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The project is the result of a partnership between ATD-Quart-Monde (ATD), the Secours Catholique (SCCF) and the French Statistical Office (Insee), conducted in 2022, with a view to better understand and measure great poverty and more specifically some hidden dimensions. The project is based on the active participation of people having an experience of poverty; it aims at identifying how the tools used by INSEE to measure poverty are consistent with the experiences of people that are actually living poverty day by day. Some local groups of people experiencing poverty have been formed, coordinated by animators from the associations with an approach that promotes trust and active participation of people. The project is punctuated by regular meetings of local groups and three plenary meetings with everyone. The project is divided in two phases : • phase 1 : build a base of knowledge and common benchmarks to define poverty. • phase 2 : specific work on two dimensions : “social isolation” and “institutional mistreatment”, with a view to identify common points and divergences between Insee tools and people’s experienc

This is the U.S. Census Bureau’s first release of the National Experimental Wellbeing Statistics (NEWS) project. The NEWS project aims to produce the best possible estimates of income and poverty given all available survey and administrative data. We link survey, decennial census, administrative, and commercial data to address measurement error in income and poverty statistics. We estimate improved (pre-tax money) income and poverty statistics for 2018 by addressing several possible sources of bias documented in prior research. We address biases from (1) unit nonresponse through improved weights, (2) missing income information in both survey and administrative data through improved imputation, and (3) misreporting by combining or replacing survey responses with administrative information. Reducing survey error substantially affects key measures of wellbeing: We estimate median household income is 6.3 percent higher than in the survey estimate, and poverty is 1.1 percentage points lower. These changes are driven by subpopulations for which survey error is particularly relevant. For householders aged 65 and over, median household income is 27.3 percent higher than in the survey estimate and for people aged 65 and over, poverty is 3.3 percentage points lower than the survey estimate. We do not find a significant impact on median household income for householders under 65 or on child poverty. Finally, we discuss plans for future releases: addressing other potential sources of bias, releasing additional years of statistics, extending the income concepts measured, and including smaller geographies such as state and county.

This document has been updated for technical reasons upon request from the authors

This document has been updated for technical reasons upon request of its author on 14 November 2023 at the start of the session.

The standard of living of the population is characterised not only by the amount of income per capita, but also by the ability to meet the needs at the expense of this income, i.e. the volume of consumption of goods and services.Growth of prices undoubtedly reduces the purchasing power of monetary income and leads to a deterioration in living standards. At the same time, high inflation affects the rich and the poor differently. It particularly affects low-income groups. In order to support the most vulnerable segments of the population during the period of rising prices, the state applies various instruments aimed at mitigating the effects of increased inflation (indexation of wages, social payments and benefits, discounts on socially important goods, etc.).The presentation provides an assessment of the impact of inflationary processes on the living standards of the population.

The Brazilian Household Food Insecurity Measurement Scale (EBIA) has been the official measure of household food insecurity (FI) in Brazil. EBIA was included in the National Household Budget Survey (Pesquisa de Orçamentos Familiares – POF 2017-2018) for the first time. The main objective of this paper is to identify the food costs of the vulnerable populations at risk of food insecurity (FI) in Brazil. The methodology is based on the construction of corresponding spatial price indexes obtained from POF conducted in 2017-2018, which collected data from different Brazilian geographical areas. It is worth noting that Brazil also does not evaluate official spatial price indexes which specifies differences in the cost of living among different Brazilian regions. Following the EBIA, we identify the vulnerable population as one that is at risk of moderate and severe food insecurity. The paper points to relevant disparities in price indexes between the regions of the country for this already population vulnerable to food insecurity that represents 60% of the Brazilian population. The geographic context that presented the greatest positive variation in relation to Brazil was the Metropolitan Region of São Paulo, with the price of chicken +10.5% higher. The five geographic contexts of the Northeast recorded the chicken price index higher than the average in Brazil. With prices below Brazil, the Federal District stood out. The average household expenses with products classified as fresh or minimally processed represented 55.5% of the total expenses for Brazil, while spending on ultra-processed foods, 26.3%. To the best of our knowledge this is the first time that is possible to investigate, simultaneously, data based on food expenditure and on food insecurity in the same national survey in Brazil. This study also offers a food regional price index for both, the whole population and the vulnerable one. Finally, these indexes can be used in future studies to provide information for public policies on poverty.

