FISCAL POLICY AND ECONOMIC DEVELOPMENT IN NIGERIA (1960 - 2011)

BY

Ibi, E. E. and Opue, J. A. University of Calabar,

Cross River State, Nigeria.

E-mail for correspondence:ngaji74@yahoo.com

ABSTRACT

This study investigated the impact of fiscal policy measures on economic development in Nigeria. The Augmented Dickey-Fuller and Phillips-perron unit root test were first conducted. The cointegration test was then performed using Johansen Maximum Likelihood procedure. The granger causality test, the impulse response test, and then the variance decomposition test were performed. The collective results reveal that fiscal policy measures have not been effective in the development of the Nigerian economy when compared to monetary policy measures. Based on this, it was recommended, amongst others, that fiscal discipline, through prudent accountability, efficiency in revenue generation and above all, spending according to domestic demands, should be ensured in order to avoid economic decline in Nigeria.

Key Word: Fiscal policy, Economic Development..

INTRODUCTIONFiscal policy is generally believed to be associated with growth or more precisely, it is

held that appropriate fiscal policy measures in particular circumstances can be used to stimulate economic development or growth (Barro and Sala-i-Martin, 1992). More recent literature, however, places increasing weight to the role of expansionary fiscal policy and its potential role in stimulating economic growth (Tabellini and Daveri, 1997). A number of studies have empirically examined the productivity of government spending for various countries and cross-sections of countries. They include panel studies such as Landau (1983), Kormendi and Meguire (1985), Grier and Tullock (1989), Kneller et al (1998); cross-sectional studies such as Landau (1986), Ram (1986), Barro (1991), Chan and Gustafson (1991), Easterly and Rebello (1993), Lin (1994), Devarajan et al (1996) and Folster and Henrekson (2001); and time series such as Dunne and Nikolaidou (1999). More recently, authors have begun to exploit the utility of new techniques. Cooray (2009) uses an extended neoclassical production function to incorporate two dimensions of the government - the size and the quality dimensions and estimates the model on a cross section of 71 economies. The results show that both the size and quality of government are important for economic growth.

However, according to Onoh (2007), the bane of the Nigerian economy is the apparent lack of integration of macroeconomic plans and the absence of harmonization and coordination of fiscal policies among the three tiers of government. Others are misplaced priorities, misapplication and misappropriation of scarce fiscal resources, high domestic and external debt profiles, and corruption in high places, lack of accountability and transparency and the lack of credible international image. These inadequacies which have resulted in low levels of macroeconomic equilibrium have further been jeopardized by the frequent altering of the political structure of Nigeria into states.

After many years of literally beating about the bush with nothing to show for, in terms of poverty eradication and improved welfare for the majority of the populace, the Nigerian authorities had to return to the drawing board. The poor macroeconomic performance was diagnosed and traced to the problems of misplaced priorities, misapplication and

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misappropriation of scarce fiscal resources, high domestic and external debt profiles, etc. The conclusion was that if macroeconomic stability was to be achieved, then the activities of the three tiers of government have to be well harmonized and coordinated. In addition, fiscal efficiency, accountability and transparency have to be incorporated into the laws governing the macroeconomic management of Nigeria. The final goal of macroeconomic policy becomes possible only if the economy is well managed (Mark, 2003).

To confirm the various postulations outlined, this study aims at investigating the impact of fiscal policy measures on the development of the Nigerian economy by focusing on the relative effectiveness of government expenditure and the resultant fiscal deficits on the economy.

The specific objectives are as follows:1. To analyze the two-way linkage of fiscal policy and economic development in Nigeria.2. To analyze the two-way linkage of fiscal policy and monetary policy in Nigeria.3. To analyze the two-way linkage of monetary policy and economic development in Nigeria.4. To examine and compare the magnitudes of monetary and fiscal policies on economic

development in Nigeria.5. To analyze the two way linkage of public debt and economic development in Nigeria.

