From Infection to Inflation: GLOBAL CRISES HIT HARD POOR AND VULNERABLE HOUSEHOLDS IN LATIN AMERICA AND THE CARIBBEAN From Infection to Inflation: GLOBAL CRISES HIT HARD POOR AND VULNERABLE HOUSEHOLDS IN LATIN AMERICA AND THE CARIBBEAN LAC Team for Statistical Development:   Regional Poverty and Inequality Update  Poverty & Equity Global Practice From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean © 2023 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW Washington DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org This work is a product of the staff of The World Bank with external contributions. The findings, inter- pretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. 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The risk of claims resulting from such infringement rests solely with you. If you wish to re-use a component of the work, it is your responsibility to determine whether permission is needed for that re-use and to obtain permission from the copyright owner. Examples of compo- nents can include but are not limited to, tables, Figures, or images. Design and illustrations: Manthra Comunicacion ACKNOWLEDGMENTS This report was produced by the Poverty and Equity Global Practice in the Latin America and Caribbe- an Region of the World Bank. The core team was led by Sergio Olivieri and consisted of Ivan Gachet, Diana Sanchez Castro, Jaime Fernandez, Kelly Montoya, Karen Barreto Herrera, Cicero Braga, and Her- nan Winkler. Oliver Balch edited and enriched the document, and Pamela Gunio provided outstanding administrative assistance. The team worked under the supervision and guidance of Ximena Del Carpio (previous Practice Manager, ELCPV) and Carlos Rodriguez-Castelan (current Practice Manager, ELCPV). The team would like to thank the peer reviewers, Nandini Krishnan (Lead Economist, GPV03), Christoph Lakner (Program Manager, DECIS), and Marcela Melendez (Senior Advisor, LCRCE) for their comments and David Sislen (Practice Manager, SLCUR), Maria Gonzalez (Program Manager, SLCSO) and Hugo Ñopo (Senior Economist, ELCPV) for their remarks. The document also benefited from country-specific inputs received from: Maria Davalos, Alejandro De la Fuente, Jacobus Joost De Hoop, Roy Katayama, Gabriel Lara Ibarra, Monica Robayo, Lourdes Rodriguez Chamussy, Gustavo Canavire, Javier Romero, Lu- ciana De la Flor Giuffra, Vincenzo Di Maro, Carolina Mejia-Mantilla, Eliana Rubiano, Trinidad Saavedra Facusse, Mariel Siravegna, Agustin Arakaki, Ricardo Campante, Kiyomi Cadena, Christian Gomez Canon, Juan Manuel Monroy, Ana Rivadeneira, Fabio Saia Cereda, Diego Tuzman, Erika Schutt, Angela Lopez, Sofia Hidalgo, Silvia Granados, Luigi Butron, Constanza Vergara, Leah Arabella, and Luis Flores. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the World Bank’s views, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. Nothing herein shall constitute or be considered a limitation upon or waiver of the privileges and immunities of the World Bank, all of which are specifically reserved. The numbers presented in this brief are based on two regional data harmonization efforts known as the Socio-Economic Database for Latin America and the Caribbean (SEDLAC) and the Labor Data- base for Latin America and the Caribbean (LABLAC), joint efforts of the World Bank and the Center of Distributive, Labor and Social Studies (CEDLAS) at the National University of La Plata in Argentina. They increase the cross-country comparability of selected findings from official household and labor surveys. For that reason, the numbers discussed here may be different from official statistics reported by governments and national offices of statistics. Such differences should not be interpreted in any way as a claim of methodological superiority because both sets of numbers serve the same important objectives: regional comparability and the best possible representation of the facts of individual coun- tries. The welfare aggregate used in this study is income based. From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean ABOUT THE POVERTY REPORTS IN LAC The Poverty and Labor Brief (PLB) and Poverty and Inequality Monitoring (PIM) series present the latest trends in poverty, inequality, and shared prosperity in Latin America and the Caribbean (LAC) using comparable regional household and labor force surveys (SEDLAC and LABLAC, respectively). The re- ports are produced by the Latin America and Caribbean Team for Statistical Development (LAC TSD) in the Poverty and Equity Global Practice. PLBs and PIMs are designed to inform fact-based decision-making and discussion by providing readers with detailed and comparable statistics related to the World Bank’s twin goals of eradicating extreme poverty and boosting shared prosperity. PLBs offer a deeper explanation of the labor market as well as other issues related to poverty dynamics. PIMs, on the other hand, tend to be shorter and more specific. Along with the previous reports, many of the indicators reported in the series are available at the coun- try level in the LAC Equity Lab website at www.worldbank.org/equitylab. RECENT POVERTY BRIEFS: The Gradual Rise and Rapid Decline of the Middle Class in Latin America and May 2021 the Caribbean June 2018 Stagnant Poverty Reduction in Latin America (PIM) May 2017 Social Gains Show Signs of Stagnation in Latin America (PIM) May 2016 Inequity in Access to Opportunities April 2016 A Slowdown in Social Gains (PIM) Working to End Poverty in Latin America and the Caribbean: Workers, Jobs, June 2015 and Wages Social Gains in the Balance: A Fiscal Policy Challenge for Latin America and February 2014 the Caribbean Shifting Gears to Accelerate Shared Prosperity in Latin America and the June 2013 Caribbean August 2012 The Effect of Women’s Economic Power in Latin America and the Caribbean On the Edge of Uncertainty: Poverty Reduction in Latin America and the December 2011 Caribbean during the Great Recession and Beyond April 2011 A Break with History: Fifteen Years of Inequality Reduction in Latin America October 2010 Did Latin America Learn to Shield its Poor from Economic Shocks? CONTENTS Executive summary 12 I. A Global Pandemic: Impacts on poor, vulnerable, and middle-class households 15 II. Footprints of the pandemic (long-term COVID-19 impacts) 35 III. New day, new dawn? Advent of vaccines, ease of containment measures, and economic recovery 45 IV. A war and a hike in inflation together weakened the region’s recovery from the pandemic 67 V. Looking ahead 81 References 84 Annex 1: 2020 Surveys for countries in Latin America 90 Annex 2: Methodological approach 94 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean FIGURES Figure I-1: Latin America was severely affected by COVID-19 16 Figure I-2: The pandemic reverted the gains in poverty reduction and middle-class growth in LAC 20 Figure I-3: Poor populations also suffered a deterioration in living conditions 23 Figure I-4: Poverty increased significantly for households in living in urban areas 24 Figure I-5: The pandemic also pushed back gains in regional shared prosperity 26 Figure I-6: Labor incomes of households at the bottom of the income distribution were severely affected, which increased inequality 27 Figure I-7: Lockdown and safe-distancing measures implemented by governments helped to save lives 28 Figure I-8: LAC countries experienced massive job losses 29 Figure I-9: Women, youth, low-educated, and informal workers were hit hard by job losses 30 Figure I-10: Working hours and labor income plunge in the region 31 Figure I-11: Increase in public transfer partially offset labor income losses in LAC 32 Figure I-12: Employment and labor income losses were the main drivers of the rise in poverty 33 Figure II-1: Children in countries with high levels of stunting are more likely to be affected by food insecurity and loss of future income 39 Figure II-2: Expected future income losses due to stunning disproportionally affect children in poor households 40 Figure II-3: Children in households at the bottom of the income distribution have been more affected by education losses 41 Figure II-4: Education losses are expected to affect household per capita income over time 42 Figure II-5: Poverty and inequality are expected to increase over the life cycle of children affected by COVID-19’s shock on education 43 Figure III-1: Vaccination rates speeded up in the second half of 2021 46 Figure III-2: The recovery has slightly benefitted unskilled workers 48 Figure III-3: The recovery has been slow for youth, low-educated workers, and women 50 Figure III-4: While employment recovered, job quality deteriorated across the region in 2021 51 Figure III-5: Workers reallocated to small and less productive firms in 2021 54 Figure III-6: The deterioration of job quality was more pronounced in countries experiencing tight labor market conditions before the pandemic 55 Figure III-7: Vulnerable groups experienced a more severe deterioration in job quality 56 Figure III-8: Countries faced a slow and uneven recovery in working hours 58 Figure III-9: The mild recovery of labor income in 2021 did not offset income losses and remained far behind pre-pandemic levels 60 Figure III-10: Public transfers plunge in 2021 in LAC 61 Figure III-11: Poverty declined and the middle class recovered, but neither returned to pre-pandemic levels in 2021 62 Figure III-12: Employment and labor income were the drivers of poverty reduction in 2021 64 Figure III-13: Incomes of the bottom income deciles increased significantly, and inequality declined in 2021 65 Figure III-14: Many households faced food insecurity and covered basic needs by using their savings 66 Figure IV-1: Since 2020, inflation has increased systematically in LAC 69 Figure IV-2: Poor households may be particularly affected by the rise of food and fuel prices 70 Figure IV-3: While employment is expected to recover in most countries, labor income still lags behind pre-pandemic levels 73 Figure IV-4: Despite a mild recovery, poverty levels and the size of the middle class are still not expected to reach pre-pandemic levels in 2022 74 Figure IV-5: Additional inflation would push many people into poverty and out of the middle class in LAC 75 Figure IV-6: Living conditions of poor households are expected to deteriorate due to additional inflations in LAC 76 Figure IV-7: The rise of food and fuel prices is projected to have a disproportional impact on households at the bottom of the income distribution 78 Figure IV-8: Inequality is expected to increase due to additional inflation 79 Figure IV-9: Poverty reduction is expected to be driven by labor income 80 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean TABLES Table I-1: Changes in the profile of poor and middle class households during the pandemic 25 Table II-1: The ‘new poor’ in LAC are more likely to be high-skilled, work informally, and live in urban areas 63 Table III-1: High-skilled urban workers in informal employment are expected to face downward mobility due to additional inflation in LAC 77 BOXES BOX I-1: Poverty levels for LAC countries, with data collected at multiple points during the pandemic 17 BOX I-2: New poverty, plus vulnerable and middle-class lines 21 BOX II-1: School closures are expected to negatively affect women’s job prospects in the long run 37 BOX II-2: Evidence-based strategies to mitigate the negative effects of the pandemic on children’s education 44 BOX III-1: Quality of jobs in LAC 52 BOX III-2: Labor market transitions 57 BOX IV-1: Effects of fuel and fertilizer price on poor households 70 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean EXECUTIVE SUMMARY Latin America and the Caribbean (LAC) have faced extraordinary challenges over the last three years that reverted the social gains of the previous two decades. The Covid-19 pandem- ic, sluggish economic growth, fiscal constraints and increased debt stress, inflationary pressures, and the collateral effects of the Russian invasion of Ukraine have taken a toll on the region. These shocks have hit poor and vulnerable populations the most, increasing poverty and inequality; and moving millions out of the middle class. Yet, the region has been relatively resilient in recovering from these crises, albeit in uneven fashion. By the end of 2022, the labor markets had improved, poverty had receded, and the mid- dle class had recovered, despite economic growth fluctuations and political instability across the region. Still, the LAC region has not completely regained its pre-pandemic socio-economic conditions in many countries and a closer look at the distributional impacts over these years could help identify opportunities. The COVID-19 pandemic resulted in severe health impacts and a reversal in many of its socio- economic gains. The region’s gross domestic product (GDP) contracted by 6.4 percent in 2020, causing hardships for all but the richest households and increasing inequality. The socio-economic impact of the pandemic came on the back of several years of sluggish economic growth, which had weakened the ca- pacity of the region’s governments to react. Low-skilled workers, women, and those working in the infor- mal economy were most affected by the economic shock of COVID-19; almost one in ten (8.3%) working adults lost their jobs. During the pandemic, around 19 million people fell back into poverty, mostly in urban areas. The living conditions of poor populations also deteriorated, with Peru posting a particularly sharp widening of its poverty gap. The rise in poverty came despite impressive government efforts to cushion COVID-19’s negative impacts on people’s livelihoods. The one notable exception in this regard is Brazil, where a successful cash transfer program compensated for almost all the income losses experienced by poor households. A rare upside of the pandemic was the increase in internet use, which was seen most strongly in respect of digital banking. This shift in behavior helped foster financial inclusion, although in- ternet access remains far from universal or evenly spread. The unprecedented disruption to education and health during the COVID-19 pandemic will leave lasting scars on human capital accumulation and the welfare of an entire LAC genera- tion. During the pandemic, children across the region encountered severe disruptions to their learning, with more than 75 percent of their total instruction time lost due to school closures. Parents and gov- ernments both tried to provide alternative schooling opportunities, but learning losses remained severe, especially for children in poor households. In addition, a rise in food shortages impacted many children’s physical and cognitive development, with levels of child stunting expected to rise as a result. The com- bined effect of these twin educational and health shocks is likely to be a reduction in future labor incomes for today’s school-age cohort. In 2045, they are projected to be earning 6.4 percent less than they would have earned were it not for the pandemic only due to learning losses. This income reduction will result in an estimated increase in poverty rates of around 1.7 percentage points, equivalent to five million more people falling into poverty than would otherwise be the case. A year after the onset of COVID-19, the region is bouncing back, yet not sufficiently fast to put the worst effects of the pandemic behind it. A combination of uneven access to vaccines, sporadic containment measures, and an enfeebled labor market has stopped welfare standards from returning to their pre-pandemic levels. By 2021, a proportion of the nine million jobs lost during the pandemic had 12 been regained, but employment levels remain 3.1 percentage points below the immediate pre-pandemic period. Furthermore, many of these regained jobs are of a lower quality than before. Most notably, levels of self-employment and informality have grown, while formal labor has seen a shift away from secure employment in large firms to insecure work in small firms. In the long term, this may well result in negative effects on the region’s productivity, as well as on labor demand. More immediately, households are experi- encing an ongoing deterioration of their welfare standards. Fiscal constraints have reduced ’governments’ capacity for public transfers (which helped mitigate some of the pandemic’s worst effects, especially in Brazil), while private remittances have also fallen. As a result, many poor households are struggling to meet their basic needs. Even though poverty was reduced in 2021, around ten million additional people remained impoverished. Moreover, the pandemic eroded their financial assets, leaving them less able to cope with shocks in the future. This situation contrasts with the experience of the richest quintile, who have bounced back quickly, enlarging the region’s already sizeable economic divide. The knock-on effects of global events have stymied LAC’s recovery in the early stages of the post-pandemic period. Russia’s invasion of Ukraine in early 2022 and its impact on international fuel and food prices have caused average inflation in the region to reach 8.9 percent (excluding Argentina). This sharp rise in prices is hampering households’ purchasing power and causing employment quality (but not quantity) to remain below previously projected levels. Poor households are especially vulnerable in light of their experience of food insecurity and reduced savings during the pandemic. While some benefit has come to net food-producer households from higher commodity prices, this has been offset (in part or in total) by higher input costs, notably fertilizers. At the same time, LAC countries have limited fiscal room for additional social programs. Poverty levels are subsequently forecast to hit 31.0 percent in 2022 (excluding Brazil), a rise of 1.3 percentage points on 2019 levels (equivalent to around eight million individ- uals). Inequality is also expected to grow, with a projected half-point rise in the Gini coefficient compared to the period prior to the Ukraine conflict. The weak economic outlook for 2023 will slightly improve employment and labor incomes, but poverty will remain above pre-pandemic levels. On average, employment in LAC is likely to be 0.9 percent above total employment in 2019, representing a creation of about 1.5 million jobs across the region. This job creation will be accompanied by lower quality, leaving many workers exposed to higher levels of vulnerability and income shocks. Moreover, average labor income would increase slightly (0.3 percent), which is not enough to reach pre-pandemic levels (i.e., 3 percent below its pre-pandemic levels). Excluding Brazil, poverty rates at US$6.85 a day (2017 PPP) are expected to reduce 0.2 percentage points, lifting 300,000 individuals out of poverty in 2023. Considering Brazil, poverty rates are also expected to decline from 28.6 percent in 2022 to 28.3 percent in 2023. Yet, poverty levels will remain above pre-pan- demic levels (1.1 percentage points excluding Brazil and 0.1 percentage points including Brazil). A slight recovery in the middle class is also expected in 2023, but total numbers still below pre-pandemic levels. Excluding Brazil, the middle class will mildly increase from 34.3 percent in 2022 to 34.5 in 2023, 1.5 per- centage points below pre-pandemic levels. Yet, including Brazil, the middle class is expected to decrease by 0.1 percentage points from 37.1 percent in 2022 to 37.0 percent in 2023, with about one million people falling out of this population segment. Despite the challenges ahead, the LAC region has the potential to overcome them in its tra- ditional areas of comparative advantage and the opportunities arising from resilient green growth. As shown in the report The Promise of Integration: Opportunities in a Changing Global Economy, the region needs to complement long-term structural reforms to reduce systemic risk, raise the level and 13 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean quality of education, invest in infrastructure, and ensure well-functioning financial markets with a com- prehensive approach to integrate the region into the global economy, particularly the US and European markets. In addition, it needs to take advantage of its comparative advantage in the green economy. The transition to the green economy could be an opportunity to improve well-being in the region by cre- ating new quality jobs, enhancing labor incomes, and contributing to poverty reduction. However, this transition will demand a significant change to labor markets in LAC countries. This change will create new jobs but at the same time it could potentially destroy jobs and displace workers in many sectors. This process will require investment in human capital for the training and reskilling of workers. It will also demand well-designed social programs to protect the most vulnerable during the transition (e.g., active labor market programs), as well as incentives for informal workers to shift to new productive firms involved in green technologies. 14 I A Global Pandemic: IMPACTS ON POOR, VULNERABLE, AND MIDDLE-CLASS HOUSEHOLDS 15 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean A Global Pandemic: IMPACTS ON POOR, VULNERABLE, AND MIDDLE-CLASS HOUSEHOLDS L atin America and the Caribbean (LAC) has been one of the regions in the world most severely affected by the COVID-19 pandemic since the first case was identified in Brazil in February 2020. As of December 31, 2022, almost 80 million positive COVID-19 cases were reported. Of these, 1.7 mil- lion resulted in deaths. This makes LAC the third most affected region with the highest numbers behind Europe and the Western Pacific.1 Official tracking data show that Brazil, Argentina, Mexico, and Colom- bia represent 75 percent of the region’s reported cases. The two largest countries in the region – Brazil and Mexico – have seen the highest death numbers, with more than 693,734 and 331,450, respectively. With approximately 36 million infections, Brazil is the sixth-worst affected country worldwide, after the United States, China, India, France, and Germany. Also, countries in the region have been disproportion- ally affected by excess mortality rates (i.e., the number of deaths above what is usually expected at that time of year) due to COVID-19. Peru, Mexico, Bolivia, and Ecuador have suffered peaks in mortality that are more than triple the size of those usually experienced across the pandemic period. Brazil and Uru- guay, meanwhile, were hit much harder in 2021 than in 2020. Within countries, there are also substantial differences in respect of the disease’s impact. For example, urban centers registered higher numbers of identified COVID-19 cases than rural areas. FIGURE I-1: LATIN AMERICA WAS SEVERELY AFFECTED BY COVID-19 Panel A. Confirmed cases Panel B. Confirmed deaths Source: WHO COVID-19 Dashboard Note: Confirmed cases of COVID-19 in these maps are as of December 31, 2022. 1 See WHO COVID-19 Dashboard for regular updates at https://covid19.who.int/. The latest update for this report is as of December 31, 2022. 16 The region’s economic growth plummeted due to the health crisis with significant socioeconomic impacts. The combination of several years of sluggish economic growth, limited progress in poverty reduction, and an unstable social environment meant that the region was ill-prepared to deal with such a global crisis.2 Economic activity slowed down due to government-imposed national lockdowns, travel restrictions, school closures, social-distancing measures, risk aversion among households and firms, and spillovers from a shrinking global economy.3 As a result, LAC’s gross domestic product (GDP) contracted by 6.4 percent in 2020, the deepest among the six emerging markets (China, South Africa, Brazil, Mexico, India, and Russia) and developing economies. However, there were significant differenc- es across countries in the region.4 Brazil’s economy declined by almost 4 percent, while the economies of Mexico and Argentina shrank by 8.0 percent and almost 10 percent, respectively. For its part, Central America saw a contraction in its overall GDP of 7.6 percent (World Bank, 2021a).5 BOX I-1: POVERTY LEVELS FOR LAC COUNTRIES, WITH DATA COLLECTED AT MULTIPLE POINTS DURING THE PANDEMIC Generally, poverty is monitored with indexes estimated as the annual average, given that significant changes are not expected over a short period after accounting for seasonal variation. For instance, in 2018 and 2019, poverty rates presented a small quarterly varia- tion or were on par with or slightly below annual poverty rates in four LAC countries with continuous surveys – namely, Argentina, Colombia, the Dominican Republic, and Peru. However, this stable behavior throughout the year only holds in 2020. Following the on- set of the pandemic, poverty rates showed staggering increases in the second and third quarters of 2020. In 2020, countries’ continuous surveys revealed significant income variability resulting from both the peak of the pandemic and the progressive lifting of restrictions on move- ment and economic activity. For instance, most countries suffered a drastic increase in poverty rates in the second quarter of 2020 before declining gradually in the third quarter. This behavior reflected containment measures that constrained economic activity and resulted in significant job and income loss, thereby increasing the size of the bottom quin- tile of the income distribution. Overall, the COVID-19 pandemic considerably increased poverty in each of the four countries, with surveys at the quarterly and annual levels. 2 The last quarter of 2019 shocked the LAC region with an outbreak of strikes, protests, and riots, resulting in considerable vio- lence on the streets of Chile, Colombia, Ecuador, and other countries. Protesters had many demands, mostly related to eco- nomic policies and better opportunities, while others were clearly political in nature. However, levels of violence were high across the board, leading to large numbers of dead and injured as well as significant material damage (World Bank, 2020a). 3 For more details on government responses, see IMF, Policy Responses to COVID-19, at https://wsww.imf.org/en/Topics/ imf-and-covid19/Policy-Responses-to-COVID-19#H 4 The decline in GDP growth in the region was more severe than during the Debt Crisis of the 1980s and the 2009 global financial crisis when the economy contracted by 2.5 and 1.9 percent, respectively (World Bank, 2021b; World Bank, 2021c). 5 GDP growth for this report version corresponds to estimates for March 23, 2023. 17 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean The second-quarter poverty levels rose by 20 percentage points above the annual average using the $6.85 line/day (2017 Purchasing Power Parity [PPP]). More specifically, poverty reached up to 62.6 percent in Peru (compared to an annual rate of 43.0 percent), 52 per- cent in Colombia (with a 42.2 percent annual rate), 16.4 percent in Argentina (14.1 percent annual rate), and 26.3 percent in the Dominican Republic (23.2 percent annual rate). Comparatively, however, increases in Argentina and the Dominican Republic were not as stark as in Peru and Colombia, where annual poverty rates were more than 10 percentage points above their 2019 levels (Figure B.I-1 – Panel A). The increase in poverty rates also affected the extremely poor population, exacerbating existing inequalities. The second-quarter poverty levels, which are calculated using the $2.15 line/day (2017 PPP), increased up to 9 percentage points above the annual average. Although poverty rates also rose in Argentina (0.7 percentage points) and the Dominican Republic (0.8 percentage points), the increases were not as pronounced as in Peru (8.6 percentage points) and Colombia (7.1 percentage points) (Figure B.I – Panel B). In most LAC countries, surveys are collected during the second half of the year. For instance, Bolivia, Costa Rica, and Ecuador implement household surveys every year in October, July, and December. This information allows for a year-on-year comparison of welfare and poverty indicators. In the specific case of COVID-19, however, this approach might mean that the peak of the pandemic is not fully captured. FIGURE B.I-1. MOST COUNTRIES EXPERIENCE A SIGNIFICANT INCREASE IN POVERTY IN THE SECOND QUARTER OF 2020 Panel A. Poverty headcount under the $6.85 a day line by quarter (2017 PPP) 2018 2019 2020 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 70 62,6 60 50 52,0 Percentage (%) 40 30 26,3 20 16,4 10 0 ARG COL DOM PER ARG (annual) COL (annual) DOM (annual) PER (annual) Figure B.I-1. 