More than half of the respondents in a global survey were at least slightly struggling with paying for basic needs as of September 2022. The single commodity that most people were struggling with was energy and utilities, followed by food. On the other hand, fewest were affected by credit card repayments. Rising inflation rates have seen cost of living surge in 2022, which has especially affected energy and certain types of food.

There is more to housing affordability than the rent or mortgage you pay. Transportation costs are the second-biggest budget item for most families, but it can be difficult for people to fully factor transportation costs into decisions about where to live and work. The Location Affordability Index (LAI) is a user-friendly source of standardized data at the neighborhood (census tract) level on combined housing and transportation costs to help consumers, policymakers, and developers make more informed decisions about where to live, work, and invest. Compare eight household profiles (see table below) —which vary by household income, size, and number of commuters—and see the impact of the built environment on affordability in a given location while holding household demographics constant.*$11,880 for a single person household in 2016 according to US Dept. of Health and Human Services: https://aspe.hhs.gov/computations-2016-poverty-guidelinesThis layer is symbolized by the percentage of housing and transportation costs as a percentage of income for the Median-Income Family profile, but the costs as a percentage of income for all household profiles are listed in the pop-up:Also available is a gallery of 8 web maps (one for each household profile) all symbolized the same way for easy comparison: Median-Income Family, Very Low-Income Individual, Working Individual, Single Professional, Retired Couple, Single-Parent Family, Moderate-Income Family, and Dual-Professional Family.An accompanying story map provides side-by-side comparisons and additional context.--Variables used in HUD's calculations include 24 measures such as people per household, average number of rooms per housing unit, monthly housing costs (mortgage/rent as well as utility and maintenance expenses), average number of cars per household, median commute distance, vehicle miles traveled per year, percent of trips taken on transit, street connectivity and walkability (measured by block density), and many more.To learn more about the Location Affordability Index (v.3) visit: https://www.hudexchange.info/programs/location-affordability-index/. There you will find some background and an FAQ page, which includes the question:"Manhattan, San Francisco, and downtown Boston are some of the most expensive places to live in the country, yet the LAI shows them as affordable for the typical regional household. Why?" These areas have some of the lowest transportation costs in the country, which helps offset the high cost of housing. The area median income (AMI) in these regions is also high, so when costs are shown as a percent of income for the typical regional household these neighborhoods appear affordable; however, they are generally unaffordable to households earning less than the AMI.Date of Coverage: 2012-2016 Date Released: March 2019Date Downloaded from HUD Open Data: 4/18/19Further Documentation:LAI Version 3 Data and MethodologyLAI Version 3 Technical Documentation_**The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates**

Title: Location Affordability Index - NMCDC Copy

Summary: This layer contains the Location Affordability Index from U.S. Dept. of Housing and Urban Development (HUD) - standardized household, housing, and transportation cost estimates by census tract for 8 household profiles.

Notes: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas.

Prepared by: dianaclavery_uo, copied by EMcRae_NMCDC

Source: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas. Check the source documentation or other details above for more information about data sources.

Feature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=447a461f048845979f30a2478b9e65bb

UID: 73

Data Requested: Family income spent on basic need

Method of Acquisition: Search for Location Affordability Index in the Living Atlas. Make a copy of most recent map available. To update this map, copy the most recent map available. In a new tab, open the AGOL Assistant Portal tool and use the functions in the portal to copy the new maps JSON, and paste it over the old map (this map with item id

Date Acquired: Map copied on May 10, 2022

Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 6

Tags: PENDING

As of January 2024, the prices of essential goods in the Philippines increased compared to the same month in the previous year. With the exception of rice, most basic goods noted a significant increase in prices. For instance, the price of six kilograms of meat rose from nearly 1,600 Philippine pesos in 2022 to 1,843 Philippine pesos in 2024. In addition, the cost of eight kilograms of vegetables increased from 698 to 857 Philippine pesos.

This paper investigates the relationship between housing prices and the quality of public schools in the Australian Capital Territory. To disentangle the effects of schools and other neighbourhood characteristics on the value of residential properties, we compare sale prices of homes on either side of high school attendance boundaries. We find that a 5 percent increase in test scores (approximately one standard deviation) is associated with a 3.5 percent increase in house prices. Our result is in line with private school tuition costs, and accords with prior research from Britain and the United States. Estimating the effect of school quality on house prices provides a possible measure of the extent to which parents value better educational outcomes.

