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Around 700 million people live on less than $2.15 per day, the extreme poverty line. Extreme poverty remains concentrated in parts of Sub-Saharan Africa, fragile and conflict-affected areas, and rural areas.

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Too often, there is a wide gap between policies as articulated and their attainment in practice—between what citizens rightfully expect, and what they experience daily. Policy aspirations can be laudable, but there is likely to be considerable variation in the extent to which they can be realized, and in which groups benefit from them. For example, at the local level, those who have the least influence in a community might not be able to access basic services. It is critical to forge implementation strategies that can rapidly and flexibly respond to close the gaps.

Enhancing learning, improving data

From information gathered in household surveys to pixels captured by satellite images, data can inform policies and spur economic activity, serving as a powerful weapon in the fight against poverty. More data is available today than ever before, yet its value is largely untapped. Data is also a double-edged sword, requiring a social contract that builds trust by protecting people against misuse and harm, and works toward equal access and representation.

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The COVID-19 pandemic demonstrated that years of progress in reducing poverty can quickly disappear when a crisis strikes. Prevention measures often have low political payoff, with little credit given for disasters averted. Over time, populations with no lived experience of calamity can become complacent, presuming that such risks have been eliminated or can readily be addressed if they happen. COVID-19, together with climate change and enduring conflicts, reminds us of the importance of investing in preparedness and prevention measures comprehensively and proactively.

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Overall, with more than 60 percent of the world’s extreme poor living in middle-income countries, we cannot focus solely on low-income countries if we want to end extreme poverty. We need to focus on the poorest people, regardless of where they live, and work with countries at all income levels to invest in their well-being and their future.

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We continue to work closely with countries to help them find the best ways to improve the lives of their least advantaged citizens.

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Subjective Perceptions of Poverty and Objective Economic Conditions: Czechia and Slovakia a Quarter Century After the Dissolution of Czechoslovakia

• Original Research
• Published: 12 April 2019
• Volume 145 , pages 523–550, ( 2019 )

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• Martina Mysíková   ORCID: orcid.org/0000-0002-6340-4753 1 ,
• Tomáš Želinský   ORCID: orcid.org/0000-0001-7198-0278 2 ,
• Thesia I. Garner 3 &
• Jiří Večerník   ORCID: orcid.org/0000-0002-0535-3118 1  

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Studies into the relation between subjective perceptions of individuals and objective economic conditions have usually resulted in ambiguous empirical findings. Whilst most studies perceive subjective welfare as being operationalized by indicators of happiness or life satisfaction, we narrow the approach to an economic domain of subjective well-being—perceptions of poverty. We argue that our approach better reflects the economic dimension, as the former may include numerous non-economic domains. We use a case study of two countries—Czechia and Slovakia—which underwent early economic transition as a common state in 1989–1992, then became independent states in 1993, after the dissolution of Czechoslovakia. We base our findings on three historical data sets covering a period from around the end of the communist era to the early years after the split, and recent data from EU Statistics on Income and Living Conditions (2005–2016). Despite initially small differences in subjective poverty levels in socialist Czechoslovakia, a considerably larger drop in economic performance during the transition period in Slovakia than in Czechia resulted in a sharp widening of the subjective poverty gap. The recent data suggests that, despite a high degree of actual economic convergence of Slovakia and Czechia, the gap in subjective perceptions of poverty is declining at a remarkably slower pace. We argue that relatively fast economic growth is not necessarily associated with a commensurate decline in subjective poverty perceptions. Our results thus support the Easterlin Paradox, although we substitute happiness by an economic dimension of subjective well-being.

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Source : SOCO 1995, EU-SILC 2005–2016. Own computations

Source : The period 1990–2009 is based on the CZSO (2012) data, the period 2010–2017 is based on Eurostat data (variable nama_10_pc)

Source : Social Stratification in Eastern Europe 1993. Own computations

Source : EEA W3–W6. Own computations

Source : Social Consequences of Transition data (1995). Own computations

Source : EU-SILC 2005–2016. Own computations

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See Van den Bosch ( 1993 ) for a brief review of the two model-based approaches and Deleeck et al.’s ( 1992 ) approach.

Household Questionnaire, Section 17: “Concerning your family's food consumption over the past 1 month, which of the following is true?” with the following responses: “1: It was less than adequate for your family’s needs; 2: It was just adequate for your family’s needs; 3: It was more than adequate for your family’s needs; 4: Not applicable”. “Adequate” is defined as: “no more nor less than what the respondent considers to be the minimum consumption needs of the family”.

Variable YINC-7990 with the following question: “Which of the following best describes [yours/yours and your spouse's/yours and your partner's] financial condition?” and the corresponding categories: “1—very comfortable and secure; 2—able to make ends meet without much difficulty; 3—occasionally have some difficulty making ends meet; 4—tough to make ends meet but keeping your head above water; 5—in over your head”.

Even today, the subjective approach is regarded as neglected within the welfare concepts, though it remains a conceptually appealing way to define poverty (Ravallion 2014 ). Although economists have long been skeptical about subjective variables (Bertrand and Mullainathan 2001 ), their skepticism about subjective data seems, to some extent, to have been overcome today (Deaton and Stone 2013 ).

Most of the countries joined the survey in 2005.

While a targeted sociological survey on the poor population, ready to be launched by the mid-1980s, was not allowed during the communist era, poverty was opened as a research issue at the beginning of the economic transition.

The first wave did not contain the subjective variables of our interest. The second wave collected information on household disposable income using a single question (as opposed to the next waves) which makes our indicator highly incomparable. The last wave collected data on income only at intervals, which hinders construction of our indicator.

Households reporting that their actual income was absolutely sufficient to get along, consisting of 23% of Czech and 6% of Slovak households, were thus not asked the MIQ. For the rest, the reported minimum income was always higher than or equal to the actual income. Thus, if the respondents’ income was not absolutely sufficient, they were assumed to have reported a higher minimum income needed.

Note that the number of categories of the scale-evaluated questions differs across the data sets used, from four in EEA to six in EU-SILC. This raises comparability issues, which are partly offset by focusing on Slovak–Czech ratios instead on the levels of “feeling poor” or “inability” incidence across the surveys.

EU-SILC variable HY020—as actual income corresponds to annual income, one twelfth of the reported value is taken into account. EU-SILC is usually conducted in spring in Czechia and Slovakia, and the income reference period corresponds to the previous calendar year, while the subjective questions are related to the current situation. We are aware of possible inconsistencies between the current and previous year reference periods. However, the income reference period is considered to provide the best approximation of current income, as suggested by Eurostat ( 2010a ), and it is also used in this sense in official statistics.

The reason for the arbitrarily chosen threshold of 75% was that approximately the same share of the Czech population reported “insufficient income” as the share of persons designated as “at risk of poverty”—the official indicator—in 2015 (documented by Večerník and Mysíková 2016 ).

Regarding the possible limitations of the “individual” method and the arbitrarily stated poverty line, note that we cannot utilize any model-based estimation of the poverty line (SPL) because a part of the SOCO 1995 survey respondents were not asked the MIQ (almost a quarter of respondents in CZ).

Note that the scale differs in SOCO and in EU-SILC.

The categories are derived based on the population size and density of the municipality. The definition of the degree of urbanization has changed slightly over time in EU-SILC definitions (compare Eurostat 2010b , 2016 ).

The regression results mostly show a statistically insignificant difference between those paying a mortgage and those paying rent; however, if the coefficient is statistically significant, those paying a mortgage are less likely to feel poor.

Our definition of the work intensity indicator is different from the one applied by Eurostat in official statistics (see Ward and Ozdemir 2013 ). Here we simply add up the number of months worked during the year by all household members aged 16+ and divide it by 12 times the number of household members aged 16+. Our purpose is to control for the share of household members who actively contribute to the household budget, while the Eurostat definition is aimed to identify socially excluded persons.

The NUTS2 level includes 8 Czech and 4 Slovak regions.

Employment rate for population aged 15–64, Eurostat database (variable lfst_r_lfe2emprt).

Eurostat database (variable nama_10r_2gdp).

Data on expenditures stem from Household Budget Surveys, provided by the Czech and Slovak Statistical Offices.

For the sake of space, we do not provide full results of the logistic regressions models or comment on them, but they are available upon request.

As with the official statistics, all computations based on EU-SILC are weighted by the individual weights provided in the datasets.

Indeed, the variance of the two indicators of subjective poverty defined above differs, especially in Slovakia: while the variance of the insufficient income indicator ranges from 0.07 to 0.14 in CZ and from 0.16 to 0.25 in SK throughout the period 2005–2016, the respective figures are 0.06–0.09 and 0.09–0.11 for the inability indicator.

Slovakia suffered from weaker economic performance than Czechia for decades, followed by a rapid convergence with Czechia through the 1980s. Slovakia attained 61% of the Czech GDP in 1948, whereas its GDP reached 88% of that of Czechia at the end of the communist era in 1989 (see, e.g., Vintrová 2008 , 2009 for further details).

The consistency of the data on actual household disposable income over survey waves is rather low as the income questions varied in each wave. The reason was to improve the validity of the variable by dividing one original summary question into several asking about individual sources of income. However, as a consequence, the indicator of insufficient income is unstable over time.

The SK dummy shows a relative outcome, meaning that the perceived subjective poverty might have increased in Slovakia, decreased in Czechia, or any combination that might have led to the wider SK–CZ gap in Model B compared to Model A. In order to shed more light on what was happening in each country, we ran a model with pooled data for all years and included year dummies together with their interaction terms with the SK dummy. The results showed that both the Czechs and Slovaks are more likely to perceive their household income as insufficient once the economic conditions are controlled for. However, this difference is substantially greater in Slovakia than in Czechia. Further, this difference is decreasing over time in both countries. Finally, the results of the pooled model with economic controls (Model B) indicate that, in Czechia, the likelihood an individual will perceive his/her income as insufficient (with the last year as a reference point) was decreasing gradually (from relatively low likelihood at the beginning). In Slovakia, the relatively high likelihood at the beginning was sharply decreasing up to 2010.

The 2007 jump appears when the regional GDP is added to the regressors. Without the regional GDP, the odds ratio for SK accounts for 10.4 in 2007, and the peak occurs in 2006 (19.5), similarly to Model A.

Finally, we conclude that the fact that the subjective questions are responded to by one household member and attributed to all other household members has only a negligible impact on the results (compare Fig.  6 and the left panel of Fig.  12 for insufficient income, and Fig.  7 and the right panel of Fig.  12 for inability). Apart from a loss in statistical significance in some years, the qualitative results and trends remain the same.

Respondents state the income variables in their national currency, i.e. Slovak crown till 2008 and Euro since 2009 in Slovakia, and Czech crown in the Czech Republic for the whole period. Eurostat converts the income variables into Euros (and provides the exchange rate applied each year). The Czech Republic has not adopted the Euro, and the exchange rate moved from 32 CZK/EUR in 2005 to 25 CZK/EUR around 2012 and to 27 CZK/EUR in 2016. In Slovakia, the exchange rate dropped from 39 SKK/EUR to 34 SKK/EUR between 2005 and 2007, and the Slovak crown was replaced by the Euro at the irrevocably fixed exchange rate of 1 EUR = 30.1260 SKK on 1 January 2009.

