In the 2018 financial year, the mean household balance of accounts with superannuation funds in Australia amounted to approximately 213.700 Australian dollars. Private trusts amounted to just over 40,000 Australian dollars.

In Belgium, forecasts of consumers regarding household savings for the upcoming year provide insight into the country's economic climate. From January 2018 to December 2019, the overall forecasts of Belgian consumers regarding household savings were pessimistic. However, from January 2020, optimistic forecasts outweighed negative ones, until October of 2022. In January 2025, the balance of consumer forecast was 18, meaning that the positive forecasts were higher than the negative forecasts by 18 percentage point. The recent positive forecasts seem to indicate a shift in Belgian consumer's perceptions of household savings. Such forecasts are used to calculate the Belgian consumer confidence indicator, providing insight into the economic landscape of the country.

Solomon Islands Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Benefit data was reported at 0.000 SBD mn in Mar 2024. This stayed constant from the previous number of 0.000 SBD mn for Dec 2023. Solomon Islands Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Benefit data is updated quarterly, averaging 0.000 SBD mn from Mar 2015 (Median) to Mar 2024, with 37 observations. The data reached an all-time high of 0.000 SBD mn in Mar 2024 and a record low of 0.000 SBD mn in Mar 2024. Solomon Islands Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Benefit data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Solomon Islands – Table SB.IMF.FSI: 2019 Methodology: Sectoral Financial Statement: Balance Sheet: Quarterly.

Poland Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Benefit data was reported at 0.000 PLN mn in Mar 2023. This stayed constant from the previous number of 0.000 PLN mn for Dec 2022. Poland Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Benefit data is updated quarterly, averaging 0.000 PLN mn from Mar 2022 (Median) to Mar 2023, with 5 observations. The data reached an all-time high of 0.000 PLN mn in Mar 2023 and a record low of 0.000 PLN mn in Mar 2023. Poland Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Benefit data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Poland – Table PL.IMF.FSI: 2019 Methodology: Sectoral Financial Statement: Balance Sheet: Quarterly.

As of between April 2022 and June 2023, the median amount of savings that adults had in their balance in the United States decreased significantly. The average savings balance also decreased, according to a survey. The reason for the disparity between the median and mean values is that the answers of those respondents with very high savings that distort the results of the mean, but not the median.

The financial indicators are based on data compiled according to the 2008 SNA "System of National Accounts, 2008". Many indicators are expressed as a percentage of Gross Domestic Product (GDP) or as a percentage of Gross Disposable Income (GDI) when referring to the Households and NPISHs sector. The definition of GDP and GDI are the following:

Gross Domestic Product:
Gross Domestic Product (GDP) is derived from the concept of value added. Gross value added is the difference of output and intermediate consumption. GDP is the sum of gross value added of all resident producer units plus that part (possibly the total) of taxes on products, less subsidies on products, that is not included in the valuation of output [System of National Accounts, 2008, par. 2.138]. GDP is also equal to the sum of final uses of goods and services (all uses except intermediate consumption) measured at purchasers’ prices, less the value of imports of goods and services [System of National Accounts, 2008, par. 2.139]. GDP is also equal to the sum of primary incomes distributed by producer units [System of National Accounts, 2008, par. 2.140].

Gross Disposable Income:
Gross Disposable Income (GDI) is equal to net disposable income which is the balancing item of the secondary distribution income account plus the consumption of fixed capital. The use of the Gross Disposable Income (GDI), rather than net disposable income, is preferable for analytical purposes because there are uncertainty and comparability problems with the calculation of consumption of fixed capital. GDI measures the income available to the total economy for final consumption and gross saving [System of National Accounts, 2008, par. 2.145].

