impact of institutions on macroeconomic performance in

Impact of Institutions on Macroeconomic

Performance in Nigeria: 1980-2013

193 Eastern European Business and Economics Journal Vol.2, No. 3, (2016): 193-221

Martins Iyoboyi Department of Economics and Development Studies,

Federal University Dutsinma, Nigeria P.M.B 5001, Dutsinma, Katsina State, Nigeria

[email protected], [email protected]

Ummu Ahmad Jalingo Department of Economics, Bayero University Kano, Nigeria

P.M.B 3011, Kano State, Nigeria [email protected]

Ahmad Tsauni

Department of Economics, Bayero University Kano, Nigeria P.M.B 3011, Kano State, Nigeria

[email protected] Reviewers: Mohamed Reda ABONAZEL, Institute of Statistical Studies and Research, Egypt; Mohamed BEN MIMOUN, Umm Al-Qura University, Saudi Arabia; Gerasimos SOLDATOS, American University of Athens, Greece; Blanka ŠKRABIĆ PERIĆ, University of Split, Croatia.

Abstract In this paper, the Vector Error Correction Model was used to investigate the impact of institutions on macroeconomic performance in Nigeria for the period 1981-2013. Data were drawn from secondary sources. Three institutional measures were employed in the study, namely contract intensive money, revenue source volatility and quality of service delivery. Accounting for structural breaks in the series, a long-run equilibrium relationship was found between the macroeconomic performance and institutional indicators. Short-run bidirectional causality between institutions and Nigeria’s macroeconomic performance was found. There is evidence of long-run unidirectional causality from revenue source volatility to macroeconomic performance and bidirectional causality between macroeconomic performance and contract intensive money, and between macroeconomic performance and the quality of service delivery. The institutional indicators were found to be endogenous. Property rights institutions should be improved through financial deepening. There is need to diversify the economy and improve the quality of service delivery through adequate provision of electricity.

Keywords: causality, cointegration, impulse response functions, institutions, variance decomposition, vector error correction model JEL classification: O14, O25, E61, E63

194

Iyoboyi M, Jalingo U, Tsauni A. 2016. Impact of Institutions on Macroeconomic Performance in Nigeria: 1980-2013. Eastern European Business and Economics

Journal 2(3): 193-221.

Introduction

Institutions are the humanly devised constraints that structure political, economic and social interaction (North, 1990). The rules which shape opportunities and incentives in an economic system are defined by institutions. Hodgson (2006) defines institutions as durable systems of established as well as embedded social rules that structure and guide social interactions towards achieving societal goals. Ekpo (2013) considers institutions as synonymous with the effectiveness of the state to enforce law and order, considering that a state with weak institutions tends to retard growth and development. Implicit in any definition of of institutions are social factors which to some extent influence the behavior of human beings. Any definition of institutions will include the provision that institutions are all rules or forms of conduct, devised with the aim of reducing uncertainty due to imperfect information and limited rationality, controlling the environment and reducing transaction costs (Menard and Shirley, 2005).

Early insights into the role that institutions can play in economic performance date back to Smith (1776) who postulates that without a certain degree of confidence in the justice of government, little development can take place in commerce and manufacturing. The rule of law is therfore imperative in macroeconomic performance. He also asserts that the differences in growth rates via differences in investment rates were a function of the rule of law and property rights. Thus, the institutional framework has been identified in the literature as an important source of growth, and although its importance had been acknowledged after Smith (Lewis, 1955; Ayres, 1962), it was only recently that it was subjected to more consistent empirical examination (Knack & Keefer, 1995; Mauro, 1995; Hall & Jones, 1999; Rodrik, 1999; Acemoglu et al., 2002; Doucouliagos & Ulubasoglu, 2006; Knowles & Weatherston, 2007; Fao, 2008; Abdel-Latif & Schmitz, 2009; Benyishay & Betancourt, 2010; Cammack & Kelsall, 2010).

In 2014, Nigeria became Africa’s largest economy and the 26th largest in the World. The country is Africa’s highest oil exporter and the world’s tenth largest oil producing country. Nigeria is currently the 8th highest net oil exporter in the world. The country has realized over US$ 600 billion in oil revenues since 1960, a figure greater than the resources used by the Marshall Plan in rebuilding Europe after World War II. Nigeria’s economy is heavily dependent on natural resources: oil and gas constitute

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98% of total exports, 80% of government revenues and around 20% of gross domestic product (Central Bank of Nigeria, 2010).

Nigeria provides a case where the enforcement of efficient legal institutions, protection of property rights and rule of law, long recognized as prerequisites for economic prosperity has been haphazard. Poor institutions force the economy to a low-level equilibrium due to the disincentives created by economic agents, whose activities can be non-productive. This is because the sorry state of the economy can be partly explained by the nature of its mainstay, which to a large extent has tended to affect the functionality of its institutions. Oil, a natural resource with which the country is heavily endowed plays a dominant factor in Nigeria socio-economic setup. The impact that natural resource abundance can play in a country’s development process is well documented. This is particularly serious given that Nigeria is hugely endowed with crude oil, the exploitation of which may be partly responsible for its macroeconomic performance over time, in addition to the quality of its institutions.

One major lacuna in previous empirical studies on Nigeria is the scanty and inadequate assessment of the impact of the institutions on the country’s drive towards macroeconomic performance, as only few studies have examined this. For example, Bakare (2013) examined the linkage between the business environment (investment climate) and the performance of the industrial sector in Nigeria and found a negative relationship, while corruption and political instability were found to seriously constrain industrial capacity. In the same vein, Osabuohien et

al. (2013), on the importance of institutions in the development and sustenance of some important segments (finance, education, technology, industry and trade) on Nigeria advocates the need for the incorporation of Nigeria’s traditional norms and values and the commitment of the state to building formidable institutions for sustainable growth. Ubi et al.

(2012), on the dynamics of institutional failure and its implications on employment capacity for the Nigerian economy, adopted education and electricity supply as proxies for institutions. They conclude that weak institutions are majorly responsible for low employment levels. A major gap found previous studies on Nigeria is the lack of assessment of the dynamic linkages and causality flows between the institutional variables and the macroeconomic performance indicator and if there is reverse causality. This gap is part of the motivation for the present study.