Research Hypotheses In the light of the above objectives, the following hypotheses have been proposed in

alternative forms (H1):Hypothesis 1 (H1): A mutually reinforcing two-way linkage between Fiscal Policy and Economic

Development exists in Nigeria. Hypothesis 2 (H1): A mutually reinforcing two-way linkage between Fiscal Policy and Monetary

Policy exists in Nigeria.Hypothesis 3 (H1): A mutually reinforcing two-way linkage between Monetary Policy and

Economic Development exists in Nigeria.Hypothesis 4 (H1): Fiscal Policy significantly affects Nigeria’s Economic Development than

Monetary Policy. Hypothesis 5 (H1): A mutually reinforcing two-way linkage between Public Debt and Economic

Development exists in Nigeria.Model specification: The Vector Autoregressive Model

In order to test the two-way linkage between Fiscal Policy and Economic Growth, in the current study, multivariate Granger causality tests are conducted to examine possible causal relationships among some variables comprised of gross domestic product (GDP), public revenue (PR), public expenditure (PEX), broad money supply (MS) and public debt (PD), all expressed in natural logs. These tests are based on the following vector autoregressive (VAR) model as shown in equation 1.1; all variables are systematically and endogenously considered at first.

The specified model is as follows:

[GDP t

P Rt

PDt

P EX t

MSt

]=A0+A1[GD Pt−1

PRt−1

PDt−1

P EX t−1

MSt−1

]+A2[GD Pt−2

PRt−2

PDt−2

P EX t−2

MSt−2

]+…+ A s[GD P t−s

PRt−s

PDt− s

P EX t−s

MSt−s

]+εt…(1.1)

Where,

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GDP=Gross Domestic Product (proxy for Economic Development), PR=Public Revenue, PEX=Public Expenditure, PD=Public Debt, MS=Broad Money Supply, A0=vector of constant terms, are all matrices of parameters (i=1, 2, …, s), and εt ~ IN (0, 1).

The fiscal policy measures consist of government expenditure (PEX), tax revenue (PR) and budget surplus (PR-PEX) while the monetary policy measure is proxied by broad money supply (MS), etc. (see Ajayi, 1974).

In order to analyze the causal linkages it is necessary to check whether the variables are stationary. According to Granger (1969), standard tests for causality are valid only if there exists cointegration. Therefore, a necessary precondition to causality testing is to check the cointegrating properties of the variables under consideration.

PRESENTATION AND ANALYSIS OF EMPIRICAL RESULTUnit root test

We test for the presence of unit roots and identify the order of integration for each variable using the Augmented Dickey-Fuller (ADF) statistic in which the null hypothesis is non stationary. The Newey and West method is applied to choose optimal lag lengths automatically based on Schwarz Criterion (SC), which was found to be nine (9) for all variables. ADF tests conducted on the logged variables of GDP, MS, PEX, PR, and PD, differentiated by their order of integration are reported in Table 1.2. The Phillip-Perron unit root test was also conducted to substantiate the ADF unit root test. This is reflected in table 1.3.

As shown in Table 1.2 for the variables in question, it is evident that we generally cannot reject the presence of a unit root at conventional levels of statistical significance except in the case of GDP which is stationary at level. To see whether the other variables are integrated of order one, at 5 percent level, we performed the ADF test on the first differences. The results show that the first differences of the series were all stationary, thus, rejecting the null hypothesis of unit root.

The Phillips-Perron result of table 1.3 also reveals that GDP is also integrated of order one. Therefore, based on these analyses, we conclude that the series involved in the estimation procedure are regarded as I(1) and so, suitable to make co-integration test.

Cointegration testSince it has been determined that the variables under examination, i.e. from equation

(3.1), GDP, MS, PD, PEX and PR, are integrated of order one (namely, I(1)), then the cointegration test is performed. The testing hypothesis is the null of non-cointegration using the Johansen maximum likelihood procedure. So the proper way to test for the relationships between the variables is certainly to test for a cointegrating equation. In testing cointegration relationships, we use the Maximum Likelihood Estimation (MLE) method of Johansen and Juselius. In selecting optimal lag length for the cointegration test, we adopt the Schwartz Information Criterion (SIC) and Schwartz Criterion. The cointegration test results performed for the five equations are reported in Table 1.4 below.

