INTRODUCTION
There is an ongoing argument among scholars concerning the direction of causality between bank-related and stock exchange-based financial development and savings, investment and economic growth. As far as economic growth causality studies are concerned, a considerable number of empirical works have been conducted on a number of countries though with conflicting results (see Nyasha and Odhiambo, 2015; Rehman et al., 2015; Acaravci et al., 2009). There are four views that have been empirically proven to exist in literature, that is, the supply-leading hypothesis, demand-following hypothesis, bidirectional-causality view and the fourth view stipulating that financial development and economic growth have no causal relationship (Nyasha and Odhiambo, 2015). The supply leading hypothesis claims that financial development stimulates economic growth (see Bayar et al. 2014; Masoud, 2013; Nazir et al., 2010; Tachiwou, 2010; Nowbusting and Odit, 2009; Caporale et al., 2004; Boubakari and Jin, 2010), and the demand following hypothesis claims that growth instigates the demand for financial commodities (see Odo et al., 2016; Isu and Okpara, 2013; Carby et al., 2012; Paramati and Gupta, 2011; Baliamoune-Lutz, 2003; Onwumere et al., 2012). The bi-directional causality hypothesis stipulates that financial progression and economic growth are bi-directionally causal while the fourth view states that financial progression has no relationship with economic growth (see Nyasha and Odhiambo, 2015; Acaravci et al., 2009).
However, causality studies that focused on the additional variables used in this study have not been as numerous and as widely researched on as the finance-growth nexus. The relationship between financial development and investment is articulated as having four main conclusions by Muyambiri and Odhiambo (2017), that is:
-
Financial development Granger-causes investment (Xu, 2000; Caporale et al., 2005, Rousseau and Vuthipadadorn 2005; Chaudry, 2007; Carp, 2012; Hamdi et al., 2013; Asongu, 2014);
-
Investment Granger-causes financial development (Odhiambo, 2010);
-
There is a bidirectional causality between financial development and investment (Shan et al., 2001; Shan and Jianhong, 2006; Lu et al., 2007; Nazlioglu et al., 2009; Huang, 2011); and
-
No causal relationship exists between the two variables (Majid, 2008; Shan and Morris, 2002; Marques et al., 2013).
Conversely, most of the studies conducted to evaluate the causal relationship between either of the variables employed in this study, made use of mostly bank-related financial development indicators while ignoring the stock exchange-based side of the financial sector. In addition to the contradictory results that came from such studies, there has been no study to be best of our current knowledge that has sought to investigate the multivariate causal relationship between bank-related financial development, stock exchange-based financial development, savings and investment in one study especially for a country like Botswana. Given these existing gaps, this study takes advantage of the multivariate causality analysis framework using the autoregressive distributed lag bounds testing approach to assess such a relationship.
