Economic institutions are considered as the fundamental cause of economic growth. Economic institutions affect economic growth through allocation of resources like physical and human capital. Unfortunately, there is dearth of empirical studies showing the impact of economic institutions on growth of the Economic Community of West African States (ECOWAS).

This study investigates the impact of economic institutions on economic growth of the ECOWAS.

The study applied cause and effect relationship. The study used econometric research techniques of unit root and co-integration tests to establish the time series properties of the data; the vector error correction and co-integration regression models to estimate the population parameters. The research data comprised data obtained from the United Nations Conference on Trade and Development (UNCTAD), the Transparency International (TI) and Heritage Foundation databases. The variables employed were the real gross domestic product (GDP) per capita (RGDPPC), corruption perception index (CPI), property rights protection (PROPRGT), private investment per capita (INVESPC), government expenditure per capita (GOEXPPC) and trade openness (TRAOPN).

The results of the data analysed showed that economic institutions represented by the property rights index engender RGDPPC growth in ECOWAS. The CPI could not stimulate RGDPPC growth in ECOWAS. The results also show that all the other variables stimulated growth except trade openness.

The study concludes that good economic institutions, private investments, and government intervention by providing security, economic and social infrastructural facilities are conducive for economic growth in the ECOWAS region. The study recommended that more efforts be made at curbing corruption in the region.

Economic institutions are regarded as fundamental causes of economic growth (Acemoglu

Several reasons have been advanced for the importance of economic institutions in stimulating economic growth. One of the reasons is that economic institutions determine the incentives given to the main performers in the economy; the outcomes of economic processes are influenced by the economic institutions. Through these incentives, economic institutions influence investment in physical and human resources, research and development (R&D), technology and the organisation of production (Acemoglu, Johnson & Robinson

It has also been argued that economic growth causes good economic institutions. Valeriani and Peluso (

The Economic Community of West African States (ECOWAS) formulated the objective of ‘striving to enhance the well-being of its citizens and to promote growth’, among other goals. The Economic Community is aware of the need to have good economic institutions to realise this objective. To achieve good economic institutions, it encourages the member states to embrace democratic practices, promote rules of law and property rights. To encourage democratic practices in the region, ECOWAS intervened in Côte d’Ivoire in 2011 and Gambia in 2017 after the incumbent presidents, President Laurent Gbagbo and President Yahya Jammeh called elections, lost and decided not to hand over. This study is therefore undertaken to ascertain to what extent has the promotion of these good economic institutions impacted on per capita growth in the ECOWAS states?

To answer the above question, this study is designed to test the alternative hypothesis that economic institutions and some approximate factors stimulating economic growth have promoted economic growth in the region. Existing empirical works on the impact of economic institutions on the economic growth of ECOWAS are sparse and most of them are based on individual countries. For example, the work of Okoh and Ebi (

The remainder of this paper is organised as follows: The ‘Theoretical and empirical literature review’ section reviews both theoretical and empirical literature; The ‘Research methodology’ section explains the research methodology applied in this study, the sources of data employed, the measurement of variables applied, the statistical methods of analysis employed and the model used in the analysis. The ‘Results and analysis’ section presents and interprets the results of the data analysed, and conducts diagnostic tests on the results to establish their reliability. The ‘Summary and concluding remarks’ section summarises and concludes the study.

This section reviews the theoretical literature as well as the existent empirical research relating to the role of economic institutions in promoting economic growth.

Researches, such as North (

Przeworski and Curvale (

Acemoglu et al. (

These models accept that institutions do exist. The models are based on representative agents who are assumed to be well behaved and have property rights and agents exchange goods and services in the markets. However, the models do not acknowledge that differences in income and growth rates are not explained by differences in institutions or variations in institutions. Acemoglu et al. (

The second wave of the more recent growth models, particularly those of Romer (

Romer (

The neoclassical and the endogenous models have become the traditional tools for economic growth explanation (Acemoglu et al.

The arguments in favour of institutions in promoting economic growth are many. Economic institutions matter for economic growth because they influence the incentives for the key performers in the economy (Easterly

A review of the empirical literature is presented in this section. Lehne, Mo and Plekhanov (

Using the appropriate measures of some factors affecting economic institutions, the study found that democracy improved economic institutions and that history had a significant impact on economic institutions. Other findings were that geographical factors such as economic openness and resource abundance had a substantial impact on economic growth. The study further found that resource abundance tends to encourage bad economic institutions.

Okoh and Ebi (

Valeriani and Peluso (

Tamilina and Tamilina (

The study found that economic institutions that are evolutionary, affected economic growth in relation to their quality ratings. Good economic institutions promoted growth. In revolutionary methods, the effect of the quality of economic institutions on growth does not reflect their index in the short run, but in the long run they do.

