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Study on the mechanism of energy abundance and its effect on sustainable growth in regional economies: A case study in China
Resources Policy 47 (2016) 1–8
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Resources Policy journal homepage: www.elsevier.com/locate/resourpol
Study on the mechanism of energy abundance and its effect on sustainable growth in regional economies: A case study in China Sanmang Wu a,b,n, Yalin Lei a,b a b
School of Humanities and Economic Management, China University of Geosciences, No. 29 Xueyuan Road, Haidian District, Beijing 100083, PR China Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Land and Resource, Beijing 100083, PR China
art ic l e i nf o
a b s t r a c t
Article history: Received 21 April 2015 Received in revised form 15 October 2015 Accepted 15 October 2015
In the present work, the effects of energy abundance on sustainable economic growth and the mechanisms of this phenomenon were investigated using panel data from 31 areas (including provinces, autonomous regions, and municipalities) in China during the period from 2003 to 2012. The results indicate that the so-called “resource curse thesis” is not supported by the development of China at the provincial level. When several important variables—such as fixed capital, human capital, innovation input, regional openness, and foreign direct investment—are controlled, energy abundance exhibits a significantly positive correlation with economic growth rate. The so-called “Dutch disease”, human capital, and institutional quality are three primary transmission routes through which energy abundance affects economic growth. Among these routes, human capital exhibits the maximum positive transmission effect. Employment in the manufacturing industry can be crowded out by energy abundance, and the institution will be weakened to a certain degree. To promote sustainable economic growth in resource-abundant regions, long-term planning, optimization of industrial structure, and nurturing a good business environment should be supported and developed. & 2015 Elsevier Ltd. All rights reserved.
Keywords: Energy abundance Sustainable economic growth Resource curse Transmission mechanism
1. Introduction The relationship between natural resources and economic growth has always been an important research topic in the field of economics. Researchers, however, still cannot reach a consensus on this relationship, and no inexorable law has been observed (Wright, 1990). According to the thoughts of early scholars of the classical school, natural resources play an active role in promoting economic growth. This was especially true, they wrote, for countries that are rich in mineral resources because they have a greater opportunity to develop quickly. For example, at the end of the 19th century, the United States led the world in industrial development. During this period, the US was also the world’s largest mineralresource producing and exporting country. In other countries with abundant mineral resources, such as Chile, Russia, Canada, and Australia, the development of extractive industries promoted the industrialization process and sustainable economic growth (Wright and Czelusta, 2007). Since the mid- to late-20th century, however, in some countries that were rich in resources (such as some countries in South America), economic deterioration n Corresponding author at: School of Humanities and Economic Management, China University of Geosciences, No. 29 Xueyuan Road, Haidian District, Beijing 100083, PR China. E-mail address: [email protected] (S. Wu).
http://dx.doi.org/10.1016/j.resourpol.2015.10.006 0301-4207/& 2015 Elsevier Ltd. All rights reserved.
occurred, accompanied by widespread poverty. During the same period, the economy boomed in such resource-poor economic entities as Japan, Singapore, and Korea. Accordingly, the traditional viewpoint that natural resources absolutely contributes to economic growth was gradually overturned. In 1993, the concept of the resource curse was first put forward by Auty. According to his research, abundant resources are not always beneficial to the economic development of a country but rather a limitation for some cases. Afterwards, several scholars proved the hypothesis of the resource curse through empirical studies on both the transnational and regional levels (Sachs and Warner, 1995, 2001; Gylfason, 2001; Gylason and Zoega, 2006; Papyrakis and Gerlagh, 2004, 2006). Since the reform and opening up of China, the country’s economy has developed rapidly, with an average annual growth rate of 9.2% from 1978 to 2013. Chinese economic development, however, has exhibited a severe imbalance among its various regions. The economic growth rates of the eastern region were obviously higher than those of the central and western regions.1 During the period from 1978 to 2013, the average annual economic growth rate in eastern China rose to 9.5%, while those of central and western China were only 8.2% and 7.8%, respectively. China’s natural resources, especially the energy resources of coal, oil, and natural gas, are mainly distributed in the provinces (including autonomous regions and municipalities) of central and western
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China. According to Chinese government statistics for 2013, the proved coal reserves in China were 236.29 billion tons, 92.8% of which were distributed in central and western China; China’s proved land-accessible oil reserves were 2.86883 billion tons, 70.9% of which were distributed in central and western China; and the proved land-accessible natural gas reserves of China were 4311.6 billion cubic meters, 97.3% of which were distributed in central and western China (National Bureau of Statistics of China, 2014). Based on these data, can we conclude that the slower economies of central and western China were affected by the constraint of resource abundance? Can we further conclude that the phenomenon of the resource curse is essentially true in central and western China? If yes, how can the resource curse be dispelled and sustainable economic growth be achieved in the areas studied in central and western China? The existing studies regarding the relationship between natural resources and economic growth focused mainly on the effects of generalized natural resources on sustainable economic development. However, little attention was paid to the effects of specific natural resources, such as the energy resources of coal, oil, and natural gas. To fill the gap in the literature, the effects of energy abundance on the sustainable development of regional economies and the related mechanisms were explored in the present work, with the provinces of China as the study subjects.
2. Literature review 2.1. Proposal of resource curse and progress in empirical research The resource curse thesis can be traced back to the 1540s, an era during which huge amounts of silver flowed into Spain from its American colonies while the Spanish Empire declined. According to legend, the emperor of the Aztec (Mexico), Motecuhzoma II, put a curse on any land grabbers to the effect that they would suffer from illnesses. As the Aztec were colonially ruled by Spain, a huge amount of silver flowed into Spain. Regardless of whether the legend was true or not, this curse was realized after the death of King Phillip II. Spain’s overseas trade began to decline sharply and foreign commodities gradually took over the market that had been previously dominated by Spain. Spanish industry declined steadily and eventually stagnated. The influx of silver also brought on the so-called “Dutch disease” to Spain. Matsuyama (1992) first constructed the prototype of the Dutch disease model. On this basis, Auty (1993) put forward the concept of the resource curse. He studied the cases of resource exporters, and concluded that natural resource abundance was a curse rather than a blessing. By analyzing the development histories of economic entities in transition, Auty (2001, 2003) further concluded that the rise of actual exchange rates caused by the export of resources aggravated the problems of those economic entities. In particular, in some countries that lack effective political systems, abundant natural resources may impair the urgency of reform, distort economic development (such as rent-seeking and corruption), and hinder economic transition. By comparing resource-poor countries in the Caspian region to neighboring resource-rich ones that were in transition, it was found that resource-rich countries after transition exhibited a reverse J-shaped development track, while those of resource-poor countries were V-shaped. 1 According to the classification standard of China National Bureau of statistics, the eastern region includes Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan; the central region includes Heilongjiang, Jilin, Shanxi, Anhui, Jiangxi, Henan, Hubei, and Hunan; and the western region includes Sichuan, Chongqing, Guizhou Yunnan, Tibet, Shaanxi, Inner Mongolia, Guangxi, Gansu, Qinghai, Ningxia, and Xinjiang.
Sachs and Warner (1995) developed the theory of the resource curse and established the model of the Dutch disease. Through the empirical analysis of data from 95 countries and regions during the period 1970–1990, they found that almost all resource-rich countries experienced retarded economic growth after the 1970s. It was Dutch disease that made the primary-commodity exporting countries lag in long-term competitiveness. Sachs and Warner (2001) further analyzed the causes responsible for the differences of economic growth in several Latin American and East Asian countries. They speculated that because the economies of these Latin American countries relied on the export of primary products or of manufactured products made up of primary products, the manufacturing sectors were inhibited with increasing returns. Their economic growth was thus hindered by the Dutch disease or other means. East Asian countries characterized by export-oriented economies, however, exported labor-intensive products and capital- and technology-intensive products in sequence, which stimulated the development of the manufacturing sectors with increasing returns. Therefore, those countries avoided the Dutch disease. After pioneering research by Auty (1990, 1993, 2001) and Sachs and Warner (1995, 2001), the resource curse thesis was continuously verified and discussed by case and empirical analysis. Overall, the subsequent literature can be classified into three types: namely, supporting the resource curse thesis, falsifying the thesis, and analyzing the thesis’s range and conditions of application. In support of the resource curse thesis, Tornell and Lane (1993) and Torvik (2002) successively developed and applied the rentseeking model. They found that resource abundance was involved in nonproductive activities, which stimulated resource rents. Accordingly, the negative monotonic relationship between natural resources and economic growth was proved. Using the ratio of resource rents to GDP as an index to evaluate the degree of resource abundance, Atkinson and Hamiltion (2003) found this index to be negatively correlated with the growth rate of per capita GDP. Papyrakis and Gerlagh (2004) performed an empirical test on the direct and indirect effects of natural resources on economic growth using the data from 39 countries. Their results indicated that the negative effects of natural resources on economic growth far exceeded the positive effects. Arezki and Ploeg (2007) conducted a systematic empirical test on the cross-sectional data of many countries. With factors such as geography, openness, and institutional quality controlled, they adopted institutional quality and openness as the instrumental variables. They found that the export of natural resources directly imposed negative effects on per capita income. In some counties with low openness, the resource curse had a more serious negative effect. Based on the examinations of data from several states in the United States, Papyrakis and Gerlagh (2007) concluded that the resource curse thesis held across various regions in the country. They found that in Alaska and Louisiana, the economy developed quite slowly over a long time period, a phenomenon that was attributed primarily to their abundant natural resources. Literature supporting the falsification of the resource curse includes a study by Davis (1995), who investigated 22 mineralbased economic entities as an entirety, compared them to nonminerals-based economic entities and found that no resource curse existed in the entirety. Using model analysis, Mikesell (1997) concluded that no inherent necessary connection existed between resource abundance and economic growth rate. The differences of economic growth among developing countries resulted mainly from differences in factors influencing endogenous growth, and the underlying causal relationship for endogenous growth could not be accounted for by resource endowment alone. In addition, Wright and Czelusta (2007) refuted the resource curse thesis by examining cases of successful resource-based economic growth.
