) over the period l981-2014 [10]. They employed the econometric methodology of the vector correction model; the study shows that in the short-run, market capitalization ratio and turnover ratio have significant negative effects on aggregate national output (GDP). The study also shows the positive effects of the value traded ratio as well as the negative effect of the inflation rate in GDP though not significant. The longrun estimate shows that all the exogenous variables have a significant negative impact on GDP and that changes in market capitalization ratio, value traded ratio and turnover ratio produce more than proportionate changes in GDP. With an adjustable speed of about 91.12 percent, the model presents an inherent capacity to overcome short-run disequilibrium. The study established that stock market development constitutes a significant determinant of economic growth in Nigeria.

Magwera empirically analyze the relationship between stock market development and economic growth in Zimbabwe for the period 1989 to 2014 [11]. They employed vector error correction model (VECM) to analyze GDP per capita growth rate as a proxy for economic growth and stock market capitalization as a ratio to GDP, stock market turnover, the stock traded total value as a proxy for stock market development indicators. Their study reveals that the long-run relationship was negative and the short-run coefficients were insignificant.

Eleanya, Ugochukwu, and Ishaku investigate the causal relationship between the stock market and aggregate economic performance of South Africa using data that spans from 1995Q1 to 2013Q4 [12]. In their investigation, they used real GDP as a proxy for economic performance and investment ratio, saving ratio, the rate of inflation, turnover ratio, and the total value of the shares traded ratio, and market capitalization ratio as a proxy for stock market development indexes. Vector Error Correction (VEC) model and Granger causality test were employed for their analysis of the variables and found out a long-run relationship between real GDP and the liquidity variables and there is no causality between the liquidity variables.

Research Methodology

Research design, nature and sources of data

The study employs the ex-post-facto research design. The study is carried out on three (3) exchanges in the Sub-Sahara African countries for the period 1997–2017. The choice of the exchanges is based on the vibrancy and availability of data. The data used are classified as time series data.

Method of data collection

Data for this study are collected from the World Bank Indicators Database, the United Nations Development Programme Database the Stock Exchange of Mauritius, the Nigerian Stock Exchange, and the Johannesburg Stock Exchange for the period 1997 to 2017.

Model specification

The model specification is underpinned to the neo-classical, endogenous and Schumpeter theories of economic growth and is in line with the objectives of the study. The six models are as follows:

Stock market size and human development index model: This model is used to test the first hypothesis and is adapted from the Ikikii and Nzomoi model :

GDP = ƒ (SMC, STV)

Where: GDP is a gross domestic product, SMC is stock market capitalization, and STV is stock market traded volume [16].

The focus of this model is in line with the Schumpeter School of Thought that a well developed financial system will absolutely produce educated entrepreneurs that will engage in a process of resourcefulness. The study considered STV as a liquidity indicator of the market because it deals with a number of shares that exchanged hands between buyers and sellers in the market and as such it was replaced with listed companies equities, which is an indicator variable because it deals with the number of companies present in the market (population). Also, the stock market size is expected to be affected by other country-specific; as such a control variable (i.e. lending interest rate) is introduced into the model. Again, GDP as a proxy for economic growth was replaced with the human development index as a proxy for economic development.

The functional form of the model is thus:

HDI = ƒ(MCAP, LCE, LIR)…………………………………………………….. Model 1

The multivariate equation expression is thus:

HDI = a0 + a1MCAP + a2LCE + a3LIR + ɛ…………………………………….. Equation 1

Where: HDI = Human development index

MCAP = Stock market capitalization

LCE = Listed companies equities

LIR = Lending interest rate

ɛ = Stochastic error term within a confidence interval of 5%

a0 = Constant term

a1, a2 and a3 = Coefficients of the independent variables

Stock market liquidity and human development index model: This model is used to test the second hypothesis and is adapted from the Ifeoluwa and Motilewa model:

GROWTH = ƒ (TVT_GDP)

Where: TVT_GDP is the stock market liquidity indicator [17].

The focus of this model is in line with the Neo-Classic School of Thought that investing in a stock market raises the liquidity position of an economy. The study replaces TVT_ GDP with stock market turnover ratio and stock market value traded as a proxy for stock market liquidity and inflation as a control variable (country specific). The introduction of inflation as control is because the liquidity variables are not expected to act alone. Again, GROWTH as a proxy for economic growth was replaced with the human development index as a proxy for economic development.

