Copper mining in Chile and its regional employment linkages

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Copper mining in Chile and its regional employment linkages Viviana Fernandez1 Business School of Universidad Adolfo Ibañez, Avenida Diagonal Las Torres 2700, Office 512-C, Santiago, Chile

A R T I C LE I N FO

A B S T R A C T

Keywords: Concordance Employment co-movement Labor productivity

This article gauges employment interactions among different economic sectors of relevant copper-producing regions in Chile, such as Tarapacá, Antofagasta, Atacama, Coquimbo, and O′Higgins. To that end, concordance measures of employment during peaks and troughs, and rolling correlations are computed. The estimation results show that the strength of employment co-movement of mining & quarrying with other economic sectors—within a given region and across regions —varies among the geographic locations under analysis. In particular, employment linkages between the Antofagasta and Metropolitan Regions have weakened over time. In addition, this article provides background information on the world copper market and Chile's mining sector. In particular, time series of copper production and labor productivity of private and state-owned mining firms operating in Chile are presented. Specifically, historical figures show that falling ore grades have had as a counterpart an increase in electricity costs per metric ton of refined copper during 2001–2015. This is specially the case for concentrator plants, which experienced an 87.1%-increase in electricity costs during that time period.

1. Introduction As early as the first half of the XIX century, copper was already one of Chile's main export products. At that time, production was highly labor intensive and came from a great number of minor high-grade mines (10% in some instances) whose individual output did not exceed 20,000 metric tons per year. Toward the end of the XIX century and the beginning of the XX century, there was a sizeable increase in world copper demand due to the development of the electric power industry and the expansion of the construction industry. At the same time, capital-intensive technological innovation in the United States made large-scale exploitation of ores with relatively low grades of 1–2% profitable. American firms started investing in the underground mine of El Teniente in 1904 and in the open-pit mine of Chuquicamata in 1911. Toward 1924 both mines accounted for 80% of Chile's total copper mine production (Meller, 2007, page 31).2 Recent statistics on exports, GDP composition, fiscal revenue, and foreign direct investment (FDI) give account that mining—copper, in particular—still plays an essential role in Chile's economy. Indeed, according to statistics from the Central Bank of Chile (www.bcentral.cl),

during the period of 2003–2016 copper cathodes and concentrates averaged altogether 50.8% of total exports, with a minimum of 37.0% in 2003 and a maximum of 58.2% in 2010. In the same 14-year period, agriculture, forestry & fishery exports averaged 7.0% while industry exports, 37.8% of total exports. With respect to the contribution to GDP, mining averaged 12.7% of GDP during the period of 2008–2015 (with a maximum of 16% in 2010 and a minimum of 8.8% in 2015), as opposed to 17.6%, 11.0%, and 9.9% of financial & entrepreneurship services; industry; and commerce, restaurants & hotels, respectively. On the other hand, the tax burden on large-scale private mining companies (GMP-10), in addition to the contribution of publicly-owned mining companies (EME) to Chile Public Treasury, has altogether represented a sizeable percentage of total fiscal revenue in the recent past. For instance, in the period of 2003–2015, GMP-10 taxes plus EME contribution averaged 17.5% of total fiscal revenue, with a minimum of 4.9% in 2003 and a maximum of 34.0% in 2006. (Source: Cochilco 2016 Yearbook).3 Moreover, the mining share of actual FDI averaged 31.9% in the period of 2003–2015, and 32.9% in the period of 1974–2015.4 In particular, mining reached unusually high shares of total actual FDI

E-mail address: [email protected]. Website: http://www.uai.cl/academicos/cuerpo-academico/viviana-fernandez, The author would like to thank the financial support provided by FONDECYT Grant No. 1170037 as well as the valuable comments received from two anonymous reviewers. 2 In 2015, Chuquicamata and El Teniente (both part of Corporación del Cobre, Codelco) accounted for only 13.5% of Chile's total copper mine production. (Source: Comisión Chilena del Cobre, Cochilco, 2016 Yearbook). 3 A gross specific mining tax (mining royalty) began to be levied on GMP-10 companies in 2006. 4 In the period of 1974–2015, the second and third largest average shares of actual FDI corresponded to the sectors of electricity, gas & water, and industry: 18.0% and 13.0%, respectively. 1

https://doi.org/10.1016/j.resourpol.2018.03.017 Received 21 July 2017; Received in revised form 29 March 2018; Accepted 31 March 2018 0301-4207/ © 2018 Elsevier Ltd. All rights reserved.

Please cite this article as: Fernandez, V., Resources Policy (2018), https://doi.org/10.1016/j.resourpol.2018.03.017

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during the commodity super-cycle5 and afterwards: 45.9% in 2008, 61.1% in 2011, and 53.2% in 2013. (Source: Cochilco 2016 and 2012 Yearbooks). Recent literature on Chile's copper industry has dealt with regional aspects of mining, labor productivity, trends in copper production, the impact of copper mining on the economic and social development in the Antofagasta Region (Chile's largest copper producer), and with Chile's commodity dependence. Examples include, among others, Aroca (2001) who, on the basis of an input-output matrix for the Antofagasta Region, measured the impact of the mining sector on output, income, and employment; Garcia et al. (2001), who analyzed labor productivity changes in the Chile copper industry during 1978–1997; Nishiyama (2005), who examined the rapid growth of copper consumption in China and production in Chile in the period of 1985–2003; Jara et al. (2010), who studied labor productivity in the copper-mining industry of Chile and Peru during 1992–2009; Lagos and Blanco (2010), who analyzed the impact of copper mining on economic and social development in the Antofagasta Region during 1985–2008; Aroca and Atienza (2011), who, on the basis of an input–output approach, measured the extent that economic resources generated by the mining industry of the Antofagasta Region impacted other Chilean regions; and, Ebert and La Menza (2015), who assessed Chile's commodity dependence. This article goes in line with Nishiyama (2005), in that it provides an overview of the world and Chile copper-mining industry; and, with Lagos and Blanco (2010) and with Aroca and Atienza (2011), in that it quantifies the impact of copper mining on economic activity. The two main contributions of this article are its thorough analysis of Chile mining industry, particularly in what refers to labor productivity, and its approach to gauging employment linkages of copper-producing regions in Chile. This article is organized as follows. Section 2 presents the data sources and methodology used throughout this study. Section 3.1 in turn provides background information on copper production worldwide, while Section 3.2 focuses on Chile's copper mining sector. Section 4 studies intra- and extra-regional effects of copper mining by measuring employment co-movement of relevant copper mine producers, such as the Tarapacá, Antofagasta, Atacama, Coquimbo, and O′Higgins Regions. Section 5 closes by summarizing the main findings of this study.

by preserving the growth rates of the 1986–2009 series. In addition, from 2013 onwards, INE broke the Electricity, gas, and water (EGW) supply sector into two categories: Electricity, gas, steam and air conditioning; and, Water supply, waste water disposal, waste management and decontamination. Hence, from 2013 onwards the summation of these two sectors was considered as EGW for the estimation of Section 4. 2.2. Concordance in employment cycles Section 4 focuses on regional aspects of copper mining in Chile by analyzing employment cycles at a regional and national level. Such an analysis is based on the methodology by Cashin et al. (1999), and McDermott and Scott (2000), which can be briefly described as follows. Consider two time series, Xi and Xj, and let Sit be a dummy variable that takes on the value of 1 when Xi is in a boom phase and 0 when Xi is in a slump phase. Such a dummy variable is determined by means of Bry and Boschan (1971) algorithm.6 Define Sj,t for Xj in the same fashion. The degree of concordance in the cycles of the two time series Xi and Xj is measured by T Cij = T−1 ⎜⎛∑ Si,t Sj,t + (1 − Si,t )(1 − Sj,t ) ⎞⎟ t=1 ⎝ ⎠

(1)

where T is the sample size and Cij measures the proportion of time the two series are in the same state.7 Critical values are determined by a response-regression surface as discussed by McDermott and Scott (2000). Details are provided in Table 1A of the Appendix. 3. Some aspects of copper mine production 3.1. Mine production worldwide In order to give a historical perspective, Fig. 1 depicts time series of mine production by continent for the period of 1950–2016. It is apparent that America has been historically the main producer, followed by Europe, Africa, Asia, and Oceania during 1950–1990 and by Asia, Europe, Oceania, and Africa from 1991 onwards. Specifically, over the period 1950–1990, the mine production shares (relative to the world) of Europe, Africa, America, Oceania, and Asia, averaged 19.7%, 19.5%, 49.5%, 3.4%, and 7.8%, respectively. By contrast during 1991–2016 such shares averaged 11.0%, 6.8%, 57.2%, 6.6%, and 18.4%, respectively. That is, Asia experienced an increase of over 10-percentage points in its mine production slice. Such Asia's share increase is mainly explained by China, as can be seen from Fig. 2, which depicts main country producers of copper ores for the period of 1950–2016. In particular, from 1990 onwards China increased its share and left behind some important mine producers, such as Australia, Russia, and Indonesia. Indeed, China's average production share over 1990–2016 was 5.0% as opposed to 5.7%, 3.3%, and 3.9% of Australia, Russia, and Indonesia, respectively. During this time period, the three main mine producers were Chile, the US, and Canada, with corresponding shares of 27.1%, 12.0%, and 5.8%. Table 1 provides further details of production shares for Australia, Canada, China, Chile, Indonesia, Peru, Russia, and the U.S during 2002–2016. Over that 15year period, these countries accounted for over 60% of world mine production.8

