research paper on world poverty

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The Social Consequences of Poverty: An Empirical Test on Longitudinal Data

Carina mood.

Institute for Futures Studies, Box 591, 101 31 Stockholm, Sweden

Swedish Institute for Social Research (SOFI), Stockholm University, Stockholm, Sweden

Jan O. Jonsson

Nuffield College, OX1 1NF Oxford, England, UK

Poverty is commonly defined as a lack of economic resources that has negative social consequences, but surprisingly little is known about the importance of economic hardship for social outcomes. This article offers an empirical investigation into this issue. We apply panel data methods on longitudinal data from the Swedish Level-of-Living Survey 2000 and 2010 (n = 3089) to study whether poverty affects four social outcomes—close social relations (social support), other social relations (friends and relatives), political participation, and activity in organizations. We also compare these effects across five different poverty indicators. Our main conclusion is that poverty in general has negative effects on social life. It has more harmful effects for relations with friends and relatives than for social support; and more for political participation than organizational activity. The poverty indicator that shows the greatest impact is material deprivation (lack of cash margin), while the most prevalent poverty indicators—absolute income poverty, and especially relative income poverty—appear to have the least effect on social outcomes.

Introduction

According to the most influential definitions, poverty is seen as a lack of economic resources that have negative social consequences—this is in fact a view that dominates current theories of poverty (Townsend 1979 ; Sen 1983 ; UN 1995 ), and also has a long heritage (Smith 1776 /1976). The idea is that even when people have food, clothes, and shelter, economic problems lead to a deterioration of social relations and participation. Being poor is about not being able to partake in society on equal terms with others, and therefore in the long run being excluded by fellow citizens or withdrawing from social and civic life because of a lack of economic resources, typically in combination with the concomitant shame of not being able to live a life like them (e.g., Sen 1983 ). Economic hardship affects the standard of life, consumption patterns, and leisure time activities, and this is directly or indirectly related to the possibility of making or maintaining friends or acquaintances: poverty is revealed by not having appropriate clothes, or a car; by not being able to afford vacation trips, visits to the restaurant, or hosting dinner parties (e.g., Mack and Lansley 1985 ; Callan et al. 1993 )—in short, low incomes prevent the poor from living a life in “decency” (Galbraith 1958 ).

The relational nature of poverty is also central to the social exclusion literature, which puts poverty in a larger perspective of multiple disadvantages and their interrelationships (Hills et al. 2002 , Rodgers et al. 1995 ; Room 1995 ). While there are different definitions of the social exclusion concept, the literature is characterized by a move from distributional to relational concerns (Gore 1995 ) and by an emphasis on the importance of social integration and active participation in public life. The inability of living a decent or “ordinary” social life may in this perspective erode social networks, social relations, and social participation, potentially setting off a downward spiral of misfortune (Paugam 1995 ) reinforcing disadvantages in several domains of life. This perspective on poverty and social exclusion is essentially sociological: the playing field of the private economy is social. It is ultimately about individuals’ relations with other people—not only primary social relations, with kin and friends, but extending to secondary relations reflected by participation in the wider community, such as in organizations and in political life (UN 1995 ).

Despite the fact that the social consequences of limited economic resources are central to modern perspectives on poverty and marginalization, this relation is surprisingly seldom studied empirically. Qualitative research on the poor give interesting examples on how the negative effects of poverty works, and portray the way that economic problems are transformed into social ones (Ridge and Millar 2011 ; Attree 2006 ). Such studies, however, have too small sample sizes to generalize to the population, and they cannot tell us much about the range of the problem. The (relatively few) studies that have addressed the association between poverty and social outcomes on larger scale tend to verify that the poor have worse social relations (Böhnke 2008 ; Jonsson and Östberg 2004 ; Levitas 2006 ), but Barnes et al. ( 2002 ) did not find any noteworthy association between poverty (measured as relative income poverty, using the 60 %-limit) and social relations or social isolation. Dahl et al. ( 2008 ) found no relation between poverty and friendships, but report less participation in civic organizations among the poor. All these studies have however been limited to cross-sectional data or hampered by methodological shortcomings, and therefore have not been able to address the separation of selection effects from potentially causal ones.

Our aim in this study is to make good these omissions. We use longitudinal data from the Swedish Level of Living Surveys (LNU) 2000 and 2010 to study how falling into poverty, or rising from it, is associated with outcomes in terms of primary and secondary social relations, including participation in civil society. These panel data make it possible to generalize the results to the Swedish adult population (19–65 in 2000; 29–75 in 2010), to address the issue of causality, and to estimate how strong the relation between economic vulnerability and social outcomes is. Because the data provide us with the possibility of measuring poverty in several ways, we are also able to address the question using different—alternative or complementary—indicators. Poverty is measured as economic deprivation (lack of cash margin, self-reported economic problems), income poverty (absolute and relative), and long-term poverty, respectively. The primary, or core, social outcomes are indicated by having social support if needed, and by social relations with friends and relatives. We expand our analysis to secondary, or fringe, social outcomes in terms of participation in social life at large, such as in civil society: our indicators here include the participation in organizations and in political life.

Different Dimensions/Definitions of Poverty

In modern welfare states, the normal take on the issue of poverty is to regard it as the relative lack of economic resources, that is, to define the poor in relation to their fellow citizens in the same country at the same time. Three approaches dominate the scholarly literature today. The first takes as a point of departure the income deemed necessary for living a life on par with others, or that makes possible an “acceptable” living standard—defined as the goods and services judged necessary, often on the basis of consumer or household budget studies. This usage of a poverty threshold is often (somewhat confusingly) called absolute income poverty , and is most common in North America (cf. Corak 2006 for a review), although most countries have poverty lines defined for different kinds of social benefits. In Europe and in the OECD, the convention is instead to use versions of relative income poverty , defining as poor those whose incomes fall well behind the median income in the country in question (European Union using 60 % and OECD 50 % of the median as the threshold). As an alternative to using purchasing power (as in the “absolute” measure), this relative measure defines poverty by income inequality in the bottom half of the income distribution (Atkinson et al. 2002 ; OECD 2008 ).

The third approach argues that income measures are too indirect; poverty should instead be indicated directly by the lack of consumer products and services that are necessary for an acceptable living standard (Mack and Lansley 1985 ; Ringen 1988 ; Townsend 1979 ). This approach often involves listing a number of possessions and conditions, such as having a car, washing machine, modern kitchen; and being able to dine out sometimes, to have the home adequately heated and mended, to have sufficient insurances, and so on. An elaborate version includes information on what people in general see as necessities, what is often termed “consensual” poverty (e.g., Mack and Lansley 1985 ; Gordon et al. 2000 ; Halleröd 1995 ; van den Bosch 2001 ). Other direct indicators include the ability to cover unforeseen costs (cash margin) and subjective definitions of poverty (e.g., van den Bosch 2001 ). The direct approach to poverty has gained in popularity and measures of economic/material deprivation and consensual poverty are used in several recent and contemporary comparative surveys such as ECHP (Whelan et al. 2003 ) and EU-SILC (e.g., UNICEF 2012 ; Nolan and Whelan 2011 ).

It is often pointed out that, due to the often quite volatile income careers of households, the majority of poverty episodes are short term and the group that is identified as poor in the cross-section therefore tends to be rather diluted (Bane and Ellwood 1986 ; Duncan et al. 1993 ). Those who suffer most from the downsides of poverty are, it could be argued, instead the long-term, persistent, or chronically poor, and there is empirical evidence that those who experience more years in poverty also are more deprived of a “common lifestyle” (Whelan et al. 2003 ). Poverty persistence has been defined in several ways, such as having spent a given number of years below a poverty threshold, or having an average income over a number of years that falls under the poverty line (e.g., Duncan and Rodgers 1991 ; Rodgers and Rodgers 1993 ). The persistently poor can only be detected with any precision in longitudinal studies, and typically on the basis of low incomes, as data covering repeated measures of material deprivation are uncommon.

For the purposes of this study, it is not essential to nominate the best or most appropriate poverty measure. The measures outlined above, while each having some disadvantage, all provide plausible theoretical grounds for predicting negative social outcomes. Low incomes, either in “absolute” or relative terms, may inhibit social activities and participation because these are costly (e.g., having decent housing, needing a car, paying membership fees, entrance tickets, or new clothes). Economic deprivation, often indicated by items or habits that are directly relevant to social life, is also a valid representation of a lack of resources. Lastly, to be in long-term poverty is no doubt a worse condition than being in shorter-term poverty.

It is worth underlining that we see different measures of poverty as relevant indicators despite the fact that the overlap between them often is surprisingly small (Bradshaw and Finch 2003 ). The lack of overlap is not necessarily a problem, as different people may have different configurations of economic problems but share in common many of the experiences of poverty—experiences, we argue, that are (in theory at least) all likely to lead to adverse social outcomes. Whether this is the case or not is one of the questions that we address, but if previous studies on child poverty are of any guidance, different definitions of poverty may show surprisingly similar associations with a number of outcomes (Jonsson and Östberg 2004 ).