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Vietnam Higher Education Market Size 2025-2029

The Vietnam higher education market size is forecast to increase by USD 616.5 million at a CAGR of 15.6% between 2024 and 2029.

The higher education market in Vietnam is expanding steadily, propelled by a growing middle class and advancements in digital learning technologies. Key drivers include the rising demand for skilled professionals in sectors like technology and healthcare, fueled by economic growth, and the adoption of online platforms and virtual classrooms, which enhance accessibility and flexibility for students.
This report offers a practical analysis for businesses and educators, detailing market size, growth forecasts through 2029, and key segments like software solutions, which lead due to their role in streamlining online education. It explores trends such as the increasing internationalization of education - evidenced by partnerships with foreign institutions - and addresses challenges like the rising cost of premium courses, which can limit access for some learners. The data is structured to support strategic planning, program development, and market entry decisions.
For stakeholders aiming to succeed in Vietnam's higher education market, this report provides clear, actionable insights into leveraging digital trends and overcoming cost barriers, ensuring they can adapt to a competitive and rapidly evolving educational landscape.

What will be the size of the Vietnam Higher Education Market during the forecast period?

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The market is experiencing growth, driven by a large and rapidly growing youth population's increasing emphasis on continuous learning and professional development. This demand is reflected In the expanding offerings of academic pursuits, ranging from undergraduate and postgraduate degrees to vocational training, professional certifications, and doctoral programs. The market encompasses a diverse range of educational modalities, including traditional classroom settings, digital technologies, and remote learning. Digital technologies are transforming higher education in Vietnam, with online learning platforms, virtual classrooms, interactive simulations, and augmented reality becoming increasingly prevalent. These innovations facilitate academic continuity and provide students with practical skills and analytical thinking abilities, essential for career progression and research endeavors.
Moreover, the integration of digital technologies supports leadership capabilities and creativity, further enhancing the value of higher education. The market's size and direction are influenced by the growing recognition of the importance of higher education in Vietnam's economy and society. As the country continues to develop, the demand for a skilled and educated workforce will only increase, ensuring the ongoing relevance and importance of the higher education sector.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Software
  Hardware


End-user

  Private colleges
  State universities
  Community colleges


Courses

  Undergraduate
  Masters
  PhD


Geography

  Vietnam

By Product Insights

The software segment is estimated to witness significant growth during the forecast period. The higher education software market in Vietnam is projected to experience substantial growth during the forecast period. This market encompasses learning management systems (LMSs), enterprise resource planning (ERP), adaptive learning software (ALS), content management systems (CMSs), and other solutions In the solutions segment. Additionally, the support segment comprises education apps, digital educational publishing, learning analytics, and other applications. The adoption of these software solutions is expected to enhance information management processes in Vietnam's higher education sector. For example, Genius Education Management, a web and mobile program, is designed to manage daily operations for schools, colleges, and universities in Vietnam.

The implementation of these tools will facilitate professional development, academic pursuits, and career progression for young individuals. Furthermore, the integration of digital technologies, such as interactive simulations, augmented reality, and remote learning platforms, will contribute to the development of a skilled workforce, personal development, and research endeavors. The growth of the higher education software market is driven by the increasing emphasis on analytical thinking, leadership capabilities, creativity, and practical skills, as well as the community advancement and earning potential associated with higher education.

Get a glance at the share of various segments. Re

This table contains data on the living wage and the percent of families with incomes below the living wage for California, its counties, regions and cities/towns. Living wage is the wage needed to cover basic family expenses (basic needs budget) plus all relevant taxes; it does not include publicly provided income or housing assistance. The percent of families below the living wage was calculated using data from the Living Wage Calculator and the U.S. Census Bureau, American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. The living wage is the wage or annual income that covers the cost of the bare necessities of life for a worker and his/her family. These necessities include housing, transportation, food, childcare, health care, and payment of taxes. Low income populations and non-white race/ethnic have disproportionately lower wages, poorer housing, and higher levels of food insecurity. More information about the data table and a data dictionary can be found in the About/Attachments section.

Cost Estimating And Quoting Software For Manufacturing Market size was valued at USD 525.1 Billion in 2023 and is projected to reach USD 1138.2 Billion by 2031, growing at a CAGR of 11.56% during the forecast period 2024-2031.