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From the foregoing, the study is motivated by the imperative of investigating the impact of institutions on Nigeria’s macroeconomic performance, using three acknowledged measures of institutions, namely contract intensive money, revenue source volatility and quality of service delivery. Empirical analyses involving the causal relationship between institutions and macroeconomic performance in Nigeria are scanty. The paper is significant as it deals with the important issue of the causality and the various links and channels of influence between the institutional set-up and macroeconomic performance, a phenomenon that is not well understood. Moreover, a detailed and systematic evaluation of the impact of institutions on macroeconomic performance is especially warranted given the need to establish the exogeneity or otherwise of institutional indicators. This paper fills that gap.

Following the introduction, section 2 covers the methodology employed. In section 3, the empirical results are presented and analyzed. The study is concluded in section 4.

Methodology

Data Sources and Measurement of Variables

Annual data for the period 1980 to 2013 were used in the study. The sources of the data are World Development Indicators (World Bank, 2014) and the Statistical Bulletins of the Central Bank of Nigeria (CBN, 2013, 2014).

Three institutional measures were used in the study, i.e. contract intensive money, revenue source volatility and quality of service delivery. These indicators were used in the study as they are most likely to lead to practical implementation because they are generated through a transparent process; the data are available, accurate and specific. In this way, abstract governance definitions were avoided, including complex composite measures. It needs to be pointed out that to ameliorate political and specificity problems, the second generation governance indicators (such as those in the present study) have been subjected to specific criteria that indicators must meet, with a view to ensuring that indicators are (a) politically acceptable; (b) suitable for rigorous quantitative analyses; and, (c) operationally relevant.

A major objective of these indicators is that they can be used by governments to assess the level of performance and progress in many

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key areas of governance. Because this measure is objective and that the data can be computed virtually for any country, it is given preference as an institutional indicator in this study. The utility of such an indicator is not dependent on the nature of the questions and the quality of respondents as it is with the subjective measures. Using it therefore makes it possible to avoid the vagaries of depending on subjective-based rankings and indices.

The macroeconomic performance measure used in the study is real gross domestic product per capita. The choice is justified in that RGDP per capita is embracive of all economic activities, and therefore reflects the degree of aggregate production and thus the performance of an economy over time.

Contract intensive money was measured as:

=2 −

2 (1)

where: CIM - Contract intensive money M2 - broad money supply (M2); COC - currency outside circulation. Revenue source volatility was computed as the standard deviation of

the growth rate of oil revenue as a proportion of total revenue, in line with the trend in the econometric literature. Quality of service delivery was measured as electricity consumption (in kilowatts) per capita. Openness was used as a control variable and measured as total trade (imports plus exports) divided by GDP. To correct for heteroscedasticity, the variables were transformed into natural logs. The estimations were done using Eviews9 and the JMulti4 packages.

Model Specification

The model adopted in the study is the vector error correction model (hereafter VECM). The choice of this econometric technique is informed by two major considerations. First is that the dynamic relationships among the variables used in the study are better reflected in a VECM framework. A major persuasion in the VECM is that each variable is allowed to speak for itself, without having to impose relationship for the variables. This eliminates the problem of misspecification. Consequently, the variables are treated in a system of behavioural

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equations, each one with its own equation, thereby precluding the problem of simultaneous equation bias. It is to be noted that VECM is a variant of Vector Autoregressive (VAR) methods, and used when there is evidence of cointegration among variables. It has been demonstrated that when a set of variables are cointegrated and of the order I(1), the short-run analysis of the system should incorporate error correction term with a view to modelling the adjustment for the deviation from its long-run equilibrium (Engle & Granger, 1987).

The VECM specifications of the variables used in the study are presented in five endogenous variables, i.e. Real Gross Domestic Product Per Capita (hereafter RGDP), Contract Intensive Money (CIM), Revenue Source Volatility (RSV), Quality of Service Delivery (QSD) and Openness (OPN), and presented in equations (2) through (6) as follows:

tit

k

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iit

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it

k

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iit

k

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itt

OPNQSDRSV

CIMRGDPECMRGDP

1

1

115

1

114

1

113

1

112

1

1111111

εδδδ

δδδγ

+∆+∆+∆+

∆+∆++=∆

−

−

=

−

−

=

−

−

=

−

−

=

−

−

=

−

∑∑∑

∑∑ (2)

tit

k

i

iit

k

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iit

k

i

i

it

k

i

iit

k

i

itt

OPNQSDRSV

CIMRGDPECMCIM

2

1

125

1

124

1

123

1

122

1

1211212

εδδδ

δδδγ

+∆+∆+∆+

∆+∆++=∆

−

−

=

−

−

=

−

−

=

−

−

=

−

−

=

−

∑∑∑

∑∑ (3)

tit

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OPNQSDRSV

CIMRGDPECMRSV

3

1

135

1

134

1

133

1

132

1

1311313

εδδδ

δδδγ

+∆+∆+∆+

∆+∆++=∆

−

−

=

−

−

=

−

−

=

−

−

=

−

−

=

−

∑∑∑

∑∑ (4)

tit

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OPNQSDRSV

CIMRGDPECMQSD

4

1

145

1

144

1

143

1

142

1

1411414

εδδδ

δδδγ

+∆+∆+∆+

∆+∆++=∆

−

−

=

−

−

=

−

−

=

−

−

=

−

−

=

−

∑∑∑

∑∑ (5)

tit

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it

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OPNQSDRSV

CIMRGDPECMOPN

5

1

155

1

154

1

153

1

152

1

1511515

εδδδ

δδδγ

+∆+∆+∆+

∆+∆++=∆

−

−

=

−

−

=

−

−

=

−

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=

−

−

=

−

∑∑∑

∑∑ (6)

