Public Expenditure, Corruption and Economic

Growth: Evidence from Nigeria using ARDL

Modelling Approach

MAJEKODUNMI, OwolabiEconomics Department, Faculty of Social and Management

Sciences, Olabisi Onabanjo University, Ago-Iwoye, Ogunstate

Abstract

In this paper, the growth differential effect of public

investment, public consumption and corruption control was

analysed between 1970 and 2011 using the autoregressive

distributed lag (ARDL) framework for cointegration, long-run,

and shot-run models. The bounds testing analysis indicated

existence of cointegration between considered set of variables

in the ARDL model. The long-run and short-run estimates

indicated that public investment was economic growth enhancing

but reversal evidence with interaction with corruption was

reported. This indicates that corruption is a menace in

Nigeria and hindering the effectiveness of public investment

in Nigeria. Also, further evidence indicated that equilibrium

is fully restored within 2years for any distortion in the

short-run. Emanating from the above, proper policy

recommendations were proffered.

1

Keywords: Public Expenditure, Corruption, Bureaucracy,Economic Growth, ARDL Modelling, Nigeria

JEL Code: C22, E02, F43, H50

I. INTRODUCTION

Corruption has become the lead topic of debate among all

major international development agencies. The World Bank, for

example, has identified corruption as the single greatest

obstacle to economic and social development, and has given

priority to anti-corruption initiatives in its strategies for

improving the quality of governance, Bardhan (1997), Jain

(2001), Rose-Ackerman (1999) and Tanzi (1998) of most concern

is corruption within society’s state institutions. Public

officials, politicians, bureaucrats and legislators, hold

unique positions of power and responsibility, the abuse of

which can cause significant and long-lasting damage to many

aspects of socioeconomic development. Such abuse can manifest

in a variety of ways, including bribery, embezzlement,

extortion and fraud that may offer substantial personal gains

at little risk of prosecution. Dishonest behavior at one level

in public office is often contagious and often supported by

dishonest behavior at other levels (Blackburn, Bose and Haque,

2011). Based on the above reasons public sector corruption is

perceived to be especially harmful, especially pervasive and

especially difficult to fight and to eliminate or reduced to

the bearest minimum in developing economy and Nigeria as well.

2

There are different ways corruption can reduce economic

growth. Corruption can act as a tax and can lower incentive to

invest. Corruption can increase the ability of agents to get

resources from central and local governments. Therefore,

public resources reward the more able people, not the best

entrepreneurs. Corruption can distort the composition of

government expenditure as corrupt politicians may be expected

to invest in large, non-productive projects from which it is

easier than in productive activities to exact large bribes

(Mauro, 1998b.)

Recent years have seen a large number of papers on the

causes and consequences of corruption. Most of these papers

are theoretical or qualitative analyses. The first

comprehensive econometric research to assess the impact of

corruption on economic growth is by Mauro (1995). On the basis

of cross-country data, Paolo Mauro finds a significant

negative relation between a corruption index, built using

information assembled from the correspondents of Business

International in 70 countries in the early 1980s, and the rate

of growth. According to the findings of Mauro, policies to

fight corruption could be very beneficial to growth. A country

that improves its standing on the corruption index, say, 6 to

8, 0 being the most corrupt, 10 the least, will experience a 4

percentage point increase in its investment rate and a 0.5

percentage point increase in its annual GDP growth rate.

(Mauro, 1998a).

3

Most of the existing literature on the long-run economic

consequences of corruption (Shleifer and Vishny, 1993;

Ehrlich, 2000). Focuses on rent seeking in the provision of

public services. A government official controls the offer of a

service against private demand. He or she has some

discretionary power on the offer and can restrict it in

several ways e.g. denying permission or delaying its release.

Bribes are the extra-price charged by bureaucrats to private

customers, and arise like rents. The economic consequences of

this phenomenon concern distortions in resources allocation

mainly in terms of less private investment, and a reduced rate

of human capital formation. In Ehrlich (2000), corruption is

an economic activity that requires some political capital.

Effort devoted to the accumulation of this kind of knowledge

has an alternative use in human capital production. Corruption

reduces economic growth through a negative influence on

investments in human capital.

Nigeria is one of the most populated countries in the

World and endowed with vast human and natural resources.

