Sub-national economic effects of the resources sector in Chile

Valdes, Rodrigo Article Sub-national economic effects of the resources sector in Chile Journal of Applied Economics Provided in Cooperation with: University of CEMA, Buenos Aires Suggested Citation: Valdes, Rodrigo (2021) : Sub-national economic effects of the resources sector in Chile, Journal of Applied Economics, ISSN 1667-6726, Taylor & Francis, Abingdon, Vol. 24, Iss. 1, pp. 141-153, https://doi.org/10.1080/15140326.2021.1880243 This Version is available at: https://hdl.handle.net/10419/314121 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Journal of Applied Economics ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/recs20 Sub-national economic effects of the resources sector in Chile Rodrigo Valdes To cite this article: Rodrigo Valdes (2021) Sub-national economic effects of the resources sector in Chile, Journal of Applied Economics, 24:1, 141-153, DOI: 10.1080/15140326.2021.1880243 To link to this article: https://doi.org/10.1080/15140326.2021.1880243 © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. View supplementary material Published online: 21 Apr 2021. Submit your article to this journal Article views: 1752 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=recs20 ARTICLE Sub-national economic effects of the resources sector in Chile Rodrigo Valdes Escuela de Negocios y Economía, Facultad de Ciencias Económicas y Administrativas, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile ABSTRACT The significant contributions of resource sectors to national economies are well known; however, information on sub-national impacts is much scarcer. Using Chile as a case study, this paper investigates how disturbances in the price of copper impact the economies where this mineral is produced. The proposed method combines estimates of long-term copper prices with a general equilibrium model to simulate the differential effects of copper price variations. The results suggest that price variations affect the design and implementation of public and private policies, at economic and business levels. ARTICLE HISTORY Received 20 March 2019 Accepted 16 January 2021 KEYWORDS Prices; regional economies; resources sector; Chile 1. Introduction The copper sector differs from many other productive sectors in that it requires high amounts of initial investment compared to other businesses, its projects have long time horizons, and that it deals with an uncertain exhaustible resource (Marshall, Silva, & Meller, 2002). Large fluctuations in any of these variables affect mining performance, which can be transmitted to the rest of the economy through, e.g.,, tax collection, the exchange rate, and return on investment (Lagos, 1997). As a result, price fluctuations in natural resources and their effects on the economic performance of resource-rich countries have attracted significant academic interest in the field of resource economics (see Baffes & Savescu, 2014; Irwin, Sanders, & Merrin, 2009; Ruehle & Kulkarni, 2011; Tilton & Lagos, 2007). While previous studies have analyzed copper price variability, demand estimates, and production linkages, very few articles have discussed mining sectors’ regional interrelationships – let alone price fluctuation effects on domestic, regional production economies in developing countries. Historically, Chile’s economic and social development has been tied to production and exports from copper mining. Particularly, the income generated in private and public sectors places Chile as the largest copper producer in the world (USGS, 2018). In 2017, exports of this mineral represented 59% of the total Chilean exports (Chilean Central Bank, 2018); from 1980 to 2013, mining activity accounted for 17.9% of the fiscal revenue and 27% of the total foreign investment and over the last 15 years, its share of national GDP averaged 8%−11%. CONTACT Rodrigo Valdes [email protected] Escuela de Negocios y Economía, Facultad de Ciencias Económicas y Administrativas, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2830, Piso 7, Valparaíso, Chile Supplemental material for this article can be accessed here. JOURNAL OF APPLIED ECONOMICS 2021, VOL. 24, NO. 1, 141–153 https://doi.org/10.1080/15140326.2021.1880243 © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In the above context, this paper examines the impact of copper price volatility at the sub-national level, analyzing Chile’s economic growth as a relationship between the mining sector and regional economies. The paper is organized as follows: Section two presents existing forecasting and long-term adjustment methods for international 0 50 100 150 200 250 300 350 400 450 1936 1938 1940 1942 1944 1946 1948 1950 1952 1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 Price (USD cents/lb.) Year 280 290 300 310 320 330 340 350 2018 2022 2024 2026 2028 2030 2032 2034 2035 2038 Price (USD cents/lb. Year Positive Shock Price (USD cents/lb.) Negative Shock Price (USD cents/lb.) 142 R. VALDES commodity prices; Section three describes copper price formation mechanisms among international markets; Section four reviews the importance of copper mining in Chile; Sections five and six show the estimation strategy; and Section seven presents the results and discussions. The final section concludes. JOURNAL OF APPLIED ECONOMICS 143 2. International price of commodities: forecasting and long-term adjustments Copper price forecasting directly influences decisions in public and private mining sectors. Therefore, its methods directly affect tax revenues depending on the type of shock, shortor long-term prices, and the duration of adjustments (Ruehle & Kulkarni, 2011). Different price projection approaches have been developed in the copper sector, in general or otherwise. For example, Issler et al. (2014) combined forecast