METHODOLOGY
Shadowing Nyasha and Odhiambo (2015), the estimated ARDL model is given as follows.
| \[\mathrm{\Delta}\text{INV}_{t} = \propto_{0} + \sum_{i = 1}^{n}{\propto_{1i}{\mathrm{\Delta}INV}_{t - i}} + \sum_{i = 0}^{n}{\propto_{2i}{\mathrm{\Delta}BFA}_{t - i}} + \sum_{i = 0}^{n}{\propto_{3i}{\mathrm{\Delta}MFA}_{t - i}} + \sum_{i = 0}^{n}{\propto_{4i}{\mathrm{\Delta}GDP}_{t - i}} + \sum_{i = 0}^{n}{\propto_{5i}{\mathrm{\Delta}GDS}_{t - i}} + \alpha_{6}\text{INV}_{t - 1} + \alpha_{7}\text{BFA}_{t - 1} + \alpha_{8}\text{MFA}_{t - 1} + \alpha_{9}\text{GDP}_{t - 1} + \alpha_{10}\text{GDS}_{t - 1} + \varepsilon_{1t}\] |
(1) |
| \[\mathrm{\Delta}\text{BFA}_{t} = \beta_{0} + \sum_{i = 1}^{n}{\beta_{1i}{\mathrm{\Delta}BFA}_{t - i}} + \sum_{i = 0}^{n}{\beta_{2i}{\mathrm{\Delta}INV}_{t - i}} + \sum_{i = 0}^{n}{\beta_{3i}{\mathrm{\Delta}MFA}_{t - i}} + \sum_{i = 0}^{n}{\beta_{4i}{\mathrm{\Delta}GDP}_{t - i}} + \sum_{i = 0}^{n}{\beta_{5i}{\mathrm{\Delta}GDS}_{t - i}} + \beta_{6}\text{BFA}_{t - 1} + \beta_{7}\text{INV}_{t - 1} + \beta_{8}\text{MFA}_{t - 1} + \beta_{9}\text{GDP}_{t - 1} + \beta_{10}\text{GDS}_{t - 1} + \varepsilon_{2t}\] |
(2) |
| \[\mathrm{\Delta}\text{GDS}_{t} = \rho_{0} + \sum_{i = 1}^{n}{\rho_{1i}{\mathrm{\Delta}GDS}_{t - i}} + \sum_{i = 0}^{n}{\rho_{2i}{\mathrm{\Delta}INV}_{t - i}} + \sum_{i = 0}^{n}{\rho_{3i}{\mathrm{\Delta}BFA}_{t - i}} + \sum_{i = 0}^{n}{\rho_{4i}{\mathrm{\Delta}MFA}_{t - i}} + \sum_{i = 0}^{n}{\rho_{5i}{\mathrm{\Delta}GDP}_{t - i}} + \rho_{6}\text{GDS}_{t - 1} + \rho_{7}\text{BFA}_{t - 1} + \rho_{8}\text{MFA}_{t - 1} + \rho_{9}\text{INV}_{t - 1} + \rho_{10}\text{GDP}_{t - 1} + \varepsilon_{3t}\] |
(3) |
| \[\mathrm{\Delta}\text{GDP}_{t} = \gamma_{0} + \sum_{i = 1}^{n}{\gamma_{1i}{\mathrm{\Delta}GDP}_{t - i}} + \sum_{i = 0}^{n}{\gamma_{2i}{\mathrm{\Delta}INV}_{t - i}} + \sum_{i = 0}^{n}{\gamma_{3i}{\mathrm{\Delta}BFA}_{t - i}} + \sum_{i = 0}^{n}{\gamma_{4i}{\mathrm{\Delta}MFA}_{t - i}} + \sum_{i = 0}^{n}{\gamma_{5i}{\mathrm{\Delta}GDS}_{t - i}} + \gamma_{6}\text{GDP}_{t - 1} + \gamma_{7}\text{BFA}_{t - 1} + \gamma_{8}\text{MFA}_{t - 1} + \gamma_{9}\text{INV}_{t - 1} + \gamma_{10}\text{GDP}_{t - 1} + \varepsilon_{4t}\] |
(4) |
| \[\mathrm{\Delta}\text{MFA}_{t} = \delta_{0} + \sum_{i = 1}^{n}{\delta_{1i}{\mathrm{\Delta}MFA}_{t - i}} + \sum_{i = 0}^{n}{\delta_{2i}{\mathrm{\Delta}INV}_{t - i}} + \sum_{i = 0}^{n}{\delta_{3i}{\mathrm{\Delta}BFA}_{t - i}} + \sum_{i = 0}^{n}{\delta_{4i}{\mathrm{\Delta}GDP}_{t - i}} + \sum_{i = 0}^{n}{\delta_{5i}{\mathrm{\Delta}GDS}_{t - i}} + \delta_{6}\text{BFA}_{t - 1} + \delta_{7}\text{INV}_{t - 1} + \delta_{8}\text{MFA}_{t - 1} + \delta_{9}\text{GDP}_{t - 1} + \delta_{10}\text{GDS}_{t - 1} + \varepsilon_{5t}\] |
(5) |
The multivariate causality model is then presented as follows:
