How’s Life in Latin America?

Measuring Well-being for Policy Making

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28 October 2021

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2. Material conditions in Latin America

Abstract

Poverty, extreme poverty and income inequality have declined considerably in the 11 focal countries since 2000, but have remained high. Since around 2014, household income and consumption have stagnated, while satisfaction with living standards began to fall in the region. While until 2019, employment levels were comparatively high in the region, recent data show that the COVID-19 crisis had a negative impact on employment and unemployment. Across Latin America as a whole, more than half of all workers are in informal employment and typically lack access to social programmes and protection against unfair dismissal. Regarding housing quality, on average for the focal countries with available data, only around half of households had access to sanitation services, and only 70% had access to clean drinking water.

Introduction

The OECD Well-being Framework encompasses three dimensions of current well-being related to material conditions: Income and Wealth (here amended to Income and Consumption), Work and Job Quality, and Housing (OECD, 2020[1]). Together, these dimensions describe people’s economic well-being or consumption possibilities (such as their ability to access essential goods and services, and their opportunities to participate in the labour market). Material conditions determine people’s ability to meet their needs (such as food, water, clothing and shelter) and wants (such as transport, entertainment and communication) as well as shaping (and in turn, being shaped by) other aspects of people’s lives, such as access to quality education and health care.

The 11 focal countries that are covered in this report were selected because of their current status as “high-income” or “upper middle-income” countries, according to the World Bank classification based on Gross National Income (GNI) per capita.1 All 11 countries have experienced substantial improvements in GDP and average household consumption expenditure over the last two decades. Despite heterogeneity across countries, poverty, extreme poverty and income inequality have all declined considerably in these countries since 2000, while people’s satisfaction with their own living standards has increased. However, the positive picture painted by these medium-term developments blurs when focusing on changes in more recent years, and particularly since the mid-2010s when the collapse of commodity prices translated into weaker GDP growth. Since around 2014, household income and consumption levels have stagnated, while satisfaction with living standards began to fall in the region. There are also indications that, in the focal countries with available data, poverty and extreme poverty began to increase again from around 2017. Income inequality has also remained high in the region, despite the significant reductions of the last two decades, and the pace of reducing inequality has slowed since the mid-2010s. The devastating impact of the COVID-19 pandemic on economic conditions is worsening material living standards across the region, potentially wiping out years (or decades) of progress in combatting poverty and inequality and further slowing convergence with higher-income countries.

Work and housing also remain key challenges for the region, especially in the context of the pandemic, where poor working and housing conditions have been key factors driving the spread of the virus. While up to 2019 employment levels were comparatively high in the region, including in the focal countries, recent data show that the COVID-19 crisis has had a clear negative impact on employment and unemployment levels. Moreover, beyond the quantity of employment, the low quality of employment, and in particular the prevalence of informality, has meant that jobs are particularly precarious. Across Latin America as a whole, more than half of all workers are in informal employment and typically lack access to social programmes and protection against unfair dismissal. As a result, during the pandemic, many workers had to choose between obeying stay-at-home orders and earning an income. Regarding housing quality, on average for the focal countries with available data, only around half of households had access to sanitation services in 2017, and only 70% had access to clean drinking water. With only one in two households having access to the Internet, most people in the focal countries struggled to access remote work or education options or to follow adequate sanitation procedures during the pandemic.

Income and consumption

People’s access to adequate economic resources is an essential component of their current well-being. The flow of income and the stock of wealth that individuals and households can draw upon determines their ability to meet their needs and wants, as well as their freedom to choose the lives that they want to live, including the goods and services they want to consume and access. To have a full picture of these material conditions at the individual or household level requires a consideration of income, consumption and wealth.2 However, the lack of comparable data on wealth stocks in Latin American countries means that it is currently not possible to evaluate this latter aspect of economic resources in this chapter.

Across Latin America, the high levels of economic growth from the early 2000s to the mid-2010s are reflected in increased levels of national income per capita. However, this growth was tied to a commodity price boom,3 and when commodity prices started to falter from around 2014 onwards, gains in average income and expenditure, as well as reductions in poverty and inequality, began to stagnate or even reverse. Latin America is the world’s most unequal region, with income inequality being a clear and persistent feature of its countries (ECLAC, 2018[2]). The average Gini index for income inequality in the Latin American and Caribbean (LAC) region has consistently been higher than every other global region for decades, despite the recent extended period of reduction (World Bank, 2016[3]).

Average income and consumption

As mentioned above, the 11 countries that are the focus of this report are all high-income and upper-middle income countries (defined according to thresholds of national income per capita). The average GNI per capita of the LAC 11 focal group (USD 16 711 at 2017 PPP) was around USD 1 000 higher than the regional LAC average (USD 15 754) in 2019 (Figure 2.1, Panel A). The average increase in GNI per capita for the LAC 11 was also larger than the increase in the regional average since 2000. This reflects the substantial improvements over the last two decades in a small number of the focal countries, particularly in Chile, Costa Rica, the Dominican Republic and Uruguay. As is often the case, the average masks a wide variation between the countries, with GNI per capita in Ecuador (USD 11 044) being less than half that in Chile (USD 23 261) in 2019. This in turn is substantially lower than the OECD average (USD 44 573). Further, the gap in national income per capita between the OECD and the LAC region overall, as well as with the focal group specifically, widened over the period since 2000.

Household consumption expenditure is an important indicator of households’ material living standards, as it informs on household spending on consumption goods and services (which is in turn an important component of GDP totals).4 While there are caveats to using this indicator as an exact measure of household spending, it is still important to look at because, in the absence of direct measures of household disposable income, information on household consumption shows how gains in national income may be translating into tangible change in the economic situation of individuals and families (see the following section on Issues for statistical development for more detail). The average value of household final consumption expenditure in the focal countries for which data are available increased from USD 7 340 in 2000 to USD 9 996 in 2019 (Figure 2.1). These levels were only marginally lower for the LAC regional average in both years (USD 7 269 in 2000 and USD 9 930 in 2019). National income and household final consumption expenditure per capita in the focal countries remain well below OECD levels, despite considerable increases over the last two decades. The cross-country variation and rates of increase are broadly similar across the two indicators, with Chile, Costa Rica, the Dominican Republic and Uruguay showing the largest improvements over the period since 2000 in both household consumption expenditure and GNI per capita (Figure 2.1, Panel B).

Note: The regional averages for Latin America and the Caribbean and the OECD are World Bank's calculations (Panel A) or are based on World Bank calculations (Panel B). In panel B, LAC 10 excludes Uruguay as data in 2017 PPP USD are not available. Household final consumption expenditure is the market value of all goods and services, including durables (such as cars, washing machines, and home computers), purchased by households. It excludes purchases of dwellings but includes imputed rent for owner-occupied dwellings. It also includes payments and fees to governments to obtain permits and licenses. Household consumption expenditure includes the expenditures of non-profit institutions serving households, even when reported separately by the country. Data are converted to constant 2017 international dollars using purchasing power parity rates for GDP for GNI and for private consumption for household final consumption expenditure.

Source: World Bank Database, https://data.worldbank.org/indicator/NY.GNP.PCAP.PP.KD (Panel A) and OECD calculations based on World Bank Database, https://data.worldbank.org/indicator/SP.POP.TOTL and https://data.worldbank.org/indicator/NE.CON.PRVT.PP.KD (Panel B).

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Comparing long-term trends in national income and household consumption expenditure per capita in the region shows that, while the 2008-2009 economic crisis had a lesser impact in the focal group than in the OECD on average, the end of the commodity price boom in 2013-2014 has led to stagnation in both income and consumption in the region as a whole, whereas these continued to rise in the OECD.

Note: OECD averages are World Bank calculations (Panel A) or are based on World Bank calculations (Panel B). In panel B, LAC 10 excludes Uruguay as data in 2017 PPP USD are not available.

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Satisfaction with living standards

LAC households’ own perceptions of their material living conditions can provide insights into how people in the region have experienced changes over the last 10 years. Figure 2.3 shows levels and trends in the share of people who say they are satisfied with their own standard of living. Panel A compares levels of satisfaction in the earliest three-year period for which data are available (2006-2009) with the latest three-year period (2017-2019), i.e. prior to the COVID-19 pandemic. The majority of focal countries (8 out of 11) experienced an increase in satisfaction with living standards, with the average level in the focal group increasing by 7 percentage points from 65% to 72%. Countries that recorded the largest rises in national income and consumption expenditures per capita between 2006-9 and 2017-19 also experienced the largest increases in satisfaction levels. When looking at long-term trends (Figure 2.3, Panel B), the focal group average shows a fairly steady increase between 2006 and 2014 (interrupted only by a dip around 2008, the year of the global financial crisis), to a level (75%) that is close to the OECD average. However, the improvements in satisfaction with living standard faltered since 2014, with a slight decrease followed by stagnation in more recent years. This closely follows developments in macro-economic measures of GNI and household final consumption expenditure per capita described above. These patterns differ significantly from those prevailing in the OECD area, where satisfaction levels increase after 2016, following a decade of broad stability.

When measuring the correlation between the three indicators, the coefficient of determination (R²) between the percentage change in satisfaction with living standards and the percentage change in GNI per capita between 2006-9 and 2017-19 was 0.57 (Figure 2.3, Panel C), while it was 0.33 between satisfaction and household final consumption expenditure (Figure 2.3, Panel D). While this shows that a substantial proportion of the cross-country variance in satisfaction with living standards is explained by differences in GNI and household final consumption expenditure per capita, it also shows that a large share of the variance is not explained by macro-economic variables.

Note: The charts show the share of people who reply “Satisfied” to the question, “Are you satisfied or dissatisfied with your standard of living, all the things you can buy and do?”. In Panel A, LAC regional average comprises 19 Latin American and Caribbean countries, including the focal countries. In Panel B, LAC regional average comprises 15 Latin American and Caribbean countries, including the focal countries. OECD 16 average includes Canada, Chile, Colombia, Denmark, France, Germany, Israel, Italy, Lithuania, Mexico, Poland, Spain, Sweden, Turkey, the United Kingdom and the United States. Panels C and D show the percentage change in the respective variables, comparing pooled values for the three-year period over 2006-8 with pooled values for 2017-19. For Uruguay, the latest time period for GNI per capita refers to 2017-18 rather than 2017-19.

Source: Gallup World Poll (database), https://www.gallup.com/analytics/232838/world-poll.aspx (for satisfaction with living standards), OECD calculations based on World Bank Database, https://data.worldbank.org/indicator/SP.POP.TOTL and https://data.worldbank.org/indicator/NE.CON.PRVT.PP.KD (Panel C) and World Bank Database, https://data.worldbank.org/indicator/NY.GNP.PCAP.PP.KD (Panel D).