Definition of Debt:
Debt is a commonly used concept, defined as a specific subset of liabilities identified according to the types of financial instruments included or excluded. Generally, debt is defined as all liabilities that require payment or payments of interest or principal by the debtor to the creditor at a date or dates in the future. Consequently, all debt instruments are liabilities, but some liabilities such as shares, equity and financial derivatives are not debt [System of National Accounts, 2008, par. 22.104]. According to the SNA, most debt instruments are valued at market prices. However, some countries do not apply this valuation, in particular for securities other than shares, except financial derivatives (AF33). In this dataset, for financial indicators referring to debt, the concept of debt is the one adopted by the SNA 2008 as well as by the International Monetary Fund in “Public Sector Debt Statistics – Guide for compilers and users” (Pre-publication draft, May 2011). Debt is thus obtained as the sum of the following liability categories, whenever available / applicable in the financial balance sheet of the institutional sector:special drawing rights (AF12), currency and deposits (AF2), debt securities (AF3), loans (AF4), insurance, pension, and standardised guarantees (AF6), and other accounts payable (AF8). This definition differs from the definition of debt applied under the Maastricht Treaty for European countries. First, gross debt according to the Maastricht definition excludes not only financial derivatives and employee stock options (AF7) and equity and investment fund shares (AF5) but also insurance pensions and standardised guarantees (AF6) and other accounts payable (AF8). Second, debt according to Maastricht definition is valued at nominal prices and not at market prices.

To view other related indicator datasets, please refer to:
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Abstract

The study includes a merged core data file from the 7 country RuralStruc surveys conducted in 2007-2008.

Geographic coverage

Areas covered in the data are selected rural areas in the following regions:

• in Kenya: Bungoma, Nakuru North, Nyando

• in Madagascar: Alaotra, Antsirabe, Itasy, Morondava

• in Mali: Diema, Koutiala, Macina, Tominian

• in Mexico: Tequisquiapan (Queretaro), Sotavento (Veracruz)

• in Morocco: Chaouia, Saiss, Souss

• in Nicaragua: El Cua, El Viejo, La Libertad, Muy Muy, Terrabona

• in Senegal: Casamance, Mekhe, Nioro, Senegal River Delta.

For more detailed information on geographic coverage, data users can refer to the RuralStruc National Reports.

Analysis unit

The basic unit of observation and analysis that the study describes is the rural household, with the exception of Mali.The preference for rural and not only farm households was justified by the objective of identifying more precisely agriculture's role with respect to other rural activities and sources of income. This option was not neutral, as it refers to analytical categories whose definition are more complicated than one may believe a priori, like the definition of what “rural” is, its characterization varying between countries. The Program National teams considered the following definitions for rural housholds:

-Kenya: "The household was defined as a family living together, eating together, and making farming and other household decisions as a unit"'

-Madagascar :" Le ménage est un ensemble de personnes avec ou sans lien de parenté, vivant sous le même toit ou dans la même concession, prenant leur repas ensemble ou par petits groupes, mettant une partie ou la totalité de leurs revenus en commun pour la bonne marche du groupe, et dépendant du point de vue des dépenses d'une même autorité appelée chef de ménage », le chef de ménage étant la personne reconnue comme tel par l’ensemble des membres du ménage".

-Mali : "La Loi d’Orientation Agricole (LOA), dans ses articles 10 à 28, définit ce que sont les exploitations agricoles au Mali. « L’exploitation agricole est une unité de production dans laquelle l’exploitant et/ou ses associés mettent en oeuvre un système de production agricole. Elles sont classées en deux catégories : l’exploitation agricole familiale et l’entreprise agricole. L’exploitation agricole familiale est constituée d’un ou de plusieurs membres unis librement par des liens de parenté ou des us et coutumes et exploitant en commun les facteurs de production en vue de générer des ressources sous la direction d’un des membres, désigné chef d’exploitation, qu’il soit de sexe masculin ou féminin. Le chef d’exploitation assure la maîtrise d’oeuvre et veille à l’exploitation optimale des facteurs de production. Il exerce cette activité à titre principal et représente l’exploitation dans tous les actes de la vie civile. Sont reconnus comme exerçant un métier Agricole, notamment, les agriculteurs, éleveurs, pêcheurs, exploitants forestiers".

-Maroc : "L’unité ménage renvoie au groupe domestique qui est défini comme une unité de résidence, de production et de consommation. Le plus souvent, le groupe domestique a pour noyau une famille, à laquelle peuvent s’ajouter des parents éloignés ou des « étrangers ». Il peut aussi se composer de plusieurs familles nucléaires comme il peut rassembler des personnes sans aucun lien de parenté".