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Acknowledgements

This work was supported by the European Regional Development Fund—Project “CSDA Research” (No. CZ.02.1.01/0.0/0.0/16_013/0001796) and by the Slovak Scientific Grant Agency (VEGA 2/0002/19). Tomáš Želinský further acknowledges support for visiting the Institute of Sociology of the Czech Academy of Sciences financed within the Protocol on cooperation in the field of education, youth and sports between the Ministry of Education, Science, Research and Sports of the Slovak Republic and the Ministry of Education, Youth and Sports of the Czech Republic. The EU-SILC datasets were made available on the basis of Contract No. 265/14 between the European Commission, Eurostat, and the Institute of Sociology of the Czech Academy of Sciences. Thanks for additional data information go to the Statistical Office of the Slovak Republic and the Czech Statistical Office. The authors especially wish to thank Andrew Clark, who provided valuable comments and ideas as a discussant of this paper at the IARIW 2018 General Conference. The authors would also like to thank the anonymous reviewers for their helpful and constructive comments which substantially contributed to the overall readability of the final version of this paper. Responsibility for all conclusions drawn from the data lies entirely with the authors.

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Martina Mysíková & Jiří Večerník

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Regression analysis of different definitions of insufficient income indicator as the dependent variable, SK dummy (odds ratio/coefficients), 2005–2016. Notes : Respondents aged 16+. All coefficients of the SK dummy are statistically significant at the 1% level. Model A controls for demographic characteristics. Model B additionally controls for socioeconomic and regional macroeconomic characteristics (see Sect.  3 on the list of control variables). GDP is missing in 2016. (Robust standard errors were used.)

Logistic regression of inability (1–2)—SK dummy (odds ratio), 2005–2016. Notes : Inability indicator (1–2): the dependent variable equals 1 if the answer was “great difficulty” or “difficulty”. Respondents aged 16+. Coefficients in Model A are statistically significant at the 1% level; in Model B, empty marks represent coefficients which are not statistically significant at the 10% level, other coefficients of the SK dummy are statistically significant at least at the 10% level. Model A controls for demographic characteristics. Model B additionally controls for socioeconomic and reginal macroeconomic characteristics (see Sect.  3 on the list of control variables). GDP is missing in 2016. (Robust standard errors were used.)

Logistic regression of insufficient income and inability—SK dummy (odds ratio), subsample of respondents answering household questionnaire, 2005–2016. Notes : Only respondents aged 16+ responsible for answering household questionnaire are included. Empty marks represent coefficients which are not statistically significant at the 10% level; all other coefficients of the SK dummy are statistically significant at the 5% level. Model A controls for demographic characteristics. Model B additionally controls for socioeconomic and reginal macroeconomic characteristics (see Sect.  3 on the list of control variables). GDP is missing in 2016. (Robust standard errors were used.)

Source : EU-SILC 2015 and 2016 (own computations); Eurostat database for GDP (current prices, Euro per capita—variable nama_10_pc)

Spatial distribution of mean wage, GDP, insufficient income and inability (NUTS2 regions), 2015–2016.  Notes : Maps were created using (c) EuroGeographics for the administrative boundaries “NUTS 2013” shapefiles.

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Mysíková, M., Želinský, T., Garner, T.I. et al. Subjective Perceptions of Poverty and Objective Economic Conditions: Czechia and Slovakia a Quarter Century After the Dissolution of Czechoslovakia. Soc Indic Res 145 , 523–550 (2019). https://doi.org/10.1007/s11205-019-02102-2

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Goal 1: End poverty in all its forms everywhere

Eradicating extreme poverty for all people everywhere by 2030 is a pivotal goal of the 2030 Agenda for Sustainable Development. Extreme poverty, defined as surviving on less than $2.15 per person per day at 2017 purchasing power parity, has witnessed remarkable declines over recent decades.

However, the emergence of COVID-19 marked a turning point, reversing these gains as the number of individuals living in extreme poverty increased for the first time in a generation by almost 90 million over previous predictions.

Even prior to the pandemic, the momentum of poverty reduction was slowing down. By the end of 2022, nowcasting suggested that 8.4 per cent of the world’s population, or as many as 670 million people, could still be living in extreme poverty. This setback effectively erased approximately three years of progress in poverty alleviation.

If current patterns persist, an estimated 7% of the global population – around 575 million people – could still find themselves trapped in extreme poverty by 2030, with a significant concentration in sub-Saharan Africa.

A shocking revelation is the resurgence of hunger levels to those last observed in 2005. Equally concerning is the persistent increase in food prices across a larger number of countries compared to the period from 2015 to 2019. This dual challenge of poverty and food security poses a critical global concern.

Why is there so much poverty

Poverty has many dimensions, but its causes include unemployment, social exclusion, and high vulnerability of certain populations to disasters, diseases and other phenomena which prevent them from being productive.

Why should I care about other people’s economic situation?

There are many reasons, but in short, because as human beings, our well- being is linked to each other. Growing inequality is detrimental to economic growth and undermines social cohesion, increas- ing political and social tensions and, in some circumstances, driving instability and conflicts.

Why is social protection so important?

Strong social protection systems are essential for mitigating the effects and preventing many people from falling into poverty. The COVID-19 pandemic had both immediate and long-term economic consequences for people across the globe – and despite the expansion of social protection during the COVID-19 crisis, 55 per cent of the world’s population – about 4 billion people – are entirely unprotected.

In response to the cost-of-living crisis, 105 countries and territories announced almost 350 social protection measures between February 2022 and February 2023. Yet 80 per cent of these were short-term in nature, and to achieve the Goals, countries will need to implement nationally appropriate universal and sustainble social protection systems for all.

What can I do about it?

Your active engagement in policymaking can make a difference in addressing poverty. It ensures that your rights are promoted and that your voice is heard, that inter-generational knowledge is shared, and that innovation and critical thinking are encouraged at all ages to support transformational change in people’s lives and communities.

Governments can help create an enabling environment to generate pro- productive employment and job opportunities for the poor and the marginalized.

The private sector has a major role to play in determining whether the growth it creates is inclusive and contributes to poverty reduction. It can promote economic opportunities for the poor.

The contribution of science to end poverty has been significant. For example, it has enabled access to safe drinking water, reduced deaths caused by water-borne diseases, and improved hygiene to reduce health risks related to unsafe drinking water and lack of sanitation.

Facts and Figures

Goal 1 targets.

• If current trends continue, 575 million people will still be living in extreme poverty and only one-third of countries will have halved their national poverty levels by 2030.
• Despite the expansion of social protection during the COVID-19 crisis, over 4 billion people remain entirely unprotected. Many of the world’s vulnerable population groups, including the young and the elderly, remain uncovered by statutory social protection programmes.
• The share of government spending on essential services, such as education, health and social protection, is significantly higher in advanced economies than in emerging and developing economies.
• A surge in action and investment to enhance economic opportunities, improve education and extend social protection to all, particularly the most excluded, is crucial to delivering on the central commitment to end poverty and leave no one behind.
• The global poverty headcount ratio at $2.15 is revised slightly up by 0.1 percentage points to 8.5 percent, resulting in a revision in the number of poor people from 648 to 659 million. ( World Bank)

Source: The Sustainable Development Goals Report 2023 

1.1  By 2030, eradicate extreme poverty for all people everywhere, currently measured as people living on less than $2.15 a day

1.2 By 2030, reduce at least by half the proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions

1.3  Implement nationally appropriate social protection systems and measures for all, including floors, and by 2030 achieve substantial coverage of the poor and the vulnerable

1.4 By 2030, ensure that all men and women, in particular the poor and the vulnerable, have equal rights to economic resources, as well as access to basic services, ownership and control over land and other forms of property, inheritance, natural resources, appropriate new technology and financial services, including microfinance

1.5  By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disasters

1.A  Ensure significant mobilization of resources from a variety of sources, including through enhanced development cooperation, in order to provide adequate and predictable means for developing countries, in particular least developed countries, to implement programmes and policies to end poverty in all its dimensions

1.B  Create sound policy frameworks at the national, regional and international levels, based on pro-poor and gender-sensitive development strategies, to support accelerated investment in poverty eradication actions

• United Nations Development Programme
• UN Children’s Fund
• International Monetary Fund
• UN Global Compact
• UN International Strategy for Disaster Reduction

Fast Facts: No Poverty

Infographic: No Poverty

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Economic Stability

About This Literature Summary

This summary of the literature on Poverty as a social determinant of health is a narrowly defined examination that is not intended to be exhaustive and may not address all dimensions of the issue. Please note: The terminology used in each summary is consistent with the respective references. For additional information on cross-cutting topics, please see the Discrimination , Employment , Housing Instability , and Incarceration literature summaries. 

Related Objectives (4)

Here's a snapshot of the objectives related to topics covered in this literature summary. Browse all objectives .

• Reduce the proportion of people living in poverty — SDOH‑01
• Increase employment in working-age people — SDOH‑02
• Increase the proportion of children living with at least 1 parent who works full time — SDOH‑03
• Reduce the proportion of families that spend more than 30 percent of income on housing — SDOH‑04

Related Evidence-Based Resources (3)

Here's a snapshot of the evidence-based resources related to topics covered in this literature summary. Browse all evidence-based resources .