| \[\mathrm{\Delta}\text{INV}_{t} = \alpha_{0} + \sum_{i = 1}^{n}{\alpha_{1i}{\mathrm{\Delta}INV}_{t - i}\ } + \sum_{i = 1}^{n}{\alpha_{2i}\mathrm{\Delta}\text{BFA}_{t - i}\ } + \sum_{i = 1}^{n}{\alpha_{3i}\mathrm{\Delta}\text{MFA}_{t - i}\ } + \sum_{i = 1}^{n}{\alpha_{4i}{\mathrm{\Delta}GDP}_{t - i}\ } + \sum_{i = 1}^{n}{\alpha_{5i}{\mathrm{\Delta}GDS}_{t - i}\ } + \alpha_{6}\text{ECT}_{t - 1} + \mu_{1t}\] |
(6) |
| \[\mathrm{\Delta}\text{BFA}_{t} = \ \beta_{0} + \sum_{i = 1}^{n}{\beta_{1i}{\mathrm{\Delta}INV}_{t - i}\ } + \sum_{i = 1}^{n}{\beta_{2i}{\mathrm{\Delta}BFA}_{t - i}\ } + \sum_{i = 1}^{n}{\beta_{3i}{\mathrm{\Delta}MFA}_{t - i}\ } + \sum_{i = 1}^{n}{\beta_{4i}{\mathrm{\Delta}GDP}_{t - i}\ } + \sum_{i = 1}^{n}{\beta_{5i}{\mathrm{\Delta}GDS}_{t - i}\ } + \beta_{6}\text{ECT}_{t - 1} + \mu_{2t}\] |
(7) |
| \[\mathrm{\Delta}\text{GDS}_{t} = \ \rho_{0} + \sum_{i = 1}^{n}{\rho_{1i}\mathrm{\Delta}\text{INV}_{t - i}\ } + \sum_{i = 1}^{n}{\rho_{2i}\mathrm{\Delta}\text{BFA}_{t - i}\ } + \sum_{i = 1}^{n}{\rho_{3i}\mathrm{\Delta}\text{MFA}_{t - i}\ } + \sum_{i = 1}^{n}{\rho_{4i}\mathrm{\Delta}\text{GDP}_{t - i}\ } + \sum_{i = 1}^{n}{\rho_{5i}\mathrm{\Delta}\text{GDS}_{t - i}\ } + \rho_{6}\text{ECT}_{t - 1} + \mu_{3t}\] |
(8) |
| \[\mathrm{\Delta}\text{GDP}_{t} = \gamma_{0} + \sum_{i = 1}^{n}{\gamma_{1i}{\mathrm{\Delta}GDP}_{t - i}} + \sum_{i = 1}^{n}{\gamma_{2i}{\mathrm{\Delta}INV}_{t - i}} + \sum_{i = 1}^{n}{\gamma_{3i}{\mathrm{\Delta}BFA}_{t - i}} + \sum_{i = 1}^{n}{\gamma_{4i}{\mathrm{\Delta}MFA}_{t - i}} + \sum_{i = 1}^{n}{\gamma_{5i}{\mathrm{\Delta}GDS}_{t - i}} + \gamma_{6}\text{ECT}_{t - 1} + \mu_{4t}\] |
(9) |
| \[\mathrm{\Delta}\text{MFA}_{t} = \delta_{0} + \sum_{i = 1}^{n}{\delta_{1i}{\mathrm{\Delta}MFA}_{t - i}} + \sum_{i = 1}^{n}{\delta_{2i}{\mathrm{\Delta}INV}_{t - i}} + \sum_{i = 1}^{n}{\delta_{3i}{\mathrm{\Delta}BFA}_{t - i}} + \sum_{i = 1}^{n}{\delta_{4i}{\mathrm{\Delta}GDP}_{t - i}} + \sum_{i = 1}^{n}{\delta_{5i}{\mathrm{\Delta}GDS}_{t - i}} + \delta_{6}\text{ECT}_{t - 1} + \mu_{5t}\] |
(10) |
where
\(\text{INV}\)= investment to GDP ratio.
\(\text{BFA}\) = accelerator-augmented index of bank-related financial development index, calculated as the means-removed average (of M3 to GDP, domestic credit to private sector to GDP ratio, and total domestic credit to GDP ratio) multiplied by the growth rate of GDP per capita.
\(\text{MFA}\) = accelerator-augmented index of stock exchange-based financial development index, calculated as the means-removed average (of stocks traded, total value to GDP ratio, market capitalisation to GDP ratio, and the turnover ratio) multiplied by the growth rate of GDP per capita.
\(\text{GDP}\)= real GDP growth rate.
\(\text{GDS}\)= gross domestic savings.
ECT = error-correction term,
\(\propto_{0}\), \(\beta_{0}\), \(\rho_{0}\), \(\gamma_{0}\) and\(\ \delta_{0}\)= respective constants,
\(\propto_{1},\ldots, \propto_{10}\),\(\ \beta_{1},\ldots,\beta_{10}\), \(\rho_{1},\ldots,\rho_{10}\), \(\gamma_{1},\ldots,\gamma_{10}\) and \(\delta_{1},\ldots,\delta_{10}\)=respective coefficients,
\(\mathrm{\Delta}\) = difference operator,
\(n\) = lag length,
\(\varepsilon\) = error term and \(\mu\) = white-noise error-term.
