Simulations of Policy Responses During the COVID-19 Crisis in Argentina: Effects on Socioeconomic Indicators

As in most countries, the COVID-19 pandemic has caused a severe crisis in Argentina. The preventive and mandatory social isolation policy (ASPO in Spanish), established by the national government on March 20, 2020, had the primary objective of immobilizing the population to prevent further transmission and to gain time to prepare the health system for the care of patients. The ASPO had been extended for more than 200 days, being one of the longest isolation periods in the world and greatly affecting the country’s economic activity. According to the National Institute of Statistics and Census (INDEC) of Argentina, economic activity fell by 25 percent in April and by 20 percent in May (compared to the same months of 2019). While many productive and essential services continued normally (e.g., food production, health services), other less essential services were significantly reduced (e.g., transportation, construction, domestic services). Those that required a physical presence in the workplace (e.g., manufacturing, construction) were also restricted, and others were directly suspended (e.g., tourism, recreation). In addition, some activities were adapted and carried out remotely (e.g., many professional services, education). This reduction in economic activity has put the job stability of many people at risk, affecting their income levels and deteriorating social indicators.

In this context, the Argentine government has implemented a series of economic policy responses to face the crisis. To guarantee access to food and to sustain the income of less well-off sectors, it established Emergency Family Income (IFE in Spanish). This was an exceptional monthly payment of $10,000 pesos (around US$120, about 60 percent of the minimum wage) during April, June, and August 2020 to unemployed people, informal workers, low-income self-employed workers, and domestic workers. The social protection system was also reinforced. Beneficiaries of cash transfer programs (e.g., holders of the Universal Child Allowance (AUH) and Universal Allowance for Pregnancy (AUE)) received a bonus payment, and retirees and pensioners received an extra payment.¹ This package of measures had an estimated fiscal cost of approximately four points of GDP, and had been in place from the beginning of the crisis until the end of 2020 for activities that were affected by the crisis.²

This paper simulates the effects of policy responses to the COVID-19 pandemic on households’ employment and income distribution, using household survey and administrative data on employment and wages by economic sectors. The underlying hypothesis is that the COVID-19 crisis strongly deteriorated the Argentine economy and household welfare, but the implemented public policies collaborated to counteract (at least partially) these negative effects. To examine this hypothesis, we analyze the impact of the COVID-19 crisis on households’ income, unemployment, poverty, and inequality. Women may have been the most affected by the crisis: the most affected economic sectors are mainly occupied by women (e.g. domestic service) and school closures may have forced them to childcare at home. We then explore heterogeneous effects by gender and age groups. For this purpose, we simulate impacts on welfare at the household level using household survey data, administrative data on employment and wages by economic sectors, and government cash transfers.

We start by considering two different scenarios. We first characterize a pre-COVID-19 scenario in terms of socioeconomic indicators using microdata from Argentina’s household survey corresponding to the first quarter of 2020. We then simulate the effects on unemployment and household income for three quarters after the beginning of the pandemic (i.e., post-COVID-19 scenarios).³ To this aim, we use the latest available figures on the evolution of economic activity and past changes on employment and wages by sector. All scenarios also include public assistance (i.e., policy responses through cash transfers), in line with what the Argentine government is doing to mitigate the crisis, to get a general sense of how effective these policies were in counteracting those effects.