The functional form of the model is thus:

HDI = ƒ(TOR, TVT, INFR)……………………………………………………….. Model 2

The multivariate equation expression is thus:

HDI = a0 + a1TOR + a2TVT + a3INFR + ɛ………………………………………Equation 2

Where: HDI = Human development index

TOR = Turnover ratio

TVT = Value traded

INFR = Inflation rate

ɛ = Stochastic error term within a confidence interval of 5%

a0 = Constant term

a1, a2 and a3 = Coefficients of the independent variables

A priori expectation

a1, a2 and a3 ≥ 0 for the two equations

Method of data analysis

The study employs Augmented Dickey-Fuller (ADF) test of unit root test for stationarity, Johansen Co-integration test for long-run equilibrium relationship among the variables, and OLS-Ordinary Least Squares (NLS-Nonlinear Least Square and ARMA-Autoregressive-Moving-Average) to estimate the variables. All statistical tools are to be employed from E-views. The choices of these statistical tools are that they are easy to interpret and are easy to understand.

Result and Discussion

Augmented Dickey-Fuller (ADF) unit root tests

An Augmented Dickey-Fuller (ADF) unit root test was employed to test the stationarity of the variables. The ADF tests were done at levels. The decision rule is to reject stationary if ADF statistics are greater than the values of critical values at 1%, 5% and 10% in absolute terms or accept stationary if ADF statistics is less than the critical value of 1%, 5% and 10% in absolute terms. If the results of the variables are stationary at 1 (0) according to Kozhan, 2010, one can proceed to estimate the variables. The results of the ADF test are presented below in Tables 1-3 for Mauritius, Nigeria, and South Africa respectively.

Variables ADF Statistics 1% Critical Values 5% Critical Values 10% Critical Value Order of Integration Level of Significance
HDI -3.441545 -3.886751 -3.052169 -2.666593 1(0) 0.8806 (10%)
MCAP -3.859796 -3.808546 -3.020686 -2.650413 1(0) 0.3430 (10%)
TOR -5.021848 -3.808546 -3.020686 -2.650413 1(0) 0.0007 (5%)
TVT -3.457984 -3.808546 -3.020686 -2.650413 1(0) 0.1399 (10%)
LCE -3.760573 -3.808546 -3.020686 -2.650413 1(0) 0.8086 (10%)
INFR -3.505303 -3.808546 -3.020686 -2.650413 1(0) 0.1290 (10%)
LIR -3.606904 -3.808546 -3.020686 -2.650413 1(0) 0.8481 (10%)

Table 1. Mauritius ADF unit root test (Source: Researcher’s computation from E-Views).

Variables ADF
Statistics
1% Critical Values 5% Critical Values 10% Critical Value Order of Integration Level of Significance
HDI -7.170999 -3.808546 -3.020686 -2.650413 1(0) 0.0000 (5%)
MCAP -3.846812 -3.808546 -3.020686 -2.650413 1(0) 0.0697(5%)
TOR -3.418049 -3.808546 -3.020686 -2.650413 1(0) 0.1496 (10%)
TVT -3.116221 -3.808546 -3.020686 -2.650413 1(0) 0.2407 (10%)
LCE -3.505195 -3.808546 -3.020686 -2.650413 1(0) 0.8708 (10%)
INFR -3.145311 -3.808546 -3.020686 -2.650413 1(0) 0.0392 (5%)
LIR -3.425578 -3.808546 -3.020686 -2.650413 1(0) 0.5491 (10%)

Table 2. Nigeria ADF unit root test (Source: Researcher’s computation from E-Views).

Variables ADF Statistics 1% Critical Values 5% Critical Values 10% Critical Value Order of Integration Level of Significance
HDI 3.045122 -3.808546 -3.020686 -2.650413 1(0) 0.9953 (10%)
MCAP -3.408439 -3.808546 -3.020686 -2.650413 1(0) 0.5574 (10%)
TOR -3.542544 -3.808546 -3.020686 -2.650413 1(0) 0.0175 (5%)
TVT -3.149536 -3.808546 -3.020686 -2.650413 1(0) 0.6745 (10%)
LCE -3.728020 -3.920350 -3.065585 -2.673459 1(0) 0.3995 (10%)
INFR -4.160451 -3.831511 -3.029970 -2.655194 1(0) 0.0050 (5%)
LIR -3.915669 -3.857386 -3.040391 -2.660551 1(0) 0.3182 (10%)

Table 3. South Africa ADF unit root test (Source: Researcher’s computation from E-Views).