What are the Likely Social Consequences of Poverty?

We have concluded that poverty is, according to most influential poverty definitions, manifested in the social sphere. This connects with the idea of Veblen ( 1899 ) of the relation between consumption and social status. What you buy and consume—clothes, furniture, vacation trips—in part define who you are, which group you aspire to belong to, and what view others will have of you. Inclusion into and exclusion from status groups and social circles are, in this view, dependent on economic resources as reflected in consumption patterns. While Veblen was mostly concerned about the rich and their conspicuous consumption, it is not difficult to transfer these ideas to the less fortunate: the poor are under risk of exclusion, of losing their social status and identity, and perhaps also, therefore, their friends. It is however likely that this is a process that differs according to outcome, with an unknown time-lag.

If, as outlined above, we can speak of primary and secondary social consequences, the former should include socializing with friends, but also more intimate relations. Our conjecture is that the closer the relation, the less affected is it by poverty, simply because intimate social bonds are characterized by more unconditional personal relations, typically not requiring costs to uphold.

When it comes to the secondary social consequences, we move outside the realm of closer interpersonal relations to acquaintances and the wider social network, and to the (sometimes relatively anonymous) participation in civil or political life. This dimension of poverty lies at the heart of the social exclusion perspective, which strongly emphasizes the broader issues of societal participation and civic engagement, vital to democratic societies. It is also reflected in the United Nation’s definition, following the Copenhagen summit in 1995, where “overall poverty” in addition to lack of economic resources is said to be “…characterized by lack of participation in decision-making and in civil, social, and cultural life” (UN 1995 , p. 57). Poverty may bring about secondary social consequences because such participation is costly—as in the examples of travel, need for special equipment, or membership fees—but also because of psychological mechanisms, such as lowered self-esteem triggering disbelief in civic and political activities, and a general passivity leading to decreased organizational and social activities overall. If processes like these exist there is a risk of a “downward spiral of social exclusion” where unemployment leads to poverty and social isolation, which in turn reduce the chances of re-gaining a footing in the labour market (Paugam 1995 ).

What theories of poverty and social exclusion postulate is, in conclusion, that both what we have called primary and secondary social relations will be negatively affected by economic hardship—the latter supposedly more than the former. Our strategy in the following is to test this basic hypothesis by applying multivariate panel-data analyses on longitudinal data. In this way, we believe that we can come further than previous studies towards estimating causal effects, although, as is the case in social sciences, the causal relation must remain preliminary due to the nature of observational data.

Data and Definitions

We use the two most recent waves of the Swedish Level-of-living Survey, conducted in 2000 and 2010 on random (1/1000) samples of adult Swedes, aged 18–75. 1 The attrition rate is low, with 84 % of panel respondents remaining from 2000 to 2010. This is one of the few data sets from which we can get over-time measures of both poverty and social outcomes for a panel that is representative of the adult population (at the first time point, t 0 )—in addition, there is annual income information from register data between the waves. The panel feature obviously restricts the age-groups slightly (ages 19–65 in 2000; 29–75 in 2010), the final number of analyzed cases being between 2995 and 3144, depending on the number of missing cases on the respective poverty measure and social outcome variable. For ease of interpretation and comparison of effect sizes, we have constructed all social outcome variables and poverty variables to be dichotomous (0/1). 2

In constructing poverty variables, we must balance theoretical validity with the need to have group sizes large enough for statistical analysis. For example, we expand the absolute poverty measure to include those who received social assistance any time during the year. As social assistance recipients receive this benefit based on having an income below a poverty line that is similar to the one we use, this seems justifiable. In other cases, however, group sizes are small but we find no theoretically reasonable way of making the variables more inclusive, meaning that some analyses cannot be carried out in full detail.

Our income poverty measures are based on register data and are thus free from recall error or misreporting, but—as the proponents of deprivation measures point out—income poverty measures are indirect measures of hardship. The deprivation measure is more direct, but self-reporting always carries a risk of subjectivity in the assessment. To the extent that changes in one’s judgment of the economic situation depend on changes in non-economic factors that are also related to social relations, the deprivation measure will give upwardly biased estimates. 3 As there is no general agreement about whether income or deprivation definitions are superior, our use of several definitions is a strength because the results will give an overall picture that is not sensitive to potential limitations in any one measure. In addition, we are able to see whether results vary systematically across commonly used definitions.

Poverty Measures

• Cash margin whether the respondent can raise a given sum of money in a week, if necessary (in 2000, the sum was 12,000 SEK; in 2010, 14,000 SEK, the latter sum corresponding to approximately 1600 Euro, 2200 USD, or 1400 GBP in 2013 currency rates). For those who answer in the affirmative, there is a follow-up question of how this can be done: by (a) own/household resources, (b) borrowing.
• Economic crisis Those who claim that they have had problems meeting costs for rent, food, bills, etc. during the last 12 months (responded “yes” to a yes/no alternative).
• Absolute poverty is defined as either (a) having a disposable family income below a poverty threshold or (b) receiving social assistance, both assessed in 1999 (for the survey 2000) or 2009 (for the survey 2010). The poverty line varies by family type/composition according to a commonly used calculation of household necessities (Jansson 2000 ). This “basket” of goods and services is intended to define an acceptable living standard, and was originally constructed for calculating an income threshold for social assistance, with addition of estimated costs for housing and transport. The threshold is adjusted for changes in the Consumer Price Index, using 2010 as the base year. In order to get analyzable group sizes, we classify anyone with an income below 1.25 times this threshold as poor. Self-employed are excluded because their nominal incomes are often a poor indicator of their economic standard.
• Deprived and income poor A combination of the indicator of economic deprivation and the indicator of absolute poverty. The poor are defined as those who are economically deprived and in addition are either absolute income-poor or have had social assistance some time during the last calendar year.
• Long - term poor are defined as those interviewed in 2010 (2000) who had an equivalized disposable income that fell below the 1.25 absolute poverty threshold (excluding self-employed) or who received social assistance in 2009 (1999), and who were in this situation for at least two of the years 2000–2008 (1990–1998). The long-term poor (coded 1) are contrasted to the non-poor (coded 0), excluding the short-term poor (coded missing) in order to distinguish whether long-term poverty is particularly detrimental (as compared to absolute poverty in general).
• Relative poverty is defined, according to the EU standard, as having a disposable equivalized income that is lower than 60 % of the median income in Sweden the year in question (EU 2005). 4 As for absolute poverty, this variable is based on incomes the year prior to the survey year. Self-employed are excluded.

Social and Participation Outcomes

Primary (core) social relations.

• Social support The value 1 (has support) is given to those who have answered in the positive to three questions about whether one has a close friend who can help if one (a) gets sick, (b) needs someone to talk to about troubles, or (c) needs company. Those who lack support in at least one of these respects are coded 0 (lack of support).
• Frequent social relations This variable is based on four questions about how often one meets (a) relatives and (b) friends, either (i) at ones’ home or (ii) at the home of those one meets, with the response set being “yes, often”, “sometimes”, and “no, never”. Respondents are defined as having frequent relations (1) if they have at least one “often” of the four possible and no “never”, 5 and 0 otherwise.

Secondary (fringe) Social Relations/Participation

• Political participation : Coded 1 (yes) if one during the last 12 months actively participated (held an elected position or was at a meeting) in a trade union or a political party, and 0 (no) otherwise. 6
• Organizational activity : Coded 1 (yes) if one is a member of an organization and actively participate in its activities at least once in a year, and 0 (no) otherwise.

Control Variables

• Age (in years)
• Educational qualifications in 2010 (five levels according to a standard schema used by Statistics Sweden (1985), entered as dummy variables)
• Civil status distinguishes between single and cohabiting/married persons, and is used as a time-varying covariate (TVC) where we register any changes from couple to single and vice versa.
• Immigrant origin is coded 1 if both parents were born in any country outside Sweden, 0 otherwise.
• Labour market status is also used as a TVC, with four values indicating labour market participation (yes/no) in 2000 and 2010, respectively.
• Global self - rated health in 2000, with three response alternatives: Good, bad, or in between. 7

Table  1 shows descriptive statistics for the 2 years we study, 2000 and 2010 (percentages in the upper panel; averages, standard deviations, max and min values in the lower panel). Recall that the sample is longitudinal with the same respondents appearing in both years. This means, naturally, that the sample ages 10 years between the waves, the upper age limit being pushed up from 65 to 75. Both the change over years and the ageing of the sample have repercussions for their conditions: somewhat more have poor health, for example, fewer lack social support but more lack frequent social relations, and more are single in 2010 (where widows are a growing category). The group has however improved their economic conditions, with a sizeable reduction in poverty rates. Most of the changes are in fact period effects, and it is particularly obvious for the change in poverty—in 2000 people still suffered from the deep recession in Sweden that begun in 1991 and started to turn in 1996/97 (Jonsson et al. 2010 ), while the most recent international recession (starting in 2008/09) did not affect Sweden that much.