EMPRICAL RESULTS
Stationarity tests are employed to ensure that all variables are integrated of maximum order 1. Otherwise, the ARDL bounds test methodology will break down if there are variables integrated of an order greater than 1. The Perron (1997) PPURoot unit root and the Augmented Dickey-Fuller Generalised Least Square tests unit root tests were employed to check the order of integration. The results for the test of stationarity of the variables are presented in Table 1.
|
Table 1. Stationarity Test Results
|
Dickey-Fuller Generalised Least Square (DF-GLS)
|
|
Variable
|
Stationarity in levels
|
Stationarity in differences
|
|
|
With intercept, no trend
|
With intercept and trend
|
With intercept, no trend
|
With intercept and trend
|
|
INV
|
-2.7471*
|
-2.7773
|
-6.2222***
|
-6.2291***
|
|
GDP
|
-4.5213 ***
|
-5.4507 ***
|
-
|
-
|
|
BFA
|
-1.7833*
|
-2.0434
|
-9.9352***
|
-11.0932***
|
|
MFA
|
-4.0963**
|
-4.9413*
|
-
|
-
|
|
GDS
|
-2.1037**
|
-2.5491
|
-5.5152***
|
-5.5653***
|
|
Perron (1997) PPURoot
|
|
Variable
|
Stationarity in levels
|
Stationarity in differences
|
|
INV
|
-6.3488***
|
-6.6408***
|
-
|
-
|
|
GDP
|
-6.3130***
|
-6.2841***
|
-
|
-
|
|
BFA
|
-6.4923***
|
-7.0091**
|
-
|
-
|
|
MFA
|
-5.6991*
|
-5.1882
|
-6.7414***
|
-6.4492***
|
|
GDS
|
-4.0141
|
-4.3253
|
-6.3954***
|
-6.2451***
|
Note: *, ** and *** denote stationarity at the 10%, 5% and 1% significance levels respectively
|
Table 1 confirms that the ARDL bounds testing procedure is appropriate for the data and it is therefore employed. Table 2 reports the results of the bounds F-test for co-integration.
|
Table 2. Bounds F-Test for Cointegration Results
|
Dependent Variable
|
Function
|
F-statistic
|
Cointegration Status
|
|
INV
|
F(INV| GDP, BFA, MFA, GDS)
|
5.1612***
|
Cointegrated
|
|
BFA
|
F(BFA| GDP, INV, MFA, GDS)
|
6.5637***
|
Cointegrated
|
|
MFA
|
F(MFA| GDP, BFA, INV, GDS)
|
1.0799
|
Not cointegrated
|
|
GDP
|
F(GDP| INV, BFA, MFA, GDS)
|
3.3418
|
Not cointegrated
|
|
GDS
|
F(GDS| GDP, BFA, MFA, INV)
|
3.8044*
|
Cointegrated
|
|
Asymptotic Critical
|
|
|
1%
|
5%
|
10%
|
|
Pesaran et al. (2001:301) Table CI(iii) Case III
|
I(0)
|
I(1)
|
I(0)
|
I(1)
|
I(0)
|
I(1)
|
|
3.74
|
5.06
|
2.86
|
4.01
|
2.45
|
3.52
|
Note: *, ** and *** denotes significance at the 10%, 5% and 1% significance levels respectively
|
The results from the bounds cointegration test indicate that three out of the five equations have a long run relationship. Consequently, the multivariate Granger causality test is run and the results are reported in Table 3. The equations with a cointegrated relationship are estimated, as expected, with the inclusion of an error correction term. Otherwise, no error correction term is included.