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Income poverty

Reducing poverty remains a primary policy objective for all countries in the region. While poverty is a multidimensional issue that goes beyond material conditions (see Chapter 6 for a discussion on the use of multidimensional poverty measures in the region), low income remains a major determinant of deprivation for millions of people across Latin America. Figure 2.4 shows income-based measures of absolute and extreme poverty based on the measures calculated by the Economic Commission for Latin America and the Caribbean (ECLAC) (see Box 2.1 for an explanation of the different poverty thresholds). Since 2000, there has been huge progress in reducing both absolute poverty and extreme poverty in the region, particularly in the focal countries. On average, across 7 of the 11 focal countries for which the earliest and latest data are available, the share of people living in absolute poverty more than halved between 2000 and 2019, from 44% to 20.4%, while the share living in extreme poverty dropped from 11.6% to 4.7% (Figure 2.4, Panels A and B). This is a much steeper decrease than for the region overall, where absolute poverty rates fell from 45.2% to 30.5% and extreme poverty rates from 12.2% to 11.4%. Particularly large reductions in absolute poverty were achieved in Uruguay (from 43.7% to 3%), Chile (from 42.8% to 10.7%) and Peru (from 43.7% to 15.4%).

Note: Absolute poverty and extreme poverty rates are calculated by ECLAC and represent the share of people living in households that do not have an income sufficient to buy a basic food basket as well as other necessary goods and services (in the case of absolute poverty) and those who do not have sufficient income to buy even the basic food basket (in the case of extreme poverty) (ECLAC, 2019[4]).The latest available year is 2018 for Mexico and 2017 for Chile. The earliest available year is 2001 for Argentina and Ecuador, 2004 for Peru, 2005 for Paraguay and 2007 for Uruguay. In Panels A and B, LAC 7 average excludes Brazil, Colombia, Costa Rica and the Dominican Republic, due to incomplete time series. In Panels C and D, LAC 6 average includes Argentina, Colombia, Ecuador, Paraguay, Peru and Uruguay. LAC is the regional average for Latin America and the Caribbean calculated by ECLAC.

Source: ECLAC Statistics, CEPALSTAT database, https://cepalstat-prod.cepal.org/cepalstat/tabulador/ConsultaIntegrada.asp?idIndicador=3328&idioma=i.

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However, even before the pandemic hit, there were signs of stagnating or reversing trends in poverty reduction in countries with available data. Figure 2.4, Panels C and D show trends in absolute poverty and extreme poverty for the LAC regional average, as well as for the average of the focal countries with data available from 2017 to 2019 (Argentina, Colombia, Ecuador, Paraguay, Peru and Uruguay). After 2013-2014, in six focal group countries, the fall in both absolute and extreme poverty began to slow, and average rates show a slight increase since 2017. The tendency towards greater poverty since 2014 is even clearer when looking at the LAC regional average.

Box 2.1. Different approaches to measuring income poverty in Latin America

There is no single statistical measure of poverty. In general terms the concept of poverty refers to “a situation in which people are unable to reach a given standard, social norm or desirable status” (ECLAC, 2019[4]). This can reflect the inability to meet basic needs for survival, such as access to adequate food and decent shelter, or it can be interpreted more broadly as being unable to fully participate in society in other ways. Poverty is increasingly recognised as being a multifaceted phenomenon, having many different causes, implications and manifestations that can impact every dimension of people’s lives. Multidimensional approaches to measuring poverty have been particularly important for informing and guiding policy in Latin American countries in recent years (see Chapter 6 for further discussion).1

However, measures of poverty that focus on income alone remain meaningful sources of information on material deprivation within a society, and many countries produce measures of income poverty based on the thresholds deemed most relevant for their own national circumstances. The problem with national measures of poverty is that they are not comparable across countries, implying that alternative measures are needed for international comparisons, even if these do not necessarily correspond to official figures produced by national statistical offices. The most prominent international measures of income-poverty are based on monetary thresholds that are common (in absolute value) across countries (or groups of countries). This is the case of the extreme poverty lines associated with SDG indicator 1.1, which looks at the share of people living on less than USD 1.90 per day (revised up from the original threshold of USD 1.25 per day). The World Bank relies on three international poverty lines: USD 1.90 per day for the international measure of extreme poverty; USD 3.20 per day for measuring poverty in lower middle-income countries; and USD 5.50 per day for poverty in upper middle-income countries.

However, monetary thresholds that are common across countries have their drawbacks, as higher poverty lines can fail to capture the true extent of income deprivation. For this reason, the regional absolute poverty rates calculated by the Economic Commission for Latin America and the Caribbean (ECLAC) are used as the primary indicator of income poverty in this chapter. These measures were selected by regional experts as the most appropriate indicators to track progress towards achieving SDG target 1.1 in Latin America (ECLAC, 2019[5]). ECLAC’s approach calculates the poverty line as a monetary value that considers two components: first, the cost of acquiring a basic food basket and, second, the cost of other goods and services, expressed as a ratio between total expenditure and food expenditure.2 The basic food basket is constructed to satisfy the average energy requirements of the population in each country, using a structure of goods and prices given by consumption patterns observed in a reference group and adjusted for basic dietary balances. The extreme poverty line is calculated as the value necessary to purchase the basic food basket without additional goods and services, while the absolute poverty line adds to the costs of the food basket those of the non-food components.

Figure 2.5 below compares income poverty levels (as measured by the regional poverty lines calculated by ECLAC) with the international poverty lines set at USD 1.90 (the official SDG indicator), USD 3.20 and USD 5.50 (the World Bank monetary poverty measures for lower and upper middle-income countries, respectively). As is clear from the chart, different poverty measures give very different results. In most of the focal countries, the international extreme poverty line (<USD 1.90 per day) captures only a portion of the share of extreme poverty as calculated with ECLAC’s methodology. On average across the focal countries, the extreme poverty rate was only 1.9% in 2019 by the World Bank measure, compared with 5.4% using the ECLAC measure (Figure 2.5, Panel A).

Note: Data refer to 2019, except for Mexico (2018) and Chile (2017). LMIC stands for Lower Middle-Income Class and UMIC stands for Upper Middle-Income Class. LAC is the regional average for Latin America and the Caribbean as calculated by ECLAC for regional poverty and extreme poverty and as calculated by the World Bank for the international poverty lines. In Panel A, OECD 27 excludes Australia, the Czech Republic, Germany, Hungary, Iceland, Japan, New Zealand, Slovenia and Switzerland, due to incomplete data. In Panel B, OECD 33 excludes Australia, Japan, New Zealand and Slovenia, due to incomplete data.

Source: ECLAC Statistics, CEPALSTAT database, https://cepalstat-prod.cepal.org/cepalstat/tabulador/ConsultaIntegrada.asp?idIndicador=3328&idioma=I and World Bank, World Development Indicators, https://data.worldbank.org/indicator/SI.POV.DDAY (Panel A), https://data.worldbank.org/indicator/SI.POV.LMIC and https://data.worldbank.org/indicator/SI.POV.UMIC (Panel B).

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The indicators discussed above measure absolute poverty in that they consider deprivation in reference to a fixed threshold. Relative poverty measures, such as the share of the population living on income at a given threshold below the median income level, are widely used in higher-income countries. For example, the 50% threshold is used to measure poverty in the OECD, and the 60% threshold in the European Union. The rationale behind this is that, beyond meeting their basic needs for survival, human beings need to be able to participate in the society in which they live to a reasonable extent, which would be precluded by living on an income substantially below the median (Townsend, 1979[6]). This report does not include a measure of relative poverty, as in the LAC region median incomes (as measured in surveys) are so low that relative measures provide lower poverty counts than absolute measures. For example, whereas in 2019, 30.5% of the LAC population were living in absolute poverty, according to ECLAC’s regional poverty measure only 19.1% were living in relative poverty (based on the 50% of median income threshold).3

Throughout this report, unless otherwise stated, measures of poverty and extreme poverty refer to the regional absolute measures calculated by ECLAC.

Notes

1. Multidimensional poverty indices (MPIs) are widely used in the region, and in some cases (such as Mexico) are used as the official poverty measure. This report does not include the MPI as one of the well-being indicators for two reasons. First, definitions of multidimensional poverty, and the methodology for calculating MPIs, differ between countries, and there is not yet a comparable global or regional approach. And second, including a composite index (combining multiple indicators) within a broader dashboard may lead to duplication of issues (e.g. housing quality or educational attainment, which are often included within MPIs). However, the importance of taking a multidimensional approach to measuring poverty, and the role of the MPI in particular, are recognised and explored in more detail in Chapter 6.

2. The selection of non-food goods and services focuses on household items that are acquired regularly, such as housing, health care, clothing and footwear, transportation, furnishings and appliances, education and other items of expenditure. For a more detailed explanation of ECLAC methodology, see (ECLAC, 2019[4]).

3. ECLAC Statistics, CEPALSTAT database: https://cepalstat-prod.cepal.org/cepalstat/tabulador/ConsultaIntegrada.asp?idIndicador=3307&idioma=e.

Income Inequality

Latin America is recognised as being the most unequal region in the world, with income inequality being one of the clearest and most persistent aspects of that inequality (ECLAC, 2018[2]). Figure 2.6 shows levels and trends in the Gini coefficient and the S80/S20 income share from 2000 to 2019. The Gini coefficient is one of the most frequently used indicators to depict inequality, expressing how far the income distribution of a country deviates from a perfectly equal distribution on a 0 to 1 scale, with 0 representing a completely equal distribution and 1 a completely unequal distribution. The S80/20 ratio shows the income share of the richest 20% as a proportion of the share accruing to the poorest 20%.

Note: The Gini coefficient depict income inequalities expressing how far the income distribution of a country deviates from a perfectly equal distribution on a 0 to 1 scale, with 0 representing a completely equal distribution and 1 a completely unequal distribution. The S80/20 ratio shows the income share of the richest 20% as a proportion of the share accruing to the poorest 20%. Income data for Latin American countries are not standardised in whether they refer to pre-or post-tax income and generally refer to individual workers. The data for the OECD average (marked with an *), on the other hand, is taken from the harmonised OECD Income Distribution database and refers uniquely to post-tax and equivalised income (i.e adjusted to account for economies of scale in the household). The latest available is 2018 for Costa Rica and Mexico, and 2017 for Chile. The earliest available year is 2001 for Ecuador; 2004 for Peru; 2005 for Paraguay; and 2007 for Uruguay. In Panel A and B, LAC 7 excludes Brazil, Colombia, Costa Rica and the Dominican Republic, due to incomplete time series, and OECD 19 includes Austria, Canada, the Czech Republic, Finland, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Norway, Poland, Portugal, the Slovak Republic, Slovenia, Spain, Switzerland and the United Kingdom. In Panel C and D, LAC 7 average excludes Brazil, Chile, Costa Rica and the Dominican Republic, due to incomplete time series, and OECD 19 includes Australia, Austria, Canada, the Czech Republic, Estonia, Finland, France, Greece, Israel, Latvia, Lithuania, Norway, Poland, Portugal, the Slovak Republic, Slovenia, Spain, Sweden and the United Kingdom. LAC is the regional average for Latin America and Caribbean as calculated by ECLAC.

Source: ECLAC Statistics, CEPALSTAT database, https://estadisticas.cepal.org/cepalstat/WEB_CEPALSTAT/estadisticasIndicadores.asp (Panel A and C), ECLAC Statistics, CEPALSTAT database, https://estadisticas.cepal.org/cepalstat/WEB_CEPALSTAT/estadisticasIndicadores.asp (Panel B and D) and OECD Income Distribution database, https://stats.oecd.org/Index.aspx?DataSetCode=IDD for OECD countries.