18 Panel B. Poverty headcount under the $2.15 a day line by quarter (2017 PPP) 2018 2019 2020 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 18 16,5 16 14,5 14 12 Percentage (%) 10 8 6 4 1,7 2 0 1,8 ARG COL DOM PER ARG (annual) COL (annual) DOM (annual) PER (annual) Source: World Bank staff calculations based on SEDLAC (CEDLAS and World Bank). Figure B.I-2. Note: Since the numbers presented here are based on Socio-Economic Database for Latin America and The Caribbean (SEDLAC), which is a regional data harmonization effort that increases cross-country comparability, they may differ from official statistics reported by governments and national statistical offices. - Continuous Surveys for Argentina, Colombia, the Dominican Republic, and Peru. - For Argentina, poverty rates are generally estimated with two quarterly datasets combined to avoid seasonality issues. Therefore, the estimates presented need to be taken with caution for quarter-to-quarter comparisons. - For Peru, estimates are based on the Annual version of Encuesta Nacional de Hogares (ENAHO). The COVID-19 pandemic rapidly eroded most poverty reduction gains of the recent past. In the early 2000s, the region made significant progress in the fight against poverty, with the poorest households benefitting the most from sustained economic growth up until 2014. Since then, poverty reduction in the region has been modest, with a gradual increase in the size of the middle class. This trend was abruptly interrupted by the pandemic when poverty rose for the first time in decades. Ex- cluding Brazil, regional poverty rates measured at $6.85 a day (2017 PPP) increased from 29.4 percent in 2019 to 34.4 percent in 2020, with approximately 19 million people falling into poverty (Figure I-2 – Panel A). This sharp increase is heavily influenced by a rise in poverty of 14.2 percentage points in Peru during 2020. The rise was the highest in the region, doubling the increase seen in Colombia (7.4 per- centage points) and Costa Rica (6.2 percentage points) (Figure I-2– Panel B). As a result, poverty rates in many LAC countries have been set back by seven years or more.6 However, when Brazil is included, there are no significant changes in poverty in the region between 2019 and 2020. The effectiveness of 6 For instance, poverty rates in Argentina reverted to the levels in 2008, Costa Rica to levels in 2008-09, and Peru to levels in 2009-10. 19 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean the fiscal package that provided direct cash transfers targeted to informal or own-account workers and low-income families, benefiting 67 million individuals prevented Brazil from joining the rest of the LAC region in observing their poverty levels increase (World Bank, 2022).7 FIGURE I-2: THE PANDEMIC REVERTED THE GAINS IN POVERTY REDUCTION AND MIDDLE-CLASS GROWTH IN LAC 55 50 45 Percentage (%) 40 35 34,4 Panel A. 29,4 Poverty headcount at $6.85 per 30 28,7 day (2017 PPP), 25 28,2 2000 2006 2008 2009 2004 2005 2020 2002 with and without Brazil 2003 2007 2001 2010 2016 2018 2019 2014 2015 2012 2013 2017 2011 $6.85 per day (2017 PPP) with Brazil $6.85 per day (2017 PPP) without Brazil Panel B. 14,2 Figure I-2 (PANEL A): 7,4 Changes in socioeconomic 6,2 Percentage (%) 4,9 5,8 3,0 2,9 3,1 2,7 classes by country (2020 vs. 2019) 0,8 0,7 1,7 1,2 2,1 2,2 1,7 0,2 2,1 -2,5 -1,8 -2,0 -4,1 -3,5 -3,6 -5,2 -5,5 -4,8 -6,0 -7,4 -11,4 Peru Colombia Costa Rica Ecuador Dominican Argentina Paraguay Uruguay Bolivia Brazil-PNADC Republic (urban) Poverty $6.85 Vulnerable $6.85-$14 Middle Class $14-$81 Figure Source: World Bank staff calculations I-2on based (PANEL B): and the World Bank). SEDLAC (CEDLAS Note: Since the numbers presented here are based on Socio-Economic Database for Latin America and The Caribbean (SEDLAC), which is a regional data harmonization effort that increases cross-country comparability, they may differ from official statistics reported by governments and national statistical offices. The LAC aggregate is based on 18 countries in the region for which microdata are available. In cases where data are unavailable, values have been interpolated or extrapolated using data from the World Bank Development Indicators (WDI) and then pooled to create regional estimates (2014 backward) and microsimulations (from 2015 onwards). Due to important methodological changes in Mexico’s official household survey in 2016 that created a break in the poverty series, we have created a break in the LAC-18 aggregate. 7 Despite being one of the countries hit the hardest by COVID-19, Brazil introduced a social protection program (Auxílio Emergencial) that stood out for its generosity, speed, and coverage, as well as for its innovative design. The program reached 55.6 percent of the population and was targeted mainly at informal and own-account workers. It employed a registration process that combines ad- ministrative records and registration using a website or an app. For benefits payments, digital bank accounts were created without cost for beneficiaries at the federal savings bank, CAIXA. This new bank account included possibilities for digital banking operations without needing physical cards or digital transactions, thus avoiding agglomerations in the payment process. The initiative was originally designed to act as a temporary unconditional cash transfer program. In the end, however, it was extended through three waves between April 2020 and October 2021 (World Bank, 2021d). 20 The middle class was not excluded from this massive shock and reduced its size in most LAC countries. The COVID-19 pandemic reversed the gradual rise in the size of LAC’s middle class (see BOX I-2). By 2019, 36 percent of the population in Latin America was considered middle class (excluding Brazil), amounting to approximately 132 million people. Yet, this number contracted by 4 percentage points in 2020 due to the COVID-19 crisis, resulting in a net loss of 13.2 million people from the middle class. The decline was less severe in Paraguay, Uruguay, and Bolivia. Contrary to the rest of the region, the middle-class size in Brazil increased by 2.1 percent points. This is explained by the generous emergency transfer program implemented by the government, which not only protected low-income families but also reached broader segments of the population, including middle-class workers lifting many people from falling into the income distribution (World Bank, 2021d). While most governments in LAC coun- tries also provided direct cash transfers as a buffer for income loss, its effectiveness was not like Brazil because they were implemented for a short period of time with inefficiencies in targeting mechanisms that were often unable to reach people in the middle of the income distribution or urban areas. Also, most government responses relied on introducing new programs that combine different identification mechanisms such as open registration, social registries, tax databases, and others (Gentilini, 2022). In Peru, for instance, the government was forced to rapidly update its household registry because its social protection system failed to identify vulnerable and poor urban households and suffered from leakages at the top of the income distribution, which limited its effectiveness despite having one of the largest mitigation packages in the region (World Bank, Forthcoming). BOX I-2: NEW POVERTY, PLUS VULNERABLE AND MIDDLE-CLASS LINES Poverty lines International poverty lines (IPLs) are based on Purchasing Parity Power (PPP) exchange rates. This allows for the IPLs to be converted into a common currency while at the same time accounting for differences in relative prices across countries. The World Bank released an updated version of the PPP exchange rates in May 2020, which triggered the update of the IPLs in September 2022. Poverty data are now expressed in 2017 PPP rates versus 2011 PPP rates, as in previous edi- tions. As price levels across the world evolve, IPLs must be periodically updated to reflect the increase in the value of the lines in nominal terms. The new extreme poverty line of $2.15 per person per day, which replaces the $1.90 poverty line, is based on 2017 PPP rates. The higher poverty lines typically used to measure poverty in lower middle-income countries (LMICs) and upper middle-income countries (UMICs) are also updated from $3.20 (2011 PPP) to $3.65 (2017 PPP) and from $5.50 (2011 PPP) to $6.85 (2017 PPP), respectively. In addition to reflecting updates in nominal terms, upper-middle-income countries raised the standards by which they determine people to be poor from 2011 to 2017. Hence, the increase in the upper line is larger, while the population that does not meet the new standard is also higher in most countries than it was with the 2011 PPP rate. See https://pip.worldbank.org/home. 21 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean At the regional level, the change from 2011 to the 2017 PPP rates induces a relatively small change in extreme poverty and poverty at $3.20 or $3.65 a day in the 2011 PPP or 2017 PPP. The 2017 PPP slightly increased historical estimates by less than 0.3 percentage points at $2.15 in 2011 and 0.9 percentage points at $3.65 in 2017. These shifts represent 1.9 and 4.8 million additional people in extreme poverty and poverty, respectively. However, poverty downward trends are similar for both 2011 and 2017 PPP rates, irrespective of the threshold. While there are no significant changes in poverty levels at both $2.15 and $3.65 a-day lines (2017 PPP), poverty increases markedly in the LAC region when using the $6.85 line (2017 PPP) relative to the $5.5 line (2011 PPP).* In 2017, regional poverty changed by 5 percentage points, or 27 million more poor people, with the 2017 PPP. Vulnerability and middle-class lines Monitoring poverty is not enough to describe the evolution of living conditions in LAC. It is important not only to lift poor households above the minimum income threshold (poverty line) but also to protect vulnerable households (those close to the poverty line) from falling back into poverty. Moreover, as countries grow and move toward middle-class income status (which characterizes most LAC countries today), it becomes imperative to analyze the transi- tions into the middle class over time. Thus, at any point in time, an individual can be classified as being poor (based on the IPL for LMICs and/or UMICs), as being vulnerable, or as being in the middle class. Following the new international poverty lines, Fernandez, Olivieri, and Sanchez (2022) pro- posed a two-step methodology for updating vulnerability and middle-class lines in LAC, expressed in 2017 PPP rates. Findings indicate that updating the $13 line in 2011 PPP to 2017 PPP results in a $14 per person per day vulnerability line (lower bound). The study also es- timates a middle-class line (upper bound) of $81 per person per day in 2017 PPP, compared with $70 per person per day in 2011 PPP. Overall, the vulnerable population (share of the population living between $5.5 and $13 a day, or $6.85 and $14 a day expressed in 2011 PPP or 2017 PPP, respectively) decreases 5.4 percent- age points with the 2017 PPP. This represents 31 million fewer vulnerable people in the region for 2017. This decrease in vulnerability when comparing the 2011 and 2017 PPP lines is mainly explained by the almost equivalent increase in the poverty rate below the $6.85 poverty line. On the other hand, the middle class has slightly increased in the region and remains the largest socioeconomic group in the LAC region since 2010. This group is measured as the share of the total population between $13 and $70 a day or $14 and $81 a day expressed in 2011 PPP or 2017 PPP, respectively. While the middle class has experienced considerable growth, changes in vul- nerable populations have been relatively small between 2010 and 2019. In 2020, during the first year of the pandemic, vulnerability increased by 1.8 percentage points while the middle class decreased by 1.8 percentage points (2017 PPP), down to 2015-2016 middle-class levels. *Jolliffe, et al. (2022) point out that the change in PPP rates only accounts for the increase between $5.5 and $6.32, quite far from the final $6.85 line. The relatively high increase in the upper-middle-income line is partially driven by real upward shifts in the national poverty lines of upper-middle-income countries. Part of this can be explained by some of these countries now being high-income countries. (For further details, see Jolliffe, et al. 2022). 22 More importantly, the COVID-19 crisis significantly deteriorated the living conditions of poor households. Despite no changes in the average poverty gap in Latin America, the poverty gap increased significantly in the Andean and Central American countries during the pandemic. In Ande- an countries, the poverty gap widened by nearly five percentage points from 11.6 percent in 2019 to 16.5 percent in 2020. However, this level is still three percentage points lower than in Central American countries and four times higher than the countries in the Sothern Cone in 2020 (Figure I-3). After the health shock, Peru went from being ranked fifth in 2019 to second in 2020 as the country with the largest poverty gap, only behind Colombia and surpassing Ecuador. This reappraisal marked a reverse of decades of progress for Peru in improving living conditions. FIGURE I-3: POOR POPULATIONS ALSO SUFFERED A DETERIORATION IN LIVING CONDITIONS Poverty gap 24,0 19,0 18,3 17,3 16,5 14,4 14,3 14,0 15,1 11,4 11,8 11,6 Percentage points 11,5 11,5 10,9 11,0 10,5 8,8 7,0 6,9 6,9 6,6 6,3 6,2 5,8 5,6 4,5 4,3 4,3 3,9 3,7 3,2 2,4 1,7 1,1 2019 2020 Figure I-3: Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank). Note: Since the numbers presented here are based on Socio-Economic Database for Latin America and The Caribbean (SEDLAC), which is a regional data harmonization effort that increases cross-country comparability, they may differ from official statistics reported by governments and national statistical offices. The LAC aggregate is based on 18 countries in the region for which microdata are available. In cases where data are unavailable, values have been interpolated or extrapolated using data from the World Bank Development Indicators (WDI) and then pooled to create regional estimates (2014 backward) and microsimulations (from 2015 onwards). Due to important methodological changes in Mexico’s official household survey in 2016 that created a break in the poverty series, we have created a break in the LAC-18 aggregate. Brazil and Mexico are not part of the aggregate of subregions. The Andean region is the aggregate of Bolivia, Colombia, Ecuador and Peru. The Central America Region is the aggregate of Costa Rica, Guatemala, Honduras, Nicaragua, Panama, El Salvador, and the Dominican Republic and Southern Cone Region is the aggregate of Argentina, Chile, Paraguay and Uruguay. Households living in urban areas were more likely to experience increases in poverty due to the health crisis. While rural poverty rates are still higher than those in urban areas, the share of urban poor in cities exceeds those in rural areas. It has increased significantly across LAC countries due to the COVID-19 pandemic. Stringent health containment measures brought a large share of economic activity to a halt in urban areas, leaving many urban poor and vulnerable without means of subsistence almost overnight. Also, the prevalence of informality and their lower digital inclusion worsened the shock among the urban poor 23 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean (Talierico O’Brien & Ñopo, 2022). Excluding Brazil, urban poverty rates measured at $6.85 a day (2017 PPP) increased by six percentage points from 22 percent in 2019 to 28 percent in 2020, representing about two third of the total poor under $6.85 a day (2017 PPP) poverty line in the region (Figure I4– Panel A). However, when Brazil is included, urban poverty rates only increased by one percentage point across region between 2019 and 2020 (Figure I-4– Panel B). This slight increase is explained by the effectiveness of the emergency package implemented in Brazil in targeting low-income families, informal workers, and the self-employed in urban areas (World Bank, 2022). FIGURE I-4: POVERTY INCREASED SIGNIFICANTLY FOR HOUSEHOLDS IN LIVING IN URBAN AREAS 90 66 80 Urban poor (share of total poor) 65 70 64 Poverty rate (%) 60 63 50 62 40 61 30 Panel A. 20 60 LAC with Brazil 10 59 0 58 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Rural poverty rate $6.85 per day (2017PPP) Urban poverty rate $6.85 per day (2017PPP) Urban poor (share of total poor), right axis 90 Figure I-4: (PANEL A): 66 80 Urban poor (share of total poor) 65 70 64 Poverty rate (%) 60 63 50 62 Panel B. 40 61 LAC excluding Brazil 30 20 60 10 59 0 58 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Rural poverty rate $6.85 per day (2017PPP) Urban poverty rate $6.85 per day (2017PPP) Urban poor (share of total poor), right axis Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank). Figure Note: The LAC aggregate is based on 18 countries in the region for which microdata I-4: (PANEL are available. B): data are unavailable, In cases where values have been interpolated or extrapolated using WDI data and then pooled to create regional estimates (2014 backward) and microsimulations (from 2015 onwards). Due to important methodological changes in Mexico’s official household survey in 2016 that created a break in the poverty series, we have created a break in the LAC-18 aggregate. 24 The increase in urban poverty and the decline in the middle classes changed the profile of poor populations during the pandemic. For instance, the poor during COVID-19 are more likely to have higher levels of education than those living below the poverty line before the pandemic. Excluding Brazil, the poor with at least some secondary education or more increased from 43.4 percent in 2019 to 49.7 percent in 2020 (Table I-1). Yet, those with higher education levels are expected to be better suited to benefit from any future recovery in jobs and opportunities as the region recovers from the pandemic. Also, the profile shows that the new poor are more likely to be working-age adults and unemployed than the existing poor. They are also less likely to work as salary workers or self-employed compared to pre-pandemic levels. In addition, the poverty profile profiles suggest that the new poor are more likely to work outside agriculture and in the services sectors. TABLE I-1: CHANGES IN THE PROFILE OF POOR AND MIDDLE CLASS IN LAC DURING THE PANDEMIC   LAC with Brazil LAC excluding Brazil   2020 2019 2020 2019   Poor Middle Class Poor Middle Class Poor Middle Class Poor Middle Class Age Groups 0-14 36.3 13.3 36.3 14.0 35.8 15.2 38.2 16.0 15-24 18.3 13.7 18.5 14.1 18.0 15.0 17.1 15.4 25-40 22.8 25.0 23.0 25.4 21.4 23.8 20.3 24.1 41-64 18.4 33.1 18.3 32.1 19.4 31.3 18.1 30.7 65+ 4.2 14.9 4.0 14.3 5.5 14.8 6.3 13.9 Education Average Years of Education 5.9 9.3 5.4 9.3 6.4 10.0 5.7 9.9 Never attended 14.3 7.6 16.3 7.5 12.4 5.9 14.2 6.0 Incomplete Primary 35.4 22.1 40.6 21.0 26.2 13.7 29.9 13.4 Complete Primary 8.9 7.3 8.5 7.7 11.8 10.0 12.5 10.2 Incomplete Secondary 17.7 9.3 15.7 10.4 23.5 15.3 23.8 16.4 Complete Secondary 17.4 23.3 14.9 23.2 17.8 20.4 14.0 20.7 Incomplete Tertiary 4.1 11.9 2.8 12.5 5.4 15.3 4.0 15.1 Complete Tertiary 2.3 18.5 1.2 17.6 3.0 19.4 1.6 18.1 Informality Informal Workers 79.0 32.9 79.8 33.2 87.5 44.1 90.0 45.9 Sector Agriculture 36.4 6.6 32.6 5.5 43.0 6.9 43.8 6.0 Industry 17.1 19.8 18.8 19.6 15.5 19.7 15.5 19.4 Services 46.4 73.6 48.6 74.9 41.5 73.4 40.7 74.6 Type of employment Employer 0.8 4.5 0.9 4.8 1.1 3.7 1.3 4.3 Not salaried 11.7 1.9 9.4 1.8 16.6 2.9 16.5 2.8 Salaried worker 28.2 63.6 35.0 65.4 22.4 61.3 25.8 62.4 Self-employed 33.7 23.3 33.0 22.8 42.3 25.4 44.3 25.4 Unemployed 25.5 6.6 21.7 5.3 17.7 6.8 12.1 5.0 Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank). Note: The LAC aggregate is based on Argentina, Bolivia, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, Peru, Paraguay, and Uruguay for which microdata are available for both 2019 and 2020. 25 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean The COVID-19 pandemic also pushed back gains in regional shared prosperity.8 Prior to the economic slowdown, shared prosperity in LAC was relatively high. Between 2016 and 2019, income growth for the bottom 40 percent increased by 1.6 percent, 0.5 percentage points higher than the income growth for the total population of 1.1 percent (Figure I-5 – Panel A). During this period, the top five performers were Bolivia, El Salvador, the Dominican Republic, Paraguay, and Panama, with an average rate of total growth income of about 3.4 percent, compared to 5.5 percent for the bottom 40. This gain reversed for most countries after the COVID-19 outbreak. Between 2019 and 2020, the region reported a negative shared prosperity premium and growth in total income of -0.7 percent and -5.9 percent, respectively.9 For Argentina, Ecuador, Costa Rica, Colombia, and Peru, which were the worst five performers during this period, growth in the income of the bottom 40 declined between 11.8 percent and 26.3 percent (Figure I-5 – Panel B). FIGURE I-5: THE PANDEMIC ALSO PUSHED BACK GAINS IN REGIONAL SHARED PROSPERITY 8,0 6,0 4,0 2,0 0,0 -2,0 -4,0 -6,0 PRY URY PER BRA MEX BOL HND CHL ARG SLV PAN LAC DOM COL ECU Panel A. CRI Share prosperity, 2016-19 Growth Total Growth b40 30,0 20,0 Panel B. 10,0 Figure I-5: (PANEL A): Share prosperity, 2019-20 0,0 -10,0 -20,0 -30,0 PRY PER BRA MEX BOL ARG LAC DOM ECU COL CRI Growth Total Growth b40 Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank). Note: Since the numbers presented here are based on Socio-Economic Database for Latin America and The Caribbean (SEDLAC), which is a regional data harmonization effort that increases cross-country comparability, they may differ from official statistics reported by governments and national statistical offices. The LAC aggregate is based on 18 countries in the region for which microdata Figure I-5: (PANEL B): are available. In cases where data are unavailable, values have been interpolated or extrapolated using data from the World Bank Development Indicators (WDI) and then pooled to create regional estimates (2014 backward) and microsimulations (from 2015 onwards). For 2016-19, shared prosperity in the Dominican Republic was computed using data from 2017-19, for Mexico using data from 2016-18, and for Chile using data from 2017-20. For 2019-20, shared prosperity for Mexico used data from 2018-20. 8 Shared prosperity measures the extent to which economic growth is inclusive by focusing on household consumption or income growth among the poorest population rather than on total growth. 9 The shared prosperity premium is the difference between the income growth rate of the poorest 40 percent of the popula- tion and the annualized growth rate for the whole population. 26 Inequality increases during the pandemic despite substantial government efforts to cush- ion the impact of the health crisis in most countries. The Gini coefficient (excluding Brazil) in- creased by 0.5 points during the pandemic, from a 2019 value of 48.9 to 49.4 in 2020. But the opposite occurs when including Brazil. With Brazil, the increase in inequality fades and even declines, indicating the effectiveness of the mitigation program implemented by the Brazilian government to cope with the adverse effects of the pandemic (Figure I-6 – Panel A). The increase in inequality (excluding Brazil) is also reflected by the growth incidence curve, which plots growth rates at each quantile of per capita income. Between 2015 and 2019, except for the bottom decile (which already had a decline in income growth of 0.4 percent), there was a moderate average increase in income growth across the income distribution of 1 percent. This indicates that countries in the region experienced stagnation in income growth. By contrast, most percentiles registered income losses between 2019 and 2020 when exclud- ing Brazil. Losses during this period for wealthier households were nearly half those for the bottom percentile. This amounts to a reversal of regional inequality trends that worsened during the COVID-19 pandemic. When Brazil is included, however, the bottom of the distribution experienced relative income increases due to government transfers that mitigated the impact of the crisis (Figure I-6 – Panel B). FIGURE I-6: LABOR INCOMES OF HOUSEHOLDS AT THE BOTTOM OF THE INCOME DISTRIBUTION WERE SEVERELY AFFECTED, WHICH INCREASED INEQUALITY 58 56 54 Gini coe cient 52 51,1 50 49,8 49,4 48 48,96 Panel A. 46 Gini coefficient 44 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Gini with Brazil Gini without Brazil 5,0 Growth Rate (Annualized) 0,0 -5,0 Figure I-6: (PANEL A): Panel B. Growth incidence curves -10,0 -15,0 -20,0 1 2 3 4 5 6 7 8 9 10 Deciles of Per Capita Household Income LAC excl. Brazil (2015-19) LAC excl. Brazil (2019-20) LAC with Brazil (2015-19) LAC with Brazil (2019-20) Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank). Note: Since the numbers presented here are based on Socio-Economic Database for Latin America and The Caribbean (SEDLAC), which is a regional data harmonization effort that increases cross-country comparability, they may differ from official statistics reported by governments and national statistical offices. The LAC aggregate is based on 18 countries in the region for which microdata are available. Figure using In cases where data are unavailable, values have been interpolated or extrapolated (PANEL I-6: WDI B): pooled to create regional data and then estimates (2014 backward) and microsimulations (from 2015 onwards). Due to important methodological changes in Mexico’s official household survey in 2016 that created a break in the poverty series, we have created a break in the LAC-18 aggregate. 27 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean What were the underpinning factors that contributed most to these changes in poverty and inequality? Lockdown and safe-distancing measures extensively contributed to saving lives. Between April and September 2020, LAC registered a sustained decrease in new COVID-19 cases and deaths because of containment and closure policies, such as school shutdowns and movement restrictions (Figure I-7). As these policies became stronger (reflected in a higher stringency index), countries were able to flatten the curve and decongest their emergency health services. The result was a demonstrable decline in the spread and mortality of the disease. FIGURE I-7: LOCKDOWN AND SAFE-DISTANCING MEASURES IMPLEMENTED BY GOVERNMENTS HELPED TO SAVE LIVES COVID-19 & stringency in Latin America and the Caribbean, April – September, 2020 60 6 50 5 40 4 30 3 20 2 10 1 0 0 73 75 77 79 81 83 85 87 89 91 Cases Deaths Source: Ballon, et al. (2021b) Despite the positive effects on public health, Figure I-7: non-pharmaceutical interventions also led to dramatic declines in employment and working hours in Latin American countries. In 2020, about 14 million jobs were lost in the region, more than two thirds of which occurred in Brazil, Colombia, and Peru. Job losses in each of these three countries numbered more than two million (Figure I-8 – Panel A).10 However, Costa Rica registered the highest job losses relative to total employment, with a drop of 14.2 percent in 2019. Next were Peru and Colombia, with a decline of 12.7 percent and 11 percent of total employment in 2019, respectively. In Brazil, Panama, and El Salvador, the drop in employment relative to total employment in 2019 was in line with the LAC average of 8.3 percent (Figure I-8 – Panel B). 