Trade in Natural Resources in the Interregional Input-Output System for Chile International Workshop on General Equilibrium Modeling, Universidad Adolfo Ibañez Viña del Mar, December 3-4, 2018 Eduardo Haddad Keyi Ussami Raphael Fernandes
Research team NEREUS Eduardo Amaral Haddad (coordinator) Ademir Antônio Moreira Rocha Bruno Proença Pacheco Pimenta Denise Leyi Li Karina Simone Sass Keyi Ando Ussami Lucas Cardoso Correa Dias Raphael Pinto Fernandes Sofia Marques Arantes Department of Economics, University of Sao Paulo 2
Outline 1. Introduction 2. Database 3. Measurement of Value Added in Exports 4. Results 5. Next Steps Department of Economics, University of Sao Paulo 3
Introduction Research on natural resources accounting related to international trade flows has boosted in the last few years with the development of worldwide input-output systems and the stronger concern with the future of resources availability in the context of global climate change. Accountability of the pressure on the use of the world s natural resources has reached the political debate, as attempts to characterize countries according to their historical, current and expected role played in this process has reopened political fissures (Victor et al., 2014). Department of Economics, University of Sao Paulo 4
Introduction Similarly to nations, regions within countries can also be characterized by their pressure on the demand for natural resources. As shown by Hoekstra and Chapagain (2008), local water depletion is often closely tied to the structure of the global economy. For regions within a country, the national economy adds another layer to the relevant structural hierarchy to understand resources uses. Department of Economics, University of Sao Paulo 5
Introduction This analysis reports on the results of an application with an interregional input-output matrix for Chile, developed by Haddad et al. (2018). We estimate, for each flow originated in one of the Chilean regions, measures of trade in value added, water and carbon emissions that are further used to calculate ours indexes. The parsimonious approach proposed in Los et al. (2016), based on hypothetical extraction, serves as the methodological anchor. Department of Economics, University of Sao Paulo 6
Database: Chilean interregional input-output system, 2014 Department of Economics, University of Sao Paulo 7
List of sectors S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 Agropecuario-silvícola y Pesca Minería Industria manufacturera Electricidad, gas, agua y gestión de desechos Construcción Comercio, hoteles y restaurantes Transporte, comunicaciones y servicios de información Intermediación financiera Servicios inmobiliarios y de vivienda Servicios empresariales Servicios personales Administración pública Highly agreggated in natural resources intensive industries Department of Economics, University of Sao Paulo 8
List of regions R1 XV De Arica y Parinacota R2 I De Tarapacá R3 II De Antofagasta R4 III De Atacama R5 IV De Coquimbo R6 V De Valparaíso R7 RMS Región Metropolitana de Santiago R8 VI R9 VII Del Maule R10 VIII Del Biobío Del Libertador General Bernardo O'Higgins R11 IX De La Araucanía R12 XIV De Los Ríos R13 X De Los Lagos R14 XI Aysén del General Carlos Ibáñez del Campo R15 XII De Magallanes y de la Antártica Chilena Department of Economics, University of Sao Paulo 9
