Figure 23 Latin America: projection of the extreme poverty rate to 2030 in various scenarios of per capita GDP growth and income distribution change, and a simulation of the impact of COVID-19 on extreme poverty in 2020

eat or use the toilet, which increase health personnel’s risk of being infected by the virus. At the same time, women working in this sector are still responsible for dependants or people in need of care within their households. They must continue to go to work in addition to this responsibility, which increases their excess workload and stress.

The care crisis, which has worsened in the current context, has a major impact on paid domestic work, a sector that employs one in every seven women in the region (ILO, 2016). Paid domestic workers’ vulnerability is a result of deregulation, the fact that they are less likely to be able to exercise their right to join a trade union or bargain collectively, and the low value afforded to their work by society. This vulnerability is exacerbated when, on the one hand, the increased demand for care falls on their shoulders in the face of school closures, greater demand for health care and the need to raise hygiene standards in the home, and when, on the other hand, domestic workers cannot do their jobs because of social distancing recommendations or restrictions on movement and are uncertain whether their wages will be paid, especially those who do not have a formal contract.

When economies are hit by an unprecedented health crisis, the economic measures adopted to alleviate the impacts of the situation must not involve spending cuts that could affect progress towards greater gender equality or curtail women’s autonomy. In particular, it is important that women’s time should not become, once again, an adjustment variable in governments’ efforts to address the region’s new economic scenarios.

4. Will extreme poverty be eliminated by 2030?

The regional framework of indicators for monitoring the Sustainable Development Goals in Latin America and the Caribbean 24 is important to analyse the prospects for reducing extreme poverty, given that SDG 1 proposes ending poverty in all its forms everywhere and its first target is to eliminate extreme poverty by 2030 (proxy indicator P-1.1.1). 25 A country’s rate of extreme poverty at a given moment is determined by the combination of average household income, the structure of distribution of this income and the extreme poverty line. This schematic view facilitates the design of scenarios to evaluate the effects of different combinations of average income growth and of reductions in inequality in poverty by 2030. 26 We compare two types of scenarios: excluding and including the impacts of COVID-19.

(a) Simulations prior to the outbreak of COVID-19

In a first scenario, with annual per capita GDP growth of 1% and no change in income concentration, the incidence of regional poverty would be 8.9% in 2030, well above the 3% target for extreme poverty (see figure 22).

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See Economic Commission for Latin America and the Caribbean (ECLAC), Report on the activities of the Statistical Coordination Group for the 2030 Agenda in Latin America and the Caribbean (LC/CEA.10/6), Santiago, 2019; and Statistical Coordination Group for the 2030 Agenda in Latin America and the Caribbean, Report on the prioritization of indicators for regional statistical follow-up to the Sustainable Development Goals in Latin America and the Caribbean (LC/CE.17/3), Santiago, 2018. The extreme poverty threshold for the target, measured as income per person below the international poverty line (equivalent to US$ 1.90 per day based on 2011 purchasing power parity), is too low for the countries of the region, so it is considered more appropriate to use the extreme poverty line based on the cost of a basic food basket. The target of eradicating extreme poverty is difficult to simulate, owing to the sensitivity of results to the characteristics of household surveys in capturing low income. The surveys generally contain observations on households with incomes close to zero, which, in addition to households that have scarce resources, also include those who did not respond to income questions or misreported extremely low values. Given that the income reported in the survey is scaled up under the simulation, the presence of observations with income equal or very close to zero can affect the results significantly. Therefore, in practical terms, a scenario is simulated where the extreme poverty rate is 3%. This does not mean that an extreme poverty rate of 3% is synonymous with eradication, rather that, given the characteristics of the methodology used, it is not useful to simulate a lower incidence.