10 The pandemic’s impact on Latin American labor markets has been profound and long-lasting. The pandemic is an unusual economic shock that affected both labor supply and demand (Baqaee & Farhi). On the supply side, lockdowns and mobility restrictions in the region limited the ability of people to supply work. At the same time, these interventions combined with firms’ supply chain problems to limit consumer demand. Consequently, the demand for labor fell significantly worldwide (Chetty, Friedman, Hendren, Stepner, & others, 2020). 28 FIGURE I-8: LAC COUNTRIES EXPERIENCED MASSIVE JOB LOSSES Job losses, 2020-19 0,0 -0,2 -0,1 -0,1 -0,1 0,0 Change in millions 2020-19 -0,8 -0,3 -0,3 -2,3 -2,0 -7,7 -13,9 Panel A. El Salvador Costa Rica Argentina Paraguay Panama Dominican Colombia Bolivia Ecuador Uruguay Brazil LAC Peru Republic Millions of workers Figure I-8: (PANEL A): 0,7% Panel B. -0,7% -0,7% As percentage of -3,3% % cahnge 2020-19 employment in 2019 -7,0% -8,4% -8,3% -7,4% -8,3% -8,3% -11,0% -12,7% -14,2% El Salvador Paraguay Panama Costa Rica Brazil Bolivia Argentina Colombia Dominican Ecuador Peru Uruguay LAC Republic Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank). Figure Note: Since the numbers presented here are based on Socio-Economic Database Latin (PANEL I-8: for America andA): The Caribbean (SEDLAC), which is a regional data harmonization effort that increases cross-country comparability, they may differ from official statistics reported by governments and national statistical offices. The LAC figure is the average of the 12 countries shown in the figure. The data from El Salvador and Panama were unavailable for 2020, so the job losses were simulated using the LAC average. Job losses refers to changes in total employment. Job losses had a significantly negative impact on vulnerable groups in the region. The workers hit the hardest by the COVID-19 crisis were the youth, low-educated individuals, and those in informal employment and the bottom-40. Employment losses represented about 15 percent of the total em- ployment of these groups in 2019. Employment losses for women represented 10.7 percent of total employment in 2019, which is about 4.3 percentage points higher than the figure for men (Figure I-9). This difference in job losses is explained by the fact that women in the region were more likely to work in face-to-face occupations, such as tourism, and in occupations that were unlikely to be undertaken from home, such as personal care services (Del Carpio & Romero Haaker, 2021). In 2020, the gap in job losses was the highest by education level. Job losses for workers with primary education or less were 29 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean about 11 percentage points higher than for workers with tertiary education. However, loss rates were similar for youth, informal workers and the bottom-40 – at about 14 percent of the total employment of these groups in 2019. FIGURE I-9: WOMEN, YOUTH, LOW-EDUCATED, AND INFORMAL WORKERS WERE HIT HARD BY JOB LOSSES Job losses, 2020-19 -2,3% -6,5% -6,2% -6,3% -6,9% % change 2020-19 -8,2% -8,2% -10,7% -11,5% -13,4% -14,8% -15,9% -15,9% Female Male Young Prime age Senior Less than primary Primary complete Secondary complete Tertiary complete Formal Informal Bottom 40 Top 60 Gender Age Education Sector Poverty Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank). Figure Note: The LAC aggregate is based on 12 countries for which microdata areI-9: available in both 2019 and 2020. Job losses refer to changes in total employment. Loss of employment had an immediate impact on working hours and labor income across countries. Between 2019 and 2020, working hours in LAC decreased by 3.3 hours per week.11 Some coun- tries in the region were more affected than others. In Argentina, Costa Rica, Peru, and Ecuador, for in- stance, the decline in working hours in 2020 ranged from 7.1 percent to 10.8 percent. (Figure I10 – Panel A). Also, most countries showed a drop in labor incomes due to the COVID-19 crisis. Peru and Ecuador had the highest reduction in labor income, with rates exceeding 15 percent between 2019 and 2020 – more than double the LAC average (Figure I-10 – Panel B). Excluding the case of Brazil, these reductions in em- ployment and labor income, respectively contributed 4.5 and 3.6 percentage points to the region’s total increase in poverty (i.e., 6.7 percentage points) (Figure I-11 – Panel A). Contrary to the rest of the region, labor income in Brazil increased by 3.5 percent between 2019 and 2020. One factor contributing to this rise is the fact that most workers who kept their jobs had higher incomes than those who lost their jobs or left the labor market. This is reflected in the contribution of labor income to the movement in poverty, which fell from 4.5 to 2.3 percentage points. Given the importance of labor income to alleviating poverty in LAC – especially over the last twenty years – the labor income plunge in most countries represents a risk to the region’s longer-term recovery and its poverty reduction. 11 The plunge in working hours was above the LAC average for workers with secondary complete or less, seniors, self-em- ployed, and those in the bottom 40, ranging from 4.1 percent to 4.9 percent between 2020 and 2019. 30 FIGURE I-10: WORKING HOURS AND LABOR INCOME PLUNGE IN THE REGION Job losses, 2020-19 2,9% 0,1% % change 2020-19 -0,7% -0,2% -1,5% -0,5% -3,3% -3,3% -3,3% Panel A. -7,1% -8,0% Change in working hours, -8,3% -10,8% 2019-20 Dominican Panama Salvador Paraguay Costa Rica Bolivia Argentina Colombia Brazil LAC Ecuador Uruguay Peru Republic Figure I-10: (PANEL A): 3,5% -0,4% % change 2020-19 Panel B. -4,2% Change in labor income, -7,7% -7,3% -6,6% -9,2% -7,3% -7,3% 2019-20 -10,8% -12,9% -15,7% -17,0% Peru Ecuador Paraguay Argentina Colombia LAC Salvador Panama Uruguay Costa Rica Bolivia Brazil Dominican Republic 2020-19 Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank). Note: Since the numbers presented here are based on Socio-Economic Database for Latin America and The Caribbean (SEDLAC), which is a regional data harmonization effort that increases cross-country comparability, they may differ from official statistics reported by Figure I-10: (PANEL B): governments and national statistical offices. The LAC figure is the average of the 12 countries shown in the figure. The data for El Salvador and Panama were unavailable in 2020, so the working hours and labor income were simulated using the LAC average. Mitigation policies and private transfers partially offset labor income losses in most countries. Governments throughout the region implemented large-scale mitigating policies to protect the most vul- nerable populations. Examples include the expansion of cash transfers, changes to social insurance plans, and the extensive use of employee furlough programs (Gentilini, Almenfi, Orton, & Dale, 2020).12,13 Public transfers have been particularly important, representing 4.7 of total per capita income in 2020, which is an 12 In advanced countries, large-scale policy interventions focused on support for the worst-affected groups, which significant- ly helped to reduce the adverse impact of the pandemic (Chetty, et al., 2020). 13 Without the mitigation measures, the region was projected to add 28 million poor people: instead, for the region as a whole, two million fewer people were in poverty in the region in 2020 than in 2019 (World Bank, 2021e). 31 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean increase of 3.4 percentage points from the 2016-19 period. Excluding Brazil, these transfers drove regional poverty rates down by 2.3 percentage points during the pandemic. This proportion more than doubles when Brazil is included (Figure I-11). Public transfers contributed the most to pushing poverty down in Peru, the Dominican Republic, and Brazil in 2020 (3, 5.7, and 9.1 percent, respectively). FIGURE I-11: INCREASE IN PUBLIC TRANSFER PARTIALLY OFFSET LABOR INCOME LOSSES IN LAC Shapley decomposition of income source to changes in poverty rate 6,7 4,5 3,6 Percentage points Panel A. 0,9 LAC, excluding Brazil, 2019-20 0,0 -2,3 Labor earnings Share who are employed Public transfers Remittances Other Total change 2,6 2,3 Figure I-11: (PANEL A): Percentage points Panel B. -0,6 -0,4 LAC, with Brazil, 2019-20 -1,2 -5,2 Labor earnings Share who are employed Public transfers Remittances Other Total change Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank). Note: The Shapley decomposition includes Argentina, Bolivia, Brazil, Colombia, Costa Rica, the Dominican Republic, Ecuador, Paraguay, Figure I-11: (PANEL B): Peru, and Uruguay, countries for which data are available for both 2019 and 2020. International remittances also helped households cope with the adverse effects of the pan- demic, particularly in Central America. After a drop in the inflow of remittances in the first few months after the health outbreak, remittance flows remained resilient in Latin America, increasing by 6.5 percent in 2020 (Ratha, Ju Kim, Plaza, & Seshan, 2021f). The Dominican Republic and Mexico regis- tered a significant increase in 2020 of 14.3 and 10.4 percent (5.5 and 1.2 percentage points higher than the 2015-19 average), respectively. El Salvador, Guatemala, Nicaragua, and Jamaica also increased in- flows of remittances in 2020, ranging from 3.5 percent to 17.7 percent (World Bank, 2021a).14 14 Remittances inflows are calculated as year-to-year percent change, using the sum of January-November inflows for the Dominican Republic, El Salvador, and Guatemala; January-October inflows for Bolivia, Colombia, Honduras, Mexico, Nica- ragua, and Paraguay; January-September inflows for Jamaica; and January-June for Ecuador (World Bank, 2021a). 32 The increase in remittances is plausibly explained by improvements in the employment situation in the United States in the second semester of 2020 (although still short of pre-pandemic levels) and the large share of LAC adults working in the United States (particularly from Central American countries). Also of potential importance was the shift in flows from informal to formal channels in 2020.15 In sum, employment losses and labor income reductions contributed the most to severely de- pleting peoples’ livelihoods in 2020. Despite significant efforts by governments to mitigate the ad- verse effects of the pandemic, 4.5 and 3.6 percent of the increase in poverty was driven by employment and labor income, respectively (Figure I-12– Panel A). Public transfers buffered the impact by contributing to the reduction in poverty by 2.3 percentage points. However, when including Brazil, the overall contribu- tion of public transfers nearly compensates for the significant reduction in the household labor compo- nent (Figure I-12 – Panel B). Moreover, across the income distribution, public transfers alone compensated for the decline in income growth due to the loss of employment and labor income to the bottom four deciles of the income distribution in the region, including Brazil (Figure I-12 –Panel A).16 When excluding Brazil, however, public transfers are shown to have only partially coped with the significant drop driven by labor income and employment along the income distribution (Figure I-12 – Panel B). FIGURE I-12: EMPLOYMENT AND LABOR INCOME LOSSES WERE THE MAIN DRIVERS OF THE RISE IN POVERTY Growth Incidence Curves 6,5 Annualized growth (%) 4,4 2,3 23,7 19,2 15,4 12,6 10,1 7,8 5,4 3,3 -6,1 -5,2 -4,4 -3,2 -2,5 -9,2 -7,9 -7,0 -6,8 -6,5 -3,1 -3,9 -3,4 -2,6 -6,0 -5,1 -4,5 -7,7 Panel A. -9,9 -9,8 LAC, with Brazil, 2019-20 1 2 3 4 5 6 7 8 9 10 Share who are employed Labor earnings Remittances Public transfers Other Total change Figure I-12: (PANEL A): 15 In the case of Mexico, Dinarte, et al. (2021) found that the increase in the volume of remittances from formal channels was larger among municipalities that were previously more reliant on informal channels (for example, near a border crossing). The travel ban made it impossible to physically transport cash or assets from the United States to Mexico. Consequently, the financial system became the only option to send and receive international transfers, which also increased the number of bank accounts opened since the beginning of COVID-19. 16 Between June and September 2020, government transfers in Brazil accounted, on average, for about half of the income of those in the poorest quintile and a third of the income among those in the second quintile. Government transfers were also significant for those in the middle of the distribution, representing about 20 percent of their overall income between May and September 2020 (World Bank, 2022). 33 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean Annualized growth (%) 11,8 8,4 6,3 5,0 3,9 2,9 2,1 -2,8 -7,5 - 6,8 - 6,0 - 5,1 - 4,0 -10,1 - 8,5 -7,9 -3,8 -14,9 - 5,2 - 4,3 -7,1 - 6,1 -2,4 -10,2 - 8,5 -12,6 -15,2 -1,7 Panel B. -17,8 -2,0 LAC, excluding Brazil, -3,5 2019-20 1 2 3 4 5 6 7 8 9 10 Share who are employed Labor earnings Remittances Public transfers Other Total change Figure I-12: (PANEL B): Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank). Note: Growth Incidence Curves (GIC) by income sources include Argentina, Bolivia, Brazil, Colombia, Costa Rica, the Dominican Republic, Ecuador, Paraguay, Peru, and Uruguay, countries for which data are available for 2019 and 2020. Despite the high socioeconomic costs of the health crisis, the pandemic accelerated internet access and financial inclusion, as well as the use of digital technologies for working, learning, accessing basic services, and communicating. Since the onset of the pandemic, uptake of the in- ternet has increased significantly worldwide, with approximately 800 million people going online for the first time (ITU, 2021). In LAC, many activities and services shifted online shortly after mobility restrictions came into effect, including the implementation and improvement of teleworking practices by firms that were able to adapt. A boost in e-commerce and e-services was also evident throughout the region as several supermarkets and restaurants shifted to online delivery services and as people increasingly began to use digital services to make utility payments or receive digital payments via cash transfer programs (e.g., in the case of Brazil).17 On average, 15 percent of adults in the region made a utility payment from an account for the first time after the outbreak of COVID-19 – double the average for developing econo- mies. That said, important differences were evident across countries. In Bolivia, for instance, nearly one in four (23 percent) adults fell into this category for first users. This group represented about 80 percent of Bolivians who reported making utility payments using an account. In Colombia, Ecuador, Honduras, and Peru, on the other hand, the proportion making digital payments for the first time was about 15 percent, accounting for about two-thirds of those who use an account to make a utility payment. In Brazil, 18 per- cent of adults made such payments for the first time, almost doubling the share of adults making utility payments digitally from an account (Demirguc-Kunt, Klapper, Singer, & Ansar, 2022).18 Yet, uneven access to digital technologies has undermined vulnerable households’ ability to benefit from digitalization. For instance, households with lower internet connectivity experienced higher job loss rates, higher income losses, and lower access to high-quality remote learning, which could increase inequality in the long run (Ballon, Mejía-Mantilla, Olivieri, Lara-Ibarra, & Romero, 2021). 17 In the LAC region, the COVID-19 crisis has caused a structural shift of demand toward digital e-commerce that is likely to continue in the years to come. In the early weeks after the onset of the crisis, for example, MercadoLibre registered a year-on-year increase in demand of 100 percent for essential goods and pharmacy products. The pandemic also created opportunities for second-gen- eration “niche” platforms operating in specific market segments that have traditionally been excluded from large e-commerce platforms. For instance, the Brazilian platform Compre Local introduced a new initiative that allows customers to locate and buy items from small businesses in their neighborhoods using a simplified payment solution (World Bank, 2020b). 18 Despite a growth in internet coverage and financial inclusion during the pandemic, LAC still faces many challenges when it comes to reducing the digital gap across the region. Hurdles include the high cost of internet provision, low coverage in rural areas, and poor quality of internet connections (Srinivasan, Comini, Koltsov, & Gelvanovska-Garcia, 2022). 34 II Footprints of the pandemic (LONG-TERM COVID-19 IMPACTS) 35 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean Footprints of the pandemic (LONG-TERM COVID-19 IMPACTS) T he COVID-19 pandemic was an unprecedented shock that negatively impacted the well-being of individuals in LAC countries. Its effects spread beyond the immediate aftermath, affecting future well-being in the medium and long term. The immediate impact of non-pharmaceutical interventions was a significant decline in welfare with significant job losses, a drop in labor incomes, and an increase in poverty and inequality across LAC countries. Yet, the pandemic also affected other dimensions of well-being, such as the decline in mental health and its negative consequences, includ- ing depression and anxiety (Banks, et al., 2021; Kovacevic, et al., 2022). A spike in domestic violence was also witnessed following the implementation of lockdowns (World Bank, 2022; UN Women, 2020a). Further, the delivery of essential health services was disrupted as healthcare systems directed most of their efforts to treating individuals with confirmed cases of COVID-19 (UN Women, 2020b).19 In the long term, the high toll on human capital accumulation due to school closures can affect not just an indi- vidual’s future earnings and well-being but also overall economic growth in the region. This may have long-term negative consequences on poverty and equity. The unprecedented disruption to education and health due to the COVID-19 pandemic will leave lasting scars on human capital accumulation and the welfare of an entire generation in the LAC region. The pandemic is likely to have a long-term adverse impact on the learning process and future earnings of those children who experienced a disruption in their education at the peak of the health crisis. About 170 million students in LAC countries were deprived of in-person education for roughly half the effective school days, which could lead to this entire generation experiencing a 12-per- cent decrease in their lifetime earnings (World Bank & UNESCO, 2022). Also, school closures increased the childcare responsibilities of parents at home, with most of the burden falling on women (BOX II- 1). Furthermore, school closures not only deprived children of opportunities for social interaction that promote growth, well-being, self-esteem, and learning; they also led to the loss of access to critical services, such as meal delivery and routine immunizations (World Bank & UNESCO, 2022). Before the pandemic, approximately 85 million children in the LAC region used to receive meals from school feed- ing programs.20 In addition, the decline in labor incomes across the region due to the pandemic has exposed households to an alarming increase in food insecurity levels. For instance, at least 40 percent of households in Bolivia, Colombia, Ecuador, Guatemala, Honduras, Peru, and the Dominican Republic reported running out of food during the lockdown (Ballon, Cuesta, Olivieri, & Rivadeneira, 2020). High- er rates of child stunting are expected as a result, the knock-on effects of which hamper physical and 19 In LAC countries, people of all ages experienced significant disruptions in healthcare services that could have significant long-lasting impacts, including preventative and treatment care for children who missed out on key primary care services, vaccinations, and nutrition programs, through to adults in need of diabetes and cardiovascular care, and older adults who experienced decreases in chronic disease care (Herrera, et al., 2022). 20 In LAC countries, schools play an important role in the direct provision of health and nutrition services. Before the pandemic, approximately 85 million children in the LAC region used to receive meals from school feeding programs. For 11 percent of these children (most of whom come from vulnerable households), this meal represented their primary daily source of food that provided balanced nutrition (World Bank & UNESCO, 2022). 36 cognitive development.21 In turn, this negatively affects schooling performance and, in the long term, harms income and productivity.22 LAC countries suffered one of the most prolonged spells of school closures due to the pan- demic, deepening stark educational challenges in the region related to low levels of learning. LAC, along with South Asia, is the region that experienced the longest school closures worldwide. Schools in these two regions were fully or partially closed for an average of 387 and 429 days, respectively. These lost days represented more than 75 percent of total instruction time during the pandemic period (Muñoz-Najar, et al., 2021). This is triple the duration of school closures in Western Europe (World Bank, UNICEF and UNESCO, 2021). While nearly every government made efforts to mitigate learning losses by offering remote learning opportunities for students, these were typically a poor substitute for in-person learning. Learning losses are expected to be particularly severe for poor students who had limited access to the internet.23 Despite efforts to provide alternative modes of learning, recent World Bank estimates of school closures in LAC suggest that learning poverty (i.e., the share of children who are not able to read proficiently when reaching late primary schooling) increased from 51 percent before the pandemic to 62.5 percent afterwards. This is equivalent to an increase of 7.6 million learners from poor households (World Bank, 2021). It is calculated that almost two in every three primary school students in the region, on aver- age, are likely to be unable to read or understand a simple age-adequate text. Further, estimates suggest that 10 months of school closures in the region could have caused an average loss of 1.3 years of schooling and could increase the school dropout rate in LAC by 15 percent. This will increase the learning gap in the region and, in turn, affect the region’s long-term productivity and economic growth (World Bank, 2021). BOX II-1: SCHOOL CLOSURES ARE EXPECTED TO NEGATIVELY AFFECT WOMEN’S JOB PROSPECTS IN THE LONG RUN School closures significantly increased the childcare needs of parents, impacting women the most. Not only did women suffer more significant job losses than men during the pan- demic, but they were also more likely to have jobs incompatible with remote work, which increased the probability of job losses, particularly for women with children.* For instance, in households with children, women with jobs incompatible with remote work were almost 21 Stunting is associated with chronic undernutrition during the critical period of early childhood development, which can have long-lasting effects on brain development and cognitive functioning (Grantham-McGregor, 1995). Empirical evidence has shown that stunted children have deficits in cognition and school achievement from early childhood to late adoles- cence. Stunted children have also shown lower self-esteem, more anxiety, depressive symptoms, and anti-social behavior (Chang, et al., 2002; Walker, et. al, 2007). 22 Skills formation is developed over time. Early periods in a child’s life cycle are crucial to developing cognitive, linguistic, social, and emotional competencies that strongly affect learning at later stages of life and productivity in the long term (Cunha, et al., 2006; Heckman, 2006). 23 Countries in the region made valiant efforts in a short period of time to set up strategies to cope with the education crisis. Limited internet connectivity led to the implementation of increasingly multi-modal solutions, where traditional means of communication (such as TV, radio, and printed materials) complemented Internet-based solutions, and where teachers and parents were supported to make remote learning more inclusive (World Bank, 2021). Results from the High-Frequency Population Survey show that school closures in LAC appear to be associated with attendance rates 10 percent lower than enrollment rates for the given year and 12 percent lower than pre-pandemic attendance rates. Also, the results from the survey indicate that 25 percent of students attending educational activities of any kind were not engaged in the educa- tional process while at home. The surveys also demonstrated that inequities in both attendance and engagement rates disproportionately affected specific groups, usually the most vulnerable populations (World Bank, 2022). 37 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean 14 percentage points more likely to lose their jobs than men with similar characteristics and whose jobs were also incompatible with remote work. In households without children, the difference is 9 percentage points lower. However, women were only 2.8 percentage points more likely than men to lose their jobs during the pandemic in jobs compatible with remote work (Berniell, et al., 2021). This implies that having a job compatible with remote work was more important for women than for men in respect of avoiding job losses. The adverse effects for women due to the increasing childcare burden when schools closed during the pandemic could be dire as they faced lost earnings and difficulties finding a job after spells of unemployment. For instance, time out of the workforce for women could reduce their accumulation of experience and training, lowering their career progression and wages in the fu- ture (Albrecht, et al., 1999). Further, women are now more likely to rethink their career decisions in light of the pandemic. This could mean that the crisis will lead to a future drop in women’s labor supply, reducing households’ ability to self-insure against income shocks (Bluedorn, et al., 2021). However, the legacy of the COVID-19 pandemic is also likely to lead to positive changes in the labor market, such as increased access to telecommuting and other work flexibilities. Over time, these might reduce gender inequality in the labor market (Alon, et al., 2022). * Gender differences in job losses are also explained by the fact that women were also more likely to work in sectors more affected by the pandemic and in occupations incompatible with remote work, such as trade, personal services, education, and hospitality (Cucagna, Haaker, & Javier, 2021). How would an increase in stunting impact future incomes? The rise in food insecurity could have a potentially negative effect on child nutrition and long-term human capital formation. A substantial share of households in the region experienced food insecurity. In May 2020, at the height of the first wave of the virus, Honduras registered the highest incidence, with more than half of households (53 percent) reporting running out of food due to a lack of money or resources. Lower rates of food insecurity followed the relaxation of lockdown measures. How- ever, there was still a considerably large percentage of households without enough resources to buy food in August 2020, with approximately one-third of households in Honduras and the Dominican Re- public still facing food insecurity (Ballon, Mejía-Mantilla, Olivieri, Ibarra, & Romero, 2021). This certainly would negatively affect early childhood nutrition in the region, with inevitable long-term consequences for the development of affected children’s cognitive skills and future incomes. This is particularly true in countries where the prevalence of stunting is larger than the LAC average, such as Nicaragua (14.1 percent), Honduras (19.9 percent), Haiti (20.4 percent), Ecuador (23.1 percent), and Guatemala (42.8 percent) (Figure II-1).24 Children from vulnerable households are more susceptible to higher stunting that could affect the development of their cognitive skills because they face higher levels of poverty, including limited access to quality health care and affordable nutrition (Currie & Almond, 2011).25 24 Stunting refers to a failure to grow to the proper height for a child’s age, mainly due to poor nutrition in pregnant women, babies, and toddlers. Stunting has been shown to result in life-long health problems, lower schooling attainment, and lower wage earnings in adulthood (Shekar, et al., 2017). 