-Mexico : "El Instituto Nacional de Estadística Geografía e Informática (INEGI) usa el concepto de localidad que define como “todo lugar ocupado por una vivienda o conjunto de viviendas, de las cuales al menos una está habitada. El lugar es reconocido comúnmente por un nombre dado por la ley o la costumbre”, y por otro considera que una localidad es rural cuando tiene menos de 2 500 habitantes. El INEGI define también en concepto de hogar como una “unidad doméstica [que] hace referencia a una organización estructurada a partir de lazos o redes sociales establecidas entre personas unidas o no por relaciones de parentesco, que comparten una misma vivienda y organizan en común la reproducción de la vida cotidiana a partir de un presupuesto común para la alimentación, independientemente de que se dividan otros gastos”.

-Nicaragua : "Se define hogar como el número de personas comparten una olla común. Un hogar puede estar compuesto de una o más familias. La definición oficial en Nicaragua de rural es aquel territorio que “comprenden los poblados de menos de 1000 habitantes que no reúnen las condiciones urbanísticas mínimas indicadas y la población dispersa.” INEC, 2007".

-Senegal : "Le rural se définit par opposition à l’urbain, constitué par les villes et les communes, même à dominance rurale. Au Sénégal, les populations d’une commune sont de facto considérées comme des urbains ; or, plusieurs communes sont composées à plus de la moitié par des agriculteurs. Le ménage rural se définit comme un groupe familial résidant en milieu rural au sein duquel s’organisent la production agricole et/ou non agricole, la préparation et la consommation des repas. Traditionnellement, le ménage rural se confond avec le ménage agricole ; toutefois, on note de plus en plus que la nourriture du ménage rural provient de moins en moins de la production ou des revenus tirés de l’agriculture au sens large : production agricole, élevage, pêche et foresterie. L’unité familiale de production et de consommation16 ne coïncide pas forcément avec l’unité de résidence, ker en wolof ou galle en pulaar".

For detailed information on the rationale corresponding to the definition of rural households, the data users can refer to the National Reports, available as External Resources.

Universe

The universe covered by the study includes rural households and all household members that were selected in the study areas.

Kind of data

Sample survey data [ssd]

Sampling procedure

With the objective of 300 to 400 surveyed households per region (i.e. between 900 and 1,200 surveys per country),the Program National teams engaged in the sampling process in two steps. The first step was the selection of the localities to be surveyed, with consideration of regions' characteristics and national team expertise. The second step was the sampling itself, which was based on existing census lists or intentionally prepared locality household lists. Then, households were selected at random, targeting a sufficient number of households per locality allowing representativeness at local level.

In the seven countries, 8,061 rural households' surveys were selected for the sample in 26 regions and 167 localities (depending on the settlement structure), and 7,269 were successfully interviewed and kept for the analysis. In Mali, the 634 household surveys (at the family farm level) were completed by 643 interviews with dependent households and 749 interviews with women.

Mode of data collection

Face-to-face [f2f]

Research instrument

The merged dataset was constructed from variables extracted from national datasets.

For details on questions relating to these variables, see the attached questionnaires for each country survey. Each country questionnaire was derived and adapted from a questionnaire template which was designed collectively by the RuralStruc Program Coordination team and the national teams.

The original page and question numbers for each variable is included in the variable descriptions.

Cleaning operations

Secondary editing of the data in this core dataset included:

(i) Data in local currency units (for example, incomes, prices, sales of agricultural products) were converted to international dollars ($ PPP), for comparability across national surveys. Purchasing Power Parity conversion rates were calculated using the World Bank Development Data Platform. They refer to the period January 2007 to April 2008. The conversion rates between $1 PPP and local currency units are the following: - Kenya: 34 Kenyan Shilling - Madagascar: 758.7 Ariary - Mali: 239.6 CFA Franc - Mexico: 7.3 Mexican Peso - Morocco: 4.8 Dirham - Nicaragua: 6.7 Cordoba - Senegal: 258.6 CFA Franc