Though, the country lacks adequate capital resources to foster

growth and depend mainly on natural resources (such as crude

oil) revenue that persistently fluctuates with crude oil price

volatility. The resource wealth and high external fiscal

financing have resulted to high government spending and

persistent fiscal spending with little or no significant

trickle-down effect on productivity and human welfare. Over

the years from 1999 till date Nigerian governments (federal,

state and local ) had spend and are still spending huge amount4

of money on infrastructural developments like building of

roads, hospitals, schools, generation of electricity etc.

despite all these huge amount pumped into these sectors our

roads are still death traps, our hospitals are still not in

good shape, schools are in a dilapidated states, our energy

sector is still providing epileptic power supply, youth

unemployment is at increase day in day out. The security level

in the country is still very low. huge resources will continue

to be used and wasted in addressing all these problems and no

significant change will be witness if a critical and swift

action/ measure is not taken to tackle the menace of

corruption in our society especially in the government and

the political circles.

It hampers budget equilibrium, diminishes expenditure

efficiency and distorts its allocation between different

budgetary functions. The state budget equilibrium is

undermined in a corrupt political context. Corruption reduces

state revenue (Tanzi, 1998). The impact of corruption on the

amount of public spending is controversial. Mauro (1997)

emphasized that corruption had no significant impact on the

level of public spending. If corruption increases the official

level of expenditure, it reduces the part of it which is

really laid out on the community since the other part is

captured by corrupt agents. A study carried out in Ugandan

primary schools shows notably that only 30 % of the expense

per pupil had actually reached schools between 1991 and 1995

(Ablo and Reinikka, 1998). Corruption raises the cost of

expenditure and reduces the quantity of output provided by the5

state (Shleifer and Vishny, 1993). But does corruption only

reduce the portion of allocated expenditure in total

expenditure or does it also have a net impact on allocated

expenditure level as a share of GDP? The same level of

spending and for a given budgetary function, public spending

is less efficient in countries with high levels of corruption:

corrupt public agents tend to favour investment projects which

generate highest bribes and not necessarily the most efficient

(Shleifer and Vishny, 1993).

Corruption diminishes the impact of public spending on

social outcomes and alters the quality of public services.

Reducing corruption would enable to improve human development

through the reduction of infant mortality and the improvement

of primary school rates (Gupta, Davoodi and Tiongson, 2000).

Corruption, on the other hand, is a multi-dimensional

variable and can be perceived and approached from various

angles. However, for the purpose of this paper, the subject

matter of corruption shall be streamlined and defined to be

illegal profiteering(mismanagement of public funds) by a

government official using his or her position as a

representative of the government, corruption, Barreto(2000)

asserts, can therefore take place in any economic transaction

involving the public sector.

In Nigeria corruption is viewed, as a means of survival

in the face of combination of economic decline, undemocratic

policies, rampant right violations, clamp down, massive

unemployment and wide poverty (Ahmed, 2006). Evidence from

corruption perception index (CPI) by transparency6

international for different years also highlighted the poorest

countries as the most corrupt countries. Oluwatayo I. B.

(2006) and Ikpeze et al, (2005) discovered politicization to

be another main constraint placed on the performances of

public spending. According to them individuals in appointive

positions in government or bureaucracy see themselves as a

representative of their group with the mission to get them

their fair share of the ‘national, state or local cake’ by

whatever means.

According to International Transparency report (2012),

Nigeria is ranked 35th (139th out of 176 countries) the most

corrupt nation in the world. As Nigeria continue to be ranked

high as one of the most corrupt nations in the world, fiscal

spending accelerates with its attendant no return on economic

growth, has yielded empirical question; what is the nexus

among public expenditure, corruption and economic growth in

Nigeria? The foregoing empirical question forms the focus of

this study in examining the effect of corruption and

bureaucracy on public expenditure and on economic growth; and

as well examines the interactive effect of corruption and

public expenditure on economic growth in Nigeria between 1970

and 2011.

This article is made up of five sections. In section 2,

literature reviews. Section 3 describes the data and

econometric method. In section 4, we present empirical

results. Then, section 5 conclusion and policy recommendation

7

in combating corruption in developing countries Nigeria

inclusive.

II LITERATURE REVIEWS

There are a lot of empirical write-ups and journals

addressing the issue of corruption, public spending and

economic development. Below are reviews of such journals.