25 The long-lasting effects of stunning could also increase the risks of overweight/obesity and diet-related non-communica- ble diseases later in life. They also trigger the intergenerational transmission of malnutrition (Osendarp, et al., 2021). 38 FIGURE II-1: CHILDREN IN COUNTRIES WITH HIGH LEVELS OF STUNTING ARE MORE LIKELY TO BE AFFECTED BY FOOD INSECURITY AND LOSS OF FUTURE INCOME Share of stunting children (0-5 years old) 23,1 20,4 19,9 14,1 Percent (%) 12,7 11,9 12,1 11,2 11,5 10,6 10,8 9,0 8,5 8,6 8,7 7,8 8,0 7,0 6,5 5,9 6,1 4,6 1,6 CHL PRY DOM BRA URY CUB ARG SUR JAM CRI TTO GUY VEN PER SLV COL LAC MEX BOL NIC HND HTI ECU Source: UNICEF/WHO/World Bank joint child malnutrition estimates for 2020. Figure II-1 https://data.unicef.org/resources/dataset/malnutrition-data/ Future income losses in LAC countries are expected due to increased stunting for the gener- ation of children affected by the COVID-19 pandemic, with the most significant impacts felt by households at the bottom of the income distribution.26 On average, annual per capita income in LAC countries is predicted to decline by 0.65 percent in future incomes due to stunting in the pre-pan- demic situation. However, the additional drop attributable to COVID-19 is minimal, at about 0.012 per- cent. This assumes an estimated increase of 2 percent in the number of stunted children in LAC due to COVID-19 from available estimates (Gasparini and Laguinge, 2022), which explains the very small effects of future incomes of new cases driven by the pandemic. (Figure II-2 – Panel A). More significant drops in per capita incomes are projected for countries with stunting rates higher than the LAC average. For instance, the decline in average income in Guatemala is considerable; at around 2.53 percent in the pre-pandemic situation and 0.056 percent for the additional cases due to the pandemic. Further, a fall in per capita income above the LAC average is projected for Bolivia, Ecuador, Nicaragua, and Honduras. Also, the impact of stunting on future income is unevenly distributed across households. The predicted decline in future incomes due to stunting in the pre-pandemic situation and due to additional stunning cases attributable to the pandemic is concentrated in households in the bottom deciles of the income distribution. In contrast, no changes are expected in the top deciles. This phenomenon could increase poverty and inequality in the long run (Figure II-2 – Panel B). 26 Estimates of the adverse effects of stunning due to the COVID-19 pandemic correspond to a recent study by Gasparini and Laguinge (2022). 39 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean FIGURE II-2: EXPECTED FUTURE INCOME LOSSES DUE TO STUNNING DISPROPORTIONALLY AFFECT CHILDREN IN POOR HOUSEHOLDS Estimated fall in future incomes in LAC (0-5 years old) 0,0% 0,00% -0,5% -0,01% Panel A. -1,0% -0,02% Percent (%) Percent (%) By country -1,5% -0,03% -2,0% -0,04% -2,5% -0,05% -3,0% -0,06% GTM BOL ECU NIC HND PAN LAC CRI ARG MEX SLV PER DOM COL PRY BRA Pre-pandemic Due to Covid-19 (Right) 0,0% 0,00% -1,0% Figure II-2: (PANEL A): -0,02% Panel B. -2,0% -0,04% By household per Percent (%) Percent (%) -3,0% capita income deciles -0,06% -4,0% -0,08% -5,0% -0,10% -6,0% -7,0% -0,12% 1 2 3 4 5 6 7 8 9 10 Pre-pandemic Due to Covid-19 (Right) Source: Gasparini and Laguinge (2022). Figure II-2: (PANEL B): How would education losses due to the COVID-19 pandemic affect poverty and inequality in the long term? Education losses across LAC countries affected countries differently, but in all cases the im- pacts were more severe for households at the bottom of the income distribution.27 On average, the estimated education losses due to pandemic-related school closures in LAC countries amounted to 59 percent of a school year missed (Figure II-3 – Panel A). Ecuador, Bolivia, Mexico, and Paraguay had the largest education losses in LAC, ranging from 76 to 85 percent of the school year. In contrast, Chile and Uruguay were the least affected countries in the region, with education losses nearly half the LAC average. Across the board, however, students from poorer households evidenced a greater likelihood of suffering from education losses during the pandemic. Notably, estimated education losses for students 27 Results presented in this section correspond to a recent study on the impacts of COVID-19 on education in LAC by Bracco, et al. (2022) . 40 in the bottom decile of the income distribution amounted to 81 percent of the school year, which is 3.7 times higher than average education losses for students in the top decile (Figure II-3 – Panel B). This difference is explained by a decline of about two percentage points in enrolment rates of children and young people (6-24 years old), indicating that a large share of students dropped out of school (or did not enroll in the higher track) due to the pandemic in 2020. FIGURE II-3: CHILDREN IN HOUSEHOLDS AT THE BOTTOM OF THE INCOME DISTRIBUTION HAVE BEEN MORE AFFECTED BY EDUCATION LOSSES Predicted education losses 85% 78% 78% 76% 65% % of school year 62% 59% 59% 56% 52% 47% Panel A. 44% By country 34% 32% URY CHL ARG BRA CRI DOM PER LAC COL SLV PRY MEX BOL ECU 81% 74% Figure II-3: (PANEL A): 68% Panel B. 63% 59% By household per % of school year 53% 48% capita income deciles 40% 31% 22% 1 2 3 4 5 6 7 8 9 10 Per capita income deciles Source: Bracco, et al. (2022) Note: Estimates of education losses are based on the methodology developed by Neidhöfer, et al. (2021) and microdata from national Figure II-3: (PANEL A): available surveys. Learning losses across LAC countries will likely harm the future earnings of children affected by the COVID-19 pandemic. The projected impact of the health crisis on labor incomes is a decline of about 6.4 percent in 2045, resulting in a 4.3-percent decline in average household per capita income 41 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean (Figure II-4).28 Yet, assuming mitigation measures implemented by the government or parental reac- tions to the loss of days of school during the pandemic (such as online learning or parents investing time to assist their children with their schoolwork), the projected negative impact of school closures on labor income and household per capita effects reduces by more than half. The negative impact on high- er earning for cohorts directly affected by the pandemic at their prime age in the labor market worsened for the group of school dropouts. For cohorts directly affected by the pandemic, when they reach their prime age (i.e., 30-45 years old), labor income is projected to decline by 15 percent and household per capita income by 10.5 percent. This is a consequence of many children and young adults affected by the pandemic entering the labor market with fewer skills than they would otherwise have had. Conse- quently, they will have lower expected lifetime earnings. The negative effect is expected to fade away as the generation affected by the pandemic grows older and leaves the labor market. FIGURE II-4: EDUCATION LOSSES ARE EXPECTED TO AFFECT HOUSEHOLD PER CAPITA INCOME OVER TIME Patterns of household per capita income 600 595 590 2017 PPP dollars 585 580 575 570 2060 2040 2068 2066 2048 2046 2050 2064 2044 2020 2030 2058 2056 2054 2028 2038 2026 2036 2062 2024 2034 2042 2070 2052 2074 2022 2032 2072 No adjustment Full adjustment Source: Bracco, et al. (2022) Note 1: No adjustment: values assuming no government Figure II-4 or parental reactions to the loss of days of school during the pandemic. Full adjustment: values assuming both government and parental reactions to the loss of days of school during the pandemic. Note 2: Values in monthly 2017 PPP dollars. Note 3: Unweighted mean of the following countries: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Mexico, Peru, Paraguay, and Uruguay. The projected decline in households’ per capita income for the cohorts hit by education losses due to the pandemic would also imply a substantial increase in income poverty and a slight change in inequality in the future. In 2045, the projected impact in poverty rates measured at $6.85 a day (2017 PPP) would be an increase of 1.7 percentage points (Figure II-5 – Panel A). For the 28 The impact of the shock on education during the pandemic is projected to grow as the directly affected cohort enters the labor market, reaching its peak in 2045. 42 cohort directly affected by education losses during the pandemic at their prime age in the labor market, almost five million people are projected to fall into poverty – assuming no mitigation measures were implemented by governments, parents, or both. The projected increase in poverty would still be about 0.7 percent even in the most positive scenario (i.e., when mitigation steps were fully implemented). However, the impact is projected to be more severe for those who dropped out of school – with an estimated increase in poverty of more than 10 percentage points at their prime age. Moreover, as with the income dynamics, the adverse impact of education losses on poverty is projected to last for a very long period and is expected to only be absorbed when the affected cohorts leave the labor market. The dynamics in the case of inequality are rather different, with slight shifts in the Gini coefficient over time (Figure II-5 – Panel B). As the effects of the shock are felt most by workers who are young and poor, the projected impact is a small increase in inequality. However, inequality is projected to narrowly decrease as the cohort affected by education losses gets older and wealthier. In any case, changes are small and become even smaller when mitigation measures by governments and parents are considered. FIGURE II-5: POVERTY AND INEQUALITY ARE EXPECTED TO INCREASE OVER THE LIFE CYCLE OF CHILDREN AFFECTED BY COVID-19’S SHOCK ON EDUCATION 22,5 22,0 Percentage (%) 21,5 21,0 20,5 Panel A. 20,0 Poverty headcount at $6.85 per day (2017 19,5 PPP) 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050 2052 2054 2056 2058 2060 2062 2064 2066 2068 2070 2072 2074 No adjustment Full adjustment 0,452 Figure II-5: (PANEL A): 0,450 Gini coe cient 0,448 Panel B. 0,446 Gini coefficient 0,444 0,442 0,440 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050 2052 2054 2056 2058 2060 2062 2064 2066 2068 2070 2072 2074 No adjustment Full adjustment Source: Bracco, et al. (2022) Note 1: No adjustment: values assuming no government or parental reactions to loss of days of school during the pandemic. Full Figure adjustment: values assuming both government and parental II-5: (PANEL reactions B):of school during the pandemic. to loss of days Note 2: Poverty line of $6.85 a day at 2017 PPP. Note 3: Unweighted mean of the following countries: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Mexico, Peru, Paraguay, and Uruguay. 43 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean BOX II-2: EVIDENCE-BASED STRATEGIES TO MITIGATE THE NEGATIVE EFFECTS OF THE PANDEMIC ON CHILDREN’S EDUCATION Addressing the adverse long-term effects caused by the COVID-19 pandemic on education requires a cohesive, evidence-based learning recovery program, sup- ported by strategies that help students to an accelerated learning recovery. The RAPID Framework for Learning Recovery and Acceleration offers five policy actions to sup- port the education system not only in implementing an equitable recovery (including moni- toring and planning) but also in supporting more effective teaching, learning, and students’ psychosocial well-being. Doing so requires a coordinated effort involving teachers, school leaders, parents, and policymakers. This can help students return to their pre-pandemic learning trajectories. These policy actions focus on five strategies: Reach every child and keep them in school. This policy involves reopening schools safely, implementing campaigns to promote students to return to the classroom by involving families in children’s education and removing fees, offering school meals, and providing families with cash transfers. It also includes early warning systems to identify students at risk of dropping out. These systems should monitor enrollment and help educators to better understand the various reasons forcing students to drop out. Assess learning levels. This strategy involves assessing the impact of the pandemic on the education system to identify learning gaps and areas of weakness among students. It requires establishing pre-pandemic learning goals and levels, assessing learning levels na- tionally and sub-nationally, and producing disaggregated data to measure students’ learning performance. In this way, educators can make more informed decisions on where and how to mobilize resources at a system level so as to prevent learning loss and drop-out, particularly among the most vulnerable students. Prioritize teaching the fundamentals. This policy involves focusing on learning recovery ef- forts on key foundational concepts and skills/competencies in order to progress to more com- plex topics for future learning, including literacy, numeracy, and socioemotional skills. This not only requires changing teaching methods to meet students’ learning needs but also adjusting the curricula and learning materials to focus on core skills and knowledge at respective grades. Increase the efficiency of instruction, including through catch-up learning. This policy involves helping students recover from learning loss at school by supporting initiatives that in- crease the amount of learning within the classroom. These initiatives include the regular training of teachers and employing learner-focused recovery strategies such as individualized self-learn- ing programs, tutoring and coaching, accelerated learning programs, and catch-up programs for dropouts. Also, technology can play a crucial role in closing learning gaps, recovering learning losses, and enhancing education to enhance education delivery. Develop psychosocial health and well-being. This policy involves assuring access to critical mental health and psychosocial support. Addressing the mental health and psychosocial needs of children and youth, as well as supporting their well-being, is essential in and of itself, but it is also critical to ensuring that they can learn. Source: World Bank, et al. (2022). 44 III New day, new dawn? ADVENT OF VACCINES, EASE OF CONTAINMENT MEASURES, AND ECONOMIC RECOVERY 45 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean New day, new dawn? ADVENT OF VACCINES, EASE OF CONTAINMENT MEASURES, AND ECONOMIC RECOVERY I n 2021, LAC countries started to recover unevenly with the advent of vaccines and the relaxation of non-pharmaceutical measures. Average GDP growth in the region recovered, growing by 7 percent in 2021. This was due to better external conditions, the relaxation of mobility restrictions, vaccination campaigns, and the expansion of domestic credit. However, these improvements were still insufficient to return the GDP rate to its pre-pandemic level (World Bank, 2022a). Moreover, second-wave mobility restrictions in the first quarter of 2021 (due to the appearance of the highly virulent Omicron variant), coupled with uneven access to vaccines, undermined the speed of recovery. Across the region, vaccine rollout was slow in the first semester of 2021, with about 30 percent of the population vaccinated. Two notable exceptions are Chile and Uruguay, where successful vaccination campaigns reached more than half of their respective populations (Figure III-1 – Panel A). In addition, significant gaps occurred in vaccination rates across groups. On average, vaccination rates across the region for college graduates and formal workers were about seven percentage points higher than for non-college graduates and informal workers. For wealthy individuals (defined as those with a high number of assets), vaccination rates were three percentage points higher than those with fewer assets (Olivieri, Ortega, & Rivadeneira, 2022). Vaccine rollout accelerated in the second semester of 2021 after initial distribution problems were solved and vaccine availability increased. This was particularly true in Ecuador, Argentina, Colom- bia, Panama, Costa Rica, Peru, and Brazil. By the end of 2021, vaccination rates across the region aver- aged almost 70 percent, excluding Haiti (Figure III-1 – Panel B).29 FIGURE III-1: VACCINATION RATES SPEEDED UP IN THE SECOND HALF OF 2021 Chile 13,7% 41,1% Uruguay 22,9% 30,2% Dom. Republic 19,0% 9,7% Argentina 15,4% 6,6% Brazil 11,2% 10,4% Costa Rica 7,1% 12,5% El Salvador 5,6% 12,4% Mexico 7,4% 9,7% Panama 6,0% 7,9% Colombia 6,8% 6,4% Panel A. Ecuador 7,3% 4,4% Bolivia 8,0% 2,9% As of May 31, 2021 Peru 4,8% 4,0% Paraguay 3,8% 1,3% Honduras 2,5% 0,5% Guatemala 2,0% 0,4% Haiti 0,0% 0,0% 10,0% 20,0% 30,0% 40,0% 50,0% 60,0% Share of people only partly vaccinated Share of people with a complete initial protocol Figure III-1: (PANEL A): 29 Vaccine rollout for the Caribbean and Central America showed little progress by the end of 2021, with a vaccination rate of about 30 percent, due to low vaccine acceptance undermining the recovery in those countries (Margolies, et al., 2022). 46 Chile 84,4% 3,9% Argentina 73,6% 11,7% Uruguay 78,3% 2,3% Ecuador 70,5% 8,5% Panel B. Brazil 66,6% 10,6% Costa Rica 68,5% 7,9% As of December 31, 2021 Colombia 54,6% 19,3% Peru 64,9% 8,1% Panama 63,7% 7,1% El Salvador 65,6% 5,2% Mexico 57,1% 7,1% Dom. Republic 50,8% 11,0% Paraguay 43,5% 7,7% Honduras 41,2% 5,9% Bolivia 37,6% 8,5% Guatemala 26,3% 10,4% Haiti 0,4% 0,7% 0,0% 10,0% 20,0% 30,0% 40,0% 50,0% 60,0% 70,0% 80,0% 90,0% Share of people with a complete initial protocol Share of people only partly vaccinated Source: Our World in Data, at https://ourworldindata.org/. Figure III-1: (PANEL B): Despite the recovery, there are concerns about how this process affects households’ welfare. The pandemic significantly impacted households’ welfare through employment losses and the plunge in labor income, partially cushioned by public transfers (Chapter 1). Thus, during the recovery phase, attention should initially focus on the feasibility of job retention or the creation of enough new jobs for employment to return to pre-pandemic levels, plus the nature of these jobs. A second area of focus should be the fiscal sustainability of maintaining public transfers as in 2020 or the likelihood of domes- tic or international transfers being sustained through private efforts. Has the recovery process been sufficient to return labor incomes to pre-pandemic levels? Labor market indicators show recovery signs, with employment rates improving slowly, unevenly, and still below pre-pandemic levels in most countries. After an 8.3-percent drop in employment in 2020 relative to 2019, approximately nine million jobs were regained across the re- gion in 2021. Yet, one year after the onset of the pandemic, employment in LAC was still 3.1 percent- age points below total employment in 2019. In 2021, employment was above pre-pandemic levels in only four countries: Argentina, Bolivia, Ecuador, and Paraguay. The improvement was significant for Ecuador and Argentina, with new jobs representing 6.8 percent and 5.2 percent of total employment in 2019. High vaccination rates partly explain this in both countries at the end of 2021, as well as a large share of workers entering the labor market (particularly in Ecuador). Employment recovery in the region also benefitted from the boost given to economic activity by the relaxation of mobility restrictions, on the one hand, and by increases in vaccination rates among trading partner countries, on the other.30 In contrast, employment recovery has been slow in Brazil, El Salvador, and Panama, with no significant improvements in total employment compared to pre-pandemic levels. As a result, these countries are lagging behind the region’s average recovery rate (Figure III-2 – Panel A). 30 Deb, et al. (2022) find evidence of significant spillover effects of vaccination rates across borders on economic activity mea- sured by increases in NO2 emissions. An increase of one standard deviation in COVID-19 vaccination rates in main trading partner countries led to a 0.13 standard deviation increase in NO2 emissions. These authors also find that economic gains are lower if strict containment measures are in place as such measures constrain economic activity even with a high vacci- nation rollout. 47 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean The sluggish regional employment recovery is influenced by the poor performance of the construction and services sectors, which are typically big employers of unskilled workers.31 Between them, these two sectors employ about half the region’s workforce. Yet, they were also the hardest-hit sectors during COVID-19 as well as being the least able to transition to telework modali- ties. Unlike other economic sectors, the unskilled services and construction sectors were alone in fail- ing to surpass or at least return to their pre-pandemic employment levels in 2021. Total employment remained 1.2 percentage points below where it was in 2019 (Figure III-2 – Panel B). More positively, the outbreak of the health crisis was met with a surge in jobs linked to agriculture and skilled services. In the case of agriculture, this indicates that jobs in the region have been reallocated to low-produc- tivity sectors. This is especially true in Peru, Ecuador, and Brazil, which have witnessed increases of 4.1, 2.8, and 0.8 percentage points, respectively. At the same time, the rise in employment in skilled services suggests that some workers have adapted to the challenges of the pandemic by working remotely or on digital platforms. Others have developed e-commerce businesses or other techno- logical innovations in the skilled services sector. This shift could feasibly change labor demand and supply trends in the region.32 FIGURE III-2: THE RECOVERY HAS SLIGHTLY BENEFITTED UNSKILLED WORKERS Change in total employment relative to 2019 10% Ecuador Argentina 5% Paraguay Bolivia 0% % change 2021-19 Peru Dominican Uruguay Costa Rica LAC Republic -5% Colombia Brazil El Salvador Panel A. Panama By country -10% -15% -16% -14% -12% -10% -8% -6% -4% -2% 0% 2% % change 2020-19 Figure III-2 (PANEL A): 31 Unskilled services include commerce and trade, hotels and restaurants, and transport and storage. 32 The COVID-19 pandemic will most likely accelerate the transformation process of jobs, which could contribute to increasing regional inequality. Despite the rapid adaptation of firms and workers to technological innovations, challenges surround the region’s capacity to benefit from further technology development and adoption in the future. For instance, training in relevant skills will be key to adapting and taking advantage of the new opportunities in the post-pandemic world, particu- larly for low-skilled, mid-career, and informal workers that lack the skills for the transformation process taking place in the labor market (Beylis, Jaef, Sinha, & Morris, 2020). 48 1,5% Agriculture 1,0% 0,5% % change 2021-19 Skilled services Panel B. Manufacture 0,0% By economic sector -0,5% -1,0% Unskilled services & construction -1,5% -2,0% -1,5% -1,0% -0,5% 0,0% 0,5% 1,0% 1,5% % change 2020-19 Source: World Bank staff III-2based calculations Figure B):(CEDLAS and the World Bank). on SEDLAC (PANEL Note: Since the numbers presented here are based on Socio-Economic Database for Latin America and The Caribbean (SEDLAC), which is a regional data harmonization effort that increases cross-country comparability, they may differ from official statistics reported by governments and national statistical offices. The LAC figure is the average of the 12 countries shown in the figure. Data for El Salvador and Panama were unavailable for 2020, so the employment change was simulated using the LAC average. Uruguay 2021 refers to the second semester and is not comparable with previous rounds. Unskilled services include commerce and trade, hotels and restaurants, and transport and storage. Skilled services include public utilities, financial services, communication, teaching, and other professional services. Young people, low-educated workers, and women have been experiencing a slow employ- ment recovery. While employment for workers with tertiary education rebounded fast in 2021 and increased by five percentage points relative to 2019, employment for workers with primary educa- tion emerged from the pandemic six percentage points below pre-COVID-19 levels. This is despite this section of the workforce recovering about half of all job losses by 2021. Meanwhile, no signs of recovery were seen for workers with less than primary education since the start of the pandemic. As for women’s employment levels, these remain five percentage points below where they were prior to the pandemic. This gap between pre- and post-pandemic is three times higher than that for men, despite both women and men experiencing similar recovery rates.33 In addition, employment has re- covered faster for prime-age and senior workers, although neither has reached levels seen before the pandemic. For young workers, the recovery still has a long way to go; employment figures were still 33 For females with children, re-entering the labor market has been particularly hard. For instance, throughout the pandemic, the likelihood of mothers with small children (0-5 years old) working was consistently lower than the average for women overall. Furthermore, a large share of mothers in this category was not unable to find a job (43 percent compared to 37 percent among all females). The constraints to work relate to increases in unpaid housework, such as cleaning, cooking, and childcare, as well as to time spent helping children with schoolwork. On average, women were more likely than men to report increases in the time spent on domestic work (32 versus 25 percent), childcare (43 versus 33 percent), and support for children’s education (50 versus 37 percent) (See del Carpio, et al., 2022; Mejía-Mantilla, et al., 2022). 