Impacts of mineral resources: Evidence from county economies in China

Energy Policy 136 (2020) 111088

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Impacts of mineral resources: Evidence from county economies in China Xiaoping He *, Dunguo Mou China Center for Energy Economics Research, Xiamen University School of Economics, China

A R T I C L E I N F O

A B S T R A C T

JEL classification: C26 E24 R10

the literature proposes that a booming minerals sector leads to a development curse. The mineral markets in China experienced a prolonged boom over the period of 2000–2010. We empirically examine the effects of mineral resources on employment in county economies during the boom. We consider the endogeneity of the resource measure and employ an instrumental variables approach to resolve the problem. We find the mining boom exerts a significant “crowding out” effect on the manufacturing employment in mineral-resourcedependent counties, but benefits the employment in services. Because the increase in mining employment is sizeable in a mining boom, the overall employment in the resource-dependent counties has shown a small growth. These results are robust to alternative samples. Our findings confirm the argument that resource booms undermine manufacturing sectors through deindustrialization effects, though little evidence shows the existence of a resource curse in overall employment. We conclude that for a developing economy with rich mineral re­ sources and a large population, it would be hard to following the road of industrialization relying on manufacturing.

Keywords: Resource dependence Local economy Employment Instrumental variable

1. Introduction China has a long history of exploiting and utilizing of mineral re­ sources. Local mining economies in the country were shaped not only by the long-term historical process, but also by the government-led allo­ cation of economic resources over the past decades. During the period of the First Five-Year Economic Plan (1951–1955), the mining cities accounted as much as 50% of state-level investment projects, which directly led to the first round of mining boom in these cities. In the 1980s, the mining economies ushered in the second round of booming, owing to the outline of the national industrialization strategy. Up to the end of 1990s, the mining outputs from mining cities have achieved a fraction of 50% in China’s total mining outputs. While mining cities have contributed tremendously to the national industrialization, some of them are confronted with multiple challenges, in terms of industry structure, employment, and environmental pollution. There is wide concern that natural resources would be a curse more than a bless. A comprehensive understanding of the role of mineral resources needs an insight into labour market. This is particularly important for a transition economy where the mineral resources are quickly depleting and the employment problems are increasingly prominent. The primary purpose of our work is to explore the role of natural resources in development of local economies, from the employment

perspective. The resources of our interest are the non-renewable re­ sources in general, such as minerals and fuels, since they have been regarded as the point source of the resource curse (Mehlum et al., 2006; Isham et al., 2005; Havranek et al., 2016). We first look to the literature for inspiration of how natural resources may be link with labour market, and then empirically examine the relationship between mineral-resource-dependence and local employ­ ment by instrumental variable regressions. We find in the boom, the resource curse significantly works for the manufacturing employment in mineral-resource-dependent counties, while the employment in services benefit slightly from the boom. Because the increase in mining employment is sizeable in a mining boom, the overall employment in the resource-dependent counties has shown a small growth. These findings confirm the argument that resource booms undermine manufacturing sectors through deindustrialization effects. We conclude it would be hard for a developing economy with rich resources and a large popu­ lation to follow the industrialization pattern relying on manufacturing. The rest of this article proceeds as follows: Section 2 presents the literature, Section 3 describes the identification strategy and model, Section 4 outlines the sample, data and variables, Section 5 presents empirical results. Section 6 concludes.

* Corresponding author. E-mail addresses: [email protected] (X. He), [email protected] (D. Mou). https://doi.org/10.1016/j.enpol.2019.111088 Received 30 March 2019; Received in revised form 18 September 2019; Accepted 1 November 2019 Available online 12 November 2019 0301-4215/© 2019 Elsevier Ltd. All rights reserved.

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2. Literature

2011; Caselli and Michaels, 2009). When using the data at local-level within a country, the institutional factors that confound the relation­ ship between resources and economic performance are usually common across the individuals. Hence, the estimation bias due to institutional differences is not a primary concern, and conclusions on the role of natural resources from such studies tend to be neutral or even positive. As far as labour market is concerned, most studies that explore the link between resources and employment focus on developed countries. Brown (2014) investigates the boom of natural gas industry from 2001 to 2011 in the central US, and finds it has exerted positive effects on local employment and wages, partly due to the labour mobility between counties and the small share of manufacturing in the local economy. Weber (2014) explores the decade-long boom of natural gas in the south central US, and comes to similar conclusion: it has not resulted in the conditions associated with the resource curse; any crowding-out was sufficiently small that each gas-related mining job has created at least one non-mining job. Similarly, Michaels’ (2011) find that oil abundance has promoted long-term growth of the oil-rich counties in the US. Being among the few paying attention to developing countries, Kotsadam and Tolonen (2016) provide evidence that the mining activities in African continent have created local boom-bust economies, with transient and gender-specific employment effects. The literature has employed various measures for abundance in natural resources. It is mining activities rather than the resource reserves that actually influence the economy. The amount of resource flows, for examples mining production or employment (Black et al., 2005), and exports or their share in the economy (Sachs and Warner, 2001; Zhan, 2017), is typically used as the measure for natural-resource abundance. Such measures can hardly avoid the endogeneity problem because they are the result of economic activities, and hence can be more usefully interpreted as a proxy for resource dependence. Many empirical workers (Stijns, 2005; Brunnschweiler and Bulte, 2008; Havranek etl al. 2016) have emphasized the importance of differentiating between resource dependence (the degree to which countries depend on natural resource exports) and resource abundance (a stock measure of resource wealth). The former is endogenous to underlying structural factors so its associ­ ation with economy may be not cause and effect. Another choice of the natural resource variable is the measure of resource abundance, by looking at the amount of resource stocks (Brunnschweiler and Bulte, 2008; Michaels, 2011; Arezki and Van der Ploeg, 2011; Allcott and Keniston, 2017). In contrast to resource dependence, resource abundance is shaped by a long and complex process of mineralization, conditional on the geography and geology, and hence can be regarded as exogenous to economic activity. For instance, Michaels’ (2011) identify the binary variable of resource abundance by whether the county locates over an oil field with rich reserves. As far as studies on China are concerned, the relationship between natural resources and economic growth has been the concerned focus, such as Fan et al. (2012), Su et al. (2016), Wu and Lei (2016) and Wu et al. (2018). Other research interests include the relationships between resource abundance and financial development (Yu and Chen, 2011), and resource abundance and consumption growth (Zhang et al., 2009), and so on. Overall speaking, these studies provide mixed evidence on relationships between resource abundance and various indicators of economic performance. Empirical findings of Fan et al. (2012), Wu and Lei (2016), and Su et al. (2016) show there is little evidence to support the resource curse hypothesis, while Yu and Chen (2011), Zhang et al. (2009) and Wu et al., (2018) confirm a negative link between resource abundance and economic performance. The majority of related studies use panel data at the provincial level, and a few researches use the data at the prefectural level cities. Indicators such as output value of mineral

