Abstract
Paraguay’s social assistance programs were useful to mitigate the impacts of the COVID-19 pandemic shock on the vulnerable population. Among other measures, the country deployed a novel cash transfer program, “Pytyvõ,” which targeted informal sector workers affected by the shock. Our analysis shows that it effectively prevented a larger erosion of poverty and inequality indicators. However, the results also show important opportunities to enhance the targeting strategy. The Pytyvõ program experience provides lessons on the benefit deployment in a timely matter and highlights the need for more efficient methods, such as implementing a beneficiary system that could be extended to existing or new social assistance programs.
Effectiveness of Social Assistance Programs in Paraguay During Covid-19 Times1
Paraguay’s social assistance programs were useful to mitigate the impacts of the COVID-19 pandemic shock on the vulnerable population. Among other measures, the country deployed a novel cash transfer program, “Pytyvõ,” which targeted informal sector workers affected by the shock. Our analysis shows that it effectively prevented a larger erosion of poverty and inequality indicators. However, the results also show important opportunities to enhance the targeting strategy. The Pytyvõ program experience provides lessons on the benefit deployment in a timely matter and highlights the need for more efficient methods, such as implementing a beneficiary system that could be extended to existing or new social assistance programs.
A. Some Stylized Facts
1. Paraguay’s economy was hit hard by the COVID-19 pandemic in 2020. While the aggregate contraction of the economy (at about -0.8 percent) was less severe than most countries in the region (LAC average contraction was about 6 percent), some sectors were disproportionately affected, such as hotels, services to households, and commerce services. The government’s initial measures of mobility restrictions and lockdowns affected tourism and trade in the services sector limiting production in these sectors.
Real GDP Growth
(In percent)
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: Penn World Tables, WEO and IMF staff calculations.Real GDP Growth
(In percent)
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: Penn World Tables, WEO and IMF staff calculations.Real GDP Growth
(In percent)
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: Penn World Tables, WEO and IMF staff calculations.
GDP Growth by Sector: 2020
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Source: IMF staff calculations based on national authorities’ data.GDP Growth by Sector: 2020
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Source: IMF staff calculations based on national authorities’ data.GDP Growth by Sector: 2020
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Source: IMF staff calculations based on national authorities’ data.2. Not surprisingly, lockdown measures and sluggish global economic activity negatively affected the labor market dynamics. Labor force participation and employment shrank in 2020, while labor income levels fell across different sub-groups. The shock affected women disproportionately, as they moved more intensively to unemployment or inactivity, largely explained by the role of women in household care with children, elderly and sick members. As a result, the changes in labor income seemed higher for men.
Labor Force Participation Rate by Gender
(Percent)
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: DGEEC and IMF staff calculations.Labor Force Participation Rate by Gender
(Percent)
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: DGEEC and IMF staff calculations.Labor Force Participation Rate by Gender
(Percent)
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: DGEEC and IMF staff calculations.
Labor Income Growth Rate
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: IMF/WB staff calculations based on national authorities’ data.Labor Income Growth Rate
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: IMF/WB staff calculations based on national authorities’ data.Labor Income Growth Rate
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: IMF/WB staff calculations based on national authorities’ data.3. Less favorable labor conditions led poverty indicators to retract some of the progress seen in previous years. Paraguay showed significant declines in poverty rates since the beginning of the century, only slowing down after 2014. However, the Covid-19 pandemic reverted this trend, especially in urban areas where poverty increased significantly. This is partly explained by the behavior of labor incomes for urban workers, who were affected by harsher mobility restrictions and the consequent rapid decline in economic activity. Rural workers saw a slight increase in poverty rates in part explained by the behavior of the agricultural sector but also by the compensation they received through the transfer programs deployed by the government, which accounts for a larger share of rural labor incomes compared to the urban ones. At the same time, inequality continued to reduce during the pandemic, mostly due to a shift to less favorable conditions across the income distribution.
Moderate Poverty Rates per year
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: National authorities’ data.Moderate Poverty Rates per year
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: National authorities’ data.Moderate Poverty Rates per year
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: National authorities’ data.