49 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean 8.3 percentage points below pre-pandemic levels in 2021 (Figure III-3).34 An obstacle undermining employment recovery in the region was the uneven access to vaccination of vulnerable groups. For instance, vaccinated individuals in LAC had a probability of employment 3.5 percentage points high- er than unvaccinated individuals during the first semester of 2021. Vaccination also increased the likelihood of employment for females and college graduates by about four percentage points and for workers ages 18 to 35 by six percentage points (Olivieri, Ortega, & Rivadeneira, 2023). FIGURE III-3: THE RECOVERY HAS BEEN SLOW FOR YOUTH, LOW-EDUCATED WORKERS, AND WOMEN Total employment by socio-economic groups relative to 2019 10,0% 5,0% Tertiary Secondary % change 2021-19 0,0% Senior (55-65) Male Female Prime age (25-54) -5,0% LAC Youth (15-24) Primary -10,0% -15,0% Less than primary -20,0% -20,0% -15,0% -10,0% -5,0% 0,0% % change 2020-19 Figure Source: World BankIII-3-cambio staff calculations based on SEDLAC (CEDLAS and the World Bank). Note: Since the numbers presented here are based on Socio-Economic Database for Latin America and The Caribbean (SEDLAC), which is a regional data harmonization effort that increases cross-country comparability, they may differ from official statistics reported by governments and national statistical offices. The LAC figure is the average of the 12 countries. Data for El Salvador, Panama and Uruguay were unavailable for 2020, so the changes in total employment by education level were simulated using the LAC average. Despite the recovery in regional employment levels, the quality of jobs has deteriorated. In 2021, formal employment in the region fell 1.4 percent on average compared with pre-pandemic levels. Ecuador, Dominican Republic, Panama, and Peru experienced the most significant drops in formal em- ployment, ranging from 2.3 percent to 6 percent. By contrast, self-employment in LAC has grown by 1.5 percentage points on average since the onset of the pandemic, with larger increases in Colombia, Panama, Bolivia, and Costa Rica (Figure III-4). This indicates that workers are coping with the crisis by becoming 34 Youth workers entering the labor market during a crisis face a worse start to their careers than historically expected and experience difficulties in recovering from this setback (Silva, Sousa, Packard, & Robertson, 2021). For instance, Von Wachter (2020) shows that young people entering the labor market in a typical recession experience a rise of 3-4 per- centage points in unemployment rates and a decline in income of about 10-15 percent. Genda, et al. (2010) show that the effect of high unemployment at graduation is more negative and persistent for high school graduates than for col- lege graduates in Japan. In Ecuador, an increase of 1 percentage point in the unemployment rate at entry reduces the likelihood of employment by 1-1.7 percentage points over the next 12 years. This negative effect is persistent for young female workers for the 10 years following their entry into the labor market (Gachet, 2021). 50 self-employed rather than entering unemployment (See Box II-1).35 This trend is worrisome in a region with a disproportionate share of self-employed and informal workers before the pandemic (i.e., 85 per- cent of self-employed workers were in informal jobs in 2019). Consequently, informality may function as a buffer for employment in the aftermath of the health crisis in the region due to lower entry costs, particularly for workers for whom unemployment is not an option (Silva, Sousa, Packard, & Robertson, 2021). In Argentina, the increase in formal employment in 2021 is explained by public sector employ- ment growth in urban areas. In Brazil, the increase in formal employment could be explained by the dif- ferent compositions of the labor force. First, in 2021 the unemployment rate was still higher than in the pre-pandemic period. In such a weak labor market, individuals typically working in the informal sector were more likely to be unemployed. In addition, overall labor force participation rates were also below pre-pandemic levels, partially reflecting the government’s support to cushion the economic shock and keep informal workers out of the labor force. FIGURE III-4: WHILE EMPLOYMENT RECOVERED, JOB QUALITY DETERIORATED ACROSS THE REGION IN 2021 Change in formal employment and self-employment, 2021-19 2,5% 2,7% 1,6% 2,2% 1,6% 1,5% 1,7% 1,6% 0,5% 1,3% 1,5% 1,2% % change 2021-19 0,1% 0,9% 1,3% -0,5% -0,3% -0,4% -1,4% -1,3% -1,7% -2,3% -3,8% -3,7% -6,0% Ecuador Dominicana Panama Peru Paraguay LAC Colombia Bolivia Costa Rica El Salvador Uruguay Brazil Argentina Formality Self-Employment Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank). Since the numbers presented here are based on Socio-Economic Database for Latin America and The Caribbean (SEDLAC), which is a regional data harmonization effort that increases cross-country comparability, they may differ from official statistics reported by Figure III-4 governments and national statistical offices. The LAC figure is the average of the 12 countries shown in the figure. Uruguay 2021 refers to the second semester and is not comparable with previous rounds. 35 Also, women were more likely to take up caregiver or unpaid working roles, particularly given COVID-19-induced school closures and confinement measures, thereby leading to their possible permanent exit from the labor market (Torres, et al., 2021). For instance, according to High-Frequency Phone Surveys (HFPS), women workers were 44 percent more likely than male workers to lose their jobs at the onset of the COVID-19 health crisis (Cucagna, et al., 2021). 51 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean BOX III-1: QUALITY OF JOBS IN LAC As employment recovers in LAC countries from the onset of the COVID-19 pan- demic, there is a persistent deterioration in the quality of employment. The Job Quality Index (JQI) combines four dimensions that characterize a good job (Brummund, Mann, & Rodriguez-Castelan, 2018); namely, income, benefits, security, and satisfaction. In the case of LAC, the JQI has reverted on average for more than a decade (2009 levels). There is a negative correlation between employment recovery and the JQI in LAC countries after 2020. Although employment levels return to the pre-pandemic situation, job quality does not (Figure B.III-1 – Panel A). Ecuador, Paraguay, and Bolivia exhibited employment levels above pre-pandemic levels but poorer quality. The decline in job quality is also shown in countries with employment levels just below pre-pandemic levels, such as Uruguay, Cos- ta Rica, Dominican Republic, and Peru. Argentina shows an increase in employment and the JQI, which could be explained by an increase in public employment concentrated in urban areas and historically low levels of job quality in 2019. * Lower benefits and job security levels than in pre-pandemic have driven the deteriora- tion in the JQI across LAC countries. Peru, Bolivia, Colombia, and Panama have the most sig- nificant decline in the JQI due to a worsening in benefits and job security (Figure B.III-1 – Panel B). This implies that workers in these countries face higher levels of vulnerability and are more ex- posed to shocks. A cut in benefits is also present in Ecuador, Dominican Republic, and Paraguay. Income has a smaller contribution to the drop in the index than the other two dimensions across countries, with the largest declines in Ecuador, Panama, and Peru. FIGURE B.III-1. DESPITE THE ONGOING RECOVERY IN EMPLOYMENT TO PRE-PANDEMIC LEVELS, THE QUALITY OF JOBS HAS STILL DETERIORATED 2% ARG* SLV 0% URY PRY -2% BRA LAC CRI DOM JQI 2021-19 -4% Panel A. COL BOL Change -6% PAN in total employment and Job Quality -8% ECU Index 2021 vs. 2019 PER -10% -10% -8% -6% -4% -2% 0% 2% 4% 6% 8% Employment 2021-19 Figure B.III-1 Panel A: 52 ECU 5% ARG* PAN 0% SLV -5% PER Panel B. -10% Change in dimensions of Job Quality Index 2021 vs. BOL -15% CRI 2019 -20% BRA COL URY DOM PRY LAC Income Benefits Satisfied Security Figure B.III-1 Panel B: Source: World Bank staff calculations based on SEDLAC (CEDLAS and World Bank). *Argentina only has urban coverage and is not included in computing the correlation between the JQI and employment in Panel A. Uruguay 2021 refers to the second semester and is not comparable with previous rounds. The Job Quality Index (JQI) is a multi-dimensional measure of job quality based on Brummundi, Mann, and Rodriguez-Castelan (2018) that includes four dimensions of job quality as follows: Job income: Minimum income of well-being. The JQI defines the Upper-Middle Income Class poverty line of $6.85 per day (2017 Purchasing Power Parity) as the minimum income of well-being. Even for an individual with all the other dimensions, if their wage is below the poverty line, the JQI is equal to zero. Job benefits: The job provides health insurance or retirement benefits. Some countries provide a minimum level of health and pension coverage; nonetheless, the worker sometimes receives extra coverage as part of job benefits in those countries. Job security: The worker has a contract; the job is permanent, or the worker has kept the job for a long enough period to consider a job secure. The JQI assumes a cut-off of three years of tenure to consider the job permanent and a contract and stable work indicators when available. Job satisfaction: The worker is satisfied with their job. As a proxy of this dimension, we assume that the worker does not have a second job. Workers have also reallocated from large firms to small firms.36 On average, the share of work- ers in large firms has declined by two percentage points across the region, while employment in small firms has increased. Reallocation of workers to small firms has been the highest in the Dominican Re- public, Peru, and Paraguay, with increases of 4.4, 4.2, and 3.5 percentage points, respectively. Large firms in the Dominican Republic, Panama, and Bolivia had the most significant decline in employment. Contrary to the general trend in the region, Argentina witnessed a drop in employment in small firms of 1.4 percentage points, double the decline in large firms (Figure III-5).37 This suggests that employment 36 Firms with five workers or fewer are defined as ‘small firms’ and those with more than five workers as ‘large firms.’ 37 Since the pandemic started, the region has seen a reallocation of workers from large and medium-sized firms to small firms. (See Mejía-Mantilla, et al., 2022; World Bank, 2022c). 53 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean in large firms is more cyclical than in small firms. This is an undesirable outcome for the recovery in the region, given that small firms are associated with higher informality, lower labor incomes, and lower productivity, which, in turn, lead to higher inequality and lower economic growth. An additional problem of higher informality in the region is that most informal, self-employed workers do not pay taxes on their earnings. Similarly, they typically do not contribute to social security or receive employ- ment benefits (Perry, et al., 2007). FIGURE III-5: WORKERS REALLOCATED TO SMALL AND LESS PRODUCTIVE FIRMS IN 2021 Change in the share of employment by firm size, 2021-19 4,2% 4,4% 3,5% 3,2% 2,9% 2,9% 2,0% 2,1% % change 2021-19 0,7% 1,0% -1,4% -0,7% -1,4% -0,7% -2,0% -2,0% -2,3% -2,6% -3,2% -3,1% -4,5% -4,9% Argentina El Salvador Uruguay Brazil Colombia LAC Ecuador Panama Bolivia Paraguay Peru Dominican Republic Small Large Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank). Note: Since the numbers presented here are based on Socio-Economic Database for Latin America and The Caribbean (SEDLAC), which is a regional data harmonization effort that increases cross-country comparability, they may differ from official statistics reported by governments and national statistical offices. The LAC figure is the average of the 11 countries shown in the figure for which data are Figure III-5: available for both 2019 and 2021. Uruguay 2021 refers to the second semester and is not comparable with previous rounds. The worsening of the quality of jobs was more severe in countries where labor market con- ditions were already challenging. Employment reallocation to small firms is lower in countries with higher pre-pandemic formality rates, like Uruguay and Costa Rica. In contrast, countries with lower pre-pandemic formality rates, such as Bolivia, Peru, and Paraguay, have experienced a higher reallocation of workers to small firms (Figure III-6 – Panel A). Also, pre-pandemic formality rates do not appear to have prevented employment shifts towards self-employment during the pandemic— notably, workers in countries with lower pre-pandemic levels of formality experience higher realloca- tion of workers toward self-employment. (Figure III-6 – Panel B). 54 FIGURE III-6: THE DETERIORATION OF JOB QUALITY WAS MORE PRONOUNCED IN COUNTRIES EXPERIENCING TIGHT LABOR MARKET CONDITIONS BEFORE THE PANDEMIC Pre-pandemic formality rates, size of the firm, and self-employment 5% Peru Dominican Republic % Change in share of small firms 2021-19 4% Paraguay Ecuador Panama 3% Bolivia LAC 2% Colombia Costa Rica 1% Panel A. El Salvador Brazil Uruguay Size of the firm 0% -1% Argentina -2% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% Pre-pandemic formality rate 50% Figure III-6: Panel A. 45% Bolivia Colombia Share of self-employed workers in 2021 40% Dominican Republic Panel B. Self-employment 35% Peru Ecuador Panama Paraguay 30% LAC Brazil Uruguay 25% El Salvador Argentina 20% Costa Rica 15% 10% 20% 30% 40% 50% 60% 70% 80% Pre-pandemic formality rate Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank). Note: Since the numbers presented here are based on Socio-Economic Database for Latin America Figure and The Caribbean III-6: Panel B. (SEDLAC), which is a regional data harmonization effort that increases cross-country comparability, they may differ from official statistics reported by governments and national statistical offices. The LAC figure is based on data from 11 countries (Panel A) and 12 countries (Panel B) for which data are available for both 2019 and 2021. Uruguay 2021 refers to the second semester and is not comparable with previous rounds. 55 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean Vulnerable groups have not only suffered from sluggish employment recovery but have also experienced a rapid deterioration in job quality. This dynamic could lead to increasing in- equality in the long run. By 2021, transitions to self-employed had been higher for women than men (a 1.8 vs. 1.2 change in percentage points, respectively). Yet, female formality rates have decreased less than those for men. This is encouraging given that women in several LAC countries face higher informality rates than men when entering the labor market (Figure III-7). Formal employment has decreased by 2.7 percentage points for youth relative to pre-pandemic levels, with only two-thirds of young workers transitioning to self-employment.38 Although the self-employment reallocation has been similar across education levels, the decline in formality rates for workers with secondary education was nearly double that of those with primary and tertiary education. FIGURE III-7: VULNERABLE GROUPS EXPERIENCED A MORE SEVERE DETERIORATION IN JOB QUALITY Change in formal employment and self-employment by socio-economic groups in LAC 2,2% 2,2% 1,8% 1,9% 1,5% 1,6% 1,5% 1,5% 1,3% 1,2% 1,2% 1,0% % change 2021-19 1,0% -0,2% -0,5% -0,4% -0,7% -1,2% -1,4% -1,5% -1,7% -2,0% -1,9% -2,7% -3,1% Female Male Youth Prime age Senior Less than Primary Secondary Tertiary Poor Vulnerable Middle (15- 24) (25-54) (55-65) primary class Total Gender Age Education Poverty Formality Self-Employment Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank) Note: Since the numbers presented here are based onFigure III-7 Socio-Economic Database for Latin America and The Caribbean (SEDLAC), which is a regional data harmonization effort that increases cross-country comparability, they may differ from official statistics reported by governments and national statistical offices. The LAC aggregate is based on data from 12 countries for which data are available for both 2019 and 2021. 38 The large decline in formality and the increase in self-employment among younger workers could reflect lower levels of job security related to tenure among such workers. Young workers are disproportionally likely to be hired on temporary con- tracts, which leaves them without access to many benefits and protections against dismissal (Gatti, et al. (2014); Kuddo, Robalino, and Weber (2015). 56 BOX III-2: LABOR MARKET TRANSITIONS Labor transitions reflect poor employment conditions since the onset of the COVID-19 pandemic as many previously employed people remain unemployed, have not returned to work, or have moved to informality. Results from the High-Fre- quency Population Survey (HFPS) show that transition flows of workers into unemployment and inactivity between February 2020 and mid-2021 come either from those previously em- ployed in a formal job or from inactive workers who entered the labor market after the pan- demic started (Figure B.III-2 – Panel A). Also, transition flows show a relatively low probability of re-entering the formal labor market after the transition to informality, unemployment, or inactivity during the pandemic. In addition, labor transitions show persistent gender gaps during the pandemic. Despite a slightly lower share of women than men (13 percent vs. 16 percent) transitioning from formal to informal employment, the share of women employed in the formal sector before the pandemic who then transitioned to unemployment is four percentage points higher than the equivalent for men. The share doubles for women transitioning from for- mal employment to inactivity. The gaps are even higher for women working in the informal sector. About two-thirds of women working in the informal sector before the pandemic transitioned to inactivity in 2021, which is 2.6 times higher than the rate for men. Family care responsibilities could be an explanatory factor here as roles traditionally assigned to women may have influenced their decision as to whether to work, be unemployed, or stay out of the labor force altogether (del Carpio, et al., 2022). FIGURE B III-2: WORKERS TRANSITION TO INFORMALITY DURING THE RECOVERY Transitions between employment, unemployment, and inactivity Panel A. Panel B. Panel C. All workers Men Women Source: World Bank and UNDP (2021) High-Frequency Phone Survey, Phase 2, Wave 1. 57 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean The region exhibited a deterioration in good-quality jobs, reflecting a slow recovery in work- ing hours, albeit too quiet to return to the pre-pandemic situation. Despite a significant recov- ery in 2021, average hours worked in the region are still 1.2 percent below their pre-pandemic levels, with an uneven recovery across countries. For instance, working hours in El Salvador, Uruguay, Paraguay, and the Dominican Republic have recovered or surpassed pre-pandemic levels. In contrast, working-hour rates in Peru, Costa Rica, Ecuador, and Panama remain below the LAC average (Figure III-8 – Panel A). The reduction was particularly high in Ecuador and Panama, where working hours are still 5.9 and 7.1 percentage points lower than pre-pandemic levels, respectively. In Ecuador, this is explained by regula- tory changes that allowed firms to reduce hours and wages to protect jobs but sacrificed their quality. In Panama, on the other hand, it is simply evidence of a major decline in the employment situation.39 This decline in hours worked could result from an optimal labor supply decision if hourly labor was to rise. In this case, the income effect would dominate the substitution effect, and people would willingly choose to work fewer hours. However, this is not presently the case.40 The recovery in hours worked has been uneven across socio-economic groups. By 2021, total working hours recovered faster for women than for men. The rate for women was also higher than the LAC average, albeit 0.8 percent below the pre-pandemic level. Yet, the gap between men and women in worked hours remains the same as before the pandemic. For their part, youth workers experienced a decline in hours worked of 0.7 percent in 2020. However, the situation bounced back in 2021, push- ing their working hours 0.4 percent above pre-pandemic levels. Meanwhile, the total number of hours worked by less educated and senior workers recovered at a slower rate in 2021 (Figure III-8 – Panel B). FIGURE III-8: COUNTRIES FACED A SLOW AND UNEVEN RECOVERY IN WORKING HOURS Working hours relative to 2019 4% 2% 0% -3,3% -2% -1,2% % change -4% -6% -8% -10% -12% Panel A. Argentina Costa Rica Peru Ecuador El Salvador Panama LAC Dominica Republic Paraguay Colombia Uruguay Brazil Bolivia By country 2020-19 2021-19 39 In Ecuador, the Humanitarian Law, approved in 2020, introduced a set of policies designed to mitigate the pandemic’s negative effects. These included renegotiation of labor contracts by mutual agreement, a fixed-term emergency contract that allowed part-time for one year and renewed for up to one more year, and a reduction of the workday and wages. Figure III-8: Panel A. 40 The decline in hours seems to be driven by demand. The decline in hours worked is an equilibrium result in the sense that it comes about from the interaction of labor supply and labor demand. Brinca, et al. (2021) argue that negative labor supply shocks are related to the pandemic’s public health effects and public health responses, while labor demand shocks reflect economic forces that may persist beyond the public health crisis. If hours worked and labor move in the same direction, these movements are more likely to be caused by a demand shock. However, if hours and hourly wages move in opposite directions, a supply shock is more likely. For the case of the United States, Brinca, et al. (2021) find that around two-thirds of the decline in the growth rate of hours at the onset of the pandemic was due to a supply shock, with significant heterogeneity across sectors. 58 0,5% Youth (15-24) 0,0% % change 2021-19 -0,5% Tertiary Panel B. Female By socioeconomic group -1,0% Primary LAC Secondary Prime age -1,5% - (25-54) Male Senior (55-65) -2,0% Less than primary -2,5% -6,0% -5,0% -4,0% -3,0% -2,0% -1,0% 0,0% 1,0% % change 2020-19 Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank). Note: Since the numbers presented here are based on Socio-Economic Database for Latin America and The Caribbean (SEDLAC), which is a regional data harmonization effort that increases cross-country comparability, they may differ from official statistics Figure III-8: Panel B. reported by governments and national statistical offices. The LAC figure is the average of the 12 countries shown in the figure for which data are available for 2019 and 2021. Uruguay 2021 refers to the second semester and is not comparable with previous rounds. Data for El Salvador, Panama and Uruguay were unavailable for 2020, so working hours were simulated using the LAC average. Working hours correspond to the main job. Despite household members’ efforts in the labor market, labor incomes were significantly behind pre-pandemic levels. Moreover, labor income has recovered at a lower rate than hours worked. In 2021, labor income in the region increased by an average of 2.3 percent. Two notable ex- ceptions are Argentina and El Salvador, where labor income recovered at higher rates than the re- gional averages (7.9 percent and 8.7 percent, respectively). Brazil witnessed a decrease of 2.9 percent in labor income between 2021 and 2019, after posting an increase between 2020 and 2019 of 3.5 percent (Figure III-9 – Panel A). Labor income losses were still affecting most groups in 2021. On average, labor income is about 7 percent lower than its pre-pandemic levels for workers at all education levels, except those with less than primary education (for whom income is 0.5 percent higher). For prime-age workers, labor income recovered by three percentage points compared with 2020 but is still 5 percent below pre-pandemic levels. Despite a significant recovery, the labor income of men in 2021 remained 7 per- cent below its pre-pandemic levels, triple the income losses of women (Figure III-9 – Panel B). 59 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean FIGURE III-9: THE MILD RECOVERY OF LABOR INCOME IN 2021 DID NOT OFFSET INCOME LOSSES AND REMAINED FAR BEHIND PRE-PANDEMIC LEVELS Labor income relative to 2019 5% 0% -4,9% -7,3% -5% % change -10% -15% -20% Panel A. Colombia El Salvador Bolivia Panama Costa Rica Brazil Ecuador Paraguay Argentina Uruguay Peru Dominican Republic LAC By country 2020-19 2021-19 1% Less than primary 0% -1% Figure III-9: Panel A. % change 2021-19 -2% Panel B. Female -3% By socioeconomic group -4% -5% Prime age (25-54) LAC Senior (55-65) -6% Youth (15-24) Male Tertiary -7% Primary Secondary -8% -13% -11% -9% -7% -5% -3% -1% 1% % change 2020-19 Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank). Note: Since the numbers presented here are based on Socio-Economic Database for Latin America and The Caribbean (SEDLAC), which is a regional data harmonization effort that increases cross-country comparability, they may differ from official statistics reported by governments and national statistical offices. The LAC figure is the average of the 12 countries shown in the figure for which data are available for 2019 and 2021. Uruguay 2021 refers to the second semester and is not comparable Figure Panel III-9:rounds. with previous B. Data for El Salvador, Panama and Uruguay were unavailable for 2020, so working hours were simulated using the LAC average. What happened to public aid one year after the pandemic’s onset? In 2021, public transfers declined in most countries, given fiscal constraints across LAC coun- tries. After increasing by 3.5 percentage points between 2019 and 2020, fiscal transfers declined by 2.0 percentage points in 2021 compared to 2020. However, they are still 1.5 percentage points above pre-pandemic levels. Peru, Argentina, Brazil, and the Dominican Republic had the highest drop in fiscal transfers, ranging from 2.4 percentage points to 6.7 percentage points in 2021 (Figure III-10). The decline in fiscal transfers is mostly explained by the limited fiscal space available to most governments in the region (World Bank, 2022b). In Brazil, the decline in public transfers is also explained by the discontin- uation of the emergency transfer program (Auxílio Emergencial) in 2021, which provided a lifeline for many households at the onset of the pandemic (World Bank, 2022d). 60 FIGURE III-10: PUBLIC TRANSFERS PLUNGE IN 2021 IN LAC Change in public transfer relative to 2019 8,0% 8,0% 5,0% 5,1% 4,1% 3,5% Percentage points 2,8% 2,0% 1,2% 1,4% 1,5% 1,0% 0,3% 0,2% 0,2% 0,2% -0,6% -0,4% -1,4% -1,5% -1,6% -2,0% -2,4% -3,7% -6,6% -6,7% El Salvador Bolivia Brazil Dominican Republic Argentina Panama Paraguay Costa Rica LAC Colombia Ecuador Peru Uruguay 2020-19 2021-19 Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank). Since the numbers presented here are based on Socio-Economic Database for Latin America and The Caribbean (SEDLAC), which is a regional data harmonization effort that increases Figure III-10 cross-country comparability, they may differ from official statistics reported by governments and national statistical offices. The LAC figure is the average of the 12 countries shown in the figure for which data are available for 2019 and 2021. Uruguay 2021 refers to the second semester and is not comparable with previous rounds. Data for El Salvador and Panama were unavailable for 2020, so changes in transfers were simulated using the LAC average. The region’s slow yet uneven post-pandemic recovery saw poverty levels decline in 2021, al- though they remain above their pre-pandemic levels. Taking a poverty line of below $6.85 per day (2017 PPP), the poverty rate in LAC (excluding Brazil) during 2021 stood at 31.4 percent. This poverty level is three percentage points lower than in 2020 but two percentage points higher than in 2019 (Figure III11). Around ten million people have fallen into poverty since the onset of the COVID-19 pandemic. When Bra- zil is included in the calculation, however, poverty rates are shown to increase by two percentage points during this period – from 28.2 percent in 2019 to 30.3 percent in 2021 (Figure III-11). This is explained by a gradual reduction of Brazil’s emergency transfer and a labor market that had not fully recovered in 2021.41 The recovery helped increase the size of the middle class but not enough to reach pre-pan- demic levels. The total population in the region (minus Brazil) who registered as middle class recovered from 32.0 percent in 2020 to 34.5 in 2021, yet this remains 1.5 percentage points below pre-pandemic levels (Figure III-11). When Brazil is included, the loss in the region’s middle class nearly doubles, with about 12 million people falling out of this socioeconomic segment in the two years following the start of the pandemic. Losses of labor income and a deterioration in the quality of employment across the region have driven this shrinking of the middle class and the increase in poverty. Thus, the improvement in labor market conditions is the region’s main challenge in the aftermath of the COVID-19 pandemic. 