Table 1 above shows Mauritius Augmented Dickey-Fuller unit root test for stationarity of the variables. The result shows that HDI, TVT, LCE, INFR, and LIR have an ADF statistics value of -3.441545, -3.457984, -3.760573, -3.505303, and -3.606904 respectively that are greater than 5% and 10% critical level values in absolute term. MCAP, TOR, and ASI have an ADF statistical value of -3.859796, -5.021848 and -4.674109 respectively that is greater than 1%, 5% and 10% critical values in absolute term. The result reveals that the variables are stationary at 1 (0). Thus, ordinary least squares of data estimation can be applied in the analysis of data.

Table 2 above shows the Augmented Dickey-Fuller unit root test for stationarity of the variables. The result shows that HDI, MCAP, have ADF statistics value of -7.170999, -3.846812 and respectively that are greater than 1%, 5% and 10% critical level values in absolute term while TOR, TVT, LCE, INFR, EXR, and LIR has ADF statistics value of -3.418049, -3.116221, -3.505195, -3.145311, and -3.425578 respectively that are greater than 5% and 10% critical level values in absolute term but less than 1% critical level values in absolute term. The result reveals that the variables are stationary at I (0). Thus, the ordinary least square of data estimation can be applied in the analysis of data.

Table 3 above shows the Augmented Dickey-Fuller unit root test for stationarity of the variables. The result shows that INFR, and LIR have ADF statistics value of -4.160451 and -3.915669 respectively that is greater than 1%, 5% and 10% critical level values in absolute term. HDI, MCAP, TOR, TVT, and LCE have ADF statistics value of 3.045122, -3.408439, -3.542544, -3.149536, and -3.728020 respectively that is greater than 5%, and 10% critical level values in absolute term but less than 1% critical value in absolute term. The result reveals that the variables are stationary at 1 (0). Thus, ordinary least squares of data estimation can be applied in the analysis of data.

Johansen Co-integration test

Johansen co-integration test is conducted to ascertain the existence of the long - run relationship among the variables for each of the models in the study. The Johansen co-integration test contains two types of co-integration tests. These are unrestricted co-integration, rank test (Trace) and unrestricted co-integration, rank test (Maximum Eigenvalue). According to Johansen, Erik & Par (2007), the decision rule is to accept the null hypothesis if the probability of the critical value is greater than the 5% level of significance [18]. Otherwise, we reject the null hypothesis [19].

Mauritius Co-integration test

The stock market size model that examined the long-run relationship between stock market size variables; stock market capitalization, listed companies’ equities, and economic development; human factor development was tested for the null hypothesis of no co-integration on the assumption of the linear deterministic trend. The model includes HDI, MCAP, LCE, and LIR (LIR is a control variable) [20,21]. The result of the co-integration is presented in Table 4. The result of the Trace and Maximum-Eigen probability was above the 5% level of significance. Thus, it becomes necessary to accept the null hypothesis of no cointegration. The study then indicates that there is no co-integration among the variables in the stock market size model. This connotes that there is no longrun relationship between stock market size and economic development in Mauritius.

Hypothesized
No of CE(s)
Eigenvalue Unrestricted Co-integration Rank Test (Trace) Unrestricted Co-integration Rank Test (Maximum Eigenvalue)
    Trace Statistics 5% Critical Value Prob.** Maximum Eigenvalue Statistics 5% Critical Value Prob.**
None* 0.964196 87.67001 47.85613 0.0000 63.26424 27.58434 0.0000
At most 1 0.517380 24.40577 29.79707 0.1838 13.84197 21.13162 0.3781
At most 2 0.426198 10.56380 15.49471 0.2398 10.55396 14.26460 0.1780
At most 3 0.000518 0.009838 3.841466 0.9207 0.009838 3.841466 0.9207

Table 4. Mauritius result of the Co-integration among variables of stock market size model, HDI =ƒ (MCAP, LCE, LIR) (Source: Researcher’s computation from E-Views).

The stock market liquidity model that examined the longrun relationship between stock market liquidity variables; stock market turnover ratio, stock market value traded, and economic development; human factor development was tested for the null hypothesis of no co-integration on the assumption of the linear deterministic trend. The model includes HDI, TOR, TVT, and INFR (INFR is a control variable). The result of the co-integration is presented in Table 5. The result of the Trace and Maximum-Eigen probability was above the 5 % level of significance. Thus, it becomes necessary to accept the null hypothesis of no cointegration [22]. The study then indicates that there is no co-integration among the variables in the stock market liquidity model. This connotes that there is no long-run relationship between stock market liquidity and economic development in Mauritius.