Fiorino and Galli, (2012), in their study corruption and

growth: evidence from the Italian religions, estimated a

dynamic growth model for the period of 1980-2004 addressing

both the potential bias of the measures of corruption and the

endogeneity between corruption and economic development, finds

strong evidence of a negative correlation between corruption

and growth. Moreover, since government intervention has been

traditionally used to reduce income differentials between the

northern and the southern regions, they also analyse the

interaction between corruption and government expenditure.

Their results indicate that corruption undermines the positive

impact that public expenditures have on economic growth.

Blackburn and Haque, (2011) presented a dynamic general

equilibrium analysis of public sector corruption and economic

growth. Corruption arises because of an opportunity for

bureaucrats to appropriate public funds by misinforming the

government about the cost and quality of public goods

provision. They establish the existence of multiple

development regimes, together with the possibility of

multiple, frequency-dependent equilibria. The predictions of

our analysis accord strongly with recent empirical evidence.

8

Blackburn and Forgues-Puccio (2007) who also show how

corruption can foster inequality by compromising the

effectiveness of redistributive policy, and in Blackburn and

Sarmah (2008) who show how corruption can influence

demographic outcomes (life expectancy in particular) through

its impact on the provision of public health expenditures. The

present paper goes further than these, providing an account of

the corruption–development feedback nexus for the purpose of

explaining why the incidence of corruption is not only higher

in poor countries than in rich countries, but also more

variable among middle-income countries.

Rose-Ackerman (1999) tells of the millions of dollars of

non-existent stationary that was ‘purchased’ by the Government

Press Fund in Malawi, and describes how telephone

specifications in another African country contained the

useless requirement that the equipment must be robust to

freezing temperatures (a requirement that could be satisfied

by only one telephone manufacturer from Scandinavia). These,

and countless other, examples bear testimony to the problems

that face many developing countries. The scale of the offences

involved and the ingenuity of those behind them are often

quite astonishing, and there is little doubt that the

misappropriation of public resources has contributed

significantly to the plight of deprived of nations

Tanzi and Davoodi (1997) find that corruption leads to a

diversion of public funds to where bribes are easiest to

collect, implying a bias in the composition of public spending

towards low-productivity projects at the expense of value9

enhancing investments. The same authors conclude that, as a

result of corruption, the amount of public investment tends to

rise, while the quality of this investment tends to fall

Abbott (1988) reports the instance in Haiti when a prominent

member of the Duvalier regime had 150 km of rail track pulled

up and sold for scrap metal, pocketing the proceeds for

himself. Hardin (1993) recounts the case of the Turkwell Gorge

Dam project in Kenya, the final cost of which was more than

double the amount of initial estimates due to the recoupment

of bribe payments by the French contractor.

III. Theoretical Framework and Model Specification

3.1 Theoretical Framework

Models of endogenous growth have pursued theoretical

framework where persistent economic growth is conditioned on

public investment spending such as human capital accumulation,

as in Lucas (1988), Romer (1990), and Romer (1994). The

proponents opined that growth rate of output is endogenously

determined within the economic environment. The implication of

these models is that public capital investment is the driving

force in the growth process of an economy. The theoretical

consideration which this study is anchored on stems from the

generalization of the capital accumulation of growth and the

accessible channels of capital investment in developing

countries in which associated consensus is still controversial

in literature.

The proponents of endogenous growth model indicate that a

society with higher incentives for capital investments would

10

generate higher growth; it is not clear how the social

incentives for capital investment should be structured across

the real sectors to foster growth. This is an important issue

since different structures will lead to different compositions

of government capital investments (e.g. economic, social,

security and community components) which may or may not have

differential impact on the productivity growth.

From the above, we consider an economy where final output

is dependent two distinct factors of production, physical

capital and labour. For a Cobb-Douglass production function

with constant return to scale technology:

Yt=AtKtβLt

1−β, (0<β<1 ) (3.1)

Where Yt , Kt , and Lt denote gross domestic product, physical

capital stock, and total labour force at time t. Time-variant

technological level ( At ) is influenced by factors contributing

to the enhancement of efficiency and knowledge environment.

In this paper, we argued that the government has growth

or productivity incentives on investment and consumption

spending. Our proposed argument indicates that physical

capital is conceptualized as public investment and consumption

spending driving economic growth over time. This also supports

earlier argument of Josephat et. al. (2000), Nitoy et. al.