41 Despite contributing to poverty reduction in Brazil, government transfers were reduced substantially in September 2020 and discontinued in December. This resulted in a sharp drop in real per capita income of the bottom 40 percent and left vulnerable populations much more susceptible to the socioeconomic shocks induced by the pandemic, such as job losses and increases in prices (World Bank, 2022d). 61 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean FIGURE III-11: POVERTY DECLINED AND THE MIDDLE CLASS RECOVERED, BUT NEITHER RETURNED TO PRE-PANDEMIC LEVELS IN 2021 Headcount poverty trends 38,4 36,6 35,7 36,0 34,4 34,5 Percentage (%) 32,8 33,1 32,8 32,2 32,4 32,0 31,4 31,0 30,3 29,4 28,7 28,2 2019 2020 2021 2019 2020 2021 LAC with Brazil LAC without Brazil $6.85 per day (2017PPP) Vulnerable $6.85-$14 per day (2017PPP) Middle Class $14-$81 per day (2017PPP) Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank). Figure III-11 Since the numbers presented here are based on Socio-Economic Database for Latin America and The Caribbean (SEDLAC), which is a regional data harmonization effort that increases cross-country comparability, they may differ from official statistics reported by governments and national statistical offices. The LAC aggregate is based on 18 countries in the region for which microdata are available. In cases where data are unavailable, values have been interpolated or extrapolated using data from the World Bank Development Indicators (WDI) and then pooled to create regional estimates (2014 backward) and microsimulations (from 2015 onwards). Households living in urban areas, working-age adults, those with higher levels of educa- tion, and salary workers were more likely to experience increased poverty and downward mobility in 2021. Including Brazil, the ‘new poor’ living in urban areas increased from 70.5 percent in 2020 to 72.3 percent in 2021. Poverty also increased for those with incomplete primary education by 1.4 percentage points, from 35.4 percent in 2020 to 36.8 percent in 2021. They almost reached the poverty levels of those with incomplete primary. Interestingly, the profile shows that most house- holds who fell into poverty are more likely to be salary workers and unemployed in the industry and services sectors in 2021. When excluding Brazil, however, poverty in urban areas decreases by 1.4 per- centage points. Yet, the decline in urban poverty did not reach all groups. Those who did not benefit from the recovery include youth, salaried workers, the self-employed, and individuals with incom- plete secondary education or more, (Table II-1). 62 TABLE I I-1: THE ‘NEW POOR’ IN LAC ARE MORE LIKELY TO BE HIGH-SKILLED, WORK INFORMALLY, AND LIVE IN URBAN AREAS Poverty profile, 2021-20 LAC with Brazil LAC excluding Brazil   2021 2020 2021 2020     Poor Middle Class Poor Middle Class Poor Middle Class Poor Middle Class Age Groups 0-14 35.1 13.4 36.3 13.3 37.0 15.7 35.8 15.2 15-24 18.2 13.2 18.3 13.7 17.4 15.1 18.0 15.0 25-40 23.5 24.9 22.8 25.0 21.0 23.9 21.4 23.8 41-64 19.2 32.8 18.4 33.1 18.7 31.3 19.4 31.3 65+ 4.1 15.8 4.2 14.9 6.0 14.0 5.5 14.8 Education Average Years of Education 5.9 9.5 5.9 9.3 6.1 10.0 6.4 10.0 Never attended 15.1 7.2 14.3 7.6 13.2 5.6 12.4 5.9 Incomplete Primary 36.8 19.8 35.4 22.1 27.1 13.4 26.2 13.7 Complete Primary 8.1 7.4 8.9 7.3 11.5 9.7 11.8 10.0 Incomplete Secondary 16.0 10.1 17.7 9.3 23.7 16.1 23.5 15.3 Complete Secondary 18.2 22.6 17.4 23.3 17.4 20.8 17.8 20.4 Incomplete Tertiary 3.7 12.7 4.1 11.9 4.9 15.4 5.4 15.3 Complete Tertiary 2.1 20.2 2.3 18.5 2.3 19.0 3.0 19.4 Area Urban 72.3 90.5 70.5 90.5 66.0 90.0 67.3 90.3 Informality Informal Workers 78.3 33.2 79.0 32.9 89.4 46.2 87.5 44.1 Sector Agriculture 31.5 6.5 36.4 6.6 40.5 6.8 43.0 6.9 Industry 18.9 19.7 17.1 19.8 15.5 20.2 15.5 19.7 Services 49.6 73.8 46.4 73.6 44.0 73.0 41.5 73.4 Type of employment Employer 0.8 4.7 0.8 4.5 1.1 4.0 1.1 3.7 Not salaried 8.7 1.9 11.7 1.9 15.1 3.0 16.6 2.9 Salaried worker 31.9 64.5 28.2 63.6 24.9 61.9 22.4 61.3 Self-employed 32.5 23.9 33.7 23.3 44.2 25.7 42.3 25.4 Unemployed 26.1 5.1 25.5 6.6 14.7 5.3 17.7 6.8 Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank). Note: The LAC aggregate is based on Argentina, Bolivia, Brazil, Colombia, Costa Rica, Dominican Republic, Ecuador, Peru, Paraguay, and Uruguay, for which microdata are available. 63 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean Contrary to 2020, the main drivers of poverty reduction in 2021 have been the recovery in em- ployment and labor income. For LAC (excluding Brazil), 2.0 and 2.3 percent of the poverty reduction was driven by improved labor income and employment, respectively. This improvement compensated for the negative impact on poverty resulting from lower public transfers and remittances (Figure III-12 – Panel A). Yet, when Brazil is included, the recovery of labor income and employment only offset half of the negative contribution to the increase in poverty of lower public transfers (Figure III-12 – Panel B). This result indicates that the dynamics of poverty reduction in 2021 were reverted when compared to 2020. FIGURE III-12: EMPLOYMENT AND LABOR INCOME WERE THE DRIVERS OF POVERTY REDUCTION IN 2021 Shapley decomposition of income source to changes in poverty rate 1,1 Percentage points -0,3 -0,4 Panel A. -2,0 -2,3 LAC excluding Brazil, 2020-21 -4,0 Labor earnings Share who are employed Public transfers Remittances Other Total change 4,2 Figure III-12 Panel A 3,6 Percentage points Panel B. 1,0 LAC with Brazil, 2020-21 0,4 -0,7 -1,3 Labor earnings Share who are employed Public transfers Remittances Other Total change Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank). Note: The Shapley decomposition includes Argentina, Bolivia, Brazil, Colombia, Costa Rica, the Dominican Republic, Ecuador, Paraguay, Peru, and Uruguay, countries for which data are available for both 2020 and 2021. In 2021, inequality reverted to pre-pandemic levels due to a faster recovery in bottom percen- tiles. The Gini coefficient (excluding Brazil) decreased by 1.1 points during the pandemic, from 49.4 to 48.3 Figure III-12 Panel B 64 for 2020 and 2021, respectively (Figure III-13 – Panel A). This decline is explained by a faster increase in the bottom income deciles (16.6 percent annual growth) than the top income deciles. When including Brazil, however, the Gini coefficient increased by 0.5 points, from 49.8 in 2020 to 50.3 in 2021. This increase indicates the decline in the mitigation measures implemented by the Brazilian government. Thus, most percentiles registered income losses when Brazil is included. The exception is the upper deciles, who expe- rienced a slight increase in their average incomes in 2021 (Figure III-13 – Panel B). FIGURE III-13: INCOMES OF THE BOTTOM INCOME DECILES INCREASED SIGNIFICANTLY, AND INEQUALITY DECLINED IN 2021 51,1 50,3 49,8 49,4 Gini coe cient 49,0 48,3 Panel A. Gini coefficient 2019 2020 2021 Gini with Brazil Gini without Brazil 20,0 15,0 Growth Rate (Annualized) 10,0 Figure III-13 Panel A Panel B. 5,0 Growth incidence curves 0,0 -5,0 -10,0 -15,0 -20,0 1 2 3 4 5 6 7 8 9 10 Deciles of Per Capita Household Income LAC excl. Brazil (2019-20) LAC excl. Brazil (2020-21) LAC with Brazil (2019-20) LAC with Brazil (2020-21) Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank). Note: The LAC aggregate is based on 18 countries in the region for which microdata are available. In cases where data are unavailable, III-13 Panel Figure Indicators values have been interpolated or extrapolated using data from the World Bank Development Bthen pooled to create (WDI) and regional estimates (2014 backward) and microsimulations (from 2015 onwards). Despite a recovery in their welfare, households are still struggling to access food and face greater vulnerability to future shocks due to the erosion of their savings during the pan- demic. Nearly twice as many households now suffer from food insecurity compared to before the onset of the pandemic. Thus, 24 percent of households across LAC report running out of food due to the lack of money or other resources, as opposed to 13 percent before the start of the pandemic 65 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean (Figure III-14 – Panel A). By 2021, most countries appear to be worse off after the pandemic. Welfare household levels are low in Andean and Caribbean countries, with some (e.g., Jamaica, Ecuador, Be- lize, and Haiti) experiencing levels nearly half the LAC average. As a result, the most prevalent coping mechanism reported to cover basic needs in the region was the use of savings (67 percent). In Haiti, Ecuador, Peru, and Colombia, about 8 out of ten households used their savings to cope with the financial stress caused by the pandemic (Figure III-14 – Panel B).42 Even if incomes and employment were to have bounced back to pre-pandemic levels, households would still be worse off. The compul- sion to cover their basic needs during the pandemic compelled them to reduce their financial assets. This reduction in household assets also limits their ability to cope with future crises, compromising both their welfare and the region’s poverty reduction in the short term. FIGURE III-14: MANY HOUSEHOLDS FACED FOOD INSECURITY AND COVERED BASIC NEEDS BY USING THEIR SAVINGS 70% 60% 50% Percent (%) 40% 30% 24% 20% 13% Panel A. 10% Share of households 0% Antigua y Barbuda El Salvador Paraguay Argentina St. Lucia Costa Rica Jamaica Panama Guatemala Bolivia Nicaragua Brasil Colombia Dominican Rep. who ran out of food Haiti Dominica LAC Uruguay Belice Honduras Guyana Ecuador Chile Peru Mexico Pre-pandemia (Feb 2020) 2021 2020 85% 80% Figure III-14 Panel A 75% Panel B. Percent (%) 70% Share of households who 68% used their savings 65% 60% 55% 50% El Salvador St. Lucia Jamaica Panama Bolivia Argentina Paraguay Guatemala Haiti Costa Rica Nicaragua Belize Colombia LAC Honduras Dominica Ecuador Chile Uruguay Dominican Rep. Guyana Peru Mexico Source: LAC High-Frequency Population Surveys – Word Bank Figure III-14 Panel B 42 Other coping mechanisms include the entry into the labor force of non-working household members – notably, inactive adults and children (42.5 and 10.3 percent, respectively) – and the halting of rent payments or debt installments (40.3 percent) (Mejía-Mantilla, et al., 2021). 66 IV A war and a hike in inflation TOGETHER WEAKENED THE REGION’S RECOVERY FROM THE PANDEMIC 67 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean A war and a hike in inflation TOGETHER WEAKENED THE REGION’S RECOVERY FROM THE PANDEMIC A s the COVID-19 pandemic fades, inflation has emerged in LAC, jeopardizing the region’s economic recovery and threatening the weakest prospects for poverty reduction since 2020. Although the surge in inflation started in 2021, it has accelerated since February 2022, hitting its highest level in 14 years and becoming increasingly generalized in many LAC countries. By Oc- tober 2022, annual headline inflation in the region, excluding Argentina, reached 8.3 percent. This price increase nearly doubled the inflation rate in October 2021 and was three times higher than in October 2020. Such trends exceed regional central bank targets but align with those of OECD countries (Figure IV1).43 Inflation poses a significant additional challenge for many households who have suffered from food insecurity and depleted savings due to the pandemic. Higher inflation erodes the already dete- riorated value of wages and savings. This new shock affects vulnerable households the most as they cannot easily preserve their purchasing power and smooth their consumption, thus increasing their risk of falling into poverty.44 However, net food producer households may have benefited from higher com- modity prices. This is the case for regional exporter countries such as Argentina, Brazil, Chile, Colombia, Ecuador, and Peru. At the same time, the war in Ukraine raises food and fuel prices as well as production costs for agricultural products, which are highly dependent on fertilizer imports (World Bank, 2022a). This potentially mitigates the benefits accrued by net food producer households.45 In addition, LAC countries face tighter financial conditions with limited fiscal policy space. All this while the world faces many evolving risks, from a recession in the global economy and a rise in interest rates by the United States to disruptions in global supply bottlenecks and significant political and social instability (World Bank, 2022b).46 43 All countries have seen a surge in inflation but at different rates as of October 2022, ranging from less than 5 percent in Bolivia, Panama and Ecuador through to little more than 10 percent in Honduras, Nicaragua, Colombia, and Chile. Argentina is an outlier, with rates up to 88 percent as of October 2022. 44 Poorer households tend to be less able than wealthier households to protect their income and assets from an unexpected increase in inflation. This is because poor households are more reliant on labor income, have less access to financial instru- ments, and are unlikely to have assets other than cash. Also, poor households are more exposed to unexpected increases in food prices because they spend a larger share of their income on food (Ha, M. Ayhan Kose, & Ohnsorge, 2019). 45 For poor households working in agriculture, higher food prices can be a source of income growth from producing food on their farms (World Bank, 2022f). However, benefits from higher prices are most likely offset by sharp increases in input prices, especially for fertilizer and fuel. 46 In 2022, the average GDP growth in LAC is estimated to slow down to 3.8 percent after a rebound of 7 percent in 2021. Among the largest economies, GDP growth is expected to be below the LAC average in Brazil, Chile, and Mexico, but above the LAC average in Argentina and Colombia. Contrary to large regional economies, most countries in LAC are expected to grow above the regional average in 2022, with larger growth rates in the Dominican Republic, Colombia, Panama, and most Caribbean countries. 68 FIGURE IV-1: SINCE 2020, INFLATION HAS INCREASED SYSTEMATICALLY IN LAC Headline inflation, 2021-22 85% 75% 65% 55% Percent (%) 45% 35% 25% 15% 5% -5% PAN BOL ECU BRA SLV PRY DOM LAC MEX PER CRI URY GTM JAM HND OECD NIC COL CHL ARG without ARG Oct-20 Oct-21 Oct-22 Source: World Bank staff calculations based on World Bank and OECD data. Note: Inflation represents the annualized percent change in the headline consumer price index for the year in October. Figure IV-1: The sharp rises in food and fuel prices may hurt poor households in particular.47 Food prices, measured by the Food and Agriculture Organization’s Food Prices Index, are at their highest level for three decades and have risen systematically since 2021 (Figure IV-2 – Panel A). Guatemala, Jamaica, Chile, and Colombia have experienced the fastest growth in relative food prices, while Honduras, Pana- ma, Ecuador, and Costa Rica registered the lowest growth (World Bank, 2022c). Fuel prices, measured by international oil prices, have also increased in the last two years, affecting the cost of agricultural inputs. The rise in food and energy prices has negatively affected poor households in the region as they spend a larger share of their income on food and fuel. The average budget share of food spending in the consumption basket of households in the bottom 40 in LAC is 41 percent. This is 12 percentage points higher than households in the top 60 (Figure IV-2 – Panel A). Households in the bottom 40 in Bolivia, Nicaragua, Guatemala, Paraguay, and Brazil have the largest share of food spending in the consumption basket in the region, ranging from 50 percent to almost 70 percent. However, the net ef- fect of increases in food and fuel prices on poor households’ purchasing power will depend on whether households are food producers or consumers (See Box IV-1). For most net food consumer households, price increases result in welfare losses, while welfare gains should be expected for net food producers (Cuesta, Duryea, Jaramillo, & Robles, 2010). 47 In LAC, food and energy were the main contributors to inflation in 2021. They accounted for more than 90 percent of infla- tion in Costa Rica, 75 percent in Paraguay, 66 percent in Brazil, and almost 60 percent in Colombia (Jaramillo & Talierico O’Brien, 2022). 69 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean FIGURE IV-2: POOR HOUSEHOLDS MAY BE PARTICULARLY AFFECTED BY THE RISE OF FOOD AND FUEL PRICES 160 250 140 200 120 150 Panel A. 100 100 Global Real Food Price Index and Oil Price 80 50 60 0 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 Global real food index Crude WTI (Right) 70% Panel B. 60% Food share in the 50% Figure IV-2: Panel A. consumption basket Percent (%) 40% 30% 20% 10% 0% BOL NIC GTM PRY BRA MEX HND LAC CRI PER ECU COL SLV DOM URY CHL PAN Bott-40 Top-60 Source: Food and Agricultural Organization (FAO) and World Bank estimates based on available surveys. Figure IV-2: Panel B. BOX IV-1: EFFECTS OF FUEL AND FERTILIZER PRICE ON POOR HOUSEHOLDS Agricultural producers in LAC countries depend highly on fertilizer imports as the region has a limited production capacity. The rising price of fertilizers and other energy-intensive products is expected to inflate the cost of agricultural inputs, causing an increase in production costs and, eventually, in food prices. The increase in fertilizer prices could lead to lower input use levels, thus reducing crop production (particularly for small farmers who tend to use less fertilizer) and profitability because yields are more sensitive to these increases (World Bank, 2022d). At the same time, food consumers, particularly low-income households, may be unable to afford the higher food prices caused by inflation, leading to food insecurity and further exacerbating poverty. Nevertheless, focusing exclu- sively on the impacts of inflation on purchasing power would overestimate the negative 70 effects of the crisis, mainly on poor populations. Agricultural-producing households might benefit from higher food prices, but only to the extent that their food sales exceed their consumption (i.e., if they are net food producers). However, such households would also face additional production costs, which could reduce or even offset the potential addi- tional gains from higher food prices. FIGURE B.IV-1 shows these dynamics for the case of Ecuador by plotting changes in growth incidence curves, considering the effect of in- flation on purchasing power, the average effects of including net producing households’ benefits, and the net effect when including additional production costs (e.g., fertilizers). Income losses at the bottom of the income distribution are slightly lower when account- ing for the additional income of net producers, but the inclusion of higher production costs reduces their potential benefits. FIGURE B.IV-1: INCOME LOSSES DECLINE WHEN ADDITIONAL INCOME OF NET PRODUCES IS TAKEN INTO ACCOUNT Incidence curve of inflation changes in per capita income, Ecuador -1,9 Household per capita income variation With vs. Without -2,0 -2,1 inflation crisis. 2022 -2,2 -2,3 -2,4 Q1 Q2 Q3 Q4 Q5 Purchasing power Purchasing power+Agricultural households benefits Purchasing power+Agricultural households benefits-Additional costs Average impact Source: World Bank staff calculations. 2022 projections are based on 2021-2022 SEDLAC (CEDLAS-WB) microdata Figure and macroeconomic forecasts fromB.IV-1: the MTI Global Practice. Version: March 30, 2023. How has high inflation affected the recovery path in poverty reduction? Despite a slip in economic activity and inflationary pressures, most LAC countries are ex- pected to continue recovering from severe employment losses with a deterioration in their quality. On average, employment would have grown 0.5 percent above total employment in 2019, representing a creation of about one million jobs across the region. Yet, significant differences exist across countries. Employment levels would have recovered quickly and expanded beyond pre-pan- demic levels in Argentina, Ecuador, Bolivia, and Paraguay, ranging from 3.3 percent to 6.8 percent. 71 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean It would also be slightly above pre-pandemic levels in Colombia, Peru, the Dominican Republic, and Costa Rica (Figure IV-3 – Panel A). By contrast, employment would have yet to recover to pre-pan- demic levels in Uruguay, Brazil, El Salvador, and Panama (0.4, 1.7, 4.5, and 4.8.6 percent below total employment in 2019, respectively). Despite employment recovery levels, as discussed in Chapter 3, the quality of jobs has deteriorated since the onset of the pandemic. As such, no significant improve- ment in labor market outcomes (e.g., informality and self-employed) is expected in 2022.48 Faced with greater vulnerabilities, most of the region’s poor households consequently lack adequate coping mechanisms against inflationary pressures due to the erosion of their savings during the pandemic. Employment levels would reach pre-pandemic levels, but labor income recovery would still lag while fiscal aid remains depleted, increasing the risk that millions will fall into poverty. Average labor income would increase by 2.5 percent between 2021 and 2022, but it is still expected to be 3.3 percent below its pre-pandemic levels (Figure IV-3 – Panel B). This gap is projected to be significantly below average in Argentina (-10.5 percent), the Dominican Republic (-8.1 percent), Uru- guay (-5.5 percent), and Ecuador (-5.2 percent). The expected outcome in Brazil is different, with labor income set to increase by an estimated 1.7 percent between 2021 and 2022 but still 1.2 percent below its pre-pandemic level. Panama, El Salvador, and Bolivia post the best scenario, with labor incomes expected to exceed pre-pandemic levels. The slow recovery of labor income generally leaves many households vulnerable to price increases, leading to loss of purchasing power and food insecurity.49 Countercyclical fiscal policies helped households cope with the adverse effects of the pandemic but eroded governments’ fiscal space, which was already limited. This restriction in fiscal space has led to high deficits and increased government debt (World Bank, 2022a; World Bank, 2022e). A further de- cline in public transfers is therefore expected in most countries in 2022, increasing the exposure of poor households to the rise in food and fuel prices. Between 2021 and 2022, the most significant decline in the share of public transfers in total income is expected in Chile (14 percentage points), Peru (2.2 per- centage points), and Panama (2.1 percentage points). Uruguay, Costa Rica, Nicaragua, and Guatemala are projecting slower rates of reduction in public transfers. 48 Quarterly data from available surveys across the region show that informality increased or remained the same in 2022. Between Q2-2021 and Q2-2022, informality increased by 3.3 percentage points (p.p.) in Argentina, 1.2 p.p. in Chile, 0.5 p.p. in Ecuador, 0.3 p.p. in Costa Rica, and 0.1 p.p. in Brazil. In these countries, informality increased at higher rates for females than men. For instance, in Chile, while informality for females increased by 2.3 p.p. in Q2-2022, informality for males only increased by 0.4 p.p. During the same period, informality only declined in Colombia and Paraguay, by 4.1 p.p. and 1.5 p.p., respectively. 49 In LAC, food insecurity is expected to deteriorate in 2022 as rising fuel and food prices have also undermined household purchasing power. This is especially the case for poor households, who spend a larger share of their income on food and transportation. For instance, severe food insecurity in Brazil is estimated to have increased by 15.5 percent in 2022. This is 6.5 percentage points higher than in 2020, with the result that approximately 14 million more Brazilians are facing hunger (Rede PENSSAN, 2022). In Guatemala, Honduras, El Salvador, and Haiti, one of two negative scenarios are ex- pected for about 10.8 million people: either (i) experiencing food consumption gaps, reflected by high or above-usual acute malnutrition; or (ii) marginally able to meet minimum food needs but only by depleting essential livelihood assets or through crisis-coping strategies (FSIN & GNAFC., 2022). Moreover, the number of undernourished people in LAC is projected to increase by 0.52-1.13 percent in 2022, slightly lower than the projection for Asia and the Pacific, at 0.93- 1.14 percent, respectively (FAO, IFAD, UNICEF, WFP, & WHO, 2022). The negative impact of inflation on food security, as discussed in Chapter 2, may have implications for malnutrition levels and may eventually lead to worsening economic outcomes (Heltberg, 2009). 72 FIGURE IV-3: WHILE EMPLOYMENT IS EXPECTED TO RECOVER IN MOST COUNTRIES, LABOR INCOME STILL LAGS BEHIND PRE-PANDEMIC LEVELS 10% 0,5% 5% 0% % change -3,1% -5% Panel A. -10% Change in total employment, -8,3% relative to 2019 -15% -20% Costa Rica Peru Colombia Brazil El Salvador Panama LAC Republic Argentina Uruguay Ecuador Bolivia Paraguay Dominican 2019-20 2019-21 2019-22e 5% Panel B. Figure IV3: Panel A -3,3% Change in labor income, 0% relative to 2019 -5% % change -4,9% -7,3% -10% -15% -20% Peru Ecuador Dominican Republic Paraguay Argentina Colombia LAC Salvador Panama Uruguay Costa Rica Bolivia Brazil 2019-20 2019-21 2019-22e Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank). ForIV3: Figure Panel Busing microsimulations 2022, projections (own computations). Note: The LAC figure is based on data from 12 countries for which such data are available. Uruguay 2021 refers to the second semester and is not comparable with previous rounds. Version: March 30, 2023. Poverty reduction is expected to stall, and poverty levels are projected to remain above their pre-pandemic rate in 2022. Excluding Brazil, poverty rates measured at $6.85 a day (2017 PPP) are expected to be 31.0 percent in 2022. This is 0.4 percentage points lower than in 2021 but 1.6 percentage points higher than in 2019, equivalent to approximately seven million more poor individuals across the region in 2022 than in 2019. When Brazil is included, poverty rates are expected to increase slightly, by 0.4 percentage points (from 28.2 percent in 2019 to 28.6 percent in 2022). The result is a net ten million people falling into poverty (Figure IV-4). A substantial recovery in the middle class is also expected in 2022, but total numbers will still remain below pre-pandemic levels. Including Brazil, the middle class is expected to increase by 0.5 percentage points, from 36.6 percent in 2020 to 37.1 in 2022. This is 1.3 percentage points below pre-pandemic levels, resulting in a net gain of four million people into the middle class compared to 2019. 