2. Data and methodology 2.1. Data The sources for copper mine/refined production and consumption are the Chilean Copper Commission (Comisión Chilena del Cobre, Cochilco), www.cochilco.cl, the World Bureau of Metal Statistics (WBMS) via Bloomberg, and the World Metal Statistics Yearbook 2017. Other indicators, such as labor productivity, copper grade, and energy costs of mining companies located in Chile are also from Cochilco. In addition, information on porphyry-copper deposit grades for various countries is from the US Geological Survey (USGS), www.usgs.gov. The source of employment in mining & quarrying and other economic sectors in turn is the Chile National Statistics Institute (Instituto Nacional de Estadísticas, INE), www.ine.cl. Specifically, the employment levels of 1986–2009 (former national employment survey) were assembled to the 2009–2016's (new national employment survey, NENE)

6 First, a potential set of peaks and troughs is determined by the application of a turning point rule that defines a local peak in series Y as occurring at time t whenever {Yt > Yt ± k}, k = 1, …., K, while a local trough occurs at time t whenever {Yt < Yt ± k }, k = 1, …., K, where K is set to 2. The second step imposes the condition that peaks and troughs must alternate. Thirdly, the peaks and troughs are revised, or "censored", according to a range of criteria, so that the duration of a complete cycle cannot be less than a pre-specified figure (e.g., 2 years). 7 Applications of this methodology can be found, for instance, in Male (2011) and Gouveia and Correia (2008).

5 The commodity super-cycle is generally associated with the period of the late 1990's through the 2008 financial crisis (e.g., Johnson and Sharenow, 2013). The World Bank's pink sheet data (http://www.worldbank.org/en/research/commodity-markets) shows that the real annual price percent variation for base metals was 24.0%, 12.2% and 44.8% in 2004, 2005, and 2006, respectively. During the same period, the corresponding annual real increments for energy reached 17.7%, 31.0%, and 10.1%, whereas those for nonenergy, 8.5%, 4.9%, and 18.6%, respectively. (All indices are expressed in 2010 USD).

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Fig. 1. Copper mine production by continent: 1950–2016. Notes: (1) Own elaboration based on information from the World Bureau Metal Statistics (WBMS) and Bloomberg. (2) America comprises North, Central, and South America, and the Caribbean.

Fig. 2. Chile's copper mine production. (a) Chile and other major mine producing countries: 1950–2016. Note: Data is from World Bureau of Metal Statistics Yearbook 2017 and Bloomberg. (b) Codelco versus private mining firms: 1960–2015. (c) Copper mine production in Chile by geographic region: 1985–2015. (d) Mine production shares: 1990–2015. Notes (1) In Panel (c): I = Tarapacá Region; II = Antofagasta Region (II); III = Atacama Region; IV = Coquimbo Region; V = Valparaíso Region; VI = O′Higgins Region; RM = Metropolitan Region. (2) Data in Panels (b) through (d) is from Cochilco. 3

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Fig. 2. (continued) Table 1 Percentage shares of world copper mine production: 2002–2016.

Australia Canada China Chile Indonesia Peru Russia U.S.A. Total

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

6.5% 4.5% 4.2% 33.8% 8.6% 6.2% 5.1% 8.4% 66.3%

6.1% 4.1% 4.4% 36.0% 7.4% 6.2% 4.9% 8.2% 67.0%

5.8% 3.8% 5.1% 36.9% 5.7% 7.1% 5.2% 7.9% 67.9%

6.2% 3.9% 5.0% 35.2% 7.0% 6.7% 5.3% 7.6% 66.9%

5.8% 4.0% 5.8% 35.3% 5.4% 6.9% 5.1% 7.9% 66.4%

5.6% 3.8% 6.0% 35.8% 5.1% 7.7% 5.0% 7.5% 66.9%

5.7% 3.9% 6.9% 34.0% 4.2% 8.1% 5.0% 8.4% 66.5%

5.4% 3.1% 6.7% 34.0% 6.3% 8.0% 4.7% 7.4% 67.1%

5.4% 3.2% 7.3% 33.6% 5.4% 7.7% 4.4% 6.9% 65.3%

5.9% 3.5% 8.0% 32.4% 3.3% 7.6% 4.4% 6.8% 62.5%

5.4% 3.4% 9.3% 32.0% 2.3% 7.6% 4.2% 6.9% 62.4%

5.5% 3.5% 9.3% 31.6% 2.7% 7.5% 3.9% 6.8% 61.8%

5.2% 3.8% 8.8% 31.1% 2.0% 7.5% 3.9% 7.4% 60.7%

5.0% 3.6% 8.6% 29.9% 3.0% 8.8% 3.8% 7.3% 61.4%

4.5% 3.4% 8.8% 26.9% 3.2% 11.5% 3.6% 6.9% 60.9%

Note: Percentages are with respect to world copper mine production. Source: Various issues of the World Metal Statistics Yearbook.

Panels (b) through (d) of Fig. 2 show detailed information of Chile mine production over time. In particular, Panel (b) depicts annual mine production of Corporación del Cobre (Codelco) and private mining firms

for the period of 1960–2015. It is apparent that from the mid-1990s onwards mine production of private firms has exceeded that of Codelco, and the gap between the two series has widened over time. As a matter of illustration, in 2015 Codelco produced 1732 metric tons (MT) as compared with 4032 MT of private firms. Panel (c) in turn depicts mine production in Chile by geographic region for the period of 1985–2015. To a great extent, the most important producer is the Antofagasta Region (II), where the mines of Chuquicamata (Codelco), Radomiro Tomic (Codelco), Gaby (Codelco), Escondida, El Abra, Anglo-American North,9 among others, are located. Other relevant producers are the regions of Tarapacá (I), e.g., Cerro

8 Regarding refined copper, figures from the WBMS show that Chile, China, Japan, Russia, and the U.S accounted for over 50% of production every year of the 2002–2016 period. In particular, China's production share increased considerably from 10.6% in 2002 to 35.9% in 2016. Moreover, during the same time period, China was the largest consumer of refined copper by a large margin. For instance, in 2016 China's consumption share reached 49.9% followed by the 7.6% and 5.3% shares of the US and Germany, respectively.

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Fig. 3. Labor productivity. (a) Average labor productivity (MT/worker): 1978–2015. (b) Productivity index of GPM10 and Codelco: 2005–2015. Note: In Panel (a), employment is defined as total direct employment in large-, medium- and small-scale mining. Employment figures for 1978–1994 are from Meller (2002, page 76) and for 1995–2015 from Cochilco. Mine production is from the latter. In Panel (b), GPM-10 stands for the 10 largest private copper companies. Data is from Cochilco Yearbook 2016. (c) Labor productivity of the mining sector during 1995–2013: selected countries. Notes: (1) The series for the US is from the Bureau of Labor Statistics, www.bls.org, and it corresponds to an annual index of labor productivity in the mining industry. (2) The series for Canada and Australia are from the OECD, https://stats.oecd.org/Index. aspx?DataSetCode=PDBI#., and they correspond to an index of gross value added per person employed, at constant prices, in the mining and utilities sector.

mines for the period of 1990–2015. It is evident from the figure that Chuquicamata and El Teniente have lost ground over time. Indeed, Chuquicamata share dropped from 42.9% in 1990 to 5.4% in 2015, while that of El Teniente, from 18.9% to 8.2% during the same time period. By contrast Escondida, which started operating in 1990, has become the largest producer with 20% of total Chile mine production in 2015. Such a share is considerable greater than those of El Teniente, Collahuasi, Anglo-American South, Los Pelambres, and Chuquicamata

Colorado and Collahuasi; Atacama (III), e.g., Salvador (Codelco), Candelaria, and Caserones; Coquimbo (IV), e.g., Los Pelambres; Valparaíso (V), e.g., Andina (Codelco), Anglo-American South (Chagres and El Soldado); O′Higgins (VI), e.g., El Teniente (Codelco), and the Metropolitan Region (RM), e.g., Anglo-American South (Los Bronces). Lastly Panel (d) presents production shares of some selected large

9

Mantos Copper from 2015 onwards.