Davis and Hopkins (

The statistical analytical method employed five yearly sets of data for eight periods starting from 1961/1965 to 1996/2000. It employed a regression model which stated that the per capita income is a function of years of schooling plus the Gini coefficient representing inequality, plus a variable for economic institutions which is property rights.

The results of the study showed that economic institutions promoted growth but that income inequality depressed growth. The study further demonstrated that investment also promoted economic growth.

Zouhair (

The theoretical perspective of the study emphasised the fact that the empirical literature examining the impact of institutions on economic growth is increasing since the seminal work of North (

The estimation of the model applied in the study used the generalised method of moments (GMM) of Arellano and Bond (

Docquier (

Ferrini (

Pereira and Teles (

When economic variables were controlled or moderated, the study demonstrated that political institutions matter for recipient democracies, and not for consolidated democracies. Consolidated democracies have already internalised the effects of the political system on their economic growth. In recipient democracies, there is a need to internalise good political institutions that will promote economic growth to ensure the continued growth of the economy.

This section discusses the research methods applied. It presents the data sources, explains the measurements of variables, the statistical methods applied in the study and the model used in estimating the model.

This study applied data obtained from the United Nations Conference on Trade and Development’s (UNCTAD) database. The data collected spanned the period from 1990 to 2015. The data obtained from UNCTAD comprised data for the real gross domestic product per capita (RGDPPC), government expenditure per capita (GOEXPPC), investment expenditure per capita (INVESPC) and trade openness (TRAOPN) per capita. Data were also obtained from Transparency International (TI)’s Corruption Perception Index (CPI) for the period from 1996 to 2015, and the Heritage Foundation’s Property Rights Index from 1995 to 2015.

The variables used in this study are RGDPPC, GOEXPPC, INVESPC, and TRAOPN. The RGDPPC is measured in thousands of US dollars at 2005 constant prices. The GOEXPPC and INVESPC are also measured in US dollars and at 2005 constant prices. TRAOPN is computed as the value of imports plus the value of exports in a given year and the result is divided by the real GDP of the year in which it is computed.

Economic institutions are measured based on the CPI as published by TI and the property rights index published by the Heritage Foundation. The range of values used is from 1, indicating very high corruption or very low level of property rights protection, to 10, indicating complete absence of corruption or complete property rights protection.

This study applied combinations of both cross-sectional data and time series data, and as a result, it estimates the parameters of the regression using vector auto-regression and co-integrating regression models. The study investigates the impact of economic institutions on economic growth. It is a well-known fact that good economic institutions promote economic growth, which in turn, causes good economic institutions. There is a bi-causality between the two variables. To establish this bi-causality, this study applied the vector error correction (VEC) model. Because the study applied panel data which involves data that may be co-integrated, the employment of this method may yield consistent and efficient estimated parameters (Baltagi

To test for bi-causality between the dependent variable and the regressors that represent economic institutions, this study estimated two more regression models using CPI and PROPRGT as dependent variables. These are stated in

The expected signs of the estimated parameters are:

This study also used fully modified ordinary least square (OLS) (FMOLS) and dynamic OLS (DOLS) co-integrating regression models in estimating the parameters of the study. As there is co-integration among the variables studied, the co-integrating regression models may yield consistent and efficient estimated parameters. The co-integrating regression method of DOLS makes use of the past, present and future values of the regressors. The DOLS regression is estimated using the formula (

The panel co-integrating relationships are designed to study long-run relationships that are featured in macroeconomic settings. Such long-run relationships are predicted by economic theories. Researchers seek to find out whether the predictions of the theories largely hold. Two approaches have been recommended for estimating panel co-integrating regression models. Phillips and Moon (

Before applying the results of regression models in testing the hypothesis of this study, the study carries out diagnostic tests to ascertain that the results are not spurious regression. The diagnostic tests are tests for multi-collinearity and the test for autocorrelation and heteroscedasticity. The tests applied for multi-collinearity are un-centred variance inflation factors (VIF). The joint test applied for autocorrelation and heteroscedasticity is autoregressive conditional heteroscedasticity (ARCH) in the residuals as suggested by Newey and West (

This study applied time series analytical methods. The time series analytical techniques applied are unit root tests, co-integration tests, the VEC regression model and the co-integrating regression models. The study also conducted diagnostic tests to check the reliability of the regression models applied.

