An Assessment of the Impact of the Fiscal and Monetary Policy Coordination on Macroeconomic Performance in Nigeria (1970-2012)

An Assessment of the Impact of the Fiscal and Monetary Policy Coordination on Macroeconomic Performance in Nigeria (1970-2012) 1Adenaike, A. S., 2Sennuga, M. A; and 3Adegboyega S. B. Abstract This paper evaluates the efficacy of macroeconomic performance vis-à-vis fiscal and monetary policy coordination for sustainable economic growth in Nigeria. The method adopted in this paper was econometric analysis, which employs unit root test, co-integration test and vector error correction mechanism (ECM). Both preliminary analysis, i.e., long-run (static) relationships and the final analysis, i.e., short-run (dynamic) relationships show that prudent expenditure policy, good revenue generation mechanism along with moderate broad money supply policy raise economic growth rate in a good policy environment. This analysis also reveals that sound fiscal policies together with healthy monetary policy reduce inflationary pressures in Nigeria. Therefore, efficient fiscal-monetary policy measures help to suppress inflationary tendencies and enhance factor productivity when properly coordinated. Finally, sound fiscal policy along with healthy monetary policy eliminates and/or minimizes inflationary crises in an environment of fiscal dominance. Keywords: Economic reform, fiscal policy, monetary policy, inflation rate and economic growth 1Department of Economics, Olabisi Onabanjo University, Ago-Iwoye. 2Department of Economics, Tai Solarin College of Education, Omu-Ijebu. 3Department of Economics, Olabisi Onabanjo University, Ago-Iwoye. Introduction An economy in which fluctuations are partly due to the combination of aggregate demand effects and nominal rigidities, fiscal policy has the potential to reduce fluctuations in aggregate demand and thus increase welfare. This has long been a theme of Keynesian macroeconomics. Whereas for monetary policy the major trade-off is between price and output stability, the trade-off for fiscal policy is between output stabilization and the distortions from tax and spending policies (Blanchard and Fisher, 1989). However, the emergence of new theories of endogenous growth has indeed renewed interest in the potential role of policy coordination in promoting economic growth and development. Economic growth cannot be possible without the combined role of fiscal and monetary policy measures. There are different ways of looking at fiscal policy roles. One is to emphasize the role of inflationary finance/tax, while the other is to emphasize the role of fiscal deficit or balanced. Experts emphasize that with full employment of resources achieved, inflationary finance can be used as an instrument to finance investments, hence growth and development, in developing countries (Tanzi, 1978; Aghevli and Khan, 1977; Asogu, 1991). But sad enough, a full employment situation rarely holds in Sub-Saharan Africa countries. Perhaps, inflationary finance through investment may not have the desired effect on growth and development, while fiscal balance has been seen to have a positive effect on long-run growth, but fiscal deficit has a negative effect on growth (Easterly and Levine, 1994). The role has also been examined by economists from different angles and with various degrees of emphasis. In particular, the study of Barro (1990) suggests a simple endogenous growth model with government. Departing from the standard characterization of government consumption financed by distortionary taxes as in Easterly (2005), in Barro’s model, public investment (roads, ports, sanitation, schools, etc.) complements private investment. Government spending is financed by a straight income tax. Since public investment raises the productivity of private investment, higher taxes can be associated with an increase or a decrease in overall growth. But because the private sector ignores the additional tax revenues and public investment generated by its private investment, it tends to invest too little. Therefore, recent models of economic growth can generate long-term growth without relying on exogenous changes in technology or population. Some of the models amount to theories of technological progress (Romer 1986) and others to the theories of population change (Barro 1988). A general feature of these models is the presence of constant or increasing returns in the factors that can be accumulated (Lucas 1988; Romer 1989; Rebelo 1991). However, in this study, the geometric distributed lag model will be employed, after application of the so-called Koyck (1954) transformation. This will establish the dynamic link between fiscal-monetary policy and inflation rate. The model will make current inflation rate a function of current and past government expenditure and revenue levels as well as broad money supply, where the lag coefficients have a geometrically decaying pattern as the model will involve an infinite number of lagged variables. In many studies the resultant model is hence called the Koyck model. Koyck, L. M. (1918-1962) was a Dutch economist who studied and worked at the Netherlands School of Economics, which is now called the Erasmus University Rotterdam. The aim of this paper is to evaluate the efficacy of macroeconomic performance vis-à-vis fiscal and monetary policy coordination for sustainable economic