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Published online by Cambridge University Press: 28 April 2025
Latin America was one of the regions hardest hit by the COVID-19 pandemic. This paper aims to assess the evolution of family income inequality and its components from the onset of the pandemic to the end of 2021 in six Latin American countries: Argentina, Brazil, Colombia, Costa Rica, Peru and Uruguay. The unequalising impact of the worsening of the labour market during the contraction period was associated with the significant loss of informal jobs. This effect was partially offset by the equalising role of cash transfer policies. During the recovery period, the distributive impacts of these income sources were the opposite of those observed during the contraction period, as most countries gradually reduced or ceased these transfers while labour incomes partially rebounded. Two years into the COVID-19 pandemic, income inequality in most countries either remained the same or had decreased compared to 2019, even though total family incomes are still below the levels of that year.
América Latina ha sido una de las regiones más afectadas por la crisis de la pandemia de COVID-19. El objetivo de este trabajo es evaluar la dinámica de la desigualdad de ingresos familiares y sus componentes desde el inicio de la pandemia hasta fines de 2021 en Argentina, Brasil, Colombia, Costa Rica, Perú y Uruguay. El empeoramiento laboral durante el periodo inicial de contracción tuvo un impacto desigualador que se asoció a la pérdida significativa de empleos informales. Este efecto fue parcialmente compensado por las políticas de transferencias monetarias implementadas. Durante el periodo de recuperación se observa el impacto opuesto de estas fuentes, ya que la mayoría de los países redujeron o eliminaron gradualmente esas transferencias a medida que el empleo y los ingresos laborales se fueron recuperando. Dos años después del inicio de la pandemia de COVID-19, excepto en Colombia y Costa Rica, la desigualdad de ingresos es igual o inferior a 2019. Ello se produjo a pesar de que los ingresos familiares totales se encuentran aún por debajo de los niveles de ese año.
A América Latina tem sido uma das regiões mais afetadas pela crise da pandemia de COVID-19. O objetivo deste trabalho é avaliar a dinâmica da desigualdade da renda familiar e de seus componentes, desde o início da pandemia até o final de 2021 em Argentina, Brasil, Colômbia, Costa Rica, Peru e Uruguai. A deterioração do trabalho durante o período inicial de contração teve um impacto desigual associado à perda significativa de empregos informais. Esse efeito foi parcialmente compensado pelo papel equalizador das políticas de transferência de renda. Durante o período de recuperação, o impacto dessas fontes de renda se reverteu, já que a maioria dos países reduziu gradualmente ou cessou essas transferências à medida que o emprego e, consequentemente, a renda do trabalho, parcialmente se recuperaram. Dois anos após o início da pandemia de COVID-19, exceto na Colômbia e na Costa Rica, a desigualdade de renda nos demais países permaneceu igual ou diminuiu em comparação com 2019. Isso ocorreu apesar do fato de a renda familiar total ainda estar abaixo dos níveis daquele ano.
1 Data used in this paragraph comes from IMF, World Economic Outlook: Recovery during a Pandemic (Washington, DC: IMF, 2021) and ECLAC, Preliminary Overview of the Economies of Latin America and the Caribbean 2020 (Santiago: ECLAC, 2021).
2 ILO, 2022 Labour Overview of Latin America and the Caribbean (Lima: ILO Regional Office, 2022).
3 For progress in income distribution see Cord, Louise et al., ‘Inequality Stagnation in Latin America in the Aftermath of the Global Financial Crisis’, Review of Development Economics, 21: 1 (2017), pp. 157–81CrossRefGoogle Scholar; for its reversal see, among others, Cornia, Giovanni Andrea, ‘Inequality Trends and their Determinants: Latin America over the Period 1990–2010’, in Cornia, Giovanni Andrea (ed.), Falling Inequality in Latin America: Policy Changes and Lessons (Oxford: Oxford University Press, 2014), pp. 23–48CrossRefGoogle Scholar; Maurizio, Roxana, ‘Transitions to Formality and Declining Inequality: Argentina and Brazil in the 2000s’, Development and Change, 46: 5 (2015), pp. 1047–79CrossRefGoogle Scholar; Stampini, Marco et al., ‘Poverty, Vulnerability, and the Middle Class in Latin America’, Latin American Economic Review, 25: 4 (2016), pp. 1–44CrossRefGoogle Scholar; Carlos Rodríguez-Castelán et al., ‘Understanding the Dynamics of Labor Income Inequality in Latin America’, Policy Research Working Paper WPS 7795, World Bank, 2016; Verónica Amarante, Marco Galván and Xavier Mancero, ‘Inequality in Latin America: A Global Measurement’, CEPAL Review, 108 (2016), pp. 25–44; Julian Messina and Joana Silva, ‘Twenty Years of Wage Inequality in Latin America’, The World Bank Economic Review, 35: 1 (2021), pp. 117–47; World Bank, ‘Shifting Gears to Accelerate Shared Prosperity in Latin America and the Caribbean’, Latin America and the Caribbean Poverty and Labor Brief, World Bank, 2013, http://hdl.handle.net/10986/15265 (all URLs last accessed 29 Dec. 2024).
