Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

This statistic shows the median household income in the United States from 1990 to 2023 in 2023 U.S. dollars. The median household income was 80,610 U.S. dollars in 2023, an increase from the previous year. Household incomeThe median household income depicts the income of households, including the income of the householder and all other individuals aged 15 years or over living in the household. Income includes wages and salaries, unemployment insurance, disability payments, child support payments received, regular rental receipts, as well as any personal business, investment, or other kinds of income received routinely. The median household income in the United States varies from state to state. In 2020, the median household income was 86,725 U.S. dollars in Massachusetts, while the median household income in Mississippi was approximately 44,966 U.S. dollars at that time. Household income is also used to determine the poverty line in the United States. In 2021, about 11.6 percent of the U.S. population was living in poverty. The child poverty rate, which represents people under the age of 18 living in poverty, has been growing steadily over the first decade since the turn of the century, from 16.2 percent of the children living below the poverty line in year 2000 to 22 percent in 2010. In 2021, it had lowered to 15.3 percent. The state with the widest gap between the rich and the poor was New York, with a Gini coefficient score of 0.51 in 2019. The Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality and a score of one indicates a society where one person would have all the money and all other people have nothing.

The US Family Budget Dataset provides insights into the cost of living in different US counties based on the Family Budget Calculator by the Economic Policy Institute (EPI).

This dataset offers community-specific estimates for ten family types, including one or two adults with zero to four children, in all 1877 counties and metro areas across the United States.

Interesting Task Ideas:

1. See how family budgets compare to the federal poverty line and the Supplemental Poverty Measure in different counties.
2. Look into the money challenges faced by different types of families using the budgets provided.
3. Find out which counties have the most affordable places to live, food, transportation, healthcare, childcare, and other things people need.
4. Explore how the average income of families relates to the overall cost of living in different counties.
5. Investigate how family size affects the estimated budget and find counties where bigger families have higher costs.
6. Create visuals showing how the cost of living varies across different states and big cities.
7. Check whether specific counties are affordable for families of different sizes and types.
8. Use the dataset to compare living standards and economic security in different US counties.

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Quality of Life Index (higher is better) is an estimation of overall quality of life by using an empirical formula which takes into account purchasing power index (higher is better), pollution index (lower is better), house price to income ratio (lower is better), cost of living index (lower is better), safety index (higher is better), health care index (higher is better), traffic commute time index (lower is better) and climate index (higher is better).

Current formula (written in Java programming language):

index.main = Math.max(0, 100 + purchasingPowerInclRentIndex / 2.5 - (housePriceToIncomeRatio * 1.0) - costOfLivingIndex / 10 + safetyIndex / 2.0 + healthIndex / 2.5 - trafficTimeIndex / 2.0 - pollutionIndex * 2.0 / 3.0 + climateIndex / 3.0);

For details how purchasing power (including rent) index, pollution index, property price to income ratios, cost of living index, safety index, climate index, health index and traffic index are calculated please look up their respective pages.

Formulas used in the past

Formula used between June 2017 and Decembar 2017

We decided to decrease weight from costOfLivingIndex in this formula:

index.main = Math.max(0, 100 + purchasingPowerInclRentIndex / 2.5 - (housePriceToIncomeRatio * 1.0) - costOfLivingIndex / 5 + safetyIndex / 2.0 + healthIndex / 2.5 - trafficTimeIndex / 2.0 - pollutionIndex * 2.0 / 3.0 + climateIndex / 3.0);

The World Happiness 2017, which ranks 155 countries by their happiness levels, was released at the United Nations at an event celebrating International Day of Happiness on March 20th. The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions. Leading experts across fields – economics, psychology, survey analysis, national statistics, health, public policy and more – describe how measurements of well-being can be used effectively to assess the progress of nations. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness.