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In equations 2 through 6, the left-hand side is expressed in first differences (denoted ∆ ), while the right-hand side has optimum lagged differences of the five variables, in addition to the one-period lagged error term (i.e. the ECM in equations 2 through 6) of the cointegrating equation, and thus the speed of adjustment. The intercept terms are denoted by γ1,… γ5 while the disturbance terms are denoted by ε1t,… ε5t. The lag length was based on the sequential modified likelihood ratio (LR), Final prediction error (FPE) and the Akaike information criterion (AIC) test statistic. The major advantage of the LR test is that it facilitates cross-equation restrictions to test shorter versus longer lags. AIC is relatively better than the Schwartz Bayesian Criterion (SBC) when selecting lags for models that the true data generating process is one of long lags (Ozcicek & McMillin, 1999). The AIC and FPE are superior to other criteria in the case of small samples (60 observations and below) for estimating autoregressive lag length, in that they minimize the chance of underestimation while maximizing the chance of recovering the true lag length (Liew, 2004). The result of the lag length selection criteria is presented in Table 1E of the Appendix. All the criteria choose 1 lag except AIC which reports 2 lags. Consequently 1 lag was employed in the estimation of the unrestricted Vector Autoregression (VAR) model, while, a higher lag (2 lags) was used for the Vector Error Correction (VECM) model, to get the residuals as whitened as possible.

Model Estimation Procedure

For time series data, pre-estimation diagnostics involve tests of unit and cointegration. The Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests which do not consider structural breaks were used. Because the ADF and PP tests have been shown to have low power in the presence of structural breaks in that it has low power which may be due to small sample size, and a tendency to "discover" unit roots that are not actually there because of the structural break (Perron, 1997), we considered the Perron and Vogelsang (1992) innovational and outlier models, with the following specifications:

Innovational Outlier Model (IOM):

titi

k

itttt eycyTBDDUy +∆∑++++= −

=−

11)( αθδµ (7a)

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Additive Model (AOM) - 2 steps:

yTBDDUy ttt~)( +++= θδµ

titc

k

itIti

k

it eycyTBDwy +∆∑++∑= −

=−−

=

~~)(~1

11

α (7b) The Innovational Outlier models represent the change occurring

gradually whereas the Additive Outlier model represents the change that occurs rapidly. Because the presence of structural breaks in a series can substantially distort standard inference procedures for cointegration, it is necessary to account for possible breaks in the data before inference on cointegration can be made.

In the presence of structural breaks, we followed the approach developed by Lütkepohl and his associates (Saikkonen & Lütkepohl, 2000; Trenkler, Saikkonen & Lütkepohl, 2008) (hereafter LST). The central assumption of the LST approach is that for the data generation process of a vector-valued yt, its deterministic component does not affect its stochastic part. The deterministic part (with possible breaks) is removed in the first stage, and a Likelihood Ratio (LR) cointegration tests executed in the second stage, using the de-trendedstochastic part of yt. Given yt, at time TBof the following data generating process, the case of a single shift in both the level and the trend is considered:

(8)

where: t is a linear time trend, µι (i = 0,1) and λi (i = 0,1) are unknown

(p×1) parameter vectors, dtand btare dummy variables given by dt= bt= 0 for t< TB, dt= 1 and bt= t −TB+ 1 for tB≥ T. To capture the unobserved stochastic error xt, the following VECM representation is employed:

TtiidNxxx ttiti

k

itt ,...,1),,0( ,111 =Ω≈+∆Γ∑+Ψ=∆ −

−

=− εε (9)

Note that the parameter vectors µ0, µ1, λ0 and λ1 are obtained in

equation (8) using GLS in the first stage, and the detrended series are computed, while in the second stage, the likelihood ratio test of the following specification is applied:

Ttxbdxy ttttttt ,...,1,1010 =++++=+= λλµµµ

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)ˆ1ln(10

∑ +=−−=

p

ri iTLR λ (10)

The use of the LST framework was favoured in the study as it is found

that their tests have better size and power properties than the Johansen et al. (2000) tests in finite samples, in the case of shifts in the level of yt.

Evidence of cointegration led to the investigation of the short-run and long-run causality between macroeconomic performance and institutions through the VECM. It needs to be pointed out however that Granger causality tests are applicable regardless of the orders of integration of the underlying variables, if a long-run equilibrium relationship between the underlying series has been established (Groenewold & Tang, 2007). However, in implementing the Granger causality test in the presence of a long-run relationship, it is essential to include a lagged error correction term within a VECM in order to capture the short-run deviations of the series from their long-run relationship (Narayan & Smyth, 2004).

There are two sources of causation in a VECM framework (i.e. one originating from the ECM term and the other from the lagged dynamic terms). Consequently, three causality tests were carried out, i.e. the short-run Granger non-causality test, the long-run causality, both of which were complemented with an overall causality. The short-run Granger non-causality test was carried out through the strong exogeneity test, while the long-run causality was executed through the weak exogeneity test. All the causality tests were carried out through the Wald test.

In addition to the causality tests, the generalized impulse response function and variance decomposition relating to the variables used are presented and analysed. With the variance decomposition, the relative degree of exogeneity and endogeneity of the variables in the VAR system can be captured, while the use of impulse response functions helps to reflect the overall dynamics of the responses by the variables to the shocks in the system.

Results and Discussion

Descriptive Statistics

The descriptive statistics for the variables employed are presented in Table 1A of the Appendix. The data comprises 34 observations for each variable. The measures of central tendency indicate that the average

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(mean) of RGDP per capita in the period of study was 687 dollars. Contract intensive money averaged 76%, implying that a significant amount of money was in circulation under the period of study. The major financial reforms within the period of study such as the bank consolidation exercise and the insurance reforms might have been responsible for this. While oil revenue as a proportion of total revenue for Nigeria averaged 76% between the period of investigation, quality of service delivery (proxied by electricity consumption per capita) averaged 89.53 kilowatts.

The average degree of openness of the Nigerian economy between 1980 and 2013 was 23%, suggesting that trade (both domestic and foreign) was not relatively high as a proportion of the country’s gross domestic product. While real gross domestic product per capita, contract intensive money, revenue source volatility, and openness have a positive skewness within the period of study, quality of service delivery was negatively skewed. Real gross domestic product per capita is positively correlated with contract intensive money, quality of service delivery and openness, while it has a negative correlation with revenue source volatility. The pair-wise correlations between the measure of macroeconomic performance used in the study (RGDP) and the explanatory variables are not excessively high, indicating the absence of multicollinearity in the estimated model.