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additional mine deepening and further ore hardness. Hence more electricity is required to crush harder mineral in large quantities and carry out the froth floatation process14 at a later stage (Lagos op cit; Calvo et al., 2016). In order to have a better sense of copper grades, Table 2 shows porphyry copper deposits sorted by grade in 2008 (source: Singer et al., 2008). Such deposits are copper ore bodies that are formed from hydrothermal fluids, which originate from a voluminous magma chamber several kilometers below the deposit itself. Porphyry copper deposits are currently the largest source of copper ore. In particular, the greatest concentration of the largest copper porphyries is in northern Chile. Almost all mines exploiting large porphyry deposits produce from open pits. (https://en.wikipedia.org/wiki/Porphyry_copper_deposit). In particular, Table 2 presents the twenty deposits with highest copper grade for Chile, the United States, Canada, and Peru in 2008. As can be seen from the depicted deposits, grades ranged from 1.30 to 0.45 in Chile, 1.10–0.59 in the US, 1.57–0.46 in Peru, and from 0.83 to 0.39 in Canada.15 The table also reports mean grades and relative standard deviations (i.e., coefficient of variation = standard deviation/mean). As can be seen, the US exhibited the highest mean (= 0.72) with the lowest relative standard deviation (= 0.19). It is worth comparing the figures of Table 2 with previous measurements of copper grades of porphyry deposits. From Singer et al. (2002), one finds that the mean and relative standard deviation of the twenty deposits with highest copper grades in Chile were 0.97 and 0.26, respectively. Clearly, within a 6-year period, the mean grade dropped to 0.69 and the relative dispersion increased to 0.30 (Table 2). In particular, Escondida's copper grade dropped from 0.97 in 2002–0.77 in 2008. El Teniente also experienced a sizeable decrease (from 0.92 to 0.67), but less so Chuquicamata (from 0.65 to 0.59). The aforementioned phenomena have had as a counterpart increasing mining costs in Chile and elsewhere. Specifically, Panel (a) of Fig. 5 shows time series of C3 total costs (= Cash costs (C1)+depreciation+indirect costs+interest) for Chile and the world for the period of 1980–2015. Both series experienced a sharp increase from 2004 onwards. In addition, in recent years Chile's C3 costs have exceeded those of the World. Panel (b) of Fig. 5 in turn presents an index of total unit costs for the period of 2005–2015 elaborated by Cochilco, which includes operating, selling, general, and administrative costs; financial costs, and non-operating expenses. As can be seen, the depicted series exhibited an increasing trend over time, being Escondida the mining company which experienced the greatest total unit costs increase. Regarding the above discussion, it is worth making the following points. First, a shift to lower grade ores may not be necessarily driven by depletion of high-grade deposits, but by dilution of ore grades through a combination of factors (West, 2011): major improvements in metallurgical processes, which turn previously sub-economic mineral concentrations into profitable ores; a movement toward high-volume and lower cost extraction; and, the economic advantages of extending the life of older mines (i. e., existing expensive capital plant and other infrastructure; technical and administrative uncertainties associated to a new mine, and costs associated with closing a mine.) As an example, West mentions the fall in copper grades in Australia from 1995 to 2005 due to the addition of the two high-tonnage and low-grade copper-gold deposits of Cadia Hill and Telfer. Second, the inverse association between copper grade and costs

in the same year: 8.2%, 7.9%, 7.6%, 6.5%, and 5.4%, respectively. 3.2. Copper mining in Chile: productivity and costs This section offers some extra background information on the Chilean mining sector by focusing on mining productivity and production costs. In particular, Panel (a) of Fig. 3 shows an annual series of labor productivity, measured as metric tons (MT) of mine production per worker for the period of 1978–2015. (The computation involves only direct employment—i.e., own labor force). As the figure shows, Chile experienced a notable increase in mining labor productivity from 1978 to 2004, mainly due to the discovery and development of new mines, and to innovation and new technology (Garcia et al., 2001). However, from 2005 onwards productivity has fallen sharply, particularly from 2005 to 2008. The decline in labor productivity from 2005 onwards can be also seen in Panel (b) of Fig. 3, where Codelco and the GPM-10 (i.e., Gran Minería Privada-10, that is, the 10 largest private mining companies operating in Chile) are depicted. The graph shows that Collahuasi and El Abra are the private companies which have undergone the most severe reduction in labor productivity in the period of 2005 − 2015.10 By contrast Codelco has experienced some increase in labor productivity from 2008 to 2011, and from 2013 to 2015.11 Panel (c) of Fig. 3 illustrates that the phenomenon of a falling mining productivity has also taken place in other countries, such as Australia, Canada, and the U.S, from the early 2000s onwards. Specifically, the depicted series for the US corresponds to an annual index of productivity in the mining industry computed by the Bureau of Labor Statistics. (Employment data are obtained from employer or establishment surveys). The series for Canada and Australia in turn are from the OECD, and correspond to an index of gross value added per person employed, at constant prices, in the mining and utilities sector. Geological factors that contribute to a falling productivity per ton of ore mined are depleting reserves and a decreasing ore grade (Ernst and Young, 2014). The latter is accompanied by two effects, namely, mine deepening, which implies extracting more material and transport it longer distances, and by more ore hardness arising from deepening of open-pit and underground mines (Lagos, 2016). Fig. 4(a) depicts copper grade by process type: concentrator plant (i.e., crushing and grounding of sulfide copper minerals to liberate the valuable minerals from waste or gangue minerals) and heap leach (i.e., extraction of oxidized copper ore bodies via a series of chemical reactions.) The average grade of Codelco mines is also depicted. As can be seen, grades showed a decreasing trend in the period of 2001–2015. This translated into an increase of electricity costs per metric ton of refined copper in the same time period, as Panel (b) of Fig. 4 shows. Such a cost increase during 2001–2015 is particularly noticeably for concentrator plants: 87.1% as opposed to 48.1% of mine, 22.9% of hydrometallurgy (LX/SX/EW)12 and to 4.9% of smelting. It is apparent from Fig. 4(b) that the most energy-intensive processes are concentration and LX/SX/EW.13 Indeed, falling ore grades translate into 10 Such a decline is partly explained by the fact that both mines experienced annual production drops around the period of 2009–2012. Indeed, according to figures from Cochilco, Collahuasi's annual production fell 6% in 2010, 11% in 2011, and 47% in 2012, while that of El Abra dropped 1% in 2009, 12% in 2010, and 16% in 2011. Mine overexploitation and management failure appear to be the causes behind such sizeable reductions. (I thank one of the reviewers for this suggestion.). 11 In a recent survey on mining productivity conducted by Ernst and Young (2014), participants associated drops in labor productivity to inexperienced teams, high labor turnover and an aging workforce, and to a focus on volume rather than efficiency. The survey covered questions on labor, capital, mineral resources, and economies of scale. 12 Leaching /Solvent extraction/Electro-winning processes. 13 In a recent study for the Chile copper mining sector, Lagos et al. (2018) estimate that copper grade should decrease at a slower rate in the forthcoming years, so that electric energy consumption per ton of copper should grow at a significantly lower rate than that observed in the period of 2001–2015. Lagos et al. also anticipate that copper mining production in Chile will be essentially based on the mine-to-mill process, while the

(footnote continued) relative importance of the Leach/SX/EW operations will decrease as close-to-surface oxide deposits run out. 14 Froth floatation is a process for separating minerals from gangue by taking advantage of differences in hydrophobicity. That is, the property of repelling water rather than absorbing it or dissolving in it. (https://en.wikipedia.org/wiki/Froth_flotation). 15 When considering all deposits with non-missing data and copper grades greater than zero, the minimum grades for Chile, the US, Peru, and Canada are 0.20, 0.10, 0.30, and 0.15, respectively.