The scores are based on answers to the main life evaluation question asked in the poll. This question, known as the Cantril ladder, asks respondents to think of a ladder with the best possible life for them being a 10 and the worst possible life being a 0 and to rate their own current lives on that scale. The scores are from nationally representative samples for 2017 and use the Gallup weights to make the estimates representative. The columns following the happiness score estimate the extent to which each of six factors – economic production, social support, life expectancy, freedom, absence of corruption, and generosity – contribute to making life evaluations higher in each country than they are in Dystopia, a hypothetical country that has values equal to the world’s lowest national averages for each of the six factors. They have no impact on the total score reported for each country, but they do explain why some countries rank higher than others.

Quality of life index, link: https://www.numbeo.com/quality-of-life/indices_explained.jsp

Happiness store, link: https://www.kaggle.com/unsdsn/world-happiness/home

Goal 10Reduce inequality within and among countriesTarget 10.1: By 2030, progressively achieve and sustain income growth of the bottom 40 per cent of the population at a rate higher than the national averageIndicator 10.1.1: Growth rates of household expenditure or income per capita among the bottom 40 per cent of the population and the total populationSI_HEI_TOTL: Growth rates of household expenditure or income per capita (%)Target 10.2: By 2030, empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other statusIndicator 10.2.1: Proportion of people living below 50 per cent of median income, by sex, age and persons with disabilitiesSI_POV_50MI: Proportion of people living below 50 percent of median income (%)Target 10.3: Ensure equal opportunity and reduce inequalities of outcome, including by eliminating discriminatory laws, policies and practices and promoting appropriate legislation, policies and action in this regardIndicator 10.3.1: Proportion of population reporting having personally felt discriminated against or harassed in the previous 12 months on the basis of a ground of discrimination prohibited under international human rights lawVC_VOV_GDSD: Proportion of population reporting having felt discriminated against, by grounds of discrimination, sex and disability (%)Target 10.4: Adopt policies, especially fiscal, wage and social protection policies, and progressively achieve greater equalityIndicator 10.4.1: Labour share of GDPSL_EMP_GTOTL: Labour share of GDP (%)Indicator 10.4.2: Redistributive impact of fiscal policySI_DST_FISP: Redistributive impact of fiscal policy, Gini index (%)Target 10.5: Improve the regulation and monitoring of global financial markets and institutions and strengthen the implementation of such regulationsIndicator 10.5.1: Financial Soundness IndicatorsFI_FSI_FSANL: Non-performing loans to total gross loans (%)FI_FSI_FSERA: Return on assets (%)FI_FSI_FSKA: Regulatory capital to assets (%)FI_FSI_FSKNL: Non-performing loans net of provisions to capital (%)FI_FSI_FSKRTC: Regulatory Tier 1 capital to risk-weighted assets (%)FI_FSI_FSLS: Liquid assets to short term liabilities (%)FI_FSI_FSSNO: Net open position in foreign exchange to capital (%)Target 10.6: Ensure enhanced representation and voice for developing countries in decision-making in global international economic and financial institutions in order to deliver more effective, credible, accountable and legitimate institutionsIndicator 10.6.1: Proportion of members and voting rights of developing countries in international organizationsSG_INT_MBRDEV: Proportion of members of developing countries in international organizations, by organization (%)SG_INT_VRTDEV: Proportion of voting rights of developing countries in international organizations, by organization (%)Target 10.7: Facilitate orderly, safe, regular and responsible migration and mobility of people, including through the implementation of planned and well-managed migration policiesIndicator 10.7.1: Recruitment cost borne by employee as a proportion of monthly income earned in country of destinationIndicator 10.7.2: Number of countries with migration policies that facilitate orderly, safe, regular and responsible migration and mobility of peopleSG_CPA_MIGRP: Proportion of countries with migration policies to facilitate orderly, safe, regular and responsible migration and mobility of people, by policy domain (%)SG_CPA_MIGRS: Countries with migration policies to facilitate orderly, safe, regular and responsible migration and mobility of people, by policy domain (1 = Requires further progress; 2 = Partially meets; 3 = Meets; 4 = Fully meets)Indicator 10.7.3: Number of people who died or disappeared in the process of migration towards an international destinationiSM_DTH_MIGR: Total deaths and disappearances recorded during migration (number)Indicator 10.7.4: Proportion of the population who are refugees, by country of originSM_POP_REFG_OR: Number of refugees per 100,000 population, by country of origin (per 100,000 population)Target 10.a: Implement the principle of special and differential treatment for developing countries, in particular least developed countries, in accordance with World Trade Organization agreementsIndicator 10.a.1: Proportion of tariff lines applied to imports from least developed countries and developing countries with zero-tariffTM_TRF_ZERO: Proportion of tariff lines applied to imports with zero-tariff (%)Target 10.b: Encourage official development assistance and financial flows, including foreign direct investment, to States where the need is greatest, in particular least developed countries, African countries, small island developing States and landlocked developing countries, in accordance with their national plans and programmesIndicator 10.b.1: Total resource flows for development, by recipient and donor countries and type of flow (e.g. official development assistance, foreign direct investment and other flows)DC_TRF_TOTDL: Total assistance for development, by donor countries (millions of current United States dollars)DC_TRF_TOTL: Total assistance for development, by recipient countries (millions of current United States dollars)DC_TRF_TFDV: Total resource flows for development, by recipient and donor countries (millions of current United States dollars)Target 10.c: By 2030, reduce to less than 3 per cent the transaction costs of migrant remittances and eliminate remittance corridors with costs higher than 5 per centIndicator 10.c.1: Remittance costs as a proportion of the amount remittedSI_RMT_COST: Remittance costs as a proportion of the amount remitted (%)SI_RMT_COST_BC: Corridor remittance costs as a proportion of the amount remitted (%)SI_RMT_COST_SC: SmaRT corridor remittance costs as a proportion of the amount remitted (%)