A large body of studies has explored the relationship between natural resources and economic development, while the conclusions are mixed. Many studies conclude that the economies more reliant on resources tend to grow slower over time. A typical argument is the well-known Resource Curse hypothesis. It was first proposed by Auty (1993) and thereafter supported by many empirical evidences (Sachs and Warner, 1995; Gylfason et al., 1999; Hausman and Rigobon, 2002; Sala-I-Martin & Subramanian, 2003; Papyrakis and Gerlagh, 2004, 2007). Sachs and Warner (2001) note, “empirical studies have shown that this curse is a reasonably solid fact.” Hirschman (1958) argue that either the hori­ zontal linkages of mining sector with other sectors or the vertical linkage within the mining sector is weak, and therefore, extraction of natural resources can hardly drive the development of resource-abundant areas. The main channels of transmission from natural resource abundance to slow economic growth can be described in terms of crowding out: a heavy dependence on natural capital tends to crowd out other types of capital (such as foreign capital, social capital, human capital, physical capital, and finical capital) and thereby inhibit economic growth (Gyl­ fason, 2006). The cross-country studies often support the resource curse hypoph­ ysis, such as Gylfason et al. (1999) and Sachs and Warner (2001). Though there may be heterogeneity in country’s experiences to use the wealth of natural resources to improve economic performance, there are two common factors underlying the failures of resource-rich countries. One is the technical difficulty of handling resource revenues that are risky, volatile, and time-limited. The other is that governance has been unable to resist short-run spending pressures and to commit to long-run investment and growth strategies (Venables, 2016). In particular, for developing countries (Mehlum et al., 2006; Caselli and Michaels, 2009), the interaction of resources with a market failure has been widely regarded as the fundamental mechanism inducing the resource-curse effects. Many other scholars have criticized the resource curse hypothesis (e. g., Davis, 1995; Sell et al., 2007; Brunnschweiler and Bulte, 2008; Havranek et al., 2016). The literature of economic geography provides the theoretical basis that regional economies may benefit from natural resources (Krugman, 1991; Rosenthal and Strange, 2004). There are also increasing empirical evidences on the positive role of resource abun­ dance (Black et al., 2005; Wright and Czelusta, 2007; Michaels, 2011; Mideksa, 2013; Weber, 2014; Brown, 2014; Allcott and Keniston, 2017). The general argument is mining activities increase the demand for la­ bour in resource-rich areas, leading to rises of wages and inflows of population. These changes in resource-abundant areas may trigger in­ vestments in local infrastructure, which in turn improve the productivity of industries in these areas. By using a meta-analysis, Havranek et al. (2016) conclude that overall support for the resource curse hypothesis is weak when potential publication bias and method heterogeneity are taken into account. All­ cott and Keniston (2017) note, whether non-mining industries would be hurt by resource extraction depends on a couple of conditions, such as whether the wages of manufacturing sector rise, whether manufacturing products are tradable, and whether there are productivity spillovers between sectors. Furthermore, Papyrakis & Gerlagh (2004) point out, estimation biases in many cross-country studies can be largely attributed to the failures of taking into account the differences in institutional factors between resource-rich countries, such as corruption, investment, governance structure, and trade terms. These problems also explain why empirical results from cross-country studies are sensitive to choices of observation period, sample countries and variables (Van der Ploeg,

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covariates to describe the relationship between resource dependence and employment, as the following expression:

extraction, the ratio of resource production to GDP, the fraction of mining workers in total employment, and minerals imports, are widely used as the resource measures. However, the potential endogeneity problem of such measures has seldom been considered, except Su et al. (2016) apply a dynamic generalized method of moments (GMM). To our best knowledge, up to date, there have extremely rare empirical studies on the relationship between natural-resource abun­ dance and labour market in China. One possible exception may be Gong and Deng (2009): based on a primary statistical analysis using provincial-level data, they conclude that the “Dutch Disease” has led to the increasing dependence of China’s employment on the export-oriented and labour-intensive industries. However, the study is short of rigorous econometric analysis. In summary, little consensus exists on effects of natural resource richness. The related studies pay more attentions on developed countries than on developing countries, widely employ GDP growth as the dependent variable, and more use cross-country data than local-level data. Importantly, while resource dependence is typically employed as the proxy for resources richness, the endogeneity problem is rarely considered. Our study is different from previous studies in four aspects. First, we focus on county economies, by using a dataset from China Population Census. Second, previous studies have rarely focused on the relationship between natural-resource abundance and labour market in developing economies. Our work enriches the literature, by exploring the role of natural resources in local economies of China from an employment perspective. Third, we make a distinction between resource dependence and resource abundance, and, unlike many other empirical studies, treat the former as endogenous. Finally, previous conclusions overall tend to be positive on impacts of resources on employment. We identify a negative link between mineral resources and manufacturing employ­ ment, while we find little evidence on the existence of a resource curse on overall employment.

ΔYi;t ¼ αj þ βXi;t0 þ γTi þ εi;t

(1)

Where ΔYi;t represents the change in employment of county iat period t. jindexes provinces. The binary variable Ti is used to indicate whether a county has received a positive treatment in terms of resource depen­ dence, Ti 2{0,1}. The vector of controls, Xi;t0 , represents the initial characteristics of individuals. εi;t is the normally distributed error term. The parameter vector ​ αj , captures the province-specific effects. β cap­ tures the impact of the initial difference at period t0 in individual characteristics. The coefficient of interest throughout the paper is γ, which captures the treatment effect of resource dependence on employment. Identifiability of the causality in Equation (1) relies on the assumption that Ti is generated from a random experiment. However, a regression model of which the independent variables include resource dependence and other characteristics would suffer from endogeneity problems for two reasons: First, to identify the causality between resource dependence and employment, the conditional independence assumption covðTi ; εi Þ ¼ 0 must be satisfied. This is a strong assumption, because it implies there is no any omitted variable that is associated with Ti . Moreover, the omitted variables that may affect both Ti and the outcome variable can lead to a spurious correlation between the two variables. Second, given the data nature, the assignment of resource depen­ dence to individual counties may suffer from the self-selection bias. In other words, Ti may be influenced by the covariates and other under­ lying factors. This is particularly true for the variable indicating resource dependence. While resource abundance is determined by nature con­ ditions and hence can be regarded as exogenous, resource dependence is always influenced by economical and technical conditions. The resulted endogeneity problem would cause a bias in identifying the causality between resource dependence and economic performance. This article applies the instrumental variable (IV) approach to address the potential endogeneity of Ti . Assuming that the assignment of resource dependence is correlated with unobserved individual’s char­ acteristics and other underlying factors, we expect that the effect of IV on local economies to be negligible once Ti is controlled for. Suppose Yi is continuous, and Ti is binary. The regression specification of employ­ ment on an endogenous variable of resource dependence and exogenous covariates is written as follows: 8 < ΔYi;t ¼ αj þ γTi þ βXi; ​ t0 þ ​ εi;t (2) : Ti ​ ¼ 1ðϑZi ​ þ ​ δWi; ​ t0 þ ​ ui Þ