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Fig. 4. Copper mining grade and electricity costs in Chile: 2001–2015. (a) Mining grade by process type. (b) Electricity consumption per metric ton (MT) by process. Notes: (1) In Panel (b), LX/SX/EW stands for Leaching /Solvent extraction/Electro-winning processes. 1 Megajoule equals 0.278 kW h (2) Source for both panels is Cochilco.

a series of new innovations and technology, including the solvent-extraction electro-winning process (SX-EW). Nowadays, new technologies include software to optimize asset utilization, devices to monitor and control activities remotely, and robotics for the automation of repetitive tasks. For instance, Rio Tinto built a remote monitoring and control facility that can connect with all mines around the world in real time (PwC Mine Report, 2017, page 21) Fourth, the behavior of labor productivity and costs may vary over the business cycle. In particular, Tilton (2001) examined the relationship between labor productivity, costs, and survival of mines during a recession. Based on evidence for the US during 1975–1990, Tilton concluded that the level of labor productivity at the beginning of the downturn is less relevant to survive than a mine's ability to increase its labor productivity and reduce its costs through innovations (e.g., more advanced and efficient technology; more flexible worker-manning schedules; changes in stripping ratios; pursuit of price concessions from suppliers; elimination of non-essential activities, such as company housing).19 In addition, Tilton found that the extent to which mines increase their labor productivity during a recession depends on the life expectancy of their reserves.

applies mostly from 2005 onwards, when technology was unable to make up for all aspects influencing costs.16 Indeed, as Fig. 5(a) shows, nominal costs in the world (and in Chile) declined from 1993 to 2003. However, the world average copper grade exhibited a decreasing trend during the same time period (e. g., Fig. 1 in Crowson, 2012; Fig. 2 in Mudd, 2009).17 Third, besides geological factors, such as ore grades, stripping ratios18 and reserves, technology and managerial practices are key drives of labor productivity as well. In this regard, Jara et al. (2010) argued that the noticeable increase in labor productivity in Peru's open-pit mining during 1992–2004 was followed by a sharp increase in wages, which reflected the introduction of world class production systems and management standards by multinational companies. On the other hand, Garcia et al. (2001) documented that the sharp jump in labor productivity in the US mining sector during 1975–1995 came largely from 16

I thank one of the reviewers for making this point. Average mining grades for Chile are available from Cochilco only from 1999 onwards. In particular, Chile average mining grade felt from 1.26 in 1999 to 0.95 in 2003. Moreover, Fig. 2 in Crowson op cit. shows that a regional weighted-average copper head grade for South and Central America generally decreased during 1996–2007. 18 Volume of waste material required to be handled in order to extract some tonnage of ore. 17

19

7

Jackson and Gilbertson (2012) also stress this point.

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peaks and troughs are later used as an input to compute a measurement of cycle concordance, according to the methodology of Section 2.2. As an extension, and in the spirit of Aroca and Atienza (2011), this section considers other country regions which may exhibit employment comovement with the Antofagasta Region: Atacama, Coquimbo, O′Higgins, Bío-Bío, and Metropolitan. Section 4.2 in turn utilizes an alternative measure of synchronicity by computing rolling correlations of annual employment percent variation for selected sectors and geographic regions.

Table 2 Porphyry copper deposits sorted by grade in 2008.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Chile deposit name

Grade

US deposit name

Grade

Mansa Mina Potrerillos Spence Collahuasi Cerro Colorado Escondida Quebrada Blanca Conchi Ujina El Teniente Los Pelambres Los Bronces/Rio Blanco Chimborazo Sierra Gorda Chuquicamata Opache Esperanza El Abra Toki Andacollo Mean Coefficient variation Peru deposit name Coroccohuayco Cerro Colorado Los Chancas Aguila Tantahuatay Antapaccay Cuajone Cotabambas Quechua Michiquillay Quellaveco Rio Blanco La Granja Toquepala Cerro Negro Constancia Magistral Cerro Verde/Santa Rosa El Galeno Toromocho Mean Coefficient variation

1.30 0.98 0.92 0.86 0.81 0.77 0.72 0.71 0.71 0.62 0.62 0.60 0.60 0.60 0.59 0.53 0.51 0.49 0.47 0.45 0.69 0.30 Grade 1.57 1.01 1.00 0.85 0.79 0.74 0.69 0.68 0.68 0.65 0.65 0.57 0.56 0.55 0.53 0.51 0.51 0.50 0.47 0.46 0.70 0.37

Christmas Sheep Mountain Bingham Gibson Contact Kirwin Copper Creek Sacaton Ray North Fork Snoqualmie River Continental/Butte Silver Bell Lakeshore Bisbee Miami-Inspiration Red Mountain Ely Sunnyside San Manuel-Kalamazoo Casa Grande West Mean Coefficient variation Canada deposit name Afton Kerr Lorraine Galaxy Gaspé Galore Creek Taseko Huckleberry Copper Mountain Bethlehem Valley Axe Granisle Lornex Big Onion Eaglehead Island Copper Sulphurets Berg Gnat Lake Mean Coefficient variation

1.10 0.99 0.88 0.78 0.77 0.76 0.75 0.72 0.68 0.68 0.67 0.66 0.66 0.66 0.63 0.63 0.61 0.61 0.60 0.59 0.72 0.19 Grade 0.83 0.75 0.66 0.59 0.56 0.55 0.53 0.48 0.47 0.45 0.43 0.43 0.43 0.43 0.42 0.41 0.41 0.41 0.40 0.39 0.50 0.25

4.1. Concordance of employment cycles Fig. 6(a) through (c) depict 3-month moving average employment series (e.g., January-March, February-April, etc.) for the Tarapacá and Antofagasta Regions and nationwide, respectively, for the period of 1986–2016. As depicted in Panel (a), commerce concentrates most employment in the Tarapacá Region, followed by construction and industry for most of the sample period. From 2010 onwards, however, mining & quarrying surpassed construction and industry in terms of employment creation. Meanwhile, in the Antofagasta Region, mining & quarrying is evidently the most important employment sector, followed by commerce. Nationwide, commerce, industry, and construction are leading sectors in employment creation, followed by mining & quarrying, and electricity, gas & water.21 It is worth mentioning that mining & quarrying employment comprises that of metal mining (copper, iron, manganese, lead & zinc, gold & silver), industrial minerals (calcium carbonate, lithium carbonate, sodium chloride, quartz, and nitrates, among others), and fuels (coal and crude oil). However, most employment corresponds with metal mining, and, in particular, with copper. Indeed, as of 2013, figures gathered by the National Service of Geology and Mining (Sernageomin), show that metal mining accounted for 87.1% of total direct mining & quarrying employment, as opposed to 10.3% and 2.6% of industrial minerals and fuels, respectively.22 Out of the 87.1% share of metal mining, 76.1% corresponded to copper, being large-scale mining the one that concentrated most direct employment (56.9%), followed by small-scale mining (10.9%), and medium-scale mining (8.5%). Table 3 provides further information on the economic importance of each sector depicted above, by computing their share with respect to regional GDP in the case of the Tarapacá and Antofagasta Regions, and nationwide for the period of 2008–2015. As can be seen from the table, mining and quarrying was the most important economic activity in both regions in that period.23 It is also worth noticing that in the Tarapacá and Antofagasta Regions and nationwide the mining & quarrying GDP share reached its peak in 2010 (57%.2, 70.7%, and 16.0%, respectively) to start declining thereafter. This is not surprising given the evolution of copper prices, as discussed below. In order to characterize the employment series Panels (a) and (b) of Table 4 present peaks and troughs of mining & quarrying, construction;

Notes: (1) Own elaboration based on Singer et al. (2008). Porphyry copper deposits are currently the largest source of copper ore. (2) Coefficient of variation=standard deviation/mean.

By contrast, during high-price periods the opportunity cost of not producing a unit of production leads mining companies to favor volume over costs because the benefits associated to more production outweigh the increased cost (and lower productivity) that result (Wentzel, 2012, page 3). 4. Regional aspects of copper mining in Chile: employment cycles

(footnote continued) reasons to consider these two regions are that they are adjacent, and that at the Tarapacá Region is located Iquique's Free Trade Zone (ZOFRI), a relevant business platform. It is also worth mentioning that the Tarapacá and Antofagasta Regions exhibited a greater annual per-capita GDP, PPP-based, than nationwide as of 2014—USD 27,873 and USD 64,668 per year, respectively, versus USD 22,910 per year nationwide— and have lower income-based poverty levels than nationwide as of 2015—7.1% and 5.4%, respectively, versus 11.7% nationwide. Source: Sociedad de Fomento Fabril (SOFOFA)’s regional fact sheets, http://web.sofofa.cl/informacion-economica/indicadores-economicos/. 21 Nationwide, another key employment sector is agriculture, stock breeding, forestry, and fishing. Specifically in the November 2016–January 2017 period, it represented 9.9% of national employment, as opposed to 19.7% of commerce, 10.6% of industry, 8.6% of construction, 2.4% of mining & quarrying, and 1.1% of electricity, gas, and water. 22 Such figures are unavailable after 2013 because they were discontinued by Sernageomin. 23 Nationwide, another key economic sector is financial and entrepreneurship services, which accounted for 15.3% of Chile GDP in 2015.