Global Cost Estimating And Quoting Software For Manufacturing Market Drivers

The market drivers for the Cost Estimating And Quoting Software For Manufacturing Market can be influenced by various factors. These may include:

Increased Demand for Efficiency: In today’s competitive manufacturing landscape, businesses are under constant pressure to optimize their operations and trim costs without compromising quality. Cost estimating and quoting software play a crucial role in achieving these objectives by streamlining the process of generating cost estimates and quotes. This software significantly reduces the time needed to produce accurate quotes, allowing manufacturers to respond faster to customer inquiries and win more business. Efficient quoting also helps in better resource allocation and production planning, reducing wastage and downtime. By providing detailed cost breakdowns and labor estimates quickly, the software allows decision-makers to make more informed and timely business decisions. Furthermore, the time saved by automating these processes can be reinvested into other critical areas of the business, such as innovation and customer service, thus improving overall operational efficiency. The ability to produce quotes rapidly without sacrificing accuracy gives manufacturers a competitive edge and is a key market driver for such software.

Automation and Reduced Errors: Automation is one of the primary advantages of cost estimating and quoting software. Manual quote generation is often prone to human errors, which can lead to costly mistakes, such as underquoting or overquoting, that affect profitability and customer satisfaction. Automating the estimating and quoting process minimizes these risks by applying standardized calculations and predefined data inputs, ensuring consistent and accurate results. This software typically incorporates complex algorithms and historical data to provide precise estimates, reducing the potential for errors that can arise from manual data entry and subjective judgments. Automation also enables real-time adjustments and updates, which are essential in a rapidly changing manufacturing environment where material costs and labor rates can fluctuate. By reducing the incidence of errors and the need for rework, manufacturers can enhance their reputation for reliability and accuracy, which in turn builds customer trust and loyalty. Efficiency gains from automation lead to cost savings and higher profitability, driving the market for such solutions.

Complexity of Modern Manufacturing: Modern manufacturing involves a highly intricate network of processes, materials, and technologies, making it increasingly complex to estimate costs accurately. From custom parts and variable production volumes to advanced machinery and diverse raw materials, the factors influencing the final cost of a manufactured product are numerous and interdependent. Cost estimating and quoting software is designed to handle this complexity by integrating various elements of the production process into a comprehensive and cohesive estimating system. This software can take into account multiple variables, such as machine run times, material costs, labor hours, and overheads, providing a holistic view of production costs. Additionally, it can cater to different manufacturing methodologies, whether it’s batch production, continuous production, or just-in-time manufacturing. The ability to accurately capture and analyze such multifaceted data is invaluable for manufacturers looking to optimize pricing strategies and maintain competitive advantage. As manufacturing continues to grow in complexity, the demand for robust and sophisticated cost estimating solutions is expected to rise.

Integration with Other Systems: The integration capability of cost estimating and quoting software with other enterprise systems is a significant market driver. Modern manufacturers utilize a range of software applications, including Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and Manufacturing Execution Systems (MES), to manage different facets of their operations. When cost estimating and quoting software seamlessly integrates with these systems, it allows for a more synchronized and efficient workflow. Real-time data exchange between systems eliminates the need for manual data entry, reducing the risk of errors and ensuring consistency across various departments. For instance, integration with ERP systems can provide immediate access to up-to-date material costs and inventory levels, leading to more accurate and realistic estimates. Similarly, CRM integration ensures that customer information and histories are readily accessible, aiding in personalized and precise quoting. This interconnectedness facilitates better decision-making, enhances productivity, and enables a more strategic approach to pricing and resource allocation, driving the adoption of integrated estimating and quoting solutions in the market.

Customization and Scalability: Manufacturers seeking adaptable and scalable solutions tailored to specific needs add to the market growth.
Compliance and Regulatory Requirements: Adhering to industry standards and regulatory requirements in estimates and quotes is a driver for adopting sophisticated software solutions.
Globalization and Competition: As manufacturers face global competition, efficient cost estimating and quoting become critical for competitive pricing.

Graph and download economic data for Producer Price Index by Commodity: Final Demand (PPIFIS) from Nov 2009 to Jan 2025 about final demand, headline figure, PPI, inflation, price index, indexes, price, and USA.