• Social Determinants of Health: Tenant-Based Housing Voucher Programs
• New Perspectives on Creating Jobs: Final Impacts of the Next Generation of Subsidized Employment Programs
• Strengthening TANF Outcomes By Developing Two-Generation Approaches To Build Economic Security

Literature Summary

The United States measures poverty based on how an individual’s or family’s income compares to a set federal threshold. 1 For example, in the 2021 definition, people are considered impoverished if their individual income is below $12,880 or their household income is below $26,500 for a family of 4. 2 After 5 consecutive years in decline, the U.S. poverty rate increased to 11.4 percent in 2020, or a total of 37.2 million people. 3  

Poverty often occurs in concentrated areas and endures for long periods of time. 1 Some communities, such as certain racial and ethnic groups, people living in rural areas, and people with disabilities, have a higher risk of poverty for a myriad of factors that extend beyond individual control. 1 , 4 – 8 For example, institutional racism and discrimination contribute to unequal social and economic opportunities. 4 Residents of impoverished communities often have reduced access to resources that are needed to support a healthy quality of life, such as stable housing , healthy foods , and safe neighborhoods. 1 , 4 , 9 Poverty can also limit access to educational and employment opportunities, which further contributes to income inequality and perpetuates cyclical effects of poverty. 1  

Unmet social needs, environmental factors, and barriers to accessing health care contribute to worse health outcomes for people with lower incomes. 10 , 11 For example, people with limited finances may have more difficulty obtaining health insurance or paying for expensive procedures and medications. 12 In addition, neighborhood factors, such as limited access to healthy foods and higher instances of violence , can affect health by influencing health behaviors and stress. 12  

Across the lifespan, residents of impoverished communities are at increased risk for mental illness, chronic disease, higher mortality, and lower life expectancy. 9 , 13 – 17 Children make up the largest age group of those experiencing poverty. 18 , 19 Childhood poverty is associated with developmental delays, toxic stress, chronic illness, and nutritional deficits. 20 – 24 Individuals who experience childhood poverty are more likely to experience poverty into adulthood, which contributes to generational cycles of poverty. 25 In addition to lasting effects of childhood poverty, adults living in poverty are at a higher risk of adverse health effects from obesity, smoking, substance use, and chronic stress. 12 Finally, older adults with lower incomes experience higher rates of disability and mortality. 6 One study found that men and women in the top 1 percent of income were expected to live 14.6 and 10.1 years longer respectively than men and women in the bottom 1 percent. 26

Poverty is a multifaceted issue that will require multipronged approaches to address. Strategies that improve the economic mobility of families may help to alleviate the negative effects of poverty. 27 – 29 For example, tax credits such as the Earned Income Tax Credit and Child Tax Credit alleviate financial burdens for families with lower and middle incomes by reducing the amount of taxes owed. 30 In addition, federal social assistance programs are designed to provide safety net services and specifically benefit individuals and families with lower incomes. 31 Two of the nation’s largest social assistance programs are Medicaid, which provides health coverage, and the Supplemental Nutrition Assistance Program (SNAP), which provides food assistance. Medicaid and SNAP serve millions of people each year and have been associated with reductions in poverty along with overall health benefits. 32 , 33 In order to reduce socioeconomic inequality, it may also be important to address factors that are associated with the health status of poor communities. 27 Additional research and interventions are needed to address the effects of poverty on health outcomes and disparities. 

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U.S. Department of Agriculture, Office of the Assistant Secretary for Planning and Evaluation. (2021, February 1). 2021 Poverty guidelines . https://aspe.hhs.gov/topics/poverty-economic-mobility/poverty-guidelines/prior-hhs-poverty-guidelines-federal-register-references/2021-poverty-guidelines

Shrider, E. A., Kollar, M., Chen, F., & Semega, J. (2021, September 14). Income and poverty in the United States: 2020 . U.S. Census Bureau. https://www.census.gov/library/publications/2021/demo/p60-273.html

Williams, D. R., Mohammed, S. A., Leavell, J., & Collins, C. (2010). Race, socioeconomic status, and health: Complexities, ongoing challenges, and research opportunities. Annals of the New York Academy of Sciences, 1186 (1), 69–101. https://doi.org/10.1111/j.1749-6632.2009.05339.x

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Brucker, D. L., Mitra, S., Chaitoo, N., & Mauro, J. (2015). More likely to be poor whatever the measure: Working-age persons with disabilities in the United States. Social Science Quarterly, 96 (1), 273–296. https://doi.org/10.1111/ssqu.12098

Rank, M. R., & Hirschl, T. A. (2015). The likelihood of experiencing relative poverty over the life course. PLoS ONE, 10 (7), e0133513. https://doi.org/10.1371/journal.pone.0133513

Singh, G. K., & Siahpush, M. (2006). Widening socioeconomic inequalities in US life expectancy, 1980–2000. International Journal of Epidemiology, 35 (4), 969–979. https://doi.org/10.1093/ije/dyl083

Phelan, J. C., Link, B. G., & Tehranifar, P. (2010). Social conditions as fundamental causes of health inequalities: Theory, evidence, and policy implications. Journal of Health and Social Behavior, 51(Suppl 1) , S28–S40. https://doi.org/10.1177/0022146510383498

Thompson, T., McQueen, A., Croston, M., Luke, A., Caito, N., Quinn, K., Funaro, J., & Kreuter, M. W. (2019). Social needs and health-related outcomes among Medicaid beneficiaries. Health Education & Behavior: The Official Publication of the Society for Public Health Education, 46 (3), 436–444. https://doi.org/10.1177/1090198118822724

Khullar, D., & Chokshi, D. A. (2018). Health, income, & poverty: Where we are & what could help . Health Affairs Health Policy Brief. https://doi.org/10.1377/hpb20180817.901935

Braveman, P. A., Cubbin, C., Egerter, S., Williams, D. R., & Pamuk, E. (2010). Socioeconomic disparities in health in the United States: What the patterns tell us. American Journal of Public Health, 100 (Suppl 1), S186–S196. https://doi.org/10.2105/AJPH.2009.166082

Belle, D., & Doucet, J. (2003). Poverty, inequality, and discrimination as sources of depression among U.S. women. Psychology of Women Quarterly, 27 (2), 101–113. https://doi.org/10.1111/1471-6402.00090

Caughy, M. O., O’Campo, P. J., & Muntaner, C. (2003). When being alone might be better: Neighborhood poverty, social capital, and child mental health. Social Science & Medicine, 57 (2), 227–237. https://doi.org/10.1016/S0277-9536(02)00342-8

Ward-Smith, P. (2007). The effects of poverty on urologic health. Urologic Nursing, 27 (5), 445–446.

Mode, N. A., Evans, M. K., & Zonderman, A. B. (2016). Race, neighborhood economic status, income inequality and mortality. PLoS ONE, 11 (5), e0154535. https://doi.org/10.1371/journal.pone.0154535

Kaiser Family Foundation. (n.d.). Poverty rate by age . https://www.kff.org/other/state-indicator/poverty-rate-by-age/

Cellini, S. R., McKernan, S. M., & Ratcliffe, C. (2008). The dynamics of poverty in the United States: A review of data, methods, and findings. Journal of Policy Analysis and Management, 27 (3), 577–605.   https://onlinelibrary.wiley.com/doi/abs/10.1002/pam.20337

Eamon, M. K. (2001). The effects of poverty on children’s socioemotional development: An ecological systems analysis. Social Work, 46 (3), 256–266.

Evans, G. W., & Kim, P. (2013). Childhood poverty, chronic stress, self-regulation, and coping. Child Development Perspectives, 7 (1), 43–48. https://doi.org/10.1111/cdep.12013

Shaw, D. S., & Shelleby, E. C. (2014). Early-starting conduct problems: Intersection of conduct problems and poverty. Annual Review of Clinical Psychology, 10 (1), 503–528. https://doi.org/10.1146/annurev-clinpsy-032813-153650

Justice, L. M., Jiang, H., Purtell, K. M., Schmeer, K., Boone, K., Bates, R., & Salsberry, P. J. (2019). Conditions of poverty, parent-child interactions, and toddlers’ early language skills in low-income families. Maternal and Child Health Journal, 23 (7), 971–978. https://doi.org/10.1007/s10995-018-02726-9

Council on Community Pediatrics, Gitterman, B. A., Flanagan, P. J., Cotton, W. H., Dilley, K. J., Duffee, J. H., Green, A. E., Keane, V. A., Krugman, S. D., Linton, J. M., McKelvey, C. D., & Nelson, J. L. (2016). Poverty and child health in the United States. Pediatrics, 137 (4), e20160339. https://doi.org/10.1542/peds.2016-0339

Wagmiller Jr, R. L., & Adelman, R. M. (2009). Childhood and intergenerational poverty: The long-term consequences of growing up poor . National Center for Children in Poverty. https://www.nccp.org/publication/childhood-and-intergenerational-poverty/

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Yoshikawa, H., Aber, J. L., & Beardslee, W. R. (2012). The effects of poverty on the mental, emotional, and behavioral health of children and youth: Implications for prevention. The American Psychologist, 67 (4), 272–284. https://doi.org/10.1037/a0028015

Riccio, J. A., Dechausay, N., Greenberg, D. M., Miller, C., Rucks, Z., & Verma, N. (2010). Toward reduced poverty across generations: Early findings from New York City’s conditional cash transfer program . MDRC.

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Transforming Poverty Alleviation Policies

Adapting the regional innovation architecture approach.

Poverty, defined as the inability of individuals to fulfill their basic needs, poses a significant challenge to achieving community welfare in many regions. Addressing this challenge requires a holistic and integrated approach that considers Indonesian society's diverse cultural and socio-economic contexts. Adapting innovative architecture is crucial in confronting Indonesia's various cultural and socio-economic landscapes. This approach provides new alternatives for regional development strategies and opens up new opportunities for transforming and adapting innovation to tackle poverty holistically and sustainably. This research aims to restructure innovation architecture and examine its potential to transform regions in poverty alleviation efforts. The study employs a qualitative research method with a literature review analysis. It explores novel approaches for developing a solution-oriented and applicable analysis and recommendations for communities. Additionally, the research analyzes several regions that have successfully implemented regional innovation and reduced local poverty levels. Data is collected through a literature search strongly relevant to the research objectives. The findings indicate that innovation architecture can drive regional transformation in poverty alleviation through 1) Enhancing collaboration and synergy among stakeholders, 2) Creating a conducive innovation ecosystem, and 3) Developing contextual and sustainable innovative solutions. These findings demonstrate the substantial potential of innovation architecture in transforming regions to achieve poverty alleviation. This aligns with existing theories emphasizing stakeholder collaboration and synergy as key factors in creating effective, innovative solutions. Innovation architecture can serve as an effective tool in transforming regions towards poverty alleviation. Proper and sustainable implementation of innovation architecture can assist regions in crafting contextual, innovative solutions and enhancing community welfare .

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• Published: 14 May 2024

2023 summer warmth unparalleled over the past 2,000 years

• Jan Esper   ORCID: orcid.org/0000-0003-3919-014X 1 , 2 ,
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Including an exceptionally warm Northern Hemisphere (NH) summer 1 ,2 , 2023 has been reported as the hottest year on record 3-5 . Contextualizing recent anthropogenic warming against past natural variability is nontrivial, however, because the sparse 19 th century meteorological records tend to be too warm 6 . Here, we combine observed and reconstructed June-August (JJA) surface air temperatures to show that 2023 was the warmest NH extra-tropical summer over the past 2000 years exceeding the 95% confidence range of natural climate variability by more than half a degree Celsius. Comparison of the 2023 JJA warming against the coldest reconstructed summer in 536 CE reveals a maximum range of pre-Anthropocene-to-2023 temperatures of 3.93°C. Although 2023 is consistent with a greenhouse gases-induced warming trend 7 that is amplified by an unfolding El Niño event 8 , this extreme emphasizes the urgency to implement international agreements for carbon emission reduction.

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Are We Talking Too Much About Mental Health?

Recent studies cast doubt on whether large-scale mental health interventions are making young people better. Some even suggest they can have a negative effect.

By Ellen Barry

In recent years, mental health has become a central subject in childhood and adolescence. Teenagers narrate their psychiatric diagnosis and treatment on TikTok and Instagram. School systems, alarmed by rising levels of distress and self-harm, are introducing preventive coursework in emotional self-regulation and mindfulness.

Now, some researchers warn that we are in danger of overdoing it. Mental health awareness campaigns, they argue, help some young people identify disorders that badly need treatment — but they have a negative effect on others, leading them to over-interpret their symptoms and see themselves as more troubled than they are.