This section analyzes how mining may interact with other economic sectors within the same geographic region and/or across different geographic regions in Chile. The focus of study is employment because time series are available at a national level and by regions from the mid1980s onwards; hence, providing with a relatively large sample size. Specifically, Section 4.1 determines peaks and troughs of employment series of mining & quarrying; construction; electricity, gas & water; commerce; and industry nationwide and of the Antofagasta and Tarapacá Regions, two key copper mining producers (Fig. 2c).20 Such 20 During the period of 1999–2015, the Tarapacá Region was the largest copper producer (12%-average share) after the Antofagasta Region (53%-average share). Two other

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Fig. 5. Mining costs. (a) C3 total costs = Cash costs (C1) + depreciation + indirect costs + interest: 1980 − 2015. (b) Index of total unit costs (2005 = 100). Notes: (1) Cost series of Panel (a) are from Brook Hunt (Wood Mackenzie) Copper Costs: Mines and Projects 2010 Edition, and from Consejo Minero, www.consejominero.cl. (2) The cost index of Panel (b) is from Cochilco Yearbook 2016. (3) In Panel (b), GMP-10 stands for the 10 largest private mining companies operating in Chile. The Index of total unit costs includes operating, selling, general, and administrative costs, financial costs, and non-operating expenses.

onwards. When looking at peaks and troughs of mining & quarrying employment nationwide, it is noticeable some overlapping with those of the Antofagasta Region. Indeed, in both cases the years of 1987, 1994, 2002, and 2012 are identified with peaks, and the years of 1992, 2000, and 2003 with troughs. With respect to the other four economic sectors under analysis, the degree of synchronicity with mining & quarrying employment cycles in the Antofagasta Region and nationwide is not clear-cut by simple inspection of Panels (a) and (b) of Table 4, respectively. Therefore, the computation of a concordance measure can shed some light on this matter, by gauging the degree of interaction of employment sectors within a region and between regions. In that regard, Panel (c) of Table 4 reports a matrix of concordance of employment cycles of the Antofagasta and Tarapacá Regions and nationwide, computed on the basis of expression (1) for the period of 1986–2016. In this case, concordance quantifies the proportion of time two employment series are in the same state (i. e, boom or slump). In particular, E1, E2, E3, E4, and E5 denote employment of mining & quarrying, construction; electricity, gas and water (EGW), commerce, and industry, respectively. And, E1-A, E1-T, and E1-N denote the mining & quarrying employment sectors of the Antofagasta Region, the Tarapacá Region and nationwide, respectively. The remaining abbreviations are analogous. Statistically significant figures at a 95% confidence level are indicated by *. (Critical values are determined

electricity, gas & water; commerce, and industry of the Antofagasta Region, and nationwide.24 As stated in Section 2.2, peaks and troughs are determined by means of the Bry and Boschan (1971) algorithm. This is implemented in the BryBoschan procedure of WinRATS 9.1, a timeseries oriented econometric package.25 In addition, in order to have a basis for assessment of peaks and troughs of mining & quarrying employment, the minimum, maximum, and median values of the real price of copper (expressed in 2016 USD/pound) of each year of the period of 1986–2016 are depicted in Fig. 7. As can be seen from Panel (a) of Table 4, peaks in mining & quarrying employment in the Antofagasta Region were usually associated with periods of increasing or unusually high copper prices, as illustrated in Fig. 7: 1989, 1994–1995, 2004, 2010, and 2012. Similarly, troughs were associated with periods of depressed copper prices during 1992–1993, 1999–2000, and with decreasing prices from 2011

24 For the sake of brevity, peaks and toughs of the Tarapacá Region are not reported in the table, but they are considered in the concordance matrix of Panel (c) of Table 4. 25 The RATS documentation states that the algorithm takes account of the following: 1. it replaces outlying observations (i.e., those too far from a preliminary trend-cycle). 2. It locates preliminary turning points by finding local maxima and minima of the adjusted trend-cycle. 3. It eliminates consecutive peaks and consecutive troughs, by keeping the most extreme in a sequence. 4. It enforces a minimum cycle length by eliminating the less pronounced peak-trough combination. 5. It does some further refinement to ensure that phases are not too short.

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Fig. 6. 3-month moving average employment series: 1986–2016. (a) Tarapacá Region. (b) Antofagasta Region. (c) Nationwide. Notes: (1) EGW stands for electricity, gas and water. (2) Source: Instituto Nacional de Estadísticas (INE). (3) Employment figures are 3-month moving averages, e.g., January-March, February-April, etc.

series were in the same state 69% of the time. Second, employment in mining & quarrying of the Antofagasta region (E2-A) was statistically concordant only with that of mining & quarrying nationwide (E2-N) 59% of the time. However, employment series in other sectors of the Antofagasta Region were statistically concordant either within the region (e.g., 72% concordance between construction (E2-A) and industry (E5-A)) or across regions (69%

according to Table 1A of the Appendix). From the matrix, it is worth highlighting the following. First, employment in mining & quarrying of the Tarapacá Region (E1-T) was statistically concordant with that of industry of the Antofagasta Region (60%); and nationwide, with employment in all of the four sectors, with the exception of EGW, during 1986–2016. In particular, E1-T was highly concordant with E2-N (construction sector nationwide), as both 10

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regions closer to Antofagasta, such as Tarapacá and Atacama. Calculations of employment concordance of the Atacama, Coquimbo, O′Higgins, Bío-Bío, and the Metropolitan Regions,27 with respect to the Antofagasta Region and nationwide, are presented in Table 5, Panels (a)-(e), for the period of 1986–2016. In general terms, the figures show that the five regions in question interacted with the Antofagasta Region, although not with its mining & quarrying sector (with the exception of the Bio-Bio Region28), but instead with other economic sectors, such as construction, commerce, and industry. When considering employment concordance with respect to the country as a whole, however, the mining & quarrying sectors of the regions under analysis displayed co-movement with that at a national level, being the O′Higgins Region29 the one with the largest concordance of 71% (Panel (c)). It is also worth noticing that employment in all of the economic sectors of the Coquimbo Region displayed synchronicity with that of construction nationwide (Panel (b)). In particular, the highest concordance of the latter was with Coquimbo's industry (70%). A similar pattern was observed for the Metropolitan Region sectors (Panel (e)) during 1986–2016, which displayed high levels of employment concordance with respect to construction nationwide. In particular, construction employment levels in the Metropolitan Region and nationwide were in the same state 90% of the time during the sample period. As a robustness check, Table 6 presents employment concordance of the Tarapacá, Atacama, Metropolitan, O′Higgins, and Bío-Bío Regions, with respect to the Coquimbo Region, for the period of 1986–2016. First, it draws one's attention that employment in the economic sectors of these five regions, with the exception of Bío-Bío, exhibited concordance with that of mining & quarrying of the Coquimbo Region. This was not generally the case with respect to the Antofagasta Region, as displayed in Table 4(c) and Table 5, Panels (a)-(e). Second, most synchronicity was observed among sectors of the Tarapacá and Metropolitan Regions with respect to the Coquimbo Region. These findings complement Aroca and Atienza (2011)’s in that Coquimbo appears as a more influential region than Antofagasta when it comes to employment co-movement. As Aroca and Atienza point out, Coquimbo is an attractive region due to its pleasant weather, low cost of living, and proximity to the Metropolitan Region.

Table 3 GDP share by economic activity: 2008–2015. Year

Mining & quarrying

Industry

Electricity, gas & water

Construction

Commerce

(a) Tarapacá Region 2008 52.5% 2009 52.1% 2010 57.2% 2011 52.1% 2012 39.5% 2013 41.9% 2014 44.8% 2015 31.7%

2.6% 2.2% 2.2% 2.4% 0.0% 2.7% 2.8% 6.2%

2.2% 2.5% 2.1% 1.9% 1.9% 1.9% 1.9% 1.9%

8.6% 9.6% 6.5% 9.6% 13.9% 11.2% 8.1% 8.3%

13.1% 11.8% 11.4% 12.0% 14.6% 14.8% 14.1% 11.6%

(b) Antofagasta region 2008 67.3% 2009 66.3% 2010 70.7% 2011 68.5% 2012 63.0% 2013 57.0% 2014 56.3% 2015 48.5%

4.4% 4.5% 3.4% 3.7% 4.1% 4.2% 3.5% 5.0%

2.5% 2.4% 1.9% 2.3% 2.7% 2.6% 2.4% 3.6%

9.2% 9.6% 8.9% 9.2% 12.4% 16.6% 18.4% 13.2%

3.2% 3.2% 2.9% 3.4% 4.0% 4.6% 4.6% 3.8%

(c) Nationwide 2008 14.0% 2009 13.1% 2010 16.0% 2011 14.9% 2012 12.8% 2013 11.1% 2014 11.2% 2015 8.8%