(2003), Adesoye et. al. (2010) and Atanda et al. (2012). This

indicates that:

Kt=f (PIt,PCt) (3.2)11

Also, following the conventional growth models, the growth of

labour force (L) is proportion to population level (POP) i.e.

Lt=POPt (3.3)

Similarly, technological progress (A) is taken to grow at a

constant rate over-time and the baseline theoretical model is

augmented with an institutional factor, corruption control

(COR). Then, we have:

Yt=AtKtβLt

1−βCORtη (3.4)

Yt=AtPItβ1PCt

β2Lt1−βCOR

tη (3.5)

Yt=AtPItβ1PCt

β2POPt1−βCOR

tη (3.6)

3.2 Model Specification and Estimation Procedure

Therefore, taking log of both sides, gives:

lnYt=θ0+β1lnPIt+β1lnPCt+(1−β )lnPOPt+ηCORt+u (3.7)

Then, the equ. (3.7) represents the main theoretical baseline

model for this study that expresses the relationship among

public spending, corruption and economic growth in Nigeria.

In line with the objective of this study, the long-run and

short-run effect of public spending and corruption on economic

growth is examined using the autoregressive distributed lag

(ARDL) framework Pesaran & Shin (1995, 1999), Pesaran et. al

(1996), Pesaran (1997), and Pesaran (2001). This framework is

distinct from traditional approach to determine the long-run

and short-run relationships among variables using the standard12

Johansen cointegration (Johansen, 1988; Johansen and Juselius,

1990) and vector error correction (VEC) procedures. In spite

of its innovative properties and popularity, the Johansen

procedure has been under scrutiny in terms of sample size

(Jeon, 2006).

There are advantages of using ARDL framework instead of the

conventional Johansen procedures as noted by Duasa (2007). The

conventional cointegration method estimates the long run

relationships within a context of a system of equations, the

ARDL method employs only a single reduced form equation

(Pesaran & Shin, 1995). The ARDL method yields consistent and

robust results both for the long-run and short-run

relationship among economic growth, public spending and

corruption. The ARDL approach does not involve pre-testing

variables, which means that the test for the existence of

relationship between variables in levels is applicable

irrespective of whether the underlying regressors are purely

I(0), purely I(1) or mixture of both. This feature alone,

given the characteristics of th cyclical components of the

data, makes the standard of cointegration technique unsuitable

and even the existing unit root tests to identify the order of

integration are still highly questionable. But, Adom (2011) as

quoted in Adom, Bekoe and Akoena (2012) indicated that there

is the need for series within an ARDL framework to satisfy two

conditions necessitate that we test for the presence of unit

root in series.

13

Duasa (2007) earlier established that with the ARDL, it is

possible that different variables have different optimal lags,

which is impossible with the standard cointegration test. Most

importantly, the model could be used with limited sample data

(30 observations to 80 observations) in which the set of

critical values were developed originally by Narayan (2004).

From equation (3.7), we basically adopt the ARDL approach to

cointegration following Pesaran et. al (2001) involves estimating

the Unrestricted Error Correction (UEC) version of the ARDL

model to describe the existence of an equilibrium in terms of

long-run and short-run dynamics without losing long-run

information. The test involves estimating the following

equation:

Δln(RYt)=c+∑i=1

aαiΔln(RYt−i)+∑

i=0

bϕiΔln(PIt−i)+∑

i=0

cλiΔln (PCt−i)+∑

i=0

dνiΔln(POPt−i )+

∑i=0

eρiΔ (CORt−i )+η1ln (RYt−1 )+η2ln (PIt−1)+η3ln(PCt−1)+η4ln (POPt−1)+η5CORt−1+εt

(3.

8)

Where Δ is first-difference operator and a,b,c,d and e are the

optimal lag lengths for each incorporated series. Note that

there is no reason that the lag-length terms are equivalent to

each other. The first part of the equation with αi,ϕi,λi,νi, and ρi

represents the short-run dynamics of the model whereas the

parameters η1−5 represents the long-run relationship.

14

The equation (3.8) is estimated using classical ordinary least

square (OLS) method. To test the existence of a long-run level

relationship the F test is used. When long-run relationship

exists, F test indicates which variable should be normalized.