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Over the past two decades, countries in the focal group and the region as a whole have achieved impressive reductions in income inequality, by both measures. On average, across the 7 LAC focal group countries for which data are available throughout the period, the Gini dropped from 0.51 in 2000 to 0.44 in 2019, and the S80/20 income share ratio from 15.1 in 2000 to 9.8 in 2019 (i.e. in 2019 the income share of the richest 20% of the population was almost ten times higher than that of the poorest 20%) (Figure 2.6, Panels A and B). Over the same period, the OECD average levels of the same measures barely changed, meaning that while income inequality in the LAC countries remains very high, there has been some convergence between the LAC region and the OECD since 2000.

However, these inequality gains cannot be taken for granted, especially in the context of the COVID-19 pandemic (see the Section on COVID impact below). Since around 2014, the pace of inequality reduction has slowed, at least for the seven focal countries for which annual data are available through the period (Figure 2.6, Panels C and D). The average reduction in the Gini coefficient for these countries was 0.03 points over the 5-year period between 2008-9 and 2012-2013 (from 0.49 to 0.46) and only a quarter of that in the subsequent 5-year period between 2014-15 and 2018-2019 (from 0.46 to 0.45).

It is worth noting here that the comparisons between countries, and between the LAC focal group or region and the OECD averages, should be treated with some caution, as the calculation of income is not standardised across LAC countries. In most LAC countries, post-tax income is recorded for dependent workers, but income from self-employment and other sources is pre-tax. In other countries (e.g. Brazil) all income is before pre-tax. Generally, income refers to individuals. The data for the OECD average, on the other hand, is taken from the harmonised OECD Income Distribution database and refers uniquely to post-tax and equivalised income. This does not negate the value of the available data for making general comparisons between countries and over time, but further underlines the need for harmonised data on household income in the region, which is addressed further in the following section on Issues for statistical development.

Income adequacy

Even people living above the poverty line may still feel economically strained based on what their income can provide. On average in the 10 countries of the focal group (LAC 10) for which data are available, 2 out of every 5 people (41%) said they had difficulties satisfying their needs based on their family income in 2018, compared with just under 1 in 4 people (23%) in the OECD countries (Figure 2.7, Panel A). This share had decreased by 9 percentage points since 2000 (when it was 50%) on average in the 10 focal countries, driven by large falls in Ecuador and Uruguay and smaller declines in Argentina, Chile, Mexico and Peru, as compared to stability or even slight increases in the other focal countries. Conversely, the LAC regional average share barely changed over the same period. Annual data show that the average share of people having difficulties satisfying their needs out of their current income edged up in both these 10 focal countries and in the LAC regional average since 2014 (Figure 2.7, Panel B).

Note: The chart is based on the question, “Does the salary you receive and your total family income allow you to cover your needs in a satisfactory manner? Which of the following statements describes better your situation?” with response categories, “It’s enough, we can save”, “It’s just enough, we don’t have major problems”, “It’s not enough, we have problems”, and “It’s not enough, we have major problems”. The data shown reflect the percentage of respondents answering, “It’s not enough, we have problems” or “It’s not enough, we have major problems”. For the OECD average, this refers to the share of people who report having difficulty or great difficulty in making ends meet. The question is asked to the household reference person, and the information is available at household level only. LAC regional average comprises 18 Latin American and Caribbean countries, including the focal countries. OECD 18 average includes Austria, Belgium, the Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Norway, Portugal, the Slovak Republic, Slovenia and Spain.

Source: ECLAC Statistics, CEPALSTAT database, https://cepalstat-prod.cepal.org/cepalstat/tabulador/ ConsultaIntegrada.asp?idIndicador=3256&idioma=i (until 2016), Latinobarometro, https://www.latinobarometro.org/latOnline.jsp (2017 and 2018) and OECD calculations based on Eurostat’s database European Union Statistics on Income and Living Conditions (EU-SILC), https://ec.europa.eu/eurostat/web/income-and-living-conditions/data/database (OECD average).

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One of the most serious forms of deprivation in material conditions is food insecurity, or uncertainty in people’s ability to obtain adequate food for themselves and their families. Food insecurity was on the rise even before the pandemic, and in 2019 just under one in three people (32%) lived with moderate or severe food insecurity (see Box 2.2).

Box 2.2. Food insecurity

Food insecurity is closely related to income adequacy and is a direct measure of people’s consumption possibilities in the most basic sense: whether they are able to consistently afford or access adequate nutrition. Latin America has seen a rise in undernourishment in recent years – or in other words, the share of the population whose habitual food consumption is insufficient to provide the dietary energy levels that are required to maintain a normal active and healthy life. The region counted 9 million more undernourished people in 2019 than in 2015 (FAO, 2020[7]).

Until recently, the complexity of the concept of food insecurity as well as differences in how it was operationalised complicated the study of the “food insecure” (Smith, Kassa and Winters, 2017[8]). However, the inclusion of a food insecurity measure in the UN 2030 Agenda (under SDG indicator 2.1.2, related to the prevalence of moderate and severe food insecurity) gave a greater impetus to developing a more harmonised methodology, leading the Food and Agriculture Organisation (FAO) to design the Food Insecurity Experience Scale (FIES). While this method has not yet been widely incorporated into official national surveys, a global survey conducted jointly by the FAO and Gallup has provided some initial results.

Figure 2.8 shows the prevalence of moderate or severe food insecurity in the population based on the Food Insecurity Experience Scale (FIES). According to this scale, people experiencing moderate food insecurity face uncertainties about their ability to obtain food and have been forced to compromise on the quality or quantity of the food they consume, while people experiencing severe food insecurity have typically run out of food and, at worst, gone at least a day without eating. Overall, across Latin America, just under one in three people (32%) lived in either moderate or severe food insecurity in 2019, up from just under one in four (23%) in 2014. In a global perspective, while the prevalence of food insecurity is higher in Africa on average, Latin America saw the largest regional increase in the prevalence of food security over the 2014-2019 period (when compared with Africa, Asia and North America and Europe). This rise in overall (moderate and severe) food insecurity broadly matches the rise in food prices (as measured by the FAO Food Consumer Price Index, Figure 2.8, right-hand axis).

Note: Data on food insecurity are derived from the Food Insecurity Experience Index. People experiencing moderate food insecurity are those reporting that they face uncertainties about their ability to obtain food or that they have been forced to reduce the quality and/or quantity of the food they consume. People experiencing severe food insecurity have typically run out of food and, at worst, gone a day (or several days) without eating. The shares of people experiencing the two types of food insecurity are plotted on the left axis. The Food Consumer Price Index measures the change over time in the general level of prices of food and non-alcoholic beverage items that households acquire, use or pay for consumption. This is done by measuring the cost of purchasing a fixed basket of consumer food and beverages of constant quality and similar characteristics, with the products in the basket selected as being representative of households’ expenditure during a specified period. The base year of the Food Consumer Price Index is 2015 (2015=100). All values refer to averages for the Latin America and Caribbean region as calculated by the Food and Agriculture Organization (FAO).

Source: (FAO, 2020[7]), “Food Security and Nutrition in the world”, http://www.fao.org/3/ca9692en/ca9692en.pdf, and FAO Statistics, http://www.fao.org/faostat/en/#data/CP

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Impact of COVID-19

The devastating impact of the COVID-19 pandemic is set to negatively affect living standards across the region, potentially wiping out years (or decades) of progress in combatting poverty and inequality and further slowing convergence with higher-income countries. ECLAC estimates suggest that in 2020, more than one in three Latin Americans were living in poverty (33.7%) and one in eight in extreme poverty (12.5%).5 Based on these estimates, the total number of people falling below the ECLAC line for absolute poverty was 209 million by the end of the year, 22 million more than in 2019 (ECLAC, 2021[9]). Of this total, 78 million people would be living in conditions of extreme poverty, with an increase of 8 million compared to 2019 (ECLAC, 2021[9]). These changes are likely to have brought the absolute poverty rate to its highest level since 2008 and extreme poverty to its highest level since 2000 (FAO, 2020[7]).

The pandemic has undoubtedly deepened the deprivation level not only of millions of people living on the edge of poverty, but also of the vulnerable middle class. In 2019, 77% of the region’s population (470 million people) belonged, according to ECLAC, to the low or lower-middle income strata, with per capita income up to three times the regional poverty line, and with insufficient savings to weather a crisis (ECLAC, 2020[10]). ECLAC estimates that 15% of those belonging to the low-income non-poor strata (with a per capita income between 1 and 1.8 times the absolute poverty line) are expected to have fallen into absolute poverty (20.8 million people) or extreme poverty (3 million people) as a consequence of the crisis (ECLAC, 2020[10]). The prevalence of food insecurity in the Latin American region is also likely to have risen, due to disruptions in food supply and income loss (FAO, 2020[7]).

ECLAC projections also suggest that inequality in household income per person (as measured by the Gini coefficient) increased by 5.6% on average between 2019 and 2020, and by 2.9% when taking into account government transfers (ECLAC, 2021[9]). As with poverty levels, income inequality is projected to have increased the most in the largest economies of the region, with the Gini coefficient estimated to have risen by 3% or more in Argentina, Brazil, Ecuador, Mexico and Uruguay, and by between 0.5% and 1.4% in the Dominican Republic, Paraguay, Guatemala, Honduras and Panama (ECLAC, 2020[10]).

Although on average people’s satisfaction with their standard of living changed little change over the past two years in the focal group, trends diverged across countries. For instance, people’s satisfaction with their standard of living declined slightly in 2020 in the Dominican Republic (from 72% to 68%) and Brazil (from 73% to 70%) (Figure 2.9) but much more in Peru (from 74% to 59%). Conversely, their satisfaction increased by three percentage points or more in Paraguay (from 71% to 74%), Argentina (from 61% to 65%) and Chile (from 68% to 77%).

Note: Data refer to the share of people who reply “Satisfied” to the question, “Are you satisfied or dissatisfied with your standard of living, all the things you can buy and do?”. LAC regional average comprises 15 Latin American and Caribbean countries, including all the focal countries. The OECD 16 average includes Canada, Chile, Colombia, Denmark, France, Germany, Israel, Italy, Lithuania, Mexico, Poland, Spain, Sweden, Turkey, the United Kingdom and the United States.

Source: Gallup World Poll (database), https://www.gallup.com/analytics/232838/world-poll.aspx.

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Issues for statistical development

Ideally, measures of current well-being should refer to households or individuals. However, information on income, consumption and wealth at the household level are not widely available for Latin American countries.6 In their absence, data compiled from countries’ Systems of National Accounts (SNA) can provide useful proxy information on income and consumption, although as these data conflate information from different sectors of the economy, such as firms, financial intermediaries and the public sector, they are imperfect measures of actual household conditions. Two different measures of average material conditions are used in this chapter. The first is Gross National Income (GNI) per capita, the indicator used by the World Bank to classify countries’ income levels, which reflects income streams accruing to all sectors of the economy, rather than being limited to households per se. The second is an SNA-based measure of household consumption expenditures, an indicator that, while pertaining to households, omits the share of current income that is saved by them, and which could support their living standards in later periods. This indicator also includes the expenditures of non-profit institutions serving households, such as hospitals and educational institutions. While both of these proxies have limits – not least the fact that they can provide only aggregate information with no consideration of patterns of distribution within countries –- the joint consideration of the two allows for a more rounded assessment of the material living standards in Latin America at the national level.