Unit Root and Cointegration Tests

The results of the unit root tests are presented in Table 1B and 1C of the Appendix. All the unit root test types indicate that that all the variables are non-stationary in levels, while all tests indicate stationarity in first differences. It is noteworthy that all three tests (ADF, PP and KPSS) of unit roots lead to the same conclusion and are thus consistent. It is therefore concluded that the variables employed are integrated of order 1, i.e. I(1), implying that all the variables have a unit root at levels.

The results of the Perron and Vogelsang (1992) unit root tests in the presence of structural breaks are presented in Table 1C of the Appendix. Both the Innovational Outlier Model and the additive outlier model indicate that the null hypothesis of a unit root is not rejected for all the variables at levels. However, the variables tend to be stationary at first difference. However, there is a difference in the break dates reported, due largely to the difference in the assumptions of the two frameworks.

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While the Innovational Outlier models represent a change that occurs gradually, the Additive Outlier model represents the change that occurs rapidly. It is important to note that the results are from the two models are consistent. On the whole, it is plausible to conclude that the variable each contains a unit root with a break. The test of cointegration and other estimations were implemented taking account of the breaks.

The cointegration test results are presented in Table 1D of the Appendix. The LR test statistics with intercept, and both trend and intercept indicate that the hypothesis of no cointegration among the variables is rejected at the 5% and 10% significance levels respectively. From the results, there is a unique cointegrating vector among the variables of interest based on LR statistics. Thus there is a long-run equilibrium relationship between the institutional, macroeconomic and control variables used in the investigation.

Causality Tests

The Granger non-causality test results for the variables of interest, i.e. macroeconomic performance (RGDP),contract intensive money (CIM), Revenue Source Volatility (RSV), Quality of Service Deliver (QSD), and openness (OPN) are presented in Table 1. The results of causality are categorised into two, namely causality from other variables to macroeconomic performance (Panel A) and causality from macroeconomic performance to other variables (Panel B).

Hypothesis Short-run Causality Weak Exogeneity (long-run causality)

Strong Exogeneity (overall causality)

Ho: ∆ CIM ↛ ∆ RGDP δ12i = 0 11δ = 0 δ12i = 11δ = 0

χ2 21.53612* 4.374572** 22.40694*

Ho: ∆ RSV ↛ ∆ RGDP δ13i = 0 11δ = 0 δ13i = 11δ = 0

χ2 5.513879** 4.374572** 10.20044*

Ho: ∆ QSD ↛ ∆ RGDP δ14i = 0 11δ = 0 δ14i = 11δ = 0

χ2 20.55734* 4.374572** 24.01924*

Ho: ∆ OPN ↛ ∆ RGDP δ15i = 0 11δ = 0 δ15i = 11δ = 0

χ2 19.45431* 4.374572** 24.41517*

Table 1a: Causality Test Results. Panel A. Causality from other variables to macroeconomic performance (RGDP)

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Hypothesis Short-run Causality Weak Exogeneity (long-run causality)

Strong Exogeneity (overall causality)

∆ RGDP ↛ ∆ CIM δ11i = 0 21δ = 0 δ11i = 21δ = 0

χ2 34.95812* 10.34176* 35.81029*

Ho: ∆ RGDP ↛ ∆ RSV δ11i = 0 31δ = 0 δ11i = 31δ = 0

χ2 34.95812* 2.500721 34.96881*

Ho: ∆ RGDP ↛ ∆ QSD δ11i = 0 41δ = 0 δ11i = 41δ = 0

χ2 34.95812* 14.17045* 45.83513*

Ho: ∆ RGDP ↛ ∆ OPN δ11i = 0 51δ = 0 δ11i = 51δ = 0

χ2 34.95812* 1.145243 35.00208*

Note: ↛ denotes “does not Granger-cause”.* and ** represent 1% and 5% level of significance respectively. Source: Authors’ computations. Table 1b: Causality Test Results. Panel B. Causality from macroeconomic performance (RGDP) to other variables

From Panel A of Table 1, the short-run causality test results indicate that contract intensive money, revenue source volatility, quality of service delivery and openness Granger-cause macroeconomic performance (proxied by RGDP) and they all statistically significant. While contract intensive money, revenue source volatility and openness are significant at 1%, quality of service delivery is significant at the 5% level.

From Panel B of Table 1, the short run causality test results indicate that macroeconomic performance proxied by RGDP Granger-causes contract intensive money, revenue source volatility, quality of service delivery and openness and are statistically significant at 1% level. Thus, in the short run, there is evidence of bidirectional causality between institutions and macroeconomic performance in Nigeria.

The weak exogeneity (long-run causality) shows evidence of unidirectional causality from macroeconomic performance (RGDP) to resource source volatility (RSV) and openness (OPN). On the other hand, there is evidence of bidirectional causality between the macroeconomic performance indicator (RGDP) and two of the institutional indicators, i.e. contract intensive money (CIM) and quality of service delivery (QSD). This can be seen in the results combining Panels A and B of Table 1. An examination of the long-run causality results in Panel A indicates that causality flows from contract intensive money to RGDP; from revenue source volatility to RGDP; from quality of service delivery to RGDP; and from openness to RGDP, all of which are statistically significant at the 5% level. There is a feedback relationship between

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macroeconomic performance (RGDP) and contract intensive money (CIM) and between macroeconomic performance (RGDP) and quality of service delivery (QSD) as indicated by the results in Panel B. It is evident from the empirical results that the institutional indicators are endogenous to the macroeconomic performance indicator (RGDP) for Nigeria. It is important to stress that based on the results, macroeconomic performance in Nigeria can be predicted on the basis of the institutional variables used in the study.

The strong exogeneity (i.e. the overall causality in the system) shows that the null hypothesis that all the variables in the system do not Granger-cause RGDP is rejected at 1% level of significance, and substantiates the unidirectional causality flowing from other variables in the system to macroeconomic performance (RGDP).