The null hypothesis for no cointegration (i.e. no long-run

relationship) among variables in equation (3.8) is:

H0: η1=η2=η3=η4=η5=0

H1: η1≠η2≠η3≠η4≠η5≠0

We proceed by conducting a bounds test for the null

hypothesis. The calculated (Wald) F-statistic is compared with

the critical value tabulated by Pesaran (1997), Pesaran et al.

(2001) and Narayan (2004). If the test statistics exceeds the

upper critical value, the null hypothesis of a no long-run

relationship can be rejected regardless of whether the under

lying order of integration of the variables is 0 or 1 or a

mixture of both. Similarly, if the test statistic falls below

a lower critical value, the null hypothesis is not rejected.

However, if the test statistic falls between these two bounds,

the result is inconclusive. When the order of integration of

the variables is known and all the variables are I(1), the

decision is made based on the upper bound. Similarly, if all

the variables are I(0), then the decision is made based on the

lower bound.

In the second step, if there is evidence of long-run

relationship (cointegration) among the policy variables, the

following long-run model (3.9) is estimated:15

ln(RYt)=c+∑i=1

kϖiln(RYt−i)+∑

i=0

lϑiln (PIt−i )+∑

i=0

mχiln (PCt−i)+

∑i=0

pμiln (POPt−i)+∑

i=0

qοiCORt−1+εt

( (3.9)

The ARDL method estimates (p+1)k number of regressions in order

to obtain the optimal lag length for each variable, where p is

the maximum number of lags to be used and k is the number of

variables in the equation. The orders of the lags ( k,l,m,p,q ) in

the ARDL model (3.9) are selected by the Akaike Information

criterion (AIC), the Schwarz Information criterion (SIC), and

Hannan-Quinn Information criterion (HIC) before the selected

model is estimated by OLS. For annual data, Pesaran & Shin

(1999) recommended choosing a maximum of 2 lags. From this,

the lag length that minimizes the criteria are selected.

The ARDL specification of the short-run dynamics can be

derived by constructing an error correction model (ECM) of the

following form:

Δln (RYt)=c+∑i=1

aμiΔln(RYt−i)+∑

i=0

bνiΔln(PIt−i)+∑

i=0

cοiΔln (PCt−i)

+∑i=0

dπiΔln(POPt−i )+∑

i=0

eθiΔCORt−i+ψECMt−1+ωt

(3.10)

Where ECMt−1 is the error correction term, defined as:

16

ECMt−1=ln(RYt)−¿(c+∑i=1

kϖiln (RYt−i)+∑

i=0

lϑiln(PIt−i)+∑

i=0

mχiln (PCt−i )¿)¿

¿

¿¿

(3.11)

All coefficients of short-run equation are coefficients

relating to the short-run dynamics of the model’s convergence

to equilibrium, and ψ represents the speed of adjustment. The

ECM indicates the speed of adjustment back to long-run

equilibrium after a short-run disturbance.

However, public investment spending are expected to be growth

enhancing, while public consumption spending can either be

positive or negative related economic growth depending on

inherent institutional framework in Nigeria. Also, corruption

control as an institutional quality indicator is expected to

further enhance public spending effectiveness to enhance

economic growth in Nigeria. Likewise, we expect the speed of

growth adjustment to long-run path to be fast, which further

inform policy simulations from the findings.

3.3 Pre and Post Estimation Diagnostic Tests

3.3.1 Pre Estimation Diagnostic Test

The time series properties of the variables incorporated in

the ARDL model (3.7) is examined using the Augmented Dickey-

Fuller unit root test in order to determine the long-run

convergence of each series to its true mean. The test involves

17

the estimation of equations with drift and trends as proposed

Dickey and Fuller (1988). The test equations are expressed as:

ΔZt=η0+η1Zt−1+∑i=1

nπiΔZt−i+νt (3.12)

ΔZt=η0+η1Zt−1+η1t+∑i=1

nπiΔZt−i+νt (3.13)

H0: η1=0

H1: η1<0

The time series variable is represented by Z, t and νt as time

and residual respectively. The equ. (3.12) and (3.13) are the

test models with intercept only, and linear trend

respectively.

