# Financial Deepening and Economic Growth Nexus in Nigeria: Supply-Leading or Demand-Following?

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review

#### 2.1. Theoretical Framework

#### 2.2. Empirical Literature

#### 2.3. Limitations of Previous Studies

## 3. Methodology

#### 3.1. Data and Sources

#### 3.2. Model Specification

#### 3.3. Description of Variables

#### 3.4. Estimation Technique

^{2}) distribution irrespective of the order of integration or cointegration properties of the variables. The Toda–Yamamoto approach fits a standard vector auto-regression and a vector error correction model on levels of the variables (not on their first differences) and therefore makes allowances for the long-run information often ignored in systems that require first differencing and pre-whitening (Clarke & Mirza, 2006 [49]). The approach employs a modified Wald test (MWALD) for restrictions on the parameters of the VAR (k), where k is the lag length of the system. The basic idea of the Toda–Yamamoto approach is to artificially augment the correct order, k, by the maximal order of integration, say dmax. Once this is done, a (k + dmax) the order of VAR is estimated and the coefficients of the last lagged dmax vectors are ignored (Caporale & Pittis, 1999 [53]).

## 4. Results and Discussion

^{2}) distribution irrespective of the order of integration of the variables. In fact, the Toda–Yamamoto approach fits a standard vector auto-regression model on levels of the variables and therefore makes allowance for the long-run information often ignored in systems that require first differencing and pre-whitening (Clarke & Mirza, 2006 [49]).

^{2}) value of 19.19907 with a probability value of 0.0378, which suggest rejection of the null hypothesis that financial deepening variables does not Granger-cause economic growth. However, there is no evidence of feedback, which means that economic growth (GR) does not Granger-cause financial development as GR was not significant at the 5% level in any of the models with financial deepening variables as the dependent variable (see Appendix A).

## 5. Conclusions and Recommendations

#### 5.1. Conclusions

#### 5.2. Recommendations

## Author Contributions

## Conflicts of Interest

## Appendix A

VAR Granger Causality/Block Exogeneity Wald Tests | |||

Date: 14 October 2015; Time: 13:32 Sample: 1970–2013 Included observations: 42 | |||

Dependent variable: BA | |||

Excluded | Chi-square | Degree of freedom | Probability |

CR | 0.414173 | 2 | 0.8129 |

GR | 0.365922 | 2 | 0.8328 |

LR | 0.236432 | 2 | 0.8885 |

MR | 0.578120 | 2 | 0.7490 |

TR | 2.820104 | 2 | 0.2441 |

All | 5.154918 | 10 | 0.8806 |

Dependent variable: CR | |||

Excluded | Chi-square | Degree of freedom | Probability |

BA | 0.798868 | 2 | 0.6707 |

GR | 2.270515 | 2 | 0.3213 |

LR | 4.072561 | 2 | 0.1305 |

MR | 3.126561 | 2 | 0.2094 |

TR | 8.506981 | 2 | 0.0142 |

All | 17.05607 | 10 | 0.0731 |

Dependent variable: GR | |||

Excluded | Chi-square | Degree of freedom | Probability |

BA | 2.091518 | 2 | 0.3514 |

CR | 2.467952 | 2 | 0.2911 |

LR | 8.359085 | 2 | 0.0153 |

MR | 3.733386 | 2 | 0.1546 |

TR | 11.78489 | 2 | 0.0028 |

All | 19.19907 | 10 | 0.0378 |

Dependent variable: LR | |||

Excluded | Chi-square | Degree of freedom | Probability |

BA | 2.642556 | 2 | 0.2668 |

CR | 0.759500 | 2 | 0.6840 |

GR | 1.256911 | 2 | 0.5334 |

MR | 0.255097 | 2 | 0.8803 |

TR | 2.917831 | 2 | 0.2325 |

All | 11.03855 | 10 | 0.3545 |

Dependent variable: MR | |||

Excluded | Chi-square | Degree of freedom | Probability |

BA | 0.594002 | 2 | 0.7430 |

CR | 0.123098 | 2 | 0.9403 |

GR | 0.373803 | 2 | 0.8295 |

LR | 9.406109 | 2 | 0.0091 |

TR | 4.127024 | 2 | 0.1270 |

All | 14.74395 | 10 | 0.1417 |

Dependent variable: TR | |||

Excluded | Chi-square | Degree of freedom | Probability |

BA | 3.140361 | 2 | 0.2080 |

CR | 5.028108 | 2 | 0.0809 |

GR | 4.495025 | 2 | 0.1057 |

LR | 3.062545 | 2 | 0.2163 |

MR | 3.382383 | 2 | 0.1843 |

All | 11.18014 | 10 | 0.3437 |

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**Figure 1.**Borrowers from Deposit Money banks per 1000 adults. Source: Authors’ computation, with data from the IMF Financial Access Survey, 2014 [4].