(ii) Data in local currency units were converted into kilo-calories, for comparability across national surveys. In all the studied zones, diets rely primarily on cereals - at least in terms of energy. Thus, the basic cereal of each zone (or basket of cereals in the case of Mali) was used as a reference. The conversion rates between Kg of cereals and Kcal are those provided by the FAO's Food Balance Sheets (FAO 2001). The prices of cereals are those used by the RuralStruc national teams to estimate the value of self-consumption. These prices correspond with the average producer sale prices (or the median in the case of Madagascar) for the surveyed year. One will note that, in general, the farm income for the poorest households largely consists of self-consumption of cereals, which are valued, therefore, at the producer sale price. The average cereal prices and kilocalorie ratios permitted calculation of a price for units of 1000 Kcal in $PPP and then to convert the estimated monetary incomes in incomes in kilocalories equivalent. For detailed information, data users can refer to the methodological annex of the synthesis report.

(iii) Recoding of the geographical component of the household identifier

For more details on data editing, the data user should refer to the variable descriptions.

Italy Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Benefit data was reported at 3,737.966 EUR mn in Jun 2024. This records an increase from the previous number of 3,732.370 EUR mn for Dec 2023. Italy Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Benefit data is updated quarterly, averaging 3,737.966 EUR mn from Jun 2022 (Median) to Jun 2024, with 5 observations. The data reached an all-time high of 4,270.604 EUR mn in Jun 2022 and a record low of 3,722.961 EUR mn in Dec 2022. Italy Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Benefit data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Italy – Table IT.IMF.FSI: 2019 Methodology: Sectoral Financial Statement: Balance Sheet: Quarterly.

Household Income Distribution Atlas: Mean and median income indicators. Annual. Municipalities.

Abstract

The Hunger Safety Net Programme (HSNP) is a social protection project being conducted in the Arid and Semi-Arid Lands (ASALs) of northern Kenya. The pilot phase has now concluded and the HSNP is beginning to scale up under Phase 2. The ASALs are extremely food-insecure areas highly prone to drought, which have experienced recurrent food crises and food aid responses for decades. The HSNP is intended to reduce dependency on emergency food aid by sustainably strengthening livelihoods through cash transfers.

Oxford Policy Management was responsible for the monitoring and evaluation (M&E) of the programme under the pilot phase, with the intention of informing programme scale-up as well as the government’s social protection strategy more generally. The M&E involved a large-scale rigorous community-randomised controlled impact evaluation household survey, assessment of targeting performance of three alternative targeting mechanisms (Social Pension; Dependency Ratio; Community-based Targeting), qualitative research (interviews and focus group discussions) to assess targeting and impact issues less easily captured in the quantitative survey, and on-going operational and payments monitoring to ensure the smooth implementation of the programme. Findings were communicated to the HSNP Secretariat, Government of Kenya and the Department for International Development (DFID) on a regular basis to inform and advise on policy revisions and development. The M&E component used the data it produced to advise the design of HSNP Phase 2, including micro-simulations of different programme targeting scenarios and review of the phase 2 targeting approach which combines proxy means testing with community-based targeting.

The impact evaluation study compares the situation of HSNP and control households at the time of their selection into the programme (baseline), with their situation 12 months (year 1 follow-up) and 24 months later (year 2 follow-up). Over this 24-month period most of the HSNP households covered by the evaluation had received 11 or 12 bi-monthly transfers (initially KES 2,150, increased to KES 3,500 by the end of the evaluation period).

The baseline data collection was completed in November 2010, the first round of follow-up data collection finished in November 2011, and the final round of fieldwork - in November 2012.

Geographic coverage

Counties of Turkana, Marsabit, Wajir, and Mandera.

Analysis unit

• individuals,
• households,
• community.

Universe

All persons living within "secure" sub-locations across all counties at the time of sampling (2008; due to sporadic insecurity across the four counties, a small portion of sub-locations were deemed to be insecure when the sample was drawn and so excluded from the sample frame).

Kind of data

Sample survey data [ssd]

Sampling procedure

At follow-up 2, in addition to attrition, the sample size is further reduced because the follow-up 2 survey covered eight fewer sub-locations, 40 rather than 48. Overall 2,436 households were surveyed (at the baseline, 5,108 households were covered).