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They indicated that mineral resources have formed the foundation of high-tech industries in many countries. The successes of the United States’ economic development from the mid-19th to mid20th centuries, and those of Norway, Australia, and Canada more recently, all proved that progress in mining and technology, and the promotion of relevant knowledge, impose significant positive effects on economic development. In a literature review, Davis and Tilton (2005) concluded that mineral resources, such as property and capital in other forms, were also a type of treasure. Taking Equatorial Guinea as an example, Same (2008) indicated that in some very poor countries virtually without any manufacturing industry, the Dutch disease was actually a spontaneous and necessary redistribution of resources in the economy. Oil revenues played a fairly significant role in national economic development and poverty reduction in Equatorial Guinea. Concerning the application range and conditions of the resource curse thesis, Torvik (2002) constructed a Dutch disease model that included “learning by doing” and concluded that the occurrence of the resource curse depends on the characterization of the present economy and productivity in the trade and nontrade sectors. Variations in relative productivity will cause fluctuations in the exchange rate and determine the degree of the Dutch disease. Additionally, Wen and King (2004) indicated that the occurrence of the resource curse depends on the quality of professionalization and transaction efficiency. When the resourcerich countries have poor organizational systems and transaction efficiency, the curse will occur. Otherwise, the curse will not occur. Other researchers argued that the occurrence of resource curse is not absolute and depends on the institutional quality of the specific country (Blute et al., 2005; Robinson et al., 2006; Mehlum et al., 2006). If a country possesses a predatory institutional environment, the resources will not contribute to economic growth. However, if a country has a favorable production-type institutional environment, it can achieve rapid economic growth by taking full advantage of its abundant resources. Based on the affirmation that the resource curse is not a regular thesis, Murshed (2004), Hodler (2006), and Boschini and Pettersson (2007) used model analysis to confirm that institutional quality and the abundance of natural resources jointly determine the occurrence of the resource curse (Murshed, 2004; Hodler, 2006; Boschini and Pettersson, 2007). 2.2. Mechanism of resource curse In earlier literature, researchers speculated that the Dutch disease was the primary mechanism of the resource curse. Specifically, Larsen (2006) decomposed this mechanism into crowdingout, overflow, and spending effects. Through the crowding-out effect, labor, capital, and techniques are crowded out of the mechanical manufacturing industry by natural resources. Labor is then attracted by the higher income of the resource-based sectors and transfers from the mechanical manufacturing industry to the resource mining and manufacturing industries. Natural resources may also overflow through techniques that are inferior to those of the manufacturing sectors, leading to low output and low economic growth. The spending effect of natural resources refers to increases in the actual exchange rate by converting a huge amount of resource revenues into increased total demand, which weakens the international competitiveness of the products from the mechanical manufacturing sectors. In a case study of Brazil, Auty (1995) concluded that policies that gave priority to the development of heavy industry inhibited development of the manufacturing sectors that were highly dependent on tariff protection and transfer payments. Brazil’s industrialization therefore matured quite slowly. In addition to the Dutch disease, the effect of natural resources on human capital is another important mechanism responsible for the occurrence of resource curse. Learner et al. (1999)
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found that material capital, rather than human capital, is actually required for the excavation of natural resources. Accordingly, the education system must provide for the formation of a labor-intensive manufacturing industry, a provision that affects the economic growth. Additionally, several scholars have speculated that institutional factors such as rent-seeking, conflict, property rights, and political processes are other important mechanisms by which the resource curse occurs (Torvik, 2002; Auty, 2001; Humphreys, 2005; Hodler, 2006; Wick and Bulte, 2006; Welsch, 2008). 2.3. Literature summary In reviewing the aforementioned research on the resource curse, some problems are found. First, few studies were performed on the relationship between energy abundance and sustainable economic growth. The recent studies on the resource curse focused mainly on the relationship between natural resources and sustainable economic growth. In some developing countries with relatively backward economies and technologies, the effects of energy resources on the national economy are actually quite remarkable. Therefore, we should study the effects of energy resources further. Second, few studies were performed on regional disparity in developing countries. In the study of the resource curse thesis, large samples are required (Xu and Shao, 2006). Compared with developed countries, developing countries exhibit more severe developmental inequality. In future studies, the data from regions of different developmental levels should be used as the samples to analyze. Third, China’s energy demand is huge because of economic development. It is an urgent need to further study on the economic problems which caused by the development of energy resources. China’s total energy consumption reached 42.6 tons of standard coal in 2014, accounting for 20% of the total global energy consumption. In the future, with the development of China economy, the demand for energy will remain a relatively rapid growth. The International Energy Agency predicted that China would overtake the United States by 2030, becoming the world largest oil consumer. Therefore, it is necessary to study on the economic problems which caused by China energy development. In the present work, the per capita reserves of three major fossil fuels (coal, oil, and natural gas) were taken as the proxy variables of energy abundance. Using the data of various provinces in China obtained from National Bureau of Statistics of China, we performed empirical analysis on the relationship between energy endowment and sustainable economic growth and analyzed the underlying mechanisms.
3. Model and data 3.1. Calculation model and variable declaration To verify the relationship between energy abundance and sustainable economic growth in China, a basic econometric model of panel data was constructed by referring to the empirical study by Shao and Yang (2010):
yit = β0 + β1Yi0 + β2 Rit + β3 Zit + β4 D + μi + εit
(1)
where i denotes the provincial cross-section unit; where denotes the provincial cross-section unit; denotes the year; is the regression coefficient; is the intercept term to reflect individual heterogeneity; and is the regression residual term. The declarations of these variables are listed below.
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(1) Explained variable: y is the annual economic growth, which is denoted by the annual growth rate of per capita gross regional product using the year of 2003 as the base period. (2) Explanatory variable:
R=
a1 × Coal + a2 × Oil + a3 × Gas , Population
which
represents
the
energy
abundance. Using this index, the basic reserves of three major energy resources were converted into standard coal reserves through energy conversion. The per capita energy abundance of the various regions was used, with the unit of ten thousand standard tons of coal per capita. By referring to the conversion formula on basic energy reserves (Xu and Wang, 2006) developed by the Chinese Academy of Sciences (CAS), the coefficients of conversion from ten thousand tons (cubic meters) of coal, oil, and natural gas to ten thousand tons of standard coal are 0.7143, 1.4288, and 13.30, respectively. (3)Control variable: Y0 ¼GDP2003, i.e., per capita GDP at 2003 (base period), represents the initial economic development level, which can be used to control the effects of regional economic disparities on sustainable economic growth. D denotes the dummy variable of geographical location, which was used to control the effects of regional disparities on sustainable economic growth. For the eastern regions, D1 ¼ 1, D2 ¼0; for the central regions, D1 ¼0, D2 ¼ 1; for the western regions, D1 ¼0, D2 ¼0. Z represents the other control variables affecting economic growth. According to economic theory and the relevant literature, these variables can be classified into five items: (1) Material capital input (INV), which is denoted by the ratio of investment of total fixed assets to GDP of the region and is used to control the effects of material capital on economic growth. (2) Human capital input (EDU_C), which is denoted by the ratio of expenditure on education to total fiscal expenditures of that region and can be used to control the effects of human capital on economic growth. (3) Technological innovation input (RD), which is denoted by the ratio of research and development input to GDP of that region. (4) Institutional condition (OPEN): The opening-up policy has played a vital role in China’s economic development for the past 30 years (Shao and Qi, 2008). The regions with greater openness exhibit more robust market economy systems, more transparent economic activities, and, thus, better institutional quality (Xu and Wang, 2006). Therefore, in the present work, we selected openness as a proxy variable to reflect the institutional quality in a region, which is denoted by the ratio of total volume of imports and exports (in yuan) to GDP. (5) Foreign direct investment (FDI): Many studies have examined the relationship between FDI and China’s economic growth and indicated that FDI significantly promoted China’s economic development and its effects should be controlled. In the present work, the control variable of FDI is denoted by the ratio of foreign investment in actual use to GDP in the study region. 3.2. Selection of samples and data sources To ensure the availability and accuracy of the data and the consistency of the statistical caliber, the data from 31 provinces (including autonomous regions and municipalities) in China during the period from 2003 to 2012 were selected as the study samples. In total, 310 observed values were included. The panel data were constructed by selecting a regional economic growth variable, an energy abundance variable and other economic
variables that affect economic growth, and then the relationship between energy endowment and sustainable economic growth was investigated. The data in the studies were collected from the China Statistical Yearbook during the period from 2004 to 2013, the statistical bulletins of national economy and social development in the 31 provinces from 2003 to 2012, the Commerce Ministry’s website (http://www.mofcom.gov.cn), and the statistical yearbook of each province.