The measurement of wealth in the region is currently very limited: only Costa Rica, Chile, Mexico and Uruguay have conducted household wealth surveys, and not on a regular basis.7 Improving information on wealth matters not only for obtaining a clearer picture of households’ financial and material assets, but also for better understanding households’ economic insecurity. Measuring economic insecurity was identified as a priority by the High-Level Expert Group on the Measurement of Economic Performance and Social Progress (Stiglitz, Fitoussi and Durand, 2018[11]). Financial insecurity is a particularly relevant indicator for identifying people who are not income-poor but are at risk of falling into poverty due to insufficient financial resources. For example, How’s Life? 2020 (OECD, 2020[1]) includes a measure of the share of people who have insufficient resources to prevent them from falling into poverty given a three-month loss of income.8 This measure provides valuable information on the (in)sufficiency of assets that could be used as buffers against shocks, highlights the distribution of economic resources, and presents joint information on income and wealth (though not on consumption).

In order to better understand the economic situation of Latin Americans from a well-being perspective, an important objective is to improve the availability and comparability of direct measures of household income as well as of household wealth. This is not a negligible task, as the definition of income across different national surveys in the region tends to differ substantially depending on, for instance, whether and how in-kind income, imputed rents and home production are treated, and whether specific income sources such as remittances, private transfers or property income are properly captured. Further, incomes may be reported on either a net or gross-of-tax basis: in the latter case (as for the official data for Brazil and Colombia), inequality measures based on pre-tax income would naturally be higher than when reporting inequalities in disposable, i.e. post- tax, income, as they do not reflect the redistributive impact of taxes. In some countries (e.g. Mexico), even when measures refer to disposable income, data on taxes are not separately reported, and it is therefore not possible to capture the full extent of redistribution (only that of public transfers) (Balestra et al., 2018[12]). Estimates of income inequality for Latin American countries also tend to differ from the OECD approach, which refers to an income metric adjusted for economies of scale in household needs (so-called equivalised household incomes9). Latin American sources tend to use income per capita as a standard, which assumes no economies of scale within households (Balestra et al., 2018[12]). Standardising the way that income data are collected and reported in the LAC region would be an important step towards having comparable direct measures of household income and income distribution.

Measurement of household economic conditions and their distribution could also be improved in other ways, such as improved frequency and coverage. Ideally, income distribution surveys should be conducted at least annually and data collected on income with reference to the previous year (rather than the previous month, as is the case for some countries in the region). Efforts should also be made to ensure that the data cover the whole of the income distribution, and especially those at the very top and the very bottom, both of which tend to be under-reported. In Latin America, inequality tends to be driven by an excessive concentration of income by a small elite in the very top 1% or even 0.1% of the distribution, even more so than in other world regions (Sánchez-Ancochea, 2021[13]). Supplementing survey data with additional information from other sources, such as tax records where possible, can help to provide more accurate figures on the “missing rich” (Stiglitz, Fitoussi and Durand, 2018[11]). Administrative data can also improve the quality of income measurement at the bottom of the income distribution. For example, many countries in the region have introduced conditional cash transfers (CCT) in recent decades, but these transfers are not always properly reported on household income surveys.10 Supplementing household surveys with administrative data from CCTs could provide more precise information of the situation of eligible households.

Finally, given the importance of the issue of food insecurity for the region, more widespread use of the Food Insecurity Experience Index in national surveys would provide valuable comparable evidence for monitoring its prevalence and intensity.

Work and job quality

For most households of working age, the regular income provided by paid work is necessary to increase and maintain their material living standards. In addition, both paid and unpaid work can provide people with a chance to fulfil their own ambitions, to develop skills and abilities, to feel useful in society and to build self-esteem. Work shapes personal identity, provides structure and can create opportunities for social relationships. Being unemployed has a large and persistent negative effect on both physical and mental health and on subjective well-being, with effects that go well beyond the income loss that unemployment brings (OECD, 2011[14]). Since most people spend a substantial share of their waking hours at work, and work for a significant part of their lives, the need for a high-quality job has been increasingly recognised by international organisations and policy makers, who have referred to jobs that provide adequate wages and benefits, are reasonably secure, and take place in a safe and supportive working environment.11

In Latin America, employment rates are high compared with the OECD average, which has been the case for at least the past two decades. However, employment rates have faltered since 2016, and unemployment has also been rising. In addition, Latin American employment is characterised by a high rate of informality, with over half of workers estimated to be in informal jobs. While it can be argued that informal employment is better than no employment, the high prevalence of informality is nonetheless a concern from the perspective of job quality, as informal jobs are not protected, regulated or well-recognised and valued. As social protection and access to health care are often tied to employment status in Latin America, informal workers are particularly vulnerable in this respect. In this context, the impact of the COVID-19 pandemic can be devastating, leading to a significant rise in unemployment, a further increase in informality as a share of total employment, and widespread poverty.

Employment and unemployment

Paid work provides essential income to individuals and families but also, particularly in Latin America, the access to health care and other forms of social protection that are tied to employment status. When looking at the average for the seven focal countries with comparable time series (Brazil, Chile, Colombia, Costa Rica, Ecuador, Paraguay and Peru), while employment rates remained stable between 2014 to 2019 (at 68%), unemployment rates rose from 6.5% to 8.4% (Figure 2.10). Over the same period, OECD average employment rates rose slightly, from 58 to 60%, and unemployment declined from 9.8% to 7.5%.

The pandemic has impacted key labour market outcomes significantly, as evidenced by the sharp changes in employment and unemployment levels between 2019 and 2020. This is addressed more in the later section on COVID-19 impacts, but Figure 2.10 already shows that employment has decreased and unemployment increased for both the LAC focal group and regional averages. While this is also true for the OECD, the magnitude of the impact has apparently been less than for the LAC region. Overall, across the seven LAC focal countries with available data, employment decreased nine percentage points between 2019 and 2020 to 58% (compared with only a one percentage-point decrease in the OECD), and unemployment increased 3.6 percentage points to 12% (compared with a 1.2 percentage-point increase in the OECD).12

Note: OECD 23 excludes Belgium, Denmark, France, Germany, Iceland, Ireland, Israel, Japan, Korea, Luxembourg, Mexico, Sweden, Turkey and the United States, due to breaks in the time series or incomplete data. LAC is the regional average for Latin America and the Caribbean as calculated by the ILO. In Panel A, LAC 7 excludes Argentina, the Dominican Republic, Mexico and Uruguay. In Panel B, LAC 6 excludes Argentina, the Dominican Republic, Ecuador, Mexico and Uruguay.

Source: ILO, https://www.ilo.org/shinyapps/bulkexplorer13/?lang=en&segment=indicator&id=EMP_2WAP_SEX_AGE_RT_A for country data and https://www.ilo.org/shinyapps/bulkexplorer34/?lang=en&segment=indicator&id=EMP_DWAP_SEX_AGE_RT_A for the Latin American and Caribbean regional average (Panel A) and https://www.ilo.org/shinyapps/bulkexplorer22/?lang=en&segment=indicator&id=UNE_DEAP_SEX_AGE_RT_A (Panel B).

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At the time of writing, 2020 labour market indicators were not available for all focal countries, so the following analysis continues by focusing on the situation before the pandemic in 2019. On average across the 10 focal countries with comparable data, the employment rate was 66% for the population aged 25 or above. This is relatively high and was four percentage points higher than the OECD average in 2019 at 61%. Across the focal countries, national employment rates differed by over twenty percentage points, ranging from 58.6% in Brazil to 80.1% in Peru in 2019 (Figure 2.11, Panel A). While most countries experienced little net change in employment between 2010 and 2019, this was not the case for every country: Uruguay experienced a decrease in the employment rate of almost 4 percentage points over this period, while Paraguay recorded an increase of the same amount.

Relatively high employment rates among the focal countries mask deeper issues with the quality and availability of labour market opportunities in the region. For example, across the eight focal countries with comparable time series, on average 9.2% of workers in 2019 had jobs that did not provide them enough working hours (Figure 2.11, Panel B). In Argentina, one in seven workers (14.6%) were involuntarily working part-time hours and were willing and able to work more hours. However, time-related underemployment is much lower in the focal countries than in the LAC region as a whole, where almost one in five workers (18.5%) would be willing and able to work more hours given the opportunity.

Note: The earliest available year is 2011 for Costa Rica, 2012 for Brazil, 2014 for Ecuador and 2015 for the Dominican Republic. LAC is the regional average as calculated by the ILO. In Panel A, LAC 10 excludes Argentina and OECD 29 excludes Belgium, Denmark, France, Ireland, Korea, Luxembourg, Sweden and Turkey, due to incomplete data. In Panel B, LAC 8 excludes Ecuador, Mexico and Uruguay, due to breaks in the time series or incomplete time series. Time-related underemployed are employed who satisfy the following three criteria during the reference period: a) are willing to work additional hours; b) are available to work additional hours, i.e. are ready, within a specified subsequent period, to work additional hours, given opportunities for additional work; and c) worked less than a threshold relating to working time, i.e. persons whose hours actually worked in all jobs during the reference period were below a threshold, to be chosen according to national circumstances.

Source: ILO, https://www.ilo.org/shinyapps/bulkexplorer13/?lang=en&segment=indicator&id=EMP_2WAP_SEX_AGE_RT_A for country data and https://www.ilo.org/shinyapps/bulkexplorer34/?lang=en&segment=indicator&id=EMP_DWAP_SEX_AGE_RT_A for the Latin American and Caribbean regional average (Panel A); https://www.ilo.org/shinyapps/bulkexplorer43/?lang=en&segment=indicator&id=TRU_DEMP_SEX_AGE_RT_A for country data, https://www.ilo.org/shinyapps/bulkexplorer27/?lang=en&segment=indicator&id=EMP_2TRU_SEX_AGE_RT_A for the Latin American and Caribbean regional average (Panel B).

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Levels of unemployment varied widely across the focal group in 2019 (Figure 2.12, Panel A). The highest unemployment rate, in Brazil (11.9%), was over three times higher than the lowest rate amongst the focal group, in Peru (3.4%). Peru, along with Ecuador and Mexico, all had unemployment rates that were lower than the OECD average in 2019 (5.9%). The average rate of unemployment for the focal group rose slightly from 6.9% in 2010 to 7.4% in 2019, and the highest country-level increases in this period were seen in Argentina (+ 2.5 percentage points) and Brazil (+4.7 percentage points).

Unemployment can have a considerable impact on workers’ well-being, not only in terms of income loss, bringing long-term scarring effects that last well beyond the period of unemployment itself (Mousteri, Daly and Delaney, 2018[15]). The negative impact of unemployment increases with its duration: long-term unemployment, lasting more than 12 months, can put a considerable burden on those affected and their families. On average across the eight focal countries with available data, long-term unemployment represented 15% of total unemployment, just more than half the OECD average rate of 27%, in 2019 (Figure 2.12, Panel B). Indeed, almost all countries, with the exception of Argentina, had long-term unemployment rates that were below the OECD average. However, this relatively positive labour market outcome in the LAC region needs to be seen in the context of the inadequacy of the regional social safety nets. Due to the limited coverage of unemployment benefits, workers cannot generally afford to stay unemployed for long, and are forced by necessity to find work again quickly, even if the only jobs available are in the informal sector.