The researchers point to unexpected results in trials of school-based mental health interventions in the United Kingdom and Australia: Students who underwent training in the basics of mindfulness , cognitive behavioral therapy and dialectical behavior therapy did not emerge healthier than peers who did not participate, and some were worse off, at least for a while.

And new research from the United States shows that among young people, “self-labeling” as having depression or anxiety is associated with poor coping skills, like avoidance or rumination.

In a paper published last year , two research psychologists at the University of Oxford, Lucy Foulkes and Jack Andrews, coined the term “prevalence inflation” — driven by the reporting of mild or transient symptoms as mental health disorders — and suggested that awareness campaigns were contributing to it.

“It’s creating this message that teenagers are vulnerable, they’re likely to have problems, and the solution is to outsource them to a professional,” said Dr. Foulkes, a Prudence Trust Research Fellow in Oxford’s department of experimental psychology, who has written two books on mental health and adolescence.

Until high-quality research has clarified these unexpected negative effects, they argue, school systems should proceed cautiously with large-scale mental health interventions.

“It’s not that we need to go back to square one, but it’s that we need to press pause and reroute potentially,” Dr. Foulkes said. “It’s possible that something very well-intended has overshot a bit and needs to be brought back in.”

This remains a minority view among specialists in adolescent mental health, who mostly agree that the far more urgent problem is lack of access to treatment.

About 60 percent of young Americans with severe depression receive no treatment, according to Mental Health America, a nonprofit research group. In crisis, desperate families fall back on emergency rooms, where teens often remain for days before a psychiatric bed opens up. There is good reason to embrace a preventive approach, teaching schoolchildren basic skills that might forestall crises later, experts say.

Dr. Foulkes said she understood that her argument runs counter to that consensus, and when she began to present it, she braced for a backlash. To her surprise, she said, many educators reached out to express quiet agreement.

“There’s definitely a fear about being the one to say it,” she said.

A deflating result

In the summer of 2022, the results of a landmark study on mindfulness training in British classrooms landed — like a lead balloon.

The trial, My Resilience in Adolescence, or MYRIAD, was ambitious, meticulous and expansive, following about 28,000 teenagers over eight years. It had been launched in a glow of optimism that the practice would pay off, improving the students’ mental health outcomes in later years.

Half of the teenagers were trained by their teachers to direct their attention to the present moment — breathing, physical sensations or everyday activities — in 10 lessons of 30 to 50 minutes apiece.

The results were disappointing . The authors reported “no support for our hypothesis” that mindfulness training would improve students’ mental health. In fact, students at highest risk for mental health problems did somewhat worse after receiving the training, the authors concluded.

But by the end of the eight-year project, “mindfulness is already embedded in a lot of schools, and there are already organizations making money from selling this program to schools,” said Dr. Foulkes, who had assisted on the study as a postdoctoral research associate. “And it’s very difficult to get the scientific message out there.”

Why, one might ask, would a mental health program do harm?

Researchers in the study speculated that the training programs “bring awareness to upsetting thoughts,” encouraging students to sit with darker feelings, but without providing solutions, especially for societal problems like racism or poverty. They also found that the students didn’t enjoy the sessions and didn’t practice at home.

Another explanation is that mindfulness training could encourage “co-rumination,” the kind of long, unresolved group discussion that churns up problems without finding solutions.

As the MYRIAD results were being analyzed, Dr. Andrews led an evaluation of Climate Schools, an Australian intervention based on the principles of cognitive behavioral therapy, in which students observed cartoon characters navigating mental health concerns and then answered questions about practices to improve mental health.

Here, too, he found negative effects. Students who had taken the course reported higher levels of depression and anxiety symptoms six months and 12 months later.

Co-rumination appears to be higher in girls, who tend to come into the program more distressed, as well as more attuned to their friends, he said. “It might be,” he said, “that they kind of get together and make things a little bit worse for each other.”

Dr. Andrews, a Wellcome Trust research fellow, has since joined an effort to improve Climate Schools by addressing negative effects. And he has concluded that schools should slow down until “we know the evidence base a bit more.” Sometimes, he said, “doing nothing is better than doing something.”

The awareness paradox

One problem with mental health awareness, some research suggests, is that it may not help to put a label to your symptoms.

Isaac Ahuvia, a doctoral candidate at Stony Brook University, recently tested this in a study of 1,423 college students . Twenty-two percent “self-labeled” as having depression, telling researchers “I am depressed” or “I have depression,” but 39 percent met the diagnostic criteria for depression.

He found that the students who self-labeled felt that they had less control over depression and were more likely to catastrophize and less likely to respond to distress by putting their difficulties in perspective, compared with peers who had similar depression symptoms.

Jessica L. Schleider, a co-author of the self-labeling study, said this was no surprise. People who self-label “appear to be viewing depression as a biological inevitability,” she said. “People who don’t view emotions as malleable, view them as set and stuck and uncontrollable, tend to cope less well because they don’t see a point to trying.”

But Dr. Schleider, an associate professor of medical social sciences at Northwestern University and the director of the university’s Lab for Scalable Mental Health, pushed back on the prevalence inflation hypothesis. She disagreed with the claim that students are overdiagnosing themselves, noting that Mr. Ahuvia’s findings suggest otherwise.

Awareness campaigns are bound to have multiple effects, helping some students and not others. And ultimately, she argued, the priority for public health should be reaching young people in the most distress.

“The urgency of the mental health crisis is so clear,” she said. “In the partnerships that I have, the emphasis is on the kids truly struggling right now who have nothing — we need to help them — more so than a possible risk for a subset of kids who aren’t really struggling.”

Maybe, she said, we need to look beyond the “universal, school-assembly-style approach,” to targeted, light-touch interventions, which research has shown can be effective at decreasing anxiety and conduct disorders, especially in younger children.

“There is a risk of throwing the baby out with the bathwater,” Dr. Schleider said. “The response can’t be ‘Forget all of it.’ It should be ‘What about this intervention was unhelpful?’”

Other researchers echoed her concern, pointing to studies that show that on average, students benefit from social and emotional learning courses.

One of the largest, a 2023 meta-analysis of 252 classroom programs in 53 countries, found that students who participated performed better academically, displayed better social skills and had lower levels of emotional distress or behavioral problems. In that context, negative effects in a handful of trials appear modest, the researchers said.

“We clearly have not figured out how to do them yet, but I can’t imagine any population-based intervention that the field got right the first time,” said Dr. Andrew J. Gerber, the president and medical director of Silver Hill Hospital and a practicing child and adolescent psychiatrist.

“Really, if you think about almost everything we do in schools, we don’t have great evidence for it working,” he added. “That doesn’t mean we don’t do it. It just means that we’re constantly thinking about ways to improve it.”

‘We want everyone to have it’

These debates are taking place a long way away from classrooms, where mental health curriculums are increasingly commonplace.

Allyson Kangisser, a counselor at Woodsdale Elementary School in Wheeling, W.Va., said the focus in her school is on basic coping skills. In the early grades, students are asked, “What things can you do to take care of yourself when you’re having big feelings?”

Starting in third grade, they take on more complex material, such as watching cartoon characters to distinguish transient stress from chronic conditions like depression. “We’re not trying to have them diagnose themselves,” Ms. Kangisser said. “We are saying, what do you feel — this one? Or this one?”

At the school’s sixth annual mental health fair last month, Woodsdale students walked through a giant inflatable brain, its lobes neatly labeled. They did yoga stretches and talked about regulating their emotions. Ms. Kangisser said the event is valuable precisely because it is universal, so troubled children are not singled out.

“The mental health fair, everybody does it,” she said. “It’s not ‘You need it, and you don’t.’ We want everyone to have it, because you just never know.”

By the time the students reach college, they will have absorbed enormous amounts of information about mental health — from school, but also from social media and from one another.

Dr. Jessica Gold, chief wellness officer for the University of Tennessee system, said the college students she sees are recognizably different — more comfortable speaking about their emotions and more willing to be vulnerable. They also overuse diagnostic terms and have the self-assurance to question a psychiatrist’s judgment.

“It’s sort of a double-edged sword,” she said. “We want people to talk about this more, but we don’t want that to lead to overdiagnosis or incorrect diagnosis or overtreatment. We want it to lead to normalizing of having feelings.”

Lucy Kim, a Yale senior who has lobbied for better mental health support on campus, described the prevalence inflation hypothesis as “disheartening, dismissive and potentially dangerous,” providing another way to discount the experiences of young people.

“As a college student, I see a generation of young people around me impacted by a depth and breadth of loneliness, exhaustion and disillusionment suggestive of a malaise that goes deeper than the general vicissitudes of life,” said Ms. Kim, 23.

Overdiagnosis does happen, she said, and so does glorification of mental health disorders. But stigma and barriers to treatment remain the bigger problem. “I can confidently say I have never heard anyone respond to disclosures of depression with ‘That’s so cool, I wish I had that, too,’” she said.

Ellen Barry is a reporter covering mental health for The Times. More about Ellen Barry

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• v.12(8); 2007 Oct

Language: English | French

The impact of poverty on educational outcomes for children

Hb ferguson.

1 Community Health Systems Resource Group, The Hospital for Sick Children

2 Department of Psychiatry, Psychology & Public Health Sciences, University of Toronto, Toronto, Ontario

Over the past decade, the unfortunate reality is that the income gap has widened between Canadian families. Educational outcomes are one of the key areas influenced by family incomes. Children from low-income families often start school already behind their peers who come from more affluent families, as shown in measures of school readiness. The incidence, depth, duration and timing of poverty all influence a child’s educational attainment, along with community characteristics and social networks. However, both Canadian and international interventions have shown that the effects of poverty can be reduced using sustainable interventions. Paediatricians and family doctors have many opportunities to influence readiness for school and educational success in primary care settings.

Depuis dix ans, l’écart des revenus s’est creusé entre les familles canadiennes, ce qui est une triste réalité. L’éducation est l’un des principaux domaines sur lesquels influe le revenu familial. Souvent, lorsqu’ils commencent l’école, les enfants de familles à faible revenu accusent déjà un retard par rapport à leurs camarades qui proviennent de familles plus aisées, tel que le démontrent les mesures de maturité scolaire. L’incidence, l’importance, la durée et le moment de la pauvreté ont tous une influence sur le rendement scolaire de l’enfant, de même que les caractéristiques de la communauté et les réseaux sociaux. Cependant, tant au Canada que sur la scène internationale, il est possible de réduire les effets de la pauvreté au moyen d’interventions soutenues. Les pédiatres et les médecins de familles ont de nombreuses occasions d’agir sur la maturité et la réussite scolaire dans le cadre des soins de premier recours.

Poverty remains a stubborn fact of life even in rich countries like Canada. In particular, the poverty of our children has been a continuing concern. In 1989, the Canadian House of Commons voted unanimously to eliminate poverty among Canadian children by 2000 ( 1 ). However, the reality is that, in 2003, one of every six children still lived in poverty. Not only have we been unsuccessful at eradicating child poverty, but over the past decade, the inequity of family incomes in Canada has grown ( 2 ), and for some families, the depth of poverty has increased as well ( 3 ). Canadian research confirms poverty’s negative influence on student behaviour, achievement and retention in school ( 4 ).