4. Empirical analysis 4.1. Descriptive statistics To depict the statistical characteristics of the variables more clearly, we first conducted descriptive statistical analyses on the variables; the results are listed in Table 1. It can be observed that the mean value of y is 14.314%, suggesting that the per capita economic growth rates for these regions during that period were generally high. The standard deviation, however, was 6.982, suggesting marked economic disparities among the various regions. The mean value of R was 238.644, and the standard deviation was 521.714. This suggests that there were great regional differences in the distribution of basic energy reserves. The energy resources are concentrated in individual provinces; e.g., the per capita standard coal of Shanxi province and Inner Mongolia both exceed 2000 tons, while the energy reserves are scarce in some provinces. For the INV variable, the mean value and standard deviation were 0.536 and 0.146, respectively. The variability of the standard deviation with respect to the mean value was close to that of y, suggesting that the expected capital investment imposes significant effects on economic growth. For the EDU_C variable, the mean value and standard deviation were 0.035 and 0.149, respectively. The variability of the standard deviation with respect to mean value was large, suggesting that the education expenditures in the various provinces were diverse. For the RD, OPEN and FDI variables, the standard deviations were large, suggesting that the intensity of investment in research and development, openness, and foreign investment in actual use varied significantly among the various provinces. 4.2. Econometric test During the econometric test, the multicollinearity among the variables was inspected using the statistics of the variance inflation factor (VIF). Subsequently, combining ordinary least squares (OLS) with cluster-robust standard error, the Lagrange multiplier (LM) test, and the Hausman test, the regression model for the panel data was identified (including pooled regression, random effect, and fixed effect). Considering that self-correlation may exist among the disturbance terms of individuals in different periods, the standard deviation of the pooled regression was estimated Table 1 Descriptive statistics of the analysis variables. Variable Observed value
Mean value Standard deviation
Minimum value
Maximum value
y R Y0 INV EDU_C RD OPEN FDI
14.314 238.644 11.663 0.536 0.149 1.182 0.353 0.021
1.014 0.000 3.686 0.238 0.090 0.120 0.046 0.001
32.997 2178.500 37.910 0.884 0.218 5.820 1.690 0.079
310 310 310 310 310 310 310 310
6.982 521.714 7.994 0.146 0.035 0.987 0.425 0.018
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Table 2 Overall tests on the resource curse of energy abundance. Variable
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
R
22.301** (2.27)
18.116* (1.70) 0.179*** ( 4.53)
18.624* (1.80) 0.215*** ( 2.77) 2.507 (1.20)
19.009* (1.80) 0.406*** ( 5.88)
19.123 (1.55) 0.129*** ( 2.59)
19.367** (2.55) 0.256*** ( 4.81)
18.681* (1.90) 0.130*** ( 4.55)
23.129** (2.52) 0.294*** ( 3.42) 4.465*** (4.20) 0.507 (1.40) 75.099*** (5.60) 90.867*** (3.49) 13.944*** (6.14) 3.459 ( 1.46) 0.266 ( 0.56) 3.777 ( 1.34) 0.222 310
Y0 OPEN
1.050* (1.90)
RD
44.617*** (3.88)
EDU_C
80.060*** (3.66)
FDI INV D1 D2 Constant term R2 Observed value
14.997*** (15.27) 0.029 310
0.057 ( 0.04) 0.062 ( 0.18) 18.129*** (26.78) 0.080 310
0.219 ( 0.40) 0.030 (0.03) 17.884*** (20.19) 0.080 310
0.428 (0.77) 0.586 ( 0.23) 18.443*** (20.14) 0.080 310
1.604 ( 1.49) 0.566 ( 0.70) 9.818*** (5.10) 0.088 310
2.154 ( 1.66) 0.899 ( 1.20) 15.865*** (20.43) 0.090 310
12.642*** (4.50) 1.253 (1.30) 0.683 (0.81) 9.601*** (6.83) 0.167 310
Note: t-values are listed in the brackets below the estimation coefficient. *** ** *
Represent the significance at the levels of 1%. Represent the significance at the levels of 5%. Represent the significance at the levels of 10%.
using the cluster-robust standard error, and the random effect was estimated using generalized least squares to eliminate the effects of heteroscedasticity, which may exist in short panel data. Finally, by introducing the proxies of the explanatory variables, a robustness test was performed on the econometric model. (1) Multicollinearity test: According to the estimation, the VIFs of the various variables were all less than 7. Except for the variables Y0 and OPEN, the VIFs of the other variables were all less than 3. Accordingly, we can conclude that the multicollinearity among different variables was not serious. (2) Panel regression: With the adoption of hierarchical regression, the effects of the control variables on several primary explanatory variables and explained variables were investigated. Finally, all of the predictive variables were included in the model. Table 2 lists the overall inspection results on the econometric model. (3) Robustness test: The average ratio of energy reserves (denoted by RS) was used as the proxy of energy abundance to re-estimate the econometric model.2 The results indicated that the regression coefficient and t-value of RS were 14.213 and 1.92, respectively, passing the significance test at the level of 15% (in model 8 from Table 2, the estimation coefficient and t-value of energy abundance R were 23.129 and 2.52, respectively). Moreover, for the other control variables, the signs of the regression coefficients were not changed, and there were only small variations in the values. In addition, EDU_C was replaced in the test by the average years of education (EDU_Y) in the study region. The results showed that the signs of the variables were not changed, and the changes in value were not large. We thus conclude that the econometric model was robust.
2 The energy reserves in the area (converted into standard coal) accounts for the proportion of total reserves in China in that year. This indicator reflects a relative level of energy abundance in a region from the view of reserves.
4.3. Interpretation of results In model 1 (reference), regression was performed only on the explained (economic growth rate y) and explanatory variables (energy abundance R). The estimation coefficient of R was 22.301, which passed the significance test at the level of 5%. The regression results suggested that abundant energy reserves significantly promoted the economic growth rate. To verify the stability of this result, model 2 controlled the initial economic level (Y0) and the geographic factor of the study region. The results indicated that at the significance level of 10%, the estimation coefficient of R decreased to 18.116. Thus, the conclusions described above were still applicable. It is worth noting that the coefficient of Y0 is 0.179, which also passed the significance test at the level of 1%. When a series of control variables were added, this coefficient was still negative. This suggested that during the sample period, the economic growth rates in these regions were conditionally convergent. In other words, the economic growth rates in the regions with lower per capita income exceeded those of the regions with higher per capita income (Xu and Wang, 2006). Regarding the issue of whether the regional economic gap was widening or narrowing, further verifications are still required. During the sample period, however, the initial economic level of a region imposed negative impacts on its economic growth rate. From the model 3 to the model 7, several control variables were found to affect economic growth: namely, institutional quality (OPEN), research and development level (RD), human capital input (EDU_C), foreign direct investment (FDI), and material capital input (INV). The calculation results agreed well with the mainstream viewpoint in economic growth theory. Technological innovation, material capital, and human capital are the key elements in economic growth, while institutional quality is an important external condition for the functioning of those factors. Foreign direct investment can greatly promote regional economic growth. The models demonstrated the positive effects of those factors. The addition of each control variable imposed no obvious effects on either the estimation coefficient of R or the significance level,
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Table 3 Test results of transmission mechanisms. Variable
R Y0 D1 D2 Observed value
The Dutch disease
Material capital
Human capital
Innovation
Institutional quality
MANU_C
MANU_R
INV
EDU_C
EDU_Y
RD
OPEN
0.067 (0.326) 0.005 (0.005) 0.148 (0.009) 0.125 (0.022) 310
0.156 (0.000) 0.012 (0.005) 0.145 (0.010) 0.052 (0.006) 310
0.178 (0.360) 0.007 (0.001) 0.089 (0.020) 0.115 (0.010) 310
0.056 (0.245) 0.012 (0.045) 0.078 (0.011) 0.023 (0.014) 310
1.720 (0.017) 0.095 (0.008) 0.277 (0.042) 0.656 (0.002) 310
1.979 (0.008) 0.095 (0.004) 0.458 (0.005) 0.089 (0.178) 310
0.458 (0.074) 0.085 (0.074) 0.089 (0.045) 0.066 (0.020) 310
Note: P-values are listed in the brackets below the estimation coefficients.
which is consistent with previous reports. Model 8 included all control variables. As shown in Table 2, the estimation coefficient of R was 23.129, which passed the significance test at the level of 5%. In this model, energy abundance still imposed positive effects on economic growth. The estimation coefficient exhibited no obvious variations during the process. Energy reserves thus play stable and significantly positive roles in regional economic growth, and the resource curse of energy abundance does not exist in the examined provinces of China.