Gini Coefficient
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: National authorities’ data.Gini Coefficient
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: National authorities’ data.Gini Coefficient
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: National authorities’ data.B. Social Programs in Paraguay in Response to COVID-19
4. The rapid implementation of new social assistance programs helped reduce the pandemic’s large social cost on the most vulnerable. As the health emergency developed, the government implemented an extensive program to reach households more vulnerable to the labor income shock. The most extended program, Pytyvõ, was implemented in mid-2020 and designed as a cash transfer for informal sector workers who lost their job. The first version of Pytyvõ was financed with 195 US million and distributed benefits to about 18 percent of the population. Each beneficiary received 548 thousand guaranies (about 80 US dollars at the average 2020 exchange rate). Public cash transfers were important for those in the lowest part of the income distribution. They represented a large share of total income for those in the two lowest quintiles of the distribution (31.3 and 15.3 percent for the first and second quintiles, respectively), while they were not much significant for individuals in the upper part of the distribution (less than 1 percent for those in the fifth quintile).
Public Transfers as Percentage of Labor Income
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: IMF/WB staff calculations based on national authorities’ data.Public Transfers as Percentage of Labor Income
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: IMF/WB staff calculations based on national authorities’ data.Public Transfers as Percentage of Labor Income
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: IMF/WB staff calculations based on national authorities’ data.5. Paraguay’s public social assistance programs also prevented a higher erosion of poverty and inequality indicators due to the pandemic. Pytyvõ and other already implemented social assistance programs effectively reduced the pandemic’s impact on moderate and extreme poverty rates. Without Tekoporã (a social program for school-age kids, pregnant women, people with disabilities, and indigenous communities in poverty conditions), Adulto Mayor (a program for the elderly), and Pytyvõ, moderate and extreme poverty rates would have been reached 30.1 and 6.4 percent, respectively. That would have represented more than 60 percent more individuals in extreme poverty that the officially estimated figure for 2020. Concerning inequality, the impact of social programs was not that significant in absolute magnitude but still helped to contain a deterioration of the Gini coefficient. Pytyvõ and the other public social programs deterred inequality from reaching higher levels.
Impact of Social Assistance Programs on Poverty
(2020)
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: IMF/WB staff calculations based on national authorities’ data.Impact of Social Assistance Programs on Poverty
(2020)
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: IMF/WB staff calculations based on national authorities’ data.Impact of Social Assistance Programs on Poverty
(2020)
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: IMF/WB staff calculations based on national authorities’ data.
Impact of Social Assistance Programs on Inequality
(2020)
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: IMF/WB staff calculations based on national authorities’ data.Impact of Social Assistance Programs on Inequality
(2020)
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: IMF/WB staff calculations based on national authorities’ data.Impact of Social Assistance Programs on Inequality
(2020)
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: IMF/WB staff calculations based on national authorities’ data.6. Targeting informal sector workers to receive the program’s benefits Pytyvõ was broadly adequate. We analyzed the probability of receiving the Pytyvõ transfer for those individuals whose relative position in the income distribution shifted one or two quintiles (to the right or the left of the distribution) between 2019 and 2020. The results in Table 1 show that among people with a job in 2019, the marginal effects on the probability of receiving Pytyvõ or Ñangareko assistance for those individuals who shifted one or two quintiles to the lowest part of the distribution are positive and significant (i.e., the probability of receiving Pytyvõ was higher for those individuals whose income was negatively affected during the pandemic). On the other hand, the marginal effect on the probability of receiving the transfer is not statistically different from zero for those whose income situation improved during the pandemic.