The impacts of institutions and openness on macroeconomic performance are next presented, using the impulse response functions at the VEC level. It is important to stress that the analysis of this nature is usually conveyed through shocks, both from each variable itself and from the shocks from other variables in the system. Based on the objectives of the study, the analysis is focused on how macroeconomic performance (proxied by RGDP) responds to shocks in other associated variables. To conserve space, the responses of other variables in the system, i.e. contract intensive money, revenue source volatility, quality of service delivery and openness to their own and other shocks are not presented.

Impulse Response Functions

The response of macroeconomic performance to the shocks in other variables in the system, for a 10-period forecast horizon, is presented in Figure 1.

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-.02

.00

.02

.04

.06

1 2 3 4 5 6 7 8 9 10

Panel A: Response of RGDP to RGDP

-.02

.00

.02

.04

.06

1 2 3 4 5 6 7 8 9 10

Panel C: Response of RGDP to RSV

-.02

.00

.02

.04

.06

1 2 3 4 5 6 7 8 9 10

Panel B: Response of RGDP to CIM

-.02

.00

.02

.04

.06

1 2 3 4 5 6 7 8 9 10

Panel D: Response of RGDP to QSD

-.02

.00

.02

.04

.06

1 2 3 4 5 6 7 8 9 10

Panel E: Response of RGDP to OPN

Figure 1: Response of Macroeconomic performance (RGDP) to Generalized One S.D. Innovations. Source: Authors’ computations.

It can be observed in Panel A of Figure 1, that macroeconomic performance (proxied by RGDP) responds to its own and to the shocks of other variables in the system. The response of RGDP to its own shock tends to be procyclical and persistent throughout the horizon, with peaks at the 5th and 7th periods. On the whole, there is a strong and regular response by macroeconomic performance to its own shocks.

Macroeconomic performance responds positively to shocks from contract intensive money (CIM) from the first period and persists over the entire period. From the graph, the response of macroeconomic performance to CIM shocks indicates that the particular institutional setting and framework in the economy is supportive of macroeconomic performance. It can be observed that there is a sharp increase in performance from the initial period or year, getting to a peak in the 2nd period, after which it falls to its lowest point in the 5th period. It is

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remarkable that the shocks tend not to die out, implying that the impact of property rights, proxied by contract intensive money is a significant explanatory factor of Nigeria’s macroeconomic performance (proxied by RGDP).

Macroeconomic performance initially responds positively to revenue source volatility shocks from the first period up to the 2nd. It is to be noted that this initial rise is sharp. Thereafter, there is a general decline in macroeconomic performance as seen from the trend line which is largely within the negative region of the graph. Thus revenue source volatility (in terms the proportion of oil revenue in Nigeria’s total revenue profile) tends to weaken macroeconomic performance. From the 3rd period and up to the end of the forecast horizon, there is a general decline in macroeconomic response. The implication of this response is that revenue volatility is translated to reduced macroeconomic performance through policy inconsistency and unpredictability. This intuition is rooted in the country’s fiscal experience. Government performance in Nigeria is inevitably linked to revenue, which is largely dictated by movements in international oil prices. Budgets are usually benchmarked on oil prices and have had to be revised on several occasions due to changes in oil prices that are exogenously determined. This has meant that policy stability is hinged on accurate forecasts of oil prices from year to year and consequently, budget volatility which is tied to revenue expectations have meant that macroeconomic performance has largely been uneven. Essentially, the economy has had to bear huge consequences originating from huge deviations between forecast revenues and what is generated from a mono-cultural source.

Service delivery shocks tends to reduce macroeconomic performance from the initial period up to the 2nd period, after which it is associated with periodic increases. The shock tends to persist and does not show any sign of abatement throughout the forecast horizon. Overall, there is no sharp tendency for performance to be positively impacted. Overall, macroeconomic performance is positively influenced by shocks from quality of service delivery. In essence, improved service delivery (reflected in electricity consumption) is performance-enhancing. There may be a wide range of reasons for the nature of macroeconomic performance response to quality of service delivery. A rise in electricity supply is associated with higher consumption, which increases investment (both domestic and foreign), leading to a rise in aggregate output, due largely to reduced production cost and consequently on the

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level of prices. Lower prices have a positive impact on consumption as purchasing power is improved. This can lead to higher investment due to higher effective demand in the economy. Thus, higher production, transmission and distribution of electricity can directly improve consumption across the production value chain and directly impact the level of macroeconomic performance.

It can be observed that macroeconomic performance (RGDP) responds positively to a shock in the openness, index, in the beginning, an indication that for the Nigerian economy, openness may be indeed good. This is especially so when tied to the production and sale of the country’s major foreign exchange earner, crude oil. However, the positive response begins to decline successively from the 2nd to the 5th periods, with peaks in the 6th period. The shock persists and not dying out at the end of the forecast period. The periodic rise and decline in performance is not surprising, given the country’s production and operating environment, which is characterized among several other factors, by relatively high interest rates, poor infrastructure, predominance of the production of one export commodity, inflation and deficit financing. Throughout the forecast horizon, shocks from openness have a positive impact on macroeconomic performance, as can be seen from Panel E. Thus macroeconomic performance is in favour of higher degrees of openness, consistent with the prediction of theory.

Variance Decomposition

The variance decomposition results are presented in Panel 1 through 5 of Table 2.

Period S.E. RGDP CIM RSV QSD OPN 1 0.052401 41.47326 4.191319 0.088681 49.65889 4.587847 2 0.066319 39.03509 4.822771 1.126405 47.60951 7.406223 3 0.068356 37.06179 6.349664 1.415086 47.03135 8.142104 4 0.074182 35.27445 6.560081 1.811605 49.28526 7.068602 5 0.080990 37.20717 5.668138 1.520351 49.04075 6.563588 6 0.085029 37.13044 5.142824 1.558711 48.19695 7.971071 7 0.090704 35.61950 5.485156 1.900539 49.00226 7.992548 8 0.095530 35.49632 4.985035 1.867487 50.27283 7.378331 9 0.098110 35.62558 4.726647 1.804900 50.20923 7.633642 10 0.101948 35.42407 4.597206 1.842956 50.14006 7.995708

