The Impact of COVID-19 on Living Standards: Addressing the Challenges of Nowcasting Unprecedented Macroeconomic Shocks with Scant Data and Uncharted Economic Behavior

In early 2020, when the COVID-19 pandemic hit countries across the world, governments implemented lockdown policies of various degrees to contain the spread of the virus.¹ Inevitably, these measures--combined with the supply and demand disruptions associated with the pandemic--caused a sharp reduction in economic activity, a fall in employment and income, and a rise in poverty and inequality. When the pandemic hit, there was an urgent need to be able to quantify its impact on living standards and poverty. Nowcasting this impact faced two significant challenges. First, the data was scant. Second, existing models used parameters, behavioral responses, and market-clearing assumptions that did not necessarily apply to the new circumstances. Given these restrictions, the first set of exercises was quite simple and, in particular, assumed that income losses were proportional across the income distribution.² It was fairly obvious, however, that those income losses would not be proportional. As an alternative, this paper proposes a modeling framework to obtain meaningful, if tentative, estimates on how incomes and their distribution can be affected by such an unusual systemic shock. Specifically, we propose a microsimulation methodology to nowcast the impact of the COVID-19 shock on inequality and poverty. We illustrate how such a framework can work in practice by applying it to the four largest countries in Latin America: Argentina, Brazil, Colombia, and Mexico.

The methodology proposed here has relatively low information requirements and a parsimonious modeling framework to address the challenges of scant data and unchartered economic behavior. The information requirements are: a recent household survey, identification of sectors most/least affected by lockdowns, and projections of aggregate GDP contractions. These three were available in the early days of the pandemic. With this information, one can nowcast a range of possible outcomes of the effects of COVD-19 on inequality and poverty and a rough estimate of the extent social assistance would need to be expanded to mitigate the pandemic’s effect on incomes. Information on the share of households who reported losing income became increasingly available. This type of additional information can be used to narrow the range of nowcasting outcomes to one. Moreover, a few months into the pandemic, there was also information on the actual expansion of social assistance, which can be used to assess how successful governments were in mitigating the impact of the pandemic.

Our proposed methodology has an important advantage compared to other “fast-delivery” exercises which assume that income losses were proportional. Based on existing information at the time, the distribution of income changed—and changed fast--during the lockdowns. In particular, “real-time” telephone surveys showed that the poorer and informal sector workers lost employment and income in a larger proportion due to the “COVID-19 effect.”³ Our microsimulation framework allows us to relax the equal loss assumption and incorporate distributional changes in the analysis. It also allows us to produce non-anonymous growth incidence curves to describe income losses across the pre-pandemic income distribution.⁴ Importantly, this framework can be easily adapted to different countries and contexts, has great flexibility, and allows one to incrementally fine-tune the projected effects as more information becomes available.⁵

Our nowcasting exercise aims to obtain meaningful estimates of the pandemic’s impact on inequality and poverty at the early stages of such a shock when data and behavioral response information are limited. The estimated impact is obtained by simulating potential income losses at the household level using microdata from household surveys. The first step of the exercise consists in obtaining the most recent household survey for the country of interest and, if needed, adjust household incomes to the month immediately preceding the pandemic. We use gross income per capita as the welfare indicator. Gross income is defined as labor income plus rents, private transfers, pensions, and government cash transfers before any direct taxes.

Next, based on the economic sector in which household members work, microsimulation is used to estimate potential income losses at the household level. The simulations first identify individuals whose income is “not at risk” because they work in sectors that are “essential”. This information can be obtained from different sources. For example, the “ILO Monitor: COVID-19 and the world of work” (ILO, 2020) classified the impact of the pandemic on output by economic sector as low, medium, and high. Individuals whose income were less likely to be at risk are those who worked in the “low” category. Another source to identify incomes not at risk is the national lockdown Decrees. Of course, the latter identify the statutory low-risk sectors which may differ from the actual ones depending on enforcement of the lockdown.⁶ The not-at-risk income category, depending on the country, can also include incomes from cash transfers programs, social security pensions, public employment, private transfers, and the income earned in “nonessential” sectors by white-collar workers with internet access at home.⁷ Depending on the country, one may also wish to exclude in the not at risk income category the incomes of informal street vendors regardless of the sector in which they work and rental incomes.

Once the not-at-risk income for each income concept is obtained, we aggregate them to the household level and estimate the at-risk income as the difference between the total gross income and the total income that is not at risk. We simulate potential income losses using a range of two key parameters: the share of households with at-risk income that actually lose income and, of those who lose income, the share of at-risk income lost. We allow both parameters to range from zero to one hundred percent and select the households who lose income (from the set of households with at-risk income) randomly.