11.2% 11.3% 10.8% 11.0% 10.8% 10.8% 10.7% 11.4%

2.7% 3.1% 2.8% 2.8% 2.5% 2.4% 2.2% 2.8%

7.3% 7.5% 6.8% 6.9% 7.4% 7.7% 7.5% 6.6%

9.8% 9.2% 9.4% 9.5% 9.9% 10.3% 10.4% 10.9%

Notes: (1) Own elaboration based on information from the Central Bank of Chile, www.bcentral.cl. (2) Mining & quarrying comprises copper mining and other mining activities. Manufacturing industry in turn involves the categories of food, beverages & tobacco; lumber & furniture; cellulose, paper & printing; oil refining; chemical products, rubber, and plastic; and, non-metallic minerals and basic mining.

concordance between Tarapacá Region commerce (E4-T) and Antofagasta Region construction (E2-A)). Third, nationwide mining & quarrying employment (E1-N) was in the same state as that of construction (E2-N) 65% of the time. The latter was in turn in sync with commerce (E4-N) and industry (E5-N) 67% and 69% of the time, respectively. On the other hand, EGW employment nationwide (E3-N) also exhibited high concordance with commerce and industry nationwide (60% and 67% of time, respectively). To summarize, the above figures for the period of 1986–2016 suggest that mining & quarrying employment in the Tarapacá and Antofagasta Regions were concordant with that of mining & quarrying nationwide (66% and 59% of the time, respectively), and that the latter in turn was concordant with that of construction nationwide (65% of the time). Moreover, mining & quarrying employment in the Tarapacá Region exhibited statistically significant linkages with that of construction, commerce, and industry nationwide (69%, 61%, and 61% of the time, respectively). As an extension, and in line with Aroca and Atienza (2011), this section considers other geographic regions as well for the purpose of gauging employment concordance. Specifically, Aroca and Atienza analyzed long-distance commuting from Antofagasta to other regions of Chile, and determined that the Tarapacá, Atacama, Coquimbo, Metropolitan, and Bío-Bío Regions were those whose economies benefited the most from direct and indirect employment creation.26 In particular, Aroca and Atienza found that the total impact of long-distance commuting on Coquimbo Region's employment level was larger than on

4.2. Employment correlations This section looks into employment co-movement by focusing on rolling bivariate correlations of employment annual percent variations. Such correlations make it possible to get a dynamic measure of employment linkages across economic sectors, within and across regions, which in turn can be linked to copper price cycles. Specifically, rolling bivariate correlations are computed by taking 24 observations (i.e., 2 years) at a time, where each observation corresponds to the annual percent variation of a given employment series (Fig. 6). The reason for taking an annual percent variation is to remove 27 Among these five regions, the Atacama and Metropolitan Regions are the ones with the highest annual per-capita GDP, PPP-based, as of 2014: USD 29.088 and USD 24,559 per year, respectively. In turn the Coquimbo, O′Higgins, and Bío-Bío Regions have corresponding annual per-capita GDP, PPP-based, as of 2014 of USD 14,803, USD 18,038, and USD 12,406 per year. In terms of income-based poverty, as of 2015 the Bío-Bío Region had the highest level among the five regions with 17.6%, followed by Coquimbo, O′Higgins, and the Metropolitan Region, with corresponding figures of 13.8%, 13.7%, and 11.7%. Source: SOFOFA's regional fact sheets. 28 Mining in the Bío-Bío Region has been historically relevant due to the exploitation of coal resources in the so-called coal basin. The closure of state and private deposits in the late 1990s, however, gives account of an activity currently depressed, in need of state contributions. Nevertheless, the Bio-Bio Region led mining exploration, with 25% of concessions, during the period of 2006–2014, according to information provided by Cochilco. Searches were focused on copper, molybdenum, bituminous coal and rare earths, being Tomé , Florida, and Trehuaco the most requested communes. 29 In this region is located El Teniente, which produced 8.2% of the national copper mining production in 2015.

26 A direct effect on employment refers to workers of a given region employed in mining & quarrying of the Antofagasta Region, while an indirect effect, to workers indirectly connected to that sector.

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Table 4 Employment concordance: 1986–2016. a) Peaks and troughs of employment series: Antofagasta Region Mining & quarrying Construction

Elect., gas & water

Commerce

Industry

Peaks 1987 1989 1994 1998 2002 2004 2010 2012

Dec-Feb Jul-Sep Nov-Jan Feb-Apr Jul-Sep Jan-Mar May-Jul Feb-Apr

1992 1995 1998 2000 2004 2007 2011 2013

Oct-Dec Apr-Jun Jun-Aug Jul-Sep Feb-Apr Aug-Oct Mar-May May-Jul

1988 1990 1992 1994 1997 2001 2005 2009 2012 2014

Mar-May Mar-May Dec-Feb Sep-Nov May-Jul Apr-Jun Aug-Oct Aug-Oct Nov-Jan Jul-Sep

1987 1990 1992 1997 1999 2001 2004 2008 2010 2013 2016

May-Jul May-Jul Nov-Jan May-Jul May-Jul Sep-Nov Apr-Jun May-Jul Sep-Nov Feb-Apr Dec-Feb

1989 1991 1995 1997 2000 2004 2008 2010 2013

Dec-Feb May-Jul Feb-Apr Aug-Oct Apr-Jun Mar-May Jan-Mar Oct-Dec Aug-Oct

Troughs 1988 1992 1996 2000 2003 2006 2011 2016

Oct-Dec Dec-Feb Sep-Nov May-Jul May-Jul Nov-Jan Feb-Apr Dec-Feb

1994 1996 2000 2002 2005 2008 2012 2015

Mar-May Dec-Feb Feb-Apr Jan-Mar Mar-May Mar-May Jan-Mar Feb-Apr

1989 1991 1992 1995 1998 2003 2007 2011 2013 2015

Jan-Mar Jan-Mar Oct-Dec Mar-May May-Jul May-Jul May-Jul Mar-May Nov-Jan Nov-Jan

1986 1988 1991 1994 1998 2000 2002 2005 2009 2012 2014

Nov-Jan May-Jul Dec-Feb Aug-Oct May-Jul Jul-Sep Jul-Sep Sep-Nov May-Jul Aug-Oct Apr-Jun