The results indicate that during the COVID-19 crisis, households would have seen their income decline by about 6 percent without the policy responses. This reduction was nonlinear along the income distribution, with the lowest income earners suffering the most in relative terms. This is strongly related to the relatively higher informality at the bottom of the income distribution. The greater negative effects of the pandemic in the less well-off part of the income distribution are in line with results from Bonavida and Gasparini (2020). Furthermore, we find suggestive evidence that the impact was not homogeneous by gender: on average, the employment rate fell more among women than men (–23 percent versus –20.8 percent compared to the second and first quarters of 2020). In early working ages (18–24) the differences are very significant: the fall in the employment rate was 63 percent for men and 80 percent for women. These differences become more pronounced when considering the presence of children. For example, for the age group 18–24, the contraction in employment for men with children was close to 57 percent, while it was 82 percent for women with children. In the 25–40 age group, the falls were 18 percent for men with children and 30 percent for women with children, respectively. Even conditioning on sectors, in 7 of the 11 analyzed sectors, women’s employment was more affected than that of men. These findings are consistent with ILO (2020) and Lustig et al. (2020) and can be associated with the overlap of work responsibilities and care (housework, childcare, and eldercare) responsibilities, which have intensified during the pandemic, especially for households with children (OECD, 2020; WEF, 2021).⁴

The policy response mitigated the impact of the crisis: households incomes decline by about 4.1 percent. In other words, the policy response reduced the average household income decline by approximately a third (-4.1 percent vs -6.0 percent). This prevented major increases in poverty and inequality. A key aspect of the satisfactory policy response was that public assistance was targeted at informal workers and at less well-off households with children. This policy response’s large offsetting effect is in line with previous findings such as Lustig et al. (2020).⁵

Given the crisis’ magnitude and the public policy responses, it is very important to determine the damages of the crisis and how effective these policies have been in mitigating it. However, any attempt of studying this phenomenon while the pandemic is going on, faces two main limitations. First, there are limited data available on how COVID-19 is affecting economic activity in real time. For example, at the moment of writing this paper, only the household survey corresponding to the first quarter of 2020 is available in Argentina. Also, information on how employment in the different sectors is being affected has been published with lags. Second, we face a limitation on how to predict the evolution of this uncertain phenomenon. Argentina, like the rest of other countries in the world, is going through the COVID-19 pandemic without certainty about when or how it will stop.

With these caveats in mind, we believe that this paper makes two contributions. First, the paper contributes to the literature studying the effects of the COVID-19 pandemic on socioeconomic variables such as unemployment, poverty, and inequality. To some extent, it fills the gap on the immediate effects of the COVID-19 crisis at the household level for most low- and middle-income countries, as remarked by Janssens et al. (2021).⁶ Since we focus on Argentina, a Latin American developing country, the paper is closely related to some previous studies for the region on this topic. Lustig et al. (2020) micro-simulate the distributional consequences of COVID-19 in Latin American countries, considering the expanded social assistance that governments introduced in response. Their findings suggest that i) the worst effects are not on the poorest but on those (roughly) in the middle of the income distribution, ii) the policy responses presented a large offsetting effect but with different intensities across countries, and iii) the increase in poverty induced by the lockdown was similar for male- and female-headed households. Moreover, Bonavida and Gasparini (2020) analyze the effects of the COVID-19 pandemic on remote employment, evaluating to what extent this type of employment is feasible for Argentine workers. They suggest that about a quarter could do it remotely, and the degree of applicability of this modality is very heterogeneous (by occupation and industry). Less compatible occupations are characterized by a higher share of informal and self-employed workers, with lower levels of education, skills, and wages. Thus, the short-term negative effects of the pandemic would be greater in the lower-income sectors, implying a significant increase in poverty and income inequality.⁷ Brum and De Rosa (2021) micro-simulate the short-run effect of the crisis on the poverty rate for Uruguay and estimate the effect of the crisis on formal, informal, and self-employed workers, finding that during the first full month of the lockdown, the poverty rate increased by approximately 3.2 points, from 8.5 percent to 11.7 percent. This represents about 110,037 additional people below the poverty line. Cash transfers implemented by the government would have had a positive but very limited effect in mitigating this poverty spike. Second, the paper provides estimates of the effectiveness of the government’s response to the pandemic with respect to its impact on household welfare. Thus, the paper brings a real time approximation on the effects of the adopted policies for policy makers, highlighting pros and cons to help design a the new round of measures to be adopted at the end of the pandemic.