4 Lustig, Nora et al., ‘Short and Long-Run Distributional Impacts of COVID-19 in Latin America’, Economía LACEA Journal, 22: 1 (2023), pp. 96–116CrossRefGoogle Scholar; Nora Lustig et al., ‘The Impact of COVID-19 on Living Standards: Addressing the Challenges of Nowcasting Unprecedented Macroeconomic Shocks with Scant Data and Uncharted Economic Behavior’, International Journal of Microsimulation, 16: 1 (2023), pp. 1–27.
5 Liliana Castilleja-Vargas, ‘La clase media andina frente al shock del Covid-19’, Documento de Discusión IDB-DP-00774, IDB, 2020, http://dx.doi.org/10.18235/0002377.
6 Lustig et al., ‘Short and Long-Run Distributional Impacts’; Lustig et al., ‘The Impact of COVID-19 on Living Standards’.
7 Castilleja-Vargas, ‘La clase media andina’.
8 Arnoldo López Marmolejo and Marta Ruiz-Arranz (eds.), Inequality and Social Discontent: How to Address them through Public Policy: Economic Report on Central America, the Dominican Republic, Haiti, Mexico, and Panama ([Washington, DC:] IDB, 2020.
9 Delaporte, Isaure, Escobar, Julia and Peña, Werner, ‘The Distributional Consequences of Social Distancing on Poverty and Labour Income Inequality in Latin America and the Caribbean’, Journal of Population Economics, 34 (2021), pp. 1385–1443CrossRefGoogle ScholarPubMed.
10 Lustig et al., ‘The Impact of COVID-19 on Living Standards’.
11 Matías Busso et al., ‘Covid-19: The Challenge of Protecting Informal Households during the COVID-19 Pandemic: Evidence from Latin America’, Discussion Paper IDB-DP-780, IDB, 2020.
12 Lucila Berniell and Dolores de la Mata, ‘COVID-19 and Inequality: Will Social Gaps Widen in Latin America and the Caribbean?’, CAF Blogs (2021), www.caf.com/en/knowledge/views/2021/12/covid19-and-inequality-will-social-gaps-widen-in-latin-america-and-the-caribbean/.
13 Ivonne Acevedo et al., ‘Higher Inequality in Latin America: A Collateral Effect of the Pandemic’, IDB Working Paper No. IDB-WP-01323, IDB, 2022.
14 Sarthak Agrawal et al., ‘COVID-19 and Inequality: How Unequal Was the Recovery from the Initial Shock?’, World Bank Poverty and Equity Global Unit, 2021, https://documents1.worldbank.org/curated/en/700711624541133306/pdf/COVID-19-and-Inequality-How-Unequal-Was-the-Recovery-from-the-Initial-Shock.pdf.
15 In Colombia and Uruguay, the change in the ways surveys were conducted makes results unable to be compared after the period under consideration.
16 Antonio R. Discenza and Kieran Walsh, Global Review of Impacts of the COVID-19 Pandemic on Labour Force Surveys and Dissemination of Labour Market Statistics (Geneva: ILO, 2021), www.ilo.org/global/statistics-and-databases/publications/WCMS_821387/lang--en/index.htm, p. 34. Moreover, whilst Mexico is not included in this article, its statistical institute (the Instituto Nacional de Estadística, Geografía e Informática, INEGI) conducted a specifically designed telephone survey (the Encuesta Telefónica de Ocupación y Empleo, ETOE) for several months during the pandemic instead of the regular survey (Encuesta Nacional de Ocupación y Empleo, ENOE) and explicitly states: ‘The ETOE employs a different operational strategy and statistical design from the ENOE, so the figures provided by the ETOE are not strictly comparable to those of the ENOE. However, they represent an approximation to the indicators traditionally measured by the ENOE, making the comparison useful as a reference measure’: https://rtc-cea.cepal.org/sites/default/files/rtc_connected/files/INEGI%20M%C3%A9xico.pdf.
17 To clarify, in this paper, we differentiate between formal and informal employment, a characteristic of the job itself. This conceptually differs from employment in the formal or informal sectors, where the unit of analysis is the establishment or firm. Non-wage earners’ jobs are classified as formal or informal using the same criteria as those applied to distinguish between formal and informal establishments. By definition, all informal non-wage earners work in the informal sector.