growth in Nigeria. The task we set out in this research study enable us not only to reconcile some of the conflicting findings of positive, insignificant, or negative effects from other studies, but also to assess the extent to which Nigeria states have tax and expenditure structures conducive to economic growth and suppresses inflationary pressures. However, this research study contributes to the empirical growth literature in what follows? Economic growth is analyzed for an individual country such as Nigeria, with data spanning over four decades (1970-2012). Economic activity in Nigeria has traditionally been viewed as one of the engines of growth and prosperity in the West African Sub-Region and in other neighboring countries. An investigation of the determinants of growth and persistent inflation growth rate in Nigeria would contribute to a better understanding of the factors that can boost economic growth and suppress inflationary pressures in the region. In addition, an investigation of the growth determinants for an individual economy can focus on the institutional and historical aspects of the country (Adenaike and Sennuga, 2012). Finally, the contributions of fiscal and monetary policy coordination to growth are investigated along with the robustness of the influence of inflation on economic growth. On a final note, the forgoing challenges make raising the following conceptual and policy questions a necessary exercise: What are the impacts of fiscal and monetary policy measures on the macroeconomic stability indices [gross domestic product (GDP) growth rate and inflation rate]? Pursuit of this issue leads to thorough understanding of fiscal and monetary institutions with a careful analysis of the economic principles which underlie budget policy. This paper is divided into four parts, thus: the first part focuses on brief background of the study, problem statement, and objective; part two reviews the related literatures; while third part highlights the analytical framework and methodological issues, empirical analysis and result interpretations; and the last part concludes the study with policy implications. Literature Review In the neoclassical growth model of Solow (1956), together with its many subsequent extensions, the long-run growth rate is driven by population growth and the rate of technical progress. Distortionary taxation or productive government expenditures may affect the incentive to invest in human or physical capital, but in the long run this affects only the equilibrium factor ratios and not the growth rate, although there will in general be transitional growth effects. Endogenous growth models such as those of Barro (1990) and King and Rebelo (1990), on the other hand, predict that distortionary taxation and productive expenditures will affect the long-run growth rate. The implications of endogenous growth models for fiscal policy have been particularly examined by Barro (1990), Jones et al. (1993), Stokey and Rebelo (1995) and Mendoza et al. (1997). Unlike the neoclassical growth model, where fiscal effects alter the level of the long-run output path, the endogenous growth model permits fiscal effects to alter the slope of the long-run output path, as illustrated for example in Barro (1990). Here, we employ an adaptation of the Bleaney et al (2001) presentation of the Barro and Sala-i-Martin (1992, 1995) model of endogenous growth. This adaptation is also used in Gray and Stone (2006) to examine the issue of Ricardian equivalence for sub-national states. According to Bleaney et al (2000), endogenous growth models, such as Barro (1990), predict that government expenditure and taxation will have both temporary and permanent effects on growth. They test this prediction using panels of annual and period-averaged data for OECD countries during 1970-95, isolating long-run from short-run fiscal effects. Their results strongly support the endogenous growth model and suggest that long-run fiscal effects are not fully captured by period averaging and static panel methods. Unlike previous investigations, their estimates are free from biases associated with incomplete specification of the government budget constraint, and do not appear to result from endogeneity of fiscal or investment variables. Barro’s (1990) model of endogenous growth implies that economic growth will initially rise with an increase in taxes directed toward “productive” expenditures (e.g., education, highways, and streets), but will subsequently decline. Previous tests of the model, including Barro (1989, 1990) and recently Bleaney et al (2001), focus on whether the linear incremental effect of taxes is positive, negative, or zero, with substantial evidence for all three conclusions. In their study, Bania et al (2006) tested for nonlinearity directly by incorporating nonlinear effects for taxes, and based on U.S. states found that the incremental effect of taxes directed toward productive government expenditures is initially positive, but eventually declines. U.S. states on average appear to under invest in expenditures on productive government activities. Hence, do taxes and government expenditures enhance or impede economic growth? This question lies at the heart of public finance and taxation policy, both at the national and sub-national levels. In an extensive summary of empirical studies of the effects of taxes on economic