In recent years, there has been increased interest in going beyond headline measures of inflation to better describe the experiences of households. The CPI for All Urban Consumers (CPI-U) targets the inflation experience of over 90 percent of households in the United States, but it may not reflect the inflation experience of an individual household or group of households. This presentation describes two ongoing research efforts at the Bureau of Labor Statistics to expand its offerings of consumer price indexes in ways that allow for a richer description of household experiences. First, in response to increasing user demand, we construct consumer price indexes for different groups along the income distribution. From 2006 to 2023, lower income households generally faced larger inflation rates than higher income households, and the gap is highest when measured using the Chained CPI, which is a closer approximation to a cost-of-living index. We explore how different budget items contribute to this gap, as well as how it changes over time. Second, we estimate a family of price indexes known as Household Cost Indexes (HCI), which aim to measure the average inflation experiences of households as they purchase consumer goods and services. These differ from the usual CPIs in two main respects. First, the upper-level aggregation of the HCIs weights households equally, unlike most headline CPIs which implicitly give more weight to higher-expenditure households. Second, the HCIs use the payments approach to value owner-occupied housing services explicitly using household outlays. In contrast, the U.S. CPIs use rental equivalence. The HCI for all urban consumers has an average 12-month change of 1.51% over December 2011 to December 2021, compared to 1.86% for the CPI-U. Roughly 95% of the difference is due to the payments approach.

The CIS Statistics Committee began in 2023 the implementation of the Project “Development of CIS Statistics”. One of the key tasks is the creation of a Unified Information and Analytical System (UIAS) based on BI technologies. Based on the results of this work, the development of a prototype of the UIAS has already been carried out, including:

- setting up the information model of the data warehouse;

- loading historical data and current information received from the NSS into the data warehouse;

- loading into the database open statistical data of other countries of the world on labor statistics indicators from the World Bank database;

- setting up interactive information panels and a catalog of publications;

- setting up a statistical portal;

- development and publication of interactive information panels on the statistical portal of the CIS Statistics Committee.

Reliable statistics are the cornerstone of sound policy. High-quality poverty statistics enable policymakers to make choices that lead to economic and social benefits for the poor. At the national and policy level, however, having more than one poverty measure can be challenging and will likely require significant dissemination efforts to make the use of additional poverty measures sufficiently widespread. Kazakhstan will share information on the use of data visualization tools on living standards and will demonstrate an interactive dashboard posted on the BNS website. In addition, they will inform about new initiatives to disseminate data on poverty and collaborate with users.

Abstract
Two practical applications combining household survey and geospatial data are presented. First, administrative level one data from household surveys are combined with geospatial data to project child poverty headcount rates at administrative level two and below. This analysis is carried out using the same indicators and thresholds across countries (to estimate child poverty nationally and at administrative level one). Small area estimates and machine learning models are used to generate the estimates at lower administrative levels. This first part of the paper includes a presentation of results and discussion of limitations of this methodology. Besides this discussion, the paper includes a second practical application combining georeferenced data and survey data. In this case, the child poverty subnational data used in
the first part are combined with high-resolution geographical data about environmental risks. Combining these two sets allows to analyze the relationship between child poverty and environmental risks which provides an important tool for Disaster Risk Reduction plans.

Two practical applications combining household survey and geospatial data are presented. First, administrative level one data from household surveys are combined with geospatial data to project child poverty headcount rates at administrative level two. This analysis is carried out using the same indicators and thresholds across countries (to estimate child poverty nationally and at administrative level one) and various machine learning models to generate the estimates (at administrative level two).
The first part of the paper includes a presentation of results and discussion of limitations of this methodology. In addition, the paper includes a practical application combining georeferenced data and survey data. In this case, the child poverty subnational data used in the first part are combined with high-resolution geographical data collected in the UNICEF Children’s Climate Risk Index (CCRI). Combining these two sets allows to analyze the relationship between child poverty and environmental risks.