Hypothesized
No of CE(s)
Eigenvalue Unrestricted Co-integration Rank Test (Trace) Unrestricted Co-integration Rank Test (Maximum Eigenvalue)
    Trace Statistics 5% Critical Value Prob.** Maximum Eigenvalue Statistics 5% Critical Value Prob.**
None* 0.805551 53.63170 47.85613 0.0130 31.11412 27.58434 0.0168
At most 1 0.567743 22.51758 29.79707 0.2706 15.93595 21.13162 0.2286
At most 2 0.288527 6.581626 15.49471 0.6267 6.467944 14.26460 0.5539
At most 3 0.005965 0.113682 3.841466 0.7360 0.113682 3.841466 0.7360

Table 5. Mauritius result of the Co-integration among variables of stock market liquidity model, HDI =ƒ (TOR, TVT, INFR) (Source: Researcher’s computation from E-Views).

Nigeria Co-integration test

The stock market size model that examined the long-run relationship between stock market size variables; stock market capitalization, list companies’ equities, and economic development; human factor development was tested for the null hypothesis of no co-integration on the assumption of the linear deterministic trend. The model includes HDI, MCAP, LCE, and LIR (LIR is a control variable) [23]. The result of the co-integration is presented in Table 6. The result of the Trace and Maximum-Eigen probability was above the 5% level of significance. Thus, it becomes necessary to accept the null hypothesis of no cointegration. The study then indicates that there is no co-integration among the variables in the stock market size model. This connotes that there is no longrun relationship between stock market size and economic development in Nigeria.

Hypothesized
No of CE(s)
Eigenvalue Unrestricted Co-integration Rank Test (Trace) Unrestricted Co-integration Rank Test (Maximum Eigenvalue)
    Trace Statistics 5% Critical Value Prob.** Maximum Eigenvalue Statistics 5% Critical Value Prob.**
None* 0.865122 66.69689 47.85613 0.0003 38.06427 27.58434 0.0016
At most 1 0.634095 28.63259 29.79707 0.0676 19.10226 21.13162 0.0939
At most 2 0.379518 9.530336 15.49471 0.3186 9.067925 14.26460 0.2805
At most 3 0.024044 0.462411 3.841466 0.4965 0.462411 3.841466 0.4965

Table 6. Nigeria result of the Co-integration among variables of stock market size model, HDI =ƒ (MCAP, LCE, LIR) (Source: Researcher’s computation from E-Views).

Hypothesized
No of CE(s)
Eigenvalue Unrestricted Co-integration Rank Test (Trace) Unrestricted Co-integration Rank Test (Maximum Eigenvalue)
    Trace Statistics 5% Critical Value Prob.** Maximum Eigenvalue Statistics 5% Critical Value Prob.**
None* 0.740002 52.58588 47.85613 0.0168 25.59454 27.58434 0.0879
At most 1 0.504254 26.99134 29.79707 0.1018 13.33214 21.13162 0.4221
At most 2 0.395325 13.65921 15.49471 0.0928 9.558234 14.26460 0.2426
At most 3* 0.194136 4.100975 3.841466 0.0428 4.100975 3.841466 0.0428

Table 7. Nigeria result of the Co-integration among variables of stock market liquidity model, HDI =ƒ (TOR, TVT, INFR) (Source: Researcher’s computation from E-Views).

The stock market liquidity model that examined the long-run relationship between stock market liquidity model variables; stock market turnover ratio, stock market value traded, and economic development; human factor development was tested for the null hypothesis of no co-integration on the assumption of the linear deterministic trend. The model includes HDI, TOR, TVT, and INFR (INFR as a control variable) [24]. The result of the co-integration is presented in s. The results from the Trace and Maximum-Eigen probability show one (1) co-integration equation. The results are based on the probability of the critical values less than 5% level of significance. The study then indicates that there is cointegration among the variables of the stock market liquidity model. This connotes that there is a long-run relationship between stock market liquidity and economic development in Nigeria.

South Africa Co-integration test

The stock market size model that examined the long-run relationship between stock market size variables; stock market capitalization, list companies’ equities, and economic development; human factor development was tested for the null hypothesis of no co-integration on the assumption of the linear deterministic trend [25-27]. The model includes HDI, MCAP, LCE, and LIR (LIR is a control variable). The result of the co-integration is presented in Table 8. The results from the Trace and Maximum-Eigen probability show one (1) cointegration equation. The results are based on the probability of the critical values less than 5% level of significance. Thus, it becomes necessary to reject the null hypothesis of no cointegration. The study then indicates that there is co- integration among the variables in the stock market size model. This connotes that there is a long-run relationship between stock market size and economic development in South Africa [28-30].