growth, Pecorino (1993) and Poot (2000) find that most estimates are either insignificant or negative, though a small number are positive. Similarly, estimates of the effects of government investment expenditures on economic growth also tend to be insignificant, though a few studies find positive effects, particularly for expenditures on education and human capital (Tella et. al, 2013). Recently, Bleaney et al. (2001) test a Barro (1990) style endogenous growth model for OECD countries over the period 1970-95, extending tests in earlier Barro (1989, 1990) cross-country studies. Based on a full specification of the government budget constraint, including distinctions between productive and nonproductive government expenditures, their results are consistent with the endogenous growth model, in that taxes reduce the long-run growth rate and productive government expenditures increase it, all else the same. While the studies surveyed by Poot and the recent Bleaney et al study are based primarily on cross-country data, there are also a number of cross-state (or cross-county) studies for the United States, including for example Helms (1985), Mofidi and Stone (1990), and more recently, Mark et al (2000) and Holcombe and Lacombe (2004). Helms and Mofidi-Stone find that taxes spent on “productive” government investments tend to enhance growth, while Holcombe-Lacombe and Mark et al find that increases in taxes tend to impede growth. Which conclusion is correct? Ironically, the Barro-style model of endogenous growth (e.g., Barro 1990) suggests that all could be right, depending on the level of taxes, composition of expenditures, and other factors. In Barro’s model, increases in taxes can enhance, have no effect on, or impede growth depending, in particular, on the initial level of taxes and how the tax revenues are spent. For example, an incremental dollar of tax revenue spent on productive government services has a much more positive effect on growth in the Barro model when taxes are initially low than when they are already high. Aschauer (1997) and Kalaitzidakis and Kalyvitis (2005) incorporate nonlinear effects for public capital, but not taxes. While Bania, Gray and Stone (2006) examined the nonlinearities predicted for the effects of taxes on economic growth. A number of empirical studies have investigated economic growth with either cross-country or panel data. Most of these studies have used the neoclassical growth model--or its extended version that includes human capital--because of its simplicity and ease of applications. Nonetheless, a number of the limitations of this model have prompted the development of endogenous growth models. An important limitation of the neoclassical model is that steady state growth depends solely on technological progress and population growth, both of which are exogenous to the model. As such, economic policies have no influence on steady state growth, although they do influence the level of output when the economy is between steady states. By contrast, endogenous growth models provide mechanisms through which changes in economic policies and accumulation of human and private physical capital stocks can generate sustained economic growth, even in the absence of exogenous technological change and population growth. In general, these models assume increasing returns to scale in reproducible factors of production (Lucas, 1988; and Romer, 1986). Lucas’ (1988) model assumes that investment in human capital has spillover effects that give rise to sustained growth. Also, Romer’s (1986) model assumes that technological change is endogenous and that private investment raises the level of technology for the whole economy. The positive externality associated with private investment gives rise to a production function that exhibits increasing returns to scale; in this model increases in private investment raise growth in the steady-state. It can be expected that private investment provides a linkage between imported technology and economic growth (Grossman and Helpman, 1991). Therefore, judging from the existing literature (both theoretical and empirical), it is desirable to undertake an assessment of the impact of the fiscal and monetary policy coordination on macroeconomic performance indicators (such as GDP growth rate and inflation rate) in order to provide a guide to the restructuring and coordination of Nigerian macroeconomic reforms. Analytical Framework, Methodology and Empirical Analysis 3.1 Analytical Framework The analytical framework was based on two empirical studies. Firstly, Barro Endogenous Growth Model which assumes that growth is influenced by policy variables other than the technical relationship between capital and labour (Barro, 1990). Such policy variables include public spending and taxation which are proxies for fiscal policy in this study. Since government policy can be fiscal policy and/or monetary policy, thus, the introduction of money supply as concurrent policy variable. Among the reasons for this is that government often prints money to finance some of its developmental programmes and in financing fiscal deficits. Following this trend and coupled with what Anderson and Carlson (1972) discuss on the central role of expectations in the Saint Louis Model. The models were used for policy analysis, specifically for studying the effect of fiscal and monetary policy on output and