Persistent socioeconomic disadvantage has a negative impact on the life outcomes of many Canadian children. Research from the Ontario Child Health Study in the mid-1980s reported noteworthy associations between low income and psychiatric disorders ( 5 ), social and academic functioning ( 6 ), and chronic physical health problems ( 7 ). Since that time, Canada has developed systematic measures that have enabled us to track the impact of a variety of child, family and community factors on children’s well-being. The National Longitudinal Survey of Children and Youth (NLSCY) developed by Statistics Canada, Human Resources Development Canada and a number of researchers across the country was started in 1994 with the intention of following representative samples of children to adulthood ( 8 ). Much of our current knowledge about the development of Canadian children is derived from the analysis of the NLSCY data by researchers in a variety of settings.

One of the key areas influenced by family income is educational outcomes. The present article provides a brief review of the literature concerning the effects of poverty on educational outcomes focusing on Canadian research. Canadian data are placed in the perspective of research from other ‘rich’ countries. We conclude with some suggestions about what we can do, as advocates and practitioners, to work toward reducing the negative impact of economic disadvantage on the educational outcomes of our children.

POVERTY AND READINESS FOR SCHOOL

School readiness reflects a child’s ability to succeed both academically and socially in a school environment. It requires physical well-being and appropriate motor development, emotional health and a positive approach to new experiences, age-appropriate social knowledge and competence, age-appropriate language skills, and age-appropriate general knowledge and cognitive skills ( 9 ). It is well documented that poverty decreases a child’s readiness for school through aspects of health, home life, schooling and neighbourhoods. Six poverty-related factors are known to impact child development in general and school readiness in particular. They are the incidence of poverty, the depth of poverty, the duration of poverty, the timing of poverty (eg, age of child), community characteristics (eg, concentration of poverty and crime in neighborhood, and school characteristics) and the impact poverty has on the child’s social network (parents, relatives and neighbors). A child’s home has a particularly strong impact on school readiness. Children from low-income families often do not receive the stimulation and do not learn the social skills required to prepare them for school. Typical problems are parental inconsistency (with regard to daily routines and parenting), frequent changes of primary caregivers, lack of supervision and poor role modelling. Very often, the parents of these children also lack support.

Canadian studies have also demonstrated the association between low-income households and decreased school readiness. A report by Thomas ( 10 ) concluded that children from lower income households score significantly lower on measures of vocabulary and communication skills, knowledge of numbers, copying and symbol use, ability to concentrate and cooperative play with other children than children from higher income households. Janus et al ( 11 ) found that schools with the largest proportion of children with low school readiness were from neighbourhoods of high social risk, including poverty. Willms ( 12 ) established that children from lower socioeconomic status (SES) households scored lower on a receptive vocabulary test than higher SES children. Thus, the evidence is clear and unanimous that poor children arrive at school at a cognitive and behavioural disadvantage. Schools are obviously not in a position to equalize this gap. For instance, research by The Institute of Research and Public Policy (Montreal, Quebec) showed that differences between students from low and high socioeconomic neighbourhoods were evident by grade 3; children from low socioeconomic neighbourhoods were less likely to pass a grade 3 standards test ( 13 ).

POVERTY AND EDUCATIONAL ATTAINMENT

Studies emanating from successive waves of the NLSCY have repeatedly shown that socioeconomic factors have a large, pervasive and persistent influence over school achievement ( 14 – 16 ). Phipps and Lethbridge ( 15 ) examined income and child outcomes in children four to 15 years of age based on data from the NLSCY. In this study, higher incomes were consistently associated with better outcomes for children. The largest effects were for cognitive and school measures (teacher-administered math and reading scores), followed by behavioural and health measures, and then social and emotional measures, which had the smallest associations.

These Canadian findings are accompanied by a large number of studies in the United States that have shown that socioeconomic disadvantage and other risk factors that are associated with poverty (eg, lower parental education and high family stress) have a negative effect on cognitive development and academic achievement, smaller effects on behaviour and inconsistent effects on socioemotional outcomes ( 17 – 19 ). Living in extreme and persistent poverty has particularly negative effects ( 18 ), although the consequences of not being defined below the poverty line but still suffering from material hardship should not be underestimated ( 20 ). Furthermore, American studies found strong interaction effects between SES and exposure to risk factors. For instance, parents from disadvantaged backgrounds were not only more likely to have their babies born prematurely, but these prematurely born children were also disproportionately at higher risk for school failure than children with a similar neonatal record from higher income families ( 18 ).

It is worth noting that international studies have consistently shown similar associations between socioeconomic measures and academic outcomes. For example, the Progress in International Reading Literacy Study (PIRLS) assessed the comprehensive literacy skills of grade 4 students in 35 countries. The Programme for International Student Assessment (PISA) assessed reading, math and science scores of 15-year-old children in 43 countries ( 21 ). At these two different stages of schooling, there was a significant relationship between SES and educational measure in all countries. This relationship has come to be known as a ‘socioeconomic gradient’; flatter gradients represent greater ‘equity of outcome’, and are generally associated with better average outcomes and a higher quality of life. Generally, the PISA and the NLSCY data support the conclusion that income or SES has important effects on educational attainment in elementary school through high school. Despite the results shown by the PISA and the NLSCY, schools are not the ultimate equalizer and the socioeconomic gradient still exists despite educational attainment. Test results can be misleading and can mask the gradient if the sample does not account for all children who should be completing the test. A study ( 13 ) completed by the Institute of Research and Public Policy demonstrated only small differences between low and high socioeconomic students when test results were compared in those students who sat for the examination. However, when results were compared for the entire body of children who should have written the examination, the differences between low and high socioeconomic students were staggering, mainly due to the over-representation of those who left school early in the low socioeconomic group.

Longitudinal studies carried out in the United States have been crucial in demonstrating some of the key factors in producing and maintaining poor achievement. Their findings have gone well beyond a model that blames schools or a student’s background for academic failure. Comparisons of the academic growth curves of students during the school year and over the summer showed that much of the achievement gap between low and high SES students could be related to their out-of-school environment (families and communities). This result strongly supports the notion that schools play a crucial compensatory role; however, it also shows the importance of continued support for disadvantaged students outside of the school environment among their families and within their communities ( 22 ).

A Human Resource Development Canada study ( 23 ) titled “The Cost of Dropping Out of High School” reported that lower income students were more likely to leave school without graduating, which agrees with international data. In a nonrandom sample for a qualitative study, Ferguson et al ( 24 ) reported that one-half of Ontario students leaving high school before graduating were raised in homes with annual incomes lower than $30,000. Finally, in Canada, only 31% of youth from the bottom income quartile attended postsecondary education compared with 50.2% in the top income quartile ( 25 ). Once again, the evidence indicates that students from low-income families are disadvantaged right through the education system to postsecondary training.

REVERSING THE EFFECTS OF POVERTY

The negative effects of poverty on all levels of school success have been widely demonstrated and accepted; the critical question for us as a caring society is, can these effects be prevented or reversed? A variety of data are relevant to this question, and recent research gives us reason to be both positive and proactive.

Early intervention

There is a direct link between early childhood intervention and increased social and cognitive ability ( 26 ). Decreasing the risk factors in a child’s environment increases a child’s potential for development and educational attainment. Prevention and intervention programs that target health concerns (eg, immunization and prenatal care) are associated with better health outcomes for low-income children and result in increased cognitive ability ( 27 ). However, it is the parent-child relationship that has been proven to have the greatest influence on reversing the impact of poverty. Both parenting style ( 28 ) and parental involvement, inside and outside of the school environment ( 29 ), impact on a child’s early development. Characteristics of parenting such as predictability of behaviour, social responsiveness, verbal behaviour, mutual attention and positive role modelling have been shown to have a positive effect on several aspects of child outcome. Parental involvement, such as frequency of outings ( 29 ) and problem-based play, creates greater intellectual stimulation and educational support for a child, and develops into increased school readiness ( 26 ).

Interventions act to advance a child’s development through a range of supports and services. Their underlying goal is to develop the skills lacking in children, that have already developed in other children who are of a similar age. There is general agreement that interventions should be data driven, and that assessments and interventions should be closely linked. A primary evaluation of a child and family support systems is, therefore, pivotal in the creation of individualized interventions to ensure success in placing children on a normative trajectory ( 30 ). Ramey and Ramey ( 30 ) determined that interventions have sustained success for children when they increase intellectual skills, create motivational changes, create greater environmental opportunities and/or increase continued access to supports.

Karoly et al ( 31 ) reported the magnitude of effects that early intervention programs have on children. Measured at school entry, they found a pooled mean effect size of around 0.3, with many programs having effect sizes between 0.5 and 0.97. This means that for many interventions, children in the program were, on average, one-half to a full standard deviation above their peers who were not in the program. Interestingly, they found that interventions that combined parent education programs with child programs had significantly higher effect sizes. Furthermore, interventions that continued beyond the early years showed significantly lower fade-out effects. The results strongly support the notion that early interventions should include the whole family and be continued beyond the early years. Constant evaluation of interventions should be completed to ensure that the benefits for children are maximized using these key components.

Highly regarded early interventions

The High/Scope active learning approach is a comprehensive early childhood curriculum. It uses cooperative work and communication skills to have children ‘learn by doing’. Individual, and small and large group formats are used for teacher-and-child planned activities in the key subject areas of language and literacy, mathematics, science, music and rhythmic movement. There has been ongoing evaluation of the approach since 1962 using 123 low-income African-American children at high risk of school failure ( 32 ). Fifty-eight children received high-quality early care and an educational setting, as well as home visits from the teachers to discuss their developmental progress. By 40 years of age, children who received the intervention were more likely to have graduated high school, hold a job, have higher earnings and have committed fewer crimes.

Similar positive effects of preschool intervention were found in the evaluation of the Abecedarian project ( 33 ). This project enlisted children between infancy and five years of age from low-income families to receive a high-quality educational intervention that was individualized to their needs. The intervention used games focused on social, emotional and cognitive areas of development. Children were evaluated at 12, 15 and 21 years of age, and those who had received the intervention had higher cognitive test scores, had greater academic achievement in reading and math, had completed more years of education and were more likely to have attended a four-year college. Interestingly, the mothers of children participating in the program also had higher educational and employment status after the intervention.

One of the oldest and most eminent early intervention programs is the Chicago Child Parent Center program. The intervention targets students who are between preschool and grade 3 through language-based activities, outreach activities, ongoing staff development and health services. Importantly, there is no set curriculum; the program is tailored to the needs of each child ( 34 ). One crucial feature of the program is the extensive involvement of parents. Multifaceted parental programs are offered to improve parental knowledge, their engagement in their children’s education and their parental skills. An evaluation of the Chicago Child Parent Center Program was completed by Reynolds ( 34 ) using a sample of 1106 black children from low-income families. They were exposed to the intervention in preschool, kindergarten and follow-up components. Two years after the completion of the intervention, the results indicated that the duration of intervention was associated with greater academic achievement in reading and mathematics, teacher ratings of school adjustment, parental involvement in school activities, grade retention and special education placement ( 34 ). Evaluation of the long-term effects of the intervention was completed by Reynolds ( 35 ) after 15 years of follow-up. Individuals who had participated in the early childhood intervention for at least one or two years had higher rates of school completion, had attained more years of education, and had lower rates of juvenile arrests, violent arrests leaving school early.