Hypothesized
No of CE(s)
Eigenvalue Unrestricted Co-integration Rank Test (Trace) Unrestricted Co-integration Rank Test (Maximum Eigenvalue)
    Trace Statistics 5% Critical Value Prob.** Maximum Eigenvalue Statistics 5% Critical Value Prob.**
None* 0.881326 78.33363 47.85613 0.0000 40.49612 27.58434 0.0007
At most 1* 0.769137 37.83751 29.79707 0.0048 27.85271 21.13162 0.0049
At most 2 0.380999 9.984795 15.49471 0.2819 9.113307 14.26460 0.2768
At most 3 0.044832 0.871488 3.841466 0.3505 0.871488 3.841466 0.3505

Table 8. South Africa result of the Co-integration among variables of stock market size model, HDI =ƒ (MCAP, LCE, LIR) (Source: Researcher’s computation from E-Views).

The stock market liquidity model that examined the long-run relationship between stock market liquidity model variables; stock market turnover ratio, stock market value traded, and economic development; human factor development was tested for the null hypothesis of no co-integration on the assumption of the linear deterministic trend. The model includes HDI, TOR, TVT, and INFR (INFR as a control variable). The result of the co-integration is presented in Table 9. The results from the Trace and Maximum-Eigen probability show no cointegration equation. The results are based on the probability of the critical values less than 5% level of significance. The study then indicates that there is no co-integration among the variables of the stock market liquidity model. This connotes that there is no long-run relationship between stock market liquidity and economic development in South Africa.

Hypothesized
No of CE(s)
Eigenvalue Unrestricted Co-integration Rank Test (Trace) Unrestricted Co-integration Rank Test (Maximum Eigenvalue)
    Trace Statistics 5% Critical Value Prob.** Maximum Eigenvalue Statistics 5% Critical Value Prob.**
None 0.743220 42.58154 47.85613 0.1431 25.83121 27.58434 0.0824
At most 1 0.308346 16.75032 29.79707 0.6585 7.004732 21.13162 0.9537
At most 2 0.289078 9.745591 15.49471 0.3008 6.482657 14.26460 0.5521
At most 3 0.157796 3.262934 3.841466 0.0709 3.262934 3.841466 0.0709

Table 9. South Africa result of the Co-integration among variables of stock market liquidity model, HDI =ƒ (TOR, TVT, INFR) (Source: Researcher’s computation from E-Views).

In summary, model 1 that states that there is no co-integration between stock market size and economic development in the long-run was accepted in Mauritius and Nigeria while it was rejected in South Africa; and in model 2 that states that there is no co-integration between stock market liquidity and economic development in the long-run was accepted in all the three countries [31,32].

Result of the Ordinary Least Square (OLS) estimation of models

Two models were formulated. These are: stock market size model, and stock market liquidity model. Their impact is reported below for each of the countries; Mauritius, Nigeria, and South Africa.

Mauritius result of the OLS estimation: On the Table 10, the result of the estimated stock market size model based on ordinary least squares (OLS) technique was analyzed to show the contribution of each of the variables of stock market size on the economic development of Mauritius. The result shows that MCAP has an insignificant negative impact on economic development (probability = 0.9854 and coefficient = -7.62E- 06). LCE has significant positive impact on economic development (probability = 0.0393 and coefficient = 0.001407). LIR, the control variable has significant negative impact on economic development (probability = 0.0229 and coefficient = -0.002977).

Variable Coefficient Std. Error t-Statistic Prob.
C 0.701137 0.035533 19.73213 0.0000
MCAP -7.62E-06 0.000411 -0.018542 0.9854
LCE 0.001407 0.000630 2.232520 0.0393
LIR -0.002977 0.001190 -2.500776 0.0229
R-squared 0.861126     
    Durbin-Watson stat
1.592545
F-statistic
Prob.(F-statistic)
35.13767
0.000000
 

Table 10. Mauritius result of the OLS estimation of the stock market size model (Source: Researcher’s computation from E-Views).

The coefficient of determination, R-squared (R2) is 0.861126 and indicates that about 86% of the changes in economic development are explained by the variation in stock market size indicators (MCAP and LCE). The F-statistic explains the overall significance of the variables of stock market size (MCAP and LCE) on economic development. The F-statistic is 35.13767 with a probability value of 0.0000 less than 5% level of significance. Based on the F-probability, the study concludes that stock market size variables have an overall significant impact on economic development in Mauritius. The coefficient of Durbin-Watson is 1.592545 and is approximately 2. This shows that the model is free of autocorrelation.