inflation rate. The St. Louis Model specified that any macroeconomic variable is a function of fiscal policy and monetary variables. Here, each of the fiscal policy variables was treated separately to avoid the problem of serial correlation and to evaluate their impact on macroeconomic variables. Secondly, the model also reviewed the empirical study of Koyck (1954) which was conducted in Amsterdam, North Holland and affirmed by Olofin et al (2009) in their study- Modelling Nigeria’s Economic Development-the Cear Model Mac-IV. The value added is the extent to which fiscal and monetary policy measures influence inflationary crises in Nigeria. In the models, we assumed along conventional lines that inflation levels are based on current government expenditure, broad money supply and the previous level of inflation. Such a previous level may refer to inflation rate in the immediately preceding period, or peak level of inflation among a set of previous period’s inflation. 3.2 Methods of Analysis The method adopted in this study was econometric analysis, which employs unit root test, co-integration test and vector error correction mechanism. The empirical model was estimated and tested using two-stage least square (2SLS) estimation technique. However, the model was tested for stationarity using Augmented Dickey-Fuller (1979) method which ensures that we are not analysing inconsistent regression. Here, we used Johansen’s (1988) procedure which utilizes vector autoregression (VAR) approach to test the model. Also, the model was tested using co-integration vector (error correction mechanism) which explored Augmented Engel and Granger (1987) test. To this effect, the unit root and co-integration tests help to circumvent the inherent limitations of traditional models as well as avoid spurious regression results (Hendry, 1986). The empirical analysis entails the follows: first, the time-series properties of the data (which explore long-run properties and short-run dynamics) are estimated; second, the existence and direction of causality between output (GDP growth rate) and fiscal-monetary policy measures (fiscal balance, government expenditure, and broad money supply) are examined; and thirdly, the base regression presents the effects of government revenue (taxes) and broad money supply on economic growth. Fourthly, the robustness of the impact of inflation is investigated by augmenting the equations by other relevant explanatory variables. Finally, the regression results are analyzed and interpreted. 3.3 Sources of Data The study employed secondary data. Relying on the time series data spanning 1970-2012 and given the time scope as well as the frequency of the data, all the macro variables have 42 observations. The assessment periods help to capture the various fiscal regimes under difference government experienced in Nigeria. The data were sourced from the Central Bank of Nigeria Statistical Bulletin (various issues), Bullion, Economics and Financial Reviews and Annual Reports and Statement of Accounts for various years. Also, other relevant publications from the World Bank, the International Monetary Fund (IMF), e-library, books, periodical journals and articles were employed as sources of data. 3.4 Empirical Analysis Models Specification: The model used for quantitative method seeks to assess the impact of fiscal and monetary policies coordination and their effect on economic growth in Nigeria. The dependent variable in the model is the level of output (gross domestic product proxy for economic growth) while the explanatory variables are fiscal balance, total government expenditures and revenues, broad money supply, inflation rate. For this section, the specification of our models mirrors the research studies of Koyck (1954) and Olofin et al (2009). The functional form of the model is expressed as: GDPt = f (TGEt, TGRt, FISCt, MSt, INFRt, GDPt-1) ………………(3.1) Assuming logarithmic linear relationship, equation (3.1) can be transformed as follows: LnGDPt = η0 + η1LnTGEt + η2LnTGRt + η3FISCt + η4LnMts + η5INFt + η6 LnGDPt-1 + ε1t ……..(3.1a) However, to overcome the problems of multicollinearity, equation 3.1a was disaggregated as LnGDPt = η0 + η1LnTGEt + η2 LnMts + η3LnGDPt-1 + ε2t ………………….. (3.1b) LnGDPt = η0 + η1LnTGRt + η2FISCt + η3LnMts + η4INFt + η5LnGDPt-1 + ε3t ………….(3.1c) We have the following a priori expectations: 0 < η0 < 1, η1 > 0, η2 > 0, η3 < 0, η4 > 0, 0 < η5 < 1, 0 < η6 < 1. Where: GDP is gross domestic product, TGE is total government expenditure, TGR is total government revenue, Ms is broad money supply, INF is the inflation rate which is our proxy for composite consumer price index, GDPt-1 is one year lag value of GDP; FISCt-1 is one year lag value of FISC; ηi’s is the parameter to be estimated; and εt is the error term. In the absence of serial correlation, multicolinearity and limitations of sample size more lags can be introduced into equation (3.1a) for example to give: LnGDPt = η0 + η1LnTGEt + η2LnTGEt-1 + η3LnTGEt-2 …………… (3.1.1) However, if it is assumed that the parameters (η2) decline according to a geometric series this expression can be rewritten as: LnGDPt = η0 + η1LnTGEt + η1λLnTGEt-1 + η1LnTGEt-2 …………… (3.1.2) Where: 0 < λ < 1. Using Koyck (1954) and Olofin et al (2009), it can be readily shown by writing out λLnGDPt-1 in full employing equation (3.1.2) that: LnGDPt = λLnGDPt-1 = η0 (1- λ) + η1LnTGEt …………… (3.1.3) And hence that: LnGDPt = η0(1- λ)+ η1LnTGEt + η2LnTGRt + η3FISCt + η4LnMts + η5INFt +λη6LnGDPt-1…….