techniques to predict monthly and quarterly commodity prices using the 1965 to 2008 IMF International Financial Statistics (IFS) database; Gargano and Timmermann (2014) subjected datasets of financial and macroeconomic predictors – e.g., industrial production, unemployment, inflation, the Australian Dollar, the Indian Rupee, and open interest in futures markets – to univariate and multivariate regressions to predict returns on CRB commodity sub-indices (industrials, metals, fats/oils, foods, textiles, etc.); Chen, Rogoff, and Rossi (2010) forecasted quarterly changes in commodity price indexes using mixed-frequency data (daily exchange rates and equity data from 1984 to 2010), finding that equity price indexes can have a predictive power similar to exchange rates; and – similarly – Rapach et al. (2013) found that information on U.S. stock prices broadly forecast stock returns in other international markets. In spite of the above advances, there has been limited development of methodological approaches to study effects of price variations on domestic and international economies. However, some sectoral studies do exist. For example, Marshall et al. (2002) – in their analysis of long-term price as a function of copper consumption growth rate, future production, and profitability established for mining projects – found evidence of heterogeneous effects at the domestic level and demonstrated that long-term variations have a direct influence on the investment strategies of copper companies; Cuddington and Jerrett (2008) used band-pass filters to extract particular cyclical components from copper price series data, and found that high and low price rally cycles may inform public budgetary adjustments; and finally, Tang (2012) proposed a reduced-form model of the stochastic long-run mean as a separate factor in order to explore the mean reversion of copper price, to generate long-term budgets for public and private agents, and to inform investment decisions. Unlike the methodological proposal of this article, however, most of the above pricing models lack non-structural explanatory variables (i.e., investment, production capacity, inventories and demand determinants) for the effects of copper price volatility over a longer period of time. My approach innovates first, by incorporating long-term trends and short-term volatility to control for asymmetric responses to positive and negative shocks in copper prices and second, by implementing general equilibrium models to analyze sectoral interrelationships of the mining sector and the effects of price fluctuations on the economy. 3. The formation of copper prices As depicted in Figure 1, international copper prices have experienced a general downward trend over time, with clearly distinguishable periods. From 1936 to 1970s, strong price fluctuations were attributable to the Great Depression and the oil crisis; in the early 144 R. VALDES 1970s, prices rose sharply and then began a steady fall to their historic lows in the early 2000s. All these variations show changes in the average prices of each cycle, and therefore influence the long-term price. Scholars have tried diverse approaches to gain further insight into this process of copper price formation. Of these, Wets and Rios (2015) blended short-term and longterm processes into a multidimensional model using several indexes. These authors examined various channels of copper price formation, and discerned some economic interdependence. The analysis in this paper builds on that approach, including asymmetric responses to positive and negative shocks on long-term copper prices, and provides novel insight into the economic implications for local economies. Indeed, to this last point, although it is widely accepted that resource sectors make a significant contribution to national and state economies, information about how they impact regional economies is much scarcer. In addressing this gap, this paper pioneers the analysis of regional effects by exploring how copper price fluctuations and sources affect the economies of the major Chilean copper-producing regions. 4. The Chilean copper mining sector Copper mining is the most economically significant productive sector in Chile: between 2008 and 2018, it was responsible for 59.1% of the total exports, 18.1% of share of total investment, and from US$39.1 to US$51.1 billion in net public revenues (Corporacion Chilena del Cobre (COCHILCO), 2018). Table 1 presents Chilean copper mining value added in terms of exports, direct foreign investment, and revenue shares. In terms of regional importance, mining cycles have had an enormous impact on the evolution, localization, and industry changes of economic activity in Chile. Because the copper cycle is geographically dispersed and capital-intensive (Badia-Miró & Yañez, 2015), multinational corporations dominate activities in the primary mining regions of Tarapaca (Region I), Antofagasta (Region II), Atacama (Region III), Coquimbo (Region IV), Valparaiso (Region V), and Libertador General Bernardo O’Higgins (Region VI). In 2016, large-scale operations – conducted by 19 private companies and the National Copper Corporation of Chile (hereafter, CODELCO) – accounted for about 6.5 million metric tons (Mt), or 95.2% of the total; medium-scale, by 22 private companies, 5% to 6% of the annual copper production; and sundry small-scale operations, for about 2% (Corporacion Chilena del Cobre (COCHILCO), 2018). Antofagasta, the leading copperproducing region, has accounted for between 53% and 65% of the national production every year since at least 2004 (National Geology and Mining Service, 2017; hereafter, SERNAGEOMIN). In 2018, the top copper-producing companies accounted for 77% of the total production: Minera Escondida Ltda. – which holds the leading private copper mine, Escondida – produced 