We subsequently aggregate the results into a ten-by-ten matrix of possible total per capita gross income losses in 10 percent intervals. Cells on this matrix show one-hundred possible total (and not just the at-risk component) per capita households’ gross income losses as a proportion of pre-pandemic gross income as we vary both the probability that households lose at-risk income (down the rows, for instance) and the share of that at-risk income they lose (across the columns). For example, the 10 percent-10 percent cell of each matrix shows the fall in total per capita gross income in percent corresponding to the case in which 10 percent of the households with at risk income lose 10 percent of their income at risk. In Table 1, which corresponds to the estimates for Argentina discussed in the applications section, in the 10 percent-10 percent cell, the fall in total per capita gross income is estimated at 0.3 percent on average. Although the choice of the size of this matrix is arbitrary, as shown below in the applications, it provides sufficient granularity for the nowcasting exercise.

Figure 1

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Our initial results for income losses provide one-hundred possibilities. To narrow them down, the first step is to match the income losses with macroeconomic predictions on aggregate fall in GDP. Multilateral organizations such as the International Monetary Fund (IMF) are usually quick in producing initial forecasts.⁸ To translate GDP losses into household income losses, one needs to assume a “pass-through.” Ravallion (2003) proposed a method to estimate such a pass-though. An application by Lakner et al. (2020) estimates a “pass-through” of GDP growth to household (gross) income growth of 0.85. We used the latter in our applications. Outcomes of income losses that are consistent with the predicted macroeconomic downturn form an “iso-loss curve” that runs through the 10x10 matrix (see highlighted cells in Table 1).

The iso-loss curve reduces the possible outcomes to a handful of options. If no other information is available, a possible path is to choose the two results closest to the corners of the matrix where either the smallest proportion of households lose much income (upper right) –we call this the "concentrated losses” scenario-- or the largest proportion of households lose smaller amounts of income (lower left) –“dispersed losses” scenario. In the absence of additional information, the concentrated and dispersed losses scenarios provide estimates for two extreme opposite situations in terms of the share of households that bear the burden of losses.

As new information is collected, the nowcasted results can be narrowed further. A few months into the pandemic, the World Bank, governments, and other agencies, resorted to high-frequency surveys to monitor its impact on people’s wellbeing. Usually, the percent of households reporting income losses was included among the information collected by these surveys. With this information, it is possible to select one outcome out of the 100 in the matrix, shown in pink highlight in the matrix above –we call this the “actual losses” scenario-.

Once the scenario or scenarios are selected, one can generate nowcast incomes for all the households included in the survey. The nowcast (post-pandemic) income distribution can be compared with the pre-pandemic distribution using the standard indicators such as Lorenz curves, the Gini or other inequality indicators, and poverty indicators. We can also provide a non-anonymous analysis of income losses across the pre-pandemic income distribution. The analysis of the income trajectories is of considerable interest when income losses (or gains) differ, perhaps greatly, among households. To describe those trajectories, we use two non-anonymous distributional comparisons: non-anonymous growth incidence curves (GIC) and pre- to post-pandemic income mobility.⁹ The latter features the share of households who remain in their pre-pandemic category of income classes (e.g., the share of pre-pandemic poor households who continue to be part of the poor after the shock) or were pushed down across categories (e.g., pre-pandemic middle-class households who are pushed into poverty—at least based on their income-- after the shock).

Finally, a nowcasting exercise of the effect of COVID-19 on living standards should also incorporate the new or expanded social assistance measures to varying degrees that were introduced by governments. Therefore, in addition to examining the pre- and post-pandemic income distributions, we construct a third distribution that simulates changes to the social assistance programs. This yields a post-policy response distribution which can be compared to the pre-pandemic distribution in turn.

Our proposed methodology has some important caveats. The microsimulations do not account for behavioral responses or general equilibrium effects, so they only yield first-order effects. Also, the results depend on the specific assumptions about income sources that are “at risk”/“not at risk” and the new and expanded social assistance programs incorporated.