(3.1.4) which is equivalent to equation (3.1a), and shows that given some restrictions, current gross domestic product can be estimated as a function of current total government expenditure, broad money supply, inflation rate and the gross domestic product of the immediately preceding period. Since the model is to be used for forecasting purposes, we are interested in both the long-run and short-run effects of a change in the levels of total government expenditure, broad money supply and inflation rate on gross domestic product. Therefore, the short-run effect for the equation (3.1.4) is measure by the elasticity coefficients defined as: δLnGDPt ∕ δLnTGEt = η1 ………… (3.1.5) δLnGDPt ∕ δLnTGRt = η2 ………… (3.1.6) δLnGDPt ∕ δFISCt = η3 ………… (3.1.7) δLnGDPt ⁄ δLnMts = η4 ……….... (3.1.8) and δLnGDPt ⁄ δINFt = η5 ……….... (3.1.9) In the long-run, inflationary rate is stable, thus: LnGDPt = LnGDPt-1 = LnGDP1-2 …………. (3.1.8) And can be restated as: LnGDPt = η0 + η1LnTGEt + η2LnTGRt + η3FISCt + η4LnMts + η5INFt + η6LnGDPt OR LnGDPt = η0 ∕ 1-η6 + η1 ∕ 1-η6 LnTGEt + η2 ∕ 1-η6 LnTGRt + η3 /1-η6 FISCt + η4 ∕ 1-η5 LnMts ….(3.1.9) And hence the long-run elasticity coefficients are defined from equation (3.1.9) as: δ LnGDPt ⁄δLnTGEt = η1 ⁄ 1- η6 ………….(3.1.10) δ LnGDPt ⁄δLnTGRt = η2 ⁄ 1- η6 ………………(3.1.11) δ LnGDPt ⁄δFISCt = η3 ⁄ 1- η6 ………………(3.1.12) δ LnGDPt ⁄δLnMts = η4 ⁄ 1- η6 and …………(3.1.13) δ LnGDPt ⁄δINFt = η5 ⁄ 1- η6 …………(3.1.14) Similarly, for equations (3.2 and 3.3), the short-run and long-run elasticity coefficients would be expressed in the same vain as in equations (3.1, and 3.1.11 to 3.1.12). Finally, the parsimonious error correction mechanism (ECM) can be specified as: ∆LnGDPt = α0 + α1∆LnTGEt-s + α2∆LnTGRt-s + α3∆FISCt-s + α4∆LnMt-ss + α5∆INFt-s + α5∆LnGDPt-1 + α5ECMt-1 + ε1t ………………………………………………………... (3.2) Empirical Models Results and Discussion 3.5.1 Unit Root Tests Table 3.1: Observed Results of the Augmented Dickey Fuller (ADF) and Phillips-Perron (PP) Test Statistics VARIABLES LEVELS 1ST DIFF. LEVELS 1ST DIFF.   ADF t-Statistic ADF t-Statistic PP t-Stat PP t-Stat  t-Stat -2.5305 -3.91695 -3.18097 -4.060021 LOGGDP -3.53308 -3.5366 -2.93316 -3.520787 INFR -2.94115 -2.94343 -2.935 -3.523623 LOGMS -2.94115 -2.94343 -2.93316 -3.520787 LOGTGE -3.53308 -2.94343 -2.935 -3.523623 LOGTGR -3.53308 -3.5366 -2.93316 -3.520787 FISC -2.94115 -2.94343 -2.93316 -3.520787 Source: Authors’ Computations The table 3.1 above reports the summary of statistics used for testing the unit roots of the time series data. It is evident from the table that all the variables became stationary series when approximately differenced. From the two types of integration tests carried out, it could be concluded that all the variables in our models contain unit roots at levels. Therefore, we safely proceed to use the co-integration method in analysing our models as conventional regression model will generate spurious results due to the integration level of the series. 3.5.2 Cointegration Test As a result of the challenges with the Engel-Granger framework for testing cointegration, our results were validated using the Johansen (1991, 1995) approach. This approach provides the number of cointegrating equations and estimates of all the cointegrating vectors in the multivariate case as contained in the table 3.2 below. Date: 01/25/14 Time: 20:06 Sample (adjusted): 1972 2012 Included observations: 41 after adjustments Trend assumption: Linear deterministic trend Series: LOG(GDP) INFR LOG(M2) LOG(TGE) LOG(TGR) FISC  Lags interval (in first differences): 1 to 1 Unrestricted Cointegration Rank Test (Trace) Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value None *  0.701428  114.6667  95.75366 At most 1  0.443310  65.10817  69.81889 At most 2  0.316552  41.09256  47.85613 At most 3  0.304897  25.48777  29.79707 At most 4  0.216132  10.57623  15.49471 At most 5  0.014338  0.592108  3.841466  Trace test indicates 1 cointegrating eqn(s) at the 0.05 level  * denotes rejection of the hypothesis at the 0.05 level  **MacKinnon-Haug-Michelis (1999) p-values Unrestricted Cointegration Rank Test (Maximum Eigenvalue) Hypothesized Max-Eigen 0.05 No. of CE(s) Eigenvalue Statistic Critical Value None *  0.701428  49.55857  40.07757 At most 1  0.443310  24.01562  33.87687 At most 2  0.316552  15.60479  27.58434 At most 3  0.304897  14.91153  21.13162 At most 4  0.216132  9.984126  14.26460 At most 5  0.014338  0.592108  3.841466  Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level  * denotes rejection of the hypothesis at the 0.05 level  **MacKinnon-Haug-Michelis (1999) p-values The Johansen cointegration test results are showed in the above table 3.2. The trace and max-eigen tests conducted indicate the existence of one (1) cointegrating equation each at the 0.05 significance level. The consistency in the test results confirms the existence of long-run relationship among the variables in the models. 