Database: water and CO 2 Source: Eora 2014: a Global Multi-Region Input-Output Database Lenzen et al. (2012; 2013). (75 industries) Water (WFN - Water Footprint Network): Total (Green, Blue and Gray) water used in Mm 3 /year, Department of Economics, University of Sao Paulo 10
Database: green, blue, gray and total water Green water Evapotranspiration: 70 Lake, river, reservoir Rain: 100 Water withdrawal: 100 Evaporation: 10 Surface run-off: 10 Soil: 10 Blue water Evaporation: 10 Irrigation: 100 Soil and Surface run-off: 20 Blue water Evapotranspiration: 70 Lake, river, reservoir Gray water Pollutants: 1 Required amount of freshwater to assimilate and keep quality standards: 100 Department of Economics, University of Sao Paulo 11
Database: more about water Doublecounting? Water footprint (Mm³/yr) % Water footprint (m³/yr/hab) Green 32,492 57.7% 1,870 Blue 9,966 17.7% 574 Grey 13,862 24.6% 798 Total 56,320 100.0% 3,242 Chile s average per person: 1,600 m³/yr/hab (source: water footprint calculator) Department of Economics, University of Sao Paulo 12
Database: water and CO 2 Source: Eora 2014: a Global Multi-Region Input-Output Database Lenzen et al. (2012; 2013). (75 industries) Water (WFN - Water Footprint Network): Green, Blue, Gray and Total water used in Mm 3 /year, Emissions (EDGAR - Emissions Database for Global Atmospheric Research): CO 2 emissions in Gigagrams (Gg/year) from fossil fuel use and energy production, excluding biomass burning. Caveat: national coefficients applied to regions. Department of Economics, University of Sao Paulo 13
Water availability Superficial: Critical regions regarding water availability Chile s average: ~54,000 m³/person/year International average: 6,600 m³/person/year Sustainable development: 2,000 m³/person/year Groundwater: Overexploited aquifers mainly in northern and central Chile Source: World Bank, 2011 Department of Economics, University of Sao Paulo 14
1 - Agropecuario-silvícola y Pesca 2 - Minería 3 - Industria manufacturera 4 - Electricidad, gas, agua y gestión de desechos 5 - Construcción 6 - Comercio, hoteles y restaurantes 7 - Transporte, comunicaciones y servicios de información 8 - Intermediación financiera 9 - Servicios inmobiliarios y de vivienda 10 - Servicios empresariales 11 - Servicios personales 12 - Administración pública l/peso Total water multiplier 4,000 3,5000 3,000 Green-direct Blue-direct Gray-direct Green-Indirect Blue-indirect Gray-indirect 2,5000 2,000 1,5000 1,000,5000,000 Department of Economics, University of Sao Paulo 15
1 - Agropecuario-silvícola y Pesca 2 - Minería 3 - Industria manufacturera 4 - Electricidad, gas, agua y gestión de desechos 5 - Construcción 6 - Comercio, hoteles y restaurantes 7 - Transporte, comunicaciones y servicios de información 8 - Intermediación financiera 9 - Servicios inmobiliarios y de vivienda 10 - Servicios empresariales 11 - Servicios personales 12 - Administración pública g CO 2 /peso CO 2 multiplier 4,000 3,5000 3,000 2,5000 2,000 1,5000 1,000,5000,000 Direct Indirect Department of Economics, University of Sao Paulo 16
RoW Trade RoW 1 RoW 2 3 Antofagasta 4 5 6 7 8 RMS 9 10 11 12 13 14 Trade Balance (billions CLP 2014) > 2,000 1,000-2,000 0-1,000 (-1,000) - 0 (-2,000) - (-1,000) (-2,000) 15 La Araucanía