Table 1

Descriptive statistics of dependent and independent variables in the LNU panel

N for variables used as change variables pertains to non-missing observations in both 2000 and 2010

The overall decrease in poverty masks changes that our respondents experienced between 2000 and 2010: Table  2 reveals these for the measure of economic deprivation, showing the outflow (row) percentages and the total percentages (and the number of respondents in parentheses). It is evident that there was quite a lot of mobility out of poverty between the years (61 % left), but also a very strong relative risk of being found in poverty in 2010 among those who were poor in 2000 (39 vs. 5 % of those who were non-poor in 2000). Of all our respondents, the most common situation was to be non-poor both years (81 %), while few were poor on both occasions (6 %). Table  2 also demonstrates some small cell numbers: 13.3 % of the panel (9.4 % + 3.9 %), or a good 400 cases, changed poverty status, and these cases are crucial for identifying our models. As in many panel studies based on survey data, this will inevitably lead to some problems with large standard errors and difficulties in arriving at statistically significant and precise estimates; but to preview the findings, our results are surprisingly consistent all the same.

Table 2

Mobility in poverty (measured as economic deprivation) in Sweden between 2000 and 2010

Outflow percentage (row %), total percentage, and number of cases (in parentheses). LNU panel 2000–2010

We begin with showing descriptive results of how poverty is associated with our outcome variables, using the economic deprivation measure of poverty. 8 Figure  1 confirms that those who are poor have worse social relationships and participate less in political life and in organizations. Poverty is thus connected with both primary and secondary social relations.

The relation between poverty (measured as economic deprivation) and social relations/participation in Sweden, LNU 2010. N = 5271

The descriptive picture in Fig.  1 does not tell us anything about the causal nature of the relation between poverty and social outcomes, only that such a relation exists, and that it is in the predicted direction: poor people have weaker social relations, less support, and lower levels of political and civic participation. Our task now is to apply more stringent statistical models to test whether the relation we have uncovered is likely to be of a causal nature. This means that we must try to rid the association of both the risk for reverse causality—that, for example, a weaker social network leads to poverty—and the risk that there is a common underlying cause of both poverty and social outcomes, such as poor health or singlehood.

The Change Model

First, as we have panel data, we can study the difference in change across two time-points T (called t 0 and t 1 , respectively) in an outcome variable (e.g., social relations), between groups (i.e. those who changed poverty status versus those who did not). The respondents are assigned to either of these groups on the grounds of entering or leaving poverty; in the first case, one group is non-poor at t 0 but experiences poverty at t 1 , and the change in this group is compared to the group consisting of those who are non-poor both at t 0 and t 1 . The question in focus then is: Do social relations in the group entering poverty worsen in relation to the corresponding change in social relations in the group who remains non-poor? Because we have symmetric hypotheses of the effect of poverty on social outcomes—assuming leaving poverty has positive consequences similar to the negative consequences of entering poverty—we also study whether those who exit poverty improve their social outcomes as compared to those remaining poor. We ask, that is, not only what damage falling into poverty might have for social outcomes, but also what “social gains” could be expected for someone who climbs out of poverty.

Thus, in our analyses we use two different “change groups”, poverty leavers and poverty entrants , and two “comparison groups”, constantly poor and never poor , respectively. 9 The setup comparing the change in social outcomes for those who change poverty status and those who do not is analogous to a so-called difference-in-difference design, but as the allocation of respondents to comparison groups and change groups in our data cannot be assumed to be random (as with control groups and treatment groups in experimental designs), we take further measures to approach causal interpretations.

Accounting for the Starting Value of the Dependent Variable

An important indication of the non-randomness of the allocation to the change and comparison groups is that their average values of the social outcomes (i.e. the dependent variable) at t 0 differ systematically: Those who become poor between 2000 and 2010 have on average worse social outcomes already in 2000 than those who stay out of poverty. Similarly, those who stay in poverty both years have on average worse social outcomes than those who have exited poverty in 2010. In order to further reduce the impact of unobserved variables, we therefore make all comparisons of changes in social outcomes between t 0 and t 1 for fixed t 0 values of both social outcome and poverty status.

As we use dichotomous outcome variables, we get eight combinations of poverty and outcome states (2 × 2 × 2 = 8), and four direct strategic comparisons:

• Poverty leavers versus constantly poor, positive social outcome in 2000 , showing if those who exit poverty have a higher chance of maintaining the positive social outcome than those who stay in poverty
• Poverty leavers versus constantly poor, negative social outcome in 2000 , showing if those who exit poverty have a higher chance of improvement in the social outcome than those who stay in poverty
• Poverty entrants versus never poor, positive social outcome in 2000 , showing if those who enter poverty have a higher risk of deterioration in the social outcome than those who stay out of poverty, and
• Poverty entrants versus never poor, negative social outcome in 2000 , showing if those who enter poverty have a lower chance of improvement in the social outcome.

Thus, we hold the initial social situation and poverty status fixed, letting only the poverty in 2010 vary. 10 The analytical strategy is set out in Table  3 , showing estimates of the probability to have frequent social relations in 2010, for poverty defined (as in Table  2 and Fig.  1 above) as economic deprivation.

Table 3

Per cent with frequent social relations in “comparison” and “change” groups in 2000 and 2010, according to initial value on social relations in 2000 and poverty (measured as economic deprivation) in 2000 and 2010

LNU panel 2000–2010. N = 3083

The figures in Table  3 should be read like this: 0.59 in the upper left cell means that among those who were poor neither in 2000 nor in 2010 (“never poor”, or 0–0), and who had non-frequent social relations to begin with, 59 % had frequent social relations in 2010. Among those never poor who instead started out with more frequent social relations, 90 per cent had frequent social relations in 2010. This difference (59 vs. 90) tells us either that the initial conditions were important (weak social relations can be inherently difficult to improve) or that there is heterogeneity within the group of never poor people, such as some having (to us perhaps unobserved) characteristics that support relation building while others have not.

Because our strategy is to condition on the initial situation in order to minimize the impact of initial conditions and unobserved heterogeneity, we focus on the comparisons across columns. If we follow each column downwards, that is, for a given initial social outcome (weak or not weak social relations, respectively) it is apparent that the outcome is worse for the “poverty entrants” in comparison with the “never poor” (upper three lines). Comparing the change group [those who became poor (0–1)] with the comparison group [never poor (0–0)] for those who started out with weak social relations (left column), the estimated probability of frequent social relations in 2010 is 7 % points lower for those who became poor. Among those who started out with frequent relations, those who became poor have a 17 % points lower probability of frequent relations in 2010 than those who stayed out of poverty.

If we move down Table  3 , to the three bottom lines, the change and comparison groups are now different. The comparison group is the “constantly poor” (1–1), and the change group are “poverty leavers” (1–0). Again following the columns downwards, we can see that the change group improved their social relations in comparison with the constantly poor; and this is true whether they started out with weak social relations or not. In fact, the chance of improvement for those who started off with non-frequent social relations is the most noteworthy, being 33 % units higher for those who escaped poverty than for those who did not. In sum, Table  3 suggests that becoming poor appears to be bad for social relations whereas escaping poverty is beneficial.

Expanding the Model

The model exemplified in Table  3 is a panel model that studies change across time within the same individuals, conditioning on their initial state. It does away with time-constant effects of observed and unobserved respondent characteristics, and although this is far superior to a cross-sectional model (such as the one underlying Fig.  1 ) there are still threats to causal interpretations. It is possible (if probably unusual) that permanent characteristics may trigger a change over time in both the dependent and independent variables; or, put in another way, whether a person stays in or exits poverty may be partly caused by a variable that also predicts change in the outcome (what is sometimes referred to as a violation of the “common trend assumption”). In our case, we can for example imagine that health problems in 2000 can affect who becomes poor in 2010, at t 1 , and that the same health problems can lead to a deterioration of social relations between 2000 and 2010, so even conditioning on the social relations at t 0 will not be enough. This we handle by adding control variables, attempting to condition the comparison of poor and non-poor also on sex, age, highest level of education (in 2010), immigrant status, and health (in 2000). 11

Given the set-up of our data—with 10 years between the two data-points and with no information on the precise time ordering of poverty and social outcomes at t 1 , the model can be further improved by including change in some of the control variables. It is possible, for example, that a non-poor and married respondent in 2000 divorced before 2010, triggering both poverty and reduced social relations at the time of the interview in 2010. 12 There are two major events that in this way may bias our results, divorce/separation and unemployment (because each can lead to poverty, and possibly also affect social outcomes). We handle this by controlling for variables combining civil status and unemployment in 2000 as well as in 2010. To the extent that these factors are a consequence of becoming poor, there is a risk of biasing our estimates downwards (e.g., if becoming poor increases the risk of divorce). However, as there is no way to distinguish empirically whether control variables (divorce, unemployment) or poverty changed first we prefer to report conservative estimates. 13

Throughout, we use logistic regression to estimate our models (one model for each social outcome and poverty definition). We create a dummy variable for each of the combinations of poverty in 2000, poverty in 2010 and the social outcome in 2000, and alternate the reference category in order to get the four strategic comparisons described above. Coefficients do thus express the distance between the relevant change and comparison groups. The coefficients reported are average marginal effects (AME) for a one-unit change in the respective poverty variable (i.e. going from non-poor to poor and vice versa), which are straightforwardly interpretable as percentage unit differences and (unlike odds ratios or log odds ratios) comparable across models and outcomes (Mood 2010 ).