Since the start of 2021, prices in the global chicken meat market shot up as a result of high demand, rising costs for feed grain and food as well as a decreasing rate of chicken slaughter in the EU, South Korea and Japan. Heightened costs for shipping containers are additionally driving the growth in export prices. As of year-end 2021, worldwide production and exports of chicken meat are forecast to remain at the previous year’s level. Demand for chicken meat in China is dropping while the pig population in the country is recovering and hog prices are decreasing. Saudi Arabia’s ban on imports of chicken products from Brazil may lead to diminished exports from that country.

Urban Displacement Project’s (UDP) Estimated Displacement Risk (EDR) model for California identifies varying levels of displacement risk for low-income renter households in all census tracts in the state from 2015 to 2019(1). The model uses machine learning to determine which variables are most strongly related to displacement at the household level and to predict tract-level displacement risk statewide while controlling for region. UDP defines displacement risk as a census tract with characteristics which, according to the model, are strongly correlated with more low-income population loss than gain. In other words, the model estimates that more low-income households are leaving these neighborhoods than moving in.This map is a conservative estimate of low-income loss and should be considered a tool to help identify housing vulnerability. Displacement may occur because of either investment, disinvestment, or disaster-driven forces. Because this risk assessment does not identify the causes of displacement, UDP does not recommend that the tool be used to assess vulnerability to investment such as new housing construction or infrastructure improvements. HCD recommends combining this map with on-the-ground accounts of displacement, as well as other related data such as overcrowding, cost burden, and income diversity to achieve a full understanding of displacement risk.If you see a tract or area that does not seem right, please fill out this form to help UDP ground-truth the method and improve their model.How should I read the displacement map layers?The AFFH Data Viewer includes three separate displacement layers that were generated by the EDR model. The “50-80% AMI” layer shows the level of displacement risk for low-income (LI) households specifically. Since UDP has reason to believe that the data may not accurately capture extremely low-income (ELI) households due to the difficulty in counting this population, UDP combined ELI and very low-income (VLI) household predictions into one group—the “0-50% AMI” layer—by opting for the more “extreme” displacement scenario (e.g., if a tract was categorized as “Elevated” for VLI households but “Extreme” for ELI households, UDP assigned the tract to the “Extreme” category for the 0-50% layer). For these two layers, tracts are assigned to one of the following categories, with darker red colors representing higher displacement risk and lighter orange colors representing less risk:• Low Data Quality: the tract has less than 500 total households and/or the census margins of error were greater than 15% of the estimate (shaded gray).• Lower Displacement Risk: the model estimates that the loss of low-income households is less than the gain in low-income households. However, some of these areas may have small pockets of displacement within their boundaries. • At Risk of Displacement: the model estimates there is potential displacement or risk of displacement of the given population in these tracts.• Elevated Displacement: the model estimates there is a small amount of displacement (e.g., 10%) of the given population.• High Displacement: the model estimates there is a relatively high amount of displacement (e.g., 20%) of the given population.• Extreme Displacement: the model estimates there is an extreme level of displacement (e.g., greater than 20%) of the given population. The “Overall Displacement” layer shows the number of income groups experiencing any displacement risk. For example, in the dark red tracts (“2 income groups”), the model estimates displacement (Elevated, High, or Extreme) for both of the two income groups. In the light orange tracts categorized as “At Risk of Displacement”, one or all three income groups had to have been categorized as “At Risk of Displacement”. Light yellow tracts in the “Overall Displacement” layer are not experiencing UDP’s definition of displacement according to the model. Some of these yellow tracts may be majority low-income experiencing small to significant growth in this population while in other cases they may be high-income and exclusive (and therefore have few low-income residents to begin with). One major limitation to the model is that the migration data UDP uses likely does not capture some vulnerable populations, such as undocumented households. This means that some yellow tracts may be experiencing high rates of displacement among these types of households. MethodologyThe EDR is a first-of-its-kind model that uses machine learning and household level data to predict displacement. To create the EDR, UDP first joined household-level data from Data Axle (formerly Infogroup) with tract-level data from the 2014 and 2019 5-year American Community Survey; Affirmatively Furthering Fair Housing (AFFH) data from various sources compiled by California Housing and Community Development; Longitudinal Employer-Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES) data; and the Environmental Protection Agency’s Smart Location Database.UDP then used a machine learning model to determine which variables are most strongly related to displacement at the household level and to predict tract-level displacement risk statewide while controlling for region. UDP modeled displacement risk as the net migration rate of three separate renter households income categories: extremely low-income (ELI), very low-income (VLI), and low-income (LI). These households have incomes between 0-30% of the Area Median Income (AMI), 30-50% AMI, and 50-80% AMI, respectively. Tracts that have a predicted net loss within these groups are considered to experience displacement in three degrees: elevated, high, and extreme. UDP also includes a “At Risk of Displacement” category in tracts that might be experiencing displacement.What are the main limitations of this map?1. Because the map uses 2019 data, it does not reflect more recent trends. The pandemic, which started in 2020, has exacerbated income inequality and increased housing costs, meaning that UDP’s map likely underestimates current displacement risk throughout the state.2. The model examines displacement risk for renters only, and does not account for the fact that many homeowners are also facing housing and gentrification pressures. As a result, the map generally only highlights areas with relatively high renter populations, and neighborhoods with higher homeownership rates that are known to be experiencing gentrification and displacement are not as prominent as one might expect.3. The model does not incorporate data on new housing construction or infrastructure projects. The map therefore does not capture the potential impacts of these developments on displacement risk; it only accounts for other characteristics such as demographics and some features of the built environment. Two of UDP’s other studies—on new housing construction and green infrastructure—explore the relationships between these factors and displacement.Variable ImportanceFigures 1, 2, and 3 show the most important variables for each of the three models—ELI, VLI, and LI. The horizontal bars show the importance of each variable in predicting displacement for the respective group. All three models share a similar order of variable importance with median rent, percent non-white, rent gap (i.e., rental market pressure calculated using the difference between nearby and local rents), percent renters, percent high-income households, and percent of low-income households driving much of the displacement estimation. Other important variables include building types as well as economic and socio-demographic characteristics. For a full list of the variables included in the final models, ranked by descending order of importance, and their definitions see all three tabs of this spreadsheet. “Importance” is defined in two ways: 1. % Inclusion: The average proportion of times this variable was included in the model’s decision tree as the most important or driving factor.2. MeanRank: The average rank of importance for each variable across the numerous model runs where higher numbers mean higher ranking. Figures 1 through 3 below show each of the model variable rankings ordered by importance. The red lines represent Jenks Breaks, which are designed to sort values into their most “natural” clusters. Variable importance for each model shows a substantial drop-off after about 10 variables, meaning a relatively small number of variables account for a large amount of the predictive power in UDP’s displacement model.Figure 1. Variable Importance for Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet.Figure 2. Variable Importance for Very Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet. Figure 3. Variable Importance for Extremely Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet.Source: Chapple, K., & Thomas, T., and Zuk, M. (2022). Urban Displacement Project website. Berkeley, CA: Urban Displacement Project.(1) UDP used this time-frame because (a) the 2020 census had a large non-response rate and it implemented a new statistical modification that obscures and misrepresents racial and economic characteristics at the census tract level and (b) pandemic mobility trends are still in flux and UDP believes 2019 is more representative of “normal” or non-pandemic displacement trends.