Measurement of value added in trade (to other regions) Input output model: N different regions and RoW: x = ax + f x = (I-A) -1 f = Lf VA 1 = v 1 (I-A) -1 fi VA coefficients Department of Economics, University of Sao Paulo 18
Measurement of value added in trade (to other regions) Input output model: Hypothetical world where 1 does not trade with n (Los et al, 2016) x = ax + f 0 0 x = (I-A) -1 f = Lf 0 0 0 VA* 1,n = v 1 (I-A* 1,n ) -1 f* 1,n i VA 1,n = VA 1 - VA* 1,n Department of Economics, University of Sao Paulo 19
Measurement of value added in trade (to other regions) VA in exports to RoW: VA 1,RoW = v 1 (I-A) -1 f RoW i VA in imports from RoW: RoW has Chile s economic structure; region n from Chile imports from region n in RoW; as if the imports from RoW in Chile where produced inside the region Department of Economics, University of Sao Paulo 20
Measurement of water/co 2 in trade (to other regions) Water / CO 2 used in region 1 Water / CO 2 used in region 1 in the hypothetical world VA 1 = v 1 (I-A) -1 fi VA* 1,n = v 1 (I-A* 1,n ) -1 f* 1,n i VA 1,n = VA 1 - VA* 1,n v1: Water / CO 2 coefficients Water / CO 2 embedded in the trade flux Department of Economics, University of Sao Paulo 21
Virtual water trade balance Chile imports water from RoW Trade balance Regions that imports more water than exports Department of Economics, University of Sao Paulo 22
CO 2 trade balance Trade balance Department of Economics, University of Sao Paulo 23
Methodology Share of value added content in exports from R1 to R2 in total value added traded Department of Economics, University of Sao Paulo 24
Methodology Share of water content in exports from R1 to R2 in total water traded Department of Economics, University of Sao Paulo 25
Methodology Location quotient of traded water to value added Department of Economics, University of Sao Paulo 26
Methodology The proposed index, TWI, can be compared to similar metrics related to other natural resources. Economic activity demand different scarce resources whose availability varies across regions within a country. Similarly, we can calculate a Trade-Based Index of CO 2 Emissions based on DVA and DCO 2. Department of Economics, University of Sao Paulo 27
Methodology The TWI and the Trade-Based Index of CO 2 emissions (TEI) can be interpreted as (a) if greater than 1, exports from the region use more intensively water resources compared to its contribution to value added creation; and (b) if lower than 1, the opposite. Department of Economics, University of Sao Paulo 28
Trade-based index of Water Intensity (TWI) Total Water Index O / D R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 ROW R1-1.72 2.21 0.90 1.10 3.99 2.54 2.69 1.77 2.20 0.88 1.01 1.08 0.69 1.06 2.15 R2 0.52-0.57 0.51 0.47 0.85 0.71 0.65 0.49 0.52 0.31 0.30 0.33 0.36 0.35 0.26 R3 0.26 0.27-0.28 0.17 0.15 0.14 0.16 0.11 0.12 0.10 0.09 0.10 0.19 0.15 0.14 R4 0.65 0.84 0.93-0.42 0.93 0.59 0.88 0.42 0.51 0.23 0.25 0.29 0.34 0.57 0.37 R5 1.16 1.68 1.91 0.95-3.38 2.61 2.70 1.82 2.03 0.90 0.98 1.03 0.56 0.96 0.83 R6 0.63 0.73 0.89 0.63 0.66-1.15 1.23 0.83 0.94 0.50 0.49 0.49 0.46 0.41 0.95 R7 0.19 0.24 0.27 0.26 0.24 0.40-0.36 0.21 0.21 0.15 0.13 0.13 0.16 0.12 0.42 R8 1.32 1.96 2.45 1.22 1.52 4.23 3.27-2.46 2.76 