Table 1.4 shows the cointegration test results. Since calculated λmax (= 139.7459) and Trace (= 428.2405) are above the critical values (33.8769) and (69.8189) respectively at 5 percent level, we can clearly reject the null hypothesis stating there is no cointegration. In the second null hypothesis stating one versus two cointegrating vectors, we also reject the null hypothesis since the calculated λmax (= 111.8083) and Trace (=288.4946) are above the critical values (27.5843) and (47.8561) respectively. Furthermore, in the two cointegrating vectors versus three cointegrating vectors, we also reject the null hypothesis since the calculated λmax (=

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91.8675) and Trace (=176.6863) are above the critical values (21.1316) and (29.7971) respectively.

However, when it comes to three versus four cointegrating vectors we also reject the null hypothesis of no cointegration since λmax (=59.9677) and Trace (84.8188) are greater than the critical values (14.2646) and (15.4947) respectively. Finally, in the case of four versus five cointegrating vectors, we also reject the null hypothesis since λmax (=24.8511) and Trace (= 24.8511) are both greater than the critical values of 3.8415.

Hence, we conclude that we have five cointegrating vectors at 5 percent level of significance. Eviews-5.0 also reports the normalized cointegrating vectors. According to the normalized cointegrating vectors, the coefficients obtained gives a stable long-run relationship between the variables.

However, the result of Table 1.4 which suggests the presence of cointegration provides strong evidence in the causality among these variables. Hence, there is need for the Granger causality test for the VAR model of 1.1. Granger causality test

If there exists a cointegrating vector between the variables in question, that is GDP, PEX, PR, PD and MS, there is causality among these variables at least in one direction (Granger, 1988). Thus, Granger causality tests can be used to examine the nature of the relationship. The results of the granger causality tests reported in Table (1.5) indicate that:-(i) Monetary policy measures Granger cause economic development, and Economic development Granger cause monetary policy measures, which means that there is evidence of two-way Granger causality from GDP to MS and vice versa with feedback. This leads us to conclude that there is long-run relationship between the two variables. (ii) Public debt Granger cause Economic development and Economic development Granger cause Public debt, which means that there is evidence of two way Granger causality from GDP to PD and vice versa with feedback. This leads us to conclude that there is long-run relationship between the two variables. (iii) Fiscal policy measures (PEX) Granger cause Economic development and Economic development Granger cause Fiscal policy measures, which means that there is evidence of two-way Granger causality from GDP to PEX and vice versa with feed-back. This leads us to conclude that there is long-run relationship between the two variables. (iv) Public revenue Granger cause Economic development, and Economic development Granger cause Public revenue, which means that there is evidence of two-way Granger causality from GDP to PR and vice versa with feedback. This leads us to conclude that there is long-run relationship between the two variables. (v) Fiscal policy measures (PEX) Granger cause Monetary policy measures (MS), and Monetary policy measures does not Granger cause Fiscal policy measures, which means that there is evidence of one-way Granger causality from PEX to MS. This leads us to conclude that PEX has significant effect on MS, but MS does not have a significant effect on PEX. (vi) Public revenue Granger cause Monetary policy measures, and Monetary policy measures also Granger cause Public revenue, which means that there is evidence of a by-directional Granger causality from PR to MS, with feedback. This leads us to conclude that there exists long-run relationship between the two variables. Impulse response analysis

To further illustrate the manner of response of the sample to the shocks, we conduct the impulse response function (IR) analysis for the VAR model of equation (1.1). We examined the effect of the responses of GDP to GDP, MS, PR, PE, and PD; the response of MS, to MS, GDP, PR and PE; the response of PE to PE, MS, GDP; and PR to PR, MS and GDP, over a 6 year

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period after the beginning of the shock. The one standard deviation confidence band is obtained by Monte Carlo integration method.

As shown in Figure 1.1, the first and second line shows the response of Economic development (GDP) to itself and other variables. The impulse response of GDP to the shocks of MS was positive and insignificant except in the 2nd, 3rd and 6th year where it was insignificant. The impulse response of GDP to the shocks of and PD was positive and significant except in the 3rd, 5th and 6th year where it was insignificant. The response of GDP to the shocks of PEX was negative and insignificant down to the fifth year period and then remains positive; while the response of GDP to the shocks of Public Revenue (PR) was totally negative all through the 6 th

year but significant except in the 5th and 6th year.The third line of Figure 1.1 shows the response of Monetary policy measures (MS) to

itself and other variables. GDP has a positive but insignificant effect, Fiscal policy measures (PEX) has a positive and insignificant effect which reaches its peak in the fifth (5 th) year and then drops down in the 6th year; PR has an insignificant effect fluctuating between positive and negative in the 2nd and 3rd year and then became positive all through to the 6th year.