3.3.2 Post Estimation Diagnostic Test

The specified long-run and short-run ARDL models (3.9 and

3.10) are estimated through the use of Classical Least Square

Estimator and other time series diagnostic tests are employed

such as Ramsey RESET test for the entire structural stability

of the model in line with underlining classical assumptions;

residual diagnostic tests like Histogram normality test,

Breusch Godfrey serial correlation LM test, Breusch-Pagan-

Godfrey (BPG) and ARCH Heteroskedasticity tests.

3.4 Data Requirements and Sources

The time series data required for this paper are real gross

domestic product, government recurrent expenditure, and

18

capital expenditure. These data were sourced from the Central

Bank of Nigeria (CBN) Statistical Bulletin, December 2012 and

control of corruption was sourced from the Worldwide

Governance Indicator (WGI) July, 2013. This study employed

annual data that spans from 1970 to 2011.

IV. EMPIRICAL FINDINGS

The empirical results and discussion on the growth

differential effect of public investment and consumption

spending and corruption control index in Nigeria between a

decade after independence (1970) and 2011 are presented under

this section. The summary statistics of the incorporated time

series are presented in section 4.1, while the estimated

regression models were presented in subsequent sub-sections.

4.1 Summary Statistics

Table 4.1: Descriptive Statistics for Growth, Public Spending and Corruption

COC PC PI RY POPGPOP_N

G

Mean -1.109479909.

5219782.

8281644

.82.535 102.2

Standard Error 0.028

130820.9 50328.4

35264.3

0.029 4.8

Median -1.122 37231.6 26194.8266464

.62.492 98.8

Standard Deviation 0.183

847816.0

326165.1

228538.7

0.187 31.1

Kurtosis -0.467 3.9 1.8 -0.11.691 -1.0

Skewness -0.106 2.1 1.6 0.81.492 0.3

Range 0.7713309627

.31152622

.9829942

.80.726 105.1

Minimum -1.510 716.1 173.6 4219.0 2.32 57.4

19

6Year (Minimum) 1978 1970 1971 1970 1997 1970

Maximum -0.7393310343

.41152796

.5834161

.83.052 162.5

Year (Maximum) 1990 2010 2009 2011 1978 2011Count 42 42 42 42 42 42

Source: Authors’ Computation

Figure 4.1:

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

0.00

50,000.00

100,000.00

150,000.00

200,000.00

250,000.00

300,000.00Real Growth, Public Consumption and

Investment Spending (Pre-SAP and SAP era)

PC PI RY

Figure 4.2:

20

1994

1996

1998

2000

2002

2004

2006

2008

2010

0.00

500,000.00

1,000,000.00

1,500,000.00

2,000,000.00

2,500,000.00

3,000,000.00

3,500,000.00 Real Growth, Public Consumption and Investment Spending (Post-SAP era)

PC PI RY

Table 4.2: Average Values of Growth, Public Spending and Corruption Indicators across Structural Periods

Structural Eras Period

PC(N’mill)

PI(N’mill)

RY(N’mil

l) COR

POPGrowth (%)

POP(mill)

2nd NDP1970-1974

1006.52 520.38 7011.4

-1.166

8 2.46 60.3

3rd NDP1975-1980

3526.983

5306.067

29757.66

-1.251

2 2.93 70.3

4th NDP1981-1985

5701.48

5486.94

195020.9

-1.166

8 2.55 81.7

SAP Era1986-1993

41621.38

23116.01

243313.9

-0.888

8 2.51 96.4

Post-SAP era

1994-1998

135751.5 196730

292703.2

-1.133

4 2.34 112.7

Democratic Era

1999-2002

546840.6

374388.3 357890

-1.215

7 2.39 125.3

21

NEEDS Era2003-2007

1224034

484839.4

559422.6

-1.163

7 2.48 139.9Global Economic & Debt Crises

2008-2011

2695558

1019669

750418.5

-0.986

9 2.51 156.5

Overall Eras

1970-2011

479909.5

219782.8

281644.8

-1.109

1 2.54 102.2Source: Authors’ Computation

Figure 4.3: Output Growth and Public Spending across structural erasin Nigeria

1970-1974

1975-1980

1981-1985

1986-1993

1994-1998

1999-2002

2003-2007

2008-2011

1970-2011

0

500000

1000000

1500000

2000000

2500000

3000000

PCPIRY

4.2 Unit Root Test Results

The result of the time series properties of the incorporated

variables in the multiple regression model in the previous

section is not reported in this sub-section. This serves as a

pre-test before the estimation of the long-run regression

model in the next sub-section that captures the effect of

public spending and corruption on economic growth in Nigeria

between 1970 and 2011.