Table 11 shows the result of the estimated stock market liquidity (SML) model based on the ordinary least squares (OLS) technique. The SML was analyzed to show the contribution of each of its variables on the economic development of Mauritius. The result shows that TOR has a significant negative impact on economic development (probability = 0.0194 and coefficient = -0.009601). TVT has significant positive impact on economic development (probability = 0.0003 and coefficient = 0.026846). INFR, the control variable has significant negative impact on economic development (probability = 0.0044 and coefficient = -0.008333).

Variable Coefficient Std. Error t-Statistic Prob.
C 0.746662 0.027008 27.64589 0.0000
TOR -0.009601 0.003720 -2.580540 0.0194
TVT 0.026846 0.005917 4.537232 0.0003
INFR -0.008333 0.002541 -3.279584 0.0044
          R-squared 0.698054     
    Durbin-Watson stat
1.612682
           F-statistic
Prob.(F-statistic)
13.10046
0.000111
 

Table 11. Mauritius result of the OLS estimation of the stock market liquidity model (Source: Researcher’s computation from E-Views).

The coefficient of determination, R-squared (R2) is 0.698054 and indicates approximately 70% of the changes in economic development are explained by the variation in stock market liquidity indicators (TOR and TVT). The F-statistic explains the overall significance of the variables of stock market liquidity (TOR and TVT) on economic development. The F-statistic is 13.10046 with a probability value of 0.000111 less than 5% level of significance. Based on the F-probability, the study concludes that stock market liquidity variables have an overall significant impact on economic development in Mauritius. The coefficient of Durbin-Watson is 1.612682 and is approximately 2. This shows that the model is free of autocorrelation.

Nigeria result of the OLS estimation: On Table 12 the result of the estimated stock market size model based on ordinary least squares (OLS) technique was analyzed to show the contribution of each of the variables of stock market size on the economic development of Mauritius. The result shows that MCAP has a significant negative impact on economic development (probability = 0.0120 and coefficient = -0.007193). LCE has an insignificant positive impact on economic development (probability = 0.1237 and coefficient = 0.003113). LIR, the control variable has significant negative impact on economic development (probability = 0.0039 and coefficient = -0.037724).

Variable Coefficient Std. Error t-Statistic Prob.
C 0.628620 0.395969 1.587547 0.1308
MCAP -0.007193 0.002559 -2.811049 0.0120
LCE 0.003113 0.001922 1.619546 0.1237
LIR -0.037724 0.011310 -3.335340 0.0039
R-squared 0.456768 Durbin-Watson stat 1.745632
F-statistic
Prob.(F-statistic)
4.764728
0.013738
 

Table 12. Nigeria result of the OLS estimation of the stock market size model (Source: Researcher’s computation from E-Views).

The coefficient of determination, R-squared (R2) is 0.456768 and indicates that about 46% of the changes in economic development are explained by the variation in stock market size indicators (MCAP and LCE). The F-statistic explains the overall significance of the variables of stock market size (MCAP and LCE) on economic development. The F-statistic is 4.764728 with a probability value of 0.013738 less than 5% level of significance. Based on the F-probability, the study concludes that stock market size variables have an overall significant impact on economic development in Mauritius. The coefficient of Durbin-Watson is 1.745632 and is approximately 2. This shows that the model is free of autocorrelation.

Table 13 shows the result of the estimated stock market liquidity model based on ordinary least squares (OLS) technique was analyzed to show the contribution of each of the variables of stock market liquidity on the economic development of Nigeria. The result revealed that TOR has an insignificant positive impact on economic development (probability = 0.2805 and coefficient = 0.009748). TVT has an insignificant negative impact on economic development (probability = 0.7292 and coefficient = -0.008461). INFR, the control variable has an insignificant positive impact on economic development (probability = 0.3463 and coefficient = -0.008828).

Variable Coefficient Std. Error t-Statistic Prob.
C 0.235817 0.113032 2.086286 0.0523
TOR 0.009748 0.008745 1.114757 0.2805
TVT -0.008461 0.024035 -0.352019 0.7292
INFR 0.008828 0.009114 0.968592 0.3463
R-squared 0.185971 Durbin-Watson stat 1.843906
F-statistic
Prob.(F-statistic)
1.294592
0.308450
 

Table 13. Nigeria result of the OLS estimation of the stock market liquidity model (Source: Researcher’s computation from E-Views).

The coefficient of determination, R-squared (R2) is 0.185971 and indicates that about 19% of the changes in economic development are explained by the variation in stock market liquidity indicators (TOR and TVT). The F-statistic explains the overall significance of the variables of stock market liquidity (TOR and TVT) on economic development. The F-statistic is 1.294592 with a probability value of 0.308450 greater than a 5% level of significance. Based on the F-probability, the study concludes that stock market liquidity variables have an overall insignificant impact on economic development in Nigeria. The coefficient of Durbin-Watson is 1.843906 and is approximately 2. This shows that the model is free of autocorrelation.