Regression Results

As detailed above, we use changes over time in poverty and social outcomes to estimate the effects of interest. The effect of poverty is allowed to be heterogeneous, and is assessed through four comparisons of the social outcome in 2010 (Y 1 ):

• Those entering poverty relative to those in constant non-poverty (P 01  = 0,1 vs. P 01  = 0,0) when both have favourable social outcomes at t 0 (Y 0  = 1)
• Those exiting poverty relative to those in constant poverty (P 01  = 1,0 vs. P 01  = 1,1) when both have favourable social outcomes at t 0 (Y 0  = 1)
• Those entering poverty relative to those in constant non-poverty (P 01  = 0,1 vs. P 01  = 0,0) when both have non-favourable social outcomes at t 0 (Y 0  = 0)
• Those exiting poverty relative to those in constant poverty (P 01  = 1,0 vs. P 01  = 1,1) when both have non-favourable social outcomes at t 0 (Y 0  = 0)

Poverty is a rare outcome, and as noted above it is particularly uncommon to enter poverty between 2000 and 2010 because of the improving macro-economic situation. Some of the social outcomes were also rare in 2000. This unfortunately means that in some comparisons we have cell frequencies that are prohibitively small, and we have chosen to exclude all comparisons involving cells where N < 20.

The regression results are displayed in Table  4 . To understand how the estimates come to be, consider the four in the upper left part of the Table (0.330, 0.138, −0.175 and −0.065), reflecting the effect of poverty, measured as economic deprivation, on the probability of having frequent social relations. Because these estimates are all derived from a regression without any controls, they are identical (apart from using three decimal places) to the percentage comparisons in Table  3 (0.33, 0.14, −0.17, −0.07), and can be straightforwardly interpreted as average differences in the probability of the outcome in question. From Table  4 it is clear that the three first differences are all statistically significant, whereas the estimate −0.07 is not (primarily because those who entered poverty in 2010 and had infrequent social relations in 2000 is a small group, N = 25).

Table 4

Average marginal effects (from logistic regression) of five types of poverty (1–5) on four social outcomes (A-D) comparing those with different poverty statuses in 2000 and 2010 and conditioning on the starting value of the social outcome (in 2000)

Right columns control for sex, education, age, immigrant status, health in 2000, civil status change between 2000 and 2010, and unemployment change between 2000 and 2010. P values in parentheses. Excluded estimates involve variable categories with N < 20. Shaded cells are in hypothesized direction, bold estimates are statistically significant ( P  < 0.05). N in regressions: 1A: 3075; 1B: 3073; 1C: 3075; 1D: 3069; 2A: 3144; 2B: 3137; 2C: 3144; 2D: 3130; 3A: 3074, 3B: 3072; 3C: 3074; 3D: 3068; 4A: 2995; 4B: 2988; 4C: 2995; 4D: 2981; 5A: 3128; 5B: 3121; 5C: 3128; 5D: 3114

In the column to the right, we can see what difference the controls make: the estimates are reduced, but not substantially so, and the three first differences are still statistically significant.

The estimates for each social outcome, reflecting the four comparisons described above, support the hypothesis of poverty affecting social relations negatively (note that the signs of the estimates should differ in order to do so, the upper two being positive as they reflect an effect of the exit from poverty, and the lower two being negative as they reflect an effect of entering poverty). We have indicated support for the hypothesis in Table  4 by shading the estimates and standard errors for estimates that go in the predicted direction.

Following the first two columns down, we can see that there is mostly support for the hypothesis of a negative effect of poverty, but when controlling for other variables, the effects on social support are not impressive. In fact, if we concentrate on each social outcome (i.e., row-wise), one conclusion is that, when controlling for confounders, there are rather small effects of poverty on the probability of having access to social support. The opposite is true for political participation, where the consistency in the estimated effects of poverty is striking.

If we instead follow the columns, we ask whether any of the definitions of poverty is a better predictor of social outcomes than the others. The measure of economic deprivation appears to be the most stable one, followed by absolute poverty and the combined deprivation/absolute poverty variable. 14 The relative poverty measure is less able to predict social outcomes: in many instances it even has the non-expected sign. Interestingly, long-term poverty (as measured here) does not appear to have more severe negative consequences than absolute poverty in general.

Because some of our comparison groups are small, it is difficult to get high precision in the estimates, efficiency being a concern particularly in view of the set of control variables in Table  4 . Only 14 out of 62 estimates in models with controls are significant and in the right direction. Nonetheless, with 52 out of 62 estimates in these models having the expected sign, we believe that the hypothesis of a negative effect of poverty on social outcomes receives quite strong support.

Although control variables are not shown in the table, one thing should be noted about them: The reduction of coefficients when including control variables is almost exclusively driven by changes in civil status. 15 The time constant characteristics that are included are cross-sectionally related to both poverty and social outcomes, but they have only minor impacts on the estimated effects of poverty. This suggests that the conditioning on prior values of the dependent and independent variables eliminates much time invariant heterogeneity, which increases the credibility of estimates.

Conclusions

We set out to test a fundamental, but rarely questioned assumption in dominating definitions of poverty: whether shortage of economic resources has negative consequences for social relations and participation. By using longitudinal data from the Swedish Level-of-living Surveys 2000 and 2010, including repeated measures of poverty (according to several commonly used definitions) and four social outcome variables, we are able to come further than previous studies in estimating the relation between poverty and social outcomes: Our main conclusion is that there appears to be a causal relation between them.

Panel models suggest that falling into poverty increases the risk of weakening social relations and decreasing (civic and political) participation. Climbing out of poverty tends to have the opposite effects, a result that strengthens the interpretation of causality. The sample is too small to estimate the effect sizes with any precision, yet they appear to be substantial, with statistically significant estimates ranging between 5 and 21 % units.

While these findings are disquieting insofar as poverty goes, our results also suggest two more positive results. First, the negative effects of poverty appear to be reversible: once the private economy recovers, social outcomes improve. Secondly, the negative consequences are less for the closest social relations, whether there is someone there in cases of need (sickness, personal problems, etc.). This is in line with an interpretation of such close relations being unconditional: our nearest and dearest tend to hang on to us also in times of financial troubles, which may bolster risks for social isolation and psychological ill-being,

Our finding of negative effects of poverty on civic and political participation relates to the fears of a “downward spiral of social exclusion”, as there is a risk that the loss of less intimate social relations shrinks social networks and decreases the available social capital in terms of contacts that can be important for outcomes such as finding a job (e.g., Lin 2001 ; Granovetter 1974 ). However, Gallie et al. ( 2003 ) found no evidence for any strong impact of social isolation on unemployment, suggesting that the negative effects on social outcomes that we observe are unlikely to lead to self-reinforcement of poverty. Nevertheless, social relations are of course important outcomes in their own right, so if they are negatively affected by poverty it matters regardless of whether social relations in turn are important for other outcomes. Effects on political and civic participation are also relevant in themselves beyond individuals’ wellbeing, as they suggest a potentially democratic problem where poor have less of a voice and less influence on society than others.

Our results show the merits of our approach, to study the relation between poverty and social outcomes longitudinally. The fact that the poor have worse social relations and lower participation is partly because of selection. This may be because the socially isolated, or those with a weaker social network, more easily fall into poverty; or it can be because of a common denominator, such as poor health or social problems. But once we have stripped the analysis of such selection effects, we also find what is likely to be a causal relation between poverty and social relations. However, this effect of poverty on social outcomes, in turn, varies between different definitions of poverty. Here it appears that economic deprivation, primarily indicated by the ability of raising money with short notice, is the strongest predictor of social outcomes. Income poverty, whether in absolute or (particularly) relative terms, are weaker predictors of social outcomes, which is interesting as they are the two most common indicators of poverty in existing research.

Even if we are fortunate to have panel data at our disposal, there are limitations in our analyses that render our conclusions tentative. One is that we do not have a random allocation to the comparison groups at t 0 ; another that there is a 10-year span between the waves that we analyze, and both poverty and social outcomes may vary across this time-span. We have been able to address these problems by conditioning on the outcome at t 0 and by controlling for confounders, but in order to perform more rigorous tests future research would benefit from data with a more detailed temporal structure, and preferably with an experimental or at least quasi-experimental design.

Finally, our analyses concern Sweden, and given the position as an active welfare state with a low degree of inequality and low poverty rates, one can ask whether the results are valid also for other comparable countries. While both the level of poverty and the pattern of social relations differ between countries (for policy or cultural reasons), we believe that the mechanisms linking poverty and social outcomes are of a quite general kind, especially as the “costs for social participation” can be expected to be relative to the general wealth of a country—however, until comparative longitudinal data become available, this must remain a hypothesis for future research.