1986 1989 1993 1995 1999 2002 2004 2009 2012 2015

Feb-Apr Feb-Apr Dec-Feb Nov-Jan Sep-Nov Oct-Dec Nov-Jan May-Jul Mar-May Mar-May

(b) Peaks and troughs of employment series: Nationwide Mining & quarrying

Construction

Peaks 1986 1987 1990 1994 1996 2002 2012

May-Jul Jan-Mar Apr-Jun Dec-Feb Aug-Oct Apr-Jun Aug-Oct

1989 1993 1998 2002 2008 2013 2016

Troughs 1987 1992 1994 2000 2003

Aug-Oct Sep-Nov Oct-Dec Oct-Dec Feb-Apr

1990 1994 1999 2003 2009 2015

Elect., gas & water

Commerce

Industry

Nov-Jan Sep-Nov Feb-Apr Nov-Jan Jul-Sep Nov-Jan Feb-Apr

1987 1996 1999 2002 2004 2007 2011

Feb-Apr Sep-Nov Feb-Apr Dec-Feb Apr-Jun Jul-Sep Jan-Mar

1994 1999 2003 2005 2008 2010 2014

Nov-Jan Oct-Dec Dec-Feb Dec-Feb Oct-Dec Nov-Jan Jan-Mar

1989 1998 2001 2003 2004 2007 2012 2015 2016

Sep-Nov Jan-Mar Oct-Dec Mar-May Sep-Nov Mar-May Aug-Oct May-Jul Aug-Oct

Aug-Oct Jul-Sep Aug-Oct Jul-Sep Jul-Sep Dec-Feb

1990 1997 2000 2002 2005 2009 2012

May-Jul Nov-Jan Sep-Nov Oct-Dec Jul-Sep Jan-Mar Jun-Aug

1996 2000 2004 2005 2009 2012 2015

Jun-Aug Aug-Oct May-Jul Sep-Nov Mar-May Jul-Sep Jun-Aug

1990 2001 2002 2004 2005 2009 2013 2016

Nov-Jan Dec-Feb May-Jul Mar-May Jul-Sep Feb-Apr Feb-Apr Feb-Apr

(c) Concordance matrix: Tarapacá Region, Antofagasta Region, and nationwide

E1-T E2-T E3-T E4-T E5-T E1-A E2-A E3-A E4-A E5-A E1-N E2-N E3-N E4-N E5-N

E1-T

E2-T

E3-T

E4-T

E5-T

E1-A

E2-A

E3-A

E4-A

E5-A

E1-N

E2-N

E3-N

E4-N

E5-N

1.00 0.49 0.51 0.50 0.53 0.54 0.53 0.56 0.41 0.60* 0.66* 0.69* 0.45 0.61* 0.61*

1.00 0.49 0.59* 0.55 0.47 0.66* 0.43 0.56 0.60* 0.43 0.62* 0.72* 0.63* 0.68*

1.00 0.51 0.49 0.41 0.55 0.62* 0.36 0.51 0.45 0.56 0.56 0.54 0.45

1.00 0.51 0.50 0.69* 0.42 0.58* 0.48 0.48 0.56 0.58* 0.71* 0.61*

1.00 0.46 0.65* 0.47 0.53 0.64* 0.64* 0.67* 0.58* 0.57* 0.72*

1.00 0.50 0.54 0.44 0.44 0.59* 0.49 0.48 0.56 0.51

1.00 0.42 0.58* 0.72* 0.50 0.65* 0.59* 0.66* 0.63*

1.00 0.44 0.51 0.55 0.52 0.42 0.50 0.49

1.00 0.54 0.54 0.58* 0.62* 0.55 0.52

1.00 0.56 0.61* 0.58* 0.60* 0.56

1.00 0.65* 0.46 0.48 0.56

1.00 0.55 0.67* 0.69*

1.00 0.60* 0.67*

1.00 0.62*

1.00

Notes: (1) E1: mining & quarrying; E2: construction; E3: electricity, gas and water; E4: commerce; E5: industry. (2) E1-A, E1-T, and E1-N denote the mining & quarrying employment sectors of the Antofagasta Region, the Tarapacá Region and nationwide, respectively. The remaining abbreviations are analogous. (3) * indicates statistically significant at the 95% level. Critical values are determined according to Table 1A of the Appendix.

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Fig. 7. Evolution of the real price of copper: 1986–2016. Note: The data is from Cochilco. Nominal prices are deflated by the U.S. Producer Price Index (PPI, all commodities), and expressed in 2016 US dollars/pound.

other hand, co-movement of the mining & quarrying and the commerce sectors of the Antofagasta and Coquimbo Regions, respectively, seemed less economically relevant than those of the Antofagasta and Tarapacá Regions. By contrast the employment interactions between the Antofagasta and Metropolitan Regions of Panel (f) show that co-movement has weakened over the years. For instance, prior to 2002, in general there was numerically and statistically significant co-movement between the mining & quarrying sector of the Antofagasta Region and those of construction, commerce, and industry of the Metropolitan Region. In most recent years (e.g., 2014–2016), however, the correlations between mining & quarrying of the Antofagasta Region with industry and commerce of the Metropolitan Region have considerably dropped, whereas the correlation with the construction sector of the Metropolitan Region has become in essence statistically insignificant. Lastly, Panel (g) shows that, in contrast with the Antofagasta Region, the mining & quarrying sector of Coquimbo has generally comoved over time with industry and commerce of the Metropolitan Region, particularly during periods of booming copper prices (Fig. 7). This is in agreement with the evidence of Table 6, previously discussed.

seasonal components. (This procedure is customary in Instituto Nacional de Estadistica (INE)’s statistical reports). In turn the window length of 24 observations is chosen so that a reasonably large sample size is available for constructing confidence intervals for the bivariate correlations. The outcome of these calculations is presented in Fig. 8, Panels (a) through (e) for the period of 1988–2016. In each graph, the dashed gray lines represent a 90%-confidence interval based on normality.30 If the estimated correlation falls into this interval, it is not statistically different from zero. In particular, Panel (a) depicts bivariate correlations of mining & quarrying with the sectors of construction, EGW, commerce, and industry of the Antofagasta Region, and similarly in Panel (b) for the country as whole. In turn Panels (c), (d), (e), and (f) consider interactions between employment sectors of the Tarapacá and Antofagasta Regions, of the Coquimbo and Antofagasta Regions, of the Antofagasta and Metropolitan Regions, and of the Coquimbo and Metropolitan Regions, respectively. From Panel (a), it is apparent that commerce and EGW are the sectors that most co-moved with mining & quarrying through time in the Antofagasta Region, and that construction and industry did it so to a lesser extent. By contrast, Panel (b) shows that nationwide co-movement of mining & quarrying employment with that of industry, commerce, and construction seemed strongest, particularly during 2006–2012 (i. e, period containing the final phase of the commodity super-cycle). When looking at regional interactions in Panel (d), during copper price booms (e.g., 1988–1999, 1997, 2010–2012) employment comovement between the mining & quarrying sectors of the Tarapacá and Antofagasta Regions was particularly strong. Something similar occurred with employment of the mining & quarrying sector of the Antofagasta Region and commerce of the Tarapacá Region. In particular, one may conjecture that Iquique's Free Trade Zone (ZOFRI) may have played a role in this association, by attracting mining & quarrying earnings from the Antofagasta Region during copper price booms. In the case of the Antofagasta and Coquimbo Regions, depicted in Panel (e), there were employment linkages between the two regions’ mining & quarrying sectors during the mid-2000 and during 2010–2012 approximately. In addition, employment linkages between mining & quarrying of the Antofagasta Region and industry of the Coquimbo Region were large in magnitude in recent years (2013 onwards). On the

5. Conclusions This article presented an overview of the world copper market and Chile copper mining industry in regard to mine production, costs, labor productivity and ore grade. In addition, this article focused on employment interactions among different economic sectors of relevant copper-producing regions in Chile. The two main findings are the following. First, as figures for Australia, Chile, the US, and Canada show, decreasing labor productivity in the mining sector is a widespread phenomenon. In particular, Collahuasi and El Abra were the private companies operating in Chile which underwent the most severe reduction in labor productivity in the period of 2005–2015. Such decline may be partly attributed to geological factors, such as decreasing ore grades. In this regard, figures from the US Geological Survey show that grades of porphyry copper deposits worldwide declined relatively fast between 2002 and 2008. Falling ore grades have as a counterpart an increase of electricity costs per metric ton of refined copper. In particular, figures for Chile show that during 2001–2015 concentrator plants experienced an 87.1%-increase in electricity costs (Megajoules/MT of refined copper) as opposed to 48.1% of mine, 22.9% of hydrometallurgy (LX/SX/EW) and 4.9% of smelting. Indeed, falling ore grades entail more mine

30 In large samples, Pearson bivariate correlation is distributed as N(0, 1/T), where T is the sample size.

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Table 5 Employment concordance of various regions with Antofagasta Region and nationwide: 1986–2016. (a) Atacama region E1-A E1-AT E2-AT E3-AT E4-AT E5-AT