South Africa result of the OLS estimation: On Table 14 the result of the estimated stock market size model based on ordinary least squares (OLS) technique was analyzed to show the contribution of each of the variables of stock market size on the economic development of South Africa. The result shows that MCAP has a significant positive impact on economic development (probability = 0.0093 and coefficient = 0.000302). LCE has an insignificant positive impact on economic development (probability = 0.1948 and coefficient = 0.000113). LIR, the control variable has an insignificant negative impact on economic development (probability = 0.5144 and coefficient = -0.001661).

Variable Coefficient Std. Error t-Statistic Prob.
C 0.547893 0.042141 13.00130 0.0000
MCAP 0.000302 0.000103 2.934164 0.0093
LCE 0.000113 8.38E-05 1.349792 0.1948
LIR -0.001661 0.002494 -0.665917 0.5144
R-squared 0.397407 Durbin-Watson stat 1.587929
F-statistic
Prob.(F-statistic)
3.737137
0.031391
 

Table 14. South Africa result of the OLS estimation of the stock market size model(Source: Researcher’s computation from E-Views).

The coefficient of determination, R-squared (R2) is 0.397407 and indicates that about 40% of the changes in economic development are explained by the variation in stock market size indicators (MCAP and LCE). The F-statistic explains the overall significance of the variables of stock market size (MCAP and LCE) on economic development. The F-statistic is 3.737137 with a probability value of 0.031391 less than 5% level of significance. Based on the F-probability, the study concludes that stock market size variables have an overall significant impact on economic development in South Africa. The coefficient of Durbin-Watson is 1.587929 and is approximately 2. This shows that the model is free of autocorrelation.

On Table 15 the result of the estimated stock market liquidity model based on ordinary least squares (OLS) technique was analyzed to show the contribution of each of the variables of stock market liquidity on the economic development of South Africa. The result shows that TOR has an insignificant negative impact on economic development (probability = 0.5145 and coefficient = -0.000677). TVT has a significant positive impact on economic development (probability = 0.0050 and coefficient = 0.000661). INFR, the control variable has an insignificant positive impact on economic development (probability = 0.9153 and coefficient = 0.000269).

Variable Coefficient Std. Error t-Statistic Prob.
C 0.614512 0.021078 29.15423 0.0000
TOR -0.000677 0.001018 -0.665728 0.5145
TVT 0.000661 0.000205 3.225658 0.0050
INFR 0.000269 0.002490 0.107896 0.9153
R-squared 0.430967 Durbin-Watson stat 1.831418
F-statistic
Prob.(F-statistic)
4.291753
0.019913
 

Table 15. South Africa result of the OLS estimation of the stock market liquidity model(Source: Researcher’s computation from E-Views).

The coefficient of determination, R-squared (R2) is 0.430967 and indicates that about 43% of the changes in economic development are explained by the variation in stock market liquidity indicators (TOR and TVT). The F-statistic explains the overall significance of the variables of stock market liquidity (TOR and TVT) on economic development. The F-statistic is 4.291753 with a probability value of 0.019913 less than 5% level of significance. Based on the F-probability, the study concludes that stock market liquidity variables have an overall significant impact on economic development in South Africa. The coefficient of Durbin-Watson is 1.831418 and is approximately 2. This shows that the model is free of autocorrelation.

In summary, the results of the OLS estimations in: Model 1, stock market size has an overall significant impact on the economic development of the three SSA countries, Mauritius, Nigeria, and South Africa. Model 2, stock market liquidity has an overall significant impact on the economic development of the three SSA of Mauritius, Nigeria, and South Africa.

Impact of stock market size on the economic development of SSA countries

Test of hypothesis 1: Stock market size does not have a significant impact on the economic development of SSA countries.

The result that tests whether there is a long-run relationship between stock market size and economic development in SSA countries is presented in Tables 4, 6 and 8 for Mauritius, Nigeria, and South Africa respectively. The result of the Johansen co-integration test for Mauritius and Nigeria shows that there is no co-integration among the variables of the stock market size model while that of South Africa shows that there is co-integration among the variables of the stock market size model. This implies that there is no long-run relationship between stock market size and human development index as a proxy for economic development in Mauritius and Nigeria while in South Africa there is a long-run relationship between stock market size and economic development.