As described earlier, our proposed methodology can be applied to any country with a recent household survey from before the pandemic and macroeconomic prediction on aggregate fall in GDP. Here, we illustrate how this methodology works in practice by applying it to the four largest countries in Latin America: Argentina, Brazil, Colombia, and Mexico. We use the most recent household survey available in each country from before the pandemic and update the information to December 2019. Specifically, the surveys used here are: Argentina: Encuesta Permanente de Hogares (EPH (2019)), Brazil: Pesquisa Nacional por Amostra de Domicilios Continua (PNADC (2019)), Colombia: Gran Encuesta Integrada de Hogares (GEIH (2019)), Mexico: Encuesta Nacional de Ingresos y Gastos de los Hogares (ENIGH (2018)). The household surveys for Brazil, Colombia, and Mexico are representative at the national level. In Argentina, the survey covers only urban areas that represent around 62 percent of the population. For simplicity, we will refer to “Argentina” except in tables and figures where we shall add “urban.”

We construct the gross income aggregating labor income plus rents, private transfers, pensions, and government cash transfers before any direct taxes. To maintain comparability across countries, we exclude consumption of own production and the rental value of owner-occupied housing.^10,11 We update gross incomes for Mexico to 2019 using the GDP per capita growth rate for 2019 multiplied by the “pass-through” of 0.85. Also, for Mexico, we update gross incomes to take into account the significant reforms introduced to the cash transfers system in 2019.¹²

The “not at risk” income is estimated as the income derived from work in sectors that are “essential”. For Argentina and Colombia, the lockdown measures stated explicitly which sectors are essential. For Brazil and Mexico, we use the ILO definition of essential sectors.¹³ The not-at-risk income also includes incomes from cash transfers programs, social security pensions, public employment, private transfers (e.g., remittances),¹⁴ and the income earned in “nonessential” sectors by white-collar workers who are CEO’s, managers, and researchers with internet access at home.^15,16 The not at risk income excludes incomes of informal street vendors and rental incomes.

We aggregate the not-at-risk income at the household level and estimate the at-risk income as the difference between the total gross income and the total income that is not at risk. Figure 1 shows the composition of per capita gross income by centile of the pre-pandemic income across five categories: cash transfers, social security pensions, government salaries, incomes not-at-risk, and incomes at-risk. The first three categories are incomes coming from the public sector. Note that the share of income that is not at risk –everything but the dark blue area– is not equal across the income distribution as many studies assume, nor is it uniformly decreasing in income as it would be if the poorest were most at risk. Rather, it is U-shaped with the greatest risk in the middle of the income distribution rather than either extreme.¹⁷ The very poorest households have an income floor from existing targeted cash transfers (albeit low) that protect an important share of their income. The richest households also have relatively lower income at risk than the middle. In Colombia and Mexico, this is due to income from social security pensions and employment in the public sector. In Argentina and Brazil, households in the higher deciles have less of their income coming from at risk sectors.¹⁸

We proceed to simulate potential income losses using the two key parameters. These are: the share of households with at-risk income that actually lose income and, of those who lose income, the share of at-risk income lost. As described above, in the 10 percent-10 percent cell of each matrix, we show the fall in total per capita gross income in percent corresponding to the case in which 10 percent of the households with at risk income lose 10 percent of their income at risk. For instance, in the 10 percent-10 percent cell for Argentina, the fall in total per capita gross income is estimated at 0.3 percent (see Panel (a) in Table 2).

Then, to narrow the possible outcomes down, we choose the scenarios for which the decline in per capita gross income comes closest to the macroeconomic predictors. Here, we use the IMF predictions for 2020 adjusted to per capita growth rates using data on population growth for the latest year available and assume the “pass-through” of GDP growth to household (gross) income growth of 0.85. As shown by the iso-loss curve (outcomes of income losses that are consistent with the predicted macroeconomic downturn), the previous step reduces the possible outcomes to between five or six depending on the country (highlighted cells in Table 2). When no other information is available, we choose the two results closest to the corners of the table where either the smallest proportion of households lose larger amounts of income –"concentrated losses” scenario-- or the largest proportion of households lose smaller amounts of income –“dispersed losses” scenario.

When additional information regarding the share of households that declare having lost income is available, we can choose a specific outcome within the matrix. In our exercise, the World Bank’s High-Frequency Monitoring Dashboard (2020) shows that 71.7 percent of households experienced a decline in their total income in Colombia.¹⁹ For Mexico, Universidad Iberoamericana (2021) finds that between April and December 2020, on average, 64 percent of households experienced a decrease in total income.²⁰ Since there is no information for Argentina and Brazil, we used 70 and 80 percent, respectively, the average for Latin America according to two major studies for the region.²¹ For each country, the scenario for which we have information on the projected GDP fall and the proportion of households who report losing income are the following: Argentina: total gross per capita income losses equal 9.4 percent with 70 percent of households losing 50 percent of their income; Brazil: 4.2 percent with 80 percent of households losing 20 percent; Colombia: 7.1 percent with 70 percent of households losing 30 percent; and, Mexico: 7.9 percent with 60 percent of households losing 40 percent. We show the “actual losses” scenario for each country in pink in Table 2.