The evaluation sub-locations were selected from a sample frame of all secure sub-locations in each district. In each district 12 sub-locations were selected with PPS (Probability Proportional to Size) with implicit stratification by population density such that there is an even number of selected sub-locations per new district.

The evaluation sub-locations were sorted within districts by population density and paired up, with one of the pair being control and one being treatment. The sampling strategy for the quantitative survey was designed in order to enable a comparison of the relative targeting performance of three different targeting mechanisms. These are:

• Community-based targeting (CBT): The community collectively selected households they consider most in need of transfers, up to a quota of 50% of all households in the community;
• Dependency ratio targeting (DR): Households were selected if individuals under 18 years old, over 55 years old, disabled or chronically ill made up more than a specified proportion of all household members;
• Social pension (SP): All individuals aged 55 or older were selected.

For both the treatment and control sub-locations there are an equal number of CBT, SP and DR sub-locations. Assignment of targeting mechanisms to sub-locations was done randomly across the same pairs that were defined to assign treatment and control status. In all the evaluation sub-locations, the HSNP Admin component implemented the targeting process. In half the sub-locations the selected recipients started receiving the transfer as soon as they were enrolled on the programme - these are referred to as the treatment sub-locations. In the other half of the evaluation sub-locations, the selected recipients were not to receive the transfer for the first two years after enrolment - these are referred to as the control sub-locations.

The households in the treatment sub-locations that are selected for the programme are referred to as the treatment group. These households are beneficiaries of the programme. In control sub-locations the households that are selected for the programme are referred to as the control group. These households are also beneficiaries of the programme but only begin to receive payments two years after registration. The targeting process was identical in the treatment and control sub-locations. The following population groups can thus be identified and sampled: - Group A: Households in the treatment sub-locations selected for inclusion in the programme; - Group B: Households in control sub-locations selected for inclusion in the programme but with delayed payments; - Group C: Households in treatment sub-locations that were not selected for inclusion in the programme; - Group D: Households in control sub-locations that were not selected for inclusion in the programme.

Because targeting was conducted in both treatment and control areas, households were sampled in the same way across treatment and control areas. Selected households (groups A and B) were sampled from HSNP administrative records. Sixty six beneficiary households were sampled using simple random sampling (SRS) in each sub-location (in two of sub-locations this was not possible due to insufficient numbers of beneficiaries in the programme records). In cases of household non-response replacements were randomly drawn from the remaining list of non-sampled households. This process was strictly controlled by the District Team Leaders.

Non-selected households (groups C and D) were sampled from household listings undertaken in a sample of three settlements within each sub-location. These settlements were randomly sampled. The settlement sample was stratified by settlement type, with one settlement of each type being sampled. Settlements were stratified into three different types: 1. Main settlement (the main settlement was defined as the main permanent settlement in the sub-location, often known as the sub-location centre and usually where the sub-location chief was based. As there was always one main settlement by definition, the main settlement was thereby always selected with certainty). 2. Permanent settlements (permanent settlement is defined as a collection of dwellings where at least some households are always resident, and/or there is at least one permanent structure). 3. Non-permanent settlements.

As concern community level data, community questionnaires were conducted in every community for which at least one household interview was attached. A community was defined as a settlement or a sub-section of a settlement if that settlement had been segmented due to its size. Due to missing data, a small proportion of households are not linked to any community data.

The above explanation is taken from "Kenya HSNP Monitoring and Evaluation Component: Impact Evaluation Final Report 2009 to 2012". For more details please refer to this report in Related Materials section.

Sampling deviation

The reduction in the number of sub-locations surveyed at follow-up 2 was the result of decisions made by the programme and its stakeholders, rather than a technical decision by the evaluation team. This reduction in sample size is unfortunate for a number of reasons. Firstly, it undermines the study design to the extent that the smaller sample size reduces the ability to detect impact with statistical significance. Secondly, it affects the balance of the sample, meaning that treatment and control populations are less balanced at baseline than they were with the original sample structure. Lastly, the sample was designed to be seasonally balanced across the whole calendar year, which is no longer the case as sub-locations that would have been surveyed in the latter and early part of the calendar were dropped. Another implication of the reduced sample at follow-up 2 is that the baseline estimates presented in this report differ from those presented in the baseline and follow-up 1 impact reports. This is because the estimates now relate to slightly different populations.