In 2023, the lowest 20 percent of income consumer units spent about 41.3 percent of their total expenditure on housing. Consumer units belonging to the highest 20 percent of income spent only 29.2 percent on housing. Additionally, those in the highest income quintile spent 17.7 percent of their total expenditure on personal insurance and pensions, while the lowest 20 percent spent only 2.1 percent.

Abstract

Urban education is emerging as a significant topic of discussion in Tanzania and other Sub-Saharan African (SSA) countries, particularly focusing on the challenges faced by the population residing in impoverished urban areas. Learners from low-income households in urban settings encounter more difficulties in their educational journey compared to their more privileged counterparts. Tanzania, like many SSA nations, is undergoing substantial urbanization, marked by a notable rise in rural-to-urban migration, projected to reach approximately 55% of the country’s population by 2050. Understanding the current state of urban education is crucial for developing plans to address the escalating demands of urban education in the future. The study aimed to address these issues through the following research questions: 1. What are the schooling patterns among children living in urban poor households in Tanzania – including those with special needs? 2. How do urban poor communities perceive and understand education as a right in the context of urbanization in Tanzania? and, 3. What available education opportunities exist for children with special needs and living in poor urban households? In collaboration with HakiElimu and technical support from the National Bureau of Statistics, APHRC conducted a cross-sectional concurrent mixedmethods study. Quantitative data were gathered from 1,200 randomly selected low-income households in Dar es Salaam and Dodoma, along with input from 98 educational institutional heads from schools enrolling learners from the sampled households. Qualitative data were obtained through focus group discussions with caregivers/parents, in-depth interviews, and key informant interviews involving opinion leaders and policymakers. The study received ethical and administrative approvals from relevant authorities. Data analysis focused on 2,150 children aged 5-17, reflecting the basic education schooling age in Tanzania. A wealth score, derived from household belongings, was categorized into three equal tertiles for analysis purposes. The key results highlights have been presented below and thereafter a set of key study recommendations.