This section presents the results of data estimated using unit root tests, co-integration tests and regression models using the VEC and co-integrating regression model. The regression results are subjected to second-order tests to ascertain the degree of reliability that can be placed on them. The second-order tests conducted are mainly of two types – coefficient diagnostic tests using VIF and coefficient variance decomposition and residual-based tests.

Unit root test.

Variable | Number of differencing | LLC |
Breitung |
IPS |
|||
---|---|---|---|---|---|---|---|

Stat. | Prob. | Stat. | Prob. | Stat. | Prob. | ||

CPI | At a level | −1.77 | 0.04 | −1.16 | 0.12 | −1.85 | 0.02 |

1st difference | −8.96 | 0.00 | −6.95 | 0.00 | −7.66 | 0.00 | |

GOEXPPC | At a level | 0.50 | 0.69 | 3.61 | 1.00 | 3.91 | 1.00 |

1st difference | −3.54 | 0.00 | 1.33 | 0.91 | −4.81 | 0.00 | |

INVESPC | At a level | −5.12 | 0.00 | −2.79 | 0.00 | −4.90 | 0.00 |

1st difference | - | - | - | - | - | - | |

PROPRGT | At a level | −0.95 | 0.17 | −1.81 | 0.04 | −0.17 | 0.43 |

1st difference | −8.35 | 0.00 | −7.33 | 0.00 | −6.35 | 0.00 | |

RGDPPC | At a level | 1.30 | 0.10 | 2.45 | 0.99 | 1.73 | 0.95 |

1st difference | −5.13 | 0.00 | −2.17 | 0.02 | −5.67 | 0.00 | |

TRAOPN | At a level | 0.92 | 0.82 | 2.35 | 0.99 | 0.57 | 0.72 |

1st difference | −5.93 | 0.00 | −3.65 | 0.00 | −7.08 | 0.00 | |

1(1) | −7.91 | 0.00 | −2.08 | 0.02 | −10.66 | 0.00 |

Stat., statistic; Prob., probability; CPI, corruption perception index; GOEXPPC, government expenditure per capita; INVESPC, investment per capita; PROPRGT, property rights protection; RGDPPC, real gross domestic product per capita; TRAOPN, trade openness.

Unit root test.

Variable | Number of differencing | ADF-Fisher |
PP-Fisher |
||
---|---|---|---|---|---|

Stat. | Prob. | Stat. | Prob. | ||

GDP^{it} |
At a level | 47.3 | 0.02 | 46.4 | 0.04 |

1st difference | 110 | 0.00 | 240 | 0.00 | |

GOEXPPC | At a level | 17.91 | 1.00 | 20.81 | 0.89 |

1st difference | 82.6 | 0.00 | 383.1 | 0.00 | |

INVESPC | At a level | 31.9 | 0.00 | 89.7 | 0.00 |

1st difference | 178.6 | 0.00 | 378.8 | 0.00 | |

PROPRGT | At a level | 31.9 | 0.43 | 36.9 | 0.18 |

1st difference | 89.8 | 0.00 | 492 | 0.00 | |

RGDPPC | At a level | 17.2 | 0.96 | 17.2 | 0.97 |

1st difference | 89.2 | 0.00 | 148.9 | 0.00 | |

TRAOPN | At a level | 32.3 | 0.35 | 35.2 | 0.24 |

1st difference | 406.2 | 0.00 | 464.8 | 0.00 |

Stat., statistic; Prob., probability; CPI, corruption perception index; GOEXPPC, government expenditure per capita; INVESPC, investment per capita; PROPRGT, property rights protection; RGDPPC, real gross domestic product per capita; TRAOPN, trade openness.

Kao residual co-integration test.

Type of test | Probability | |
---|---|---|

Augmented Dickey–Fuller (ADF) | −6.157 | 0.00 |

Residual variance | 0.758 | - |

HAC variance | 0.783 | - |

HAC, Heteroscedasticity- and -autocorrelation-consistent

From the results of the unit root tests in

Panel data regression results (real gross domestic product is the dependent variable, except Models 2 and 3).