Mode of data collection

Computer Assisted Personal Interview [capi]

Portugal Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Benefit data was reported at 13,005.812 EUR mn in 2023. This records a decrease from the previous number of 14,934.000 EUR mn for 2022. Portugal Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Benefit data is updated yearly, averaging 17,186.537 EUR mn from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 20,694.631 EUR mn in 2008 and a record low of 11,218.559 EUR mn in 2011. Portugal Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Benefit data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Portugal – Table PT.IMF.FSI: 2019 Methodology: Sectoral Financial Statement: Balance Sheet: Annual.

Abstract copyright UK Data Service and data collection copyright owner. The qualitative research aimed to investigate the feasibility of using home service as a means of raising levels of financial literacy and providing support services for the financially excluded. It was recognised in the proposal that home service provision for savings was in decline. However, when the research began, approximately two years after the proposal had been written, very little industrial branch, home service insurance was still in existence. Also, unfortunately, the one large company still operating in the field (which has subsequently closed for new business) refused the researchers access. Therefore the objectives were changed to include an investigation of the demise of industrial branch insurance and its implications. Nevertheless, in-depth research was carried out with three organisations that offered new business to clients. In finding that a sub-prime or near-prime market for credit delivered at the home, either through face-to-face or electronic media, was expanding dramatically, efforts were rebalanced in this area to investigate their implications for financial literacy and social exclusion. The research also aimed to examine changes in financial regulation that might balance the aim of improving financial literacy for marginal groups but meet the demands of providers for some margin of profit at the lower end of the market. Researchers followed a pilot for a modified ordinary branch sales process testing out the delivery system for new, lower cost products due to come online later in 2004. Main Topics: The data set contains transcript material from interviews with: key informants in home service insurance companies; home collected credit companies; regulatory agencies; focus groups with users of door-to-door delivered financial services companies. The particular focus of this research project was the apparent anachronism of the survival into the early twenty-first century of a relic form of financial services delivery system, that is, door-to-door provision, in an era of largely branch-based and increasingly, telephone and internet-mediated systems. Door-to-door delivery systems for financial services emerged first in the nineteenth century, and signalled the development of mass retail financial markets. Evolving from the earliest Friendly Societies, the pioneers were the industrial branch insurance companies that took advantage of the high density populations in urban areas to sell services to working class households. This sector, which became known as Home Service in the 1960s, continued to thrive until Financial Services Authority regulations in the 1990s began to threaten the viability of its door-to-door operations. By late 2003, all Home Service insurance companies had abandoned new industrial branch business and had either outsourced their door-to-door collections of existing business to specialist agencies or converted them to the status of ordinary branch, where payments are generally collected through remote electronic means. The material in this database provides perspectives on door-to-door financial services from both the industry and its customers. Purposive selection/case studies Face-to-face interview Telephone interview

The Housing Affordability Index, calculated by the Runstad Center for Real Estate Studies, measures the ability of a middle-income family to carry the mortgage payments on a median-price home. When the index is 100 there is a balance between the family’s ability to pay and the cost. Higher indexes indicate housing is more affordable.

For example, an index of 126 means that a median-income family has 26 percent more income than the bare minimum required to qualify for a mortgage on a median-price home. An index of 80 means that a median-income family has less income than the minimum required.

accounting-balances adrh atlas-de-distribucio_n-de-renta-de-los-hogares average-household-income average-household-net-income average-income-by-unit-of-consumption average-net-income-per-person average-per-person-gross-income consumo-y-distribucio_n-de-la-renta consumption-and-distribution-of-income estadi_sticas household-income-distribution-atlas media-de-la-renta-por-unidad-de-consumo median-household-income median-income-by-unit-of-consumption mediana-de-la-renta-por-unidad-de-consumo nivel-calidad-y-condiciones-de-vida quality-of-life-and-living-conditions renta-bruta-media-por-hogar renta-bruta-media-por-persona renta-mediana-por-hogar renta-neta-media-por-hogar renta-neta-media-por-persona saldos-contables statistics territorial-units_ unidades-territoriales

In the financial year 2021, a majority of Indian households fell under the aspirers category, earning between 125,000 and 500,000 Indian rupees a year. On the other hand, about three percent of households that same year, accounted for the rich, earning over 3 million rupees annually. The middle class more than doubled that year compared to 14 percent in financial year 2005.