The empirical results of the multivariate Granger causality test are reported in Table 3.
|
Table 3. Granger-Causality Test Results
|
Investment (I), Bank-related Financial Development (BG), and Savings (S)
|
|
Dependent Variable
|
F-statistics (probability)
|
|
|
ECTt
[t-statistics]
|
|
∆INVt
|
∆BFAt
|
∆MFAt
|
∆GDPt
|
∆GDSt
|
|
∆INVt
|
-
|
1.0580
(0.387)
|
1.7160
(0.234)
|
4.6903**
(0.040)
|
4.1479*
(0.053)
|
-0.83473**
[-3.1077]
|
|
∆BFAt
|
4.1163**
(0.044)
|
-
|
9.4632**
(0.010)
|
0.61525
(0.557)
|
0.0060698
(0.939)
|
-0.19494*
[-1.7746]
|
|
∆MFAt
|
7.2592**
(0.011)
|
0.15822
(0.856)
|
|
0.96271
(0.415)
|
0.55967
(0.588)
|
|
|
∆GDPt
|
2.0094
(0.190)
|
3.4138*
(0.066)
|
1.2810
(0.324)
|
|
2.4408
(0.131)
|
|
|
∆GDSt
|
0.75629
(0.407)
|
1.6111
(0.251)
|
3.8186*
(0.082)
|
6.3678**
(0.019)
|
-
|
-0.88920***
[-4.2538]
|
Note: *, ** and *** denotes significance at the 10%, 5% and 1% significance levels, respectively
|
The results in Table 3 reveal that they are only unidirectional causal relationships amongst a number of the variables under discussion. Economic growth is found to Granger-cause investment and savings both in the short-run and long run. Only bank-related financial development is found to Granger-cause economic growth in Botswana in the short run.
Inherently, investment, according to the results, precedes financial development. However, there is only a short-run unidirectional causal relationship from investment to stock exchange-based financial development. The same unidirectional relationship in both the short run and the long run is found from investment to bank-related financial development. Therefore, consistent with Odhiambo (2010), the results show that it is chiefly investment that drives the bank-related and stock exchange-based financial sectors. To induce financial sector development, there is need to put in place policies that encourage increased investment. Nevertheless, investment is found to be Granger-caused by economic growth and savings in both the long run and the short run in Botswana.
Notwithstanding that stock exchange-based financial development is Granger-caused by only investment, it precedes both bank-related financial development and savings in both the short run and the long run.
As already noted, investment and stock exchange-based financial development Granger-cause bank-related financial development in the short run and long run. The only variable that is Granger-caused by bank-related financial development is economic growth and this is only in the short run. This finding tends to confirm the findings of Bayar et al., 2014; Masoud, 2013; Nazir et al., 2010; Tachiwou, 2010; Nowbusting and Odit, 2009; Caporale et al., 2004; and Boubakari and Jin, 2010.
Savings Granger-cause investment in both the long run and the short run. Stock exchange-based financial development and economic growth Granger-cause savings in both the short run and the long run.
Table 4 summarises the results of the Granger-causality tests.
|
Table 4. Summary of Granger-causality test results
|
Dependent Variable
|
Direction of Causality AND SIGNIFICANT VARIABLES
|
PERIOD of Causality
|
|
Short Run
|
Long Run
|
|
GDP
|
⇒INV, GDS
|
✔
|
✔
|
|
INV
|
⇒BFA
|
✔
|
✔
|
|
|
⇒MFA
|
✔
|
-
|
|
MFA
|
⇒BFA
|
✔
|
✔
|
|
|
⇒GDS
|
✔
|
✔
|
|
BFA
|
⇒GDP
|
✔
|
-
|
|
GDS
|
⇒INV
|
✔
|
✔
|
NB: GDP=Economic growth, GDS=Savings, INV=investment; BFA=bank-related financial development; MFA=stock exchange-based financial development, ⇒indicates direction of causality, ✔indicates presence of causality in respective period.
|
CONCLUSION
In this paper, the causal relationship between financial development, split into bank-related and stock exchange-based financial development, savings, and investment and economic growth has been empirically examined for the period of 1976 to 2014 for Botswana with the aid of a multivariate Granger-causality model. The study results show that it is chiefly investment that drives the bank-related and stock exchange-based financial sectors in the short run. However, the same deduction is true for bank-related financial development in the long run. Inherently, results also show that stock exchange-based financial development drives bank-related financial development and savings in both the short run and the long run. While, savings are found to Granger-cause investment. Economic growth is found to Granger-cause investment and savings both in the short-run and long run. Only bank-related financial development is found to Granger-cause economic growth in Botswana.
Therefore, to induce financial sector development in the short run, there is need to put in place policies that encourage increased investment. These must focus on the economic growth and savings that have been found to precede investment as per the results of this study.
-
See Muyambiri and Odhiambo (2015) for a fuller examination of the sequential development of the finance sector in Botswana