Schooling patterns: The findings indicate that 98.1% of children have attended school, with a slightly higher percentage among girls. Among learners with special needs (2.3% of the total), only 10% have received schooling, revealing an access gap for these vulnerable children. Six out of ten (60%) schools have provisions for learners with special needs, while those without reported inadequate facilities. In the academic year 2022, the majority of learners in pre-primary, primary, and ordinary level secondary education were enrolled in government schools (79.9%, 87.3%, and 90.6%, respectively). This underscores significant government control over enrollment spaces for learners from low-income urban households. The primary factor influencing school choice was the cost, suggesting the success of Tanzania’s free primary and secondary education initiatives. However, among the surveyed schools, the pupil-teacher ratio exceeded the government’s recommended number (40) across all levels. Additionally, the average class size surpassed 100 learners per class in primary school signaling quality issues.

Perceptions of Right to Education The right to education is categorized into three sub-themes: policy and strategies, law enforcement, and the school feeding program. 1. Policy and Strategies: Stakeholders acknowledged the state’s obligation to protect and promote the RTE. Examples cited included the Tanzania Education and Training Policy of 2014 (2023 edition) which cites the successes in the provision of the fee-free primary and secondary education, illustrating the government’s efforts to ensure universal access to education. 2. Law Enforcement: Local government authorities (LGAs) actively encourage parents to send their children to school. Those who fail to comply are presented to legal enforcement agencies, emphasizing the commitment to ensuring children’s attendance. However, implementation varies from one LGA to the other. 3. School Feeding Program (SFP): The government’s dedication to protecting the RTE is evident through the implementation of school feeding programs (SFPs).

Geographic coverage

Urban informal settlements of Tanzania, specifically in Dar es Salaam and Dodoma

Analysis unit

Households: The study analyzes household characteristics, such as size, head of household information, and wealth tertiles. Individuals (Children): The study focuses on children aged 3-19, specifically looking at their schooling patterns, participation, and characteristics. Schools: The study includes a school survey, analyzing school facilities, teacher qualifications, pupil-teacher ratios, and other school-level factors. Community: The study explores the perceptions and roles of community leaders and the community in relation to the right to education.

Universe

The survey covered households with school going children aged 3 to 19 years in selceted urban informal settlements in Dar esr Salaam and Dodoma

Sampling procedure

A sample of 1,200 households and 2,593 children aged 3 to 19 years was randomly selected through proportional allocation by region, ward, street, household head sex and age category.

The initial stage involved the listing of eligible households in the selected informal settlements. The initial criteria used was to ensure the household had at least one child who was aged 3-19 years. The listing exercise adopted a systematic approach: Starting from the furthest point of the enumeration areas, research assistants identified and listed the first eligible households. They would then skip to the fifth household. If the fifth household was not eligible, they would move to the next until they identified an eligible one. As a result, 3,567 households with 7,742 children aged 3-19 years were reached.

Using the data obtained from listing as a sampling frame, a sample of 1,200 households and 2,593 children aged 3 to 19 years was randomly selected through proportional allocation by region, ward, street, household head sex and age category. The household sample size was designed to allow estimation of key schooling indicators. The following indicators were considered in estimating the minimum sample size: enrollment, out of school, and attendance and primary and secondary school intake rates.

We utilised primary school net enrolment rate of 81.33%2 (World Bank 2018) for it gives the highest sample size.Besides, we made the following assumptions i) a design effect of 1.5 ii) average number of people aged between 3 and 19 years per household as two; iii) a 5% level of significance, which corresponds to 1.96 critical value for the standard normal distribution corresponding to a Type I error); and iv) a 5% margin of error respectively. The minimum estimated sample size was 2,389 children aged 3-19 years from 1,195 households after adjusting for a 90% response rate. The sample stratified proportionately between the two study sides based on their population and thereafter, randomly sampled households to participate in the study based on the listing.

Sampling deviation

The minimum estimated sample size was 2,389 children aged 3-19 years from 1,195 households after adjusting for a 90% response rate.

