Resource Boom and Economic Growth: Empirical Evidence from China

This study investigates the relationship between resource boom, industrial structure and economic growth of China. Compared with these developed countries with a mature industrial structure, China is still in the process of the industrialization. We first examine the effect of a resource boom in China and provides a quantitative example illustrating that local governments with a resource boom should pay more attention to the initial industrial structure to launch any policy to avoid the “resource curse”. Introduction The core model of “Dutch disease” is based on the experiences of developed countries, such as Holland and Australia (Bandara, Jayatilleke S. 1995). Comparing to developed countries, the most distinguished features of developing countries, such as China is the immature industrial structure. We propose an alternative mechanism of a resource blessing for the developing countries. The mechanism is based on the interaction between a resource boom, industrial structure and economic growth. We show that a resource boom will change the industrial structure, and if a country has an immature industrial structure, such as a sufficiently large agriculture sector or a small non-resource manufacturing sector, resource boom can enjoy a blessing with a resource boom. We extend the literature by the following features: examine the impact of a resource boom on the industrial structure and propose that industrial structure condition is an essential factor for an economy with natural abundance; make a clear distinction between resource boom and price boom: a price boom is a rise of price of natural resources, a resource boom is an increase in the stock of natural resources, and narrow our research on the resource boom; focus on the industrial-structure transmission channel of natural resource boom and propose that the initial industrial structure is an essential factor for an economy to experience a blessing or curse with a resource boom. Empirical analysis In this section, we use Chinese city-level data to provide evidence in support of the key predictions of the model. We employ the data of 255 major cities covering 26 provinces taken from volumes of the China City Statistical Yearbook over the period 2001–2011. First, we identify the effect of resource booms on industrial structure. Second, we examines whether there is a resource “curse” or “blessing” in China and then verify the industrial-structure mechanism of a “resource blessing” effect. Impact of a resource boom on industrial structure. Our model predicts that a natural resource boom has not only the economic growth impact, but also the structural impact on the local economy. We test these hypotheses with Chinese city-level data under the following specification: , 0 1 , -1 2 , 3 , , Re i t i t i t i t i t IS IS VC α α α α e = + + + + (1) 5th International Conference on Social Science, Education and Humanities Research (SSEHR 2016) © 2016. The authors Published by Atlantis Press 128 The dependent variable , i t IS is the output share of different sectors over the whole economy. , i t VC represents other control variables, including the degree of openness, institutions and the prices of natural resources. The lagged value of natural resource prices allow us to exploit the significant time variation in the level of the prices, while also mitigating concerns about omitted variable biases and endogeneity. To control for omitted city characteristics, city fixed effects are included and errors are clustered by city. Year fixed effects are also included so as to capture time trends affecting all the cities in the sample. Finally, the level of GDP per capita is also included as a control. Our model predicts not only the structural impact but also the level impact of a resource boom. We also test the level impact predictions with Chinese city-level data and estimate the following specification: , 0 1 , -1 2 , 3 , , Re i t i t i t i t i t Output Output VC α α α α e = + + + + (2) The dependent variable , i t Output is the output level of the different sectors over the whole economy. All the control variables in (2) are the same as (1). The empirical results of (1) and (2) are shown in Table 1. In line with our theoretical predictions, column 1 and 2 document a negative coefficient on the interaction of resource booms with the output share and level of the agriculture sector. As expected, column 3 and 4 document a positive coefficient on the interaction of resource booms with the output share and level of the industrial sector. Since “Dutch disease” predicts that “resource curse” arise with a resource boom by shrinking the manufacturing sector, we need to confirm whether our results reflect “Dutch disease” or not. Column6 thus includes interactions of a resource boom with the share of manufacturing sector. Our model shows that a resource boom may shrink the traditional part of the manufacturing sector but will increase the resource-based part of the manufacturing sector. Our findings is opposite with the “Dutch disease” and confirms our model’s prediction that a resource boom may shrink the traditional part of the manufacturing sector but increase the resource-based part of the manufacturing sector. Finally, the findings from the regression analysis in column 3 and 4 shows that a resource boom have a significantly positive effect on the output share of the service sector and a significantly negative effect on the output level of the service sector. Impact of a resource boom on economic growth. We examine whether there is a resource “curse” or “blessing” in China and and estimate the following specification: 0 1 , -1 2 , 3 , , Re t i t i t i t i t y y VC α α α α e = + + + + (3) Where i represents the prefecture-level city; t represents different years; , Rei t represents the level of resource abundance; , i t VC represents other control variables in the model, including the degree of openness and institutions and , i t e represents the random disturbance term. The results from the system GMM regression models appear in Table 2. Model 1 in Table1 provides supportive evidence that, even after controlling for several additional variables, the coefficients of Re is positive, which reflects a positive association between a resource boom and the growth of the local economy. Our empirical result of “resource blessing” is similar to some literatures (Liu, Chengfang, Linxiu Zhang, Renfu Luo, Scott Rozelle, and Linxiu Zhan 2009). In order to test the hypothesis 4, we add a cross-product term of , Rei t in Model 2 in Table2: 2 0 1 , -1 2 , 3 , 4 , , Re Re t i t i t i t i t i t y y VC α α α α α e = + + + + + (4) The coefficient of , Rei t in Model 2 of Table1 is significantly negative at the 5% level. It supports the curvilinear relationship between a resource boom and economic growth predicted by hypothesis 4. If the initial industrial structure is highly resource-based, then there will be less innovation activity with a resource boom and the economic growth will be lower. Hence excess resource dependence will hurt the “resource blessing” effect of the local economy with a resource boom