Later intervention

A common question concerns the stage at which it is too late for interventions to be successful. Recent findings (N Rowen, personal communication) from an uncontrolled community study in Toronto, Ontario, have suggested that a multisys-temic intervention as students transition to high school can produce dramatic results. The Pathways to Education project began because of a community (parents) request to a local health agency to help their children succeed in high school. The community consisted mainly of people from a public housing complex, with the majority of families being poor, immigrants and from visible minority groups. The Pathways project grew out of a partnership between the community, the health centre and the school board, and was funded by a variety of sources. The core elements of the program include a contract between the student, parents and project; student-parent support workers who advocate for the student at school and connect parents to the project and/or school; four nights a week of tutoring (by volunteers) in the community; group and career mentoring located in the community; and financial support, such as money for public transit and scholarship money for postsecondary education dependent on successful academic work and graduation. The Pathways project has been running for six years, and the results for the first five cohorts of students have been exciting. In comparison to a preproject cohort, the absentee and academic ‘at-risk’ rate (credit accumulation) has fallen by 50% to 60%, the ‘dropout’ rate has fallen by 80% to a level below the average for the board of education and the five-year graduation rate has risen from 42% to 75%. Of the graduates, 80% go on to college or university, compared with 42% before the Pathways project. While these initial results must be replicated in other communities, they suggest that, even at the high school level, interventions can be startlingly effective, even in a community with a long history of poverty, recent immigration and racism. As the proponents of Pathways move to replication, they will need to be careful to untangle the effects of community commitment, school board collaboration and the rich set of collaborations that have been a hallmark of this first demonstration project. Nevertheless, Pathways has made it clear that Canadian communities possess the capacity to change the education outcomes of their children and youth. While it takes resolve and resources to achieve such effects, initial analysis suggests that over the lifetime of the students, each dollar invested will be returned to Canada more than 24 times ( 36 )!

Schools make a difference

Canadian and international research on educational outcomes has revealed important data on the effects of schools and classrooms. Frempong and Willms ( 37 ) used complex analyses of student performance in mathematics to demonstrate that Canadian schools, and even classrooms, do make a difference in student outcomes (ie, students from similar home backgrounds achieve significantly different levels of performance in different schools). Furthermore, schools and classrooms differ in their SES gradients (ie, some schools achieve not just higher scores, but more equitable outcomes than others). These general findings were corroborated by Willms ( 38 ) using reading scores from children in grade 4 and those 15 years of age from 34 countries. Once again, it was demonstrated that schools make a difference and that some schools are more equitable than others. According to Thomas ( 10 ), activities other than academics, such as sports and lessons in the arts, have been shown to increase student’s school readiness despite SES. These activities should be encouraged in all schools to maximize school readiness. A key to making schools more effective at raising the performance of low SES students is to keep schools heterogeneous with regard to the SES of their students (ie, all types of streaming result in markedly poor outcomes for disadvantaged children and youth).

WHAT CAN WE DO?

Balancing the consistent evidence about the pervasive negative impact of poverty on educational outcomes with the hopeful positive outcomes of intervention studies, what can we do in our communities to attenuate the effects of poverty and SES on academic success? Here are some important actions:

• Advocate for and support schools which strive to achieve equity of outcomes;
• Advocate for and support intervention programs that provide academic, social and community support to raise the success of disadvantaged children and youth;
• Make others aware of the short-, medium- and long-term costs of allowing these children and youth to fail or leave school;
• Never miss a personal opportunity to support the potential educational success of the children and youth who we come into contact with;
• Advocate for system changes within schools to maximize educational attainment (eg, longer school days and shorter summer vacations); and
• Advocate for quality early education and care to minimize differences between children’s school readiness before entering school.

Paediatricians and family doctors have many opportunities to influence readiness for school and educational success in primary care settings. Golova et al ( 39 ) reported intriguing results from a primary care setting. They delivered a literacy promoting intervention to low-income Hispanic families in health care settings. At the initial visit (average age 7.4 months), parents received a bilingual handout explaining the benefits of reading aloud to children, literacy-related guidance from paediatric providers or an age-appropriate bilingual children’s board book. Control group families received no handouts or books. At a 10-month follow-up visit (mean age 17.7 months), there was no difference between groups on a screening test for language scores; however, intervention families read more often to their children, reported greater enjoyment of reading to children and had more children’s books in their homes. Given this suggestive finding, there are a number of points that paediatricians and family doctors should consider as they deliver primary care:

• Observe and encourage good parenting – mutual attention and contingency of interaction (taking turns and listening to each other), verbal behaviour (amount of talking and quality), sensitivity and responsiveness (awareness to signs of hunger, fatigue, boredom and providing an appropriate response), role modelling and reading to their children;
• Encourage parents to increase their knowledge of child development, particularly age-appropriate needs of and activities for their children. Explain to them, for instance, how ear infections can severely affect a student’s language development, and that good nutrition and hygiene can lower the frequency and severity of infections;
• Encourage parents who do not have their children in institutionalized care to attend parent-child centres and programs. These programs usually do not charge fees and require no formal arrangements. Examples are the Ontario Early Years Centres, the Aboriginal Head Start Program in Northern communities, and programs related to the Alberta Children and Youth Initiative;
• Indicate the importance of parental support and networks – keep a message board in your office and post a list of community-based organizations in your neighborhood; and
• Keep in mind that poverty is not always obvious. One in five low-income families is headed by a parent who works full-time all year; thus, it is often difficult to tell if a family is in need ( 40 ).

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Approaches to subjective poverty in economic and sociological research

• Martin Lačný

Poverty is a complex phenomenon which has been the subject of research across the social sciences. There have been varying approaches to defining and measuring poverty, especially with regard to research focus. In economic and sociological research, the concept of subjective poverty, which is particularly interesting in terms of psychological research into poverty, represents an alternative to the predominant objective measures of poverty. This article reviews the approaches to poverty used in economic and sociological research, paying special regard to representative approaches to subjective poverty, including subjective poverty lines and outlines the aspects relevant to psychological research into poverty.

Introduction

While there have been different approaches to and concepts for defining and measuring poverty, inferior living conditions and their serious individual and social consequences have remained dominant. The scale of poverty ranges from the imminent threat of starvation and increased risk of certain diseases, to the inability to participate in the services normally available to the rest of society. The economic concept of poverty deals with subsistence income levels. Several levels are taken into account: (1) the existential subsistence minimum, the lowest standard of living at which only the most basic living needs can be satisfied (extreme poverty line); (2) social subsistence minimum, reflecting ability to meet socially recognized needs (poverty line); (3) minimal comfort, society’s minimum level for a comfortable standard of living.

In the economic sciences, poverty is understood as a comparative concept based on the consensus of those who view the phenomenon of poverty from the outside. This is generally based on statistics ( Riegel, 2007 ).

In sociology, there are particular views of specific aspects of poverty. For example, Shildrick and Rucell (2015) describe a functionalist approach in which poverty is understood to be a consequence of circumstance. In other words, the poor share the general values of society but are unable to implement them because of low income, insufficient qualifications and so on. Lewis, Webley, and Furnham (1995) have discussed conflict theory which emphasizes that poverty inhibits the ability to fight for the distribution of scarce resources, while interaction theory underlines the stigma of poverty, because poverty is not only an economic deprivation but is involved in self-concept as well.

Gordon (1972) interpreted radical theories which distinguish the primary labour market (typically relative stability and high income) from the secondary labour market (unstable with low income), resulting from bad interaction between governments and trade unions. Townsend (1979) described the theory of minority groups and subculture theory . The former approach distinguishes between primary poverty (resulting from the death of the breadwinner, illness, injury) and secondary poverty (resulting from alcoholism, mismanagement, irregular income, etc.). Subculture theory identifies the common features of poorer segments of societies in different countries. These include values, interpersonal relationships, family and community structures, similar patterns of consumption and time spending and differences in economic, intellectual or emotional level compared with the majority of society.

Psychology has the ability to go deeper into the objective and subjective components of poverty. However, Džuka, Babinčák, Kačmárová, Mikulášková, and Martončik (2017) consider the initial state of psychological research into poverty to be an ‘open space’ in Slovakia. In other countries, research has only just started to reveal the psychological aspects of poverty and has not yet focused on a comprehensive understanding. They point out that, globally, the psychological research on poverty enjoys significant attention, especially that relating to the subjective causes and psychological consequences of poverty. The aim of this article is to review the representative approaches to subjective poverty and outline the aspects that are important for psychological research into poverty: first, the basic approaches to defining the variables for conceptualizing poverty (financial resources, capabilities and the multidimensional approach); second, the identification of objective and subjective poverty; and finally, third, subjective poverty lines and subjective sub-indicators used in poverty measures.

Income vs capabilities

In the economic research, there are two basic approaches to defining the variables for conceptualizing poverty. The first is to define poverty according to financial resources (income, wealth, consumption level, etc.) and is a conventional and widely used approach in the methodologies of the World Bank ( Ravallion, 1994 , 2010 ; Ravallion & Chen, 1997 ). Income poverty measures are taken from the utilitarian understanding of wealth, with an emphasis on individual utility as the key variable. In this context, and along with an additional set of rigorous assumptions (including the completeness of markets, the absence of externalities and public goods, the absence of increasing economies of scale, the specification of cardinal utility functions), income is seen as a measure of individual welfare as all welfare-relevant goods can be purchased on the competitive market. Income shortfalls can then be understood as economic welfare shortfalls or poverty. Most analysts are not primarily interested in short-term or life-cycle fluctuations in income. Instead, they rely on expenditure as a more stable indicator of long-term or lifetime resources and thus of welfare. There is wide agreement that household consumption or income aggregates should be normalized for cost-of-living differences, including differences in household size. However, there are different views as to how this normalization should be done. Equally, neither income nor consumption (even with seemingly appropriate normalizations) are considered sufficient for measuring welfare ( Ravallion, 2012 ). The weaknesses in this approach are mainly related to the suitability and interpretation of utility as a measure of welfare. There is also the problematic interpersonal comparability of utility for which there is no suitable empirical procedure in addition to strict cardinal utility functions ( Klasen, 2000 ).

The alternative approach is based on the idea that poverty may lie in the lack of basic benefits, basic skills or capabilities, and that some of these cannot be purchased as there is no sufficient supply in the market system ( Sen, 1992 , 1999 ). The capability approach to measuring poverty defines poor people as those who have limited freedom or chance of realizing their own lifestyles. Financial resources are but one of the means of achieving a certain level of well-being in this sense and the definition of poverty should therefore focus directly on ‘well-being outcomes’. Another argument is that in economic behaviour it is possible to identify differences in a person’s ability to convert income into utility. This multifactorial approach to defining and measuring poverty and well-being is therefore focused on observing and measuring the capabilities of individuals and households. Poverty is then defined as the inability of a person to achieve a minimum level of ‘vital capabilities’ (e.g. the inability to be healthy, inability to be rich, well dressed, have housing) [1] .