Note: In Panel A, the earliest available year is 2011 for Costa Rica; 2012 for Brazil; 2014 for Argentina; and 2015 for the Dominican Republic. LAC 10 excludes Uruguay, due to a break in the time series. LAC is the regional average as calculated by the ILO. OECD 33 excludes Belgium, Denmark, Ireland and Sweden, due to breaks in the time series. In Panel B, the latest available year is 2018 for Peru. The earliest available year is 2012 for Costa Rica; 2013 for Ecuador; and 2015 for the Dominican Republic. LAC 8 excludes Brazil, Peru and Uruguay, due to breaks in the time series. LAC regional average comprises 12 Latin American and Caribbean countries, including the 8 focal countries with available data. OECD 32 excludes Belgium, Denmark, Ireland, Sweden and Turkey, due to breaks in the time series.

Source: ILO, https://www.ilo.org/shinyapps/bulkexplorer22/?lang=en&segment=indicator&id=UNE_DEAP_SEX_AGE_RT_A (Panel A),

National Continuous Employment Survey of Costa Rica and https://www.ilo.org/shinyapps/bulkexplorer23/ ?lang=en&segment=indicator&id=UNE_TUNE_SEX_AGE_DUR_DT_A for all countries except Costa Rica (Panel B).

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Informality

Informal work13 provides income where jobs in the formal sector may not be available, but it implies a lack of social protection coverage and a greater degree of vulnerability to job insecurity, poor working conditions and lower earnings (ILO, 2018[16]). Close to 40% of all Latin American workers are not protected by any safety net, but this reaches a level of 65% for informal workers (OECD et al., 2020[17]).

Overall, well over half of workers (57%) were in informal employment across the focal countries in 2019, close to the levels registered in 2010 (58%) (Figure 2.13, Panel A). The prevalence of informality fell over the past decade in half of the focal countries for which data are available, with Peru, Paraguay and Colombia experiencing particularly large reductions (9, 8 and 6 percentage points, respectively). As with other employment indicators so far in this section, there are wide differences in the prevalence of informality across the focal countries, ranging from 24% in Uruguay to 69% in Ecuador.

Informal employment as a share of non-agricultural employment is sometimes preferred to measure informality, as informal work tends to be prevalent in agricultural employment, which can skew the results for countries with large agricultural sectors. This measure is included in the UN Global Framework to monitor SDG target 8.3.1, related to the creation of decent and productive jobs. However, the prevalence of informality among the focal countries drops only slightly when excluding the agricultural sector, to an average rate of 52% on average, with cross-country differences remaining broadly similar to those highlighted in Figure 2.13.

Note: The indicator presents the share of employment that is classified as informal employment in the total economy. For the full description of job categories comprising informal employment, see: https://ilostat.ilo.org/resources/concepts-and-definitions/description-informality/

The earliest available year is 2011 for Brazil and Costa Rica; 2014 for Ecuador; and 2015 for the Dominican Republic. LAC 8 excludes Chile, Mexico and Uruguay, due to breaks in the time series or incomplete time series. LAC regional average comprises 12 Latin American and Caribbean countries, including the 8 focal countries with available data.

Source: ILO, https://www.ilo.org/shinyapps/bulkexplorer23/?lang=en&segment=indicator&id=EMP_NIFL_SEX_ECO_RT_A.

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Earnings

Earnings are a core component of job quality and a major determinant of people’s income and living standards. On average, real wages rose only slightly in the focal group from 2010 to 2019, with hourly wages increasing from 4.9 to USD 5.3 and monthly wages from 821 to USD 906 (as measured in 2017 PPP) (Figure 2.14, Panels A and B).

These average trends mask the fact that wages have tended to increase at a much faster rate for workers at the lower end of the distribution over the last two decades in Latin America (Messina and Silva, 2017[18]). This has led to significant reductions in both wage inequality and in-work poverty (Figure 2.14, Panels C and D). Overall, in the seven focal countries for which data are available, the share of employees living below the poverty line (as calculated by ECLAC) fell from 26% in 2000 to 10% in 2019. On average, for the nine focal countries with data available, the Gini coefficient of labour income fell from 0.49 in 2010 to 0.46 in 2019.

Note: In Panel A, the latest available year is 2017 for Chile and 2016 for Peru. The earliest available year is 2012 for Brazil; 2014 for Ecuador; and 2015 for Chile and the Dominican Republic. LAC 8 excludes Argentina, Colombia and Uruguay, due to breaks in the time series or incomplete time series. The LAC regional average comprises 11 Latin American and Caribbean countries, including the 8 focal countries with available data. OECD 31 excludes Australia, Canada, Colombia, Iceland, Japan and New Zealand, due to breaks in the time series or incomplete time series. In Panel B, the latest available year is 2017 for Chile. The earliest available year is 2011 for Costa Rica and Uruguay; 2012 for Brazil; 2013 for Mexico; and 2014 for Ecuador. LAC 6 excludes Argentina, Chile, Colombia, Peru and Uruguay, due to breaks in the time series or incomplete time series. The LAC regional average comprises 9 Latin American and Caribbean countries, including the focal countries. OECD 28 excludes Australia, Canada, Chile, Colombia, Hungary, Iceland, Japan, Lithuania and New Zealand, due to breaks in the time series or incomplete time series. Panel C shows the share of the employed population whose income is below the absolute poverty line as calculated by ECLAC. In this panel, the latest available year is 2018 for Mexico and 2017 for Chile. The earliest available year is 2001 for Ecuador, 2004 for Peru, 2005 for Paraguay and 2007 for Uruguay. LAC 7 excludes Brazil, Colombia, Costa Rica and the Dominican Republic, due to breaks in the time series. LAC is the regional average for Latin America and the Caribbean calculated by ECLAC. Panel D shows the Gini coefficient of labour income. Labour income includes all sources of income that workers report receiving related to their job, which includes monetary labour income in terms of cash payments and earnings, as well as non-monetary in-kind benefits (transportation, dwelling, food) that are received as job payments and are expressed in terms of money (even if they were received in-kind). For Argentina, the Gini coefficient for urban monetary income is considered instead of total labour income, due to incomplete data. In this panel, the latest available year is 2018 for all countries except for Argentina (2019) and Chile (2017). The earliest available year is 2011 for Chile, 2012 for Brazil and 2014 for Mexico. LAC 9 excludes Argentina and Mexico due to incomplete time series. LAC regional average comprises 13 countries, including the 9 focal countries with available data. Values for Chile include adjustments to national accounts.

Source: OECD calculations based on ILOSTAT, https://ilostat.ilo.org/data and World Development Indicators, https://data.worldbank.org/ (Panel A and B), CEPALSTAT, https://cepalstat-prod.cepal.org/cepalstat/tabulador/ConsultaIntegrada.asp?idIndicador=3336&idioma=e (Panel C) and SEDLAC, see https://www.cedlas.econo.unlp.edu.ar/wp/en/estadisticas/sedlac/estadisticas/#1496165509975-36a05fb8-428b (Panel D).

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Job security

Labour market security – which captures the major risks that workers may face in the labour market and their economic consequences –- is one of the three key aspects of job quality in the OECD Job Quality Framework, alongside with earnings and the work environment (Cazes, Hijzen and Saint-Martin, 2015[19]). The degree of job insecurity perceived by workers, or the level of concern that people feel about the possibility of losing their jobs, is one important indicator in this respect.14 Figure 2.15 shows that while perceived job insecurity has fallen across most of the focal countries since 2000, it remained widespread even before the pandemic. On average across the focal countries, three in five people (60%) in 2018 were concerned about losing their job in the following 12 months. While fully comparable data are not available for OECD countries, Eurofound data from 2015 suggest that only one in five Europeans (16.6%) thought it was likely that they would lose their job in the following 6 months. Perceived job insecurity is highest in Brazil, with 70% of respondents reporting that they were concerned about losing their job in 2018, a share almost unchanged from the 2000 level of 71%.

Note: The indicator reflects the share of people responding “worried” or “very worried” to the question: “How worried would you say you are about becoming unemployed or unemployable in the next twelve months?”. OECD EU 24 average is included to provide a broad indication of the situation in the OECD. The question phrasing and the time period are different from the question included in the Latinobarometro (i.e the OECD EU 24 is the percentage of people that think they might lose their job in the next 6 months and includes Austria, Belgium, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, the Netherlands, Norway, Poland, Portugal, the Slovak Republic, Slovenia, Spain, Switzerland and Turkey).

Source: Latinobarometro (database), http://www.latinobarometro.org/latOnline.jsp; OECD calculations based on the European Working Condition Surveys 2005, 2010 and 2015.

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Work environment

Job quality also encompasses a wide range of non-economic aspects of people’s working environments, ranging from the nature of the work tasks assigned to each worker to the physical and social conditions under which these tasks are carried out, the characteristics of the firm or organisation where work takes place, the scheduling of working time, the prospects that the job provides to workers and the intrinsic rewards associated with the job ( (OECD, 2017[20]); (ECLAC, 2019[5])). Two indicators of the work environment of particular relevance in Latin America are long working hours and occupational injuries.

Very long working hours can negatively impact people’s physical and mental health as well as their work-life balance by leaving little time for family, socialising or unpaid work in the home (OECD, 2017[20]). Close to one in five employees (20.6%) in the focal countries worked 50 hours or more in their primary job in 2018, a share that is almost twice as high as the OECD average rate (10.9%) (Figure 2.16, Panel A). Since 2000, almost all countries in the group experienced a marked decrease in the share of people working very long hours, with the exception of Mexico, where rates have increased by 6 percentage points. In addition, many people in Latin America have more than one job, which further increases the burden of working time. In 2018, 24% of employees in the focal group worked 60 hours or more across all their jobs (ranging from 45.8% in Mexico to 5.8% in Chile, Figure 2.16, Panel B) – roughly six times higher than the OECD average of 4.2%.

Note: Data for Latin American countries refer to actual hours worked, while data for the OECD refer to usual hours worked. The latest available year is 2019 for Costa Rica and 2017 for Chile. The earliest available year is 2001 for Brazil; 2002 for Paraguay; and 2004 for Argentina, Colombia and Peru. In Panel A, LAC 8 excludes Costa Rica, the Dominican Republic and Ecuador, due to incomplete data. OECD Total refers to the percentage of employees working 50 hours or more per week. In Panel B, LAC 8 excludes the Dominican Republic, Ecuador and Paraguay, due to incomplete data.

Source: The Key Indicators of Informality based on Individuals and their Household (KIIbIH) database, oe.cd/kiibih-database and OECD Incidence of employment by usual weekly hours worked (database), https://stats.oecd.org/Index.aspx?DataSetCode=USLHRS_I.

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Workers’ safety is a fundamental aspect of job quality. According to available data, safety levels and the underlying trends differ significantly across the focal countries (Figure 2.17). For example, while Argentina and Chile experienced substantial improvements in the rates of both fatal and non-fatal injuries between 2010 and 2018, in Costa Rica fatal injuries, which were already high, increased further over the same period. Despite an improvement in the rate of non-fatal injuries in Costa Rica, just under one in ten workers experienced a non-fatal injury in the workplace in 2018.