5. Transmission mechanisms for the effects of energy abundance on sustainable economic growth As stated above, energy abundance, rather than being a vector of the resource curse, significantly promoted the sustainable economic growth of the studied provinces in China. What, however, are the underlying mechanisms? In the studies regarding the specific mechanisms of resource curse, the transmission mechanisms were considered as the underlying cause for the phenomenon. Therefore, the transmission mechanism and its degree of influence are also important research topics in this field (Papyrakis and Gerlagh, 2004; Xu and Shao, 2006; Shao and Qi, 2008; Fang et al., 2011). Existing literature indicates that the transmission mechanisms include deterioration of trade conditions (Davis and Tilton, 2005), the Dutch disease (Corden and Neary, 1982; Matsuyama, 1992; Sachs and Warner, 1995, 1999), institutional weakness (Sachs and Warner, 1999; Gylfason, 2001; Sala-i-Martin, 2003), the crowding-out of investment (Sachs and Warner, 1995, 1999; Gylfason, 2001), and war and conflict (Collier and Hoeffler, 2005; Brunnschweiler and Bulte, 2009). Papyrakis and Gerlagh (2004) proposed a tool for estimating the transmission mechanism that connects natural resource abundance to sustainable economic growth (Papyrakis and Gerlagh, 2004). According to their studies, the contribution proportions of investment rate, openness, trade conditions, education investment, and institutional efficiency were 47%, 24%, 25%, 13%, and 7%, respectively. Through multiple transmission mechanisms, the driving factors that are quite important to sustainable economic growth were crowded out by natural resource abundance, and thus, the economic growth was hindered. In our study, the investigation of transmission mechanisms has two goals: the first is to discuss whether and how the resource curse transmission mechanisms still work when energy reserves exhibit positive correlation with sustainable economic growth; the second is to further explore the underlying causes of why energy reserves can promote sustainable economic growth based on the paradigm of transmission mechanisms. As described above, the Dutch disease (the crowding-out of the manufacturing industry), unreasonable capital allocation (the crowding-out of investment), the crowding-out of human capital (or education investment), the
crowding-out of innovation, and institution weakening are significant transmission mechanisms by which resource abundance affects sustainable economic growth. In the present work, those possible transmission mechanisms were examined. By consulting the econometric model of transmission mechanism established by Papyrakis and Gerlagh (2004), the panel data models were established as follows:
Zit = α0 + α1Rit + α2 Y0 + α3 D + μit
(2)
where Zit denotes the variable of transmission mechanism, corresponding to the control variable Z in model 1, and the other variables had the same definitions as described above. According to the definition by Papyrakis and Gerlagh (2004), the regression coefficient β2 in model 1 represents the direct effect of energy reserves on sustainable economic growth, and α1β3 represents the indirect effect. The sum of β2 and α1β3 represents the total effect of energy reserves on sustainable economic growth. The panel data were estimated by the FGLS in a random effect model, and the examination results of transmission mechanisms are listed in Table 3. To verify whether the crowding-out of manufacturing industry effect was generated by energy abundance, i.e., the occurrence of the Dutch disease in the conventional sense, the variables MANU_C and MANU_R were introduced. The two represent capital and human inputs, respectively, in manufacturing industry.3 The results suggested that energy abundance promoted capital input rather than the crowding-out effect. However, this promotion was not remarkable. A possible reason is that the cooperative effects among various industries induced by abundant energy exceeded the crowding-out effect, and thus, the total effect was positive. For human input in the manufacturing industry, however, the crowding-out effect induced by energy abundance was quite significant. This suggests that abundant energy would affect to various degrees the distribution of employment among various sectors, causing a transfer from the manufacturing sectors to the energy production sectors. For the effects of energy abundance on human capital, we performed the examination from two aspects, i.e., the effects on education capital input (EDU_C) and on human capital accumulation (EDU_Y).4 The results suggested that energy abundance imposed a significantly positive effect on human capital accumulation. In addition, the effect on material 3 The employment ratio of manufacturing industry was denoted by the ratio of the persons employed in manufacturing industry to total employed persons in the study region. 4 Average years of education was denoted by the average years of education in the study region. The computational formula can be written as (the population of illiteracy 1þ the population who received elementary education 6þ the population who received junior middle school education 9þ the population who received senior middle school education 12þ the population who received college education or above 16)/total population in the study region.
S. Wu, Y. Lei / Resources Policy 47 (2016) 1–8
Table 4 Estimation coefficients of various variables in overall test of the resource curse (Model 9). Variable
R
Y0
D1
D2
INV
Estimation coefficient P-value
14.45 (0.089)
0.607 (0.021)
2.788 (0.054)
1.87 (0.152)
14.752 (0.052)
Variable
FDI
RD
MANU_R
EDU_Y
OPEN
Estimation coefficient P-value
48.721 (0.025)
0.035 (0.89)
10.100 (0.01)
1.890 (0.000)
5.562 (0.049)
capital input was positive but not very significant, whereas the effects on the level of research and development and institutional quality were remarkably negative. To a certain extent, energy abundance restrained the intensity of research and development investment and openness in the study region and was adverse to technological innovation and the construction of an open, transparent, and favorable economic system. Considering that human capital (EDU_C) was not significant, but human capital accumulation (EDU_Y) was quite significant in the examination of transmission mechanisms, EDU_C was replaced by EDU_Y in the present work to calculate the effect of transmission mechanisms. In addition, MANU_R was also added into model 1, and this extended model (named model 9) was used to verify the resource curse. Table 4 lists the estimation coefficients of various variables. By comparing Tables 3 and 4, it can be found that in the routes of R, Z, and y, the variables of MANU_R, EDU_Y, and OPEN exhibited obvious significance. That is, the Dutch disease, human capital, and institutional quality, which are represented by the employment ratio of the manufacturing industry, the average years of education, and openness, respectively, are the three primary transmission mechanisms by which energy abundance affects economic growth. After the transmission mechanisms were identified, the indirect effects on sustainable economic growth caused by energy abundance through those three transmission mechanisms were calculated by referring to the calculation method of Papyrakis and Gerlagh (2004). The results (Table 5) suggest that, among the three transmission mechanisms, the direct effect of human capital is the greatest and positive, with a value of 3.251. The direct effect of openness was 2.547, and that of the Dutch disease, which crowded out the employment in the manufacturing industry, was 1.576. Energy abundance hindered sustainable economic growth through the latter two effects. Compared with the direct effect induced by energy abundance (corresponding to the estimation coefficient of R in model 9, 14.45), the indirect effects retarding the sustainable economic growth could not threaten overall sustainable economic growth. Consequently, on the whole, energy abundance is positively correlated with sustainable economic growth in the studied provinces.
Table 5 Indirect effects of transmission mechanisms. Transmission mechanism
α1
β3
α1β3
MANU_R EDU_Y OPEN
0.156 1.720 0.458
10.100 1.890 5.562
1.576 3.251 2.547
7
6. Conclusions and policy suggestions 6.1. Research conclusions
(1) At the provincial level in China, the resource curse of energy abundance is absent. The test results provided here indicated that when a series of important variables, such as fixed assets, human capital, innovation investment, regional openness, and foreign direct investment, are controlled, energy abundance exhibits significantly positive correlations with the economic growth rate. Among those variables, foreign direct investment, fixed assets investment, education investment, and policies promoting openness are the primary motivating factors in sustainable economic growth in the studied provinces. (2) The transmission mechanisms through which energy abundance affects sustainable economic growth play a significant role. Energy abundance imposed positive effects on human capital and thus promoted economic growth. Energy abundance imposed indirect crowding-out effects on openness and the manufacturing industry, thus retarding sustainable economic growth. The direct positive effects of energy abundance, however, were larger. Therefore, no curse occurred on the whole. 6.2. Policy suggestions
(1) To achieve sustainable development, scientific plans for economic development should be made in resource-based regions. For a long time, there have been few scientific and comprehensive top-level designs and plans made by Chinese governments in resource-intensive regions. In those regions, therefore, the economy developed rapidly during the period when resources were abundant and degraded seriously after the resources were exhausted. Some locations even become ghost cities, such as Yumen in Gansu province and Fuxin in Liaoning province. In the future, long-term planning should be carried out in resource-rich regions to achieve sustainable development. (2) To prevent the Dutch disease, industrial structure should be further optimized and improved. Energy abundance imposed the crowding-out effect on employment in the manufacturing industry to a certain degree, an effect that would certainly have negative impacts on human capital accumulation in the manufacturing industry. Accordingly, the accumulation of technical knowledge was adversely affected, which degraded the manufacturing industry and its cooperative development with other industries. In that case, we did see evidence of the natural resource curse. (3) High-quality market environments should be established, backed by an improved legal system, with surveillance by social networks, the media, and individuals. Well-established institutions contribute to regional economic development. Energy abundance, however, may degrade institutional quality to a degree. Corruption frequently takes place when local governments and/or sectors have resource monopoly advantages. Concentration of power can lead to resource allocation imbalances and resource waste, which are averse to reaching the full utilization of resource endowment advantages. Therefore, local governments, in combination with the power of public opinion, should consider the balance and supervision between various sectors when designing institutions relevant to economic development. Thus, the executive force of policies can be enhanced, and the serious economic and social problems induced by excessive power in the hands of the resource production sectors can be avoided.
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Acknowledgment The authors are grateful for financial support from the National Natural Science Foundation of China under Grant nos. 71003066 and 71133003.