3. Empirical model and identification strategy Production factors of an economy can be reallocated because of an external shock. Naturally, we expect the pattern of labour allocating between sectors in local economies rich in natural resources be different from other economies. In minerals-abundant economies, mining activ­ ities may affect non-mining sectors through various channels. For instance, a boom of mining industry may generate a change in wages or resource prices, leading to a change in production costs of non-mining sectors. Since non-mining sectors have to compete for labour in local market, their production cost would go up if the wages in local market increase with the boom. If the boom produces an agglomeration effect leading to an inflow of population, the negative effect of wage rising on other sectors may be weakened. The mining boom may also generate positive spillovers of productivity, hence benefiting the non-mining sectors. The net effect of a mining boom would be the result of interactions between various channels. Since a resource shock may lead to a struc­ tural change in terms of production inputs, the structural difference in sectoral employment between local economies can be attributed to the difference in resource dependence. Further, effects of resource depen­ dence on employment may vary with sectors, since the substitution between input factors varies with sectors.

The regression model (2) is composed of one main equation and one auxiliary equation, where 1 (∙) is an indicator function, taking the value of one if the statement in brackets is true, and zero otherwise. The endogenous variable, Ti , is identified by the IV Zi . A vector of cova­ riates, Wi; ​ t0 , is introduced in the auxiliary equation to control for other county-level initial attributes that may influence the distribution of resource endowment. The covariates ​ Wi; ​ t0 may be different from Xi; ​ t0 . The random error terms, εi;t and ui , follow a two-dimension normal distribution, with a correlation coefficient of ρ. The parameter to be estimated is γ. Resource dependence may impact employment through γ. Resource dependence and employmentmay both depend on the corre­ lated variables observed or unobserved. The endogeneity assumption of Ti cannot be rejected if ρis significantly different from 0, which can be expressed as ρðui ; εi Þ 6¼ 0. The IV, Zi , is assumed to be unrelated with the outcome variable unless through the controls included in the regression.

3.1. Empirical model We use a reduced-form function of the resource treatment and

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Obviously, Model (2) compares counties of dependent on resources or not, within the same province over the same period and with similar county-level characteristics. The parameters can be estimated by the maximum likelihood estimation (MLE) method. We will report heteroscedasticity-robust standard errors, to address the concerns for any potential heteroscedasticity.

“underground” factors rather than “above-ground” (such as geography) factors (such as technological differences). Hence, the spatial distribu­ tion of the mining tax revenues of the time can be regarded as largely exogenous. Second, historical mining activities are often related to present mining activities, due to the “path dependence” effect. Thus, the vari­ able of resource dependence is related to the indicator of historical mining tax collection. This means the correlation assumption on the relationship between the instrument and the endogenous variable can be satisfied. Finally, the mining tax levied in 1912 (the first year of Beiyang administration) is hard to directly affect the present local economies for three reasons: (i) Although modern mining industries in China emerged as early as in the late 19th century, the scale had been very small thereafter for a long time. In 1949, total production from various mines in China fell short of the output of single medium-size mine today (Liu and Zhao, 2013); (ii) In the period of Beiyang administration (1912–1928), the government was unable to effectively collect tax that mining revenues were almost ignorable in the national income; (iii) China’s taxation system has experienced great changes over the past century. Fig. 1 describes the location of the county-level mining cities in 1999 and the counties with a record of mining tax revenue in 1912. The similarity in distribution between two groups in the central, northeast and southeast regions indicates the potential correlation between pre­ sent resource dependence and historical mining tax collection.

3.2. Measuring resource dependence If the resource-stock information is available, the resource abun­ dance indicator based on that information can be used as the resource measure. Then, the endogeneity problem is not a primary concern in empirical analysis. However, systematic data on regional resource re­ serves in China is unavailable (Zhan, 2017), which lead to the difficulty for the present work to apply the resource-stock method. Moreover, because this stream of methods relies on estimating the coverage and amount of specific type of resources, it is not well applicable in the case of considering multiple categories of mineral resources. Therefore, our work adopts resource dependence as the resource measure, while taking account of the endogeneity problem. The re­ sources in discussion throughout our empirical work refer to the mineral resources that are non-renewable, including coal, metals, oil and gas, chemicals, non-ferrous metals, gold, metallurgy and chemical raw ma­ terials. Being non-renewable, these resources may be the point source of the resource curse (Mehlum et al., 2006; Isham et al., 2005; Havranek et al., 2016), and be more prone to rent-seeking and conflicts (Boschini et al., 2007). We identify the status of resource dependence of a county by whether the county had been categorized as mining cities before the treatment period of 2000–2010. China Mining Yearbook (MYEO, 2002) defined 426 town- and prefecture-level mining cities up to 1999. Specifically, a city was categorized as mining city because its economy of the time satisfied at least one of the following conditions:

4. Sample and data We empirically investigate the effect of resource dependence on local employment over the period of 2000–2010. The reason for examining this period is that assignment of resource dependence was according to the information on the 1999 category of mining cities. With the prefecture-level cities and the counties in Tibet being excluded, the sample is composed of 1903 counties, of which 246 counties were resource-dependent, and 1657 not. In 1999, 64% of the resourcedependent counties had fossil energy as the main mineral; mining sec­ tors on average accounted for nearly 4% of the total employment of counties, and 17% of the county GDP. Between 2000 and 2010, mineral markets in China have experienced a prolonged boom; the mineral prices have increased considerably and continuously, owing to the boom (Fig. 2). We expect the minerals boom to influence the resource-dependent counties more than other counties. We examine the impact of resource dependence on changes in countylevel economy, in terms of overall employment, and sectoral employ­ ment in mining, manufacturing and services. Outcome variables all take logarithm of the difference in employment scale between 2000 and 2010, hence reflecting the long-term change in employment. To control for the effects of prior mining activities on local labour markets, total employment at the county level in 1990 was included in the main equation. To control for the pre-treatment difference in indi­ vidual characteristics between counties, the county-level values of total population, per capita GDP, population density, gender ratio, and edu­ cation level for the year of 2000 are included in the main equation. In both equations, a set of province dummies is used to control for the unobservable province-specific effects. Our empirical analysis also considers the differences in geographical and geological characteristics between counties. In addition to leading to the imbalance in endowment of mineral resources, these differences may also lead to the imbalance in economic development. Resourcedependent cities in China generally locate far from the central cities, therefore being disadvantaged in transportation. The disadvantages in terms of location often cause the population outflowing in search of better possibilities of survival. To control for impacts of geographical characteristics on resource dependence, three dummies are introduced in the auxiliary equation, respectively representing the mountainous