22

The Augmented Dickey Fuller (ADF) unit-root test results

indicated that among the considered time series variables, log

of real GDP, public investment spending, public consumption

spending, population level and corruption control index were

found not to reject the null hypotheses “no stationary” at

levels. This indicates that the series does not revert to

their long-run mean at level based on the random walk with

drift and without drift models. However, after the first

difference of these series do not reject the null hypothesis

“unit root”. This implies that the first difference

transformed series of the incorporated variables in the ARDL

models are stationary. This further suggests that the first

differenced series are mean reverting at levels and do converge to their

long-run equilibrium.

4.3 ARDL Bound Testing

Prior before the estimation of the Unrestricted Error

Correction (UEC) version of the ARDL model to describe the

existence of a long-run equilibrium among the incorporated

series in model (3.8), the optimal lag was selected using

several criteria as indicated in Table 4.3. Although, for

annual data, Pesaran & Shin (1999) recommended choosing a

maximum of 2 lags but the 3rd lag was found more optimal and

was selected for the ARDL estimation.

Table 4.3: Optimal Lag Selection Criteria for the ARDL Unrestricted Error Correction (UEC) Model [3.8]

Lag Adj. R2 S.E AIC SIC HQC1 0.2066 0.3025 0.6750 1.1394 0.84292 0.3385 0.2796 0.5812 1.2637 0.8261

23

3 0.7100 0.1875 -0.2089 0.6961 0.11314 0.6618 0.1821 -0.3758 0.7562 0.0233

Note: S.E is Standard error of regression; AIC is Akaike Information criterion; SIC is Schwarz criterion; and HQC is Hannan-Quinn criterion

Table 4.4: Bounds test for the existence of a level relationship

Wald F-Statistic

10% 5% 1%

Upper

bound

I(1)

LowerboundI(0)

UpperboundI(1)

Lower

bound

I(0)

Upper

bound

I(1)

LowerboundI(0)

FRY (RY|PI,PC,COR,POP)¿10.3877

3.345

2.402

3.905

2.850

5.173

3.892

Source: Narayan, 2004 and Authors’ Computation

The calculated Wald F-statistic value (10.39) as shown on

Table 4.4 was found greater than the tabulated upper and lower

bounds statistics reported in Narayan (2004) for 43

observations for each of the significance levels. This

indicates that there is existence of long-run relationship or

cointegration among economic growth, public investment, public

consumption and corruption in Nigeria between 1970 and 2011.

4.5 Long-Run and Short-Run Estimates

The dynamic and static versions of the long-run estimates of

the relationship among economic growth, public investment,

public consumption and corruption in Nigeria between 1970 and

2011 were estimated as reported on Table 4.5 and 4.6

respectively.

24

The Hendry’s (1995) general-to-specific modelling iteration

approach was adopted to estimate the autoregressive

distributed lag (ARDL) model (3.9) and the results were

reported in Table 4.5. The estimated ARDL(1, 1, 3, 0, 0)

model was selected on the basis of the Akaike and Schwarz

Information criteria. The R-squared result indicated that 94%

of the total changes in output was dynamically explained by

simultaneous changes in public investment, public consumption,

population and corruption control level in Nigeria.

Table 4.5: Long-Run Estimates (ARDL Version)Dependent Variable: lnRYARDL (1, 1, 3, 0, 0) selected based on Schwarz criterionSample (adjusted): 1973 2011

Regressor Coeff.Std.Error t-Stat. Prob.