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Matsuyama, K., 1992. Agricultural productivity, comparative advantage, and economic growth. J. Econ. Theory 58, 317–334. Mikesell, R.F., 1997. Explaining the resource curse, with special reference to mineral exporting countries. Resour. Policy 23, 191–199. Mehlum, H., Moene, K., Torvik, R., 2006. Institutions and the resource curse. Econ. J. 116, 1–20. Murshed, S.M., 2004. When does natural resource abundance lead to a resource curse? [Z/OL]. 〈http://pubs.iied.org/pdfs/9250IIED.pdf〉. National Bureau of Statistics of China, 2014. China Statistical Yearbook. China Statistics Press, Beijing. Papyrakis, E., Gerlagh, R., 2004. The resource curse hypothesis and its transmission channels. J. Comp. Econ. 32, 181–193. Papyrakis, E.R., Gerlagh, R., 2006. Resource abundance and economic growth in the United States. Eur. Econ. Rev. 4, 253–282. Papyrakis, E., Gerlagh, R., 2007. Reource abundance and economic growth in the United States. Eur. Econ. Rev. 51, 1011–1039. Robinson, J.A., Ragna, T., Thierry, V., 2006. Political foundations of the resource curse. J. Dev. Econ. 79, 447–468. Sachs, J.D., Warner, A.M., 2001. The curse of natural resources. Eur. Econ. Rev. 45, 827–838. Sachs, J.D., Warner, A.M., 1995b. Natural resource abundance and economic growth. NBER Working Paper No. 5398. National Bureau of Economic Research, Cambridge, MA. Sachs, J.D., Warner, A.M., 1999. The big push, natural resource booms and growth. J. Dev. Econ. 59; , pp. 43–76. Same, A.T., 2008. Mineral-Rich Countries and Dutch Disease: Understanding the Macroeconomic Implications of Windfalls and the Development Prospects: The Case of Equatorial Guinea. World Bank Policy Research Working Paper No. 4585. Shao, S., Qi, Z., 2008. Energy exploitation and economic growth in Western China: an empirical analysis based on the resource curse hypothesis. Econ. Res. J. 1, 147–160 (in Chinese). Sala-i-Martin, Xavier, X., 2003. Addressing the natural resource curse: an illustration from Nigeria. NBER working Paper No. W9804. Tornell, A., Lane, P.R., 1993. The voracity effect. Am. Econ. Rev. 3, 22–46. Torvik, R., 2002. Natural resources, rent seeking and welfare. J. Dev. Econ. 67, 455–470. Wright, G., 1990. The origins of American industrial success: 1879–1940. Am. Econ. Rev. 80, 651–680. Wright, G., Czelusta, J., 2007. Resource-based growth past and present. In: Lederman, D., Maloney, W.F. (Eds.), Natural Resources, Neither Curse nor Destiny. World Bank, Washington, DC, pp. 183–211. Wen, M., King, S.P., 2004. Push or pull? The relationship between development, trade and primary resource endowment. J. Econ. Behav. Organ. 53, 569–591. Wick, K., Bulte, E.H., 2006. Contesting resources—rent seeking conflict and the natural resource curse. Public Choice 128, 457–476. Welsch, H., 2008. Resource abundance and internal armed conflict types of natural resource and the incidence of “new war”. Ecol. Econ. 10, 10–16. Xu, K., Shao, J., 2006. Natural resources abundance and economic growth: are-examination of the “Recourse curse” hypothesis. J. World Econ. 11, 38–47 (in Chinese). Xu, K., Wang, J., 2006. An empirical study of a linkage between natural resource abundance and economic development. Econ. Res. J. 1, 78–89 (in Chinese).
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Resources Policy journal homepage: www.elsevier.com/locate/resourpol
Study on the mechanism of energy abundance and its effect on sustainable growth in regional economies: A case study in China Sanmang Wu a,b,n, Yalin Lei a,b a b
School of Humanities and Economic Management, China University of Geosciences, No. 29 Xueyuan Road, Haidian District, Beijing 100083, PR China Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Land and Resource, Beijing 100083, PR China
art ic l e i nf o
a b s t r a c t
Article history: Received 21 April 2015 Received in revised form 15 October 2015 Accepted 15 October 2015
In the present work, the effects of energy abundance on sustainable economic growth and the mechanisms of this phenomenon were investigated using panel data from 31 areas (including provinces, autonomous regions, and municipalities) in China during the period from 2003 to 2012. The results indicate that the so-called “resource curse thesis” is not supported by the development of China at the provincial level. When several important variables—such as fixed capital, human capital, innovation input, regional openness, and foreign direct investment—are controlled, energy abundance exhibits a significantly positive correlation with economic growth rate. The so-called “Dutch disease”, human capital, and institutional quality are three primary transmission routes through which energy abundance affects economic growth. Among these routes, human capital exhibits the maximum positive transmission effect. Employment in the manufacturing industry can be crowded out by energy abundance, and the institution will be weakened to a certain degree. To promote sustainable economic growth in resource-abundant regions, long-term planning, optimization of industrial structure, and nurturing a good business environment should be supported and developed. & 2015 Elsevier Ltd. All rights reserved.
Keywords: Energy abundance Sustainable economic growth Resource curse Transmission mechanism
1. Introduction The relationship between natural resources and economic growth has always been an important research topic in the field of economics. Researchers, however, still cannot reach a consensus on this relationship, and no inexorable law has been observed (Wright, 1990). According to the thoughts of early scholars of the classical school, natural resources play an active role in promoting economic growth. This was especially true, they wrote, for countries that are rich in mineral resources because they have a greater opportunity to develop quickly. For example, at the end of the 19th century, the United States led the world in industrial development. During this period, the US was also the world’s largest mineralresource producing and exporting country. In other countries with abundant mineral resources, such as Chile, Russia, Canada, and Australia, the development of extractive industries promoted the industrialization process and sustainable economic growth (Wright and Czelusta, 2007). Since the mid- to late-20th century, however, in some countries that were rich in resources (such as some countries in South America), economic deterioration n Corresponding author at: School of Humanities and Economic Management, China University of Geosciences, No. 29 Xueyuan Road, Haidian District, Beijing 100083, PR China. E-mail address: [email protected] (S. Wu).
http://dx.doi.org/10.1016/j.resourpol.2015.10.006 0301-4207/& 2015 Elsevier Ltd. All rights reserved.
occurred, accompanied by widespread poverty. During the same period, the economy boomed in such resource-poor economic entities as Japan, Singapore, and Korea. Accordingly, the traditional viewpoint that natural resources absolutely contributes to economic growth was gradually overturned. In 1993, the concept of the resource curse was first put forward by Auty. According to his research, abundant resources are not always beneficial to the economic development of a country but rather a limitation for some cases. Afterwards, several scholars proved the hypothesis of the resource curse through empirical studies on both the transnational and regional levels (Sachs and Warner, 1995, 2001; Gylfason, 2001; Gylason and Zoega, 2006; Papyrakis and Gerlagh, 2004, 2006). Since the reform and opening up of China, the country’s economy has developed rapidly, with an average annual growth rate of 9.2% from 1978 to 2013. Chinese economic development, however, has exhibited a severe imbalance among its various regions. The economic growth rates of the eastern region were obviously higher than those of the central and western regions.1 During the period from 1978 to 2013, the average annual economic growth rate in eastern China rose to 9.5%, while those of central and western China were only 8.2% and 7.8%, respectively. China’s natural resources, especially the energy resources of coal, oil, and natural gas, are mainly distributed in the provinces (including autonomous regions and municipalities) of central and western
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China. According to Chinese government statistics for 2013, the proved coal reserves in China were 236.29 billion tons, 92.8% of which were distributed in central and western China; China’s proved land-accessible oil reserves were 2.86883 billion tons, 70.9% of which were distributed in central and western China; and the proved land-accessible natural gas reserves of China were 4311.6 billion cubic meters, 97.3% of which were distributed in central and western China (National Bureau of Statistics of China, 2014). Based on these data, can we conclude that the slower economies of central and western China were affected by the constraint of resource abundance? Can we further conclude that the phenomenon of the resource curse is essentially true in central and western China? If yes, how can the resource curse be dispelled and sustainable economic growth be achieved in the areas studied in central and western China? The existing studies regarding the relationship between natural resources and economic growth focused mainly on the effects of generalized natural resources on sustainable economic development. However, little attention was paid to the effects of specific natural resources, such as the energy resources of coal, oil, and natural gas. To fill the gap in the literature, the effects of energy abundance on the sustainable development of regional economies and the related mechanisms were explored in the present work, with the provinces of China as the study subjects.
2. Literature review 2.1. Proposal of resource curse and progress in empirical research The resource curse thesis can be traced back to the 1540s, an era during which huge amounts of silver flowed into Spain from its American colonies while the Spanish Empire declined. According to legend, the emperor of the Aztec (Mexico), Motecuhzoma II, put a curse on any land grabbers to the effect that they would suffer from illnesses. As the Aztec were colonially ruled by Spain, a huge amount of silver flowed into Spain. Regardless of whether the legend was true or not, this curse was realized after the death of King Phillip II. Spain’s overseas trade began to decline sharply and foreign commodities gradually took over the market that had been previously dominated by Spain. Spanish industry declined steadily and eventually stagnated. The influx of silver also brought on the so-called “Dutch disease” to Spain. Matsuyama (1992) first constructed the prototype of the Dutch disease model. On this basis, Auty (1993) put forward the concept of the resource curse. He studied the cases of resource exporters, and concluded that natural resource abundance was a curse rather than a blessing. By analyzing the development histories of economic entities in transition, Auty (2001, 2003) further concluded that the rise of actual exchange rates caused by the export of resources aggravated the problems of those economic entities. In particular, in some countries that lack effective political systems, abundant natural resources may impair the urgency of reform, distort economic development (such as rent-seeking and corruption), and hinder economic transition. By comparing resource-poor countries in the Caspian region to neighboring resource-rich ones that were in transition, it was found that resource-rich countries after transition exhibited a reverse J-shaped development track, while those of resource-poor countries were V-shaped. 1 According to the classification standard of China National Bureau of statistics, the eastern region includes Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan; the central region includes Heilongjiang, Jilin, Shanxi, Anhui, Jiangxi, Henan, Hubei, and Hunan; and the western region includes Sichuan, Chongqing, Guizhou Yunnan, Tibet, Shaanxi, Inner Mongolia, Guangxi, Gansu, Qinghai, Ningxia, and Xinjiang.