3.5.3 Empirical Estimation- Long-Run Static and Short-Run Dynamic Models However, due to the facts that the data series are non-stationary and the vector of the variables in the equations appear to be cointegrated, we evaluate the second phase of the Engel-Granger technique leading to the estimation of error correction form of stochastic model. This model represents the short-run behaviour and the adjustment to the long-run model. Hence, the residual from the cointegrating model lagged one period was used as the error correction mechanism (ECM) in the dynamic model 3.2 (appendix, table 3.4). All the diagnostic test statistics are quite satisfactory and the magnitude of the coefficients confirms the absence of redundant regressors. Judged by the significance of t-statistics and the p-value, the coefficients are well determined. The disequilibrium error term, ECMt-1, is statistically significant and negative (as expected) in all the equations. The significance of the error term confirms the existence of the long-run relationship between the variables in the ECM. Of particular interest is the coefficient on the ECMt-1 in the gross domestic product model 3.2 (appendix, table 3.4). The ECMt-1 induces about 5.26% adjustment per period in the model, that is, the preceding period’s disequilibrium is eliminated in the current period. In addition, the model is statistically significant and the overall statistical fit is good. This is a clear evidence of the goodness-of-fit of the models. The value of the adjusted R2 reveals that the short-run dynamic model 3.2 accounts for at least 99.3% changes in the economic growth. The marginal significance level (p-value) of the F-statistics is zero. The null hypothesis of the F-statistics is rejected for all choices of significance level. Hence, we conclude that as groups, the regression coefficients are significantly different from zero (0). The economic growth model in the table has statistically significant coefficients for broad money supply, total government expenditure (TGE), inflationary pressure and the past levels of gross domestic product. Evidently, estimates from the vector ECM (short-run model) reveal that (TGE) in the first period lagged has a positive and significant effect on economic growth in Nigeria, that is, a 1% increases in total government expenditure has about 0.12% increases in economic growth. However, in the long-run model (equation 3.1 table 3.3), the result depicts that the total changes in the dependent variables (total government expenditure, broad money supply and inflation) accounted for 99.62% changes in the economic growth (proxy by gross domestic product). From the model, while the broad money supply is not statistically significant and the coefficient is 0.028, the inflation rate, total government expenditure and revenue are statistically significant with positive coefficient of about 0.04, 0.41 and 0.45 respectively. Conclusion and Policy Implications The study reveals that as government expenditure increases, as a result of the increase in economic activities of government, there exhibited an appreciable increase in GDP growth rate (economic growth) as well as a manageable increase in the inflation rate. Moreover, a reduction in the broad money supply (M2) reflects a reduction in the inflation rate but with a positive effect on GDP growth rate. Our findings suggest, consistent with Barro-style models, that increases in taxes spent on public infrastructure and other productive investments increase the growth rate of state real personal income per capita, but at a declining rate, so that the impact of taxes depends both on where they are spent, as well as on the initial level of taxes. We also provide empirical assessments on the extent to which state and local taxes and corresponding public investments are optimal, too low, or too high in terms of growth in state real personal income per capita. However, the results supported the endogenous growth-type model which implied that: the aggregate production function exhibits increasing returns to scale; the impact of an increase in public investment on growth is large, significant, and robust; increases in government investment have a positive impact on growth; and growth is boosted by economic policies that foster external competitiveness and a prudent fiscal stance. Both preliminary analysis, i.e., long-run (static) relationships and the final analysis, i.e., short-run (dynamic) relationships shows that prudent expenditure policy along with moderate broad money supply policy raises economic growth rate in a good policy environment. This analysis shows that prudent fiscal policy together with healthy monetary policy reduces inflationary pressures in Nigeria. Therefore, efficient monetary-fiscal policy measures help to suppress inflationary tendencies and enhance factor productivity when properly coordinated. Finally, sound fiscal policy along with healthy monetary policy eliminates and/or minimizes inflationary crises in an environment of fiscal dominance. Also, it is evidence from this paper that Nigeria’s government has yet to commit to a sound strategy for coordinating fiscal-monetary policy for effective management of the economic resources. The question, then, is how the policy environment should be organized in order for it to facilitate the accumulation of production factors and their efficient allocation, as well as the introduction of enhanced technologies. Economic policies at the micro level should clearly aim to develop and sustain efficient markets, while macro policy should be geared towards guaranteeing macroeconomic stability. It has also become increasingly clear that a supportive environment of efficient institutions is crucial for