Local Government Spending and Economic Growth in Guangdong: The Key Role of Financial Development. Chi-Chuan LEE
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1 2017 International Conference on Economics and Management Engineering (ICEME 2017) ISBN: Local Government Spending and Economic Growth in Guangdong: The Key Role of Financial Development Chi-Chuan LEE School of Management, Beijing Normal University Zhuhai, Zhuhai, China Keywords: Government Spending, Economic Growth, Financial Development. Abstract. This paper investigates how financial development shapes the relation between local government spending and regional economic growth by applying the GMM technique on dynamic panels. Using panel data of 21 prefecture-level cities in Guangdong for the period and after considering the financial development for each region, our findings show that local government spending and regional economic growth have a significantly positive relationship, which supports Wagner s Law. However, the level of financial development may mitigate the positive relation between government spending and economic growth, but the impact of financial development is different for Pearl River Delta and non-pearl River Delta. Introduction It is widely recognized that government spending and financial development constitute a potentially important dual mechanism for sustaining a country s development in terms of economic and financial aspects. From an economic perspective, Law of the Increasing Extension of State Activity postulates a positive relationship between economic development and government spending [1]. Even though there is a considerable amount of empirical literature devoted to understanding the direct impact of government spending on economic growth, there is no consensus among analysts due to inconclusive results therein [2 5]. From a financial perspective, the supply-leading and demand-following theories postulate that either financial development speeds up economic growth or economic activity pushes forward financial development [6 7]. Although financial development has been extensively analyzed and discussed in both the theoretical and empirical literature due to its relevance to an economic system, the results are mixed and conflicting [8]. Given the importance of government spending and financial development to an economy, there is a distinct lack of analyses dealing with the interrelationships among government appending, economic growth, and financial development in a unified framework. This paper examines the role of financial development in explaining the variation across prefecture-level cities and over time for the relation between government spending and economic growth in China. This paper contributes to the literature in several respects. First, most studies in this line of research focus on developed countries, with little attention given to developing countries. The impact of government spending on growth in China is an important issue worth further investigation given its massive economic size, rapid growth, and continuing policy reforms. Second, while some efforts have been made at looking at growth regression in China, most of them are conducted either using whole country data or focusing on provincial data. Government spending at the prefecture-level has rarely been used in previous analyses. From a policy perspective, the prefecture is the most stable frame of reference, making any empirical study more rigorous [9]. Third, there are several theoretical justifications for regional differences in the level of economic growth. To account for the geographical pattern of regional disparities, we classify Pearl River Delta (PRD) and non-pearl River Delta (non-prd) from the whole dataset. Discussing the regional effects enables policy-makers and authorities to determine when to respond to an economic shock. To fill the gap in the literature, a detailed prefecture-level analysis will be undertaken in Guangdong Province for the period To the best of our knowledge, this study is the first to investigate the correlations among
2 government expenditure, financial development, and economic growth for prefecture-level cities in Guangdong. Theoretical Structure and Empirical Model This study applies the two-step dynamic panel data approach suggested by Arellano and Bover [10] and Blundell and Bond [11] and uses the dynamic panel generalized method of moments (GMM) technique to estimate the cross-city regression. The system estimator avoids the weak instrument problem and provides a more flexible variance-covariance structure for the moment conditions. Compared with conventional static panel data regression models, such as fixed-effect or random-effect models, the panel GMM technique is much more consistent and efficient in estimating the coefficients of equations and in solving for the problems of endogeneity, heteroskedasticity, and autocorrelation. Given the considerations of the theoretical and empirical studies described above, we specify the following basic model:. (1) Here, and denote the time period and the individual cities, respectively. Term is a measure of regional economic growth, proxied by GDP, that is observed in city at time ; term represents the local government expenditure. The term comprises the set of control variables pertaining to the city, such as investment (INV) and population (POP). While is the estimated persistence coefficient for regional economic growth, a significant implies that growth will last from one year to the next. The testable hypothesis is that if the coefficient of is positive, then Wagner s Law holds. are estimated parameters. Finally, is the error term, and and are time and city effects, respectively. Equation (1) is the benchmark specification, designed to investigate the impact of local government expenditure on regional economic growth. However, the relation may be generated by different mechanisms at different levels of local financial development. To check for this possibility, we next modify the benchmark specification by adding an explicit interaction term of financial development on the relationship between government expenditure and regional economic growth. As a result, our extended model is:. (2) Here, refers to the measures of local financial development. This paper hypothesizes four possible outcomes in relation to the coefficients and of equation (2). If and, then government expenditure has a positive effect on economic growth, and financial development affects the relation favorably. If and, then government expenditure has a positive effect on economic growth, and financial development affects the relation adversely. If and, then government expenditure has a negative effect on economic growth, and financial development adversely impacts that negative effect. If and, then government expenditure has a negative effect on economic growth, and financial development favorably impacts that negative effect. Empirical Results To perform the empirical analysis, our data consist of a panel of 21 prefecture-level cities and their gross domestic product (GDP), total government spending (GEXP), total investment in fixed assets (INV), population (POP), and the ratio of total deposits and loans over GDP (FIR) for the period All data required were taken from Guangdong Statistical Yearbook and expressed in natural logarithm. Apart from population, all the data series are transformed into real terms by the consumer price index (CPI). Table 1 presents descriptive statistics and correlations of all variables.