Mode of data collection

Face-to-face [f2f]

Research instrument

Household Questionnaire included household membership and their characteristics , social-economic characteristics, including food security, household shocks, household poverty well-being, and household schedule.

The individual schooling history questionnaire included detailed schooling information about individuals aged between 3 and 19 years, which consist of schooling information (enrolment, type of school enrolled, participation in preschool among others) for the year (2022), and 5 years retrospectively based on the age of the child.

The parental or guardian involment questionnaire helped sought information on parental involvement in their children's schooling including homework support, details of last schooling year(s), parental perception of student schooling experience, feeding and costs of schooling.

The institutional questionnaire contains information on institutional heads, staff and fees.

Cleaning operations

Data quality assessments continued during the data collection period by assessing for consistency of the responses as well as comparing data collected by field workers against spot check data collected by the senior research team and field supervisors.

Upon data collection completion, data were rigorously checked for consistency and outliers.

Data cleaning was carried out using Stata v.17.0.

Response rate

90%

Sampling error estimates

The following assumptions were made i) a design effect of 1.5 ii) average number of people aged between 3 and 19 years per household as two; iii) a 5% level of significance, which corresponds to 1.96 critical value for the standard normal distribution corresponding to a Type I error); and iv) a 5% margin of error respectively.

Therefore, the minimum estimated sample size was 2,389 children aged 3-19 years from 1,195 households after adjusting for a 90% response rate.

The present study deals with a special part of sectorial planning: provision of housing. The provision of housing in the Federal Republic of Germany (BRD) is divided in three different areas. Those areas are: Construction and housing industry, the social structure of the inhabitants and the physical structure of housing and housing estates. Governmental intervention measures mainly address those three areas: they try to regulate the housing provision and the rental prices through financial subsidies, the social distribution of housing through definition of target groups and the housing standards through urban planning and technical guidelines. Therefor the scientific investigation of housing provision needs to be about economic, sociological and urban and architectural aspects and needs to relate those aspects. The study of Kerstin Dornhöfer uses an integrated approach of the investigation of housing provision looking at those three aspects. The objective of the study is to develop criteria for the evaluation, planning and implementation of measures for housing provision. “The state controlled housing provision has its origin in the historical development before the Second World War. Besides the material basis of housing provision in the BRD also knowledge about and experiences with comprehensive steering instruments and its effectiveness resulted from the historical development of housing supply and its state controlled steering. This raises the question to what extent this knowledge and experiences had an impact on governmental policies concerning housing provision in the BRD. The description and analysis of the investigation is based on the following guiding questions:- Which steering instruments the BRD uses to achieve higher effectiveness concerning the socio-political postulate of improving the housing circumstances for the broad masses of people?- Could the dependence of housing provision and is governmental steering on the development of the total capital and on landed property , construction and housing construction capital be eliminated or at least gradually controlled?- What was the impact of governmental steering in the BRD?- How did it come to the current discrepancies in spite of all reform efforts and directing interventions?- What conditions were problematic for the improvement of housing circumstances for the broad masses of people? What are the relevant determinants for housing provision? The first part of this study deals with the description of housing provision for broad masses of people since the foundation of the BRD. This time is divided into four periods; each period begins with an important change in laws that indicated a change in in the governmental steering and transformations of economic and social circumstances. The description of the different periods helps to see the governmental steering instruments and its effectiveness regarding the historical circumstances. In the second part of the study the governmental objectives and steering instruments will be questioned and the circumstances of implementation will be identified based in three criteria. Those criteria are: (1) Housing standards and housing quality; (2) rental price (income-rent ratio); (3) Social distribution (broad masses of people as the target group of governmental steering). The question behind this is; if the thesis, which resulted from the historical development of housing provision before the Second World War, that governmental steering only takes place when the economic circumstances require and allow the public intervention and when public pressure forces governmental intervention, is also valid for the BRD.” (Dorhöfer, K., a. a. O., S. 11-13). Data tables in HISTAT:A. Federal Republic of Germany A.01 Development of population, housing stock and occupation density, BRD and West-Berlin (1950-1975)A.02 Ratio of housing stock and private households by size (1950-1974)A.03 Housing completions in the Federal Republic of Germany (1950-1975)A.04 Financing of housing construction in the Federal Republic of Germany, in percent (1950-1975)A.05 Building owners of housing in the Federal Republic of Germany, in percent (1950-1975)A.06 Price indices for residential buildings, cost of living, land without buildings and rents (1950-1975)A.07 Average monthly expenditures per four person worker-household with average income (1950-1975)A.08 Total cost of an apartment in social housing and average land prices in DM (1950-1975)A.09 Average living area, number of rooms per apartment, equipped with central heating system and bathroom in the BRD (1952-1975)A.10 Proportion of apartments per number of rooms per apartment in the Federal Republic of Germany (1952-1973)A.11 Construction activity of non-profit housing companies (1951-1975)A.12 Number of non-profit housing companies and number of members of housing cooperatives (1950-1975)A.13 Housing stock of the nonprofit housing companies and monthly rent (1951-1975) B. West- Berl...