Panel 1. Variance Decomposition of RGDP

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Period S.E. RGDP CIM RSV EC OPN 1 0.025595 0.000000 100.0000 0.000000 0.000000 0.000000 2 0.034898 0.400707 87.35591 1.023106 6.001651 5.218625 3 0.042008 0.362184 85.11530 1.184222 5.066789 8.271501 4 0.046623 2.446803 85.11466 0.970275 4.245715 7.222544 5 0.051969 2.149769 86.73461 0.788761 3.505447 6.821413 6 0.055552 2.060560 85.85635 0.699875 3.668050 7.715163 7 0.059219 1.828972 86.75947 0.638369 3.422215 7.350973 8 0.063574 1.845879 87.40309 0.579649 3.227096 6.944288 9 0.067136 1.813104 87.36630 0.520371 3.017903 7.282327 10 0.069929 1.727747 87.44276 0.519707 2.788901 7.520889

Panel 2. Variance Decomposition of Contract intensive money (CIM)

Period S.E. RGDP CIM RSV QSD OPN

1 0.118210 0.000000 3.636599 96.36340 0.000000 0.000000 2 0.137286 5.219257 3.190981 72.33254 2.520722 16.73650 3 0.154144 4.268897 3.741814 66.12452 2.051548 23.81322 4 0.169919 6.352607 6.894810 63.12761 2.957187 20.66778 5 0.180953 6.148837 6.119264 61.76148 3.064069 22.90635 6 0.193875 5.439636 8.317206 60.59288 2.818057 22.83222 7 0.199097 5.310989 8.465621 60.56982 2.905415 22.74815 8 0.209166 5.397839 8.501535 61.88607 2.712901 21.50165 9 0.217329 5.654121 8.398189 61.62517 2.803215 21.51930 10 0.225985 5.516803 9.066095 60.61761 3.025420 21.77407

Panel 3. Variance Decomposition of Revenue Source Volatility (RSV)


1 0.077524 0.000000 38.08437 12.55681 22.60978 26.74904 2 0.105943 9.310562 25.64474 7.743429 26.55461 30.74666 3 0.112889 8.435515 22.99681 6.902758 23.69798 37.96694 4 0.132674 6.897097 31.86270 6.768027 23.10446 31.36772 5 0.147941 6.703839 28.85092 6.610042 26.79641 31.03880 6 0.156546 6.116260 27.61022 5.934714 24.68715 35.65165 7 0.163473 5.755136 28.64833 5.898903 23.75980 35.93782 8 0.177051 6.600116 28.30290 5.405591 25.26179 34.42960 9 0.185370 6.586590 27.19963 5.300419 25.55401 35.35935 10 0.192201 6.137690 28.10310 5.162924 24.64443 35.95186

Panel 4. Variance Decomposition of Quality of Service Delivery (QSD)

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1 0.064732 0.000000 2.150005 16.76873 0.000000 81.08126 2 0.068265 0.000244 3.368520 18.68267 0.042352 77.90622 3 0.071273 1.877031 3.913376 21.35693 0.462005 72.39065 4 0.079093 1.533790 3.652981 19.39448 0.385585 75.03317 5 0.085448 1.326649 5.826405 20.71204 0.570733 71.56417 6 0.089260 1.235568 5.523775 22.42667 0.524792 70.28920 7 0.092095 1.262909 5.704795 22.14495 0.538706 70.34864 8 0.097279 1.385035 5.682597 22.59780 0.721397 69.61317 9 0.101927 1.291238 6.137530 22.95046 0.917996 68.70277 10 0.105052 1.252323 6.191179 23.60353 0.865737 68.08723

Panel 5. Variance Decomposition of Openness (OPN)

Table 2. Variance Decomposition (VEC Level). Source: Authors’ computations.

The results of the variance decomposition reported in Panels 1 through 5 of Table 2 show the contribution of each variable in the system to its own shock in explaining the proportion of forecast error variance at the end of 10 years. In terms of each variable’s own shocks, the results indicate 35% for RGDP (a proxy for macroeconomic performance), 87% for contract intensive money (CIM), 61% revenue source volatility (RSV), 25% for quality of service delivery (QSD) and 68% for openness respectively. The implication of the results is that contract intensive money is the most exogenous variable in the VAR system, in that at the end of 10 years’ horizon, macroeconomic performance (RGDP), revenue source volatility (RSV), quality of service delivery (QSD), and the degree of the country’s openness (OPN) account for only 1.73%, 0.52%, 2.79% and 7.52% respectively of the shocks to contract intensive money.

An examination of Panel 1 of Table 2 indicates that quality of service delivery explains 50.14% of the variation in macroeconomic performance (represented by RGDP), as against contract intensive money (4.60%), revenue source volatility (1.84%), and openness (7.80%). The implication of this result is that institutions are helpful in explaining movements in Nigeria’s macroeconomic performance over the period of study. It is important to emphasize that macroeconomic shocks are explained more by the existence of institutions (i.e. contract intensive money, revenue source volatility and quality of service delivery) than the control variable (i.e. openness which is about 7.80%) in the system. From the estimated shock proportions in Panel 1, it can be

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seen that, on the aggregate, contract intensive money, revenue source volatility and quality of service delivery account for about 57% shocks in RGDP. Thus, it is evident that institutions are crucial factors explaining the performance of the Nigerian economy over the period of investigation.

The results of the variance decomposition of contract intensive money reported in Panel 2 indicate that shocks of about 67% are due to itself, while shocks from RGDP, revenue source volatility, quality of service delivery and openness are 1.73%, 0.52%, 2.79% and 7.52% respectively. The implication of the results is that contract intensive money does not seem to be highly responsive to the other variables in the system. This result is important given the institutional variable used to reflect the degree of confidence and trust in the system. From the table, it can be inferred that the state of macroeconomic performance, the political environment (proxied by revenue source volatility), the social environment (represented by the quality of service delivery) and the exposure of the country through trade, do not seem to muster or generate the needed trust and confidence for growth. In other words, poor political and social environment harm the promotion of property and contractual rights and consequently the trust and confidence required for improved macroeconomic performance.