Bibi (2005) has noted that the measure of so-called self-reliant poverty (a concept which originates from Sen’s capability approach) should ultimately identify those households in greatest difficulty—those at the bottom of the distribution of capabilities-to-generate-minimum-necessary-income [2] .

On one hand, this approach overcomes multiple issues in defining poverty found in approaches based solely on financial resources—for example heterogeneity in human behaviour, the effect of public goods on individual well-being and the methodological pitfalls associated with the utilitarian calculus of utility. On the other hand, it is difficult to select the relevant skills and set the trade-offs (relative weights) across the dimensions. There is also a lack of reliable data on non-monetary variables suitable for international comparison. The selection of components is usually discussed and modified with regard to the social context in which the research is being conducted. The necessary information is collected through questionnaires (e.g. income, education, ownership of consumables, means of transport, housing, access to healthcare, access to credit). The weights of the individual components in the selection can then be derived empirically via Principal Component Analysis (PCA) or Multiple Correspondence Analysis (MCA) ( Klasen, 2000 ).

The standardized scores for the individual components are then used to define quintiles representing a certain degree of poverty or well-being (similarly, the DHS Wealth Index has been created).

A brief summary of the discussed characteristics of both approaches can be seen in Table 1 .

Comparison of the financial resources and capability approaches to defining poverty

Source: Own

Nevertheless, as Ravallion (2012) states, recognizing that welfare is ‘multi-dimensional’ and that income is an incomplete metric does not mean we can credibly collapse multiple dimensions into a single, unidimensional space. The basic problem is that we do not know the prices for valuation. The prevailing practice is essentially to make ad hoc assumptions about welfare function (and hence the weights).

Based on the multidimensional approach to poverty , several globally used measures have been developed such as the Human Development Index (HDI), Physical Quality of Life Index (PQLI) and the Global Multidimensional Poverty Index (MPI). The widely used MPI index consists of ten sub-indicators grouped into three dimensions (education, health and living standards). According to the MPI, an individual whose total weight of deprivation indicators is equal to or greater than 3 would be considered poor ( Alkire et al., 2015 ; Alkire, Roche, & Vaz, 2017 ). The MPI measures poverty on an individual level making it possible to provide a detailed overview of the spectrum of deprivation faced by the poor. It enables international comparisons between countries, regions and at the global level, as well as comparisons of poverty within ethnic groups, and urban and rural communities. Although the HDI also considers education, health and living standards, in the MPI each of these dimensions is described by more than one sub-indicator. Yet, neither the HDI nor the MPI capture the moral, emotional and spiritual dimensions of poverty.

Objective vs subjective poverty

Objective poverty is defined by factors that are not dependent on personal opinion and individuals’ perceptions. It is based on an analysis of the available socio-economic information on households or household files. In contrast to the prevailing objective approaches, the subjective concept of poverty is based on the belief that the individual is best placed to assess the urgency of his/her needs and to hierarchize his/her satisfaction in a given social reference framework. In this context, relative poverty or deprivation refers to a situation where an individual evaluates his/her life situation as unfavourable when compared to the life situation of other reference groups or persons within the same reference group.

However, neither the psychological nor economic theories of poverty offer much insight into what constitutes a relevant comparison group. Researchers therefore have to rely on some potentially strong identifying assumptions. Additionally, Clark (2018) points out the bias effect resulting from a subjective comparison of one’s situation with a reference group – namely that individuals say they are happier when they earn more, but less happy when others earn more. In their older work, Clark and Oswald (1996) analysed British Household Panel Survey (BHPS) data and showed that job satisfaction among British employees rose with the individual’s income but fell in the case that the income of the peer group (defined as other people with the same job and demographic characteristics) increased.

The subjective approach to poverty has mainly been associated with Dutch-Flemish economics (e.g. B. Van Praag, H. Deleeck). In this approach, the identification of subjective poverty is based on respondents’ answers to the question of what level of income is required to meet their basic needs. The core data is obtained from respondents’ self-assessments in sample surveys. Each respondent is asked to rate his or her economic welfare, or a broader concept such as life satisfaction or happiness, on an ordinal scale (Cantril ladder). Alternatively, the respondent provides money metrics of points on qualitative welfare scales, such as minimum income needed to make ends meet (Minimum Income Question – MIQ) ( Ravallion, 2012 ).

There are three main ways in which these subjective data are used to inform poverty measurements:

As a means of testing objective poverty lines, by regressing self-rated welfare on income normalized by the poverty line plus the variables that went into the construction of the poverty line, which should be jointly insignificant if those lines accord with subjective welfare;

To calibrate a composite welfare index using the subjective welfare regression coefficients as the weights;

To derive a poverty line in the income space, defined as the income level at which some critical level of subjective welfare is reached in expectation.

The Participatory Poverty Assessment – PPA can be considered an alternative approach to examining poverty. In this approach, poor people’s attitudes are taken into account in analysing subjective poverty and formulating strategies for reducing poverty through public policy. In the newer PPA approaches, researchers do not seek to prescribe the criteria under which poverty is assessed. Rather, they focus primarily on the perceptions of respondents and their well-being. PPA can help to identify poverty in the following ways: by detecting perceptions about trends and factors influencing poverty (e.g. uncertainty, inflation, market trends, declining environmental quality); by selecting significant welfare indicators which can be further verified using conventional research methods (e.g. uncertainty level verification); by examining trends in areas that are difficult to track using conventional quantitative methods. The benefits of a participatory approach lie above all in identifying the hidden dimensions of poverty and opportunities to analyse causality and the processes by which people become poor (i.e. the psychological causes of poverty) and how they get out of poverty ( Norton et al., 2001 ).

Tosun et al. (2018) created a different approach which was used in CUPESSE [3] to collect data on various indicators related to the concept of economic self-sufficiency . It also explores the role that families play in the journey from education to employment. In their survey, economic self-sufficiency has several objective aspects such as income independence (whether and to what extent respondents provide for themselves through paid jobs or self-employment—versus relying on their family or the state) and their housing situation. The respondents’ subjective perceptions relate to self-assessed economic conditions (to what extent during the six months prior to the interview had they been able to: 1. pay their bills by themselves, 2. afford decent housing, 3. afford extras like trips or hobbies and 4. put some money aside), and includes a subjective indicator of financial satisfaction (measured on a four-point ordinal Likert scale). Although the concept of economic self-sufficiency is not primarily about poverty, it can be used to identify a sample of respondents who are subjectively dissatisfied with their economic situation and describe the extent to which individuals are able to provide for themselves without external assistance. However, as Tosun et al. (2018) have noted, a fully independent income does not always imply a decent standard of living, let alone satisfaction with one’s economic situation.

Poverty lines

Apart from the subjective poverty lines, there are subjective sub-indicators used in poverty measures based on prevailing objective variables, which are usually used alongside objective measures in order to refine their outcomes.

When investigating developing countries, researchers define the poverty threshold on a monetary basis (most often on the basis of an absolute income, such as the income poverty range or the value of household expenditures) and use a range of other objective variables. These include the daily nutritional value of available food, type of habitation, type of access to water, type of toilet, main cooking source, main heating source, number of durable consumer goods (e.g. bicycle, telephone, radio, TV, stove, car, etc.), access to education and access to health care ( Klasen, 2000 ).

For researching satisfaction of basic needs in developed countries, there are indicators of material deprivation [4] found e.g. in the EU-SILC methodology under the material deprivation module. These include indicators such as rental arrears or delayed mortgage repayments, heating, electricity, gas or water arrears, deferred loan repayments, the ability to afford a one-week annual holiday with the family away from home, the ability to afford a meal containing meat, poultry, fish (or vegetarian equivalent) every other day, the ability to deal with unexpected expenses, ownership of a telephone, colour TV, computer, washing machine and car.

The set of variables also includes a subjective assessment of the household’s overall ability to make ends meet and an estimate of the lowest monthly income limit at which the household would be able to make ends meet. There is also a subjective assessment of the financial burden associated with total housing costs and the financial burden associated with lease purchases and loans. Variables indicating the boundary at which basic needs are met include also a subjective assessment of the quality of the physical and social environment (dwelling too dark, insufficient daylight, noise from neighbours or the street, pollution, dirt or other environmental problems, violent crimes or vandalism near the home) ( EU-SILC, 2011 ).

With regard to the complexity of such data, Annoni and Weziak-Bialowolska (2016 , p. 183) conclude that “the perfect measure of poverty in terms of economic well-being should be a combination of income, consumption and welfare”. They explain that although measuring income is not difficult, measuring consumption level and welfare is more complicated. Consequently, level of disposable income is often used as a proxy for consumption.

While risk of poverty is based on the concept of relative poverty (e.g. in the EU-SILC methodology, the at-risk-of-poverty line is defined as disposable income of below 60% of the national median equivalent disposable income), material deprivation provides an accompanying view, based on both objective and absolute criteria. Income is used as a variable in two ways: (1) to measure income distribution between households and (2) as a classification and/or substance variable in conjunction with other social indicators. In particular, it is used alongside social exclusion [5] indicators ( EU-SILC, 2013 ). Material deprivation is defined in relation to the economic conditions of the individual or household. It is the forced inability to achieve an indicative material standard that most people consider desirable or necessary for a full life. The material deprivation rate is then defined as the share of the population exposed to the forced inability to afford at least three (or four where there is a severe material deprivation rate) of a list of verified items [6] ( EU-SILC, 2015 ). Except for one isolated indicator of income poverty, a combination of this indicator and others is used.

The aggregated indicator of poverty or social exclusion (AROPE) [7] has been described by Vlačuha and Kováčová (2015) . It is based on the multidimensional approach to measuring poverty. In addition to the concept of income poverty, the indicator takes two other dimensions into account—material deprivation and exclusion from the labour market. The aggregated indicator is a combination of three sub-indicators—risk of poverty rate, material deprivation rate and low labour intensity and is defined as the number of people at risk of poverty and/or material deprivation and/or living in households with low work intensity.

The objective indicator of income poverty risk is usually supplemented by subjectively declared financial problems . The EU-SILC methodology has reported subjective testimonies of households about their ability to make ends meet (on a six-point scale, ranging from ‘very difficult’ to ‘very easy’). However, the results obtained using both indicators—objective and subjective—show very little overlap. Mysíková, Večerník and Želinský (2015) reported that the two optics are quite different, and can be referred to as ‘objective’ and ‘subjective’. It is necessary to add that the ‘objective’ calculus (at-risk-of-poverty line) is affected by the methodological approach chosen by the experts and is, ultimately, based on a political decision. On the other hand, ‘subjective’ estimations by households can be considered relatively objective as they take circumstances into account that cannot be determined from income calculation—in particular expenditure and debt burden.

