Dutch Journal of Finance and Management
Research Article
2018, 2(2), Article No: 03

The Finance – Growth Nexus in Botswana: A Multivariate Causal Linkage

Published in Forthcoming Articles section: 06 Jul 2018
Published in Volume 2 Issue 2: 27 Aug 2018
Download: 358
View: 773

Abstract

This paper evaluates the dynamic causal relationship between financial development, savings, investment and economic growth in Botswana from 1976-2014 by employing a multivariate causality model. Results reveal that it is chiefly investment that drives the bank-related and stock exchange-based financial sectors in the short run. 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 Granger-causes investment and savings, both, in the short run and long run. Further, only bank-related financial development is found to Granger-cause economic growth in Botswana.

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:

  1. 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);

  2. Investment Granger-causes financial development (Odhiambo, 2010);

  3. 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

  4. 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 Botswana1. 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.


  1. See Muyambiri and Odhiambo (2015) for a fuller examination of the sequential development of the finance sector in Botswana

  • Acaravci, S. K., Ozturk, I. and Acaravci, A. (2009). Financial development and economic growth: Literature survey and empirical evidence from Sub-Saharan African countries. South African Journal of Economic and Management Sciences, 12(1), 11-27. https://doi.org/10.4102/sajems.v12i1.258
  • Baliamoune-Lutz, M. (2003). Financial liberalization and economic growth in Morocco: a test of the supply-leading hypothesis. Journal of Business in Developing Nations, 7, 31-50.
  • Bayar, Y., Kaya, A. and Yildirim, M. (2014). Effects of stock market development on economic growth: Evidence from Turkey. International Journal of Financial Research, 5(1), 93-100. https://doi.org/10.5430/ijfr.v5n1p93
  • Boubakari, A. and Jin, D. (2010). The role of Stock Market Development in Economic Growth: Evidence from some Euronext Countries, International Journal of Financial Research, 1(1), 14-20. https://doi.org/10.5430/ijfr.v1n1p14
  • Caporale, G. M., Howells, P. G. and Soliman, A. M. (2004). Stock market development and economic growth: the causal linkage. Journal of economic development, 29(1), 33-50.
  • Carby, Y., Craigwell, R., Wright, A. and Wood, A. (2012). Finance and growth causality: A test of the Patrick's stage-of-development hypothesis. International Journal of Business and Social Science, 3(21), 129-139.
  • Isu, H. O. and Okpara, G. C. (2013). Does Financial Deepening Follow Supply Leading on Demand Following Hypothesis? A look at the Nigerian Evidence. Asian Journal of Science and Technology, 5(1), 10-15.
  • Masoud, N. M. (2013). The impact of stock market performance upon economic growth. International Journal of Economics and Financial Issues, 3(4), 788-798.
  • Muyambiri, B and Odhiambo. M. N. (2017). The casual relationship between financial development and investment in Botswana. UNISA Economic Research Working Paper Series, Working Paper 09/2017, 1-31.
  • Muyambiri, B. and Odhiambo, N. M. (2015). The evolution of the financial system in Botswana. African Journal of Business and Economic Research, 10(2-3), 87-113.
  • Nazir, M. S., Nawaz, M. M. and Gilani, U. J. (2010). Relationship between economic growth and stock market development. African Journal of Business Management, 4(16), 3473-3479.
  • Nowbusting, B. M. and Odit, M. P. (2009). Stock market development and economic growth: The case of Mauritius. International Business & Economics Research Journal (IBER), 8(2), 77-88.
  • Nyasha, S. and Odhiambo, N. M. (2015). Banks, stock market development and economic growth in South Africa: a multivariate causal linkage. Applied Economics Letters, 22(18), 1480-1485. https://doi.org/10.1080/13504851.2015.1042132