1 http://www.sofi.su.se/english/2.17851/research/three-research-departments/lnu-level-of-living .

2 We have tested various alternative codings and the overall pattern of results in terms of e.g., direction of effects and differences across poverty definitions are similar, but more difficult to present in an accessible way.

3 Our deprivation questions are however designed to reduce the impact of subjectivity by asking, e.g., about getting a specified sum within a specified time (see below).

4 In the equivalence scale, the first adult gets a weight of one, the second of 0.6, and each child gets a weight of 0.5.

5 We have also tried using single indicators (either a/b or i/ii) without detecting any meaningful difference between them. One would perhaps have assumed that poverty would be more consequential for having others over to one’s own place, but the absence of support for this can perhaps be understood in light of the strong social norm of reciprocity in social relations.

6 We have refrained from using information on voting and membership in trade unions and political parties, because these indicators do not capture the active, social nature of civic engagement to the same extent as participation in meetings and the holding of positions.

7 We have also estimated models with a more extensive health variable, a s ymptom index , which sums responses to 47 questions about self-reported health symptoms. However, this variable has virtually zero effects once global self-rated health is controlled, and does not lead to any substantive differences in other estimates. Adding the global health measure and the symptom index as TVC had no effect either.

8 Using the other indicators of poverty yields very similar results, although for some of those the difference between poor and non-poor is smaller.

9 We call these comparison groups ”never poor” and ”constantly poor” for expository purposes, although their poverty status pertains only to the years 2000 and 2010, i.e., without information on the years in between.

10 With this design we allow different effects of poverty on improvement versus deterioration of the social outcome. We have also estimated models with a lagged dependent variable, which constrains the effects of poverty changes to be of the same size for deterioration as for improvement of the social outcome. Conclusions from that analysis are roughly a weighted average of the estimates for deterioration and improvement that we report. As our analyses suggest that effects of poverty differ in size depending on the value of the lagged dependent variable (the social outcome) our current specification gives a more adequate representation of the process.

11 We have also tested models with a wider range of controls for, e.g., economic and social background (i.e. characteristics of the respondent’s parents), geography, detailed family type and a more detailed health variable, but none of these had any impact on the estimated poverty effects.

12 It is also possible that we register reverse causality, namely if worsening social outcomes that occur after t 0 lead to poverty at t 1 . This situation is almost inevitable when using panel data with no clear temporal ordering of events occurring between waves. However, reverse causality strikes us, in this case, as theoretically implausible.

13 We have also estimated models controlling for changes in health, which did not change the results.

14 If respondents’ judgments of the deprivation questions (access to cash margin and ability to pay rent, food, bills etc.) change due to non-economic factors that are related to changes in social relations, the better predictive capacity of the deprivation measure may be caused by a larger bias in this measure than in the (register-based) income measures.

15 As mentioned above, this variable may to some extent be endogenous (i.e., a mediator of the poverty effect rather than a confounder), in which case we get a downward bias of estimates.

Contributor Information

Carina Mood, Phone: +44-8-402 12 22, Email: [email protected] .

Jan O. Jonsson, Phone: +44 1865 278513, Email: [email protected] .

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By Joe Hasell, Max Roser, Esteban Ortiz-Ospina and Pablo Arriagada

Global poverty is one of the most pressing problems that the world faces today. The poorest in the world are often undernourished , without access to basic services such as electricity and safe drinking water ; they have less access to education , and suffer from much poorer health .

In order to make progress against such poverty in the future, we need to understand poverty around the world today and how it has changed.

On this page you can find all our data, visualizations and writing relating to poverty. This work aims to help you understand the scale of the problem today; where progress has been achieved and where it has not; what can be done to make progress against poverty in the future; and the methods behind the data on which this knowledge is based.

Key Insights on Poverty

Measuring global poverty in an unequal world.

There is no single definition of poverty. Our understanding of the extent of poverty and how it is changing depends on which definition we have in mind.

In particular, richer and poorer countries set very different poverty lines in order to measure poverty in a way that is informative and relevant to the level of incomes of their citizens.

For instance, while in the United States a person is counted as being in poverty if they live on less than roughly $24.55 per day, in Ethiopia the poverty line is set more than 10 times lower – at $2.04 per day. You can read more about how these comparable national poverty lines are calculated in this footnote. 1

To measure poverty globally, however, we need to apply a poverty line that is consistent across countries.

This is the goal of the International Poverty Line of $2.15 per day – shown in red in the chart – which is set by the World Bank and used by the UN to monitor extreme poverty around the world.

We see that, in global terms, this is an extremely low threshold indeed – set to reflect the poverty lines adopted nationally in the world’s poorest countries. It marks an incredibly low standard of living – a level of income much lower than just the cost of a healthy diet .

From $1.90 to $2.15 a day: the updated International Poverty Line

What you should know about this data.

• Global poverty data relies on national household surveys that have differences affecting their comparability across countries or over time. Here the data for the US relates to incomes and the data for other countries relates to consumption expenditure. 2
• The poverty lines here are an approximation of national definitions of poverty, made in order to allow comparisons across the countries. 1
• Non-market sources of income, including food grown by subsistence farmers for their own consumption, are taken into account. 3
• Data is measured in 2017 international-$, which means that inflation and differences in the cost of living across countries are taken into account. 4

Global extreme poverty declined substantially over the last generation

Over the past generation extreme poverty declined hugely. This is one of the most important ways our world has changed over this time.

There are more than a billion fewer people living below the International Poverty Line of $2.15 per day today than in 1990. On average, the number declined by 47 million every year, or 130,000 people each day. 5

The scale of global poverty today, however, remains vast. The latest global estimates of extreme poverty are for 2019. In that year the World Bank estimates that around 650 million people – roughly one in twelve – were living on less than $2.15 a day.

Extreme poverty: how far have we come, how far do we still have to go?

• Extreme poverty here is defined according to the UN’s definition of living on less than $2.15 a day – an extremely low threshold needed to monitor and draw attention to the living conditions of the poorest around the world. Read more in our article, From $1.90 to $2.15 a day: the updated International Poverty Line .
• Global poverty data relies on national household surveys that have differences affecting their comparability across countries or over time. 2
• Surveys are less frequently available in poorer countries and for earlier decades. To produce regional and global poverty estimates, the World Bank collates the closest survey for each country and projects the data forward or backwards to the year being estimated. 6
• Data is measured in 2017 international-$, which means that inflation and differences in the cost of living across countries are taken into account . 4

The pandemic pushed millions into extreme poverty

Official estimates for global poverty over the course of the Coronavirus pandemic are not yet available.

But it is clear that the global recession it brought about has had a terrible impact on the world’s poorest.

Preliminary estimates produced by researchers at the World Bank suggest that the number of people in extreme poverty rose by around 70 million in 2020 – the first substantial rise in a generation – and remains around 70-90 million higher than would have been expected in the pandemic’s absence. On these preliminary estimates, the global extreme poverty rate rose to around 9% in 2020. 7

• Figures for 2020-2022 are preliminary estimates and projections by World Bank researchers, based on economic growth forecasts. The pre-pandemic projection is based on growth forecasts prior to the pandemic. You can read more about this data and the methods behind it in the World Bank’s Poverty and Shared Prosperity 2022 report. 8

Hundreds of millions will remain in extreme poverty on current trends

Extreme poverty declined during the last generation because the majority of the poorest people on the planet lived in countries with strong economic growth – primarily in Asia.

The majority of the poorest now live in Sub-Saharan Africa, where weaker economic growth and high population growth in many countries has led to a rising number of people living in extreme poverty.

The chart here shows projections of global extreme poverty produced by World Bank researchers based on economic growth forecasts. 9

A very bleak future is ahead of us should such weak economic growth in the world’s poorest countries continue – a future in which extreme poverty is the reality for hundreds of millions for many years to come.

• The extreme poverty estimates and projections shown here relate to a previous release of the World Bank’s poverty and inequality data in which incomes are expressed in 2011 international-$. The World Bank has since updated its methods, and now measures incomes in 2017 international-$. As part of this change, the International Poverty Line used to measure extreme poverty has also been updated: from $1.90 (in 2011 prices) to $2.15 (in 2017 prices). This has had little effect on our overall understanding of poverty and inequality around the world. You can read more about this change and how it affected the World Bank estimates of poverty in our article From $1.90 to $2.15 a day: the updated International Poverty Line .
• Figures for 2018 and beyond are preliminary estimates and projections by Lakner et al. (2022), based on economic growth forecasts. You can read more about this data and the methods behind it in the related blog post. 10
• Data is measured in 2011 international-$, which means that inflation and differences in the cost of living across countries are taken into account. 4

The rapid progress seen in many countries shows an end to poverty is possible

Each of the countries shown in the chart achieved large declines in extreme poverty over the last generation. 11

The fact that rapid progress against poverty has been achieved in many places is one of the most important lessons we can learn from the available data on extreme poverty.