Marginal Effect on the Probability of Receiving Public Transfers In 2020
Marginal Effect on the Probability of Receiving Public Transfers In 2020
| Individuals whose income moved: | Total | Male | Female |
|---|---|---|---|
| 2 quintiles to the left | 0.114*** (0.0365) |
0.0858* (0.0456) |
0.163*** (0.0598) |
| 1 quintile to the left | 0.0652*** (0.0246) |
0.0749** (0.0329) |
0.0517 (0.0371) |
| No change | 0 | 0 | 0 |
| 1 quintile to the right | 0.0221 (0.0257) |
0.0176 (0.0331) |
0.0299 (0.0401) |
| 2 quintiles to the right | 0.0392 (0.0454) |
0.0680 (0.0595) |
-0.00367 (0.0696) |
Marginal Effect on the Probability of Receiving Public Transfers In 2020
| Individuals whose income moved: | Total | Male | Female |
|---|---|---|---|
| 2 quintiles to the left | 0.114*** (0.0365) |
0.0858* (0.0456) |
0.163*** (0.0598) |
| 1 quintile to the left | 0.0652*** (0.0246) |
0.0749** (0.0329) |
0.0517 (0.0371) |
| No change | 0 | 0 | 0 |
| 1 quintile to the right | 0.0221 (0.0257) |
0.0176 (0.0331) |
0.0299 (0.0401) |
| 2 quintiles to the right | 0.0392 (0.0454) |
0.0680 (0.0595) |
-0.00367 (0.0696) |
7. While the design of the Pytyvõ program allowed to reach a segment of the most vulnerable population by targeting informal sector workers, such a strategy could be fine-tuned. Broadly, Pytyvõ’s resources benefited vulnerable households. However, a non-negligible share was also assigned to non-poor and richer segments. Those conclusions are supported by comparing the share of households receiving public transfers in 2019 versus the share of households that received benefits in 2020. The participation of the latter group increased not only for the lowest quintiles and poor households but also for the highest-income and non-poor households.
% Households Receiving Public Transfers by Income Group
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
% Households Receiving Public Transfers by Income Group
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
% Households Receiving Public Transfers by Income Group
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
% Households Receiving Public Transfers by Income Quintile -National
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
% Households Receiving Public Transfers by Income Quintile -National
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
% Households Receiving Public Transfers by Income Quintile -National
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
8. An optimized system of beneficiaries would help protect the most vulnerable households more efficiently. The previous results suggest that there is scope to reallocate resources to households in the lowest part of the income distribution. To assess the potential gains of such a strategy, we calculate the impact of transferring the resources received by the fifth quintile of the distribution (the richest segment) across the beneficiaries of the four lower quintiles. The results of this simulation exercise (see charts below) show that both inequality and poverty indicators would be lower if a “different Pytyvõ” scheme had been used.
Total Poverty Rate
(in percent)
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Total Poverty Rate
(in percent)
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Total Poverty Rate
(in percent)
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Extreme Poverty Rate
(in percent)
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Extreme Poverty Rate
(in percent)
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Extreme Poverty Rate
(in percent)
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
C. Remarks and Policy Implications
9. Social assistance programs can help reduce the impact of labor income shocks and protect the most vulnerable sectors from below-the-poverty line living conditions. The program Pytyvõ, successfully implemented by the government in 2020 in response to the COVID-19 shock, effectively protected the most vulnerable segments of the population from falling below non-poverty income levels.
Inequality Index (Gini): 2020
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: IMF/WB staff calculations based on national authorities’ data.Inequality Index (Gini): 2020
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: IMF/WB staff calculations based on national authorities’ data.Inequality Index (Gini): 2020
Citation: IMF Staff Country Reports 2022, 178; 10.5089/9798400213885.002.A002
Sources: IMF/WB staff calculations based on national authorities’ data.10. The experience of the program Pytyvõ offers an opportunity to enhance the implementation of targeted social programs in Paraguay. An optimized implementation of a system of beneficiaries should be prioritized to implement similar programs in the future or adjust the existing ones. This could be reached by modernizing processes for data management, targeting, application, and payment.
11. In view of recurrent climate-related shocks and uncertain geopolitical situations affecting external prices and markets, optimizing the design of social assistance programs is key to optimizing fiscal resources’ use while preserving social and economic stability.
This analysis was prepared in collaboration with the Gustavo Canavire-Bacarreza (World Bank) and Fernando Rios-Avila (The Levy Economics Institute of Bard College).