  • Odo, S. I., Ogbonna, B.C., Agbi, P. E. and Anoke, C. I. (2016). Investigating the causal relationship between Financial Development and Economic Growth in Nigeria and South Africa. Journal of Economics and Finance, 7(2), 75-81.
  • Onwumere, J. U. J., Ibe, I. G., Okafor, R. G. and Uche, U. B. (2012). Stock Market and Economic Growth in Nigeria: Evidence from the Demand-Following Hypothesis. European Journal of Business and Management, 4(19), 1-9.
  • Paramati, S. R., and Gupta, R. (2011). An empirical analysis of stock market performance and economic growth: evidence from India, International Research Journal of Finance and Economics, 73, 133-149.
  • Perron, P. (1997). Further evidence on breaking trend functions in macroeconomic variables. Journal of econometrics, 80(2), 355-385. https://doi.org/10.1016/S0304-4076(97)00049-3
  • Pesaran, M. H., Shin, Y. and Smith, R. (2001). Bound testing approaches to the analysis of level relationship. Journal of Applied Econometrics, 16(3), 289-326. https://doi.org/10.1002/jae.616
  • Rehman, M. Z., Ali, N. and Nasir, N. M. (2015). Linkage between Financial Development, Trade Openness and Economic Growth: Evidence from Saudi Arabia. Journal of Applied Finance & Banking, 5(6), 127-141.
  • Tachiwou, A. M. (2010). Stock market development and economic growth: the case of West African monetary union. International Journal of Economics and Finance, 2(3), 97-103. https://doi.org/10.5539/ijef.v2n3p97
AMA 10th edition
In-text citation: (1), (2), (3), etc.
Reference: Muyambiri B, Chabaefe NN. The Finance – Growth Nexus in Botswana: A Multivariate Causal Linkage. Dutch Journal of Finance and Management. 2018;2(2), 03. https://doi.org/10.20897/djfm/2634
APA 6th edition
In-text citation: (Muyambiri & Chabaefe, 2018)
Reference: Muyambiri, B., & Chabaefe, N. N. (2018). The Finance – Growth Nexus in Botswana: A Multivariate Causal Linkage. Dutch Journal of Finance and Management, 2(2), 03. https://doi.org/10.20897/djfm/2634
Chicago
In-text citation: (Muyambiri and Chabaefe, 2018)
Reference: Muyambiri, Brian, and Nancy Neoyame Chabaefe. "The Finance – Growth Nexus in Botswana: A Multivariate Causal Linkage". Dutch Journal of Finance and Management 2018 2 no. 2 (2018): 03. https://doi.org/10.20897/djfm/2634
Harvard
In-text citation: (Muyambiri and Chabaefe, 2018)
Reference: Muyambiri, B., and Chabaefe, N. N. (2018). The Finance – Growth Nexus in Botswana: A Multivariate Causal Linkage. Dutch Journal of Finance and Management, 2(2), 03. https://doi.org/10.20897/djfm/2634
MLA
In-text citation: (Muyambiri and Chabaefe, 2018)
Reference: Muyambiri, Brian et al. "The Finance – Growth Nexus in Botswana: A Multivariate Causal Linkage". Dutch Journal of Finance and Management, vol. 2, no. 2, 2018, 03. https://doi.org/10.20897/djfm/2634
Vancouver
In-text citation: (1), (2), (3), etc.
Reference: Muyambiri B, Chabaefe NN. The Finance – Growth Nexus in Botswana: A Multivariate Causal Linkage. Dutch Journal of Finance and Management. 2018;2(2):03. https://doi.org/10.20897/djfm/2634
Related Subjects
Finance & Management
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Submit My Manuscript



Phone: +31 (0)70 2190600 | E-Mail: info@lectitojournals.com

Address: Cultura Building (3rd Floor) Wassenaarseweg 20 2596CH The Hague THE NETHERLANDS

Disclaimer

This site is protected by copyright law. This site is destined for the personal or internal use of our clients and business associates, whereby it is not permitted to copy the site in any other way than by downloading it and looking at it on a single computer, and/or by printing a single hard-copy. Without previous written permission from Lectito BV, this site may not be copied, passed on, or made available on a network in any other manner.

Content Alert

Copyright © 2015-2018 Lectito BV All rights reserved.