Variable | Equation 5 |
|||||||||
---|---|---|---|---|---|---|---|---|---|---|

Coefficient | Coefficient | Coefficient | Coefficient | Coefficient | ||||||

Constant | −1814 | −0.85 | 0.0002 | 0.80 | 0.0034 | 0.93 | - | - | - | - |

ECM | −0.276 | −4.78 | 0.0086 | 1.02 | 0.0885 | 0.59 | - | - | - | - |

D(RGDPPCit-1) | −0.0096 | −1.40 | −0.0064 | −0.31 | −0.5947 | −1.59 | - | - | - | - |

D(RGDPPCit-2) | −0.0653 | −0.1.00 | −0.0022 | −1.09 | −3444 | −0.96 | - | - | - | - |

CPI | - | - | - | - | - | - | 0.1903 | 1.70 | 0.0144 | 0.55 |

D(CPIit-1) | 0.5582 | 2.58 | −0.2878 | −4.33 | −1.2435 | −1.05 | - | - | - | - |

D(CPIit-1) | 0.4170 | 1.9601 | −0.2797 | −4.26 | −1.2488 | −1.07 | - | - | - | - |

PROPRGT | - | - | - | - | - | - | 0.0184 | 2.57 | 0.0063 | 3.85 |

D(PROPRGTit-1) | −0.0127 | −1.013 | −0.0049 | −1.28 | −0.0012 | −0.02 | - | - | - | - |

PROPRGTit-2 | 0.0280 | 2.30 | −0.0063 | −1.70 | −0.0744 | −1.12 | - | - | - | - |

INVESPC | 0.0044 | 0.65 | 0.0002 | 0.80 | 0.0034 | 0.93 | 0.0040 | 2.95 | 0.0016 | 1.87 |

GOEXPPC | 1.4047 | 7.41 | −0.0488 | −0.84 | −0.1774 | −0.17 | 4.722 | 24.10 | 5.7913 | 52.73 |

TRAOPN | −55 | −2.39 | 0.0110 | 1.51 | −0.0234 | −1.06 | −0.0040 | −1.22 | 0.0029 | 1.33 |

Adjusted R2 | 0.340 | - | 0.125 | 0.104 | - | 0.988 | - | 0.988 | - | |

12.11 | - | 4.10 | 3.53 | - | - | - | - | - | ||

0.00 | - | 0.00 | 0.00 | - | - | - | - | - | ||

LM stat. 1 lag | 38.27 | - | - | - | - | - | - | - | - | - |

LM stat. prob. | 0.00 | - | - | - | - | - | - | - | - | - |

Adjusted Q-stat. prob. | 0.03 | - | - | - | - | - | - | - | - | - |

Chi-square 132 |
182 | - | - | - | - | - | - | - | - | - |

Chi-square prob. | 0.00 | - | - | - | - | - | - | - | - | - |

Highest 2 | - | - | - | - | - | - | 2.41 & 2.22 | - | 2.49 & 2.12 | - |

Un-centred VIF | - | - | - | - | - | - | - | - | - | - |

Lowest 2 condition numbers | - | - | - | - | - | - | 5.38E-5 & 0.00011 | - | 3.7E-5 & 0.0007 | - |

Highest 2 eigenvalues | - | - | - | - | - | - | 0.99 & 0.16 | - | 0.99 & 0.12 | - |

Q-statistic | 22.53 | - | - | - | - | - | 1.26 | - | 3.28 | - |

Q-statistic prob. | 0.03 | - | - | - | - | - | 0.19 | - | 0.07 | - |

ECM, error correction term; D(RGDPPCit), first difference of RGDPPC; D(CPIit), first difference of CPI; R^{2}, adjusted R^{2};

The results of

The results of

However, before subjecting the results of the estimated equations to hypotheses testing, it is important to subject each of the regression results to diagnostic testing to ensure that the results are efficient. The regression models to be tested are

The diagnostic tests for the two co-integrating equations are presented in this section. From the results of coefficient diagnostic tests using VIF and coefficient decomposition, the computed un-centred VIF is less than 3.0 for both FMOLS and DOLS regression models. Thus, both FMOLS and DOLS are unlikely to exhibit multi-collinearity problems. The VIF of less than 3.0 cannot cause collinearity among regressors. The computed condition numbers show that no two variables meet one of the requirements of having multi-collinearity with two conditional numbers below 0.001. However, none of the two variables has associated eigenvalues that are in excess of 0.5. For multi-collinearity to exist, at least two condition numbers must be less than 0.001 and at least two associated eigenvalues must be greater than 0.5. The lowest two computed condition numbers for FMOLS are 5.4E-5 (0.000054) and 0.00011, and for DOLS 1.4E-5 and 0.0007. The highest associated eigenvalues are 0.99 and 0.16 for FMOLS, and 0.99 and 0.12 for DOLS. Thus, both FMOLS and DOLS have no problem of multi-collinearity. Therefore, there is no substantial evidence of linearity among the regressors.

The tests for autocorrelation and heteroscedasticity applied, make use of ARCH in the residuals. This study uses Q-statistics and its probabilities for testing the null hypothesis that states no autocorrelation and heteroscedasticity exist in the residuals of the regression. The computed Q-statistic for the first lagged value of the FMOLS and the DOLS are 1.26 and 3.28 with their respective probabilities of 0.19 and 0.07. Thus, this study cannot reject the null hypothesis of no autocorrelation and heteroscedasticity in the residuals of both the FMOLS and DOLS regression models.

