The impact of climate change on economic output across industries in Chile

earlier dataset of base year t0 by the adjustment factor: Adjr;i;b¼t0 . This procedure was applied first to link the most recent 2008–2017 series to the previous series 2003–2008, then to the series 1996–2003 and the series 1985–1996, to finally obtain the combined series 1985–2017. Therefore the adjusted GDP series used in the article are: GDPadj r;i;t ¼ GDPr;i;t ; for t 2 ½2008; 2017�; ð2aÞ GDPadj r;i;t ¼ GDPr;i;t Adjr;i;b¼2003; for t 2 ½2003; 2007�; ð2bÞ GDPadj r;i;t ¼ GDPr;i;t Adjr;i;b¼2003Adjr;i;b¼1996; for t 2 ½1996; 2002�; ð2cÞ GDPadj r;i;t ¼ GDPr;i;t Adjr;i;b¼2003Adjr;i;b¼1996Adjr;i;b¼1985; for t 2 ½1985; 1995�: ð2dÞ We create datasets of both nominal and real GDP where the real GDP is adjusted using the UF or the value of real monetary unit which is indexed to the CPI [27]. Dividing the nominal GDP series by the UF value results in a real series for Chile. The average UF data for each year is also publicly available from the Central Bank of Chile, based on daily UF values published by the Chilean Bureau of Official Statistics (INE, Instituto Nacional de Estadísticas in Spanish). The UF money index is commonly used by companies and individuals in Chile for all kinds of long term contracts, including loans, real estate purchases, rent and wages. This option is made, because there are no price series in Chile that are valid for different industries in order to obtain real quantities per industry. Table 1 shows the average value of each industry in terms of the national GDP over the period 1985 to 2017. The largest economic sectors are Min-

ing (15.1%), Financial Services (14.9%), Manufacturing (12.7%), Personal Services (11.5%) and Commerce (10.9%), with shares between 10.9% and 15.1% of the national GDP over the last 35 years. In some years, industry GDP is negative for specific regions. This occurs in regions where few firms are operating in that sector. For example, negative values for industry-region GDP can occur in years in which a firm has costs exceeding revenue on its balance sheet. We replace negative values with 0 when calculating the growth rate of industry-region level GDP, there- fore growth rates can be either -100% or missing when one of the years has a zero output value. Using weighted regressions for the value of each region-industry is also an adequate way to solve this, because those observations are attributed a zero weight. Table B1 in S2 Appendix shows that the results are robust to using weighted regressions. Furthermore, since 2008 the Central Bank of Chile computes the share of financial services costs for each region-industry. Before 2008 the financial services costs are reported for each region, but are not disaggregated to the industry level, implying that the output of each industry is slightly over-estimated before 2008. Table 2 shows the average GDP of each industry for each of the 15 regions over the period between 1985 and 2017, which shows large disparities across regions. For example, Mining represents a share close to 0% of the regions VIII, IX, X and XIV, plus a share between 1.7% and 3.7% for the regions VIII, XI, XIII and XV. However, Mining represents more than 50%

of the GDP in regions II and III, and also has a share between 15.7% and 37.1% of the value in regions I, IV, V and XII. Therefore Mining is the largest economic sector in Chile, but its resources are unequally distributed across regions. The capital region (XIII, Metropolitan Region of Santiago) represents more than 40% of the national GDP and population, being par- ticularly important. For the capital region XIII the top industries are Financial Services (23.3%), Commerce (17.4%), Personal Services (12.7%) and Manufacturing (12.3%). Therefore PLOS ONE The impact of climate change on economic output across industries in Chile PLOS ONE | https://doi.org/10.1371/journal.pone.0266811 April 28, 2022 4 / 21