between the poverty rate with and without the tax or transfer. If positive, it captures a poverty reduction effect, corresponding to a lower poverty rate after the tax or transfer.

Note that a progressive tax does not necessarily reduce inequality, F<sup>12</sup> which would instead require a positive redistribution effect, or poverty, which would require a positive poverty reduction effect. Similarly, a tax can reduce inequality but, at the same time, increase poverty. 12F<sup>13</sup> For a complete picture, measures of progressivity must therefore be combined with marginal contributions to evaluate the effects of fiscal interventions on poverty and inequality.

# 2.2 Data sources

The study relies on a variety of data sources including household surveys, government expenditures from administrative sources, and findings from earlier studies. We describe them in detail below.

## A. Household Surveys

**_PNAD-C._** The national household survey ( _Pesquisa Nacional por Amostra de Domicílios Contínua_ , PNAD-C) is the main source of information in Brazil about labor market outcomes, educational attainment indicators and households’ income sources. We use the 2019 13F<sup>14</sup> annual release of the survey data as published by the Brazilian national statistical office ( _Instituto Brasileiro de Geografia e Estatística_ , IBGE). The survey comprises 150,667 households and 443,790 individuals interviewed during the 2019 calendar year.

**_PNS._** The national health survey ( _Pesquisa Nacional de Saúde_ , PNS) 2019 interviewed 100,541 households. PNS was designed to collect information about the population’s health conditions, surveillance of chronic noncommunicable diseases (NCDs) and their associated risk factors. It also aimed to verify the performance of the national health system regarding both access and use of available services and

12 In the literature, this is known as the Lambert conundrum (Lambert 2001; Lustig & Higgins, 2018). Taxes, for instance, can be regressive according to the Kakwani index but when combined with transfers make the system more equalizing than without the regressive taxes. For a thorough discussion see Enami, Lustig, and Aranda (2018). 13 See Higgins and Lustig (2018).

14 Survey data for 2020 and 2021 was available when this paper was written. However, there are several reasons related to the unusual COVID-19 conditions in Brazil that make conducting a fiscal incidence analysis in those years less desirable. First, the years 2020 and 2021 are not considered ‘typical years’ as the pandemic had major disruptions on the economy and the labor income. The employment rate of the working-age population dropped by 5.4 percentage points between 2019 and 2020. At the same time, the government put in place emergency measures including a cash transfer program called “Auxilio Emergencial” (AE) that reached over 67 million people (circa 42% of the country’s adult population) with a generous payment of R$600 (55% of the minimum wage). Second, in 2021, AE was substantially reduced in terms of coverage and benefits, while other emergency measures expired. The labor force participation (at 60.6 percent) was far below its pre-pandemic levels of 63.9% and unemployment rates were at its highest point (14 percent) in a comparable series since 2012. Moreover, the social distancing measures established to contain the virus spread challenged the data collection by the National Statistical Office of Brazil (IBGE). Consequently, the response rates in both 2020 and 2021 were much lower than in previous years and IBGE undertook a series of mitigation measures to preserve the data (IBGE 2021, IBGE 2022). The effects of these atypical situations over income, poverty and inequality estimates were documented in Lara Ibarra and Campante Vale (2022) and Lara Ibarra and Campante Vale (2023). Specific work on the effects of AE on poverty can be found in World Bank (2022d).

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