In 2025, beef prices keep rising due to low cattle inventory and strong consumer demand, posing challenges for producers and consumers alike.

By 2035, oil prices are predicted to boom due to increasing global demand, particularly from China, amidst a structurally short supply.

Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2023. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 117.5 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.

Inflation was the most worrying topic worldwide as of January 2025, with one third of the respondents choosing that option. Crime and violence as well as poverty and social inequality followed behind. Moreover, following Russia's invasion of Ukraine and the war in Gaza, nine percent of the respondents were worried about military conflict between nations. Only four percent were worried about the COVID-19 pandemic, which dominated the world after its outbreak in 2020. Global inflation and rising prices Inflation rates have spiked substantially since the beginning of the COVID-19 pandemic in 2020. From 2020 to 2021, the worldwide inflation rate increased from 3.5 percent to 4.7 percent, and from 2021 to 2022, the rate increased sharply from 4.7 percent to 8.7 percent. While rates are predicted to fall come 2025, many are continuing to struggle with price increases on basic necessities. Poverty and global development Poverty and social inequality was the third most worrying issue to respondents. While poverty and inequality are still prominent, global poverty rates have been on a steady decline over the years. In 1994, 64 percent of people in low-income countries and around one percent of people in high-income countries lived on less than 2.15 U.S. dollars per day. By 2018, this had fallen to almost 44 percent of people in low-income countries and 0.6 percent in high-income countries. Moreover, fewer people globally are dying of preventable diseases and people are living longer lives. Despite these aspects, issues such as wealth inequality have global prominence.