For those who are not aware of such progress – which is the majority of people – it would be easy to make the mistake of believing that poverty is inevitable and that action to tackle poverty is hence doomed to fail.

The huge progress seen in so many places shows that this view is incorrect.

After 200 years of progress the fight against global poverty is just beginning

Over the past two centuries the world made good progress against extreme poverty. But only very recently has poverty fallen at higher poverty lines.

Global poverty rates at these higher lines remain very high:

• 25% of the world lives on less than $3.65 per day – a poverty line broadly reflective of the lines adopted in lower-middle income countries.
• 47% of the world lives on less than $6.85 per day – a poverty line broadly reflective of the lines adopted in upper-middle income countries.
• 84% live on less than $30 per day – a poverty line broadly reflective of the lines adopted in high income countries. 12

Economic growth over the past two centuries has allowed the majority of the world to leave extreme poverty behind. But by the standards of today’s rich countries, the world remains very poor. If this should change, the world needs to achieve very substantial economic growth further still.

The history of the end of poverty has just begun

How much economic growth is necessary to reduce global poverty substantially?

• The data from 1981 onwards is based on household surveys collated by the World Bank. Earlier figures are from Moatsos (2021), who extends the series backwards based on historical reconstructions of GDP per capita and inequality data. 13
• All data is measured in international-$ which means that inflation and differences in purchasing power across countries are taken into account. 4
• The World Bank data for the higher poverty lines is measured in 2017 international-$. Recently, the World Bank updated its methodology having previously used 2011 international-$ to measure incomes and set poverty lines. The Moatsos (2021) historical series is based on the previously-used World Bank definition of extreme poverty – living on less than $1.90 a day when measured in 2011 international-$. This is broadly equivalent to the current World Bank definition of extreme poverty – living on less than $2.15 a day when measured in 2017 international-$. You can read more about this update to the World Bank’s methodology and how it has affected its estimates of poverty in our article From $1.90 to $2.15 a day: the updated International Poverty Line .
• The global poverty data shown from 1981 onwards relies on national household surveys that have differences affecting their comparability across countries or over time. 2
• Such surveys are less frequently available in poorer countries and for earlier decades. To produce regional and global poverty estimates, the World Bank collates the closest survey for each country and projects the data forward or backwards to the year being estimated. 6
• Non-market sources of income, including food grown by subsistence farmers for their own consumption, are taken into account. This is also true of the historical data – in producing historical estimates of GDP per capita on which these long-run estimates are based, economic historians take into account such non-market sources of income, as we discuss further in our article How do we know the history of extreme poverty?

Explore Data on Poverty

About this data.

All the data included in this explorer is available to download in GitHub , alongside a range of other poverty and inequality metrics.

Where is this data sourced from?

This data explorer is collated and adapted from the World Bank’s Poverty and Inequality Platform (PIP).

The World Bank’s PIP data is a large collection of household surveys where steps have been taken by the World Bank to harmonize definitions and methods across countries and over time.

About the comparability of household surveys

There is no global survey of incomes. To understand how incomes across the world compare, researchers need to rely on available national surveys.

Such surveys are partly designed with cross-country comparability in mind, but because the surveys reflect the circumstances and priorities of individual countries at the time of the survey, there are some important differences.

Income vs expenditure surveys

One important issue is that the survey data included within the PIP database tends to measure people’s income in high-income countries, and people’s consumption expenditure in poorer countries.

The two concepts are closely related: the income of a household equals their consumption plus any saving, or minus any borrowing or spending out of savings.

One important difference is that, while zero consumption is not a feasible value – people with zero consumption would starve – a zero income is a feasible value. This means that, at the bottom end of the distribution, income and consumption can give quite different pictures about a person’s welfare. For instance, a person dissaving in retirement may have a very low, or even zero, income, but have a high level of consumption nevertheless.

The gap between income and consumption is higher at the top of this distribution too, richer households tend to save more, meaning that the gap between income and consumption is higher at the top of this distribution too. Taken together, one implication is that inequality measured in terms of consumption is generally somewhat lower than the inequality measured in terms of income.

In our Data Explorer of this data there is the option to view only income survey data or only consumption survey data, or instead to pool the data available from both types of survey – which yields greater coverage.

Other comparability issues

There are a number of other ways in which comparability across surveys can be limited. The PIP Methodology Handbook provides a good summary of the comparability and data quality issues affecting this data and how it tries to address them.

In collating this survey data the World Bank takes a range of steps to harmonize it where possible, but comparability issues remain. These affect comparisons both across countries and within individual countries over time.

To help communicate the latter, the World Bank produces a variable that groups surveys within each individual country into more comparable ‘spells’. Our Data Explorer provides the option of viewing the data with these breaks in comparability indicated, and these spells are also indicated in our data download .

Global and regional poverty estimates

Along with data for individual countries, the World Bank also provides global and regional poverty estimates which aggregate over the available country data.

Surveys are not conducted annually in every country however – coverage is generally poorer the further back in time you look, and remains particularly patchy within Sub-Saharan Africa. You can see that visualized in our chart of the number of surveys included in the World Bank data by decade.

In order to produce global and regional aggregate estimates for a given year, the World Bank takes the surveys falling closest to that year for each country and ‘lines-up’ the data to the year being estimated by projecting it forwards or backwards.

This lining-up is generally done on the assumption that household incomes or expenditure grow in line with the growth rates observed in national accounts data. You can read more about the interpolation methods used by the World Bank in Chapter 5 of the Poverty and Inequality Platform Methodology Handbook.

How does the data account for inflation and for differences in the cost of living across countries?

To account for inflation and price differences across countries, the World Bank’s data is measured in international dollars. This is a hypothetical currency that results from price adjustments across time and place. It is defined as having the same purchasing power as one US-$ would in the United States in a given base year. One int.-$ buys the same quantity of goods and services no matter where or when it is spent.

There are many challenges to making such adjustments and they are far from perfect. Angus Deaton ( Deaton, 2010 ) provides a good discussion of the difficulties involved in price adjustments and how this relates to global poverty measurement.

But in a world where price differences across countries and over time are large it is important to attempt to account for these differences as well as possible, and this is what these adjustments do.

In September 2022, the World Bank updated its methodology, and now uses international-$ expressed in 2017 prices – updated from 2011 prices. This has had little effect on our overall understanding of poverty and inequality around the world. But poverty estimates for particular countries vary somewhat between the old and updated methodology. You can read more about this update in our article From $1.90 to $2.15 a day: the updated International Poverty Line .

To allow for comparisons with the official data now expressed in 2017 international-$ data, the World Bank continues to release its poverty and inequality data expressed in 2011 international-$ as well. We have built a Data Explorer to allow you to compare these, and we make all figures available in terms of both sets of prices in our data download .

Absolute vs relative poverty lines

This dataset provides poverty estimates for a range of absolute and relative poverty lines.

An absolute poverty line represents a fixed standard of living; a threshold that is held constant across time. Within the World Bank’s poverty data, absolute poverty lines also aim to represent a standard of living that is fixed across countries (by converting local currencies to international-$). The International Poverty Line of $2.15 per day (in 2017 international-$) is the best known absolute poverty line and is used by the World Bank and the UN to measure extreme poverty around the world.

The value of relative poverty lines instead rises and falls as average incomes change within a given country. In most cases they are set at a certain fraction of the median income. Because of this, relative poverty can be considered a metric of inequality – it measures how spread out the bottom half of the income distribution is.

The idea behind measuring poverty in relative terms is that a person’s well-being depends not on their own absolute standard of living but on how that standard compares with some reference group, or whether it enables them to participate in the norms and customs of their society. For instance, joining a friend’s birthday celebration without shame might require more resources in a rich society if the norm is to go for an expensive meal out, or give costly presents.

Our dataset includes three commonly-used relative poverty lines: 40%, 50%, and 60% of the median.

Such lines are most commonly used in rich countries, and are the main way poverty is measured by the OECD and the European Union .More recently, relative poverty measures have come to be applied in a global context. The share of people living below 50 per cent of median income is, for instance, one of the UN’s Sustainable Development Goal indicators . And the World Bank now produces estimates of global poverty using a Societal Poverty Line that combines absolute and relative components.

When comparing relative poverty rates around the world, however, it is important to keep in mind that – since average incomes are so far apart – such relative poverty lines relate to very different standards of living in rich and poor countries.

Does the data account for non-market income, such as food grown by subsistence farmers?

Many poor people today, as in the past, rely on subsistence farming rather than a monetary income gained from selling goods or their labor on the market. To take this into account and make a fair comparison of their living standards, the statisticians that produce these figures estimate the monetary value of their home production and add it to their income/expenditure.

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Official definitions of poverty in different countries are often not directly comparable due to the different ways poverty is measured. For example, countries account for the size of households in different ways in their poverty measures.