functioning of the economy. The paper identifies the following broad areas of possible government policy interventions that implied: - Public policies such as direct transfer and subsidies would be highly effective when the resources for tackling poverty are channeled to the people who are genuinely poor in Nigeria. Second, it is important that beneficiaries do not become unduly dependent on the poverty alleviation programmes. Those who are truly poor should be given adequate incentive to invest and build the asset (education) that can enable them to stay out of poverty. Also, those who are productively engaged in alternative economic activities should not be allowed to participate in the poverty programmes. When a subsidy of goods consumed by the poor is planned it should be targeted to the geographical area where the people are found and should emphasize goods that non-poor people do not consume. This helps to conserve resources for the poor and minimizes free riding by rich people in the society. - In order to identify a more sustainable and less volatile source of revenue for social policy investments, as well as to stimulate job creation, the Nigerian government should consider diversifying oil revenue to agricultural production and pursuing an active industrial policy, by promoting non-oil exports as they have the potential to stimulate employment opportunities in the industries. - To strengthen household incomes and provide greater economic opportunities, the government should seek to encourage the numerous rural and agricultural policies that exist to aid food security and tackle rural poverty to synthesis and consolidate for greater effect and efficiency. - Government should improve budget transparency to tackle corruption and ensure transparency and accountability in the use of public funds. It should reinforce by stronger anti-corruption laws, mechanisms and institutions as well as sanctioning of corrupt officials to create a culture of accountability. References Adenaike, A. S. (2012). Nigerian Economic Reform: The Roles of Economic Policies in Facilitating Recovery from Crises, Journal of Educational Advancement, Vol. 6, No.1, 13-25. Aghevli, B. B. and M. S. Khan (1970). “Inflationary finance and Economic Growth,” Journal of Political Economy, Vol. 85, No. 4. Anderson, L. C. and K. M. Carlson (1972). “An econometric analysis of the relation of monetary variables on the behavior of prices and unemployment.” In Otto Eckstem, ed., 1972 ‘the econometric of price determination’ Board of Governors of the Federal Reserve System. Anyanwu, J. C. (1997), Nigeria Public Finance, Anambra: Joanee Educational Publishers Ltd. Aschauer, D.A. (1997). “How Big Should the Public Capital Stock Be? The Relationship between Public Capital and Economic Growth,” Economics Public Policy Brief Archive 43, Levy Economics Institute. Asogu, J. O. (1991). An Econometric Analysis of the Nature and Causes of Inflation in Nigeria. Bania, N., J. A. Gray, and J. A. Stone (2006) “Tax, Government Expenditures, and State Economic Growth: the Role of Nonlinearities”, University of Oregon, Engene, Retrieved from https://scholarsbank.uoregon.edu/dspace/.../UO-2006-7_Stone_NLTax.pdf on 30/07/2012. Barro, R. (1989) “A Cross-Country Study of Growth, Saving, and Government,” NBER Working Paper, 2855. Barro, R. (1990) “Government Spending in a Simple Model of Endogenous Growth,” Journal of Political Economy 98, 103-117. Bleaney, M., Gemmell, N. and R. Kneller (2001) “Testing the Endogenous Growth Model: Public Expenditure, Taxation, and Growth over the Long Run,” Canadian Journal of Economics 34, 36-57. Barro, Robert, and Xavier Sala-i-Martin (1992) ‘Public finance in models of Economic growth,’ Review of Economic Studies 59, 645-61. Barro, Robert, and Xavier Sala-i-Martin (1995) Economic Growth (New York: McGraw-Hill Blanchard, O. J. and S. Fischer (1989), Lectures on Macroeconomics, Cambridge, MA.: MIT Press. Dickey, D. A. and Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, Vol. 74. Engel, R. F. and Granger, C. W. (1987). Co-integration and Error Correction: Representation, Estimation and Testing. Econometrica, Vol. 55, 251-276. Easterly, William, and Sergio Rebelo (1994) ‘Fiscal policy and economic growth,’Journal of Monetary Economics 32, 417-458 Gray, J. and J. Stone (2006) “Ricardian Equivalence for Sub-national States,” Economics Bulletin 5, 1-12. Grossman, G. & E. Helpman, (1991), "Quality Ladders and Product Cycles", Quarterly Journal of Economics, 106: 557-586. Helms, L. (1985) “The Effect of State Local Taxes on Economic Growth: A Time Series-Cross Section Approach,” Review of Economics and Statistics 67, 574-582. Hendry, D. F. (1986). Econometric Modeling with Co-integrated Variables: An Overview. Oxford Bulletin of Economics and Statistics, Vol. 48, No. 3, pp. 201-12. Holcombe, R. and D. Lacombe (2004) “The Effect of State Income Taxation on Per Capita Income Growth,” Public Finance Review 32, 292-312. Jones, Larry E., Rodolfo E. Manuelli, and Peter E. Rossi (1993) ‘Optimal taxation in models of endogenous growth,’ Journal of Political Economy 101, 485-519 Johansen, S. (1988). Statistical Analysis of Co-integration Vectors. Journal of Economic Dynamics and control, 12: 231-254. Lucas, R. (1988), "On the Mechanics of Endogenous Growth", Journal of Monetary Economics, 22: 3-42. King, Robert, and Sergio Rebelo (1990) ‘Public policy