Note: An occupational injury is defined as any personal injury, disease or death resulting from an occupational accident. A fatal occupational injury is one that led to death within one year of the day of the occupational accident. The latest available year is 2017 for Mexico and 2016 for Costa Rica. The earliest available year is 2011 for Chile and Costa Rica. In Panel A, the LAC regional average comprises 6 Latin American and Caribbean countries, including the 4 focal countries where data are available. In Panel B, LAC regional average comprises 5 Latin American and Caribbean countries, including the focal countries.

Source: ILOSTAT, https://ilostat.ilo.org/topics/safety-and-health-at-work/.

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These data are included in the report to emphasise the importance of worker safety for job quality specifically and for well-being overall (as also evidenced by the use of the indicator in target 8.8.1 of the UN 2030 Agenda). However, it should be noted that, as these data rely on administrative records, differences between countries may also be a reflection of the quality of the underlying reporting processes. Under-reporting and double-counting of cases of occupational injury (where data from several registries are combined) may be present, and so cross-country comparisons need to take this into account.

Social protection

Social protection encompasses a broad range of policies and programmes that are designed to reduce the vulnerability of workers or people across the life cycle or in specific contingencies at a given points in time. As such, social protection programmes underpin countries’ social development and, by extension, the well-being of their populations. Social protection is a cross-cutting issue and includes benefits for children and families, maternity, unemployment, employment injuries, sickness, old age, disability and health care. Social protection is closely linked to work and job quality, in that systems of social protection are largely financed by workforce contributions, and access to many social benefits are often linked to formal employment status.

Between 2002 and 2015, social security coverage improved steadily across the LAC region, thanks to favourable economic conditions (leading to an increase in employment overall, and in formal employment specifically) and to government efforts to prioritise the reduction of poverty and vulnerability, but large gaps remain (ECLAC, 2018[2]).

Given the amplitude and heterogeneity of social protection systems, a number of indicators are necessary to evaluate coverage in detail. For example, the SDG database includes 12 separate indicators to measure progress towards SDG target 1.3 on implementing nationally appropriate social protection systems for all. Recent data are not available for all these indicators, but the measure of the share of the population covered by at least one social protection scheme does have recent and comparable data for most of the focal countries (although long time series allowing comparison over time are not generally available).

On average, across the focal group countries, only 56% of the population are covered by at least one social protection benefit15 (compared with 88% in the OECD), implying that just over two-fifths of people in the focal countries have no social protection coverage at all (Figure 2.18). There are wide differences across countries, with coverage rates in Uruguay exceeding OECD levels (93.8%), while in Peru, Paraguay and Ecuador barely one-third of the population is covered by at least one benefit.

Note: The indicator reflects the proportion of persons effectively covered by a social protection system, including social protection floors. It also reflects the main components of social protection: child and maternity benefits, support for persons without a job, persons with disabilities, victims of work injuries and older persons. Effective coverage of social protection is measured by the number of people who are either actively contributing to a social insurance scheme or receiving benefits (contributory or non-contributory). Data is collected by the ILO using the Social Security Inquiry questionnaire, which are filled in direct collaboration with government agencies – Ministries of labour, ministries of finance, social protection institutions and others.

Source: ILO, UN DESA Global SDG Indicator Database, indicator 1.3.1, https://unstats.un.org/sdgs/indicators/database/.

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Impact of COVID-19

As has already been shown in this section, the pandemic has had a pronounced impact on employment and unemployment in the region, with a 9 percentage-point drop in the average employment rate of the seven focal countries with available data, and a 3.6 percentage-point increase in unemployment between 2019 and 2020. For both indicators, the magnitude of the change has been much higher for the LAC countries than for the OECD average. Overall, the rise in unemployment across the region is lower than would be expected given the magnitude of GDP contraction, since many people of working age dropped out of the labour force (ECLAC/ILO, 2020[21]). The decline in the labour force therefore reduced pressure on the labour market (ECLAC, 2021[9]).

The lack of social protection for informal workers means that during the pandemic they have been compelled to choose between obeying stay-at-home orders or earning an income. They are at greater risk of infection because of the nature of their work (e.g. domestic workers in private homes, or workers in the hospitality and retail sectors), and less capable of coping with its effects due to low health insurance coverage and lack of access to quality health services. In addition, because of their low incomes, they have limited capacity to withstand prolonged periods of inactivity (ECLAC, 2020[22]; OECD et al., 2020[17]). The pandemic will hence not only exacerbate the vulnerability and deprivation of informal workers, but also risks increasing the share of informal employment in total employment due to dismissals and layoffs in the formal sector. Projections from the Inter-American Development Bank show that the informality rate may reach 62% across Latin America as a whole as a result of the pandemic, up from 54% in 2016 (ILO, 2018[16]; Altamirano et al., 2020[23]). As formal employment becomes more difficult to find in the context of the COVID-19 pandemic, and more people turn to informal work (often as self-employed workers), it is likely that in-work poverty will rise in the immediate future.

Issues for statistical development

Labour force surveys are widespread across the LAC region, and high-quality and comparable data on employment, unemployment, length of unemployment, underemployment, working hours and earnings are available relatively easily.

The share of informality is generally captured through questions included in household surveys, although by its very nature informal work is less easy to measure given its fluidity and lack of visibility. The ILO defines the informal economy as “all economic activities by workers or economic units that are – in law or practice – not covered or sufficiently covered by formal arrangements”, and defines informal employment as “the total number of informal jobs, whether carried out in formal sector enterprises, informal sector enterprises, or households, during a given reference period” (ILO, 2012[24]). This broad definition has been used to generate cross-country estimates of the size of informality, but the flexibility of the methodology means that national approaches are not always comparable. For example, Colombia defines informal workers based on the size of the business and the occupational category, Peru based on whether workers have access to health care, and Argentina, Costa Rica and Paraguay based on access to overall social protection more generally (INE, 2019[25]). Given the importance of informal work in the region, more comparable statistics would help support more effective and better-targeted policy making for supporting workers’ transition to formality.

Other measures of job quality are also important to develop in the Latin American context. For example, the OECD Job Quality Framework (Cazes, Hijzen and Saint-Martin, 2015[19]) highlights labour market security and the quality of the working environment, in addition to earnings, as the major drivers of job quality. It emphasises the fact that where social insurance schemes are absent or weak, and where there is a risk of very low pay (as is the case in Latin America), overall labour market insecurity is underestimated when only the risk of unemployment is considered. In order to get a more relevant and complete measure of labour market security, the Framework proposes measuring both the expected earnings loss associated with unemployment (including the degree of mitigation, if any, provided by government safety nets) as well as the prevalence of pay below a given threshold. The OECD Jobs Strategy also considers job quality as a central policy priority, while highlighting the importance of adaptability and resilience for good labour market and economic performance. The strategy provides key policy recommendations, organised around three broad principles that are relevant in the Latin American context: 1) promote an environment in which high-quality jobs can flourish; 2) prevent labour market exclusion and protect individuals against labour market risks; and 3) prepare for future opportunities and challenges in a rapidly changing labour market (OECD, 2018[26]).

Unsafe job conditions, as represented by the prevalence of occupational injuries in this section, are an extreme manifestation of a low-quality working environment. The OECD Guidelines on Measuring the Quality of the Working Environment list other factors too, such as the social environment, organisational culture, and intrinsic motivation as important features of the working environment (OECD, 2017[20]), while the OECD Well-being Framework includes a measure of job strain, which is defined as a situation where the job demands that are experienced by workers (i.e. physical demands, work intensity, inflexible working hours) exceed the resources available to them (i.e. task discretion, training, career advancement) (OECD, 2020[1]). All these measures rely on comparable surveys that probe workers about different aspects of their working environment. Data from the OECD Job Quality database show that around one in three workers experienced job strain in Mexico and Chile in 2015 (29% and 28% respectively), which is similar to the OECD average rate (OECD, 2020[1]). These kinds of measures would be useful to produce on a comparable basis for countries in the region.

Finally, subjective measures can provide useful information about the quality of people’s jobs. This section uses a subjective measure to show the levels of perceived job insecurity in the region. Comparable measures of subjective job satisfaction could also provide valuable insights on job quality.

Housing

Housing is a major element of people’s current well-being that has been identified as such in international law (e.g. the Universal Declaration of Human Rights and the International Covenant on Economic, Social and Cultural Rights). Housing is essential for shelter and for offering a sense of safety, privacy and personal space (OECD, 2011[14]). Good housing conditions are also essential for people’s health and affect childhood development (WHO, 2018[27]).

Housing is among Latin America’s major obstacles in its path to sustainable development, after decades of rapid urbanisation and the expansion of slums. The region is one of the most urbanised on the planet, with 4 out of 5 people (81%) living in an urban area in 2018 (UNDESA, 2018[28]).16 It is also the region with the largest share of the population concentrated in megacities (of 10 million inhabitants or more), with six of them (Buenos Aires, Mexico City, Sao Paulo, Rio de Janeiro, Bogota and Lima) accounting for 14% of the region’s population (UNDESA, 2018[28]). Over the period between 1950 and 1990, the share of the population living in urban areas increased from 40 to 70%; since then, the pace of urbanisation has slowed to an annual growth rate of less than 2%, which corresponds to the rate of population growth (IADB, 2016[29]). Population forecasts estimate that this trend will continue in the coming decades, with urbanisation approaching 85% by 2030 and then stabilising thereafter (UN-Habitat, 2012[30]; IADB, 2016[29]).

As a consequence of the inability of both the formal housing market and of government policies to cope with this process, a rising share of urban residents are living in slums. The demand for serviced land to accommodate urban residents has surpassed the capacity to supply it (Gilbert, 2000[31]), and governments have struggled to develop mechanisms to finance serviced land or affordable housing for lower-income groups. Lack of land planning and policy has also considerably limited the supply of low-cost housing. As a result, housing prices have risen to levels that make housing unaffordable to large parts of the population, in particular to those facing difficulty making ends meet (IADB, 2016[29]).

Despite this challenging context, indicators of housing conditions show signs of improvement, as both the share of the population living in slums and housing density have decreased in the past two decades. Access to services such as safely managed drinking water, sanitation and the internet have improved overall, but wide gaps between countries in the focal group persist. Housing deprivation in Latin America has added to the burdens and psychosocial strains imposed by social distancing and confinement during the COVID-19 pandemic, whilst also complicating the isolation of symptomatic individuals from other households and community members. It has also put a spotlight on the enduring issue of housing affordability: the crisis may increase the number of homeless people, particularly in the region’s large cities. Finally, high-speed internet access at home was essential to minimise some of the disruptions created by the sanitary crisis, yet the digital divide among focal group countries shows that some people are being left behind.

Housing conditions and overcrowding

Despite some clear progress since 2000, poor quality housing is symptomatic of widespread inequality in Latin America and the Caribbean, and the region’s cities are strongly segregated along socio-economic lines. While urban segregation is not unique to Latin America, the relatively small size of the middle class in the region, and the fact that income inequality is characterised by a high concentration of income at the very top of the distribution and a large share of the population living in poverty, means that differences in housing conditions can be stark even within neighbourhoods, with exclusive gated communities abutting informal settlements (Sánchez-Ancochea, 2021[13]). Housing quality is not only an issue for urban areas, of course, and housing disparities between urban and rural areas are explored more in Chapter 5.