The house price-to-income ratio in Australia was ***** as of the fourth quarter of 2024. This ratio, calculated by dividing nominal house prices by nominal disposable income per head, increased from the previous quarter. The price-to-income ratio can be used to measure housing affordability in a specific area. Australia's property bubble There has been considerable debate over the past decade about whether Australia is in a property bubble or not. A property bubble refers to a sharp increase in the price of property that is disproportional to income and rental prices, followed by a decline. In Australia, rising house prices have undoubtedly been an issue for many potential homeowners, pricing them out of the market. Along with the average house price, high mortgage interest rates have exacerbated the issue. Is the homeownership dream out of reach? Housing affordability has varied across the different states and territories in Australia. In 2024, the median value of residential houses was the highest in Sydney compared to other major Australian cities, with Brisbane becoming an increasingly expensive city. Nonetheless, expected interest rate cuts in 2025, alongside the expansion of initiatives to improve Australia's dwelling stock, social housing supply, and first-time buyer accessibility to properties, may start to improve the situation. These encompass initiatives such as the Australian government's Help to Buy scheme and the Housing Australia Future Fund Facility (HAFFF) and National Housing Accord Facility (NHAF) programs.

As of September 2024, Mumbai had the highest cost of living among other cities in the country, with an index value of ****. Gurgaon, a satellite city of Delhi and part of the National Capital Region (NCR) followed it with an index value of ****.  What is cost of living? The cost of living varies depending on geographical regions and factors that affect the cost of living in an area include housing, food, utilities, clothing, childcare, and fuel among others. The cost of living is calculated based on different measures such as the consumer price index (CPI), living cost indexes, and wage price index. CPI refers to the change in the value of consumer goods and services. The wage price index, on the other hand, measures the change in labor services prices due to market pressures. Lastly, the living cost indexes calculate the impact of changing costs on different households. The relationship between wages and costs determines affordability and shifts in the cost of living. Mumbai tops the list Mumbai usually tops the list of most expensive cities in India. As the financial and entertainment hub of the country, Mumbai offers wide opportunities and attracts talent from all over the country. It is the second-largest city in India and has one of the most expensive real estates in the world.

Senior housing communities classed as active adult had the lowest expense ratio in the United States in the first half of 2024. During this period, the expense ratio of this asset class was ** percent, meaning that the operational expenses amounted to ** percent of the income brought in by the property. For facilities with majority nursing care, this percentage was the highest at ** percent.