(a) For prefecture-level cities, the value of mining output was larger than RMB100 million, while for counties or towns, it was larger than 45 million yuan; (b) The share of the mining sector in GDP was larger than 5%; (c) The number of mining workers was no less than 6000; (d) None of the above conditions was satisfied, but the city had been widely regarded as an old/emerging mining city. Accordingly, we identified 246 county-level mining cities as resource-dependent, by excluding the prefecture-level and town-level cities. The treatment variable (Ti ) is assigned a value of one for min­ ing cities, and zero otherwise. Apparently, our resource measure is constructed ex post, and endogenous to previous economic activities. This would introduce the endogeneity-related bias in the OLS estimates. Hence, the IV approach is necessary for valid identification of the causal effect of resource dependence. 3.3. Instrument for resource dependence The spatial distribution of resource dependence reflects the historical mining activities, in addition to the geographical distribution of resource reserves. We construct the IV from a historical perspective, using the ratio of county’s mining tax revenue in the national total in 1912 as the instrument. The justification is described as follows: First, how much mining tax can be collected from counties is related to local conditions in mineral resources. Mining activities generally emerge first in the areas where the mineral reserves are large, shallow buried and easy to extract. In the early twentieth century, China’s ge­ ology prospecting and mining technologies were extremely undevel­ oped, which means the mining tax revenues collected from different counties mainly depended on resource endowment conditions. The difference in mining revenues mainly reflected the difference in 4

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Fig. 1. Distribution of the treated and control countiesNote: The thickest solid line is the national land border, and the dotted is a part of the territorial sea border.

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heterogeneity in individual characteristics. With regard to the initial differences between groups, compared with the non-resource-dependent counties, resource-dependent counties on average had a slightly lower size of employment population, lower levels of population density and per capita GDP, higher education level, higher proportion of women, more often occurrence of geological hazards, and more locations at mountainous areas. These differences confirm the prior assumption that the assignment of resource dependence be subject to a self-selection bias. Thus, controlling for initial differences in individual characteris­ tics and addressing endogenous problems are both necessary. The amount of mining tax revenue that was collected from resourcedependent counties was on average 394 silver dollars. This is far more than that from the other counties (93 silver dollars). Moreover, the in­ dicator value varies considerably spatially. The following empirical re­ sults will show that the variable “taxi ” is a valid instrument for the endogenous variable (Ti ), once geographical and geological character­ istics being controlled.

Fig. 2. Changes in domestic producer price index of mineralsData source: China Price Statistics Yearbook 2016. The indexes are in 1990 constant prices.

counties, the plain counties, and the counties where seismic secondary geological hazards had ever occurred.1 If these dummies have strong power in explaining the resource dependence, goodness of fit of the auxiliary equation would be improved. Because none of them is used as IV for resource dependence, they are also included the main equation. To avoid further endogenous problems owing to reverse causality, no economic and social characteristics are included in the auxiliary equa­ tion to describe resource dependence. Employment data at the county level were collected from the Na­ tional Population Census, respectively for the years of 1990, 2000 and 2010. 2 Data of other variables were mainly collected from China Social and Economic Statistics Yearbook of Counties, and China Demographic Statistics Yearbook. Values of the IV ‘tax’ were calculated as the ratio of county’s mining tax revenue to national total mining tax revenue in 1912. The tax information was collected from the First Agriculture and Commerce Statistics of the Republican China (GAD, 1993). For the counties that had experienced changes in terms of administrative name and jurisdiction range over the period of 1912–2010, the changes were verified according to Chen (2011). The county-level information on the secondary geological hazards (labelled “hazard”) were collected from Gao et al. (2011). If a county had ever experienced such hazards in history, the variable is assigned a value of one, and zero otherwise. When performing regressions, continuous variables take logarithms, except the proportion variables (such as gender and IV) and the edu­ cation variable. Table 1 presents the descriptive summary of the sample by group. Standard deviations of most variables are large, indicating obvious

5. Empirical results The validity of our identification strategy depends on two assump­ tions. First, the instrument must be an important factor in accounting for the resource-dependence variations that are observed, i.e., the instru­ ment is correlated with the resource variable. Second, the IVtaxi is not correlated with the error term in the main equation, conditional on the controls included in the regression. The first condition can be expressed as covðtaxi ;Ti Þ 6¼ ​ 0. As shown in Table 2, the instrument taxi is strongly correlated with the resource variable (Ti ) at the 1% significance level. Then, the correlation assumption is satisfied. The other condition, also called the exclusion restriction, can be expressed as ​ covðtaxi ; εi Þ ¼ 0, suggesting that historical mining tax has no direct effect on current employment. Reasonability of the assumption is mainly based on economic theories and common sense, due to the lack of widely accepted testing method. In spite of this, we follow the idea of Acemoglu et al. (2001) and Altonji et al. (2005) to substantiate the assumption. Altonji et al. (2005) develop a method to assess the importance of omitted variable bias, and the basic idea is, if the estimate of the coefficient of interest does not change as additional covariates are included in the regression, it is less likely to change when some of the missing omitted variables are added. Acemoglu et al. (2001) propose an informal version of the method of Altonji et al. (2005, by directly con­ trolling for the variables that could plausibly be correlated with both IV and economic outcomes, and checking whether the addition of these variables affects the estimates. In line with the idea of the two studies, we perform OLS regressions of the employment variable on ​ Ti ​ by controlling for the covariates, with or without inclusion of the mining tax variable. If the result changes little with the addition of the mining tax variable, we can conclude this variable has no direct effect on employment. Table 3 reports the OLS regressions of various employment variables on resource dependence, ​ Ti ​ . Columns 1–4 show that there is no sig­ nificant correlation between mining tax and employment. Columns 5–8 report the results of excluding taxi in covariates. A comparison between the two sets of results show the addition of the mining tax variable generates little change in estimates of other variables. We conclude that taxi ​ does not directly affect employment, conditional on the covariates included in the regression.