The rest of the paper is structured as follows. Section 2 details the data and the methodological approach. Section 3 presents the simulation, distinguishing between scenarios before and after COVID-19. Section 4 summarizes the key findings and offers guidelines and recommendations for public policy discussions.

The main source of our data is the Permanent Household Survey (EPH in Spanish), carried out by the INDEC. The survey covers urban areas that represent around 62 percent of the total population.⁸ It contains information on whether the household member is (labor) active and in which economic sector they work. The survey also reports information on earned income, for both labor and non-labor incomes. The latter includes cash transfers, including those from government.⁹

Economic shocks like COVID-19 can affect household welfare through different channels (World, 2020; Kokas et al., 2020; Araar et al., 2020). First, through the impact on labor income due to the direct effect of lost earnings because of illness or the need to take care of sick household members. Also, households can experiment shocks to earnings and employment, caused by a decline in aggregate demand and supply disruptions.¹⁰ Second, through the impact on non-labor income due to, for example, a decline in international remittances or changes in public transfers. Third, through the impact on consumption if prices changes or shortages of basic consumption goods take place. Finally, through service disruptions with adverse impact on non-monetary dimensions of welfare. For example, the suspension of classes and feeding programs in schools, leading to impacts on student retention, learning, and nutrition, or the potential saturation of health systems in countries with a high incidence of COVID-19. Also, disruptions in mobility can occur due to quarantines and other containment measures, which may drastically reduce public and private transportation services.

The paper focuses on the effects of COVID-19 policy responses on household labor income using different scenarios and considers government policy responses.¹¹ To measure household welfare, we mainly use monthly gross income per capita (gipc). The methodology to simulate the COVID-19 impact in different scenarios involves several steps. First, we characterize the pre-COVID-19 scenario using the EPH corresponding to the first quarter of 2020.¹² We denote this initial scenario as the pre-Covid-19 one, providing an accurate representation on how the Argentine scenario—in terms of incomes, poverty, and inequality—was before the pandemic. To measure poverty, we use the same methodology as the INDEC.¹³ That is, we compare the current household’s total income to its poverty line, based on the market value of a food basket, a non-food basket, and the number of household members.¹⁴ We rely on the Gini coefficient and Atkinson indicators to measure the impact on inequality. In this pre-COVID-19 scenario, as in the others, we explore heterogeneity of the results regarding age groups, gender, and economic sectors.

We then simulate the post-COVID-19 scenario for three quarters after the pandemic, considering the effects on employment and on labor incomes.¹⁵ We assume that labor income variation affects only households with active members and non-zero labor income. Given that the COVID-19 shock may have differential effects by economic sectors depending on productive sector characteristics, for the employment simulation, we use data on employment variations dis-aggregated by 11 sectors, provided by the Argentine Ministry of Labor, Employment and Social Security.¹⁶ We also distinguish between public and private employment and in the latter between formal and informal workers.

The simulations on employment variations involve three steps: (i) determining how much employment falls in each sector and also between formal and informal workers, (ii) determining who loses their jobs, and (iii) determining the variation on wages for those who remain employed. To determine how much employment falls, we rely on past information.¹⁷ The COVID-19 shock was the largest economic contraction that Argentina experienced after the Great Recession of 2008/2009, with output contraction being almost twice as high during the COVID-19 shock than it was in the Great Recession. Therefore, for the simulation on employment variations we use the largest quarterly drop in employment during the Great Recession, increased by the relation between output drops during the COVID-19 shock relative to those ones during the Great Recession.¹⁸ Given the challenge of simulating a crisis of unprecedented magnitude, we believe that using a major global recession as a reference could be useful, although we are aware that this assumption is not free of limitations (i.e., differences in how each crisis -Great Recession and COVID-19 affected different countries).