Out of all 50 states, New York had the highest per-capita real gross domestic product (GDP) in 2023, at 90,730 U.S. dollars, followed closely by Massachusetts. Mississippi had the lowest per-capita real GDP, at 39,102 U.S. dollars. While not a state, the District of Columbia had a per capita GDP of more than 214,000 U.S. dollars. What is real GDP? A country’s real GDP is a measure that shows the value of the goods and services produced by an economy and is adjusted for inflation. The real GDP of a country helps economists to see the health of a country’s economy and its standard of living. Downturns in GDP growth can indicate financial difficulties, such as the financial crisis of 2008 and 2009, when the U.S. GDP decreased by 2.5 percent. The COVID-19 pandemic had a significant impact on U.S. GDP, shrinking the economy 2.8 percent. The U.S. economy rebounded in 2021, however, growing by nearly six percent. Why real GDP per capita matters Real GDP per capita takes the GDP of a country, state, or metropolitan area and divides it by the number of people in that area. Some argue that per-capita GDP is more important than the GDP of a country, as it is a good indicator of whether or not the country’s population is getting wealthier, thus increasing the standard of living in that area. The best measure of standard of living when comparing across countries is thought to be GDP per capita at purchasing power parity (PPP) which uses the prices of specific goods to compare the absolute purchasing power of a countries currency.

In 2023, the United States had the highest gross household disposable income per capita in OECD countries adjusted for purchasing power parity. Their disposable income per capita was over ****** U.S. dollars. Luxembourg followed in second with around ****** U.S. dollars, with Switzerland in third.

In March 2025, inflation amounted to 2.4 percent, while wages grew by 4.3 percent. The inflation rate has not exceeded the rate of wage growth since January 2023. Inflation in 2022 The high rates of inflation in 2022 meant that the real terms value of American wages took a hit. Many Americans report feelings of concern over the economy and a worsening of their financial situation. The inflation situation in the United States is one that was experienced globally in 2022, mainly due to COVID-19 related supply chain constraints and disruption due to the Russian invasion of Ukraine. The monthly inflation rate for the U.S. reached a 40-year high in June 2022 at 9.1 percent, and annual inflation for 2022 reached eight percent. Without appropriate wage increases, Americans will continue to see a decline in their purchasing power. Wages in the U.S. Despite the level of wage growth reaching 6.7 percent in the summer of 2022, it has not been enough to curb the impact of even higher inflation rates. The federally mandated minimum wage in the United States has not increased since 2009, meaning that individuals working minimum wage jobs have taken a real terms pay cut for the last twelve years. There are discrepancies between states - the minimum wage in California can be as high as 15.50 U.S. dollars per hour, while a business in Oklahoma may be as low as two U.S. dollars per hour. However, even the higher wage rates in states like California and Washington may be lacking - one analysis found that if minimum wage had kept up with productivity, the minimum hourly wage in the U.S. should have been 22.88 dollars per hour in 2021. Additionally, the impact of decreased purchasing power due to inflation will impact different parts of society in different ways with stark contrast in average wages due to both gender and race.

Rents in Germany continued to increase in all seven major cities in 2024. The average rent per square meter in Munich was approximately **** euros — the highest in the country. Conversely, Düsseldorf had the most affordable rent, at approximately **** euros per square meter. But how does renting compare to buying? According to the house price to rent ratio, house prices in Germany have risen faster than rents, making renting more affordable than buying. Affordability of housing in Germany In 2023, Germany was among the European countries with a relatively high house price to income ratio in Europe. The indicator compares the affordability of housing across OECD countries and is calculated as the nominal house prices divided by nominal disposable income per head, with 2015 chosen as a base year. Between 2012 and 2022, property prices in the country rose much faster than income, with the house price to income index peaking at *** index points at the beginning of 2022. Slower house price growth in the following years has led to the index declining, as incomes catch up. Nevertheless, homebuyers in 2024 faced significantly higher mortgage interest rates, contributing to a higher final cost. How much does buying a property in Germany cost? Just as with renting, Munich was the most expensive city for newly built apartments. In 2024, the cost per square meter in Munich was almost ***** euros pricier than in the runner-up city, Frankfurt. Detached and semi-detached houses are usually more expensive. The price gap between Munich and the second most expensive city, Stuttgart, was nearly ***** euros per square meter.