0.45 0.54 0.43 0.49 0.39

E2-A

E3-A

E4-A

E5-A

*

0.56 0.56 0.44 0.49 0.55

0.44 0.50 0.48 0.67* 0.54

0.73 0.44 0.45 0.49 0.48

0.52 0.55 0.46 0.53 0.59*

E2-A

E3-A

E4-A

E5-A

E1-AT E2-AT E3-AT E4-AT E5-AT

E1-N

E2-N

*

*

E3-N

E4-N

E5-N

0.43 0.63* 0.49 0.56 0.47

0.56 0.60 0.46 0.56 0.60*

0.65 0.51 0.47 0.51 0.49

0.59 0.68* 0.38 0.68* 0.61

0.52 0.53 0.53 0.61 * 0.54

E1-N

E2-N

E3-N

E4-N

E5-N

*

0.65 0.56 0.62* 0.52 0.59*

*

0.58 0.67* 0.60* 0.69* 0.70*

0.49 0.48 0.62* 0.60* 0.53

0.37 0.60* 0.62* 0.69* 0.65 *

0.60* 0.59* 0.57 0.58* 0.77*

E1-N

E2-N

E3-N

E4-N

E5-N

0.71 0.60* 0.55 0.46 0.47

0.49 0.79* 0.66* 0.67* 0.57

0.48 0.55 0.59* 0.51 0.75*

0.41 0.60* 0.62* 0.63* 0.56

0.46 0.73* 0.59* 0.66* 0.67*

E1-N

E2-N

E3-N

E4-N

E5-N

0.62* 0.53 0.55 0.55 0.59*

0.38 0.71* 0.62* 0.62* 0.70*

0.41 0.46 0.58* 0.63* 0.53

0.49 0.56 0.63* 0.73* 0.65*

0.44 0.61* 0.58* 0.71* 0.74*

E1-N

E2-N

E3-N

E4-N

E5-N

*

*

0.46 0.49 0.74* 0.62* 0.56

0.51 0.58* 0.56 0.88* 0.59*

0.65* 0.67* 0.66* 0.67* 0.74*

(b) Coquimbo region E1-A E1-C E2-C E3. C E4-C E5-C

0.37 0.45 0.52 0.45 0.53

*

0.56 0.54 0.58* 0.61* 0.63*

0.45 0.60* 0.48 0.53 0.45

0.50 0.47 0.55 0.53 0.44

0.66 0.49 0.65* 0.61* 0.54

E1-A

E2-A

E3-A

E4-A

E5-A

0.55 0.55 0.48 0.44 0.48

0.51 0.61* 0.47 0.53 0.51

0.47 0.44 0.55 0.48 0.48

0.55 0.50 0.49 0.43 0.61*

0.59* 0.59* 0.52 0.54 0.60*

E1-C E2-C E3. C E4-C E5-C

(c) O′Higgins region

E1-O E2-O E3-O E4-O E5-O

E1-O E2-O E3-O E4-O E5-O

(d) Bío-Bío region

E1-BB E2-BB E3-BB E4-BB E5-BB

E1-A

E2-A

E3-A

E4-A

E5-A

0.54 0.47 0.58* 0.50 0.53

0.40 0.52 0.61* 0.58* 0.63*

0.50 0.54 0.49 0.64* 0.45

0.48 0.41 0.52 0.58* 0.44

0.48 0.53 0.52 0.59* 0.53

E2-A

E3-A

E4-A

E5-A

E1-BB E2-BB E3-BB E4-BB E5-BB

(e) Metropolitan region E1-A E1-M E2-M E3-M E4-M E5-M

0.48 0.48 0.39 0.51 0.61*

*

0.58 0.56 0.42 0.51 0.54

0.61 0.60* 0.57* 0.65* 0.67*

*

*

0.57 0.59* 0.58* 0.56 0.58*

0.56 0.56 0.64* 0.61* 0.61*

E1-M E2-M E3-M E4-M E5-M

0.70 0.65* 0.54 0.50 0.69*

0.65 0.90* 0.58* 0.65* 0.72*

Notes: (1) E1: mining & quarrying; E2: construction; E3: electricity, gas and water; E4: commerce; E5: industry. (2) A: Antofagasta, AT: Atacama, C: Coquimbo, O: O′Higgins, BB: Bío-Bío, M: Metropolitan, N: nationwide. (3) * indicates statistically significant at the 95% level. Critical values are determined according to Table 1A of the Appendix. Table 6 Co-movement with Coquimbo Region: 1986–2016.

E1-T E2-T E3-T E4-T E5-T

E1-C

E2-C

E3-C

E4-C

E5-C

0.59* 0.52 0.51 0.48 0.62*

0.63* 0.51 0.63* 0.55 0.58

0.48 0.63* 0.53 0.52 0.56

0.62* 0.66* 0.60* 0.64* 0.56

0.69* 0.62* 0.48 0.58* 0.68*

E1-C E1-O E2-O E3-O E4-O E5-O

*

0.61 0.58* 0.55 0.57 0.51

E1-AT E2-AT E3-AT E4-AT E5-AT

E1-C

E2-C

E3-C

E4-C

E5-C

0.67* 0.58* 0.42 0.45 0.58*

0.39 0.61* 0.52 0.54 0.47

0.63 0.50 0.47 0.57 0.49

0.48 0.62* 0.54 0.65* 0.63*

0.51 0.60* 0.46 0.49 0.53

E2-C

E3-C

E4-C

E5-C

0.45 0.57 0.58* 0.58* 0.50

0.50 0.56 0.54 0.53 0.64*

0.46 0.64* 0.66* 0.56 0.65*

0.46 0.76* 0.62* 0.62* 0.55

E1-BB E2-BB E3-BB E4-BB E5-BB

E1-M E2-M E3-M E4-M E5-M

E1-C

E2-C

E3-C

E4-C

E5-C

0.66* 0.60* 0.62* 0.41 0.52

0.51 0.63* 0.36 0.61* 0.64*

0.50 0.53 0.56 0.59* 0.60*

0.58* 0.66* 0.6* 0.65* 0.57

0.63* 0.66* 0.57 0.64* 0.65*

E1-C

E2-C

E3-C

E4-C

E5-C

0.52 0.54 0.43 0.48 0.53

0.48 0.70* 0.64* 0.63* 0.50

0.48 0.49 0.53 0.62* 0.47

0.36 0.65* 0.60* 0.63* 0.50

0.52 0.58* 0.55 0.63* 0.64*

Notes: (1) E1: mining & quarrying; E2: construction; E3: electricity, gas and water; E4: commerce; E5: industry. (2) T: Tarapacá, AT: Atacama, C: Coquimbo, O: O’Higgins, and BB: Bío -Bío. (3) * indicates statistically significant at the 95% level. Critical values are determined according to Table 1A of the Appendix.

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Fig. 8. Employment co-movement: 1988–2016. (a) Within Antofagasta Region. (b) Nationwide. (c) Between Tarapacá (I) and Antofagasta (II) Regions. (d) Between Coquimbo (IV) and Antofagasta (II) Regions. (e) Between Metropolitan (RM) and Antofagasta (II) Regions. (f) Between Coquimbo (IV) and Metropolitan (RM) Regions. Notes: (1) The time series represent rolling bivariate correlations of employment annual percent variations in a given pair of economic sectors (e.g., In Panel (a), Mining & quarrying/construction sectors of the Antofagasta Region.) (2) Rolling bivariate correlations are computed by taking 24 observations (i.e., 2 years) at a time, where each observation corresponds to the annual percent variation of a given employment series. (3) In each graph, the horizontal dashed lines are a 90%confidence interval. This is constructed under the assumption of a normally-distributed correlation coefficient, at least asymptotically.

Tarapacá and Antofagasta Regions was concordant with that of mining & quarrying nationwide Moreover, mining & quarrying employment in the Tarapacá Region exhibited statistically significant linkages with that of construction, commerce, and industry nationwide. Furthermore, estimation results of employment concordance of the Tarapacá, Atacama, Metropolitan, and O′Higgins Regions, with respect to the Coquimbo Region, showed that employment in some economic sectors of these four regions was in sync with that of mining &

deepening and further ore hardness, which in turn demand more electricity to crush harder mineral in large quantities. Second, concordance measures of employment during peaks and troughs and rolling correlations showed that co-movement of mining & quarrying with other economic sectors, within a given region and across regions, varies among mining producers such as the Tarapacá, Antofagasta, Atacama, Coquimbo, and O′Higgins Regions. In particular, during the period of 1986–2016 mining & quarrying employment in the 15

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Fig. 8. (continued)

On the other hand, estimation results based on rolling correlations show that employment linkage between the Antofagasta and Metropolitan Regions have weakened over time. Moreover, the mining & quarrying sector of Coquimbo has generally co-moved over time with industry and commerce of the Metropolitan Region, particularly during periods of booming copper prices. This is agreement with the results given by the employment-cycle concordance methodology.

quarrying of the Coquimbo Region during 1986–2016. Moreover, most concordance was present among sectors of the Tarapacá and Metropolitan Regions with respect to the Coquimbo Region. These findings complement Aroca and Atienza (2011)’s in that Coquimbo seems a more influential region than Antofagasta when it comes to employment co-movement. In this regard, Aroca and Atienza argue that Coquimbo is an attractive region due to its pleasant weather, low cost of living, and proximity to the Metropolitan Region. Appendix See Table 1A here.

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Table 1A Response surface for 1%, 5%, and 10% significance level of concordance statistic of series Xi and Xj. Source: McDermott and Scott (2000), page 11. C(p) = ⎡1 + exp ⎛−β1T−1/2 − β2 ⎢ ⎝ ⎣

μ 2 3 σ

−1

( ) − β ( ) ⎞⎠ ⎤⎦⎥ μ σ

+ε

Significance level

β1

β2

β3

10%

3.42 (1.16) 4.78 (0.96) 7.18 (1.35)

0.92 (0.41) 0.80 (0.35) 0.67 (0.51)

1.02 (0.60) 1.23 (0.54) 1.57 (0.89)

5% 1%

Notes: (1) C(p) is the p percentile. (2) Standard errors in parenthesis. (3) Δlog(Xi) and Δlog(Xj) are distributed as N(μ, σ2). If Xi and Xj grow at different rates, μ = min(μ1, μ2). (3) For instance, if T = 155 and μ/σ = 0.32, the 5% significance level is [1 + exp(−4.78/ (155)−1/2 − 0.8 × 0.32 − 123 × (0.32)2)]−1 = 0.68.

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