To determine who lose their jobs, we rely on a simple selection model based on individual probabilities. Specifically, we estimate logistic regressions for formal and informal workers together with unemployed individuals. The dependent binary variable takes the value of one if the individual is employed and zero otherwise. The vector of independent variables includes a set of characteristics referring to individual and household observable characteristics.¹⁹ We then use the estimated probability of those with a lower probability of being employed are more likely to lose their jobs.²⁰ To determine the variation on wages for those who remain employed, we simulate the variation on labor income for those who continue working—as in the pre-COVID-19 scenario—using data on wages from the INDEC. In this case we consider income variation for public employees, private employees, and informal workers (non-registered salaried employees and self-employed workers).²¹ Note that we follow a standard microsimulation without behavioral responses. Moreover, the microsimulation is parametric: we define those who lose their job based on the estimated probability of being employed through a logit model.

Formally, we define $Y_{i,h,0}$ as the pre-COVID-19 (2020, first quarter) total income for individual $i$ in household $h$ and $L_{i,h}$ as the labor income variation for individual $i$:²²

where $Y_{i,h,1}$ constitutes the simulated total income in the post-COVID-19 scenarios.

Since inflation in Argentina is very high, we further deflate total incomes by the price-level variation of the total basic basket between the first and three subsequent quarters that cover the post-COVID-19 scenario.²³ With these real final incomes, we re-estimate total household income and the gipc to compute poverty and inequality indicators. We denote these scenarios as first-, second-, and third-quarter-ahead scenarios without policy responses.

We then simulate government policy responses in line with what the Argentine government is doing to mitigate the crisis, considering the most relevant ones in terms of household welfare (see Appendix Section 4).²⁴ We first consider the IFE, which consisted of three exceptional monthly payments during April, June, and August 2020 of $10,000 pesos each to less well-off families. Note that this policy affects only the first and second quarters after the COVID-19 simulations. We then consider the extra payment to recipients of the AUH and the AUE as well as the payment to retirees. This was an exceptional payment of $3,000 pesos for the first and second quarter of 2020 after COVID-19. For the third quarter, the AUH payment was increased up to $6.000 (corresponding to a 5 percent increase plus 20 percent of the cash transfer that is withheld every month and received at the end of the year). In terms of the simulation, we identify in the EPH all potential beneficiaries of these programs according to eligibility criteria and then simulate the cash transfer $T_{i,h}$.²⁵ Thus, the after-policy response of individual incomes $Y_{i,h,1}^p$ are

We then compute, again, the poverty and inequality indicators for these scenarios, denoted as first-, second-, and third-quarter-ahead scenarios with policy responses. Here, a critical point regarding the evolution of labor incomes must be considered when comparing the post-COVID-19 quarters with the pre-COVID-19 scenario. Workers in Argentina benefit from a wage bonus, known as aguinaldos, that is paid bi-annually during June and December. They are consequently registered and included in the first and third quarters of our simulations but not in the second and fourth ones. Thus, to provide an accurate comparison, the pre-COVID-19 scenario should be compared with the second-quarter-ahead scenario. This is valid for both the scenario with and without policy responses. Finally, leaving aside the pre-COVID-19 scenario, each quarter can be compared between the scenario with and without policy responses, respectively, which provides a good approximation for the effects of policy responses after the pandemic.

While the health effects of the COVID-19 crisis were the initial focus of the government, its socioeconomic effects and accompanying policy responses have been receiving more attention, mainly in low- and middle-income countries. In this context we analyze the impact of the COVID-19 crisis on households’ incomes, unemployment, poverty, and inequality in Argentina. For this purpose, using a standard microsimulation methodology we simulate impacts on welfare at the household level using household survey data and administrative data on employment and wages by economic sectors. The simulations also include public cash transfers (i.e., policy responses), implemented by the government to mitigate the crisis, to get a sense of how effective these policy measures were in counteracting those negative effects.