The poverty lines shown here are an approximation of national definitions, harmonized to allow for comparisons across countries. For all countries apart from the US, we take the harmonized poverty line calculated by Jolliffe et al. (2022). These lines are calculated as the international dollar figure which, in the World Bank’s Poverty and Inequality Platform (PIP) data, yields the same poverty rate as the officially reported rate using national definitions in a particular year (around 2017).

For the US, Jolliffe et al. (2022) use the OECD’s published poverty rate – which is measured against a relative poverty line of 50% of the median income. This yields a poverty line of $34.79 (measured using 2017 survey data). This however is not the official definition of poverty adopted in the US. We calculated an alternative harmonized figure for the US national poverty using the same method as Jolliffe et al. (2022), but based instead on the official 2019 poverty rate – as reported by the U.S. Census Bureau.

You can see in detail how we calculated this poverty line in this Google Colabs notebook .

Jolliffe, Dean Mitchell, Daniel Gerszon Mahler, Christoph Lakner, Aziz Atamanov, and Samuel Kofi Tetteh Baah. 2022. Assessing the Impact of the 2017 PPPs on the International Poverty Line and Global Poverty. The World Bank. Available to read at the World Bank here .

Because there is no global survey of incomes, researchers need to rely on available national surveys. Such surveys are designed with cross-country comparability in mind, but because the surveys reflect the circumstances and priorities of individual countries at the time of the survey, there are some important differences. In collating this survey data the World Bank takes steps to harmonize it where possible, but comparability issues remain.

One important issue is that, whilst in most high-income countries the surveys capture people’s incomes, in poorer countries these surveys tend to capture people’s consumption. The two concepts are closely related: the income of a household equals their consumption plus any saving, or minus any borrowing or spending out of savings.

To help communicate the latter, the World Bank produces a variable that groups surveys within each individual country into more comparable ‘spells’ (which we include in our data download ). Our Data Explorer provides the option of viewing the data with these breaks in comparability indicated.

The international-$ is a hypothetical currency that results from price adjustments across time and place. It is defined as having the same purchasing power as one US-$ in a given base year – in this case 2017. One int.-$ buys the same quantity of goods and services no matter where or when it is spent. There are many challenges to making such adjustments and they are far from perfect. But in a world where price differences across countries and over time are large it is important to attempt to account for these differences as well as possible, and this is what these adjustments do. Read more in our article From $1.90 to $2.15 a day: the updated International Poverty Line .

​​According to World Bank data, in 1990 there were 2.00 billion people living in poverty, and in 2019 that had fallen to 0.648 billion. The average fall over the 29 years in between is: (2.00 billion – 0.648 billion)/29 = 46.6 million. Dividing by the number of days (29 x 365) gives the average daily fall: (2.00 billion – 0.648 billion)/(29 x 365) = 128,000. (All figures rounded to 3 significant figures).

The projections are generally made on the assumption that incomes or expenditure grow in line with the growth rates observed in national accounts data. You can read more about the interpolation methods used by the World Bank in Chapter 5 of the Poverty and Inequality Platform Methodology Handbook.

We use the figures presented in the World Bank’s Poverty and Shared Prosperity 2022 report. Earlier estimates were also published in Lakner, C., Mahler, D.G., Negre, M. et al. How much does reducing inequality matter for global poverty?. J Econ Inequal (2022). https://doi.org/10.1007/s10888-021-09510-w . Available online here .

Earlier estimates were also published in Lakner, C., Mahler, D.G., Negre, M. et al. How much does reducing inequality matter for global poverty?. J Econ Inequal (2022). https://doi.org/10.1007/s10888-021-09510-w . Available online here .

The figures are taken from a World Bank blog post by Nishant Yonzan, Christoph Lakner and Daniel Gerszon Mahler. The post builds on and updates the estimates published by Lakner et al. (2022).In September 2022, the World Bank changed from using 2011 international-$ to 2017 international-$ in the measurement of global poverty. The International Poverty Line used by the World Bank and the UN to define extreme poverty was accordingly updated from $1.90 a day (in 2011 prices) to $2.15 (in 2017 prices).In order to match up to the projected figures, the extreme poverty estimates shown here relate to a previous release of the World Bank’s data using data expressed in 2011 prices, which vary slightly from the latest data in 2017 prices. You can read more about this change and how it affected the World Bank estimates of poverty in our article From $1.90 to $2.15 a day: the updated International Poverty Line .Lakner, C., Mahler, D.G., Negre, M. et al. How much does reducing inequality matter for global poverty?. J Econ Inequal (2022). https://doi.org/10.1007/s10888-021-09510-w . Available online here .

We use the figures provided in the blog post, which extend the methods presented in Lakner et al. (2022).Lakner, C., Mahler, D.G., Negre, M. et al. How much does reducing inequality matter for global poverty?. J Econ Inequal (2022). https://doi.org/10.1007/s10888-021-09510-w . Available online here .

Shown are those countries with a decline of more than 30 percentage points over a period of 15 years or more.There are a number of ways in which comparability across the different household surveys on which this data is based can be limited. These affect comparisons both across countries and within individual countries over time.The World Bank’s Poverty and Inequality Platform Methodology Handbook provides a good summary of the comparability and data quality issues affecting this data and how it tries to address them. In collating this survey data the World Bank takes a range of steps to harmonize it where possible, but comparability issues remain. To help communicate the latter, the World Bank produces a variable that groups surveys within each individual country into more comparable ‘spells’. Our Data Explorer provides the option of viewing the data with these breaks in comparability indicated.

You can read more about how the World Bank sets these higher poverty lines, as well as the International Poverty Line against which it measures extreme poverty, in our article From $1.90 to $2.15 a day: the updated International Poverty Line . To the three poverty lines adopted officially by the World Bank – $2.15, $3.65 and $6.85 – we add a higher line broadly consistent with definitions of poverty in high income countries. See our article Global poverty in an unequal world: Who is considered poor in a rich country? And what does this mean for our understanding of global poverty?

For details of the methods used to produce the long-run poverty data see, Moatsos, M. (2021). Global extreme poverty: Present and past since 1820. In van Zanden, Rijpma, Malinowski and Mira d’Ercole (eds.) How Was Life? Volume II: New Perspectives on Well-Being and Global Inequality since 1820. Available from the OECD here .

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Concept and Measurement of Poverty

An Overview of the Transformation Towards Humanistic Economics

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• First Online: 27 March 2024
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• Manjula Laxman 7  

Poverty is a challenging issue for any society and state. It is not just an economic, social, psychological, and political phenomenon but also related to human dignity, equity, and justice. Poverty measurement has various approaches but has historically dominated mainstream economics—the classical and neoclassical schools’ well-known measurement approach of market poverty, which focuses on the monetary incapacity to purchase basic needs. Rowntree was a pioneer of the concept of poverty. The humanistic economics concern of poverty is a phenomenon described by equity and justice, core values of an equitable society. Equitable societies rooted in ethics and humanistic economics, a branch of economics, are discussing poverty phenomena in a normative way. The author assumes that the concept and measurement of poverty have been influenced by humanistic economic thinking; social inclusion, deprivation of capability and entitlements, child poverty, human poverty, and multidimensional poverty are examples. Moreover, there has been an impact on international institutions, which have played a crucial role in shaping poverty and eradicating poverty programs around the globe. The United Nations aims to achieve sustainable development goals, targeting a poverty-free world by 2030. The paper explores poverty’s changing concept and measurement and how ethical values have been added and turned from mainstream economics to humanistic economics.

I thank Prof. Vidyut Joshi (Director of the Centre for Equity and Development, Gujarat Vidyapith, Ahmedabad, Gujarat State, India) for his valuable suggestions.

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Laxman, M. (2024). Concept and Measurement of Poverty. In: The Palgrave Handbook of Global Social Problems. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-68127-2_456-1

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Shaping Inequality and Intergenerational Persistence of Poverty: Free College or Better Schools

We evaluate the aggregate, distributional and welfare consequences of alternative government education policies to encourage college completion, such as making college free and improving funding for public schooling. To do so, we construct a general equilibrium overlapping generations model with intergenerational linkages, a higher education choice as well as a multi-stage human capital production process during childhood and adolescence with parental and government schooling investments. The model features rich cross-sectional heterogeneity, distinguishes between single and married parents, and is disciplined by US household survey data on income, wealth, education and time use. Studying the transitions induced by unexpected policy reforms we show that the “free college” and the “better schools” reform generate significant welfare gains, which take time to materialize and are lower in general than in partial equilibrium. It is optimal to combine both reforms: tuition subsidies make college affordable even for children from poorer parental backgrounds and better schools increase human capital thereby reducing dropout risk.