3 We see that there is a high positive correlation (0.950) between GDP and GEXP, meaning an increase in government spending brings about higher economic growth in these cities. The remaining correlation coefficients are around to 0.957, all of which are acceptable when it comes to avoiding the problem of multicollinearity. Table 1 Descriptive statistics and correlation matrix. GDP GEXP INV POP FIR Descriptive statistics Mean Standard deviation Minimum Maximum Correlations GDP 1 GEXP INV POP FIR To assess the basic descriptive relation between government appending and economic growth, we start with a simple regression by using GMM for dynamic panels in order to eliminate endogeneity bias in regressors. Table 2 reports the empirical results from the basic model for the full sample, Pearl River Delta, and non-pearl River Delta, respectively. At the bottom of the table, we provide the results from the Sargan test of over-identifying restrictions. All models pass the Sargan test for over-identifying, confirming that our instruments are valid. We find that the coefficients for persistence of growth are positively significant within the range from to 0.261, implying that regional economic growth lasts from one year to the next. Regarding other control variables, the effects of investment and population are all positively significant. This means GDP increases along with INV and POP being increased. Table 2 Estimated results for the benchmark model. Explanatory variables Full Sample PRD Non-PRD Coefficient S.E. Coefficient S.E. Coefficient S.E. GDP(-1) ** ** GEXP *** INV *** *** *** POP *** *** *** Observations Sargan test (p-value) Notes: The dependent variable is the natural logarithm of real GDP. Columns 1-3 represent the results for the full sample, Pearl River Delta, and non-pearl River Delta, respectively. The estimation method is the two-step GMM dynamic panel estimator. The instruments are lagged levels for differences and lagged differences for levels. The Sargan test is the overidentification test, where the null hypothesis is that the use of instruments is not correlated with the residuals. *** and ** indicate statistical significance at the 1% and 5% levels, respectively. As to the effect of government spending on growth, the evidence shows that GEXP has a negatively significant impact in the full-sample analysis, but a positively insignificant impact on Pearl River Delta and non-pearl River Delta. In these cases, Wagner s Law does not hold. Given that the impact of local government spending on regional economic growth is uncertain, a rigorous examination is further needed. With the importance of financial development to an economy, we next explore the question of whether financial development affects the relationship between government
4 spending and economic growth. Tables 3 present the empirical results for the interaction between government spending and financial development. Explanatory variables Table 3 Results for the extended model. Full Sample PRD Non-PRD Coefficient S.E. Coefficient S.E. Coefficient S.E. GDP(-1) *** *** GEXP *** ** *** GEXP x FIR *** ** INV *** *** POP *** *** *** Observations Sargan test (p-value) Notes: The dependent variable is the natural logarithm of real GDP. Columns 1-3 represent the results for the full sample, Pearl River Delta, and non-pearl River Delta, respectively. The estimation method is the two-step GMM dynamic panel estimator. The instruments are lagged levels for differences and lagged differences for levels. The Sargan test is the overidentification test, where the null hypothesis is that the use of instruments is not correlated with the residuals. *** and ** indicate statistical significance at the 1% and 5% levels, respectively. Different from the results of the benchmark model, the effect of financial development and the direct and indirect effects of government spending are significantly positive and negative, respectively, showing that the level of financial development may mitigate the positive relation between government spending and economic growth. As far as the regional effects are concerned, the results show that the interaction term of government spending and financial development is insignificantly negative for the non-prd region. One possible reason behind the different results between PRD and non-prd regions is related to a region s development level. Conclusions and Implications This paper provides a full discussion of the interrelationships among local government spending, regional economic growth, and the level of financial development, with the aim being to investigate the issue of whether there is a positive relationship between government spending and economic growth, which would support Wagner s Law in Guangdong for the period To gain better insight into the debate concerning the relationship between these variables, we also explore the regional effects for both the Pearl River Delta and non-pearl River Delta regions. Our main findings are as follows. First, government spending does have a significantly positive impact on economic growth, thus supporting the validity of Wagner s Law. Second, the level of financial development may mitigate the positive relation between government spending and economic growth. Third, GDP increases along with investment and population being increased. Finally, the interaction term of government spending and financial development is insignificantly negative for the non-prd region. Thus, a regional effect does matter for the relationship between local government and economic growth. Acknowledgement This research was financially supported by the Department of Finance of Guangdong Province (Grant Number: Z201620).
5 References [1] A. Wagner, Finanzwissenchaft,, Leipzig: C. F. Winter, [2] T. Chang, W. Liu, S.B. Caudill, A re-examination of Wagner's Law for ten countries based on cointegration and error-correction modelling techniques, Appl. Financ. Econ. 14 (2004) [3] D. Tobin, Economic liberalization, the changing role of the state and Wagner's Law: China's development experience since 1978, World Dev. 33 (2005) [4] P.K. Narayan, I. Nielsen, R. Smyth, Panel data, cointegration, causality and Wagner's law: Empirical evidence from Chinese provinces, China Econ. Rev. 19 (2008) [5] A.M. Wu, M. Lin, Determinants of government size: evidence from China, Public Choice 151 (2012) [6] J.A. Schumpeter, The theory of economic development, Cambridge: Harvard University Press, [7] R.E.J. Lucas, On the mechanics of economic development, J. Monetary Econ. 22 (1988) [8] C.C. Lee, C.C. Lee, Y.B. Chiu, The link between life insurance activities and economic growth: Some new evidence, J. Int. Money Financ. 32 (2013) [9] S. Lee, D.W. Oh, Economic growth and the environment in China: Empirical evidence using prefecture level data, China Econ. Rev. 36 (2015) [10] M. Arellano, O. Bover, Another look at the instrumental variable estimation of error-components models, J. Econom. 68 (1995) [11]R. Blundell, S. Bond, GMM estimation with persistent panel data: An application to production functions, Econom. Rev. 19 (2000)
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