The variance decomposition of revenue source volatility in Panel 3 indicates that about 61% variation is due to its own shock, while RGDP accounts for about 5.52%, contract intensive money (about 9.07%), quality of service delivery (3.03%), and openness (about 21.77%) respectively. The implication of the results is that openness of the economy is a major factor explaining the variation in revenue source volatility in Nigeria. An interesting result is that the combined shocks due to macroeconomic performance (RGDP), contract intensive money and quality of service delivery account for about 18% and less than the shocks from openness, implying that RGDP including the political and social environment do not strengthen the country’s revenue base. In other words, revenue is not diversified through broad-based growth that is inclusive and sustainable, as a high proportion of revenue needed to implement government programmes and thus pursue macroeconomic policies comes from petroleum. It is evident from the result that shocks to Nigeria’s government revenue and consequently government effectiveness in achieving set mandates and objectives are intricately

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linked to external or exogenous factors. That this is so is well documented in the literature of oil price shocks.

In Panel 4 of Table 2, the variance decomposition of quality of service delivery shows that more of the shocks originate from contract intensive money and openness, exemplified by about 28.10% and 36% shocks accounted for by them respectively. This can be contrasted to the shocks from RGDP (6.14%) and revenue source volatility (5.16%) respectively. That quality of service shocks are accounted for mostly by openness shocks is not surprising, given the economic structure in Nigeria over the years. Specifically, the results indicate that an increase in quality of service delivery is related to the openness of the economy, and when underscored on the country’s revenue base, it becomes plausible to conclude that wide variability in revenue base which is largely determined by oil-price shocks can make or mar the provision of quality service in Nigeria. In essence, quality of service in terms of the consumption of electricity reacts to the nature and dimension of economic institutions (proxied by contract intensive money) and the extent of exposure of the country to the global environment operating environment.

The results of the variance decomposition in Panel 5 of Table 2 suggest that the degree of the openness of the Nigerian economy is not very sensitive to two of the institutional indicators, i.e. contract intensive money and quality of service delivery, which account for about 6.19% and 0.87% of the shocks, respectively. The same can be said about shocks from macroeconomic performance variable (RGDP) which is about 1.25%. It follows that greater degree of openness tends not to be induced by the nature of property rights, including the quality of service delivery in the country. It needs to be noted that the nature of openness can help explain the proportion of the variation seen in the decomposition from macroeconomic performance, contract intensive money, and quality of service delivery. Based on the indicator used (total trade as a component of GDP) and given the dominance of crude oil sale in Nigeria’s export trajectory, the results are not out of place. Nigeria’s trade flows are not in favour of higher non-oil exports. Sale of oil has been on the increase in Nigeria in the midst of low development indicators (high corruption, high level of poverty, bureaucratic bottlenecks and the like). The implication is that the mainstay of the economy (oil) continues to be exported unabated, and does not reflect the dynamics in other sectors such as agriculture and manufacturing. In

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a nutshell, variations in the level of openness are not significantly influenced by growth and two of the institutional variables used in the study. It needs to be noted also that 23.6% shocks in openness is accounted for by shocks in revenue source volatility. The implication is that to a large extent, openness of the Nigerian economy depends in part on the nature of its major revenue source, i.e. crude petroleum export which is the mainstay of the economy.

Conclusion

The study was aimed at empirically investigating the impact of institutions on Nigeria’s macroeconomic performance for the period 1980-2013, using annual data generated from the World Bank and the Central Bank of Nigeria. The analysis is based on a multivariate VECM. After accounting for structural breaks, a long-run equilibrium relationship was found between the macroeconomic performance (proxied by real GDP per capita) and institutional indicators (measured by contract intensive money, revenue source volatility and quality of service delivery). The institutional indicators were found to Granger-cause macroeconomic performance and are all statistically significant in the short run. There is short-run bidirectional causality between the institutional measures and Nigeria’s macroeconomic performance.

The long-run causality tests indicate that there is unidirectional causality from revenue source volatility to macroeconomic performance and bidirectional causality between macroeconomic performance and contract intensive money, and between macroeconomic performance and quality of service delivery. Importantly, the institutional indicators were found to be endogenous to the macroeconomic performance indicator for Nigeria in the long run.

From the variance decomposition results, contract intensive money, revenue source volatility and quality of service delivery were found to account for about 57% shocks in RGDP, indicating that institutions are crucial to the performance of the Nigerian economy over the period of investigation. These results are reinforced by the impulse response functions.

Based on the objectives of the study, the following recommendations are important:

1. Economic or property rights institutions should be improved by encouraging contract intensive money. A rise in contract intensive

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money by 1% is associated with an increase in RGDP per capita by 1.5%. One way to achieve this is through financial deepening, which encourages private sector deposit in financial institutions and discourages high volumes of currency outside circulation. Deposit interest rate regimes can be streamlined to promote higher deposits to make loanable funds more readily available. This can promote private investment and thus enhance macroeconomic performance in Nigeria. Another way is to provide greater checks on the activities of financial institutions in order to arrest potential collapse and strengthen public confidence in their ability to serve as custodians of their liquid wealth. This can encourage higher patronage by the private sector, improve contractual rights, improve deposit/savings levels, which can find their way into private investment and consequently improved performance of the economy via growth.

2. The economy should be diversified away from crude petroleum, to reduce Nigeria’s dependence on oil revenue, in order to curtail the negative impact of its volatility on the country’s macroeconomic performance. From the estimated results, a rise in revenue source volatility by 1% leads to 0.33% fall in RGDP per capita. Stated differently, the diversification of the economy away from a major revenue source can save the country from a fall in real income per capita. In essence, a reduction in revenue source volatility by 1% can improve macroeconomic performance by 0.33%. Diversification of the economy should take account of agriculture and non-oil mining which are capable of generating huge employment and spreading the gains of improved performance. The services sector should not be left out, as can be seen in the rebased GDP of Nigeria in 2014 which showed the rising trajectory of the services sector as an engine of growth and with specific reference to erstwhile neglected telecommunications and entertainment sub-sectors. The degree of diversified production base has been found to be a direct cause of the degree of a country’s macroeconomic volatility (Lahiri and Végh, 2001; Chowdhury et al., 2013). Non-volatile economic policy environment has been found to be germane to a sustainable structural diversification and improved macroeconomic performance through industrial growth (Michael & Babasanmi, 2004; García-Belenguer & Santos, 2013). Thus, there is a strong link between diversifying the Nigerian economy, promoting policy stability and improving the quality of service delivery. It is germane to note that diversification of the economy away from oil can thus strengthen

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government’s fiscal policy operations, enhance long-term planning, and consequently government effectiveness as a political institution of the state.