In 2023, the majority of households in Indonesia consisted of four to five members, accounting for about 45.5 percent of households. Furthermore, around 38 percent of households had two to three members, indicating a stable trend over the past decade for smaller household structures. Meanwhile, the share of households with more than five members has slightly declined in recent years. Household consumption Per capita household consumption in Indonesia amounted to approximately 2,400 U.S. dollars, with almost half of this expenditure allocated to food as the most basic need. In recent years, the rising prices of essential goods such as rice, beef, and electricity rates have added economic pressure especially for lower-middle-income households. This remains a concern for many households in Indonesia as they struggle to tighten their budgets and cope with the continuously increasing cost of living. Living conditions of Indonesian households Despite high rates of homeownership, many Indonesian households still face inadequate living conditions. As of 2023, around eight percent of households still lived in slums, while around one in seven households lived in insufficient housing space per capita. In addition, Indonesia has a persistent housing backlog of millions of units, reflecting the number of households that are still struggling to secure homeownership. With the increasing number of households in Indonesia, it is crucial to address this issue to improve living standards across the country.

The UK inflation rate was three percent in January 2025, up from 2.5 percent in the previous month, and the fastest rate of inflation since March 2024. Between September 2022 and March 2023, the UK experienced seven months of double-digit inflation, which peaked at 11.1 percent in October 2022. Due to this long period of high inflation, UK consumer prices have increased by over 20 percent in the last three years. As of the most recent month, prices were rising fastest in the communications sector, at 6.1 percent, but were falling in both the furniture and transport sectors, at -0.3 percent and -0.6 percent respectively.
The Cost of Living Crisis High inflation is one of the main factors behind the ongoing Cost of Living Crisis in the UK, which, despite subsiding somewhat in 2024, is still impacting households going into 2025. In December 2024, for example, 56 percent of UK households reported their cost of living was increasing compared with the previous month, up from 45 percent in July, but far lower than at the height of the crisis in 2022. After global energy prices spiraled that year, the UK's energy price cap increased substantially. The cap, which limits what suppliers can charge consumers, reached 3,549 British pounds per year in October 2022, compared with 1,277 pounds a year earlier. Along with soaring food costs, high-energy bills have hit UK households hard, especially lower income ones that spend more of their earnings on housing costs. As a result of these factors, UK households experienced their biggest fall in living standards in decades in 2022/23. Global inflation crisis causes rapid surge in prices The UK's high inflation, and cost of living crisis in 2022 had its origins in the COVID-19 pandemic. Following the initial waves of the virus, global supply chains struggled to meet the renewed demand for goods and services. Food and energy prices, which were already high, increased further in 2022. Russia's invasion of Ukraine in February 2022 brought an end to the era of cheap gas flowing to European markets from Russia. The war also disrupted global food markets, as both Russia and Ukraine are major exporters of cereal crops. As a result of these factors, inflation surged across Europe and in other parts of the world, but typically declined in 2023, and approached more usual levels by 2024.

Over the past 30 years, there has been an almost constant reduction in the poverty rate worldwide. Whereas nearly 38 percent of the world's population lived on less than 2.15 U.S. dollars in terms of 2017 Purchasing Power Parity (PPP) in 1990, this had fallen to 8.7 percent in 2022. This is despite the fact that the world's population was growing over the same period. However, there was a small increase in the poverty rate during the COVID-19 pandemic in 2020 and 2021, when thousands of people became unemployed overnight. Moreover, rising cost of living in the aftermath of the pandemic and spurred by the Russian invasion of Ukraine in 2022 meant that many people were struggling to make ends meet. Poverty is a regional problem Poverty can be measured in relative and absolute terms. Absolute poverty concerns basic human needs such as food, clothing, shelter, and clean drinking water, whereas relative poverty looks at whether people in different countries can afford a certain living standard. Most countries that have a high percentage of their population living in absolute poverty, meaning that they are poor compared to international standards, are regionally concentrated. African countries are most represented among the countries in which poverty prevails the most. In terms of numbers, Sub-Saharan Africa and South Asia have the most people living in poverty worldwide. Inequality on the rise How wealth, or the lack thereof, is distributed within the global population and even within countries is very unequal. In 2022, the richest one percent of the world owned almost half of the global wealth, while the poorest 50 percent owned less than two percent in the same year. Within regions, Latin America had the most unequal distribution of wealth but this phenomenon is present in all world regions.