and economic growth: Developing neoclassical implications,’ Journal of Political Economy 98, s126-s151 Koyck, L.M. (1954), Distributed Lags and Investment Analysis, Amsterdam: NorthHolland.Gian Kalaitzidakis, P. and S. Kalivitis (2005) “’New’ Public Investment and/or Public Capital Maintenance for Growth? The Canadian Experience,” Economic Inquiry 43, 586-600. Mark, S., McGuire, T. and L. Papke (2000) “The Influence of Taxes on Employment and Population Growth: Evidence from the Washington, D.C. Metropolitan Area,” National Tax Journal 53, 105-124. Mendoza, Enrique G., Maria Milesi-Ferretti and Patrick Asea (1997) ‘On the effectiveness of tax policy in altering long-run growth: Harberger's superneutrality conjecture,’ Journal of Public Economics 66, 99-126 Mofidi, Alaeddin and Joe A. Stone (1990) ‘Do state and local taxes affect economic growth?’ Review of Economics and Statistics 72, 686-91 Olofin, S., Iyaniwura, J., Adeniyi, J. and Olayide, S. (2009). The Cear Model Mac-IV. In Adenikinju, A. Busari, D. and Olofin, S. (eds.), Applied Econometrics and Macroeconometric Modelling in Nigeria (pp. 43-76). Ibadan, Nigeria: Ibadan University Press. Pecorino, P. (1993), "Tax Structure and Growth in a Model with Human Capital", Journal of Public Economics, 52: 251-271. Rebelo, S. (1991), "Long-Run Policy Analysis and Long-Run Growth", Journal of Political Economy, 99: 500-521 Romer, C. (1986), “New Estimates of Pre-war Gross National Product and Unemployment,” JEH June, 341- 352. Romer, David (1988) “What are the Costs of Excessive Deficits?” NBER Macroeconomics Annual, 63-98. Solow, Robert M., (1956) ‘A contribution to the theory of economic growth,’ Quarterly Journal of Economics 71, 65-94 Stokey, Nancy, and Sergio Rebelo (1995) ‘Growth effects of flat-rate taxes,’ Journal of Political Economy 103, 510-50 Tanzi, V. & H. Zee, (1993), "Time Constraints in Consumption and Savings Behavior" Journal of Public Economics, 50: 253-259. Tella, S. A., Adenaike, A. S., Sennuga, M. A. and Adekoya, E. O. (2013). ‘Equality and Priority in the Distribution of Wealth in Nigeria’, in Nationalism and Economic Justice in Nigeria. A paper presented at the International Conference in Honour of Dr. Oladipo Fashina. Department of Philosophy, Obafemi Awolowo University, Ile-Ife, Nigeria, 7th - 9th May. Usman, Shamsudden (2007), “Improving Fiscal Coordination between the Federal and State Governments”, Presentation at the Department for International Development (DFID) Country Partnership Strategy Retreat by the Honourable Minister of Finance October 3, Retrieved on 29/05/2009, from http://www.fmf.gov.ng/FMF_News.aspx?id=54. Appendices Table 3.3: Long-run (Static) Model Dependent Variable: LGDP Method: Least Squares Date: 06/12/14 Time: 09:21 Sample (adjusted): 1971 2012 Included observations: 42 after adjustments Variable Coefficient Std. Error t-Statistic Prob.   C 0.517172 0.199436 2.593181 0.0137 FSC 0.039507 0.007345 5.378813 0.0000 LM2 -0.017349 0.057511 -0.301667 0.7646 LTGR 0.448851 0.109126 4.113140 0.0002 INF 0.004292 0.001447 2.966736 0.0053 LGDP(-1) 0.597781 0.129939 4.600479 0.0001 R-squared 0.998083     Mean dependent var 13.24994 Adjusted R-squared 0.997817     S.D. dependent var 2.734500 S.E. of regression 0.127763     Akaike info criterion -1.145711 Sum squared resid 0.587645     Schwarz criterion -0.897472 Log likelihood 30.05993     Hannan-Quinn criter. -1.054722 F-statistic 3749.076     Durbin-Watson stat 2.093442 Prob(F-statistic) 0.000000 Source: Authors’ Computation Dependent Variable: LGDP Method: Least Squares Date: 06/12/14 Time: 09:50 Sample (adjusted): 1971 2012 Included observations: 42 after adjustments Variable Coefficient Std. Error t-Statistic Prob.   C 0.945856 0.193463 4.889086 0.0000 LTGE 0.409260 0.069154 5.918074 0.0000 LM2 -0.028352 0.057732 -0.491093 0.6262 LGDP(-1) 0.597467 0.093311 6.402940 0.0000 R-squared 0.997566     Mean dependent var 13.24994 Adjusted R-squared 0.997374     S.D. dependent var 2.734500 S.E. of regression 0.140139     Akaike info criterion -1.001968 Sum squared resid 0.746282     Schwarz criterion -0.836475 Log likelihood 25.04132     Hannan-Quinn criter. -0.941308 F-statistic 5190.875     Durbin-Watson stat 1.498518 Prob(F-statistic) 0.000000 Source: Authors’ Computation Table 3.4: Short-run (Dynamic) Model Dependent Variable: LOG(GDP)         Method: Least Squares     Date: 01/26/14 Time: 19:49     Sample (adjusted): 1971 2012     Included observations: 42 after adjustments   Variable Coefficient Std. Error t-Statistic Prob.   C 1.34573 0.171562 7.843992 0 LOG(M2) 0.168372 0.076848 2.190977 0.0347 LOG(TGE) 0.864403 0.085616 10.09628 0 LOG(TGE(-1)) 0.117986 0.223298 0.528381 0.601 LOG(TGE(-2)) 0.102179 0.16251 0.628754 0.5341 INFR -0.06353 0.038839 -1.6358 0.1131 INFR(-1) -0.02329 0.043531 -0.53499 0.5969 FISC(-1) 0.175419 0.158782 1.104776 0.2787 FISC(-2) 0.170337 0.140237 1.21463 0.2347 LOG(TGR) -9.87138 9.196245 -1.07341 0.2911 ECM(-1) -0.52647 0.203385 2.588529 0.0136 R-squared 0.993946     Mean dependent var 13.24994 Adjusted R-squared 0.993468     S.D. dependent var 2.7345 S.E. of regression 0.220999     Akaike info criterion -0.09093 Sum squared resid 1.855934     Schwarz criterion 0.074564 Log likelihood 5.909496     Hannan-Quinn criter. -0.03027 F-statistic 2079.709     Durbin-Watson stat 1.971189 Prob(F-statistic) 0       Source: Authors’ Computation PAGE \* MERGEFORMAT 23