The fourth line of Figure 1.1 shows the responses of Fiscal policy measures (PEX) to the shocks of other variables. GDP has an insignificant effect which fluctuated between positive and negative directions in the first and second year and then became positive all through to the 6 th

year; MS was insignificant, and also fluctuates in the positive and negative directions. PR was significant with positive and negative fluctuations, and then became negative from the 4 th to 6th

year. The last line of Figure 1.1 shows the responses of PR to the shocks of itself and other

variables. GDP has a negative and insignificant effect; MS has an insignificant effect which fluctuates between the negative and positive direction; while PEX also has an insignificant effect except in the 1st year and fluctuates in the positive and negative directions.

Variance decomposition While causality test indicates if a variable or group of variables is found to be helpful in

predicting other variables or group of variables, without implying true causality but rather the forecasting powers, Impulse response functions provides the direction and level of significance of the relationship existing between the variables in question. However, variance decomposition substantiates the causal affects and the impulse response effects by comparing the sizes or magnitudes of the effects on the existing relationships among the variables. Thus, showing how the sizes of the effects have changed over time.

The variance decomposition result is shown in Table 1.6. It draws from the analysis of VAR model of equation (1.1). In Table 1.6 the reported numbers indicate the percentage of forecast error in each variable that can be attributed to innovations in other variables in six (6) different time horizon or period.

The part A of Table 1.6 indicates that the changes in GDP is due to its own changes starting from 100% in the first year decreasing to 41.5% in the 3 rd year and then increasing to 46% in the 6th year. Besides, changes in GDP are also explained by around 26.5%, 13.9%, 1.7% and 11.8% by MS, PD, PEX and PR respectively in the 6th period. This indicates that the changes in GDP are mainly explained by its own variation, and MS, PD, PEX and PR but with MS having the highest magnitude among other variables.

When looking at the part B of Table 1.6, we observed that in the 1st period, the changes in MS are explained by 93% of its own shocks and goes down to 60% in the 6 th period. In the 6th

period, however, the MS variation is accounted of 6% by GDP, 26% by PEX, and only 1.6% by

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PR. Hence, the changes in MS are mainly explained by its own variation, and GDP, PEX and PR, respectively but with PEX having the highest magnitude.

Moreover, the part C of Table 1.6 indicates that the innovation of PEX is explained by 14% in the first period and increases to 17% in the 2nd, and the goes down to 6% in the 6th period by its own variation. The variation in PEX in the 6 th period is explained by GDP (30%), MS (36%), and PR (19%). Thus, the innovation of PEX is mainly explained by its own variation, GDP, MS, and PR, but with MS having the highest magnitude.

The part D of Table 1.6 shows that PR variability is attributed to shocks by itself (52%) in the first period, while 3.5% is due to changes in GDP, 10.5% is due to changes in MS, and 33.3% is due to changes in PEX. However, as time goes by, the explanatory proportion of its own innovation decreases to 33.9% in the 6th period, while GDP increased to 22.6%, MS increased to 16.8%, and PEX decreased to 15.1%. Therefore, GDP has the highest magnitude.

The conclusion of variance decomposition analysis is that:(i) Economic development (GDP) is more sensitive to changes in Monetary policy measures

(MS), than by changes in Public debt (PD) and Public revenue (PR), but not by changes in Fiscal policy measures (PEX).

(ii) MS is sensitive to changes in PEX and not to changes in GDP and PR(iii) PEX is more sensitive to changes in MS than by changes in GDP and PR.(iv) PR is more sensitive to changes in GDP than by changes in MS and PEX.