In 2023, the average wage in Mexico achieved its highest level since 2009, amounting to around 20,090 U.S. dollars per year. Moreover, the average annual wage increased for the third consecutive year. Employment conditions In light of the crucial role that employment plays as a primary source of income, perceiving their working conditions to be poor, a sentiment held by nearly half of Mexico's workforce. Furthermore, the distribution of working hours skews towards non-monetarily compensated positions, particularly impacting the female demographic on households. This imbalance poses challenges to sustaining motivation. Informal employment also exhibits a higher prevalence among females, presenting regulatory complexities. Furthermore, a perceived gender-based disparity in employment opportunities subsists, amplifying the overarching concerns. As these factors coalesce, one out of every two individuals' harbors apprehensions about potential job loss. Salaries vs. Life expenses In 2023, the minimum wage has witnessed a considerable increase. Nevertheless, it continues to fall short of meeting essential living costs. Calculations indicate that a worker should ideally earn at least twice the amount of the latest increment to adequately cover these necessary expenses. A significant portion of the population—approximately one-third—finds itself residing beneath the threshold of basic food basket expenses. Consequently, Mexico ranks as the country where grocery expenses constitute the highest percentage of earnings. Furthermore, this predicament disproportionately impacts women, as they are often remunerated at lower wage rates.

When adjusted for inflation, the 2024 federal minimum wage in the United States is over 40 percent lower than the minimum wage in 1970. Although the real dollar minimum wage in 1970 was only 1.60 U.S. dollars, when expressed in nominal 2024 dollars this increases to 13.05 U.S. dollars. This is a significant difference from the federal minimum wage in 2024 of 7.25 U.S. dollars.

The national minimum wage for federal workers in Nigeria reached ****** Nigerian naira (NGN) in 2024, which equaled about ** U.S. dollars. On July 23, 2024, this minimum wage of ****** NGN was passed into law, increasing from the previous amount of ****** NGN. According to most recent data, the monthly cost of living for an individual in Nigeria amounted to ****** NGN on average, whereas this figure added up to over ******* NGN for a family. Dependency ratio In 2023, the labor dependency ratio in Nigeria was estimated at *** percent, showing no significant change since 2012. This metric represents the proportion of dependents who are either not part of the workforce or are unemployed, in relation to the total employed population. Nigeria's compensation trends and workload statistics In 2023, individuals working in executive management and change roles garnered the highest average annual salary in Nigeria at ****** U.S. dollars. In the same year, the employed workforce in Nigeria contributed to a collective weekly workload exceeding *** billion hours. Two years earlier, the workload was estimated at about *** billion hours.

The working poverty rate in Mexico, which refers to the percentage of the population with labor income below the cost of the food basket, rose to **** percent in the fourth quarter of 2024. The share of the Mexican population at working poverty peaked in the third quarter of 2020, at ** percent.

Out of all OECD countries, Cost Rica had the highest poverty rate as of 2022, at over 20 percent. The country with the second highest poverty rate was the United States, with 18 percent. On the other end of the scale, Czechia had the lowest poverty rate at 6.4 percent, followed by Denmark.

The significance of the OECD

The OECD, or the Organisation for Economic Co-operation and Development, was founded in 1948 and is made up of 38 member countries. It seeks to improve the economic and social well-being of countries and their populations. The OECD looks at issues that impact people’s everyday lives and proposes policies that can help to improve the quality of life.

Poverty in the United States

In 2022, there were nearly 38 million people living below the poverty line in the U.S.. About one fourth of the Native American population lived in poverty in 2022, the most out of any ethnicity. In addition, the rate was higher among young women than young men. It is clear that poverty in the United States is a complex, multi-faceted issue that affects millions of people and is even more complex to solve.