Sachs and Warner (1995) developed the theory of the resource curse and established the model of the Dutch disease. Through the empirical analysis of data from 95 countries and regions during the period 1970–1990, they found that almost all resource-rich countries experienced retarded economic growth after the 1970s. It was Dutch disease that made the primary-commodity exporting countries lag in long-term competitiveness. Sachs and Warner (2001) further analyzed the causes responsible for the differences of economic growth in several Latin American and East Asian countries. They speculated that because the economies of these Latin American countries relied on the export of primary products or of manufactured products made up of primary products, the manufacturing sectors were inhibited with increasing returns. Their economic growth was thus hindered by the Dutch disease or other means. East Asian countries characterized by export-oriented economies, however, exported labor-intensive products and capital- and technology-intensive products in sequence, which stimulated the development of the manufacturing sectors with increasing returns. Therefore, those countries avoided the Dutch disease. After pioneering research by Auty (1990, 1993, 2001) and Sachs and Warner (1995, 2001), the resource curse thesis was continuously verified and discussed by case and empirical analysis. Overall, the subsequent literature can be classified into three types: namely, supporting the resource curse thesis, falsifying the thesis, and analyzing the thesis’s range and conditions of application. In support of the resource curse thesis, Tornell and Lane (1993) and Torvik (2002) successively developed and applied the rentseeking model. They found that resource abundance was involved in nonproductive activities, which stimulated resource rents. Accordingly, the negative monotonic relationship between natural resources and economic growth was proved. Using the ratio of resource rents to GDP as an index to evaluate the degree of resource abundance, Atkinson and Hamiltion (2003) found this index to be negatively correlated with the growth rate of per capita GDP. Papyrakis and Gerlagh (2004) performed an empirical test on the direct and indirect effects of natural resources on economic growth using the data from 39 countries. Their results indicated that the negative effects of natural resources on economic growth far exceeded the positive effects. Arezki and Ploeg (2007) conducted a systematic empirical test on the cross-sectional data of many countries. With factors such as geography, openness, and institutional quality controlled, they adopted institutional quality and openness as the instrumental variables. They found that the export of natural resources directly imposed negative effects on per capita income. In some counties with low openness, the resource curse had a more serious negative effect. Based on the examinations of data from several states in the United States, Papyrakis and Gerlagh (2007) concluded that the resource curse thesis held across various regions in the country. They found that in Alaska and Louisiana, the economy developed quite slowly over a long time period, a phenomenon that was attributed primarily to their abundant natural resources. Literature supporting the falsification of the resource curse includes a study by Davis (1995), who investigated 22 mineralbased economic entities as an entirety, compared them to nonminerals-based economic entities and found that no resource curse existed in the entirety. Using model analysis, Mikesell (1997) concluded that no inherent necessary connection existed between resource abundance and economic growth rate. The differences of economic growth among developing countries resulted mainly from differences in factors influencing endogenous growth, and the underlying causal relationship for endogenous growth could not be accounted for by resource endowment alone. In addition, Wright and Czelusta (2007) refuted the resource curse thesis by examining cases of successful resource-based economic growth.
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They indicated that mineral resources have formed the foundation of high-tech industries in many countries. The successes of the United States’ economic development from the mid-19th to mid20th centuries, and those of Norway, Australia, and Canada more recently, all proved that progress in mining and technology, and the promotion of relevant knowledge, impose significant positive effects on economic development. In a literature review, Davis and Tilton (2005) concluded that mineral resources, such as property and capital in other forms, were also a type of treasure. Taking Equatorial Guinea as an example, Same (2008) indicated that in some very poor countries virtually without any manufacturing industry, the Dutch disease was actually a spontaneous and necessary redistribution of resources in the economy. Oil revenues played a fairly significant role in national economic development and poverty reduction in Equatorial Guinea. Concerning the application range and conditions of the resource curse thesis, Torvik (2002) constructed a Dutch disease model that included “learning by doing” and concluded that the occurrence of the resource curse depends on the characterization of the present economy and productivity in the trade and nontrade sectors. Variations in relative productivity will cause fluctuations in the exchange rate and determine the degree of the Dutch disease. Additionally, Wen and King (2004) indicated that the occurrence of the resource curse depends on the quality of professionalization and transaction efficiency. When the resourcerich countries have poor organizational systems and transaction efficiency, the curse will occur. Otherwise, the curse will not occur. Other researchers argued that the occurrence of resource curse is not absolute and depends on the institutional quality of the specific country (Blute et al., 2005; Robinson et al., 2006; Mehlum et al., 2006). If a country possesses a predatory institutional environment, the resources will not contribute to economic growth. However, if a country has a favorable production-type institutional environment, it can achieve rapid economic growth by taking full advantage of its abundant resources. Based on the affirmation that the resource curse is not a regular thesis, Murshed (2004), Hodler (2006), and Boschini and Pettersson (2007) used model analysis to confirm that institutional quality and the abundance of natural resources jointly determine the occurrence of the resource curse (Murshed, 2004; Hodler, 2006; Boschini and Pettersson, 2007). 2.2. Mechanism of resource curse In earlier literature, researchers speculated that the Dutch disease was the primary mechanism of the resource curse. Specifically, Larsen (2006) decomposed this mechanism into crowdingout, overflow, and spending effects. Through the crowding-out effect, labor, capital, and techniques are crowded out of the mechanical manufacturing industry by natural resources. Labor is then attracted by the higher income of the resource-based sectors and transfers from the mechanical manufacturing industry to the resource mining and manufacturing industries. Natural resources may also overflow through techniques that are inferior to those of the manufacturing sectors, leading to low output and low economic growth. The spending effect of natural resources refers to increases in the actual exchange rate by converting a huge amount of resource revenues into increased total demand, which weakens the international competitiveness of the products from the mechanical manufacturing sectors. In a case study of Brazil, Auty (1995) concluded that policies that gave priority to the development of heavy industry inhibited development of the manufacturing sectors that were highly dependent on tariff protection and transfer payments. Brazil’s industrialization therefore matured quite slowly. In addition to the Dutch disease, the effect of natural resources on human capital is another important mechanism responsible for the occurrence of resource curse. Learner et al. (1999)
3
found that material capital, rather than human capital, is actually required for the excavation of natural resources. Accordingly, the education system must provide for the formation of a labor-intensive manufacturing industry, a provision that affects the economic growth. Additionally, several scholars have speculated that institutional factors such as rent-seeking, conflict, property rights, and political processes are other important mechanisms by which the resource curse occurs (Torvik, 2002; Auty, 2001; Humphreys, 2005; Hodler, 2006; Wick and Bulte, 2006; Welsch, 2008). 2.3. Literature summary In reviewing the aforementioned research on the resource curse, some problems are found. First, few studies were performed on the relationship between energy abundance and sustainable economic growth. The recent studies on the resource curse focused mainly on the relationship between natural resources and sustainable economic growth. In some developing countries with relatively backward economies and technologies, the effects of energy resources on the national economy are actually quite remarkable. Therefore, we should study the effects of energy resources further. Second, few studies were performed on regional disparity in developing countries. In the study of the resource curse thesis, large samples are required (Xu and Shao, 2006). Compared with developed countries, developing countries exhibit more severe developmental inequality. In future studies, the data from regions of different developmental levels should be used as the samples to analyze. Third, China’s energy demand is huge because of economic development. It is an urgent need to further study on the economic problems which caused by the development of energy resources. China’s total energy consumption reached 42.6 tons of standard coal in 2014, accounting for 20% of the total global energy consumption. In the future, with the development of China economy, the demand for energy will remain a relatively rapid growth. The International Energy Agency predicted that China would overtake the United States by 2030, becoming the world largest oil consumer. Therefore, it is necessary to study on the economic problems which caused by China energy development. In the present work, the per capita reserves of three major fossil fuels (coal, oil, and natural gas) were taken as the proxy variables of energy abundance. Using the data of various provinces in China obtained from National Bureau of Statistics of China, we performed empirical analysis on the relationship between energy endowment and sustainable economic growth and analyzed the underlying mechanisms.
3. Model and data 3.1. Calculation model and variable declaration To verify the relationship between energy abundance and sustainable economic growth in China, a basic econometric model of panel data was constructed by referring to the empirical study by Shao and Yang (2010):
yit = β0 + β1Yi0 + β2 Rit + β3 Zit + β4 D + μi + εit
(1)
where i denotes the provincial cross-section unit; where denotes the provincial cross-section unit; denotes the year; is the regression coefficient; is the intercept term to reflect individual heterogeneity; and is the regression residual term. The declarations of these variables are listed below.
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(1) Explained variable: y is the annual economic growth, which is denoted by the annual growth rate of per capita gross regional product using the year of 2003 as the base period. (2) Explanatory variable:
R=
a1 × Coal + a2 × Oil + a3 × Gas , Population
which
represents
the
energy
abundance. Using this index, the basic reserves of three major energy resources were converted into standard coal reserves through energy conversion. The per capita energy abundance of the various regions was used, with the unit of ten thousand standard tons of coal per capita. By referring to the conversion formula on basic energy reserves (Xu and Wang, 2006) developed by the Chinese Academy of Sciences (CAS), the coefficients of conversion from ten thousand tons (cubic meters) of coal, oil, and natural gas to ten thousand tons of standard coal are 0.7143, 1.4288, and 13.30, respectively. (3)Control variable: Y0 ¼GDP2003, i.e., per capita GDP at 2003 (base period), represents the initial economic development level, which can be used to control the effects of regional economic disparities on sustainable economic growth. D denotes the dummy variable of geographical location, which was used to control the effects of regional disparities on sustainable economic growth. For the eastern regions, D1 ¼ 1, D2 ¼0; for the central regions, D1 ¼0, D2 ¼ 1; for the western regions, D1 ¼0, D2 ¼0. Z represents the other control variables affecting economic growth. According to economic theory and the relevant literature, these variables can be classified into five items: (1) Material capital input (INV), which is denoted by the ratio of investment of total fixed assets to GDP of the region and is used to control the effects of material capital on economic growth. (2) Human capital input (EDU_C), which is denoted by the ratio of expenditure on education to total fiscal expenditures of that region and can be used to control the effects of human capital on economic growth. (3) Technological innovation input (RD), which is denoted by the ratio of research and development input to GDP of that region. (4) Institutional condition (OPEN): The opening-up policy has played a vital role in China’s economic development for the past 30 years (Shao and Qi, 2008). The regions with greater openness exhibit more robust market economy systems, more transparent economic activities, and, thus, better institutional quality (Xu and Wang, 2006). Therefore, in the present work, we selected openness as a proxy variable to reflect the institutional quality in a region, which is denoted by the ratio of total volume of imports and exports (in yuan) to GDP. (5) Foreign direct investment (FDI): Many studies have examined the relationship between FDI and China’s economic growth and indicated that FDI significantly promoted China’s economic development and its effects should be controlled. In the present work, the control variable of FDI is denoted by the ratio of foreign investment in actual use to GDP in the study region. 3.2. Selection of samples and data sources To ensure the availability and accuracy of the data and the consistency of the statistical caliber, the data from 31 provinces (including autonomous regions and municipalities) in China during the period from 2003 to 2012 were selected as the study samples. In total, 310 observed values were included. The panel data were constructed by selecting a regional economic growth variable, an energy abundance variable and other economic
variables that affect economic growth, and then the relationship between energy endowment and sustainable economic growth was investigated. The data in the studies were collected from the China Statistical Yearbook during the period from 2004 to 2013, the statistical bulletins of national economy and social development in the 31 provinces from 2003 to 2012, the Commerce Ministry’s website (http://www.mofcom.gov.cn), and the statistical yearbook of each province.