Faced with declining response rates to traditional surveys, budget constraints, and competing data providers, NSOs need to explore other data sources, establish new partnerships, and use statistical modeling techniques. Information will be presented on the work done to integrate the household budget survey with the Digital Family Card - a database for the provision of social assistance of the Ministry of Labor and Social Protection of the Population. The presentation will show how comparison of data and analysis with HBS, could help to switch to relative poverty indicators based on the median.

In the paper presented in the meeting of the UNECE Group of Experts on Measuring Poverty and Inequality held in December 2022, the authors discussed the potential of two innovative data sources and some preliminary results obtained within the project of the Italian National Statistical Institute (Istat) on revising and updating the Italian estimation methodology of absolute poverty.

Recently Istat has completed the above-mentioned project and, indeed, estimated the thresholds using two new available data sources (currently used to estimate CPIs): the scanner data for food components (largely used in prices data collection in addition to traditional methods) and, the database of rental contracts provided by the Italian Tax Office (a typical administrative data source).

The new EU regulation  for social surveys, in force since 2021, foresees a minimum coverage of income, consumption and wealth data in two existing EU social surveys (EU Statistics on income and living conditions - EU-SILC and Household budget survey - HBS). It resulted in the collection of some elements of household consumption and wealth in EU-SILC and some elements of income and wealth in HBS data. This allows data analysis on the components of Income, Consumption and Wealth (ICW) based on both surveys individually and improves the statistical capability of statistical matching between the two data sources and with the Household Finance and Consumption Survey collected by European Central Bank.

This analysis presents data based on the EU-SILC annual variables (income) and on the 2020 ad hoc module on Overindebtedness, consumption and wealth (OCW) (elements of consumption and wealth).  The results of the module are used to develop a 6-yearly module on OCW to be first collected in 2026. EU-SILC data collection in 2020 has been particularly challenging due to the significant changes of the behaviour of the households’ consumption and savings during COVID-19. In addition, different sanitary restrictions have led to adjustments to the data collection. Overall, the 2020 module data meets the quality criteria for most of the countries.

Non-pharmaceutical interventions (NPI) such as social distancing, business and school closures have been introduced worldwide to prevent the spread of COVID-19. In Georgia NPIs affected children’s lives, contributing to the emergence of new categories of poor. In-depth empirical understanding on which categories of people have most suffered the shock is still limited. By relying on Georgia Integrated Household Survey (IHS) for 2017–2021, we traced the differentiated impacts of NPIs throughout the start of the pandemic in the first two quarters of 2020. Households with children have been hit harder and although an economic rebound started to happen in the last quarters of 2020 our trend analysis suggested that these effects may last longer than the pandemic. The NPIs reshaped the characteristics of poverty, exacerbating gender and area inequalities and placing greatest burden on households with children especially in rural areas, with larger impacts for those relying on self-employment.

The project is the result of a partnership between ATD-Quart-Monde (ATD), the Secours Catholique (SCCF) and the French Statistical Office (Insee), conducted in 2022, with a view to better understand and measure great poverty and more specifically some hidden dimensions.
The project is based on the active participation of people having an experience of poverty; it aims at identifying how the tools used by INSEE to measure poverty are consistent with the experiences of people that are actually living poverty day by day. Some local groups of people experiencing poverty have been formed, coordinated by animators from the associations with an approach that promotes trust and active participation of people.