C-

17.098 8.374 -2.042 0.050lnRY(-1) 0.548 0.131 4.178 0.000lnPI 0.038 0.114 0.338 0.738lnPC -0.075 0.233 -0.322 0.750

lnPC(-1) 0.001 0.234 0.003 0.998lnPC(-2) -0.237 0.254 -0.934 0.358lnPC(-3) -0.237 0.226 -1.049 0.303

COR -0.176 0.383 -0.458 0.650lnPOP 6.029 2.608 2.311 0.028

R-squared 0.949    Akaike info criterion 0.6829

Adjusted R-squared 0.936

    Schwarz criterion 1.0668

S.E. of regression 0.308

    Hannan-Quinn criter. 0.8207

F-statistic 70.118    Durbin-Watson stat 1.8809

Prob(F-statistic) 0.000

Source: Authors’ Computation

The dynamic the long-run estimates as shown on Table 4.5

indicated that public investment and population level were

25

growth enhancing while corruption and public consumption were

growth retarding in Nigeria between 1973-2011. Similar

findings were reported for static long-run version estimates

that capture the growth effect of public investment, public

consumption and corruption in Nigeria as shown on Table 4.6

However, it was evident from Table 4.6 under model [2] and

[3], the growth effect of interaction between public spending

components and corruption control in Nigeria were negatives

and statistically significant.

Table 4.6: Long-Run Estimates (ARDL Version)Dependent variable: lnRY

1 2 3Coeff.

Std.Er

Coeff.

Std.Er

Coeff.

Std.Er

C

-31.743

* 5.863

-26.860

* 5.823

-26.762

* 5.689lnPI 0.326* 0.136 -0.095 0.211 0.396* 0.128

lnPC-

1.169* 0.204-

1.220* 0.192-

1.736* 0.278COR 0.895* 0.425 5.262* 1.790 5.543* 1.724

lnPI*COR-

0.421* 0.168

lnPC*COR-

0.433* 0.156

lnPOP11.740

* 1.71311.752

* 1.60311.830

* 1.578R-squared 0.913 0.926 0.928Adjusted R-squared 0.904 0.916 0.919S.E. of regression 0.472 0.441 0.434F-statistic 97.375 90.229 93.457Prob(F-statistic) 0.000 0.000 0.000Akaike info criterion 1.447 1.334 1.301Schwarz criterion 1.653 1.582 1.549* denotes 5% significanceSource: Authors’ Computation

26

The short-run estimates of the ARDL model (1, 1, 3, 0, 0) were

reported on Table 4.7 and indicated that in the short-run

public investment and consumption had negative but

insignificant effect on economic growth while corruption

control was slightly beneficial to economic growth in the

short-run. The error correction term (ECT) co-efficient that

explains the speed of adjustment from any distortion in the

short-run to its long-run equilibrium stood at -0.501. This

implies that 50.1% of any disequilibrium is restored in the

first year. This further suggests that the short-run

disequilibrium are fully restored to its long-run equilibrium

within 2years. This further establish true long-run

relationship among the series.

Table 4. 7: Log-linear short-run (ECM) estimatesECM for ARDL (1, 1, 3, 0, 0) selected based on Schwarz criterionDependent Variable: DlnRY

C DlnPI DlnPC DlnCOR DlnPOP ECT(-1)-0.574 -0.063 -0.089 0.768** 28.940 -0.501*0.731 0.150 0.219 0.445 28.400 0.197

Diagnostic tests

R-sqd. 0.237623Adj. R-sqd. 0.118502

S.E. of reg. 0.326957

F-stat. 1.994797Prob(F-st.) 0.106198 D. W stat 1.44195

AIC 0.745963 SIC 1.004529 HQC 0.837958Source: Authors’ Computation

27

V. CONCLUSION AND POLICY RECOMMENDATIONS

In this paper, the growth differential effect of public

investment, public consumption and corruption control have

been established and critically analyzed. The adopted

theoretical framework emanates from the endogenous growth

model. Considering our small size (1970-2011) the

28

autoregressive distributed lag (ARDL) framework of

cointegration, long-run, and shot-run models were adopted.

The bounds testing analysis indicated existence of

cointegration between considered set of variables in the ARDL

model. The long-run and short-run estimates indicated that

public investment was economic growth enhancing but reversal

evidence with interaction with corruption was reported. This

indicates that corruption is a menace in Nigeria and hindering

the effectiveness of public investment in Nigeria. Also,

further evidence indicated that equilibrium is fully restored

within 2years for any distortion in the short-run.

On this basis of the emanating findings, this study proffered

the need for government to build strong institutions in

combating corruption problem in order to strength the

governance framework and enhance the effectiveness of

government spending to promote economic growth in Nigeria.

Also, high monitoring and management priority should be

dedicated to oversee public consumption spending in order to

reduce wastage and enhance its growth potentials.

29

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