3. The quality of service delivery should be enhanced through adequate provision of electricity with a view to enhancing consumption. The Nigerian electricity sector is one of market failure in which there is huge demand but inadequate supply. Thus, a higher supply of electricity will enhance its consumption. The estimated results indicate that a 1% increase in electricity consumption per capita increases RGDP per capita by 0.74%. Increasing the production, transmission and distribution of electricity is capable of reducing production costs and promoting self-employment, leading to improved performance of the Nigerian economy. In this way, the quality and capacity of the country’s social institutions can be improved.

Developing institutions in Nigeria is central to the promotion and sustenance of higher macroeconomic performance. Improving contract intensive money (i.e. a measure of property rights laws) are critical to economic incentives. Second, it is imperative for the diversification of the economy in terms of promoting an inclusive growth and mitigating the negative impact of revenue volatility as found in the empirical study. Third, improvement in macroeconomic performance is ensured in an environment of efficient and effective service delivery. It is evident that the goal of achieving higher performance may not be realised if institutions of the desired capacity and quality are not accorded priority by government at all levels.

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Appendix: desriptive statistics and pre-estimation test results

RGDP CIM RSV QSD OPENNESS

Mean 687.0374 76.10628 75.92546 89.53584 22.55913 Median 597.9042 74.8819 75.14718 89.07658 22.41635 Std. Dev. 177.2388 8.544214 6.57388 32.02844 4.245555 Skewness 0.851787 0.355661 0.117175 -0.86777 0.493473 Kurtosis 2.230985 2.259845 2.083107 4.772022 2.254842 Jarque-Bera 4.949197 1.492896 1.268785 8.71551 2.166538 Sum 23359.27 2587.613 2581.466 3044.219 767.0105 Sum Sq. Dev. 1036649 2409.119 1426.125 33852.1 594.8164 Observations 34 34 34 34 34

Correlation matrix

RGDP 1.00000 0.814859 -0.13355 0.65947 0.688808 CIM 0.814859 1.00000 -0.04168 0.653665 0.54284 RSV -0.13355 -0.04168 1.00000 0.050187 -0.01558 QSD 0.65947 0.653665 0.050187 1.00000 0.723657 OPN 0.688808 0.54284 -0.01558 0.723657 1.00000

Table 1A: Descriptive Statistics, 1980-2013. Source: Authors’ computations.

Variable ADF PP KPSS RGDP -0.045845 -0.275185 0.931038*

CIM -0.421668 -0.288998 0.813425*

RSV -2.607414 -3.765530* 0.362390***

QSD -1.648679 -1.232140 1.240658*

OPN -0.239198 -0.096112 0.876432*

∆RGDP -3.247457** -4.678100* 0.749761

∆CIM -2.770514*** -4.817747* 0.305684

∆RSV -4.716550* -8.131648 0.054794

∆QSD -6.658638* -8.750199* 0.055955

∆OPN -5.217226* -6.193378* 0.197267

Panel 1: Unit Root Test Results (with intercept)

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Variable ADF PP KPSS RGDP -1.774359 -2.508075 0.403863*

CIM -1.149277 -1.073898 0.378392*

RSV -2.490337 -4.154297** 0.138060***

QSD -2.421075 -3.134455* 0.189363**

OPN -3.169320 -4.542072* 0.146823**

∆RGDP -4.005237** -5.316256* 0.084324

∆CIM -4.985357* -4.980353* 0.108283

∆RSV -4.787473* -8.086022 0.039345

∆QSD -6.584140* -8.559337* 0.045808

∆OPN -4.281950** -6.583061 0.056978

Panel 2: Unit Root Test Results (with intercept and a linear trend)

Note: *,** and *** denote rejection of the null hypothesis at 1%, 5% and 10% level of significance respectively. Source: Authors’ computations. Table 1B: Unit Root Tests Results (without breaks)

Innovational Outlier Model Additive Outlier Model

Variable t-statistics Break date t-statistics Break date

RGDP -4.815690 2003 -3.883699 2002 CIM -4.719203 1989 -3.937498 1991 RSV -3.543087 2008 -3.423125 2000 QSD -4.879379 1996 -4.751780 1996 OPN -3.867212 1986 -4.530003 2000 ∆RGDP -10.81517* 2003 -7.057296* 2004 ∆CIM -5.390156** 1994 -8.018925* 1994 ∆RSV -5.209896** 2006 -4.986110*** 2009 ∆ QSD -9.825767* 2002 -9.679804* 2001 ∆OPN -6.450535** 2009 -7.195870* 1988

Note: * and ** denote significant at the 1and 5 percent level. The null Hypothesis is that the series has a unit root. The break specification is trend and intercept. Source: Authors’ computations. Table 1C: Perron and Volgesang Unit Root Test Results (with breaks)

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Intercept included Trend and intercept

included

Null Alternative LR 0.90 0.95 LR 0.90 0.95

r = 0

r ≥ 1 63.22** 62.45 66.13 56.76*** 56.28 59.95

r ≤ 1

r ≥2 37.58 42.25 45.32 28.38 37.04 40.07

r ≤ 2

r ≥3 15.07 26.07 28.52 10.49 21.76 24.16

r ≤ 3

r ≥4 5.48 13.88 15.76 6.00 10.47 12.26

Note: ** and *** indicate statistical significance at the 5 and 10 per cent levels respectively. Source: Authors’ computations. Table 1D: LST Cointegration Test Results

Lag LogL LR FPE AIC SC HQ

0 165.2413 NA 4.05e-10 -7.515080 -5.453889 -6.831854

1 298.8310 150.2884* 5.80e-13* -14.30194 -11.09564* -13.23914*

2 326.0307 22.09974 9.04e-13 -14.43942* -10.08801 -12.99705

* indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion Source: Authors’ computations. Table 1E: VAR Lag Order Selection Criteria.

impact of institutions on macroeconomic performance in

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