Wu et al. The study areas are Nigeria and China. Past works on growth literature have shown numerous analytical and empirical analyses that revealed how public spending can influence GDP growth Bose et al. For example, a complementary capital stock can be seen in the public spending on education and health; this spending could stimulate an increase in the stock of human capital.
Moreover, public funds can also contribute to growth indirectly by increasing the marginal productivity of both publicly- and privately-supplied production factors. Based upon this premise the study adopted a simple version of endogenous growth theory and data that covers the period — Following the theoretical framework suggested by Ram , this paper simulates an economy comprised of two comprehensive sectors: the first is the Government sector G O and the second is the Non-Government sector NGO.
Production functions contained in the two sectors could be transcribed as:. Consequently, output in each sector varies according to the inputs of labour L and capital K and likewise, the output of the government sector Go isometrics an external consequence on the output of the non-government sector NGO.
Hence, the total inputs are specified by:. Subsequently, the total output Q is the addition of outputs in the two segments, given as:. The paper presumes that the virtual factor productivity in the two segments varies, hence it can be written:. Consequently, the symbol signifies which sector has upper marginal factor productivity. Hence, an optimistic value of indicates more input productivity in the government sector, while a pessimistic value of denotes a different result.
Therefore, by totally differentiating and manipulating the production functions of equations 3 and 5 , the paper deduces that:.
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Taking a cue from Feder and Marta et al. A constant term and a random stochastic disturbance term with the usual properties have been included. To express these relationships, standard panel techniques for the econometric estimation were adopted, taking a cue from Greene This estimation model allows great flexibility in modelling differences across the countries considered China and Nigeria. The basic framework is a regression model of the form:.
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The influence of the disturbance term u it on the dependent variable has been dominant and it became necessary to find a means of decomposing the disturbance term u it. Various econometric effects have been instituted to decompose the disturbance term u it. Furthermore, past studies have argued that the use of a random- or fixed-effects model may lead to better P-values, since this approach applies a more efficient estimator Pham, Hence, this study will adopt the model that will give unbiased estimates and that also addresses the disturbance term u it.
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Taking a cue from the studies of Arellano and Bond , the study modified the model in equation 1 in line with the objective of the study by decomposing the disturbance term u it. The disturbance term is divided into an individual specific effect component, u it , and a remainder disturbance component, v it , that differs over cross section country and time year.
To examine all the variables that affect GDP, Q it , in a cross-sectional way, data is required that will not vary over time, and hence there is a need to introduce dummy variables Barro et al. In line with the works of Pham the study therefore adopted the econometric terms of the least squares dummy variable approach LSDV for the estimation procedure:. The literature has indicated that the most widely used method to estimate the strength of coefficients is Ordinary Least Squares OLS Henderson and Parmeter This study argued that the rationality of the method relies on the fulfilment of several assumptions, e.
The works of Barro , Bose et al. In the same vein, a study by Agenor et al. Moreover, part of their reason lies in its conceptual framework and qualitative measurement problems.
Another methodological problem evidenced by these studies is that the OLS method with panel data cannot provide unbiased estimated betas and is therefore subject to biased conclusions. To address these shortcomings these studies adopted a random-effects model. This model allows different parameters cross-sectionally and can give better P-values, since this approach applies a more efficient estimator.
Hausman was also used to test the hypothesis of the effectiveness of the random effects model in the analysis. In addition, the use of a random effects model would help the robustness of the results. Consequently, the study looked into the quality of public spending more precisely in connection with the governance variable and its impacts on human development indicators. This is an endogeneity issue and it is addressed in the paper by using a large data set of — to be able to capture the public-spending-policy effect as reflected in the GDP growth, or otherwise, over time. The error term is decomposed into errors and residuals Eq.
The dummy variables Eq.
In addition, it boosts the multiple determination of the independent variables R 2 of the results and lessens errors. The coefficient of the dummy variables included in the equation will show what difference it makes to have timely access to modest public funding in the agricultural sector. It is hoped that adding the dummy variables to the decomposed errors will thus improve estimates in the random-effects model. The results of the random effects model revealed different components of government expenditure on GDP growth in the agricultural sector.
The weighted specification results show that the explanatory variables as a group significantly explained the variability in the dependent variable, which is indicated by the F-statistic and the p-values. In addition, this model shows an exceptional explanatory power, displayed by R 2 0. In Nigeria, the model results revealed that, of the four variables and two dummies considered, four variables were significant at difference level of significance.
Similarly, for China, of the four variables and two dummies considered, three variables were significant and positive at a difference level of significance Table 2. The classical growth theory suggested that capital will positively contribute to economic growth. In Nigeria, the effect of capital in the form of government expenditure on GDP growth is significant but with a negative coefficient, and, hence, the effect of public expenditure on GDP growth has an inverse relationship, but has a direct relation in the case of China Table 2. The dummy variables that were used in this analysis due to the presence of outliers aimed to capture the occurrence of public expenditure effectiveness and macro-economic stability in the growth of GDP.
Dummy D 1t has a coefficient of 0. The results revealed that government expenditure on GDP growth shows a significant positive influence for China and a negative one for Nigeria. This thus suggests that the Nigerian economy is highly capitalistic and strongly inclined to laissez-faire. Therefore, investments in GDP growth are focused on long-term improvement and not according to the business cycle. The effects are probably not observed in the time-span of the analysis.
In addition, the negative coefficient for Nigeria can also be explained by analysing the expenditure pattern. The analytical chapters are complemented by a statistical appendix of over 60 tables providing comprehensive data on various facets of world trade in goods and services. Charts and tables. All Excel tables in zip format.
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