1.1 million Mt of copper, or 19.2% of the national total; and the subsequent eight, 59%, divided between public endeavors (Chuquicamata, Andina Division, El Teniente, and Radomiro Tomic) and private companies (Los Bronces, Los Pelambres, Candelaria, and Collahuasi) (Corporacion Chilena del Cobre (COCHILCO), 2018). JOURNAL OF APPLIED ECONOMICS 145 5. Methodology To approach the complexity of these interrelations, and considering market mechanisms and price volatility, I propose a two-stage method. The first step is to forecast long-term copper prices and compare them with those forecasted by the Chilean Public Expert Committee. Briefly, prices published by the Public Expert Committee – created to guarantee an independent structural balance process and to assess medium-term projections in the public structural budget – are estimated by consulting rounds with a panel of 16 experts, each of whom are asked for average price estimates over the next 10 years (excluding the lowest and highest estimates). This study, in contrast, simulates variations in long-term prices with the Wets and Rios approach (Wets & Rios, 2015) over a 20-year horizon for each year in question. Indeed, the Wets and Rios approach for estimating long-term copper prices was chosen for this study because it, first, explicitly differentiates shortand long-term regimes; second, includes market information in the price estimations framework; and third, may be extended to incorporate any number of indexes. Generally, this process presents a reversion to an unobservable long-term marginal cost that follows a trend, notwithstanding random fluctuations in both level and time (1). The specification for i = 1. . .., n is as follows: yt i¼vi1euit ð Þþy0 iexp uiþ1 2X J j¼1 b2 ij !ðtt0Þþ X J j¼1 bij wt jwt0 j � � !" # (1) and at t0¼0, yt i¼vi1euit ð Þþyt0 iexp uiþ1 2P J j¼1 b2 ij !tþP J j¼1 bij wt j � � ! : 2 43 5(2) where t and t0 are the present and future periods, respectively; y0 i is the present value of index i (given); ui and bij are constants that need to be estimated; yt¼yt iyt n  �is the state of the system at time t; wj, j= 1, . . . , J are independent (standard) Wiener processes; viis an index to which yt ireverts in the long term; and uiis the “speed” at which yt ireverts to vi. Next, in the second of the two stages, the study implements a general equilibrium model to simulate positive and negative differential effects of copper price variations (10% increase and decrease, respectively) and their effects at sub-national level. Adapted from the OECD model (Beghin, Dessus, Roland-Holst, & van der Mensbrugghe, 1996), the base model represents Chile in 74 economic sectors, uses capital and labor as primary productive factors, and takes assumptions of capital as putty or semi-putty, full availability of labor (which grows at an exogenous rate for the period of analysis), and full availability of mining reserves. The model specifications include labor and incomegroups differentiation, trade partners, and specified productive factors (O’Ryan, de Miguel, & Miller, 2003). The dynamic structure of the model allows for the inclusion of adjustment costs when capital is released from one sector to another. Exogenous costs are given by disinvestment and related elasticities, obtained from the international literature. Investment in each sector – mainly represented as relative returns between installed capital and capital to be installed – may take intersectoral capital as immobile or 146 R. VALDES fully mobile, as depends on longor short-term vision. The closing equation of the model – a balance of payments considering net exports, foreign savings, factor payments, and transfers to and from households and government – ensures adherence to Walras’ law. The model framework takes as main indices: productive sectors or activities (i,j); types of work, or occupational categories (l); household income groups, expressed in quintiles (h); public spending categories (g); final demand spending categories (f); trade partners (r); and different types of pollutants (p). The production structure is modeled by nested functions for constant elasticity of substitution and constant elasticity of transformation (CES/CET). If constant returns to scale and minimized costs are assumed, each sector produces: min PKELiKELiþPNDiNDi(3) s.t. XPi¼αKELiKELσp i iþαNDiNDσp i i h i1=σp i(4) where KEL is a composite good of capital, energy, and labor; PKEL is the price of KEL; ND is a composite good of no-energy intermediate inputs; PND is the price of ND; XP is total ouput; α is the share of input/factor use; and σ is the substitution elasticity. Households use their income for consumption and savings, modeled by an Extended Linear Expenditure System (ELES) utility function which also incorporates minimum subsistence consumption independently from the level of income. This is given by: max U ¼X n i¼1 piln Ciθi ð Þþpsln S CPI � �;(5) subject to X n i¼1 PCiCiiþS¼YD (6) and X n i¼1 piþps¼1 (7) where U stands for consumer utility; C i is the consumption of good i; θiis subsistence consumption; S, savings; CPI, the price of savings; and p, the marginal propensity to consume each good or to save. Finally, the different types of taxes and transfers in public finances are directly defined in the model as taxes on labor (differentiated by occupational category), firms, and income (differentiated by quintile). In order to analyze the impact of copper price variations on regional economies, the paper proposes a social accounting matrix (SAM). This SAM includes only the copperproducing regions of Chile, and will serve as the basis for adapting the previously described general equilibrium model to these regions. This matrix departs from the methodological approaches developed by Riffo, Becerra, Acevedo, Morgado, and JOURNAL OF APPLIED ECONOMICS 147