Finally, we construct the post-policy response distribution, which simulates most of the additional policies each government has put in place to cushion the impact of the crisis, including both expansions of existing social assistance and introduction of new programs. Table 3 gives a brief description of each government’s policy responses in 2020 incorporated in our simulations of emergency social assistance programs.²² Note that Mexico has provided no additional social assistance (at the federal level) in the wake of the crisis.²³

For simulating the expanded social assistance, we proceed as follows. In the case of programs that existed pre-COVID, we assign the post-COVID additional payments to the households in which household members in the survey reported being beneficiaries of the existing pre-COVID programs. For the new social assistance programs, we first identify possible beneficiary households based on the definition of each program’s target population (e.g., informal workers, female household head, socio-economic level, and so on) and then assign the transfer randomly but only among the target population to match the number of total beneficiaries simulated in the survey to that reported in the administrative data.²⁴

3.3.Distribution of Losses: Non-anonymous Growth Incidence Curves and Income Mobility

The poverty and inequality comparisons above are anonymous. By (re-)ranking households from poorest to richest in each distribution, they do not consider the income trajectories of individual households. We describe trajectories using non-anonymous growth incidence curves (GIC)—in this case, “contraction incidence curves”—and pre- to post-pandemic income mobility.

3.3.1. Non-anonymous Growth Incidence Curves

Figure 2 shows the non-anonymous GIC curves for all the scenarios. Each point on the GIC curves shows the average change in per capita income (y-axis) for the households that are in the shown centile –based on their pre-pandemic income-- (x-axis). For example, under the “dispersed losses” scenario, the households in the first centile in Argentina could potentially lose about 13 percent of their pre-pandemic per capita income before the expanded social assistance; that loss becomes a gain of roughly 30 percent once we consider expanded social assistance.

Figure 2

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The bold lines in Figure 2 show that households across the entire income distribution are worse off on average (regardless of the scenario) after the pandemic, which is not surprising, but the losses tend to be higher for the middle deciles rather than the poorest, which perhaps is surprising. The latter reflects the fact that poorer households have a cushion given by the existing social assistance programs (the bottom area in Figure 1); it also reflects the fact that three types of income are both not at risk and concentrated at the top end of the pre-pandemic income distribution: social security pensions, salaries earned in the public sector, and labor earnings of white collar workers who are CEO’s, managers and researchers with internet access at home.³¹

The dotted lines in Figure 2 show the GIC curves after considering the effect of the expanded social assistance. As expected, social assistance cuts the losses and, indeed, increases the income of poor households by significantly more in Brazil where the mitigation policies have been much more ambitious compared to Argentina and Colombia. In all three countries that have new or expanded social assistance transfers those transfers favor the pre-pandemic poor.

When the pandemic struck, the first set of exercises quantifying the impact on living standards was produced fast and assumed that income losses were proportional across the income distribution. But it quickly became evident that this assumption was invalid as households with members working in informal sectors or members without the ability to telework were affected more drastically, and some households were minimally affected or not at all. Thus, the challenge was to use scant data to quantify the pandemic impact on living standards fast but not assuming that income losses would be proportional. Here, we propose a methodology with relatively low information requirements and a parsimonious modeling framework, which is also sufficiently flexible to allow the refinement of the projected effects of the shock as more information becomes available. Our proposed methodology to estimate the impact of COVID-19 or other unprecedented macroeconomic shocks on living standards allows researchers to relax the equal loss assumption and incorporate distributional changes in the analysis.

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Author details

1. Nora Lustig

   Samuel Z. Stone Professor of Latin American Economics in the Department of Economics at Tulane University, and Director of the Commitment to Equity Institute at Tulane University, New Orleans, United States

   For correspondence

   nlustig@tulane.edu

   "This ORCID iD identifies the author of this article:" 0000-0002-3171-6605

2. Valentina Martinez Pabon

   Postdoctoral associate at Yale University, New Haven, United States

   "This ORCID iD identifies the author of this article:" 0000-0002-1770-0316

3. Federico Sanz

   Consultant of the Commitment to Equity Institute and the World Bank, Washington, United States

4. Stephen D Younger

   Consultant of the Commitment to Equity Institute, Ithaca, United States