Middle-class income group and the COVID-19 pandemic

During the COVID-19 pandemic specifically during the lockdown in March 2020, loss of incomes hit the entire household income spectrum. However, research showed the severest affected groups were the upper middle- and middle-class income brackets. In addition, unemployment rates were rampant nationwide that further lead to a dismally low GDP. Despite job recoveries over the last few months, improvement in incomes were insignificant.

Economic inequality

While India maybe one of the fastest growing economies in the world, it is also one of the most vulnerable and severely afflicted economies in terms of economic inequality. The vast discrepancy between the rich and poor has been prominent since the last three decades. The rich continue to grow richer at a faster pace while the impoverished struggle more than ever before to earn a minimum wage. The widening gaps in the economic structure affect women and children the most. This is a call for reinforcement in in the country’s social structure that emphasizes access to quality education and universal healthcare services.

Ukraine Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Contribution data was reported at 0.000 UAH mn in Jun 2024. This stayed constant from the previous number of 0.000 UAH mn for Mar 2024. Ukraine Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Contribution data is updated quarterly, averaging 0.000 UAH mn from Dec 2023 (Median) to Jun 2024, with 3 observations. The data reached an all-time high of 0.000 UAH mn in Jun 2024 and a record low of 0.000 UAH mn in Jun 2024. Ukraine Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Contribution data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Ukraine – Table UA.IMF.FSI: 2019 Methodology: Sectoral Financial Statement: Balance Sheet: Quarterly.

Germany Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Benefit data was reported at 248,669.816 EUR mn in 2021. Germany Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Benefit data is updated yearly, averaging 248,669.816 EUR mn from Dec 2021 (Median) to 2021, with 1 observations. The data reached an all-time high of 248,669.816 EUR mn in 2021 and a record low of 248,669.816 EUR mn in 2021. Germany Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Benefit data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Germany – Table DE.IMF.FSI: 2019 Methodology: Sectoral Financial Statement: Balance Sheet: Annual.

Do you think that now is a good moment to save money? According to this study, from January 2018 to January 2025, Belgian consumers assessed negatively the favorability of making savings. Indeed, the balance was at -28 in January 2025, meaning that the negative responses outnumbered positive responses by 28 percentage points. This represents a similar negative appraisal than those made in the previous months. In other words, a similar share of Belgian consumers believed it was the right time to make savings. The lowest appraisal on the savings favorability was -59 in September 2019. Furthermore, the least negative appraisals was found in May of 2021.

Norway Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Contribution data was reported at 336,631.000 NOK mn in Dec 2023. This records an increase from the previous number of 315,230.000 NOK mn for Dec 2022. Norway Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Contribution data is updated quarterly, averaging 325,930.500 NOK mn from Dec 2022 (Median) to Dec 2023, with 2 observations. The data reached an all-time high of 336,631.000 NOK mn in Dec 2023 and a record low of 315,230.000 NOK mn in Dec 2022. Norway Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Contribution data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Norway – Table NO.IMF.FSI: 2019 Methodology: Sectoral Financial Statement: Balance Sheet: Quarterly.

Solomon Islands Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Contribution data was reported at 3,878.897 SBD mn in Mar 2024. This records an increase from the previous number of 3,853.969 SBD mn for Dec 2023. Solomon Islands Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Contribution data is updated quarterly, averaging 3,154.568 SBD mn from Mar 2015 (Median) to Mar 2024, with 37 observations. The data reached an all-time high of 3,878.897 SBD mn in Mar 2024 and a record low of 2,130.661 SBD mn in Mar 2015. Solomon Islands Other Financial Corporations: Pension Funds: Liabilities and Capital: Net Equity of Households in Pension Fund Reserves: Defined Contribution data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Solomon Islands – Table SB.IMF.FSI: 2019 Methodology: Sectoral Financial Statement: Balance Sheet: Quarterly.