Statistic | CR | BA | GR | LR | MR | TR |
---|---|---|---|---|---|---|

Mean | 13.08425 | 35.42234 | 4.402923 | 14.95818 | 17.33415 | 5.585111 |

Median | 12.70369 | 32.90723 | 4.649226 | 16.75500 | 10.96388 | 5.403249 |

Maximum | 38.34855 | 70.67310 | 33.73578 | 29.80000 | 73.75343 | 17.55881 |

Minimum | 3.862077 | 14.93431 | −13.12788 | 6.000000 | 5.122039 | 0.148551 |

Std. Dev. | 6.563540 | 13.24457 | 8.080945 | 6.406191 | 14.16887 | 3.500716 |

Skewness | 1.930973 | 0.653420 | 0.963436 | 0.142541 | 2.376585 | 0.881809 |

Kurtosis | 8.141648 | 2.867451 | 6.407793 | 2.151042 | 9.170601 | 4.436761 |

Jarque–Bera | 75.81047 | 3.163231 | 28.09746 | 1.470337 | 111.2264 | 9.486820 |

Probability | 0.000000 | 0.205643 | 0.000001 | 0.479425 | 0.000000 | 0.008709 |

Sum | 575.7068 | 1558.583 | 193.7286 | 658.1600 | 762.7026 | 245.7449 |

Sum Sq. Dev. | 1852.442 | 7543.001 | 2807.972 | 1764.689 | 8632.543 | 526.9655 |

Observations | 44 | 44 | 44 | 44 | 44 | 44 |

CR | BA | GR | LR | MR | TR | |
---|---|---|---|---|---|---|

CR | 1.000000 | 0.790731 | −0.339515 | −0.435276 | 0.351332 | 0.438212 |

BA | 0.790731 | 1.000000 | −0.296440 | −0.361842 | 0.346332 | 0.420567 |

GR | −0.339515 | −0.296440 | 1.000000 | 0.095854 | −0.345424 | 0.013725 |

LR | −0.435276 | −0.361842 | 0.095854 | 1.000000 | −0.143523 | −0.159583 |

MR | 0.351332 | 0.346332 | −0.345424 | −0.143523 | 1.000000 | 0.001028 |

TR | 0.438212 | 0.420567 | 0.013725 | −0.159583 | 0.001028 | 1.000000 |

Variable | ADF—Statistics | Adj. PP—Statistics | ||||
---|---|---|---|---|---|---|

Level | 1st Difference | 1st Difference Probability | Level | 1st Difference | Difference Probability | |

GR | −5.738307 ** | −8.695053 ** | 0.0000 | −5.746942 ** | −13.98251 ** | 0.0000 |

BA | −3.112282 ** | −5.727581 ** | 0.0000 | −2.105332 | −5.762296 ** | 0.0000 |

CR | −3.152003 ** | −5.877677 ** | 0.0000 | −2.504949 | −9.433591 ** | 0.0000 |

MR | −4.613153 ** | −9.848226 ** | 0.0000 | −4.709791 ** | −19.43562 ** | 0.0001 |

TR | −2.460042 | −8.538936 ** | 0.0000 | −2.289743 | −9.596494 ** | 0.0000 |

LR | −1.581782 | −10.27717 ** | 0.0000 | −2.034354 | −10.36160 ** | 0.0000 |

(a) Trace statistic co-integration result | |||

Hypothesized No. of CE(s) | Eigenvalue | Trace Statistic | 5% Critical Value |

None * | 0.641836 | 129.1621 | 94.15 |

At most 1 * | 0.582497 | 87.06481 | 68.52 |

At most 2 * | 0.477041 | 51.25285 | 47.21 |

At most 3 | 0.304575 | 24.67448 | 29.68 |

At most 4 | 0.174228 | 9.781959 | 15.41 |

At most 5 | 0.046053 | 1.933035 | 3.76 |

(b) Maximum eigenvalues co-integration result | |||

Hypothesized No. of CE(s) | Eigenvalue | Max–Eigen Statistic | 5% Critical Value |

None ^{†} | 0.641836 | 42.09731 | 39.37 |

At most 1 ^{†} | 0.582497 | 35.81197 | 33.46 |

At most 2 | 0.477041 | 26.57836 | 27.07 |

At most 3 | 0.304575 | 14.89252 | 20.97 |

At most 4 | 0.174228 | 7.848924 | 14.07 |

At most 5 | 0.046053 | 1.933035 | 3.76 |

^{†}denotes rejection of the hypothesis at the 5% level. Source: Authors’ computation.

Dependent Variable: GR | |||
---|---|---|---|

Excluded | Chi-square | Degree of freedom | Probability |

BA | 2.091518 | 2 | 0.3514 |

CR | 2.467952 | 2 | 0.2911 |

LR | 8.359085 | 2 | 0.0153 |

MR | 3.733386 | 2 | 0.1546 |

TR | 11.78489 | 2 | 0.0028 |

All | 19.19907 | 10 | 0.0378 |

Root | Modulus |
---|---|

0.884875 | 0.884875 |

0.807974 − 0.099972i | 0.814135 |

0.807974 + 0.099972i | 0.814135 |

0.126250 − 0.591362i | 0.604688 |

0.126250 + 0.591362i | 0.604688 |

−0.587398 | 0.587398 |

0.442022 − 0.342579i | 0.559235 |

0.442022 + 0.342579i | 0.559235 |

−0.323339 − 0.315568i | 0.451808 |

−0.323339 + 0.315568i | 0.451808 |

−0.155795 − 0.348988i | 0.382184 |

−0.155795 + 0.348988i | 0.382184 |

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Karimo, T.M.; Ogbonna, O.E.
Financial Deepening and Economic Growth Nexus in Nigeria: Supply-Leading or Demand-Following? *Economies* **2017**, *5*, 4.
https://doi.org/10.3390/economies5010004

**AMA Style**

Karimo TM, Ogbonna OE.
Financial Deepening and Economic Growth Nexus in Nigeria: Supply-Leading or Demand-Following? *Economies*. 2017; 5(1):4.
https://doi.org/10.3390/economies5010004

**Chicago/Turabian Style**

Karimo, Tari Moses, and Oliver Ejike Ogbonna.
2017. "Financial Deepening and Economic Growth Nexus in Nigeria: Supply-Leading or Demand-Following?" *Economies* 5, no. 1: 4.
https://doi.org/10.3390/economies5010004