Mexico was the first country in the world to establish an official multidimensional measurement of poverty. This means that, in addition to considering the inadequacy of economic resources, it considers several additional dimensions on which social policy should focus. Through a methodology that links two approaches: economic well-being and social rights, there is a conceptually approach to the issue of multidimensional poverty. This approach recognizes that the impoverished population not only faces economic resource inadequacy but is also vulnerable in the exercise of their fundamental rights due to a lack of access to food, healthcare, education, social security, and adequate housing. This approach allows for the development of a comprehensive social framework grounded in a rights-based perspective, monitoring various dimensions that influence social and human development, and guiding the formulation of public policies in support of complete and universal social inclusion.

Eradicating extreme poverty constitutes a fundamental objective within the framework of the Sustainable Development Goals (SDGs). The pivotal roles played by both the state and non-governmental organizations (NGOs) in this pursuit are undisputed. Central to this endeavor are social protection programs, encompassing not only cash assistance but also the provision of essential goods and services through in-kind transfers. These programs offer a lifeline to impoverished families, augmenting their resilience and empowering them to enhance their overall well-being. While certain countries primarily channel their efforts towards supporting the elderly population, others adopt a more inclusive approach by identifying various vulnerable groups, around which social programs and eligibility criteria are tailored.

This analysis shows how European Union Statistics on Income and Living Conditions (EU-SILC) measures the effectiveness of social transfers in poverty reduction and inform social policy development within the European Union (EU).
The methodology encompasses various indicators aiming at assessing the at-risk-of-poverty rate from different perspectives. Eurostat's findings underscore the pivotal role of social transfers in reducing poverty levels across the EU, while recognizing variations among Member States.
The relative poverty gap indicators1 highlight the depth of poverty among the at-risk-of-poverty population and households with very low work intensity. Furthermore, the analysis of persons receiving social transfers2 offers insights into the capacity of social protection systems to reach individuals in need of support, contributing to the combat against poverty and social exclusion.

Within the scope of various recommendations, the Task Force on Subjective Poverty Measures emphasizes the importance of properly utilizing the Minimum Income (or Spending) Question (MIQ/MSQ) with the intersection approach. This method is considered a fundamental approach for estimating a subjective poverty line (SPL) and identifying populations falling below this threshold. Introduced in the 1970s, the intersection approach has been employed across various contexts and comparisons for decades. To provide official data on subjective poverty using this method, it is imperative to include the MIQ/MSQ in surveys.
Unavailability of MIQ/MSQ question poses challenges when tracking trends in subjective poverty. As a result, researchers need to rely on various approximations or employ different methods. In this presentation, we introduce two distinct approaches for identifying households experiencing subjective poverty, with one approach also enabling the estimation of a SPL.

EU-SILC, the European survey on income and living conditions of the population, is a basic source of information on poverty that is harmonized at European Union level. The presentation will demonstrate the results of an analysis based on this survey that compares the determinants of subjective and objective poverty in Poland. In the EU-SILC, there are no questions that directly measure subjective poverty (the share of the population that considers itself poor). Instead, the variables included in this survey provide an opportunity to use indirect (proxy) methods to measure subjective poverty. On the basis of a question on the assessment of the ability to make ends meet (variable HS 120), an indicator of subjective economic stress was calculated, which is treated as an indirect measure of subjective poverty. In the case of so-called objective indicators, the analysis includes two indicators - „at-risk-of-poverty rate” (ARPR) and the 'severe material and social deprivation rate' (SMSD). Logistic regression models were used to analyze the impact of potential factors on the occurrence of various forms of poverty. EU-SILC 2022 (latest available data) and EU-SILC 2019 (the period before COVID-19) were used in the poverty determinant analysis. The repeated measurement (and analysis) referring to two time points can, to some extent, assess the stability of poverty risk factors.

Subjective indicators have an important complementary role to play in reaching the poorest and making their voices heard. Subjective measures reflect people's perceptions of their economic well-being across various aspects of life, including health, financial status and work. The resulting estimates may vary significantly due to different methods and cultural beliefs about well-being and poverty. The indicators are also influenced by the age, gender and region of the respondent. Kazakhstan will share information on the analysis of subjective poverty depending on age, gender and region over time. In addition, information will be provided on such a food security indicator as the Food Insecurity Scale.