{We thank our discussants Veronica Guerrieri and Minseon Park as well as Elizabeth Caucutt, Diego Daruich, Minchul Yum and seminar participants at Mannheim, Brown, the 2023 National Tax Association Meeting, the 2023 Young Economist EconTribute Conference, the 2023 German Christmas Meeting, the 2024 AEA Meetings in San Antonio, the Wharton Macro lunch and the 2024 Carnegie-Rochester-NYU conference for many useful comments. Financial support from the German Research Foundation grant no. 462655750 is gratefully acknowledged. Computations in this paper were carried out using resources of the Goethe-HLR high performance computing cluster and Amazon Web Services EC2 Cloud Computing. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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Young Hondurans’ desire to migrate is influenced by factors beyond poverty and violence

Why are so many young Hondurans heading north ?

Research by me and two colleagues , published in the peer-reviewed journal International Migration, delved into the factors that motivate young people – those ages 16 to 29 – to leave the central American country and migrate to the U.S.

We found that resilience, which we define as having access to social resources and local support, was a key factor in the decision-making process. This challenges conventional wisdom that suggests those who enjoy some form of employment and strong support networks are more inclined to not seek opportunities elsewhere.

Analyzing survey data from a sample of youth enrolled in a workforce development program in violence-prone areas, we examined young people’s sense of social connectedness, community belonging and support, and desire to migrate .

We specifically looked at how their perceptions and ability to rely on local support systems – something that has been labeled multisystem resilience – influenced their relocation plans.

The reality we found is complex and highlights the nuanced interaction between resilience and migration intentions. It turns out that young, employed Hondurans with greater safety networks have the deepest desire to leave. We hypothesize that this is because when youth are employed and have the economic and social resources to think beyond immediate needs, they are more likely to want to seek better opportunities elsewhere. Multisystem resilience gives them the resources to consider migration as an attainable option.

Because migrating is expensive and individuals in our study come from economically disadvantaged areas, unemployed youth are less likely to have migration intentions. In addition, among those who do not have a job, access to social resources does not affect their plans to move north. In this context, unemployed youth may want to use their resilience to find a job and satisfy their basic, immediate needs rather than to plan moving abroad.

Why it matters

A deeper understanding of the interplay between resilience and migration intentions could help to manage and ideally reduce the desire to leave through supportive policies, such as mentoring initiatives, and interconnected social, economic and environmental programs designed to disrupt the migration pipeline.

In recent years, hundreds of thousands of Hondurans have embarked on perilous journeys to the United States . Previous surveys we conducted also found that 55% of young Hondurans want to migrate within the next three years .

What still isn’t known

While the challenges driving migration from Honduras are clear, important questions remain unanswered about the long-term impacts and potential solutions.

Young people feel torn between leaving loved ones – and being able to provide for their families with remittances – or staying in Honduras and betting on a future of uncertainty. In many ways, Hondurans are between a rock and a hard place, because both options present difficult choices.

What’s next

More work needs to be done in establishing what innovative approaches Honduras could adopt to retain talented young adults and foster local development amid a brain drain that is depleting the country of human capital.

Our research suggests that targeted interventions, such as mentoring programs, could help at-risk youth see a future in their homeland. Such initiatives could provide the necessary support to reduce migration by helping youth through challenging life transitions, including dealing with changes at school or transitioning to adulthood. Appropriately matching mentors selected for their expertise and experience may be able to fortify individuals against the lure of migration.

We want to learn more about what specific skills and personal growth objectives may help young people build a future filled with promise and potential in their homeland.

By understanding the interrelation between resilience and migration intentions, we hope to pave the way for greater collaboration between government agencies, private sector entities and international partners to increase the desire to stay and thrive in Honduras and other Central American countries.

The University of Notre Dame’s Tom Hare and Laura Miller-Graff co-authored the study.

The Research Brief is a short take on interesting academic work.

• Many immigrants to the US are fleeing violence and persecution − here’s how the federal government can help cities absorb them
• Environmental activists are being killed in Honduras over their opposition to mining

Maria Estela Rivero Fuentes receives funding from USAID, INL and private donors.

Investments and Policy Reforms Towards Low-Carbon Transition and Resilience are in Azerbaijan’s Economic Interest, says WBG Report

BAKU, November 29, 2023 – Urgent action on climate can help Azerbaijan minimize the risks emerging from the global low-carbon transition and protect the living standards of its people, says the World Bank Group’s Azerbaijan Country Climate and Development Report (CCDR), released today.

The country’s economy is heavily dependent on oil and gas, which account for a third of GDP and 90% of exports. With existing oil reserves dwindling and expected to last another 25 years, comprehensive and effective decarbonization efforts will help diversify the economy and open up new drivers of growth, such as green hydrogen and agriculture.

Azerbaijan is also facing considerable physical risks from climate change. Almost the entire country is prone to both droughts and water scarcity, which are expected to increase in frequency and intensity due to extreme weather events. Meanwhile, its natural wealth is eroding as a result of soil degradation, desertification, and overgrazing, negatively impacting agriculture, while oil and gas extraction have also contributed to land degradation and contamination of water resources.

“Accelerating investments in decarbonization is in Azerbaijan’s interest, regardless of the pace of global decarbonization efforts. Such efforts would also be well aligned with national goals to diversify the economy,” said Rolande Pryce, World Bank Country Director for the South Caucasus . “The CCDR provides a practical pathway for the country to move from setting targets to implementing actions which can protect Azerbaijan’s economy and people from the negative impacts of climate change.”

Although Azerbaijan is a signatory to the Paris Agreement on climate change, it has not yet committed to a domestic net zero target. However, the country has set national targets of reducing greenhouse gas (GHG) emissions by 35% by 2030 and 40% by 2050 from 1990 levels. While stepping up its commitment to decarbonization, Azerbaijan is not yet on track to achieve its national targets, says the report.

The challenges of decarbonization in Azerbaijan are considerable, given the structure of the national economy. Sectors that are key to the green transition, including energy and water, are dominated by state-owned enterprises, which employ half of the country’s workforce. Charting and implementing a clear decarbonization pathway will require economic diversification and a more vibrant private sector.

The decarbonization and resilience actions outlined in the report will require large investments of an estimated US$44 billion, or about 3.2% of GDP, until 2060, when the global economy goes to net-zero.  A significant share of this cost should be resourced from commercial and private sector financing.

“To drive a low-carbon transition, Azerbaijan will benefit from diversifying its economy away from fossil fuels and harnessing the capital and know-how of the private sector—but time is of the essence. Seizing the opportunities outlined in this report will help the country future-proof its economy and safeguard the population from climate change, ” said Ivana Fernandes Duarte, IFC's Regional Manager for the South Caucasus .

A gradual but steady phase-out of fossil fuel subsidies – a measure already contained in the government’s 2022-2026 strategy – will be key to achieving the transition, especially if accompanied by targeted social protection measures to protect the poorest.

The report sets out a strategic roadmap for a resilient and net zero development pathway for Azerbaijan. Highlights include:

• Clean energy : Azerbaijan has abundant renewable energy resources, wind and solar, which could be exploited to produce green hydrogen and electricity for exports and domestic use in power generation, industry, and transport. This will require substantial public investment in enabling electricity infrastructure and private investment in renewable energy generation, which could be unlocked also through public-private partnerships (PPPs).
• Energy efficiency : Energy efficiency is a government priority and efforts in this direction should include a program of energy efficiency in public and private buildings as well as the transport sector. Stricter fuel efficiency and emissions standards, use of electric vehicles, tax incentives and financial support programs should be part of an array of offerings to encourage energy efficiency by the public and commercial users.
• Agriculture and water : Agriculture is a sector critical to Azerbaijan’s non-oil economy. The sector contributes less than 8% of GDP but accounts for 36% of total employment. However, the sector is highly vulnerable not only to extreme weather events but also to the existing water deficit in the country. Climate-proofing the sector to higher temperatures and lower water availability must include improved irrigation efficiency and the introduction of climate smart agricultural practices to improve productivity while building resilience to climate change and reducing emissions.

“Although they may appear large, the investments required to address the decarbonization and resilience transition are manageable, particularly when assessed against their expected benefits –Azerbaijan’s future prosperity depends on setting the right policy framework for public and private investments to start flowing” said Andrea Liverani, lead author of the Azerbaijan CCDR .

About Country Climate and Development Reports

The World Bank Group’s Country Climate and Development Reports (CCDRs) are new core diagnostic reports that integrate climate change and development considerations. They will help countries prioritize the most impactful actions that can reduce greenhouse gas (GHG) emissions and boost adaptation, while delivering on broader development goals. CCDRs build on data and rigorous research and identify main pathways to reduce GHG emissions and climate vulnerabilities, including the costs and challenges as well as benefits and opportunities from doing so. The reports suggest concrete, priority actions to support the low-carbon, resilient transition. As public documents, CCDRs aim to inform governments, citizens, the private sector and development partners and enable engagements with the development and climate agenda. CCDRs will feed into other core Bank Group diagnostics, country engagements and operations, and help attract funding and direct financing for high-impact climate action.

The Azerbaijan Country Climate and Development Report highlights decarbonization and resilience measures that are aligned with the government’s objectives of economic diversification by focusing on the energy system and end-use sectors (transport, building, and industry) as well as the water and agricultural nexus.

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