73 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean FIGURE IV-4: DESPITE A MILD RECOVERY, POVERTY LEVELS AND THE SIZE OF THE MIDDLE CLASS ARE STILL NOT EXPECTED TO REACH PRE-PANDEMIC LEVELS IN 2022 Headcount projections 38,4 37,1 36,6 35,7 36,0 34,4 34,5 34,3 Percentage (%) 33,1 32,8 33,5 32,8 32,2 32,3 32,4 32,0 31,0 31,4 31,0 30,3 29,4 28,7 28,6 28,2 2019 2020 2021 2022e 2019 2020 2021 2022e LAC with Brazil LAC without Brazil $6.85 per day (2017PPP) Vulnerable $6.85-$14 per day (2017PPP) Middle Class $14-$81 per day (2017PPP) Figure IV-4: Source: World Bank staff calculations based on SEDLAC (CEDLAS and the World Bank). Note: The LAC aggregate is based on 18 countries in the region for which microdata are available and then pooled to create regional estimates. In cases where data are unavailable, values have been projected using microsimulations. How has the speed-up of food and energy prices impacted households? An additional 13 million people are expected to face downward social mobility due to infla- tionary pressures in 2022.50 On average, the additional inflation in the LAC countries would have caused an increase of 1.3 percentage points in the poverty incidence or 6.9 million more poor people, holding constant the macroeconomic assumptions prior to the inflationary crisis and without consider- ing adjustments in the household consumption basket. Mexico, Brazil, and Colombia account for most of this change, adding almost five million poor people in 2022. However, the higher impacts on poverty incidence due to the inflation crisis happen in Nicaragua, Colombia, and Paraguay, where the project- ed increase in their headcount comes to 3.1, 2.6, and 2.2 percentage points, respectively (Figure IV-5). Estimates also indicate that an additional 5.9 million people will have fallen out of the middle class in 2022 due to the inflation crisis. The Dominican Republic, Chile, Nicaragua, Paraguay, and Costa Rica are expected to have the most considerable middle-class losses due to the inflation crisis, ranging from 1.7 to 2.5 percentage points. In the largest economies, additional middle-class losses due to the crisis with a projected increase of 1.4 and 0.4 percentage points in Mexico and Brazil, respectively. 50 The effects of the rise in inflation due to the war in Ukraine are estimated for 16 countries in LAC for which data are avail- able: Bolivia, Brazil, Chile, Colombia, Costa Rica, the Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mex- ico, Nicaragua, Panama, Paraguay, Peru, and Uruguay. 74 FIGURE IV-5: ADDITIONAL INFLATION WOULD PUSH MANY PEOPLE INTO POVERTY AND OUT OF THE MIDDLE CLASS IN LAC Expected 2022 headcount variations (p.p.) 3,1 2,6 2,2 2,0 1,8 1,8 1,5 1,4 1,4 1,3 1,3 1,2 Percentage points 1,0 1,0 0,5 0,5 0,4 0,4 0,4 0,3 0,2 0,1 0,0 0,1 0,0 -0,1 -0,1 -0,2 -0,1 -0,2 -0,4 -0,4 -0,5 -0,4 -0,5 -0,5 -0,7 -0,7 -0,9 -1,0 -1,0 -1,2 -1,3 -1,4 -1,5 -1,6 -1,7 -2,0 -2,0 -2,5 -2,5 BOL PAN URY HND BRA CHL GTM LAC ECU SLV DOM CRI MEX PER PRY COL NIC $3.65-$6.85 Vulnerable ($6.85-$14) Middle-Class ($14-$81) Source: World Bank staff calculations. 2022 projections Figure IV-5: are based on 2021-2022 SEDLAC (CEDLAS-WB) microdata and macroeconomic projections from the MTI Global Practice. The LAC aggregate is based on 16 countries in the region for which microdata are available and then pooled to create regional estimates. Version: March 30, 2023. More importantly, the surge in inflation contributes to the deterioration of the living condi- tions of poor households. On average, the poverty gap in LAC countries is projected to be an addi- tional 0.6 percentage points higher than pre-inflation crisis estimates. The poverty gap due to the infla- tion crisis is expected to increase the most in the Andean and Central American countries (Figure IV-6). For instance, the poverty gap in Nicaragua and Colombia is projected to widen by 1.5 and 1.6 percentage points, respectively, compared to pre-inflation crisis estimates.51 At the same time, a similar increase in the poverty gap is estimated in Mexico, Peru, Guatemala, and Paraguay, at about 0.8 percentage points compared to pre-inflation crisis estimates. The welfare of poor households is projected to deteriorate the least in Bolivia, Uruguay, and Panama. 51 In Colombia, the severe deterioration of the living conditions caused by inflation was driven by different factors, including strong demand, inflation inertia, currency depreciation, indexation of rents, and crop losses due to heavy rains caused by the La Niña phenomenon (World Bank, 2023). 75 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean FIGURE IV-6: LIVING CONDITIONS OF POOR HOUSEHOLDS ARE EXPECTED TO DETERIORATE DUE TO ADDITIONAL INFLATIONS IN LAC Expected poverty gap variations (2022 With vs. Without inflation crisis) -1,57 -1,53 -0,90 Percentage points -0,84 -0,75 -0,76 -0,59 -0,59 -0,60 -0,63 -0,45 -0,47 -0,32 -0,22 -0,12 -0,05 -0,03 PAN BOL URY BRA CHL HND ECU DOM SLV LAC CRI MEX PER GTM PRY NIC COL Figure IV-6: Source: World Bank staff calculations. 2022 projections are based on 2021-2022 SEDLAC (CEDLAS-WB) microdata and macroeconomic projections from the MTI Global Practice. The LAC aggregate is based on 16 countries in the region for which microdata are available and then pooled to create regional estimates. Version: March 30, 2023. Households living in urban areas and high-skilled adults are projected to be more likely to face downward mobility due to the inflation crisis. An estimated 5.2 million people in LAC are expected to fall into poverty, while about 400,000 people are projected to lose middle-class status in urban areas and have lower household dependency ratios. Also, the share of newly poor households with high skills is estimated to increase significantly, from 25.5 percent pre-crisis to 32.3 percent due to the inflation crisis. Furthermore, these newly poor households are expected to work less in informality and are more likely to work in services. The surge in inflation is expected to see the vulnerable popula- tion – i.e., those with a high probability of falling into poverty – also increase significantly, rising to 5.9 million people across the LAC region. However, the rate of this increase is projected to be slower than that for newly poor households across most characteristics (Table III-1). 76 TABLE III-1: HIGH-SKILLED URBAN WORKERS IN INFORMAL EMPLOYMENT ARE EXPECTED TO FACE DOWNWARD MOBILITY DUE TO ADDITIONAL INFLATION IN LAC Poverty profile (2022, With vs. Without inflation crisis)   Poor households Vulnerable households Middle-Class households   New Old New Old New Old Population Million 6.8 157.0 5.9 173.3 0.4 192.5 Household Characteristics Urban 76.8 65.3 82.7 80.0 87.3 88.5 Household size 4.8 5.0 4.0 4.2 3.0 3.3 Dependency 38.0 40.6 29.8 32.6 25.4 27.8 Household head Age (mean) 47.0 44.8 50.7 48.6 51.6 52.0 Male (%) 61.7 59.9 62.6 59.2 63.1 60.0 Education level (%)   Low Skilled 67.7 74.5 58.2 64.9 22.5 44.9 High Skilled 32.3 25.5 41.8 35.1 77.5 55.1 Sector Agriculture 20.6 33.2 12.5 15.4 16.3 7.8 Industry 24.8 21.1 25.7 25.3 16.8 22.2 Services 54.6 45.7 61.7 59.3 66.9 70.0 Labor status Informality 64.9 77.5 44.9 53.5 30.7 34.8 Source: World Bank staff calculations. 2022 projections are based on 2021-2022 SEDLAC (CEDLAS-WB) microdata and macroeconomic projections from the MTI Global Practice. The LAC aggregate is based on 16 countries in the region for which microdata are available and then pooled to create regional estimates. Version: March 30, 2023. The rise in food and fuel prices mainly affects households towards the bottom of the income distribution rather than those towards the top. While net food producers (who are typically from lower socio-economic backgrounds) may see some benefit from higher food prices, the overall impact on those at the bottom 40 is still negative. Higher fuel and fertilizer prices offset the potential gains from higher food prices for net food producers at the bottom income distribution in LAC countries. This heterogeneous effect of the inflation crisis on inequality in LAC countries is also reflected by the impact curve, which plots growth rates at each quintile of per capita income. For instance, in Colombia, Nicara- gua, El Salvador, and Paraguay, the average per capita income of the lowest quintile is projected to have reduced by more than double that of their wealthiest counterparts (Figure IV-7). 77 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean FIGURE IV-7: THE RISE OF FOOD AND FUEL PRICES IS PROJECTED TO HAVE A DISPROPORTIONAL IMPACT ON HOUSEHOLDS AT THE BOTTOM OF THE INCOME DISTRIBUTION Incidence curve of inflation changes in per capita income (2022 With vs. Without inflation crisis) Bolivia Brazil Chile Colombia 1 1 1 1 Income variation % 0 0 0 0 -1 -1 -1 -1 -2 -2 -2 -2 -3 -3 -3 -3 -4 -4 -4 -4 -5 -5 -5 -5 -6 -6 -6 -6 -7 -7 -7 -7 Costa Rica Dominican Republic Ecuador Guatemala 1 1 1 1 Income variation % 0 0 0 0 -1 -1 -1 -1 -2 -2 -2 -2 -3 -3 -3 -3 -4 -4 -4 -4 -5 -5 -5 -5 -6 -6 -6 -6 -7 -7 -7 -7 Honduras Mexico Nicaragua Panama 1 1 1 1 Income variation % 0 0 0 0 -1 -1 -1 -1 -2 -2 -2 -2 -3 -3 -3 -3 -4 -4 -4 -4 -5 -5 -5 -5 -6 -6 -6 -6 -7 -7 -7 Peru Paraguay El Salvador Uruguay Income variation % 1 1 1 1 0 0 0 0 -1 -1 -1 -1 -2 -2 -2 -2 -3 -3 -3 -3 -4 -4 -4 -4 -5 -5 -5 -5 -6 -6 -6 -6 -7 -7 -7 -7 Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5 Source: World Bank staff calculations. 2022 projections are based on 2021-2022 SEDLAC (CEDLAS-WB) microdata and macroeconomic IV-7: from the MTI Global Practice. Version: March 30, 2023. Figure projections Note: Income variation corresponds to household per capita income variation with respect to baseline scenario. The inflationary pressures have not only slowed down the region’s poverty reduction ef- forts but also increased inequality. The Gini coefficient is projected to increase by an additional 0.2 points compared to pre-inflation crisis estimates. This is explained by the deterioration of household purchasing power, compounded mainly by food, transport, and increases in energy prices (Figure IV8). Yet, significant differences exist across LAC countries. The increase in the Gini coefficient in Paraguay, Nicaragua, and Colombia, for instance, is triple that of the LAC average (0.51, 0.55, and 0.69 additional points, respectively). On the other hand, Brazil and Uruguay’s expected increase in the Gini coefficient is very low, at 0.06 points. Meanwhile, Costa Rica is expected to align closely with the LAC average. 78 FIGURE IV-8: INEQUALITY IS EXPECTED TO INCREASE DUE TO ADDITIONAL INFLATION EXPECTED VARIATIONS IN THE GINI COEFFICIENT (2022 With vs. Without inflation crisis) 0,69 0,55 0,51 0,41 Gini points 0,31 0,31 0,29 0,24 0,17 0,18 0,10 0,12 0,06 0,06 0,05 0,05 0,00 BOL PAN ECU BRA URY PER DOM GTM CHL LAC CRI MEX SLV HND PRY NIC COL Source: World Bank staff calculations. 2022 projections are based on 2021-2022 SEDLAC (CEDLAS-WB) microdata and macroeconomic projections from the MTI Global Practice. The LAC aggregate is based on 16 countries in the region for which microdata are available and Figure IV-8: then pooled to create regional estimates. Version: March 30, 2023. The main drivers of poverty reduction due to the surge in inflation are expected to be changes to household consumption patterns, government measures to cope with infla- tion, and shifts in households’ expectations. It was previously predicted that additional infla- tion in the region would have caused an increase of one percentage point in the poverty rate. How- ever, this calculation did not consider the effects of higher economic growth, employment creation, government mitigation programs, or changes in household behavior. With these mitigating factors in mind, the impact on poverty is expected to be a reduction of 0.64 percentage points compared to pre-inflation crisis projections. For LAC, 84 percent of the projected poverty reduction is driven by labor income, with the remaining 16 percent driven by non-labor income (Figure IV9). The slow recovery of both income types explains the low contribution to poverty reduction from labor and non-labor incomes. Labor incomes are still below pre-pandemic levels, while government transfers in support of poor households have reduced due to limited fiscal space in most countries in the region. In addition, low-income households and poor individuals have adapted to the initial impact of higher food and fuel prices by changing their consumption patterns to overcome a deterioration in their pur- chasing power. Estimations suggest that household adjustments have effectively counteracted the initial impact of additional inflation. On the other hand, governments are responding to the surge in 79 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean inflation by controlling prices directly (e.g., limiting food exports or keeping fuel prices at lower levels than in global markets) or by introducing tax incentives, with an estimated average fiscal cost equal to 0.3 percent of GDP for 2022 (Acosta-Ormaechea, Goldfajn, & Roldós, 2022). FIGURE IV-9: POVERTY REDUCTION IS EXPECTED TO BE DRIVEN BY LABOR INCOME Shapley decomposition of additional inflation and income sources 1,10 0,85 0,48 0,14 Percentage points 0,07 0,04 -0,02 -0,14 -0,10 -0,54 -0,91 -1,04 Additional inflation Labor Non-labor Other Poor Vulnerable Middle class Figure IV-9: Source: World Bank staff calculations. 2022 projections are based on 2021-2022 SEDLAC (CEDLAS-WB) microdata and macroeconomic projections from the MTI Global Practice. The LAC aggregate is based on 16 countries in the region for which microdata are available and then pooled to create regional estimates. Version: March 30, 2023. 80 V Looking ahead 81 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean Looking ahead O ver the last three years, Latin America and the Caribbean have faced extraordinary challenges that reverted the social gains of the previous decades. The combination of the COVID-19 pandemic, sluggish economic growth, fiscal constraints and increased debt stress, inflationary pressures, and the collateral effects of the Russian invasion of Ukraine has taken a toll on the region. These shocks have hit poor and vulnerable populations the most, increasing poverty and inequality. They have also moved millions of people out of the middle class. Yet, LAC countries have been relatively resilient in recovering from these crises, albeit in an uneven fashion. By the end of 2022, the labor markets had improved, poverty had receded, and the middle class had re- covered, despite economic growth fluctuations and political instability across the region. Even so, the region still faces challenges posed by low exports and commodity prices due to weak global demand, persistent and rising interest rates in advanced countries, uncertainty as China recovers from the re- cent COVID-19 lockdown, and significant geopolitical uncertainty from the war in Ukraine. As a result, LAC’s gross domestic product (GDP) is expected to slow to a meager 1.3 percent in 2023, which is 2.5 percentage points below GDP growth in 2022 (World Bank, 2023). The insufficient economic growth expected for 2023 will constrain the efforts to reduce poverty and inequality, to promote inclusion, and to defuse social tensions in the region. Despite the weak economic outlook for 2023, employment and labor incomes across LAC countries are expected to increase slightly. On average, employment in LAC is likely to be 0.9 percent above total employment in 2019, representing a creation of about 1.5 million jobs across the region. Still, employment levels in El Salvador, Paraguay, Panama, and Brazil during 2023 are project- ed to remain below pre-pandemic levels. More importantly, the quality of employment is not expect- ed to improve, leaving many workers exposed to higher levels of vulnerability and income shocks. Moreover, average labor income is expected to increase slightly by 0.3 percent between 2022 and 2023, thus falling short of pre-pandemic levels (i.e., 3 percent below its pre-pandemic levels). This gap with pre-pandemic levels will be more significant in Colombia, Ecuador, Uruguay, the Dominican Republic, Costa Rica, and Argentina, ranging from 3.5 percent to 12.7 percent. The labor income’s importance in alleviating poverty over the last two decades and its recent slow recovery in most countries in LAC creates a risk to the region’s longer-term recovery and continuous progress in poverty reduction. Poverty reduction will almost halt with poverty levels above pre-pandemic in 2023. Ex- cluding Brazil, poverty rates at $6.85 a day (2017 PPP) are expected to be 30.8 percent in 2023. This poverty level is 0.2 percentage points lower than in 2022, equivalent to 300,000 individuals across the region lifted out of poverty in 2023. Considering Brazil, poverty rates are also expected to decline slightly by 0.3 percentage points from 28.6 percent in 2022 to 28.3 percent in 2023. Yet, poverty level will remain above pre-pandemic levels (1.1 percentage points excluding Brazil and 0.1 percentage points including Brazil). A slight recovery in the middle class is also expected in 2023, but total num- bers will remain below pre-pandemic levels. Excluding Brazil, the middle class will mildly increase by 0.2 percentage points, from 34.3 percent in 2022 to 34.5 in 2023; this is 1.5 percentage points below pre-pandemic levels. Yet, including Brazil, the middle class is expected to decrease by 0.1 percentage points, from 37.1 percent in 2022 to 37.0 percent in 2023; this will see about one million people falling out of this population segment. 82 Despite the challenges ahead, LAC has the potential to overcome them in its traditional areas of comparative advantage and the opportunities arising from resilient green growth. As shown in the report The Promise of Integration: Opportunities in a Changing Global Economy, LAC needs to complement long-term structural reforms to reduce systemic risk, raise the level and quality of education, invest in infrastructure, and ensure well-functioning financial markets with a compre- hensive approach to integrate the region into the global economy (particularly, the US and European markets), and take advantage of its comparative advantage in the green economy.52 In particular, the transition to the green economy could be an opportunity to improve well-being in the region by creating new quality jobs, enhancing labor incomes, and contributing to poverty reduction.53 Howev- er, this transition will demand a significant change to labor markets in LAC countries, as it will create new jobs while at the same time potentially destroying jobs and displacing workers in many sectors. This process will require investment in human capital for the training and reskilling of workers. It will also demand well-designed social programs to protect the most vulnerable during the transition (e.g., active labor market programs), as well as incentives for informal workers to shift to new pro- ductive firms involved in green technologies (OECD, et al., 2022).54 52 See Maloney, et al. (2023). 53 An estimated 7 million new skilled and unskilled jobs could be created in the region by 2030 due to investment in renewable energy based on deploying solar and wind power and biomass technologies (OECD, et al., 2022; ECLAC, 2020). 54 A recent study by Di Maro, et al. (forthcoming) for LAC shows that upskilling workers in non-green occupations for green jobs working in green sectors could be facilitated by already being a part of green sectors. 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Science, 312, 1900-1902. doi:10.1126/science.1128898 Herrera, C., Veillard, J., de Colombi, N., Neelsen, S., Anderson, G., & Ward, K. (2022). Building Resilient Health Sys- tems in Latin American and the Caribbean: Lessons Learned from the COVID-19 Pandemic. Washington, D.C.: World Bank. Kovacevic, R., Bayona, J., & Gordillo-Tobar, G. (2022). As demand for mental health services soar, countries in Latin America and the Caribbean strengthen their response. World Bank Blogs, October 20. Washington, D.C.: World Bank. https://blogs.worldbank.org/health/demand-mental-health-services-soar-countries-lat- in-america-and-caribbean-strengthen-their. Muñoz-Najar, A., Gilberto, A., Hasan, A., Cobo, C., Azevedo, J. P., & Akmal, M. (2021). Remote Learning during COVID-19: Lessons from Today, Principles for Tomorrow. Washington, D.C.: World Bank. Neidhöfer, G., Lustig, N., & Tommasi, M. (2021). Intergenerational transmission of lockdown consequences: prog- nosis of the longer-run persistence of COVID-19 in Latin America. The Journal of Economic Inequality, 19, 571–598. Osendarp, S., Akuoku, J., Black, R. E., Headey, D., Ruel, M., Scott, N., . . . others. (2021). The COVID-19 crisis will ex- acerbate maternal and child undernutrition and child mortality in low-and middle-income countries. Nature Food, 2, 476–484. Shekar, M., Kakietek, J., Eberwein, J., & Walters, D. (2016). An investment framework for nutrition: reaching the global targets for stunting, anemia, breastfeeding, and wasting. doi:https://doi.org/10.1596/25292 UN Women. (2020a). COVID-19 and the care economy: Immediate action for structural transformation for a gen- der-responsive recovery. Policy Brief No.16. UN Women, New York. UN Women. (2020b). Policy Brief: The impact of COVID-19 on women. Policy Brief No.16. UN Women, New York. Walker, S., Chang, S., Powell, C., Simonoff, E., & Grantham-McGregor, S. (2007). Early Childhood Stunting Is Associ- ated with Poor Psychological Functioning in Late Adolescence and Effects Are Reduced by Psychosocial Stimulation1,2. The Journal of Nutrition, 137, 2464-2469. doi:https://doi.org/10.1093/jn/137.11.2464 World Bank (2021). Acting Now to Protect the Human Capital of Our Children: The Costs of and Response to COVID-19 Pandemic’s Impact on the Education Sector in Latin America and the Caribbean. Washington, D.C.: World Bank. World Bank (2022). The Learning Crisis in Latin America and the Caribbean and the COVID-19 Pandemic: Sober- ing Results of a Deepening Trend. Washington, D.C.: World Bank. World Bank and UNESCO (2022). Two Years After: Saving a Generation. Washington, D.C.: World Bank. World Bank, UNICEF, and UNESCO. (2021). The State of the Global Education Crisis: A Path to Recovery. Washing- ton D.C., Paris, New York: World Bank, UNESCO, and UNICEF. 86 World Bank; the Bill & Melinda Gates Foundation; FCDO; UNESCO; UNICEF; USAID. (2022). Guide for Learning Re- covery and Acceleration: Using the RAPID Framework to Address COVID-19 Learning Losses and Build For- ward Better. Washington, D.C.: World Bank. CHAPTER III Beylis, G., Jaef, R., Sinha, R., & Morris, M. (2020). Going Viral: COVID-19 and the Accelerated Transformation of Jobs in Latin America and the Caribbean. Washington, D.C.: World Bank. Brinca, P., Duarte, J., & e-Castro, M. (2021). Measuring labor supply and demand shocks during COVID-19. European Economic Review, 139, 103901. doi:https://doi.org/10.1016/j.euroecorev.2021.103901 Brummund, P., Mann, C., & Rodriguez-Castelan, C. (2018). Job quality and poverty in Latin America. Review of Development Economics, 22, 1682–1708. Cucagna, M., Haaker, R., & Javier, F. (2021). The Gendered Impacts of COVID-19 on Labor Markets in Latin America and the Caribbean. Technical Report, LAC Gender Innovation Lab. Washington, D.C.: World Bank. Deb, P., Furceri, D., Jimenez, D., Kothari, S., Ostry, J. D., & Tawk, N. (2022). The effects of COVID-19 vaccines on eco- nomic activity. Swiss Journal of Economics and Statistics, 158, 1–25. del Carpio, X., Olivieri, S., Rivadeneira, A., Winkler, H., Camacho, A., Hernández, P., . . . Tenjo, L. (2022). The non-eco- nomic recovery: The extended effects of the pandemic on labor markets in Latin America and the Caribbe- an. Washington, D.C.: World Bank. Gachet, I. (2021). COVID-19 and Youth Labor Market Inequalities in Ecuador. Washington, D.C.: World Bank. Gatti, R., Goraus, K., Morgandi, M., Korczyc, E., & Rutkowski, J. (2014). Balancing flexibility and worker protection. Washington, D.C.: World Bank. Genda, Y., Kondo, A., & Ohta, S. (2010). Long-Term Effects of a Recession at Labor Market Entry in Japan and the United States. The Journal of Human Resources, 45, 157–196. Retrieved November 10, 2022, from http:// www.jstor.org/stable/20648940 Kuddo, A., Robalino, D., & Weber, M. (2015). Balancing regulations to promote jobs. Washington, D.C.: World Bank. Margolies, A., Boaz, A., De Hoop, J., Kim, P., Mussini, M., Paffhausen, A., & Di Giorgio, L. (2022). A Shot in the Arm: New Evidence from the World Bank High Frequency Surveys on COVID-19 Vaccine Acceptance and Uptake in the Caribbean. Washington, D.C.: World Bank. Mejía-Mantilla, C., Ibarra, G., Castañeda, C., Meléndez, M., Camacho, A., Tenjo, L., & Hernández, P. (2022). Not There Yet : Slow Recovery and Many Left Behind as Latin America and the Caribbean Navigates the Ripples of the Pandemic - 2021 High-Frequency Phone Surveys - Wave 2. Washington, D.C.: World Bank. Mejía-Mantilla, C., Rivadeneira, A. M., Ximena, D. C., Olivieri, S., Castañeda, C., Lara-Ibarra, G., . . . Hernandez, P. (2021). An Uneven Recovery: Taking The Pulse of Latin America And the Caribbean Following the Pan- demic. Washington, D.C.: World Bank. Olivieri, S., Ortega, F., & Rivadeneira, A. (2022). Covid-19 Vaccination and Economic Recovery inLatin America: Evi- dence from the 2021 HFPS. Tech. rep., The World Bank Group & CUNY, Queens College. Perry, G., Maloney, W., Arias, O., Fajnzylber, P., Mason, A., & Saavedra-Chanduvi, J. (2007). Informality: Exit and exclusion. Washington, D.C.: World Bank. Silva, J., Sousa, L., Packard, T., & Robertson, R. (2021). Employment in Crisis: The Path to Better Jobs in a Post- COVID-19 Latin America. Washington, D.C.: World Bank. Torres, J., Maduko, F., Gaddis, I., Iacovone, L., & Beegle, K. (2022). The Impact of the COVID-19 Pandemic on Wom- en-Led Businesses. Policy Research working paper No. 9817. Washington, D.C.: World Bank. 87 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean Von Wachter, T. (2020). The persistent effects of initial labor market conditions for young adults and their sources. Journal of Economic Perspectives, 34, 168–94. World Bank. (2022a). Global Economic Prospects. January 2022. Washington, D.C.: World Bank. World Bank. (2022b). Consolidating the Recovery: Seizing Green Growth Opportunities. Washington, D.C.: World Bank. World Bank. (2022c). Long COVID: The Extended Effects of the Pandemic on Labor Markets in Latin America and the Caribbean. Washington, D.C.: World Bank. World Bank. (2022d). Brazil Poverty and Equity Assessment: Looking Ahead of Two Crises. Washington, D.C.: World Bank. CHAPTER IV Acosta-Ormaechea, S., Goldfajn, I., & Roldós, J. (2022). Latin America Faces Unusually High Risks. IMF Blog, April 26, 2022. Washington, D.C.: International Monetary Fund. https://www.imf.org/en/Blogs/Arti- cles/2022/04/26/blog-latin-america-faces-unusually-high-risks. Cuesta, J., Duryea, S., Jaramillo, F., & Robles, M. (2010). Distributive impacts of the food price crisis in the Andean region. Journal of International Development, 22, 846-865. doi:https://doi.org/10.1002/jid.1654 FAO, IFAD, UNICEF, WFP, & WHO. (2022). The State of Food Security and Nutrition in the World 2022. Technical Report, Repurposing food and agricultural policies to make healthy diets more affordable. Rome, Food and Agricultural Organization. FSIN & GNAFC. (2022). Global report on food crises 2022. Technical Report, Global Network Against Food Crisis and Food Security Information Network. Rome. Ha, J., Ayhan Kose, M., & Ohnsorge, F. (2019). Inflation in Emerging and Developing Economies: Evolution, Drivers, and Policies. Washington, D.C.: World Bank. Heltberg, R. (2009). Malnutrition, poverty, and economic growth. Health Economics, 18, S77-S88. doi:https://doi. org/10.1002/hec.1462 Jaramillo, C., & Talierico O’Brien, R. (2022). Inflation, a rising threat to the poor and vulnerable in Latin America and the Caribbean. Technical Report, World Bank Blogs, April 18. Washington, D.C.: World Bank. https://blogs. worldbank.org/latinamerica/inflation-rising-threat-poor-and-vulnerable-latin-america-and-caribbean. Rede PENSSAN. (2022). Food insecurity and COVID-19 in Brazil. Rede Brasileira de Pesquisa. World Bank. (2022a). New Approaches to Closing the Fiscal Gap. Latin America and the Caribbean Economic Re- view (October), Washington, D.C.: World Bank. World Bank. (2022b). Global Economic Prospects. January 2022. Washington, D.C.: World Bank. World Bank. (2022c). Inflation and Food/Fuel Prices in Latin America & the Caribbean. Annual Meetings – October 2022. Washington, D.C.: World Bank. World Bank. (2022d). Commodity Markets Outlook: The Impact of the War in Ukraine on Commodity Markets. Washington, D.C.: World Bank. World Bank. (2022e). Consolidating the Recovery: Seizing Green Growth Opportunities. Washington, D.C.: World Bank. World Bank. (2022f). Poverty and Shared Prosperity 2022: Correcting Course. Washington, D.C.: World Bank. World Bank. (2023). Macro Poverty Outlook for Colombia: April 2023. Washington, D.C.: World Bank. 88 CHAPTER V Di Maro, V., Montoya, K., Olivieri, S., & Vazquez, E. (Forthcoming). Green Jobs, Dirty Sectors, and the Implications for a Just Transition: Evidence from cross-country Comparisons with a particular focus on Latin American and Caribbean countries. Washington, D.C.: World Bank. ECLAC. (2020). Building a New Future: Transformative Recovery with Equality and Sustainability. Summary. Eco- nomic Commission for Latin America and the Caribbean, Santiago. Maloney, W., Riera-Crichton, D., Ianchovichina, E., Vuletin, G., & Beylis, G. (2023). The Promise of Integration: Oppor- tunities in a Changing Global Economy. Latin America and the Caribbean Economic Review; April 2023. Washington, D.C.: World Bank. OECD et al. (2022). Latin American Economic Outlook 2022: Towards a Green and Just Transition. OECD Publishing, Paris. World Bank. (2023). Global Economic Prospects: Latin America and the Caribbean January 2023. Washington, D.C.: World Bank. 89 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean Annex 1: 2020 SURVEYS FOR COUNTRIES IN LATIN AMERICA55 The COVID-19 pandemic affected both the data collection methodology and, in some cases, the survey questionnaire for Latin American and Caribbean countries with data in 2020. This box summarizes the main methodological changes to the construction of welfare aggregates (see Figure 1A-1). FIGURE 1A-1: METHODOLOGICAL CHANGES TO HOUSEHOLD SURVEYS IN LATIN AMERICA AND THE CARIBBEAN, 2019/2020 Data collection Data collection Changes in Geographic Dates Mode Questionnaire Coverage Argentina Bolivia Brazil F2F (2019) To Telephone or Mixed- Chile mode data collenction Questionnaire was Colombia (2020) shorter AND/OR Costa Rica included COVID questions Dominican Republic Ecuador It is only representative Data collection was sus- at the national, El Salvador urban/rural levels pended during April-July Guatemala No Household Survey for 2019-2020 Haiti No Household Survey for 2019-2020 Honduras Mexico Nicaragua No Household Survey for 2019-2020 Panama F2F (2019) Questionnaire was Paraguay To Telephone or Mixed- shorter AND/OR Peru mode data collenction included COVID (2020) questions Uruguay Note: F2F refers to “face-to-face” data collection. ARGENTINA In the second quarter of 2020, the survey was collected through phone interviews. The survey is designed as a rotating panel; 25 percent of the sample are “new” households interviewed for the first time each quarter. As a consequence, no previous contact was available to the Argentinian National Statistics In- stitute (INDEC) for these households (i.e., INDEC did not have their phone numbers). INDEC solved this problem of non-response using a propensity score model to reweight the sample, based on the probability of non-response according to household characteristics. (For details, see p. 11 in INDEC, 2020). 