4. Empirical analysis 4.1. Descriptive statistics To depict the statistical characteristics of the variables more clearly, we first conducted descriptive statistical analyses on the variables; the results are listed in Table 1. It can be observed that the mean value of y is 14.314%, suggesting that the per capita economic growth rates for these regions during that period were generally high. The standard deviation, however, was 6.982, suggesting marked economic disparities among the various regions. The mean value of R was 238.644, and the standard deviation was 521.714. This suggests that there were great regional differences in the distribution of basic energy reserves. The energy resources are concentrated in individual provinces; e.g., the per capita standard coal of Shanxi province and Inner Mongolia both exceed 2000 tons, while the energy reserves are scarce in some provinces. For the INV variable, the mean value and standard deviation were 0.536 and 0.146, respectively. The variability of the standard deviation with respect to the mean value was close to that of y, suggesting that the expected capital investment imposes significant effects on economic growth. For the EDU_C variable, the mean value and standard deviation were 0.035 and 0.149, respectively. The variability of the standard deviation with respect to mean value was large, suggesting that the education expenditures in the various provinces were diverse. For the RD, OPEN and FDI variables, the standard deviations were large, suggesting that the intensity of investment in research and development, openness, and foreign investment in actual use varied significantly among the various provinces. 4.2. Econometric test During the econometric test, the multicollinearity among the variables was inspected using the statistics of the variance inflation factor (VIF). Subsequently, combining ordinary least squares (OLS) with cluster-robust standard error, the Lagrange multiplier (LM) test, and the Hausman test, the regression model for the panel data was identified (including pooled regression, random effect, and fixed effect). Considering that self-correlation may exist among the disturbance terms of individuals in different periods, the standard deviation of the pooled regression was estimated Table 1 Descriptive statistics of the analysis variables. Variable Observed value
Mean value Standard deviation
Minimum value
Maximum value
y R Y0 INV EDU_C RD OPEN FDI
14.314 238.644 11.663 0.536 0.149 1.182 0.353 0.021
1.014 0.000 3.686 0.238 0.090 0.120 0.046 0.001
32.997 2178.500 37.910 0.884 0.218 5.820 1.690 0.079
310 310 310 310 310 310 310 310
6.982 521.714 7.994 0.146 0.035 0.987 0.425 0.018
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5
Table 2 Overall tests on the resource curse of energy abundance. Variable
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
R
22.301** (2.27)
18.116* (1.70) 0.179*** ( 4.53)
18.624* (1.80) 0.215*** ( 2.77) 2.507 (1.20)
19.009* (1.80) 0.406*** ( 5.88)
19.123 (1.55) 0.129*** ( 2.59)
19.367** (2.55) 0.256*** ( 4.81)
18.681* (1.90) 0.130*** ( 4.55)
23.129** (2.52) 0.294*** ( 3.42) 4.465*** (4.20) 0.507 (1.40) 75.099*** (5.60) 90.867*** (3.49) 13.944*** (6.14) 3.459 ( 1.46) 0.266 ( 0.56) 3.777 ( 1.34) 0.222 310
Y0 OPEN
1.050* (1.90)
RD
44.617*** (3.88)
EDU_C
80.060*** (3.66)
FDI INV D1 D2 Constant term R2 Observed value
14.997*** (15.27) 0.029 310
0.057 ( 0.04) 0.062 ( 0.18) 18.129*** (26.78) 0.080 310
0.219 ( 0.40) 0.030 (0.03) 17.884*** (20.19) 0.080 310
0.428 (0.77) 0.586 ( 0.23) 18.443*** (20.14) 0.080 310
1.604 ( 1.49) 0.566 ( 0.70) 9.818*** (5.10) 0.088 310
2.154 ( 1.66) 0.899 ( 1.20) 15.865*** (20.43) 0.090 310
12.642*** (4.50) 1.253 (1.30) 0.683 (0.81) 9.601*** (6.83) 0.167 310
Note: t-values are listed in the brackets below the estimation coefficient. *** ** *
Represent the significance at the levels of 1%. Represent the significance at the levels of 5%. Represent the significance at the levels of 10%.
using the cluster-robust standard error, and the random effect was estimated using generalized least squares to eliminate the effects of heteroscedasticity, which may exist in short panel data. Finally, by introducing the proxies of the explanatory variables, a robustness test was performed on the econometric model. (1) Multicollinearity test: According to the estimation, the VIFs of the various variables were all less than 7. Except for the variables Y0 and OPEN, the VIFs of the other variables were all less than 3. Accordingly, we can conclude that the multicollinearity among different variables was not serious. (2) Panel regression: With the adoption of hierarchical regression, the effects of the control variables on several primary explanatory variables and explained variables were investigated. Finally, all of the predictive variables were included in the model. Table 2 lists the overall inspection results on the econometric model. (3) Robustness test: The average ratio of energy reserves (denoted by RS) was used as the proxy of energy abundance to re-estimate the econometric model.2 The results indicated that the regression coefficient and t-value of RS were 14.213 and 1.92, respectively, passing the significance test at the level of 15% (in model 8 from Table 2, the estimation coefficient and t-value of energy abundance R were 23.129 and 2.52, respectively). Moreover, for the other control variables, the signs of the regression coefficients were not changed, and there were only small variations in the values. In addition, EDU_C was replaced in the test by the average years of education (EDU_Y) in the study region. The results showed that the signs of the variables were not changed, and the changes in value were not large. We thus conclude that the econometric model was robust.
2 The energy reserves in the area (converted into standard coal) accounts for the proportion of total reserves in China in that year. This indicator reflects a relative level of energy abundance in a region from the view of reserves.
4.3. Interpretation of results In model 1 (reference), regression was performed only on the explained (economic growth rate y) and explanatory variables (energy abundance R). The estimation coefficient of R was 22.301, which passed the significance test at the level of 5%. The regression results suggested that abundant energy reserves significantly promoted the economic growth rate. To verify the stability of this result, model 2 controlled the initial economic level (Y0) and the geographic factor of the study region. The results indicated that at the significance level of 10%, the estimation coefficient of R decreased to 18.116. Thus, the conclusions described above were still applicable. It is worth noting that the coefficient of Y0 is 0.179, which also passed the significance test at the level of 1%. When a series of control variables were added, this coefficient was still negative. This suggested that during the sample period, the economic growth rates in these regions were conditionally convergent. In other words, the economic growth rates in the regions with lower per capita income exceeded those of the regions with higher per capita income (Xu and Wang, 2006). Regarding the issue of whether the regional economic gap was widening or narrowing, further verifications are still required. During the sample period, however, the initial economic level of a region imposed negative impacts on its economic growth rate. From the model 3 to the model 7, several control variables were found to affect economic growth: namely, institutional quality (OPEN), research and development level (RD), human capital input (EDU_C), foreign direct investment (FDI), and material capital input (INV). The calculation results agreed well with the mainstream viewpoint in economic growth theory. Technological innovation, material capital, and human capital are the key elements in economic growth, while institutional quality is an important external condition for the functioning of those factors. Foreign direct investment can greatly promote regional economic growth. The models demonstrated the positive effects of those factors. The addition of each control variable imposed no obvious effects on either the estimation coefficient of R or the significance level,
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Table 3 Test results of transmission mechanisms. Variable
R Y0 D1 D2 Observed value
The Dutch disease
Material capital
Human capital
Innovation
Institutional quality
MANU_C
MANU_R
INV
EDU_C
EDU_Y
RD
OPEN
0.067 (0.326) 0.005 (0.005) 0.148 (0.009) 0.125 (0.022) 310
0.156 (0.000) 0.012 (0.005) 0.145 (0.010) 0.052 (0.006) 310
0.178 (0.360) 0.007 (0.001) 0.089 (0.020) 0.115 (0.010) 310
0.056 (0.245) 0.012 (0.045) 0.078 (0.011) 0.023 (0.014) 310
1.720 (0.017) 0.095 (0.008) 0.277 (0.042) 0.656 (0.002) 310
1.979 (0.008) 0.095 (0.004) 0.458 (0.005) 0.089 (0.178) 310
0.458 (0.074) 0.085 (0.074) 0.089 (0.045) 0.066 (0.020) 310
Note: P-values are listed in the brackets below the estimation coefficients.
which is consistent with previous reports. Model 8 included all control variables. As shown in Table 2, the estimation coefficient of R was 23.129, which passed the significance test at the level of 5%. In this model, energy abundance still imposed positive effects on economic growth. The estimation coefficient exhibited no obvious variations during the process. Energy reserves thus play stable and significantly positive roles in regional economic growth, and the resource curse of energy abundance does not exist in the examined provinces of China.