1.30 1.44 1.44 0.76 1.15 1.74 R9 1.42 1.74 2.26 1.34 1.49 4.28 3.00 3.63-3.03 1.41 1.62 1.56 0.92 1.27 3.25 R10 1.07 1.23 1.51 1.17 1.18 2.57 1.84 2.19 1.63-1.07 1.11 1.06 0.89 0.85 2.01 R11 1.43 2.12 2.94 1.39 1.72 4.69 3.48 4.31 2.78 3.23-1.71 1.66 0.89 1.26 3.56 R12 1.88 2.48 3.05 1.74 2.01 4.87 3.63 4.35 3.14 3.57 1.92-2.18 1.34 1.86 3.34 R13 2.55 3.33 3.98 2.20 2.66 5.88 4.68 5.36 4.11 4.56 2.59 2.99-1.70 2.62 3.99 R14 6.67 7.52 7.87 6.25 6.82 8.31 7.98 8.20 7.67 7.88 6.49 6.88 6.97-6.84 6.90 R15 1.35 1.63 1.80 1.37 1.32 2.54 1.87 2.23 1.56 1.74 1.03 1.09 1.15 1.12-1.14 R1 - De Arica y Parinacota; R2 - De Tarapacá; R3 - De Antofagasta; R4 - De Atacama; R5 -De Coquimbo; R6 - De Valparaíso; R7 - Región Metropolitana de Santiago; R8 - Del Libertador General Bernardo O'Higgins; R9 - Del Maule; R10 - Del Biobío; R11 - De La Araucanía; R12 - De Los Ríos; R13 - De Los Lagos; R14 - Aysén del General Carlos Ibáñez del Campo; R15 - De Magallanes y de la Antártica Chilena; RoW - Rest of the World. Department of Economics, University of Sao Paulo 29
Trade-based Index of CO 2 Emissions (TEI) CO 2 Emissions Index O / D R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 ROW R1-1.21 1.27 1.49 1.48 0.71 1.05 1.33 1.24 1.12 1.38 1.33 1.26 1.19 1.08 1.27 R2 0.71-0.85 0.94 0.96 0.63 0.80 0.76 0.68 0.63 0.71 0.62 0.64 0.77 0.61 0.61 R3 1.09 1.44-1.08 0.96 0.68 0.75 0.79 0.64 0.63 0.66 0.60 0.64 0.92 0.84 0.68 R4 1.65 2.64 2.19-1.56 0.84 1.01 1.15 0.63 0.62 0.76 0.59 0.73 1.34 1.30 0.90 R5 0.49 0.83 0.76 0.68-0.48 0.65 0.66 0.54 0.50 0.62 0.52 0.52 0.56 0.46 0.57 R6 1.18 1.66 1.68 1.48 1.76-1.58 1.66 1.29 1.20 1.37 1.14 1.13 1.24 0.98 1.38 R7 0.51 0.74 0.76 0.81 0.81 0.63-0.86 0.62 0.56 0.60 0.52 0.52 0.57 0.46 1.01 R8 0.71 1.07 1.06 0.97 1.15 0.63 0.89-0.70 0.64 0.81 0.63 0.65 0.78 0.53 0.76 R9 2.18 3.32 3.05 1.71 3.08 1.55 2.31 2.18-1.21 1.95 1.19 1.52 2.06 1.62 1.84 R10 2.09 3.01 2.91 1.83 2.79 1.90 2.42 2.41 1.60-2.02 1.41 1.64 1.99 1.63 2.14 R11 0.66 0.93 1.02 0.98 1.08 0.60 0.83 0.85 0.73 0.68-0.68 0.66 0.76 0.49 1.13 R12 1.28 1.63 1.61 1.53 1.70 0.96 1.29 1.24 1.10 1.03 1.29-1.08 1.38 0.94 1.47 R13 1.07 1.26 1.23 1.37 1.40 0.74 1.00 0.94 0.92 0.85 1.09 0.90-1.21 0.78 1.26 R14 0.33 0.36 0.35 0.47 0.42 0.29 0.32 0.32 0.33 0.31 0.39 0.35 0.35-0.33 0.45 R15 1.25 1.48 1.44 1.48 1.44 1.01 1.20 1.20 1.06 1.02 1.09 0.96 1.00 1.32-1.13 R1 - De Arica y Parinacota; R2 - De Tarapacá; R3 - De Antofagasta; R4 - De Atacama; R5 -De Coquimbo; R6 - De Valparaíso; R7 - Región Metropolitana de Santiago; R8 - Del Libertador General Bernardo O'Higgins; R9 - Del Maule; R10 - Del Biobío; R11 - De La Araucanía; R12 - De Los Ríos; R13 - De Los Lagos; R14 - Aysén del General Carlos Ibáñez del Campo; R15 - De Magallanes y de la Antártica Chilena; RoW - Rest of the World. Department of Economics, University of Sao Paulo 30
Trade-based Indices of Natural Resources Intensity: Water versus CO 2 Emissions Trade-Based Index of Natural Resources Intensity Department of Economics, University of Sao Paulo 31
Next steps (room for collaboration) Interregionalization of the coefficients calculated in this work Critical view of EORA database VA, Water and CO 2 emission openly by regions; Department of Economics, University of Sao Paulo 32
keyi.ussami@usp.br www.usp.br/nereus Department of Economics, University of Sao Paulo 33