From Mexico City to Buenos Aires, slums and informal settlements are generally self-built in the only available urban spaces, i.e. the ones at greatest risk to natural hazards, where crime, vulnerability and poverty are most common, creating barriers to housing improvement (McTarnaghan et al., 2018[32]). Slum dwellings also tend to be built from low-quality or unsafe construction materials and are often excluded from the provision of sanitation and essential services. Figure 2.19 shows that over the past two decades, Latin American countries have made substantial progress in reducing the share of people living in slums. In 2018, just under one in five people (17%) lived in slums in the eight Latin American countries for which data are available, compared to roughly one in four (23%) in 2000. Argentina and Brazil have experienced sharp reductions in the share of people living in slums, which halved from over 30% in 2000 to around 15% in 2018. Despite the reduction in the share of slumdwellers, the absolute number of such people is higher today than it was 20 years ago (IADB, 2016[29]).17 There are also significant differences between countries: in Peru, one in three people (33%) lived in a slum or informal settlement in 2018, compared with fewer than one in twenty (4%) in Costa Rica. There are also differences in trends: whereas the share of the population living in slums declined overall in the last two decades (both across the Latin American region as a whole and in most focal countries), in Chile, Paraguay and Ecuador there was relatively little change, while in Colombia the share living in slums actually rose (from 22% to 28%) (Figure 2.19).

Note: The earliest available year is 2005 for Chile, Costa Rica, Ecuador and Paraguay. LAC 10 average excludes Uruguay, as data are not available. LAC is regional average as calculated by the United Nations DESA. Slums/informal settlements are defined as “households whose members suffer one or more of the following ‘household deprivations’: 1) Lack of access to improved water source, 2) Lack of access to improved sanitation facilities, 3) Lack of sufficient living area, 4) Lack of housing durability and 5) Lack of security of tenure”. (UN-Habitat, 2020[33]).

Source: UN DESA Global SDG Indicator Database, indicator 11.1.1, https://unstats.un.org/sdgs/indicators/database/.

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Overcrowding is another fundamental aspect of housing deprivation, which can have negative impacts on health (contributing to the spread of respiratory disease, tuberculosis and allergies), mental health and child development. For example, overcrowding can contribute to children’s lack of concentration when doing homework or even playing, thus affecting their academic performance and contributing to failure in school (Santos, 2019[34]). Comparable time series are not widely available in the region (as with many other indicators of housing quality and affordability), but measures of housing density can give an idea of relative levels of overcrowding and its overall trends (Figure 2.20). Excluding slums and informal settlements, the share of households where more than two people share the same bedroom is 21% on average across countries in the focal group. Levels are particularly high in Paraguay (33%) and Peru (34%) (Figure 2.20, Panel A). Conversely, in Uruguay, Costa Rica and Chile, they are at least three times lower, at around 10% or below.

In the nine focal group countries for which data are available, there is an average of 0.9 people per room, close to the wider regional average (1.0) (Figure 2.20, Panel B). Barring Chile, where housing density has remained stable (0.8), it has decreased over the past two decades and sometimes quite significantly: the number of people per room has dropped by 60% in two decades in Ecuador, by 41% in Paraguay and by 30% in Colombia. In OECD data sources, housing density is calculated differently, in order to consider the differing needs of household with different compositions: in 2017, 12% of OECD households were living in overcrowded conditions, on average, compared to 34% in Mexico and 9% in Chile (OECD, 2020[1]). Further evidence suggests that household size is falling in Latin America, and that the phenomenon is driven by the desire for independence among younger adults, the presence of fewer children per household linked to the rising cost of living, and a growing number of seniors living by themselves (Euromonitor international, 2018[35]). Moreover, the region’s rapid urbanisation has also swayed preferences towards apartments rather than single-family houses – traditionally the dominant type of dwelling in the region – especially among the younger generation (Euromonitor international, 2018[35]).

Note: In Panel A, the latest year available is 2017 for Chile and 2018 for Mexico. LAC 10 excludes Argentina, as data are not available at the national level. The LAC regional average compromises 14 countries, including the 10 focal countries where data are available. Urban slums and informal settlements are not included in the indicator. One or two people living in a one-room/studio apartment are not considered deprived for the purpose of calculating the indicator. Children aged 2 or less are not added to the number of persons per household, as it is common for them to share a bedroom with their parents. In Panel B, the earliest available year is 2001 for Brazil, Colombia, Costa Rica and Paraguay; 2003 for Ecuador and Peru; 2004 for Argentina; and 2006 for Uruguay. The LAC regional average comprises 14 countries, including the 11 focal countries.

Source: ECLAC Statistics (not available online, Panel A). Socio-Economic Database for Latin America and the Caribbean (CEDLAS and The World Bank), https://datacatalog.worldbank.org/dataset/socio-economic-database-latin-america-and-caribbean (Panel B).

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Access to services

A lack of basic services, such as adequately managed drinking water or having handwashing facilities, is a clear sign of poor quality of housing as well as posing a high risk to health. While substantial progress has been made in increasing access to clean drinking water and sanitation, millions of Latin Americans still lack these services, particularly in rural areas (World Bank, 2019[36]).

Despite large disparities among the focal countries in access to safely managed drinking water services, the top-performing countries are approaching 100%, and the bottom-performing countries are slowly but steadily improving too. On average, this indicator improved by around 7 percentage points in the seven focal group countries where data are available, to reach 70% of the population, on average, across the countries. Nevertheless, this level is still 25 percentage points below the OECD average of 95%. A large gap exists between Chile and Costa Rica, where over 90% of people have access to safely managed drinking water services, and Peru and Mexico, where this share is 50% or less, with relatively little improvement since 2000 (Figure 2.21, Panel A). Colombia and Ecuador are both slightly above average, yet lag behind Costa Rica and Chile by 20 percentage points or more. When looking at trends across the focal group, the share of the population with access to safe drinking water has increased twice as much in Paraguay (up 15 percentage points) as for the LAC 7 average (7 percentage points), while Paraguay still falls below the focal and regional averages.

In most of these seven focal group countries, barely half of the population has access to improved sanitation services. The indicator shown in Figure 2.21, Panel B, tracks the share of the population that is using an improved sanitation facility – i.e. one that is not shared with other households, and where the excreta produced are either treated and disposed in situ, stored temporarily and then emptied and transported to treatment off-site, or transported through a sewer with wastewater and then treated off-site. Standing almost 40 percentage points below the OECD average (87%), the focal group average (48%) hides heterogeneous performances across countries: the share of the population with access to improved sanitation services is currently 60 percentage points higher in Chile (77%) than in Colombia (17%). Mexico, Peru and Chile are the three countries to have made most progress over the past two decades, although the level in Peru still remains below half of the population (43%). Levels have not declined in any of the focal group countries over this period, though they have remained relatively stable in Ecuador, at around 42%; improvements in Colombia have been small relative to those among other countries in the region.

Note: In Panel A, LAC 7 average excludes Argentina, Brazil, the Dominican Republic and Uruguay, as data are not available. In Panel B, LAC 7 average excludes Argentina, Costa Rica, the Dominican Republic and Uruguay, as data are not available. LAC is the Latin America and Caribbean regional average calculated by the UN DESA.

Source: WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (2019), as reported by the UN DESA Global SDG Indicator Database, indicator 6.1.1, https://unstats.un.org/sdgs/indicators/database/ (Panel A) and UN DESA Global SDG Indicator Database, indicator 6.2.1, https://unstats.un.org/sdgs/indicators/database/ (Panel B).

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Large gains have also been made in access to the Internet in households since 2009, though this remains highly unequal among the focal group countries. Internet access in the home can support social connections and provide access to both job and learning opportunities as well as to both public and private goods and services (OECD, 2020[1]). In 2019, 50% of households in the focal group of countries, on average, had Internet access at home, with levels over three times higher in Costa Rica than in the Dominican Republic (Figure 2.22). The overall trend in the focal group indicates gradual progress in households’ Internet access, with a considerable leap (16 percentage points) in 10 years. However, these improvements have also been unevenly distributed. For example, the level has remained relatively stable in Paraguay over the past decade. Meanwhile, Chile experienced the greatest increase in Internet access, by almost 45 percentage points.

Note: Data for Argentina refers to urban areas only. The latest available year is 2017 for Chile and Ecuador, and 2018 for Colombia and Mexico. The earliest year available is 2012 for Mexico, and 2013 for Colombia, Costa Rica, the Dominican Republic, Ecuador, Paraguay, Peru and Uruguay. LAC 9 excludes Argentina and Brazil, due to incomplete time series. LAC regional average comprises 15 Latin American and Caribbean countries, including the 9 focal countries with available data. For countries for which the source is the ECLAC Household Survey Data Bank (Banco de Datos de Encuestas de Hogares (BADEHOG): The age from which the ICT indicators are measured varies from country to country. In Paraguay, the population from 10 years of age is included; in Chile, Colombia, Costa Rica and Ecuador from the age of 5; in Peru and Uruguay from 6 years of age. The period over which the measurement of Internet use is made, from the date of the survey, varies from country to country. In Costa Rica, Paraguay and Uruguay, questions were asked about the use of the Internet in the last 3 months; in Chile and Ecuador in the last 12 months.

Source: ECLAC Statistics, ECLAC Household Survey Data Bank (Banco de Datos de Encuestas de Hogares (BADEHOG)) for Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Paraguay, Peru, and Uruguay; ITU World Telecomunication / ICT Indicators Database 2020, https://www.itu.int/en/ITU-D/Statistics/Pages/publications/wtid.aspx, for all the other countries.

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Impact of COVID-19

Housing deprivation is an important factor shaping the spread of COVID-19 and affecting the ability of people to protect themselves against it. In Latin America, initial cases of the pandemic were mainly associated with high socio-economic status and travel abroad. However, after this initial phase, the greatest risk of exposure to COVID-19 was among individuals living in overcrowded housing, often with little or no access to sanitation and water (Lustig and Tommasi, 2020[37]). Access to basic sanitation is still a challenge in some countries of the focal group (Colombia, Ecuador and Peru in particular, see Figure 2.21, Panel B), and it is important for containing the spread of the virus between households living in close proximity. Quarantining due to fear of passing the virus to family members poses serious difficulties in conditions of overcrowding: exposure to the virus despite people’s intentions is indeed a reality in Latin America (UN, 2020[38]), particularly when considering people’s reduced capacities to abide by social distancing measures. Evidence from June 2020 suggests that in Rio de Janeiro, the area with the highest incidence of COVID-19 cases was in Cidade de Deus, one of Brazil’s largest slums, where over one in four people tested were found to be infected (28%). Similar rates (24%) were found in Rocinha, another large slum in Rio that is home to at least 100 000 people (Rio Prefeitura, 2020[39]).

Beyond housing quality, the COVID-19 pandemic has also put a spotlight on limited housing affordability (OECD, 2020[40]; OECD, 2020[41]). As mentioned above, while people living in poor quality housing or in unsafe living conditions have faced elevated health and safety risks, workers experiencing sudden economic losses have struggled to cover their monthly rents, mortgages and utility payments without assistance (OECD, 2020[41]). This may lead to repossessions, displacements, or even homelessness, cutting people off and making them more vulnerable (Vera et al., 2020[42]). Without a roof, they have no means of self-isolating, and where they do have shelter available it is typically in hostels with limited means of isolation or protection.