Subjective poverty lines can be considered a mixture of both absolute and relative concepts. Unlike the above-mentioned subjective sub-indicators, they are based on dichotomized income variables and differ from each other in several respects.

The Leyden Poverty Line (LPL) is based on the Welfare Function of Income (WFI), which is derived from a particular survey question—the Income Evaluation Question (IEQ) [8] —and additional knowledge of the respondent’s personal characteristics ( Goedhart, Halberstadt, Kapteyn & Van Praag, 1977 ; Van Praag, Spit, & Van de Stadt, 1982 ). According to the LPL, a family is considered ‘α-poor’ if the evaluation of total family income falls below a certain level of utility α, for example 0.4 or 0.5, that is, LPL(04) and LPL(05), respectively. On the contrary, if the evaluation of the family income exceeds level α, the family is ranked among the ‘non-poor’ ( Flik & Van Praag, 1991 ). This poverty line concept makes it possible to break down the survey sample into multiple quantiles, which the other representative concept of the subjective poverty line does not (see e.g. Danisman Isik, 2018 ; Mareš & Rabušic, 1997 ; Delhausse, Luttgens, & Perelman, 1993 ).

The Subjective Poverty Line (SPL) introduced by Kapteyn, van de Geer and van de Stadt in 1985 states that families are poor if they think their incomes are not sufficient to make ends meet. This measure is based on the previously discussed Minimum Income Question (MIQ) [9] . Instead of asking about income amounts corresponding to several welfare levels, it asks for one income amount which corresponds to a specific welfare label. It is assumed to describe the boundary between being ‘poor’ and ‘non-poor’. According to the definition of the SPL, a respondent’s answer is understood as an individual’s poverty line. Ravallion (2012 , p. 9) has similarly defined the Social Subjective Poverty Line (SSPL) based on the MIQ as “the income below which people tend to think they are poor in the specific setting and above which they tend to think they are not poor”. As Flik and Van Praag (1991 , p. 321) have stated, the LPL (based on a multi-level question) seems to be theoretically superior to the SPL (based on a one-level question), as the SPL is likely to be more subject to random response fluctuations and more sensitive to varying interpretations of the one level.

One could expect that the answers to several ordered verbal labels are much more carefully selected and calibrated as the respondents have to rank several levels as opposed to being presented with just one level—make ends meet. Kapteyn, Kooreman and Willemse (1988) have pointed out that both the LPL and SPL approaches are model-based and that the responses themselves do not directly generate poverty lines. Indeed, it is necessary to estimate a model that explains inter-household variation in the responses to the survey questions. These two aspects identify two crucial methodological issues in the implementation of the SPL and LPL: the responses should measure what they are intended to measure and the model should be correctly specified and estimated. Both poverty lines depend on family composition and on the distribution of incomes and family compositions in the family’s reference group. It is also worth mentioning that one of the conditions for the existence of an SPL/SSPL is that subjective welfare is an increasing function of income, as it is invariably indicated in cross-sectional studies (see e.g. Clark, Frijters, & Shields, 2008 ; Garcia-Carro & Sanchez-Sellero, 2019 ).

The Centre for Social Policy in Antwerp has come up with its own measure called the CSP poverty line ( Deleeck, 1989 ). This is based on the Herman Deleeck question which is standardly used in a modified form also in the EU-SILC question battery. [10] The CSP method uses a small subsample of people who consider themselves to be on the margin [11] , while in both the LPL and SPL the poverty threshold is estimated based on opinions of what constitutes a poverty or non-poverty situation both for people who consider their income to be on the margin and those who view their income differently (below or above the margin). Flik and Van Praag’s reliability assessment for each of these subjective poverty lines, as measured by their standard deviation, has shown that the LPL performs best in this respect. The SPL comes next and the CSP measure ranks third ( Flik & Van Praag, 1991 ). A brief comparison of the basic characteristics of the discussed poverty lines is given in Table 2 .

Comparison of the basic characteristics of subjective poverty lines

Conclusions

According to the findings of Džuka et al. (2017) , three areas of research interest in the psychological problems of poverty have been identified: 1) research into the causes of poverty, 2) research into the psychological consequences of poverty, 3) and more recently research into the relationship between the psychological consequences of poverty and the persistence of poverty. In all cases, the way poverty is operationalized and the way the research sample of poor people is specified play an important role.

Based on our analysis of previously published scientific papers [12] , we can report that on one hand subjective data have redefined the long-standing debate on whether poverty is ‘absolute’ or ‘relative’. However, the structure of the poverty profile has turned out to be different in some respects. While objective poverty lines have often implied that larger households are poorer, this has not typically been the case in cross-sectional studies which use the subjective approach, as these tend to suggest greater economies of scale in consumption than normally assumed, at least in developing countries ( Ravallion, 2012 ). Nevertheless, it has to be taken into account that people generally only know their approximate level of income. Respondents take an estimate of their actual income as their frame of reference when answering the income evaluation and minimum income questions ( Kapteyn, Kooreman, & Willemse, 1988 ). Household income should therefore be measured twice in surveys, in order to determine the biasing effects of respondents’ systematic errors in estimating their own income (e.g. the frame-of-reference bias).

Furthermore, the reliability of the measures used to assess economic situation and the validity of the scores obtained directly depend on the problem the researcher aims to solve. For psychological research (especially that focused on the psychological consequences of poverty, or the relationship between psychological consequences and persistence in poverty), the concept of subjective poverty is particularly relevant, especially in cases where awareness of being poor affects thinking-related functions (such as the effect of poverty on working memory, self-control or attention). Depending on the research question or hypothesis, employing a subjective measure of economic situation will probably be more appropriate than addressing objective indicators. Once the researcher decides whether to employ economic, psychological or multidimensional poverty operationalization, it is of course helpful to use several measures and to properly report them in a sensitivity analysis.

However, it is also necessary to mention the long-standing scepticism amongst economists regarding subjective questions, notably when these data are used as dependent variables. The most pressing issue is thus the extent to which subjective questions are reliable, in the sense of obtaining similar answers under similar circumstances. In estimates concerning income or other gradients in subjective welfare, an attenuation bias can arise from psychological adaptation to adverse circumstances. For example, Večerník and Mysíková (2016) have pointed out that based on the social desirability argument, it is assumed households with low incomes tend to report a higher income than they actually have. On the other hand, households with a higher income are expected to make themselves ‘poorer’ in an interview situation. Income understatement usually rises with amount of income. This also applies to incomes which flow from several sources such as from multiple jobs. It has been confirmed that while earnings tend to be skewed in income reporting by household respondents, old age and other types of pension benefits do not suffer from such distortion. As a result, the income poverty of persons in households with wage earners may be overestimated in comparison with persons living in households with old aged pensioners. Angel, Heuberger and Lamei (2017) have also pointed out significant differences between estimated incomes in the Austrian EU-SILC survey data and the Austrian official register data on income. Their analysis revealed an increase in cross-sectional poverty rates for 2008– 2011 and in the longitudinal poverty rate when register data were used instead of survey data. They concluded that these changes in the poverty rate were mainly driven by the differences in employment income rather than sampling weights or other income components.

The current state of knowledge highlights the need to view poverty as a multi-causal phenomenon, and that aiming to understand its psychological causes by focusing on one dimension is a simplification. Davis and Gouws (2013) suggest that further research should take into account the current cultural context and cultural background, as well as the genetic constitution of the individual affecting his or her adaptation to the environment. Advanced analyses should therefore take into account a number of perspectives besides subjective ones, such as the impact of demographic variables (e.g. ethnicity, education, geographical location, employment) as possible causes of poverty.

Moreover, Van den Bosch et al. (1993) pointed out that both SPL and CSP assume that “minimum income” and “with some difficulty” mean the same thing to all respondents and that there is a household consensus reflecting the household’s standard of living. However, Ravallion (2012 , p. 8) claims that “one must allow for heterogeneity, such that people at the same standard of living give different answers to the subjective welfare question, or that the same person may give a different answer depending on when they are interviewed”. As such, these variations could stem from idiosyncratic ‘mood’ effects, personality traits or simply from errors. It is not the person’s stated perception of his or her welfare in an interview that is taken to be the relevant metric as this is bound to contain many factors that would not be deemed relevant. He concludes that the subjective questions are used to calibrate an interpersonally comparable welfare function based on observed covariates deemed to be relevant on a priori grounds—it is the role of these data sources in calibration that is the present focus.

Even when they have similar personalities or statistically ignorable differences, different people may apply different criteria when scaling their welfare. They have different ideas about what it means to be rich or poor, or what it means to be satisfied or dissatisfied with life. Latent heterogeneity in factors that are essentially irrelevant to welfare but influence responses to subjective welfare questions may lead us to question the implied interpersonal comparisons of welfare from subjective data.

In this respect, it may be concluded that subjective data could be used mainly to identify the weights on the dimensions of welfare for which prices are missing or unreliable and determine the poverty lines based on subjective data. From a wider point of view, as Kingdon and Knight (2006) indicate, it is possible to view subjective well-being as an encompassing concept which permits us to quantify the relevance and importance of the other approaches and their component variables.

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Musculoskeletal Research Center

Symposium Greeting

Welcome to the 2024 Washington University Musculoskeletal Research Center Symposium. This is our 13th annual Symposium. Our objective is to provide an opportunity for musculoskeletal researchers to network and talk science, and to provide a platform for early-career faculty, trainees and staff from member labs to present their work. Please take advantage of our time together to interact with your fellow attendees, including those outside your “usual circle”, and to learn something new.

This year we have expanded our geographic range to welcome attendees from beyond Saint Louis, including the University of Missouri-Columbia (Mizzou), University of Arkansas for Medical Sciences (UAMS), Vanderbilt University, University of Illinois Urbana-Champaign (UIUC), and Northeastern University.

We have an exciting, full-day program with some new twists. The oral presentations feature the 2023 Pilot & Feasibility Awardees, as well as 18 authors of top-rated abstracts who will present either 7-minute or 3-minute (poster teaser) talks. The Poster Session features 75 presenters, including 27 Poster Award Finalists. We have four invited speakers: Keynote Speaker, Prof Marjolein van der Meulen from Cornell, and Early-Stage Spotlight Speaker, Dr. Ha-Neui Kim from UAMS, as well as two career development speakers, Dr Jason Lim from Delaware and Corinne Decker from Ensoma, who are MRC alumni. Finally, we will hear from two Science SLAM! presenters during the reception.

Thanks to our abstract reviewers; each abstract was scored by three reviewers. Thank you to the organizing committee for putting together the program: Aaron Johnson, Naomi Dirckx, Gaurav Swarnkar, Isabel Mendendez and Deb Veis. In selecting presenters and poster finalists, we considered the abstract scores, as well as topic, lab and gender diversity. Lastly, a big “Thank You” to Kami McGhee for all her efforts in planning and organizing the meeting, from abstract submissions and reviews, to venue logistics, to assembling this program book.

I hope you enjoy the meeting. Matt Silva

• Presentations
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• Symposium 2024 Abstract Submission