Annex Department of Economics, University of Sao Paulo 34
Trade in value added (billion CLP) O / D R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 ROW R1-45.01 92.75 11.69 10.49 12.39 69.75 6.15 6.74 18.09 8.02 4.45 13.03 3.41 4.50 193.72 R2 23.10-173.03 18.10 18.23 25.78 125.75 12.59 13.19 40.66 16.00 9.62 26.95 7.15 8.39 1723.20 R3 65.37 200.42-136.18 205.95 292.51 1645.90 119.50 155.12 424.31 190.46 103.20 261.72 37.05 36.45 9619.41 R4 10.07 34.75 191.94-74.35 79.94 481.84 27.71 40.02 101.04 48.46 24.16 63.02 8.96 8.59 1699.38 R5 7.69 23.28 119.04 31.63-107.50 465.71 36.33 31.79 78.76 26.67 14.80 41.47 8.64 10.63 1718.45 R6 16.35 53.74 201.29 64.33 151.26-3422.54 162.23 121.01 219.32 85.23 40.70 116.70 28.00 27.67 3189.16 R7 203.31 517.59 2071.10 612.37 1481.66 5429.55-2656.32 1902.61 3257.42 1237.55 597.94 1626.57 346.78 343.60 11900.49 R8 7.42 21.92 83.79 21.05 45.88 216.79 1851.41-108.23 160.09 45.49 21.80 60.38 12.45 14.31 2559.88 R9 7.89 29.09 97.51 21.18 47.36 133.04 969.95 102.83-248.11 68.33 26.70 75.71 15.17 15.36 900.61 R10 28.81 101.07 321.25 84.43 142.21 239.90 1781.89 150.87 274.82-362.90 119.02 324.68 68.61 57.38 2150.26 R11 6.10 18.23 56.48 14.69 22.40 57.76 322.77 28.18 46.75 215.53-62.79 135.18 16.64 17.93 501.96 R12 3.88 12.64 42.16 10.74 15.17 34.80 198.95 16.98 23.66 98.03 71.86-128.29 12.32 10.28 382.54 R13 10.33 35.22 121.35 27.27 38.06 107.17 540.19 48.94 60.52 246.77 131.14 115.02-39.59 30.02 990.62 R14 2.85 12.86 47.47 4.38 7.59 47.83 178.65 17.99 15.46 61.77 13.90 10.48 40.77-22.93 289.67 R15 3.03 10.08 31.40 7.55 8.91 14.10 86.33 6.59 8.57 28.86 14.29 8.07 25.11 20.52-408.71 ROW 247.46 683.11 2,529.65 660.65 1,005.16 3,387.56 14,971.56 1,869.07 1,503.78 3,420.35 932.65 556.43 1,455.46 412.95 406.42 R1 - De Arica y Parinacota; R2 - De Tarapacá; R3 - De Antofagasta; R4 - De Atacama; R5 -De Coquimbo; R6 - De Valparaíso; R7 - Región Metropolitana de Santiago; R8 - Del Libertador General Bernardo O'Higgins; R9 - Del Maule; R10 - Del Biobío; R11 - De La Araucanía; R12 - De Los Ríos; R13 - De Los Lagos; R14 - Aysén del General Carlos Ibáñez del Campo; R15 - De Magallanes y de la Antártica Chilena; RoW - Rest of the World. Department of Economics, University of Sao Paulo 35
Trade in total water use (Mm³) O / D R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 ROW R1-55.55 146.97 7.55 8.26 35.43 126.85 11.86 8.54 28.59 5.05 3.22 10.06 1.70 3.41 298.52 R2 8.64-71.09 6.66 6.17 15.75 64.31 5.85 4.63 15.07 3.61 2.09 6.34 1.84 2.08 322.97 R3 12.14 39.08-27.17 25.40 31.92 166.02 13.96 12.70 36.50 13.38 6.96 19.13 4.96 4.01 990.61 R4 4.68 20.86 128.51-22.43 53.52 203.65 17.50 12.06 36.75 7.82 4.34 13.25 2.19 3.54 446.42 R5 6.38 28.13 163.20 21.63-260.33 870.51 70.35 41.52 114.94 17.20 10.45 30.76 3.46 7.31 1,022.41 R6 7.43 28.18 128.33 29.23 71.83-2,812.89 143.52 72.12 148.48 30.74 14.17 40.73 9.20 8.23 2,178.90 R7 27.11 89.95 399.69 116.24 250.64 1,556.99-678.16 293.14 502.30 130.28 55.36 153.53 40.60 29.19 3,556.48 R8 7.02 30.74 147.32 18.43 49.93 657.98 4,342.94-191.28 317.21 42.57 22.51 62.48 6.78 11.85 3,197.67 R9 8.05 36.37 158.17 20.30 50.71 407.99 2,089.23 267.69-539.10 68.93 30.93 84.71 10.01 14.03 2,101.48 R10 22.16 88.85 348.88 70.56 120.83 442.48 2,346.84 237.04 