for Bangladesh;<sup>15</sup> ii) 2018 Consumer Pyramids Household Survey (CMIE) for India;<sup>16</sup> iii) 2010-2011 Living Standards Survey (LSS) for Nepal;<sup>17</sup> iv) 2018-2019 Household Integrated Economic Survey (HIES) for Pakistan;<sup>18</sup> and v) 2016 Household Income and Expenditure Survey (HIES) for Sri Lanka.<sup>19</sup> After the data is aggregated into eight (8) energy and fourteen (14) non-energy, CPAT-compatible good/service categories,<sup>20</sup> households are grouped into population-weighted, per-capita consumption deciles, and budget shares are computed by dividing total consumption expenditure on each CPAT good/service category by each household’s total consumption expenditure across all goods/services.

Sector-specific percent price increases from the simulated carbon tax are obtained directly from CPAT for each fossil fuel (Table A.2). The fossil fuel-specific price changes and budget shares can be used to estimate the loss in consumption from price increases of fossil fuels (e.g., electricity, gasoline, diesel, natural gas, etc.) following the introduction of a carbon tax (that is, the ‘direct’ incidence effect). Price increases for other consumer goods (due to higher energy and fossil fuel input prices for each sector) are calculated assuming full passthrough of producer cost increases onto consumer prices domestically (that is, perfectly elastic supply curves).<sup>21</sup> Non-fuel sector price increases are obtained as the sum-product of: i) each sector’s fossil fuel intensity; and ii) the price increase of each fossil fuel induced by the climate mitigation policy. Sectoral fossil fuel intensities are obtained from direct requirements matrices of input-output tables. These tables are sourced from the GTAP-10 database,<sup>22</sup> which includes 2014 data for 65 sectors<sup>23</sup> that are, in turn, mapped to the CPAT non-fuel consumption good/service categories mentioned above to recompute equation (A). Summing the estimates across all non-fuel goods and services yields a measure of the loss in consumption from price increases of non-fossil fuel products (for example, food, clothing, housing, etc.) following the introduction of a carbon tax (that is, the ‘indirect’ incidence effect). Adding up the direct and indirect effects yields an estimate of the total incidence effect. We further adjust the total incidence effect downwards to

> 15 See: http://data.bbs.gov.bd/index.php/catalog/182 16 See: https://consumerpyramidsdx.cmie.com/ 17 See: https://microdata.worldbank.org/index.php/catalog/1000 18 See: https://www.pbs.gov.pk/content/household-integrated-economic-survey-hies-2018-19 19 See: http://www.statistics.gov.lk/IncomeAndExpenditure/StaticalInformation 20 To facilitate relative cross-country comparability of results, CPAT uses a standardized classification of goods and services across all countries, distinguishing among eight (8) fuel (coal, electricity, natural gas, oil, gasoline, diesel, kerosene, LPG) and fourteen (14) non-fuel (appliances, chemicals, clothing, communications, education, food, health services, housing, other, paper, pharmaceuticals, recreation and tourism, transportation equipment, public transportation) good/service categories. This classification is, in part, informed by the implicit carbon intensity of nonfuel goods/services (i.e., goods/services with similar carbon intensities are classified under the same category).

> 21 This assumption is less realistic for Emissions Intensive Trade Exposed (EITE) industries competing in world markets with limited ability to pass cost increases forward to final consumers.

> 22 See: https://www.gtap.agecon.purdue.edu/databases/v10/index.aspx. Given that the GTAP-10 input-output tables are relatively outdated, they are scaled such that their implicit revenue flows from a hypothetical climate mitigation policy match those of CPAT (which are primarily based on IEA (2021) data).

23 These cover the following five fossil fuels: coal (‘coa’), electricity (‘ely’), oil (‘oil’), natural gas (‘gas’, ‘gdt’) and petroleum products (‘p_c’).

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