Table 1.2: Augmented Dickey-Fuller (ADF) unit root testVariables ADF Statistic 5% Critical value Prob* RemarksLGDP - 4.180332 -2.941145 0.0022 Stationary I(0)LMSD(LMS)

-2.104597-3.401857

-2.938987-2.943427

0.99990.0034

Non stationaryStationary I(1)

LPED(LPE)

10.35897-2.342739

-2.928142-2.036942

1.00000.0099

Non stationaryStationary I(1)

LPRD(LPR)

2.987072-4.265049

-2.938987-2.941145

1.00000.0000

Non stationaryStationary I(1)

LPDD(LPD)

1.824764-7.929351

-2.941145-2943427

0.99960.0000

Non stationaryStationary I(1)

Table 1.3: Phillips-Perron (PP) unit root test.Variables ADF Statistic 5% Critical value Prob* RemarksLGDPD(GDP)

- 0.121112-13.12653

-2.925169-2.926622

0.94090.0000

Non stationaryStationary -I(1)

LMSD(LMS)

15.32465-8.267254

-2.925169-2.926622

1.00000.0000

Non stationaryStationary -I(1)

LPED(LPE)

14.95840-7.314937

-2.926622-2.928142

1.00000.0000

Non stationaryStationary -I(1)

LPR -3.206449 -2.928142 0.0261 Stationary–I(0)

LPDD(LPD)

-0.737876-4.631184

-2.925169-2.926622

0.82690.0005

Non stationaryStationary -1(1)

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L= log; D= First difference; * Mackinnon (1996) one sided P- ValueI(o) = Integrated of order zero; I(1)= Integrated of Order one.

L= log; D= First difference; * MacKinnon (1996) one sided P- ValueI (o) = Integrated of order zero; I(1)= Integrated of Order one.

Table 1.4: Result of the cointegration test Eigenvalue Ho H1 Trace test λmax test Critical values

5% (Trace)Critical value 5% (λmax)

0.9552 r=0 r≥1 428.2405 139.7459

69.8189 33.8769

0.9166 r≤1 r≥2 288.4946 111.8083

47.8561 27.5843

0.8702 r≤2 r≥3 176.6863 91.8675 29.7971 21.13160.7362 r≤3 r≥4 84.8188 59.9677 15.4947 14.26460.4243 r≤4 r≥5 24.8511 24.8511 3.8415 3.8415

* denotes significance at 5% level, r indicates the number of cointegrating vectors.

Tables 1.5: Granger causality test for equation 3.1F-Statistic Prob. Conclusion 5.19655 0.00974 MS GDP12.1144 7.3 E-05 GDP MS37.5198 5.5 E-10 PD GDP6.02486 0.00508 GDP PD5.05610 0.01102 PE GDP9.51605 0.00042 GDP PE4.57999 0.01618 PR GDP13.1470 4.1 E-05 GDP PR4.65074 0.01528 PE MS0.61674 0.54476 MS PE23.4658 1.8 E-07 PR MS79.1791 1.2 E-14 MS PR

= Granger Cause = not Granger Cause

Table 1.6 Variance decomposition of equation (1.1)(A) Variance decomposition of GDPPeriod S.E GDP MS PD PEX PR1 251314.7 100.0000 0.0000 0.0000 0.0000 0.00002 488768.4 66.8472 7.1586 11.2887 0.1342 14.57133 842291.0 41.5498 20. 5913 7.8517 0.9419 29.06544 1351177.0 43.9084 20. 9005 14.8579 1.8270 18.50625 2070621.0 43.2185 30.9163 10.9228 1.9288 13.01366 2777725.0 46.0445 26.5478 13.9654 1.6732 11.7691

(B) Variance decomposition of MS7

Period S.E GDP MS PEX PR1 11807.67 7.1947 92.8053 0.0000 0.00002 35279.55 4.9360 78.6793 0.1168 0.03523 67366.81 1.7092 83.5901 9.6480 0.56334 80676.53 2.6417 76.0794 13.5298 2.67185 108077.30 3.2677 63.2962 26.8295 1.71796 110953.90 6.3229 60.0862 26.8845 1.6344

(C) Variance decomposition of PEXPeriod S.E GDP MS PEX PR1 62602.88 11.3918 69.2326 14.1654 0.00002 67933.05 10.8876 58.8507 17.9339 1.18633 77772.49 25.8300 45.3032 14.0296 2.34574 90451.43 25.1707 35.8916 12.6472 12.44845 130290.80 32.9394 17.3033 15.2742 10.94216 206941.30 29.7791 35.6802 6.2336 18.7771

(D) Variance decomposition of PRPeriod S.E GDP MS PEX PR1 86983.94 3.5198 10.4783 33.2783 52.26762 113622.30 9.5422 8.9389 37.8859 42.99973 166219.60 26.5630 17.0058 23.6443 23.18354 202910.40 29.2603 21.6652 20.0470 19.68445 240311.80 21.2809 15.5688 18.1888 30.17416 271096.40 22.5967 16.7921 15.1035 33.8851

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Response to Cholesky One S.D. Innovations ± 2 S.E.