321.74-277.65 94.76 247.26 43.75 34.98 3,105.74 R11 6.27 27.78 119.18 14.60 27.57 194.30 805.44 87.05 93.13 499.68-76.93 161.01 10.65 16.26 1,281.21 R12 5.23 22.47 92.37 13.42 21.92 121.63 517.93 52.95 53.32 251.00 98.72-200.17 11.80 13.73 916.36 R13 18.87 84.13 346.20 42.95 72.55 451.86 1,811.89 188.25 178.41 807.63 243.62 246.26-48.26 56.38 2,834.90 R14 13.62 69.38 268.10 19.65 37.13 284.93 1,022.05 105.84 85.07 349.05 64.69 51.72 203.93-112.51 1,433.87 R15 2.93 11.75 40.50 7.40 8.46 25.73 115.68 10.55 9.57 35.93 10.60 6.31 20.70 16.43-334.29 ROW 431.19 451.92 835.05 620.68 1,563.77 3,098.05 6,753.38 4,090.12 2,826.79 4,015.40 1,860.44 1,177.78 3,623.16 1,810.08 430.56 R1 - De Arica y Parinacota; R2 - De Tarapacá; R3 - De Antofagasta; R4 - De Atacama; R5 -De Coquimbo; R6 - De Valparaíso; R7 - Región Metropolitana de Santiago; R8 - Del Libertador General Bernardo O'Higgins; R9 - Del Maule; R10 - Del Biobío; R11 - De La Araucanía; R12 - De Los Ríos; R13 - De Los Lagos; R14 - Aysén del General Carlos Ibáñez del Campo; R15 - De Magallanes y de la Antártica Chilena; RoW - Rest of the World. Department of Economics, University of Sao Paulo 36
Trade in CO 2 (Gg) O / D R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 ROW R1-39.70 85.53 12.67 11.31 6.38 53.18 5.94 6.09 14.80 8.04 4.30 11.99 2.96 3.53 179.61 R2 11.99-107.42 12.34 12.74 11.81 73.09 6.95 6.50 18.59 8.29 4.36 12.55 3.99 3.72 764.99 R3 51.99 209.61-106.72 144.56 145.40 898.38 68.47 71.90 193.79 90.91 45.37 122.58 24.87 22.17 4,782.87 R4 12.09 66.84 305.76-84.55 48.88 355.26 23.14 18.42 45.71 26.76 10.46 33.37 8.75 8.10 1,117.75 R5 2.77 14.02 66.17 15.70-37.33 221.48 17.45 12.52 28.43 12.12 5.56 15.81 3.55 3.54 713.74 R6 13.99 64.97 246.78 69.28 193.50-3,927.93 195.88 113.93 192.36 84.79 33.67 95.80 25.32 19.70 3,208.23 R7 76.15 280.14 1,153.50 362.35 877.88 2,505.03-1,665.08 862.70 1,336.66 539.42 227.24 611.76 144.30 115.45 8,737.27 R8 3.86 17.06 64.53 14.86 38.44 99.43 1,197.91-55.03 75.05 26.74 10.06 28.56 7.03 5.55 1,423.98 R9 12.50 70.32 216.69 26.40 106.27 150.54 1,633.66 163.19-219.03 97.25 23.23 84.04 22.75 18.07 1,205.10 R10 43.89 221.44 680.51 112.32 289.07 331.97 3,135.66 264.62 319.64-533.43 122.08 387.70 99.21 67.90 3,345.94 R11 2.93 12.40 41.97 10.53 17.66 25.33 194.19 17.43 24.71 106.64-30.98 64.80 9.17 6.40 412.85 R12 3.61 15.03 49.49 11.98 18.74 24.31 187.39 15.34 18.89 73.56 67.32-100.60 12.42 7.06 409.35 R13 8.02 32.23 108.90 27.30 38.83 57.42 393.47 33.46 40.33 152.04 104.46 75.62-34.83 17.12 908.50 R14 0.69 3.37 12.01 1.49 2.34 10.22 41.57 4.16 3.69 14.14 3.93 2.70 10.38-5.43 95.40 R15 2.76 10.85 33.03 8.12 9.36 10.38 75.20 5.78 6.59 21.39 11.33 5.63 18.26 19.77-334.80 ROW 234.98 597.27 2,179.30 660.75 832.13 3,909.74 12,955.53 1,509.47 1,837.15 4,357.19 937.44 630.80 1,457.50 211.57 403.76 R1 - De Arica y Parinacota; R2 - De Tarapacá; R3 - De Antofagasta; R4 - De Atacama; R5 -De Coquimbo; R6 - De Valparaíso; R7 - Región Metropolitana de Santiago; R8 - Del Libertador General Bernardo O'Higgins; R9 - Del Maule; R10 - Del Biobío; R11 - De La Araucanía; R12 - De Los Ríos; R13 - De Los Lagos; R14 - Aysén del General Carlos Ibáñez del Campo; R15 - De Magallanes y de la Antártica Chilena; RoW - Rest of the World. Department of Economics, University of Sao Paulo 37