55 World Bank. April 2022 Update to the Poverty and Inequality Platform (PIP): What’s New? (English). Global Poverty Monitoring Technical Note. Washington, D.C.: World Bank Group. 90 An evaluation of the comparability of data gathered during 2020 by INDEC is ongoing. After completing its evaluation, INDEC will issue further recommendations on the comparability with previous estimates. BOLIVIA Users of the poverty and inequality estimates for Bolivia 2020 should be mindful of the period in which the surveys were collected, especially when analyzing the impacts of the COVID-19 pandemic. In the case of Bolivia, the survey was conducted every year from October-November. As mentioned in Box 1, this may not capture the peak of the pandemic. The modules of the survey questionnaire were reduced. In particular, the dwelling module was shorter and did not contain the homeownership status or amount of housing rent. These are the variables that are used by SEDLAC for imputing the rent for owners. To make 2020 comparable with the historical series, the POV LAC team developed and tested an imputation model for homeowner distribution and their implicit rent using observed parameters from 2019 data. This methodology is already being used for imputing expected rent throughout the income distribution in Brazil’s National Continuous House- hold Sample Survey (PNADC) 2012-2015. (For more details, see Atamanov, et al., 2020). BRAZIL The PNADC data provide the main source of information for poverty monitoring in Brazil. The latest annual release included the 2020 data, published by the National Statistics Office (IBGE) in November 2021 and included in this update. The PNADC 2020 was collected throughout 2020, closely following the data collection methodology adopted in previous years. However, due to the health restrictions caused by the COVID-19 pandemic, IBGE had to adapt its data collection strategy from face-to-face interviews to phone interviews. While the questionnaire itself was not changed, alterations in the data-gathering methodology affected non-response rates. The PNADC follows a rotating panel design. There are five groups of households in the sample, each of which is interviewed five times. A different questionnaire is used each time and interviews are conducted throughout the year. Since 2012, the World Bank has used the first interview. However, for 2020, the fifth interview is used instead because the IBGE has published the social indicators and microdata for the fifth interview only. (See Annex 1 of Lara Ibarra and Vale (2022) for more details). Both the first and fifth interviews are conducted throughout the year. The main difference between the first and fifth interviews is that the latter does not include the dwelling module; thus, it does not contain the homeownership status or amount of housing rent (variables needed for imputing the rent of owner-occupiers for the SEDLAC datasets). To make 2020 comparable with the historical series, housing ownership and implicit rent are imputed using the 2019 distribution. This is analogous to the approach used in Brazil during 2012-2015 and Bolivia in 2020 (see description of 2020 Bolivia data). 91 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean In 2020, there is also evidence of significant under-coverage of the Auxilio Emergencial (AE) program in the survey. AE is a cash-transfer program that was introduced in 2020. According to administrative data, there were over 68 million AE recipients. In the survey, only about 20 million are observed. AE provided monthly transfers that could add up to a maximum of R$4,200 during 2020. For a house- hold in the bottom quintile in 2019, this is equivalent to a 50-percent increase in their income per capita. Given the magnitude of this transfer and in order to better capture the evolution of income and poverty in Brazil, Lara Ibarra and Vale (2022) imputed AE beneficiary status in the data. This allowed complementing the observed AE status as reported in the survey.56 Incorporating eligibility criteria from the AE (demographic, employment, and income), the method reached a combined AE population of 42.2 million individuals. This lead to a number of program recipients becoming more in line with the administrative records. The imputed AE status is used to construct the household annual income aggregate that underlies the poverty and inequality estimates for 2020 in Brazil that are included in this update. Additional details on the imputation exercise and the comparability of these estimates with previous rounds of the PNADC are discussed in Lara Ibarra and Vale (2022). CHILE Changes in the data collection methodology limit comparability between the National Socioeconomic Characterization Survey (CASEN) 2020 (using phone interviews) and previous survey rounds (using face- to-face). Compared to face-to-face interviews, phone surveys present additional challenges that could bias the estimates. While the Ministry of Social Development and Family of Chile (MDSF) adopted several strategies to minimize such bias, the income and poverty measures are unlikely to be fully comparable between 2020 and the historical series. Therefore, caution must be taken when comparing with previous years. COLOMBIA In 2020, the data collection was split into two parts. The first part was collected through phone in- terviews from March until July and the questionnaire was reduced. Moreover, the Colombian National Statistics Institute (DANE) imputed social programs using administrative data to identify program re- cipients to allocate public transfers. Between August and December, the second part of the survey was collected using both methods: phone and face-to-face. The first one was implemented in urban areas and the latter in rural areas. (For more details, see Castaneda, et al., 2022). COSTA RICA Due to COVID-19, data collection changed from face-to-face to phone: 45.2 percent of households in the sample were visited in person, while 54.8 percent were contacted by telephone. The survey weights 56 It should be noted that the survey did not explicitly ask for the receipt of AE. The survey only contains a generic question about other transfers. AE recipient status can be inferred from the amounts reported. 92 were calibrated using logistic regressions to minimize bias and ensure that the results for 2020 are com- parable with those of 2019. (For details, see p.27 in INEC, 2020). ECUADOR This survey was collected in December through face-to-face interviews and is representative at a na- tional, urban, and rural level. Before 2020, the survey was representative at the regional level. MEXICO The main effects of the pandemic on poverty and inequality were experienced during the second quar- ter of 2020 when the lockdown measures led to a significant amount of job losses. The household sur- vey collects information between August and November. Therefore, the previously used harmonization approach (which constructed the welfare aggregate by using information about the income received in the last month) may not correctly capture the peak of the pandemic. The new methodology constructs the welfare aggregate by using the average of the income from the last six months. This includes data from the second and third quarters of 2020 and, to some extent, reflects more accurately what hap- pened to poverty and inequality in Mexico during 2020. Earlier years were also revised. PERU From mid-March to end-September 2020, the household survey was collected through phone interviews and using a reduced questionnaire. In 2020, Peru had extensive cash transfer programs, covering a large part of the population due to the COVID-19 shock. However, there is evidence of significant under-cover- age of the recipients of these transfers in the survey. The National Statistical Office (INEI) carried out an imputation exercise using administrative records. In the SEDLAC data, a portion of the second wave of one of the cash transfers (Bono Universal), which was erroneously excluded, is imputed in addition. URUGUAY Due to the COVID-19 restrictions, the 2020 data used two methodologies for sampling and data collec- tion. Results are not strictly comparable with previous versions of the survey. 93 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean Annex 2: METHODOLOGICAL APPROACH The methodology for measuring the distributional impacts of additional inflation comprises three steps: preparing the necessary microdata and inputs, conducting microsimulations, and evaluating the impact on welfare. Specifically, the first step involves organizing and preparing the necessary data on individu- al and household characteristics and consumption patterns. The second step involves using this data to simulate the effects of additional inflation on different groups within the population. Finally, the third step involves assessing the overall impact of these changes on the welfare of the affected households. PREPARING THE MICRODATA AND INPUTS The first step involves preparing and using the most recent individual and household information to estimate consumption of food and non-food items, and to determine which households are net food producers or consumers. It also prepares food inflation inputs and calculates parameters for production costs. This section depicts a brief description of these fundamental steps. Mapping consumption and its components for 2022 The projected per capita income distribution for 2022 sets the foundations for measuring the distribu- tional impact of inflation. This is based on a macro-microsimulation methodology (i.e., top-down ap- proach), which accounts for adjustments in the labor market through employment and earnings, and non-labor income like public transfers, international remittances, and others, working at the individual and household levels.57 The microsimulation mimics pre-war macroeconomic projections for sectoral and total real Gross Domestic Product (GDP), employment structure and labor income, non-labor in- come, and total inflation. Spring Meetings 2022 macroeconomic projections were considered as the pre-war scenario. The SEDLAC 2021 harmonized household surveys were the microdata used for most countries.58 The result of this process is a simulated distribution of the per capita household income in 2017 Purchasing Power Parities (PPP). To estimate the 2022 consumption patterns at the household level, the projected real per capita household income is expressed in 2022 nominal terms using the 2017 PPPs and 2017 and 2022 Consumer Price Indices (CPI) for each country. Then, households are ordered from the lowest to the highest 2022 nominal per capita income and divided into 20 groups with equally number of households or ventiles . The income-consumption ratio per ventile of per capita income is estimated based on the last available household budget survey and the total per capita consumption for each ventile is mapped:59 57 Montoya, K., Olivieri, S. and Braga, C. (2023). Considering labor informality in forecasting poverty and inequality: a mi- crosimulation model for Latin America and Caribbean countries, Mimeo, World Bank, Washington D.C.; and Montoya, K., Olivieri, S., and Braga, C. (2023). Forecasting labor market dynamics with minimum information: an elasticity approach for Latin America and Caribbean countries, Mimeo, World Bank, Washington, D.C. 58 The SEDLAC 2022 pre-harmonized household surveys were the microdata used for Costa Rica, Ecuador, and Paraguay 59 Note that when survey data are available for income and consumption, ratios are estimated using the same source of information and ventiles are defined by per capita income. 94 (1) The total consumption is divided between food and non-food components using the reported shares by ventile of per capita income: and . Classifying households as net food producers or consumers To identify net food producer households, the agricultural per capita household income is calculated by adding up the labor income from agricultural self-employed household members and expressing it in per capita terms based on the 2022 projected per capita income. Then the household is classified as a net food producer if the agricultural per capita income exceeds the per capita food consumption at the household level; otherwise, it is a net food consumer. Formally, (2) Note that if the estimated percentage of households that would be net food producers under this iden- tification strategy is higher than the value reported for each country (See Table A2-1) at the ventile level, households are randomly selected to meet the target size within each ventile from the set of households previously identified as potential net food producers; otherwise, the identification strategy holds. Deriving the expected additional food inflation To calculate the expected additional inflation in 2022, the Spring Meetings 2022 Consumer Prices Index projected for 2022 (before the conflict in Ukraine) is benchmarked. To estimate food inflation under Spring Meetings 2022 assumptions, the average pre-war ratio between food CPI and headline CPI is used: , (3) where contains all months between January 2021 and January 2022. Then, this ratio estimates the food CPI for 2022 under Spring Meetings 2022 (SM2022) assumptions: . (4) Using each country’s CPI basket weights, the non-food CPI for 2022 is calculated under SM2022: , (5) where is the CPI basket weight for food and non-alcoholic beverages. On the other hand, the updated projection of the headline CPI was obtained from Spring Meetings 2023 projections. Neither projection disaggregates by food and non-food items. Neverthe- less, inflation projections under Spring Meetings 2023 are broadly the same as those observed in the CPI changes of individual countries. Therefore, in this scenario, actual food inflation was taken and 95 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean non-food inflation was obtained by difference. Using the same CPI basket weights, the non-food CPI for 2022 under SM2023 is estimated: , (6) Accounting for production costs The impact of food inflation on net food producer households must account not only for changes in benefits due to output price changes but also for changes in costs as input prices alter. The first set of movements is captured by food inflation, while the latter is estimated using the Input-Output matrix and the cost-push model. Thus, the following parameters are estimated based on the last available information in the country: is the average ratio between production costs and sales in agricultural-producing households; is the percentage change in production costs in response to a percentage change point in interna- tional oil price; and is the percentage change in production costs in response to a percentage change point in interna- tional fertilizer prices. ESTIMATING THE DISTRIBUTIONAL IMPACT OF ADDITIONAL INFLATION Let and be the food and non-food inflations in the benchmark scenario (using and ). Let and be the food and non-food inflations for 2022 in the SM2023 scenario (using and ). The expected additional inflation for food and non-food is calculated as the difference between the projected inflation for 2022 and the benchmark inflation: (7) Each household’s consumption is adjusted using the projected additional inflation: ; (8) ; (9) (10) Then the loss of purchasing power due to higher inflation in income terms is: (11) 96 he additional per capita income (only for net food producing households) corresponding to higher food sales prices and adjust it for higher agricultural input costs results as: (12) where , , and are defined as above and, is the percentage increase in the international oil price between the two reference periods, and is the percentage increase in fertilizers prices between the two reference periods. A new simulated per capita income is estimated, which accounts for the loss of purchasing power and additional incomes and costs in net food producing households: (13) ASSESSING THE DISTRIBUTIONAL IMPACT OF ADDITIONAL INFLATION Finally, the new simulated per capita income is expressed into 2017 PPP to calculate various poverty and distributional measures (inequality, vulnerability, middle-class, profiles, etc.) and to com- pare them to baseline projections to get estimations of additional poverty, vulnerability, or inequality for 2022 due to additional inflation. 97 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean Limitations and assumptions It is important to mention the limitations and assumptions associated with this methodology. First, the quality of projections from the methodology depends on the nature and accuracy of the data underpinning the exercise. While inflation projections are available for the total Consumer Price Index (CPI), the lack of disaggregated projections for food and non-food components of the CPI demands additional assumptions for identification purposes (i.e., the average pre-war and current ratio between food and nonfood). Second, the approach relies on past data that reflect pre-war household consumption (i.e., for food and non-food items) and income patterns. These structural relationships (i.e., consumption to income ratio, shares of food and non-food items per ventile of per capita income) are assumed to remain con- stant over the period for which the projections are made, without allowing for any changes over time or between scenarios. The older the consumption data are, the more questionable these assumptions are likely to be. This caveat relates back to the constraints imposed by the availability of data. In most countries in the LAC region, consumption data are collected every five to ten years, except for a few countries like Peru and Mexico. Third, the methodology assigns the same consumption pattern between food and nonfood items to all households within the same ventile of income per capita. More variability could be reached by in- creasing cut-off numbers in the income distribution (i.e., percentiles instead of ventiles or per house- hold using survey-to-survey imputation). For instance, the 2012 consumption patterns in Ecuador were mapped to the 2022 percentile of per capita income by considering the 2022 income brackets. Results mildly varied while the computation time increased significantly. Fourth, the impact on the net income of net food producing households depends on the magnitude of the increase in their production costs. This, in turn, depends on the specific production structure of these households. Since this information is often not available, input-output tables are used to estimate the percentage change in production costs that would result from a percentage change in oil and fertilizer prices. However, the aggregate production structure of the agricultural sector in national accounts may differ significantly from the actual structure of small-scale producer households. Finally, all ex-ante approaches raise concerns related to the validation of the central hypothesis, of which this model is no exception. In this case, the only effective validation or test would be to combine ex-ante and ex-post analysis (see Bourguignon and Ferreira, 2003). This may be partially possible with newly available sources of data. 98 Data Multiple sources of information were implemented for this exercise: those for predicting per capita in- come in 2022 for a pre-war scenario; those for forecasting food and non-food inflation; and oil and fertilizers prices. To project the per capita income, Spring Meetings 2022 macroeconomic projections for sectorial and total GDP for 2022 were considered the pre-war scenario.60 The SEDLAC 2021 harmonized household surveys were used to predict the 2022 labor market structure, average labor income, and labor pseudo-productivity.61 This process results in a simulated distribution of the per capita house- hold income in 2017 PPPs. It is important to note that formality (informality) has been defined as contributing (not contributing) to work-related retirement insurance for most countries. Table A2-1 presents the countries considered, the baseline year used for each country, the simulated years, and the informality definition used. All inputs are in real terms in 2017 USD PPP, so they already account for inflation changes. Input-output matrices and cost-push models are fundamental for estimating key parameters for net food producer households. This information was implemented by country when available and regional averages were used otherwise (see Table A2-1). Evolution and predictions for oil and fertilizer prices from ten countries were considered in the analysis.62 For the remaining countries, the average of similar countries was used. TABLE A2-1: CONSUMPTION, INCOME, INFORMALITY AND PARAMETERS BY COUNTRY Input-Output matrix & Country Consumption Survey Income Survey Informality Definition cost-push model ( , , , and ) Workers who do not receive Bolivia 2021 2014 a work-related pension insurance. - Salaried workers without the Pesquisa de Orcamentos work-registry book (“carteira”). Calibration with a CGE using Brazil 2019 Familiares 2017-2018 - Non-salaried workers who do not the 2019 IO Matrix contribute to the social security system. NO IO Matrix available. Impu- Encuesta de Presupuestos Workers who do not receive Chile 2021 tation based on the average Familiares 2017 a work-related pension insurance. of the other LAC countries 60 These projections are from the Macro Poverty Outlook Spring Meetings 2022 produced by the Macroeconomics, Trade and Investment Global Practice (MTI). Available at https://mtimodelling.worldbank.org/livempodata/mpodata.html 61 SEDLAC is a database of harmonized socio-economic statistics constructed from Latin American and Caribbean (LAC) household surveys. The SEDLAC database and project were jointly developed and are jointly maintained by CEDLAS (Uni- versidad Nacional de La Plata) and the World Bank’s LAC Team for Statistical Development (LAC TSD) in the Poverty and Equity Global Practice. SEDLAC includes information from over 300 household surveys carried out primarily in 18 LAC countries for which a comparable income aggregate (for welfare analysis) can be created: Argentina, Bolivia, Brazil, Colom- bia, Costa Rica, Chile, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, and Uruguay. 62 The analysis used the World Bank Commodity Price Data (The Pink Sheet) for all available countries (Argentina, Brazil, Colombia, Ecuador, Guatemala, Honduras, Mexico, Peru, and the Dominican Republic). For those countries without data, the price index of a similar country in the region was assigned considering the registered shipments or imports of fertilizers. For instance, the price index of Peru was assigned to Bolivia, Argentina’s to Chile, Guatemala’s to El Salvador, Nicaragua’s to Panama and Costa Rica, and Brazil’s to Paraguay and Uruguay. 99 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean Encuesta Nacional de Workers who do not receive a work-relat- Colombia Presupuestos de los 2021 2010 ed pension insurance. Hogares 2016-2017 Encuesta Nacional de Workers who do not receive a work-relat- Costa Rica Ingresos y Gastos de los 2022 2017 ed pension insurance. Hogares 2018-2019 Encuesta Nacional de Dominican Workers who do not receive a work-relat- Ingresos y Gastos de los 2021 2012-2013 Republic ed pension insurance. Hogares 2018 Encuesta Nacional de Ingresos y Gastos de los Workers who do not receive a work-relat- Ecuador 2022 2019 Hogares Urbanos y Rurales ed pension insurance. 2012 NO IO Matrix available. Impu- Encuesta de Hogares de Workers who do not receive a work-relat- El Salvador 2021 tation based on the average Propósitos Múltiples 2019 ed pension insurance. of the other LAC countries NO IO Matrix available. Impu- Encuesta Nacional de Workers who do not receive a work-relat- Guatemala 2014 tation based on the average Condiciones de Vida 2014 ed pension insurance. of the other LAC countries Encuesta Nacional de Workers who do not receive Mexico Ingresos y Gastos de los 2020 2013 work-related health insurance benefits. Hogares 2020 NO IO Matrix available. Impu- Encuesta de Consumo MTI Workers who do not receive a work-relat- Nicaragua 2014 tation based on the average 2021 ed pension insurance. of the other LAC countries NO IO Matrix available. Impu- Encuesta de Ingresos y Gas- Workers who do not receive a work-relat- Panama 2021 tation based on the average tos de los Hogares 2018 ed pension insurance. of the other LAC countries Encuesta de Ingresos y Workers who do not receive a work-relat- Paraguay 2022 2014 Gastos 2011-2012 ed pension insurance. Encuesta Nacional de Workers who do not receive a work-relat- Peru 2021 2019 Hogares 2019 ed pension insurance. NO IO Matrix available. Impu- Workers who do not receive a work-relat- Uruguay Expenditure Survey 2017 2019 tation based on the average ed pension insurance. of the other LAC countries Source: Own elaboration based on SEDLAC 100 From Infection to Inflation: Global crises hit hard poor and vulnerable households in Latin America and the Caribbean 102