1 This type of hazards is concluded because it is mainly related to geo­ morphology and geological structure, in addition to the hydrogeology (Gao et al., 2011). Therefore, it can well capture the impacts of geographical factors on the endogenous resource variable. 2 There were changes in categorizing of the service sub-sectors in the three rounds of Census, respectively conducted in 1990, 2000 and 2010. This study integrated the categories of service sectors, according to the National Industry Classification (GBT4754-2011). The employment of the services for the year of 2000 was calculated by aggregating 10 sub-sectors: geological exploration/ water management; transportation/warehousing/post/communications; wholesale or retail business and catering; finance and insurance; real estate; social services; health, sports and social welfare; education, radio, film and television industries; scientific research and technical services; state organiza­ tions, political parties and social groups. The employment in services for the year of 2010 was calculated by adding up the values of 13 sub-sectors: ware­ housing and post services; transportation, computer services and software; in­ formation services; accommodation/catering; finance; real estate; leasing and business services; scientific research and geological exploration; management of water, environment and other public facilities; household services; educa­ tion/health and social security and welfare; culture, sports and entertainment; and, public and social organizations.

5.1. Results from benchmark specification Though the results in Table 3 show a strong correlation between resource dependence and sectoral employment, the relationship cannot be interpreted as causal due to the endogeneity problem. Table 4 reports the IV regressions of employment on resource dependence in the full sample, based on Model (2). These regressions are taken as the benchmark specification. The results of test on the null 6

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Table 1 Statistical description of the sample. Observation period

Variable

Change in total employment (10,000 person)

2000–10

yl

Changes in mining employment (10,000 person)

2000–10

ymine

Changes in manufacturing employment (10,000 person)

2000–10

ymnf

Changes in service employment (10,000 person)

2000–10

yser

Total employment (10,000 person)

1990

l90

Population density (person/km2)

2000

spop0

Gender ratio (female ¼ 100)

2000

female0

Education level (year)

2000

edu0

Per capita GDP (RMB 10,000 in 2000 constant price)

2000

gdp0

Historical geological hazards

2000

hazard

Mountain county

2000

mountain

Plain county

2000

plain

Historical mining tax revenue *

1912

tax

Resource-dependent counties (246 obs.)

Non-resource-dependent counties (1657 obs.)

mean 0.06 (4.59) 0.14 (0.58) 0.99 (1.97) 1.61 (1.90) 24.91 (17.87) 301 (564) 109 (4.66) 7.20 (0.77) 0.44 (0.27) 0.220 (0.415) 0.464 (0.500) 0.181 (0.386) 394 (2413)

max

min

mean

25.59

24.15

1.63

3.41

1.47

13.56

0.98

15.07

0.05 (5.54) 0.02 (0.23) 1.43 (3.13) 1.60 (2.95) 24.93 (18.59) 333 (550) 107 (5.10) 6.95 (1.03) 0.51 (0.40) 0.172 (0.377) 0.412 (0.492) 0.334 (0.472) 93 (856)

1.24

95.79

1

7232

94

125

4.04

9.08

0.08

1.58

0

1

0

1

0

1

0

23,471

max

min

32.37

61.82

1.76

3.14

3.71

45.55

31.88

72.13

0.31

98.78

0

13,488

84

169

1.47

10.15

0.02

3.47

0

1

0

1

0

1

0

25,349

Note: Standard deviations are reported in parentheses. * The figure reported here is the tax revenue measured by Silver Dollar circulating in the country of the time (in current prices). When performing regressions, the variable “tax” is measured by the proportion of mining tax revenue of individual counties in the overall mining tax revenue of the country.

hypothesis ρðεi μi ) ¼ 0 are reported in the second to the last line. The hypothesis ρ ¼ 0 is rejected, regardless of the employment variable, providing evidence that the resource dependence, Ti , is endogenous. The estimate of taxi in the auxiliary equation is significantly positive suggests the validity of the IV. Most covariates in the auxiliary equation are significant, implying the geomorphologic and geological factors can be efficient predictors for mineral-resource dependence. The top half panel of Table 4 presents the estimation results of the main equation. The resource dependence variable shows a positive ef­ fect on total employment, mining employment and service employment, and a negative effect on manufacturing employment. In terms of magnitude, the effect on mining employment is the largest. This reveals the resource dependence has caused the mobility of labour from manufacturing to other sectors. In particular, mining activities crowd out the manufacturing employment; the crowding-out effect is larger in size than the positive spillovers in employment from mining to services. Up to now, in our work effects of resource dependence were esti­ mated in the full sample. Note one of the MYEO (2002) criteria for defining mining cities was the number of mining workers, and we assigned the treatment of resource dependence in line with the classi­ fication of mining cities. Because of the initial differences in sectoral employment between resource-dependent and non-resource-dependent counties, we concern that these differences lead to incorrect identifica­ tion of the treatment effect. To address the concern, we narrow the sample to reduce the initial imbalance in sectoral employment between groups. Then, we perform robustness tests to see whether our findings on the relationship between impact resource dependence and employment are sensitive to alternative samples. We discuss the tests in detail in the next section.

the lower limit of the MYEO mining cities. The problem is, employment scale is usually related to population size. A populous non-mining county is likely to have a larger quantity of mining workers than an underpopulated mining county, even if the latter relies more on mining industries. Moreover, for the present work, an upper limit of 6000 mining workers means almost all counties of the sample would be dropped from the sample, given there were only two counties where the initial number of mining workers was less than 6000. Apparently, nar­ rowing the sample in this way is not feasible. Instead, based on the share of the mining sector in total employment in 2000, we restricted the sample by deleting the counties with a share over a specified upper limit. We applied 2 upper limit values, 5.0% and 3.3%, respectively. The former value is among the criteria taken by MRS (2002) in defining resource-dependent cities, while the latter is the 2000 average proportion of county-level mining employment (without counting the towns and prefecture-level cities). Such a way of processing sample is meaningful. By excluding the counties of heavily dependent on mining sectors for employment, we obtained two groups that are different in term of resource dependence, but quite similar in pre-treatment employment. Thus, the resource dependence in the narrowed sample is relatively “pure” because it does not cover the difference in employment. Since the initial imbalance in sectoral employment between groups has been largely reduced, the difference between groups in changes of sectoral employment over the treatment period can be attributed to the effect of the “pure” resource dependence, conditional on the controls included in the regression. We respectively used the two narrowed samples to re-estimate Model (2). Table 5 reports the IV regressions of employment variables on resource dependence, respectively in two restricted samples. Whichever the restricted sample is used, the estimate of the IV (TaxÞin the auxiliary equation is significantly positive. This is consistent with the results ob­ tained from the benchmark specification. In the main equation for the same employment variable, using alternative sample generates small changes in estimates, in terms of parameter size and significance level.