In economics, the inflation rate is a measure of the change in price of a basket of goods. The most common measure being the consumer price index. It is the percentage rate of change in price level over time, and also indicates the rate of decrease in the purchasing power of money. The annual rate of inflation for 2023, was 4.1 percent higher in the United States when compared to the previous year. More information on inflation and the consumer price index can be found on our dedicated topic page. Additionally, the monthly rate of inflation in the United States can be accessed here. Inflation and purchasing power Inflation is a key economic indicator, and gives economists and consumers alike a look at changes in prices in the wider economy. For example, if an average pair of socks costs 100 dollars one year and 105 dollars the following year, the inflation rate is five percent. This means the amount of goods an individual can purchase with a unit of currency has decreased. This concept is often referred to as purchasing power. The data presents the average rate of inflation in a year, whereas the monthly measure of inflation measures the change in prices compared with prices one year ago. For example, monthly inflation in the U.S. reached a peak in June 2022 at 9.1 percent. This means that prices were 9.1 percent higher than they were in June of 2021. The purchasing power is the extent to which a person has available funds to make purchases. The Big Mac Index has been published by The Economist since 1986 and exemplifies purchasing power on a global scale, allowing us to see note the differences between different countries currencies. Switzerland for example, has the most expensive Big Mac in the world, costing consumers 6.71 U.S. dollars as of July 2022, whereas a Big Mac cost 5.15 dollars in the United States, and 4.77 dollars in the Euro area. One of the most important tools in influencing the rate of inflation is interest rates. The Federal Reserve of the United States has the capacity to make changes to the federal interest rate . Changes to the rate of inflation are thought to be an imbalance between supply and demand. After COVID-19 related lockdowns came to an end there was a sudden increase in demand for goods and services with consumers having more funds than usual thanks to reduced spending during lockdown and government funded economic support. Additionally, supply-chain related bottlenecks also due to lockdowns around the world and the Russian invasion of Ukraine meant that there was a decrease in the supply of goods and services. By increasing the interest rate, the Federal Reserve aims to reduce spending, and thus bring demand back into balance with supply.

The global fuel energy price index stood at 188.62 index points in January 2025, up from 100 in the base year 2016. Figures increased that month due to a rise in crude oil prices as a result of new sanctions on Russian oil and greater heating fuel demand. The fuel energy index includes prices for crude oil, natural gas, coal, and propane. Supply constraints across multiple commodities The global natural gas price index surged nearly 11-fold, and the global coal price index rose almost seven-fold from summer 2020 to summer 2022. This notable escalation was largely attributed to the Russia-Ukraine war, exerting increased pressure on the global supply chain. Global ramifications of the Russia-Ukraine war The invasion of Ukraine by Russia played a role in the surge of global inflation rates. Notably, Argentina bore the brunt, experiencing a hyperinflation rate of 92 percent in 2022. The war also exerted a significant impact on global gross domestic product (GDP) growth. Saudi Arabia emerged with a notable increase of nearly three percent, as several Western nations shifted their exports from Russia to Middle Eastern countries due to the sanctions imposed on the former.

Retail residential electricity prices in the United States have mostly risen over the last decades. In 2023, prices registered a year-over-year growth of 6.3 percent, the highest growth registered since the beginning of the century. Residential prices are projected to continue to grow by two percent in 2024. Drivers of electricity price growth The price of electricity is partially dependent on the various energy sources used for generation, such as coal, gas, oil, renewable energy, or nuclear. In the U.S., electricity prices are highly connected to natural gas prices. As the commodity is exposed to international markets that pay a higher rate, U.S. prices are also expected to rise, as it has been witnessed during the energy crisis in 2022. Electricity demand is also expected to increase, especially in regions that will likely require more heating or cooling as climate change impacts progress, driving up electricity prices. Which states pay the most for electricity? Electricity prices can vary greatly depending on both state and region. Hawaii has the highest electricity prices in the U.S., at roughly 43 U.S. cents per kilowatt-hour as of May 2023, due to the high costs of crude oil used to fuel the state’s electricity. In comparison, Idaho has one of the lowest retail rates. Much of the state’s energy is generated from hydroelectricity, which requires virtually no fuel. In addition, construction costs can be spread out over decades.