The above diagnostic tests show that the VEC regression model did not only have a serial correlation but also a heteroscedasticity problem. The consequence thereof is that the computed

This study hypothesised in its null hypothesis that corruption exerts no significant impact on RGDPPC. The computed

This study also hypothesised that property rights protection has not promoted RGDPPC in ECOWAS. The computed

The study also tested the hypothesis that GOEXPPC has not stimulated RGDPPC growth in ECOWAS. The computed

It is also hypothesised that private investments have no significant impact on RGDPPC growth in ECOWAS countries. Comparing the computed

The study has also tested the hypothesis that TRAOPN has no significant impact on the real GDP growth rate in ECOWAS. From the computed

In this study, an attempt was made to establish the link between economic institutions and RGDPPC in ECOWAS member countries. To carry out the investigation, it was necessary to establish the theoretical linkages between economic institutions and economic growth. In the theoretical arguments, it was stated that economic institutions are the fundamental cause of economic growth that explains income and productivity differences across countries of the world. It was further argued that economic institutions are by far better determinants of growth than technology, an increase in investment, government provision of services, among others. The reason is that economic institutions serve as the bedrock on which economic growth takes place. Once there are solid economic institutions in a country, other approximate determinants of growth fall in place. The study also reviewed related empirical literature that established the impact of economic institutions on economic growth in several countries, to see how the theoretical postulations applied to real-world situations.

The study explained the method of research applied in the study. The data used for the analysis were obtained from the UNCTAD database, TI and the Heritage Foundation. The independent variables used in the analyses were combinations of both approximate and fundamental causes of growth. The approximate causes of growth were represented by GOEXPPC, INVESPC and TRAOPN. The fundamental causes of growth were represented by the corruption perception index and the property rights index. The dependent variable used is the RGDPPC.

The results of data analysed established that GOEXPPC made the highest contribution to RGDPPC growth in ECOWAS countries, followed by the protection of property rights (PROPRGT). The results also showed that private INVESPC stimulated RGDPPC growth under FMOLS but not under the DOLS regression model.

On the basis of these findings, this study presents a number of policy implications of the study in this section. The study established that government provision of services as measured by the GOEXPPC has made a significant impact on economic growth in ECOWAS. This means that government expenditure is below the optimal spending limit; hence, this is the main condition to be met for government spending to have a significant positive impact on growth. Governments in ECOWAS can increase their spending within the range of their fiscal limit. However, it is important for each of the ECOWAS member countries to compute its optimal government size so that the size is not exceeded.

The study also showed that property rights protection has stimulated RGDPPC growth in ECOWAS. The reason for this may be attributed to the fact that property rights protection encourages people to work hard and own properties. It also encourages investment. There is the need to continue to ensure continuous PROPRGT as this can attract foreign investment into the region and foreign investments can help to diversify ECOWAS economies out of the primary products.

The study established that the corruption index has no significant impact on per capita GDP in ECOWAS countries. This does not mean that the resources ECOWAS countries used in fighting corruption are wasted. The implication of this finding is that if there are no significant efforts in fighting corruption, corruption can affect the quality of publicly provided services and this can retard economic growth. Moreover, if there is no sufficient effort in tackling corruption, corruption can adversely affect the reward system in the society. The consequence of this is that human efforts that are engaged in producing productive goods and services will be diverted into rent seeking.

TRAOPN has not stimulated economic growth of the ECOWAS countries. This finding is probably a reflection of the international arrangement in which developing countries are engaged in the production of primary products with the attendant fall in the terms of trade against them. If ECOWAS countries must overcome this problem, they have to learn how to process their primary products into at least semi-processed goods before exporting them.

This study has also established that private INVESPC stimulated economic growth in ECOWAS based on the FMOLS estimation method but not on the basis of the DOLS regression model. Private investments, particularly from international companies, are also the means of transferring technologies from more advanced countries to less developed countries. ECOWAS countries must not overlook the opportunities private foreign investments present. The ECOWAS countries must not only encourage them but also encourage their citizens to acquire the technique of production with the aim of diffusing them in the ECOWAS region generally. This is very important if private investments must become more useful to ECOWAS countries.

The authors declare that they have no financial or personal relationship(s) which may have inappropriately influenced them in writing this article.

L.Z.W. did most of the writing but under the supervision of P.L.R. who supervised and ensured that the standard of publication was attended.