In the metropolitan areas of Latin America, evidence suggests a pattern of “over-concentration” of COVID-19 infections and deaths, albeit with certain exceptions. This is especially the case in countries where 30% or more of the population live in “Major Administrative Divisions”, i.e. territories where the most populous cities are located (divisiones administrativas mayores, in Spanish) – as in Argentina, Chile, Costa Rica, Paraguay and Peru. Uruguay is a major exception to this pattern (ECLAC, 2021[9]).

Closing the digital divide between households and countries is a major challenge in Latin America and the Caribbean (Figure 2.22). Reliable, high-speed Internet access at home is essential for large-scale teleworking and home schooling. Between the first and second quarters of 2020, the use of teleworking solutions in Latin America tripled, and distance education grew by over 60% (ECLAC, 2020[43]). High-speed Internet access also provides an important source of public information, and acts as a critical lifeline to connect people who are socially isolated or vulnerable, and who may need remote medical assistance or community support (e.g. delivery of groceries and medicines). Although 67% of the region’s population had an Internet connection, the remaining third had limited or no access to digital technologies due to their social and economic status – in particular, their location and age (ECLAC, 2020[43]). For instance, 46% of children aged 5-12 live in households with no connectivity (ECLAC, 2020[43]). The COVID-19 pandemic and the ensuing crises therefore risk amplifying existing inequalities while, provided services can be better delivered, digital connectivity can minimise some of the disruptions created (Basto-Aguirre, Cerutti and Nieto-Parra, 2020[44]).

Governments can provide immediate support for lost employment and income, extend sick pay to excluded workers, and provide immediate shelter for homeless populations (OECD, 2020[40]). However, it is much harder to address overcrowded housing conditions and provide access to basic sanitation and digital services on a short-term basis. In this sense, poor housing conditions represent a systemic risk for the impacts of health crises, requiring a longer-term government response to build resilience. In the shorter term, people living in overcrowded and/or unsanitary conditions need to be prioritised for hospitalisation or other forms of out-of-home care in order to protect other vulnerable household members. Similarly, those living alone in very isolated circumstances are likely to need additional forms of community support and care during periods when staying at home is advised (OECD, 2020[45]).

Issues for statistical development

While information on the quality of housing material is generally included in censuses and household surveys in the region (especially as it is often included as a component of multidimensional poverty indices), definitions and methods are not widely comparable across countries (Santos, 2019[34]). Further harmonisation is also needed for calculating housing overcrowding rates worldwide. In this report, the indicator for Latin American countries focuses on people per room, whereas housing density data in the OECD is calculated with a measure that reflects the differing needs of households with different compositions. According to the preferred OECD measure, a house is considered overcrowded if less than one room is available: for each couple in the household; for each single person aged 18 or older; for each pair of people of the same gender between 12 and 17; for each single person between 12 and 17 not included in the previous categories; and for each pair of children under the age of 12 (Eurostat, 2019[46]; OECD, 2020[1]). Moreover, cross-country differences exist in how rooms are defined, kitchens in particular, and in how minimum space restrictions are applied. Kitchens are counted as rooms in Chile and Mexico, but in most OECD countries rooms exclude kitchens used exclusively for cooking. In addition, European countries exclude spaces of less than four square metres. This implies that overcrowding rates may be biased upwards in European sources, relative to those from Chile and Mexico (since fewer household spaces are counted as rooms) (OECD, 2020[1]).

Harmonised data on access to services and amenities (such as transport, medical centres, schools, etc.) are being developed on an OECD-wide basis but are not yet available. As mentioned above, internationally comparable data on homelessness (a measure of extreme housing deprivation) and people’s perceptions of their housing conditions are also lacking (OECD, 2020[1]).

Housing affordability is a crucial determinant of access to good housing. In Latin America, a relatively high house price-to-income ratio combined with inaccessible housing finance are the major determinants driving households to resort to informal solutions without the benefit of planning and safety regulations (UN-Habitat, 2016[47]). Conceptually, the lack of housing affordability is a measure of inadequate housing – since the cost of housing should not prevent the occupants from meeting their daily needs and enjoying their human rights (UN-Habitat, 2020[33]). More generally, this remains a challenge that may affect people across income levels, with strong a negative impact on territorial inequality. SDG 11.1.1 sets out a measure of inadequate housing, which is defined as the “proportion of households with net monthly expenditure on housing exceeding 30% of the total monthly income of the household” (UN-Habitat, 2020[33]). However, housing affordability may also be measured using the house rent-to-monthly household income ratio (HRIR) and the house price-to-annual household income ratio (HPIR). Housing is considered affordable when the HRIR is 25% or less and the HPIR is 3.0 or less (UN-Habitat, 2020[33]). Comparable data on housing affordability, housing prices and housing cost overburden (e.g. the share of households with housing costs such as rent, mortgages or other charges exceeding a certain share of income) would greatly enrich the understanding of housing quality in the region.

Developing comparable indicators for housing and land tenure would also contribute to a better understanding of housing security. A secure tenure guarantees that people can access and enjoy their home without fear of forced evictions, and enables them to improve their housing and living. It also gives parents the right to pass their land or housing to their children and is considered to contribute to poverty reduction and to enhance economic development and the sustainable use of resources as well as social stability (Santos, 2019[34]).

Capturing housing inequalities among different population groups (such as by sex, age or education) is challenging, because these data are typically reported at the household level. One possibility would be to consider differences between groups according to the status of the head of the household. Regional inequalities are also particularly important in the housing domain, not least given the important role that location plays in determining access to services (OECD, 2020[1]). Thus, efforts should be made to collect population-representative data on housing quality at the sub-national level.

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Notes

← 1. Throughout this report, the eleven focal countries refer to Argentina, Brazil, Chile, Colombia, Costa Rica, the Dominican Republic, Ecuador, Mexico, Paraguay, Peru and Uruguay. Gross national income (GNI) is defined as gross domestic product, plus net receipts from abroad of compensation of employees, property income and net taxes less subsidies on production. For more details see: https://data.oecd.org/natincome/gross-national-income.htm https://data.oecd.org/natincome/gross-national-income.htm

← 2. For example, the Report of the Commission on the Measurement of Economic Performance and Social Progress (known also as the Stiglitz-Sen-Fitoussi report, (Stiglitz, Sen and Fitoussi, 2009[48])) made the joint measurement of income, consumption and wealth one of its top recommendations for better understanding individual and household well-being. The 2018 follow-up report, For Good Measure, repeated this recommendation (Stiglitz, Fitoussi and Durand, 2018[11]). In the How’s Life? series, where the OECD well-being framework is applied to OECD member countries, the corresponding dimension is named Income and Wealth rather than Income and Consumption.

← 3. The region is an important supplier of a large number of products for the agricultural, mining and energy industries that make up the basket of international commodities, whose nominal value increased substantially from the early 2000s to the mid-2010s. Oil prices in current US dollars almost quadrupled between 2003 and 2013, and metal prices tripled, while food prices doubled and prices of agricultural products rose by about 50% (Gruss, 2014[49]).

← 4. The (national account) measure of household consumption includes the expenditure of non-profit institutions serving households (NPISHs), such as hospitals, universities, etc.

← 5. The estimates are derived from models based on time-series regressions using GDP per capita growth as a predictor for poverty. For a full explanation of the method, see Annex I. A1 in (ECLAC, 2021[9]).

← 6. For example, the preferred headline measure of average income used in the How’s Life? series (OECD, 2020[1]), net household adjusted disposable income, is not possible to calculate in a comparable manner. Household net adjusted disposable income is household income per capita, net of taxes and adjusted for the value of the in-kind services, such as education and health care, that are provided by governments free of charge or at subsidised prices.

← 7. The Central Bank of Costa Rica has carried out a regular Household Financial Survey since 2007; Mexico implemented the National Survey on Household Living Standards (ENNViH) in 2002, 2005–2006 and 2009–2012; Chile carried out the Household Financial Survey (EFH) in 2007, 2011–2012, 2014 and 2017; and Uruguay ran the Financial Survey of Uruguayan Households (EFHU), covering 2012–2014 and 2017 (ECLAC, 2018[2]).

← 8. This is based on a measure of households’ liquid financial assets and classifies households as economically insecure if they have liquid financial assets equivalent to less than 25% of the national relative income poverty line (which is in turn defined as 50% of the national median income).

← 9. Equivalised income refers to household income that is measured by pooling the income streams of each household member and then attributing this to each member, based on an “adjustment” to reflect differences in needs across households of different sizes and structures.

← 10. For example, a study about emergency benefits paid during the pandemic showed that household surveys captured overall spending of between BRL 23.6 billion and BRL 28.6 billion per month, whereas the Ministry of Citizenship reported spending of BRL 46 billion (Ferreira de Souza, 2021[50]).

← 11. This has been recognised by the ILO’s notion of “decent work”, as well as by the OECD definition of job quality, which focuses on earnings, labour market security (i.e. risks of job loss and the economic cost for workers) and the quality of the working environment (i.e. non-economic aspects of jobs such as the nature and content of the work performed, working-time arrangements and workplace relationships) (Cazes, Hijzen and Saint-Martin, 2015[19]). The 2018 OECD Job Strategy framework considers job quantity, job quality and labour market inclusiveness as central policy priorities (OECD, 2018[26]). In this perspective, very long working hours (whether paid or unpaid) can be detrimental to people’s well-being.

← 12. Values for the OECD average in this section are those calculated by the ILO rather than the OECD in order to ensure comparability. In general, OECD employment figures refer to the population aged 25-64, whereas the ILO data are for the population aged 25 years and above.

← 13. In 2015, ILO Recommendation no. 204 concerning the transition from the informal to the formal economy describes the “informal economy” as referring to all economic activities by workers and economic units that are – in law or in practice – not covered or insufficiently covered by formal arrangements. The informal economy does not cover illicit activities (OECD/ILO, 2019[51]).

← 14. The OECD measures labour market insecurity as the expected monetary loss that an employed person would incur upon becoming and staying unemployed, expressed as a share of previous earnings. This loss depends on the risk of becoming unemployed, the expected duration of unemployment, and the mitigation against these losses provided by unemployment benefits (effective insurance) (OECD, 2020[1]).

← 15. The definition of social protection systems in SDG indicator 1.3.1 is broad and includes contributory and non-contributory schemes for children, pregnant women with newborns, people in active age, older persons, victims of work injuries and persons with disabilities.

← 16. The urban and city estimates presented in (UNDESA, 2018[28]) are based on definitions used by countries for statistical purposes, and therefore the criteria to define an “urban area” may vary (ranging from administrative designations to demographic characteristics such as population size or population density and more “functional” characteristics such as the existence of sewage systems) (UNDESA, 2018[28]).

← 17. UN-Habitat defines the term “slum” as an area that has one or more of the following five characteristics; poor structural quality of housing; overcrowding; inadequate access to safe water; inadequate access to sanitation and other infrastructure; or insecure residential status (UN-Habitat, 2014[52]). Moreover, the Cities Alliance and the United Nations Statistics Division agreed on a more operational definition for “slums”, in view of measuring the indicator for MDG 7 Target 7.D (UN-Habitat, 2020[33]). The agreed definition, used again for SDG indicator 11.1.1., classifies a “slum household” as “one in which the inhabitants suffer one or more of the following ‘household deprivations’: lack of access to improved water source; lack of access to improved sanitation facilities; lack of sufficient living area; lack of housing durability; and lack of security of tenure.”

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