5.2. Effect of resource dependence in narrowed sample One option for narrowing the sample is to set an upper limit for the initial size of mining employment, for instance 6000 mining workers, 7

X. He and D. Mou

In spite of that, compared with the estimates obtained from the bench­ mark specification, the sign of each independent variable is consistent, and the estimates change remarkably little in size. These results reveal that potential imbalance in initial sector’s employment between groups would lead to an estimation bias is not a problem. Even for the counties that rely on mineral resources in other aspects rather than in the employment aspect, in the mining boom the treatment of resource dependence still leads to the same structural changes in sectoral employment that is in favour of mining sector, as identified earlier in the full sample. Thus, our conclusions on the employment effects of resource dependence are robust to narrowing the sample.

Note: the table displays the Pearson correlations between the variables. *, ** and *** are significance at 10%, 5%, and 1%, respectively. a T is the variable indicating for resource dependence. b Despite the instrument “tax” is weakly correlated with the education variable (edu0), the IV performs well in other aspects, for example it is not related to other covariates, not endogenous, significantly related to the variable of resource dependence; therefore, this empirical analysis ignored the weak correlation between tax and edu0.

1 0.109*** 0.223*** 0.347*** 0.156*** 0.083** 0.032 0.069*** 0.073*** 0.048** 0.053** 0.050** 0.039* 0.043* 0.074*** 0.012 0.014 0.044 0.024 0.089*** 0.083*** 0.109*** 0.035 0.042*

1

1 0.085*** 0.023 0.006 0.014 0.011 0.034 0.051 0.032 0.012 0.028* 0.026 0.019 0.002 tax Ta yl ymine ymnf yser ll90 lgdp0 lspop0 female0 edu0 b plain mountain hazard

Ta tax

Table 2 Correlations between variables.

yl

ymine

1 0.189*** 0.234*** 0.026 0.013 0.028 0.051** 0.196*** 0.093*** 0.030 0.010

ymnf

1 0.466*** 0.299*** 0.040 0.315*** 0.018 0.055** 0.029 0.004 0.047**

yser

1 0.120*** 0.083** 0.136*** 0.033 0.122*** 0.008 0.008 0.012

1

ll90

0.013 0.437*** 0.205*** 0.273* 0.195*** 0.283*** 0.021

lgdp0

1 0.223** 0.144* 0.217** 0.147** 0.185* 0.083*

1

lspop0

0.204*** 0.224** 0.208*** 0.189*** 0.059

1

female0

0.067** 0.302*** 0.232*** 0.011

edu0

1 0.259*** 0.418*** 0.051*

1

plain

0.574*** 0.033

1 0.024

mountain

1

hazard

Energy Policy 136 (2020) 111088

5.3. Further discussion The variation in the effects of resource dependence may be associ­ ated with the extent the mining sector dominates the local economy. The results in Section 4.3 show, when the upper limit of initial ratio of mining employment increases from 3.3% to 5.0%, the estimated effects of resource dependence on total, manufacturing and service employ­ ment become larger. This implies the impact intensity of resource dependence thereafter may be related to how far the economy initially was dominated by mining industries. The literature has provided evidence there may be a threshold level of resource dependence at which the resource curse would occur (Mehrara, 2009; Caselli and Michaels, 2009). The dilemma seems to have appeared in some old mining cities in China. Due to the data nature of our sample, we are unable to quantitatively test whether and where such a turning point occurs. For instance, whether would the economy be at a risk of resource curse when 10% of total employment relies on mining industries? This is impossible to test, because there are only 10 observations with an initial employment share in mining larger than 10%. Our empirical results based on different sample settings come to the same conclusion: the resource curse effect only works for manufacturing. The resource dependence exerts influences on manufacturing and services in opposing ways. This can be partly inter­ preted by the difference in products between sectors. Manufacturing goods are tradable and can be purchased from broader markets beyond local market. Development of mining industries may lead to an increased demand of the sector for labour and other factors. A labour outflow from manufacturing may be accompanied with outflows of other factors, leading to a decrease in output of local manufacturing. While the decreased supply in local manufacturing goods can be sup­ plemented by markets elsewhere, services are mainly provided locally. In a mining boom, the demand for services in manufacturing sector declines but the demand in mining sector for services increases more, and thus, the net effect of the boom on local services is positive. In summary, the dependence on mineral resources has the strongest growth effect on mining employment, with the positive effect size ranging between 1.13% and 1.20%. Next is the negative effect on manufacturing employment growth, with the effect size being around 0.33%. The effect on service employment growth is positive and weak, being about 0.30%. The resource dependence shows a positive effect on total employment growth, and the effect is modest with a size of about 0.12%. Most employment created by a boom in mineral industries is allocated to local mining sector. The crowding-out effect suffered by local manufacturing mainly comes from the mining sector. The resourcedependent areas may experience a modest growth in a mining boom, in terms of overall employment, since the negative effect on manufacturing can be offset by the positive effect on other sectors. It is unclear whether the economy would get caught in a dilemma that the overall employ­ ment worsens off, once the economy arrives at a certain threshold in resource dependence. The existing literature regarding the relationship between natural resources and employment has focused on American, and most of them 8

X. He and D. Mou

Energy Policy 136 (2020) 111088

Table 3 Validity of the IV, based on OLS estimation. variables

T Tax (IV) ll90 lgdp0 lspop0 female0 edu0 hazard plain mountain Obs. F R2

Including the IV

Without the IV

Total employment (yl)

Mining (ymine)

Manufacturing (ymnf)

Services (yser)

Total employment (yl)

Mining (ymine)

Manufacturing (ymnf)

Services (yser)

0.0061 (0.0140) 0.0715 (0.0411) 0.0194** (0.0078) 0.0010 (0.0086) 0.0049 (0.0045) 0.0006 (0.0009) 0.0317*** (0.0067) 0.0217** (0.0088) 0.0372*** (0.0113) 0.0506*** (0.0101) 1783 27.28 0.295

0.1446** (0.0634) 0.0474 (0.1708) 0.0404 (0.0480) 0.1175** (0.0477) 0.0089 (0.0296) 0.0095 (0.0067) 0.2115*** (0.0507) 0.1103 (0.0696) 0.2079*** (0.0722) 0.0918 (0.0667) 1678 11.26 0.171