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Figure 1.1: Shows Impulse response analysis

CONCLUSIONIn the light of the above findings, we conclude that the impact of fiscal policy measures

on the development of the Nigerian economy (1960 - 2011) has not been as effective as the impact of monetary policy measures. Though a mutually reinforcing two way linkage between fiscal policy measures and economic development exists in Nigeria, GDP responded negatively though insignificant, to the shocks of public expenditure. Hence, an indication that funds generated over the years has not been properly tailored in the right direction in order to boost productivity. This could be adduced to the fact that there exist some element of corruption and fiscal indiscipline in the public sector. Perhaps, the bulk of the money generated is plunged into foreign accounts, thus grossly reducing the level of investment and shutting down productivity.

Fiscal policy measures affects monetary policy measures, but monetary policy measures does not affects fiscal policy measures. This is a clear indication of the lack of synergy in the respective measures. Although the various responses to the shocks among these variables were positive, they were insignificant. This is an indication of the amount of efforts required to complement the roles of CBN with the public sector.

Economic development and monetary policy measures has bidirectional relationships. The response of GDP to the shocks of MS is positive and significant, though not in all periods. Hence, an indication of the level of effectiveness of monetary policy measures to economic development. However, GDP, though positive, did not impact significantly on MS. This is a clear indication that the level of productivity is not mature enough to influence policy actions. No invisible hand of the forces of demand and supply in this respect. The CBN is therefore encouraged to continue to implement its policies in alliance with the public in order to boost productivity.

The response of economic development to the shocks from public debt is positive and significant. It is also sensitive to the changes in public debt. Therefore, the revenue generated from loans does not affect the development of the Nigerian economy in the negative sense.

Though a bi-directional relationship between GDP and public revenue exists in Nigeria, public revenue impacts negatively on GDP. The reason for this could be attributed to the fact that part of the revenue generated could be traceable to over taxing of the industrial sector, thus, lowering output. The sensitivity of GDP to the changes in public revenue, though negative, is an eye opener for the government to ensure prudence in revenue generation for the development of the Nigerian economy.

However, on the basis of these issues we proffer that among other recommendations below, fiscal discipline, through prudence in accountability, efficiency in revenue generation and above all, spending according to domestic demands, should be ensured in order to avoid economic decline.

POLICY RECOMMENDATIONSBased on the findings, the following recommendations are put forward:(1.) Coordination of fiscal and monetary policy measures imply among others, fiat monetary

restraint which should be matched with lower deficit spending, thus ensuring fiscal discipline. Where deficit must be, this should be strictly applied to productive ventures to ensure economic growth and development.

(2.) Where deficit must be, it should be tilted to a balanced budget by evolving an efficient taxation policy, adequate to beat tax evasion, avoidance and inequity in order to boost productivity.

(3.) The level of tax revenue generated must correspond with the level of industrial development, and hence economic development of Nigeria; thus the optimality condition.

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(4.) The bulk of revenue generated through tax, loans, and from other sources should be plunged into domestic investment, and not be allowed to be plunged into foreign account by greedy Chief Executives in government.

(5.) Coordination of fiscal actions must be informed by forecasts and evaluation of results from sound econometric models.

(6.) There should be an increased synergy and policy stimulus between fiscal and monetary policy measures in order to boost economic development in Nigeria; thus ensuring a mixture of fiscal and monetary policies.

(7.) Government expenditures should be based on public demand for goods and services which should be provided at full employment level.

(8.) Government should at all times attempt to make and implement policies that will ensure accountability, equity, fairness, transparency in governance as well as avoiding time lag that sometimes distorts government programmes.

(9.) Government should design measures that will make monetary and fiscal policies more attractive to enhance economic growth and development in the country.

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