The results of the coefficient of determination (R2) or F-probability from the OLS techniques showed that stock market size could significantly explain only: 86% or 0.00000, 46% or 0.013738, 40% or 0.031391 of the factors that influence human development index as a proxy for economic development in Mauritius, Nigeria and South Africa respectively. In the overall assessment, the combined variables of this model were significant at the 5 % level of significance. Thus, the study accepted the alternative hypothesis that stock market size has a significant impact on economic development (human development factor) of SSA countries as indicated in the three countries; Mauritius, Nigeria, and South Africa.

Impact of stock market liquidity on the economic development of SSA countries

Test of hypothesis 2: Stock market liquidity does not have a significant impact on the economy development of SSA countries.

The result that tests whether there is a long-run relationship between stock market liquidity and economic development in SSA countries is presented in Tables 5, 7 and 9 for Mauritius, Nigeria, and South Africa respectively. The result of the Johansen co-integration test for the three countries; Mauritius, Nigeria, and South Africa shows that there is no co-integration among the variables of the stock market liquidity model. This implies that there is no long-run relationship between stock market liquidity and human development index as a proxy for economic development in Mauritius, Nigeria, and South Africa.

The results of coefficient of determination (R2) or F-probability from the OLS techniques showed that stock market liquidity could significantly explain only: 70% or 0.000111, 18% or 0.308450, 43% or 0.019913 of the factors that influence human development index as a proxy for economic development in Mauritius, Nigeria, and South Africa respectively. The overall estimation of the collective variables of this model was significant at the 5% level of significance in Mauritius and South Africa but insignificant in Nigeria. Thus, the study accepted the alternative hypothesis that stock market liquidity has a significant impact on economic development (human development factor) of SSA countries as shown in Mauritius and South Africa. In Nigeria, the study accepted the null hypothesis that stock market liquidity has no significant impact on economic development (human development factor) of SSA countries [9-11].

Conclusion and Recommendations

Conclusion

Based on our findings, the study accepted that all the variables of stock market size model indicators have a significant impact on the human development index in the three SSA countries; Mauritius, Nigeria and South Africa. The stock market liquidity model indicators have a significant impact on the human development index in Mauritius and South Africa while in Nigeria they are insignificant. Also, there is no autocorrelation among the variables of the two models. This shows that there is confidence in the level of reliability of the results.

The Johansen co-integration test showed at least two models having co-integration equations among the variables. No cointegration among the variables of the stock market size in Mauritius and Nigeria but in South Africa there is cointegration among the variables. This implies that there is no long-run relationship between the variables in Muaritius and Nigeria but not in South Africa.

From the ordinary least square result R2 and the F-probability of stock market size for Mauritius, Nigeria and South Africa are: 86% and 0.0000; 46% and 0.013738; and 40% and 0.031391 respectively. Conclusively, the variables of this model are all significant at 5%, meaning the null hypothesis is rejected and the alternative hypothesis accepted. This collaborate the empirical study of Rurangwa that stock market size has a major impact on economic development.

The second model of this model, the stock market liquidity has its Johansen cointegration result for the three SSA countries showing that there is no cointegration among the variables. Meaning that there is no long-run relationship among the variables of the stock market liquidity model variables. The result of the coefficient of determinant, R2 and the F-probability from the ordinary least square techniques showed that stock market liquidity model are: 70% and 0.000111; 18% and 0.308450; and 43% and 0.019913 for Muaritius, Nigeria, and South Africa respectively. Conclusively, all the variables of this stock market liquidity model in Mauritius and South Africa are significant while that of Nigeria is insiginaficant. This collaborate the empirical study of Okoye with regards to the results of Mauritius and South Africa in this study and Magwera with regards to the result of Nigeria in this study.

Recommendations

Here, the recommendations are based on the findings from the analysis of the two models as follows:

Stock market size: The study recommends that continuous reforms should be in place to bring about a more robust stock market in SSA towards accelerating the human development index of the entire SSA. The government of the various countries should encourage more companies to enlist by relaxing some of the listing requirements like allowing subscriptions list, to remain open for more than a maximum period of 28 working days. The number of days should be increased to allow for more participation. Another requirement to relax is the maximum of 10% of an offering to the staff of a company (or its subsidiaries or associated companies). The percentage should be increased to allow the employees more ownership status and commitment to the growth of the company.

Stock market liquidity: To bring about significant positive impact of stock market liquidity on economic development in Nigeria and to increase the Mauritius and South Africa stock market liquidity significant positive impact on economic development positions, government should put in place an efficient system geared towards high level of trading activities that will bring about a vibrant and free flow of information.

Reference