5. Transmission mechanisms for the effects of energy abundance on sustainable economic growth As stated above, energy abundance, rather than being a vector of the resource curse, significantly promoted the sustainable economic growth of the studied provinces in China. What, however, are the underlying mechanisms? In the studies regarding the specific mechanisms of resource curse, the transmission mechanisms were considered as the underlying cause for the phenomenon. Therefore, the transmission mechanism and its degree of influence are also important research topics in this field (Papyrakis and Gerlagh, 2004; Xu and Shao, 2006; Shao and Qi, 2008; Fang et al., 2011). Existing literature indicates that the transmission mechanisms include deterioration of trade conditions (Davis and Tilton, 2005), the Dutch disease (Corden and Neary, 1982; Matsuyama, 1992; Sachs and Warner, 1995, 1999), institutional weakness (Sachs and Warner, 1999; Gylfason, 2001; Sala-i-Martin, 2003), the crowding-out of investment (Sachs and Warner, 1995, 1999; Gylfason, 2001), and war and conflict (Collier and Hoeffler, 2005; Brunnschweiler and Bulte, 2009). Papyrakis and Gerlagh (2004) proposed a tool for estimating the transmission mechanism that connects natural resource abundance to sustainable economic growth (Papyrakis and Gerlagh, 2004). According to their studies, the contribution proportions of investment rate, openness, trade conditions, education investment, and institutional efficiency were 47%, 24%, 25%, 13%, and 7%, respectively. Through multiple transmission mechanisms, the driving factors that are quite important to sustainable economic growth were crowded out by natural resource abundance, and thus, the economic growth was hindered. In our study, the investigation of transmission mechanisms has two goals: the first is to discuss whether and how the resource curse transmission mechanisms still work when energy reserves exhibit positive correlation with sustainable economic growth; the second is to further explore the underlying causes of why energy reserves can promote sustainable economic growth based on the paradigm of transmission mechanisms. As described above, the Dutch disease (the crowding-out of the manufacturing industry), unreasonable capital allocation (the crowding-out of investment), the crowding-out of human capital (or education investment), the
crowding-out of innovation, and institution weakening are significant transmission mechanisms by which resource abundance affects sustainable economic growth. In the present work, those possible transmission mechanisms were examined. By consulting the econometric model of transmission mechanism established by Papyrakis and Gerlagh (2004), the panel data models were established as follows:
Zit = α0 + α1Rit + α2 Y0 + α3 D + μit
(2)
where Zit denotes the variable of transmission mechanism, corresponding to the control variable Z in model 1, and the other variables had the same definitions as described above. According to the definition by Papyrakis and Gerlagh (2004), the regression coefficient β2 in model 1 represents the direct effect of energy reserves on sustainable economic growth, and α1β3 represents the indirect effect. The sum of β2 and α1β3 represents the total effect of energy reserves on sustainable economic growth. The panel data were estimated by the FGLS in a random effect model, and the examination results of transmission mechanisms are listed in Table 3. To verify whether the crowding-out of manufacturing industry effect was generated by energy abundance, i.e., the occurrence of the Dutch disease in the conventional sense, the variables MANU_C and MANU_R were introduced. The two represent capital and human inputs, respectively, in manufacturing industry.3 The results suggested that energy abundance promoted capital input rather than the crowding-out effect. However, this promotion was not remarkable. A possible reason is that the cooperative effects among various industries induced by abundant energy exceeded the crowding-out effect, and thus, the total effect was positive. For human input in the manufacturing industry, however, the crowding-out effect induced by energy abundance was quite significant. This suggests that abundant energy would affect to various degrees the distribution of employment among various sectors, causing a transfer from the manufacturing sectors to the energy production sectors. For the effects of energy abundance on human capital, we performed the examination from two aspects, i.e., the effects on education capital input (EDU_C) and on human capital accumulation (EDU_Y).4 The results suggested that energy abundance imposed a significantly positive effect on human capital accumulation. In addition, the effect on material 3 The employment ratio of manufacturing industry was denoted by the ratio of the persons employed in manufacturing industry to total employed persons in the study region. 4 Average years of education was denoted by the average years of education in the study region. The computational formula can be written as (the population of illiteracy 1þ the population who received elementary education 6þ the population who received junior middle school education 9þ the population who received senior middle school education 12þ the population who received college education or above 16)/total population in the study region.
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Table 4 Estimation coefficients of various variables in overall test of the resource curse (Model 9). Variable
R
Y0
D1
D2
INV
Estimation coefficient P-value
14.45 (0.089)
0.607 (0.021)
2.788 (0.054)
1.87 (0.152)
14.752 (0.052)
Variable
FDI
RD
MANU_R
EDU_Y
OPEN
Estimation coefficient P-value
48.721 (0.025)
0.035 (0.89)
10.100 (0.01)
1.890 (0.000)
5.562 (0.049)
capital input was positive but not very significant, whereas the effects on the level of research and development and institutional quality were remarkably negative. To a certain extent, energy abundance restrained the intensity of research and development investment and openness in the study region and was adverse to technological innovation and the construction of an open, transparent, and favorable economic system. Considering that human capital (EDU_C) was not significant, but human capital accumulation (EDU_Y) was quite significant in the examination of transmission mechanisms, EDU_C was replaced by EDU_Y in the present work to calculate the effect of transmission mechanisms. In addition, MANU_R was also added into model 1, and this extended model (named model 9) was used to verify the resource curse. Table 4 lists the estimation coefficients of various variables. By comparing Tables 3 and 4, it can be found that in the routes of R, Z, and y, the variables of MANU_R, EDU_Y, and OPEN exhibited obvious significance. That is, the Dutch disease, human capital, and institutional quality, which are represented by the employment ratio of the manufacturing industry, the average years of education, and openness, respectively, are the three primary transmission mechanisms by which energy abundance affects economic growth. After the transmission mechanisms were identified, the indirect effects on sustainable economic growth caused by energy abundance through those three transmission mechanisms were calculated by referring to the calculation method of Papyrakis and Gerlagh (2004). The results (Table 5) suggest that, among the three transmission mechanisms, the direct effect of human capital is the greatest and positive, with a value of 3.251. The direct effect of openness was 2.547, and that of the Dutch disease, which crowded out the employment in the manufacturing industry, was 1.576. Energy abundance hindered sustainable economic growth through the latter two effects. Compared with the direct effect induced by energy abundance (corresponding to the estimation coefficient of R in model 9, 14.45), the indirect effects retarding the sustainable economic growth could not threaten overall sustainable economic growth. Consequently, on the whole, energy abundance is positively correlated with sustainable economic growth in the studied provinces.
Table 5 Indirect effects of transmission mechanisms. Transmission mechanism
α1
β3
α1β3
MANU_R EDU_Y OPEN
0.156 1.720 0.458
10.100 1.890 5.562
1.576 3.251 2.547
7
6. Conclusions and policy suggestions 6.1. Research conclusions
(1) At the provincial level in China, the resource curse of energy abundance is absent. The test results provided here indicated that when a series of important variables, such as fixed assets, human capital, innovation investment, regional openness, and foreign direct investment, are controlled, energy abundance exhibits significantly positive correlations with the economic growth rate. Among those variables, foreign direct investment, fixed assets investment, education investment, and policies promoting openness are the primary motivating factors in sustainable economic growth in the studied provinces. (2) The transmission mechanisms through which energy abundance affects sustainable economic growth play a significant role. Energy abundance imposed positive effects on human capital and thus promoted economic growth. Energy abundance imposed indirect crowding-out effects on openness and the manufacturing industry, thus retarding sustainable economic growth. The direct positive effects of energy abundance, however, were larger. Therefore, no curse occurred on the whole. 6.2. Policy suggestions
(1) To achieve sustainable development, scientific plans for economic development should be made in resource-based regions. For a long time, there have been few scientific and comprehensive top-level designs and plans made by Chinese governments in resource-intensive regions. In those regions, therefore, the economy developed rapidly during the period when resources were abundant and degraded seriously after the resources were exhausted. Some locations even become ghost cities, such as Yumen in Gansu province and Fuxin in Liaoning province. In the future, long-term planning should be carried out in resource-rich regions to achieve sustainable development. (2) To prevent the Dutch disease, industrial structure should be further optimized and improved. Energy abundance imposed the crowding-out effect on employment in the manufacturing industry to a certain degree, an effect that would certainly have negative impacts on human capital accumulation in the manufacturing industry. Accordingly, the accumulation of technical knowledge was adversely affected, which degraded the manufacturing industry and its cooperative development with other industries. In that case, we did see evidence of the natural resource curse. (3) High-quality market environments should be established, backed by an improved legal system, with surveillance by social networks, the media, and individuals. Well-established institutions contribute to regional economic development. Energy abundance, however, may degrade institutional quality to a degree. Corruption frequently takes place when local governments and/or sectors have resource monopoly advantages. Concentration of power can lead to resource allocation imbalances and resource waste, which are averse to reaching the full utilization of resource endowment advantages. Therefore, local governments, in combination with the power of public opinion, should consider the balance and supervision between various sectors when designing institutions relevant to economic development. Thus, the executive force of policies can be enhanced, and the serious economic and social problems induced by excessive power in the hands of the resource production sectors can be avoided.
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Acknowledgment The authors are grateful for financial support from the National Natural Science Foundation of China under Grant nos. 71003066 and 71133003.
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