0.1419*** (0.0391) 0.0835 (0.1778) 0.1300*** (0.0268) 0.0062 (0.0250) 0.0532*** (0.0158) 0.0035 (0.0033) 0.0779*** (0.0257) 0.0078 (0.0369) 0.0735** (0.0378) 0.0414 (0.0377) 1776 29.2 0.356

0.0032 (0.0179) 0.0055 (0.0952) 0.0386*** (0.0098) 0.0010 (0.0121) 0.0152** (0.0067) 0.0010 (0.0013) 0.0348*** (0.0116) 0.0039 (0.0192) 0.0003** (0.0183) 0.0373 (0.0154) 1783 12.17 0.188

0.0069 (0.0140)

0.1452** (0.0632)

0.1429*** (0.0389)

0.0033 (0.0178)

0.0193** (0.0078) 0.0012 (0.0086) 0.0049 (0.0045) 0.0005 (0.0009) 0.0317*** (0.0067) 0.0218** (0.0088) 0.0368*** (0.0113) 0.0507*** (0.0101) 1783 28.03 0.295

0.0403 (0.0480) 0.1174** (0.0476) 0.0090 (0.0296) 0.0095 (0.0067) 0.2116*** (0.0507) 0.1103 (0.0696) 0.2082*** (0.0721) 0.0917 (0.0667) 1678 11.55 0.171

0.1299*** (0.027) 0.0059 (0.025) 0.0531*** (0.016) 0.0034 (0.003) 0.0779*** (0.026) 0.0079 (0.037) 0.0740** (0.0380) 0.0413 (0.038) 1776 30.05 0.358

0.0386*** (0.0098) 0.0010 (0.0120) 0.0150** (0.0067) 0.0010 (0.0013) 0.0348*** (0.0116) 0.0039 (0.0192) 0.0003*** (0.0183) 0.0373 (0.0154) 1783 12.51 0.189

Note: All the regressions include the constant term and the province dummies and the estimates are not reported in the table. *, ** and *** are the significant level of 10%, 5%, and 1%, respectively. The values in brackets are the Clustering standard error by county. The dependent variable is difference in log employment population between 2000 and 2010. Similarly hereinafter.

2014), Brown 2014), and Allcott and Keniston (2017) provide evidences on the positive effects of resource abundance. In terms of non-manufacturing employment, Black et al. (2005), Michaels (2011), and Kotsadam and Tolonen (2016) provide evidences on the positive effects of resource abundance. In terms of manufacturing employment, Michaels (2011) find the positive effects of oil and gas abundance, while Black et al. (2005) find the effect of coal abundance is neutral. Similarly, we find little evidence on the existence of a resource curse on overall employment in a mining boom. However, we confirm a negative link between natural resources and manufacturing employ­ ment. Our empirical findings on manufacturing clearly differ from other studies. Ours are partly in line with Allcott and Keniston (2017) - they find employment in gas and oil abundant counties in the U.S. are pro-cyclical, and highly tradable manufacturing subsectors contract during oil and booms. Boschini et al. (2007) argue that countries rich in minerals are cursed only if they have low-quality institutions. This argument may partly explain the difference in conclusions between our work and others. Of course, this needs careful investigation by econo­ metric analysis, which may be the direction of our further research.

Table 4 Results of estimation, based on benchmark specification. yl T

0.1771** (0.0795) ll90 0.0188** (0.0080) lgdp0 0.0012 (0.0083) lspop0 0.0043 (0.0046) female0 0.0006 (0.0009) edu0 0.0319*** (0.0066) hazard 0.0142 (0.0092) plain 0.0548*** (0.0124) mountain 0.0448*** (0.0114) Auxiliary equation tax 1.7363** (0.6791) hazard 0.2106** (0.0928) plain 0.4746*** (0.1262) mountain 0.1228 (0.0880) Wald χ 2# 1025

ρ

Obs.

0.579** 1783

ymine

ymnf

yser

1.1957*** (0.1577) 0.0562 (0.0472) 0.1056** (0.0473) 0.0098 (0.0289) 0.0097 (0.0066) 0.2084*** (0.0493) 0.1631 (0.0752) 0.3598*** (0.0821) 0.0475 (0.0725)

0.3397*** (0.1187) 0.1301*** (0.0265) 0.0063 (0.0247) 0.0531*** (0.0156) 0.0034 (0.0033) 0.0780*** (0.0254) 0.0005 (0.0372) 0.0535 (0.0395) 0.0477 (0.038)

0.3006*** (0.0919) 0.0393*** (0.0098) 0.0017 (0.0112) 0.0149** (0.0066) 0.0009 (0.0013) 0.0336*** (0.0108) 0.0080 (0.0194) 0.0321 (0.0237) 0.0252 (0.0169)

1.4617*** (0.5218) 0.2010** (0.0956) 0.5443*** (0.1069) 0.0671 (0.0880) 497

1.9453*** (0.7268) 0.2005** (0.0977) 0.5161*** (0.1048) 0.1340 (0.0900) 1103

1.6282*** (0.5229) 0.1409 (0.1028) 0.5553*** (0.1054) 0.1253 (0.0855) 483

0.661*** 1678

0.189* 1776

0.597*** 1783

6. Conclusion and policy implications Despite labour-market effects are only a part of the economic con­ sequences of extractive industries (Weber, 2014), a deep understanding of the issue is important for China because the employment problem is increasingly prominent in some mining cities of the country. We empirically evaluated the effect of resource dependence on employment at the county level, while the endogeneity problem of the resource measure was addressed by the IV approach. We confirmed the resource-curse effect in manufacturing sector. The resource dependence shows a positive but modest effect on total employment growth, while a large fraction of the positive effect comes from the growth in mining employment. Even for the counties that initially relied on mineral re­ sources in other aspects than employment, in a mining boom the resource dependence still leads to the structural changes in employment detrimental to manufacturing. Our conclusions may apply to developing economies that are rich in

Note: ρ, correlation between the error terms of the structural and IV equation. #. Wald Chi square statistic estimate of the main equation.

examine specific fuels, such as oil, gas and coal (Michaels, 2011; Weber, 2012, 2014; Brown, 2014; Allcott &Keniston, 2017; Black et al., 2005). Overall, previous conclusions tend to be positive, on the role of re­ sources in labour markets. In term of total employment, Weber (2012, 9

X. He and D. Mou

Energy Policy 136 (2020) 111088

Table 5 Results of estimation, with restricted sample by share of mining employment. The proportion �3.3% T ll90 lgdp0 lspop0 female0 edu0 hazard plain mountain

yl

ymine

ymnf

yser

yl

ymine

ymnf

yser

0.1094** (0.0448) 0.0259*** (0.0068) 0.0044 (0.0065) 0.0083** (0.0042) 0.001 (0.0009) 0.0226*** (0.0062) 0.0180** (0.0088) 0.0412*** (0.0115) 0.0532*** (0.0108)