18 Consultations were held with members of the ILO Latin American and Caribbean Information and Labour Analysis System (SIALC, from its Spanish name) regarding the measurement of informality in the countries under study.
19 ILO, 2020 Labour Overview of Latin America and the Caribbean (Lima: ILO Regional Office, 2020); ILO, 2021 Labour Overview of Latin America and the Caribbean (Lima: ILO Regional Office, 2021).
20 ECLAC, Preliminary Overview 2020.
21 ILO, 2020 Labour Overview; Beccaria, Luis, Bertranou, Fabio and Maurizio, Roxana, ‘COVID-19 in Latin America: The Effects of an Unprecedented Crisis on Employment and Income’, International Labour Review, 161: 1 (2020), pp. 83–105CrossRefGoogle Scholar.
22 ILO, 2021 Labour Overview; ILO, 2022 Labour Overview.
23 ILO, 2021 Labour Overview.
24 A larger fall in informal than formal employment was a widespread phenomenon in the region: see ILO, 2021 Labour Overview.
25 Beccaria, Bertranou and Maurizio, ‘COVID-19 in Latin America’; ILO, 2022 Labour Overview.
26 Maurizio, Roxana, ‘Temporary Employment and its Impact on Wages in Latin America’, Iberoamerican Journal of Development Studies, 8: 1 (2019), pp. 186–215Google Scholar.
27 Data estimated by the authors using microdata from household surveys in each country. No data on this variable is available for Uruguay.
28 Roxana Maurizio, ‘Challenges and Opportunities of Teleworking in Latin America and the Caribbean’, ILO Technical Note, 2021; Catania, Sol and Maurizio, Roxana, ‘Intensidad y características del trabajo a domicilio y del teletrabajo durante la pandemia por COVID-19 y la posterior fase de recuperación del empleo en América Latina’, Revista de Economía Política de Buenos Aires, 18: 28 (2024), pp. 9–55Google Scholar.
29 Velásquez Pinto, Mario D., ‘Un análisis de la protección ante el desempleo en América Latina’, in Isgut, Alberto E. and Weller, Jürgen (eds.), Protección y formación: instituciones para mejorar la inserción laboral en América Latina y Asia (Santiago: CEPAL, 2016), pp. 87–113Google Scholar.
30 Beccaria, Luis and Maurizio, Roxana, ‘Labour Market Turnover in Latin America: How Intensive Is it and to what Extent does it Differ across Countries?’, International Labour Review, 159: 2 (2020), pp. 161–93CrossRefGoogle Scholar.
31 Data estimated by the authors using microdata from household surveys in each country.
32 Ibid.
33 Ibid.
34 Maurizio, ‘Challenges and Opportunities’.
35 Beccaria, Bertranou and Maurizio, ‘COVID-19 in Latin America’; ILO, 2022 Labour Overview.
36 ILO, 2022 Labour Overview.
37 Data estimated by the authors using microdata from household surveys in each country.
38 For a more detailed description of these policies see: UN-CEPAL, Social Panorama of Latin America 2020 (Santiago: ECLAC, 2021); ILO, ILO Monitor: COVID-19 and the World of Work, 5th edn, 2020, www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/documents/briefingnote/wcms_749399.pdf; Claudia Robles and Cecilia Rossel, Herramientas de protección social para enfrentar los efectos de la pandemia de COVID-19 en la experiencia de América Latina (Santiago: CEPAL, 2021), https://hdl.handle.net/11362/47412; Beccaria, Bertranou and Maurizio, ‘COVID-19 in Latin America’; Guillermo M. Cejudo et al., ‘Inventario y caracterización de los programas de apoyo al ingreso en América Latina y el Caribe frente a COVID-19’, Nota Técnica IDB-TN-02334, IDB, 2021, https://publications.iadb.org/es/inventario-y-caracterizacion-de-los-programas-de-apoyo-al-ingreso-en-america-latina-y-el-caribe; Marco Stampini et al., ‘Adaptive, but not by Design: Cash Transfers in Latin America and the Caribbean before, during and after the COVID-19 Pandemic’, Technical Note IDB-TN-02346, IDB, 2021, https://publications.iadb.org/en/adaptive-not-design-cash-transfers-latin-america-and-caribbean-during-and-after-covid-19-pandemic.
39 If mqjt is the aggregate amount of per-capita income of source j in period t in quintile q, the values shown in Figure 3 (and in Figure 4) for quintile q and for source j are computed as follows: (mqjt – mqj 0)/(∑j mqj 0). Quintiles are computed based on the income distribution at each sample point. Consequently, households in mqjt are not necessarily the same as those in mqj 0, and changes in quintile composition may occur.