The results indicate that during the COVID-19 crisis, households would have experienced a reduction of about 6 percent on their incomes without any policy responses. This reduction was nonlinear along the income distribution, with the lowest income earners suffering the most in relative terms. This result is strongly related with relatively higher informality at the bottom of the income distribution. The greater negative effects of the pandemic in less well-off parts of the income distribution are in line with results from Bonavida and Gasparini (2020). Furthermore, the impact was not homogeneous by gender: on average, the employment rate fell more among women than men (–23 percent versus –20.8 percent when comparing the second and first quarters of 2020). In early working ages (18–24), the differences are very significant: the fall in the employment rate was 63 percent for men and 80 percent for women. These differences become more pronounced when considering the presence of children. For example, for the 18–24 age group, the contraction in employment for men with children was close to 57 percent, while it was 82 percent for women with children. In the 25–40 age group, the contractions were 18 percent and 30 percent, respectively. Moreover, even when conditioning on sectors, in 7 of the 11 analyzed sectors, women’s employment was more affected than that of men. These findings are consistent with ILO (2020) and are associated with the overlap of work responsibilities and care responsibilities (housework, childcare and eldercare), which have intensified during the pandemic, especially for households with children (OECD, 2020; WEF, 2021). In addition, we find that the policy response cushioned by around one-third what the average drop in household income would have been. This prevented major increases in poverty and inequality. A key aspect of the satisfactory policy response was that public assistance was targeted at informal workers and at less well-off households with children. This policy response’s large offsetting effect is in line with previous findings such as Lustig et al. (2020).

Our simulations face two main limitations. First, there are limited data available on how the pandemic is affecting economic activity in real time. At the moment of writing this paper, only the household survey corresponding to the first quarter of 2020 was available in Argentina. Also, information on how employment in the different sectors was being affected has been published with lags. Second, we have lack knowledge on how to predict how the pandemic will evolve. Argentina, like the rest of the countries in the world, are going through the pandemic without certainty about when or how it will yield.

In the transition toward the end of the pandemic, policy discussions should include short- and medium-term policies. For short-term policies, policymakers and academics should focus on how to accurately target public assistance. An important lesson from the Argentine case is that as most of poorest households are employed in the informal sector, relief measures—such as those applied in this paper—considering informality becomes crucial. Thus, it becomes very important to discuss policies aimed at reducing labor informality as labor market policies play an important role in the formalization of employment. Given that most informal workers have low qualifications and work in jobs that are difficult to identify for public policies, an integrated policy approach is necessary, which includes economic, social, and labor policies (Bertranou et al., 2013). Along these lines, it is also important to address how to adapt people to the transformation of the workplace in the post-COVID-19 era since the crisis may allow wider adoptions of teleworking practices.

Also crucial in policy discussions is accurately identifying those who really need public assistance. All information about citizens, contained in the administrative records of the different divisions of the public sector, must be used. Invest in resources and modern technologies to obtain a good handling of this information should also be considered to have it available at the right times. In turn, it is important to make efforts to get this information as updated as possible. All this aims to minimize the typical errors of inclusion and exclusion that arise when targeting social policies. An adequate use of the administrative records of the Argentine social security, through its different contributory and non-contributory programs, despite its limitations, is a key aspect since it already covers most of the population (Giuliano et al., 2020).

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Author details

1. Julian Martinez-Correa

   Center for Distributive, Labor and Social Studies (CEDLAS). Institute of Economic Research of the Faculty of Economic Sciences at the National University of La Plata (UNLP), La Plata, Argentina

   "This ORCID iD identifies the author of this article:" 0000-0003-4623-5174

2. Guillermo Cruces

3. Juan Menduiña

   "This ORCID iD identifies the author of this article:" 0000-0003-2490-2802

4. Jorge Puig

   Center for Distributive, Labor and Social Studies (CEDLAS). Institute of Economic Research of the Faculty of Economic Sciences at the National University of La Plata (UNLP), La Plata, Argentina

   For correspondence

   jorge.puig@econo.unlp.edu.ar

   "This ORCID iD identifies the author of this article:" 0000-0001-5033-5608