1.1327*** (0.1834) 0.0077 (0.0507) 0.1132** (0.0493) 0.0271 (0.0307) 0.0094 (0.0072) 0.1519*** (0.0521) 0.1540** (0.0785) 0.3357*** (0.0841) 0.0251 (0.0760)

0.3205** (0.1590) 0.1674*** (0.0260) 0.0243 (0.0258) 0.0734*** (0.0154) 0.001 (0.0035) 0.1274*** (0.0283) 0.0186 (0.0384) 0.0185 (0.0409) 0.0494 (0.0400)

0.3006*** (0.0919) 0.0393*** (0.0098) 0.0017 (0.0112) 0.0149** (0.0066) 0.0009 (0.0013) 0.0336*** (0.0108) 0.008 (0.0194) 0.0321 (0.0237) 0.0252 (0.0169)

0.1236** (0.0377) 0.0246*** (0.0066) 0.0051 (0.0064) 0.0092** (0.0041) 0.0011 (0.0009) 0.0238*** (0.0060) 0.0163* (0.0087) 0.0425*** (0.0112) 0.0527*** (0.0105)

1.2008*** (0.1623) 0.0236 (0.0493) 0.1036** (0.0478) 0.0314 (0.0300) 0.0093 (0.0070) 0.1624*** (0.0506) 0.1603** (0.0772) 0.3426*** (0.0828) 0.0276 (0.0739)

0.3401** (0.1524) 0.1711*** (0.0255) 0.0207 (0.0250) 0.0676*** (0.0151) 0.0002 (0.0034) 0.1289*** (0.0276) 0.0134 (0.0376) 0.028 (0.0403) 0.0452 (0.0390)

0.3145*** (0.0917) 0.0387*** (0.0105) 0.0009 (0.0113) 0.0113 (0.0071) 0.0003 (0.0014) 0.0346*** (0.0131) 0.0011 (0.0201) 0.0223 (0.0234) 0.0338* (0.0176)

1.4459** (0.5862) 0.2106** (0.1068) 0.5474*** (0.1186) 0.1233 (0.1001) 449

1.7935** (0.8006) 0.2202** (0.1084) 0.5178*** (0.1173) 0.1885* (0.1012) 1044

1.6282*** (0.5229) 0.1409 (0.1028) 0.5553*** (0.1054) 0.1253 (0.0855) 458

1.6557** (0.6772) 0.2384** (0.1039) 0.4958** (0.1126) 0.1675* (0.0963) 1030

1.3562** (0.5473) 0.1947* (0.1026) 0.5481*** (0.1135) 0.1067 (0.0947) 480

1.7125** (0.7906) 0.2099** (0.1044) 0.5044*** (0.1125) 0.1694* (0.0970) 1080

1.4538*** (0.5190) 0.1602 (0.1088) 0.5816*** (0.1135) 0.1483 (0.0914) 477

0.724*** 1534

0.198* 1563

0.689** 1567

0.456*** 1619

0.659*** 1585

0.207* 1615

0.622*** 1619

Auxiliary equation tax 1.7450** (0.7157) hazard 0.2505** (0.1100) plain 0.5085*** (0.1178) mountain 0.1859* (0.1009) 2 992 Wald χ

ρ

Obs.

The proportion �5%

0.401** 1567

Note: When using the share of 3.3% as the upper limit to narrow the sample, 1667 counties remain in the sample, including 173 resource-dependent counties. When using the share of 5%, 1727 counties remain, including 197 resource-dependent counties.

mineral resources and have a large population, summarized as follows: First, for local economies that are rich in mineral resources, in a mining boom the mobility pattern of employment is an outflow from the manufacturing. Production factors’ outflows from and inflows to the manufacturing have entirely distinguished implications for economies that take industrialization as the target. Our findings confirm the argu­ ment of the Dutch disease theory that resource booms undermine the non-resource manufacturing sectors via deindustrialization effects (Corden and Neary, 1982). Second, the sectoral changes in employment due to resource dependence have meanings for urbanization. The primary driving force of urbanization is the inter-sectoral mobility of labour force with eco­ nomic development. In resource-dependent economies, the urbanization mainly relies on the accumulation of labour force in mining sectors to absorb the surplus labour force released from agricultural sector. Mining industries are generally capital-intensive, which means their ability to digest the rural surplus labour is limited. Meanwhile, the additional jobs in services resulted from mining booms are also limited. Thus, the intersectoral mobility of labour in a mining boom contributes little to ur­ banization, no mentioning the crowding-out effect of mining activities on manufacturing. With interaction of these different effects, in the long term the employment of the surplus labour in resource-dependent areas can hardly be resolved. Third, the industrialization of resource-dependent economies fundamentally differs from the industrialization in the general sense. While the latter is marked by the prosperity of manufacturing, the former is the prosperity of mining industries at the price of contraction in manufacturing. Due to the foundation of industrialization being weaken, the economies relying on mining industries may be of huge risk. With the depletion of mineral resources, the impetus of resourcesrelated industries to economic growth would diminish and even disap­ pear. If no new industries continue to promote the urbanization and

industrialization, these economies would inevitably go into a recession. Based on our findings, we put forward the following advices that may help to avoid the dilemma that the “bless” of resource abundance turns into a “curse”. (a) For a resource-based economy in its infancy, pay attention to developing the further processing industry of resources and extending the industry chain at the very beginning, to avoid being trapped in the situation of heavy dependence on the resources. (b) For a resource-based economy in maturity, because the economy mainly de­ pends on mining sector for employment, the priority may be to improve the labour productivity of mining by investing in advanced equipment and technological innovation, as well as to develop continual and substituted industry. High productivity of mining workers helps to in­ crease the average income of local labour. Potential options for substituted industries include services, clean energy, and environmental protection industries. Policymakers need to design supporting policies in favour of innovation and industrial diversification. (c) For those resource-exhausted regions, main challenges may be the unemployment of a large number of miners and potential social unrest. Possible solution is to develop labour-intensive industries, including micro and small businesses that can create a large number of jobs. Acknowledgements This study is supported by National Natural Science Foundation of China (No. 71573217). References Acemoglu, D., Johnson, S., Robinson, J.A., 2001. The colonial origins of comparative development: an empirical investigation. Am. Econ. Rev. 91 (5), 1369–1401. Allcott, H., Keniston, D., 2017. Dutch disease or agglomeration? The local economic effects of natural resource booms in modern America. Rev. Econ. Stud. 85 (2), 695–731.

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