West Virginia and Kansas had the lowest cost of living across all U.S. states, with composite costs being half of those found in Hawaii. This was according to a composite index that compares prices for various goods and services on a state-by-state basis. In West Virginia, the cost of living index amounted to **** — well below the national benchmark of 100. Virginia— which had an index value of ***** — was only slightly above that benchmark. Expensive places to live included Hawaii, Massachusetts, and California. Housing costs in the U.S. Housing is usually the highest expense in a household’s budget. In 2023, the average house sold for approximately ******* U.S. dollars, but house prices in the Northeast and West regions were significantly higher. Conversely, the South had some of the least expensive housing. In West Virginia, Mississippi, and Louisiana, the median price of the typical single-family home was less than ******* U.S. dollars. That makes living expenses in these states significantly lower than in states such as Hawaii and California, where housing is much pricier. What other expenses affect the cost of living? Utility costs such as electricity, natural gas, water, and internet also influence the cost of living. In Alaska, Hawaii, and Connecticut, the average monthly utility cost exceeded *** U.S. dollars. That was because of the significantly higher prices for electricity and natural gas in these states.

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

This map shows the median household income in the United States in 2012. Information for the 2012 Median Household Income is an estimate of income for calendar year 2012. Income amounts are expressed in current dollars, including an adjustment for inflation or cost-of-living increases. The median is the value that divides the distribution of household income into two equal parts. The median household income in the United States overall was $50,157 in 2012. This map shows Esri's 2012 estimates using Census 2010 geographies.

The geography depicts States at greater than 50m scale, Counties at 7.5m to 50m scale, Census Tracts at 200k to 7.5m scale, and Census Block Groups at less than 200k scale.

Scale Range: 1:591,657,528 down to 1:72,224.

For more information on this map, including the terms of use, visit us online.

This map shows the average household income in the U.S. in 2022 in a multiscale map by country, state, county, ZIP Code, tract, and block group. Information for the average household income is an estimate of income for calendar year 2022. Income amounts are expressed in current dollars, including an adjustment for inflation or cost-of-living increases.The pop-up is configured to include the following information for each geography level:Average household incomeMedian household incomeCount of households by income groupAverage household income by householder age groupThe data shown is from Esri's 2022 Updated Demographic estimates using Census 2020 geographies. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data.Esri's U.S. Updated Demographic (2022/2027) Data: Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies.Additional Esri Resources:Esri DemographicsU.S. 2022/2027 Esri Updated DemographicsEssential demographic vocabularyThis item is for visualization purposes only and cannot be exported or used in analysis.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Retirement Notice: This item is in mature support as of June 2023 and will be retired in December 2025. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.This map shows the average household income in the U.S. in 2022 in a multiscale map by country, state, county, ZIP Code, tract, and block group. Information for the average household income is an estimate of income for calendar year 2022. Income amounts are expressed in current dollars, including an adjustment for inflation or cost-of-living increases.The pop-up is configured to include the following information for each geography level:Average household incomeMedian household incomeCount of households by income groupAverage household income by householder age group Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

The American Community Survey (ACS) 5 Year 2016-2020 socioeconomic estimate data is a subset of information derived from the following census tables:B08013 - Aggregate Travel Time To Work Of Workers By Sex;B08303 - Travel Time To Work;B17019 - Poverty Status In The Past 12 Months Of Families By Household Type By Tenure;B17021 - Poverty Status Of Individuals In The Past 12 Months By Living Arrangement;B19001 - Household Income In The Past 12 Months;B19013 - Median Household Income In The Past 12 Months;B19025 - Aggregate Household Income In The Past 12 Months;B19113 - Median Family Income In The Past 12 Months;B19202 - Median Non-family Household Income In The Past 12 Months;B23001 - Sex By Age By Employment Status For The Population 16 Years And Over;B25014 - Tenure By Occupants Per Room;B25026 - Total Population in Occupied Housing Units by Tenure by year Householder Moved into Unit;B25106 - Tenure By Housing Costs As A Percentage Of Household Income In The Past 12 Months;C24010 - Sex By Occupation For The Civilian Employed Population 16 Years And Over;B20004 - Median Earnings In the Past 12 Months (In 2015 Inflation-Adjusted Dollars) by Sex by Educational Attainment for the Population 25 Years and Over;B23006 - Educational Attainment by Employment Status for the Population 25 to 64 Years, and;B24021 - Occupation By Median Earnings In The Past 12 Months (In 2015 Inflation-Adjusted Dollars) For The Full-Time, Year-Round Civilian Employed Population 16 Years And Over.

To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_ACS 5-Year Socioeconomic Estimate Data by CountyDate of Coverage: 2016-2020

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 map shows the median household income in the United States in 2012. Information for the 2012 Median Household Income is an estimate of income for calendar year 2012. Income amounts are expressed in current dollars, including an adjustment for inflation or cost-of-living increases. The median is the value that divides the distribution of household income into two equal parts. The median household income in the United States overall was $50,157 in 2012. This map shows Esri's 2012 estimates using Census 2010 geographies. The data shown is from Esri's 2012 Updated Demographics. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data. This map shows Esri's 2012 estimates using Census 2010 geographies.The map is designed to be displayed in conjunction with the Canvas basemap with a transparency of 25%. To use it on other basemaps, try a transparency of 25-50%.Information about the USA Median Household Income map service used in this map is here.

Large white, Grade A chicken eggs, sold in a carton of a dozen. Includes organic, non-organic, cage free, free range, and traditional."

Drone Live Map Streaming to EOCs Market Outlook

According to our latest research, the Global Drone Live Map Streaming to EOCs market size was valued at $1.25 billion in 2024 and is projected to reach $4.87 billion by 2033, expanding at a robust CAGR of 16.5% during the forecast period of 2025–2033. A major factor propelling the growth of this market globally is the increasing need for real-time situational awareness in emergency management and public safety operations. Live map streaming from drones to Emergency Operations Centers (EOCs) enables rapid decision-making, enhances operational efficiency, and significantly improves response times during critical incidents such as natural disasters, large-scale public events, and security threats. The integration of advanced imaging technologies, AI-driven analytics, and seamless cloud connectivity further accelerates the adoption and scalability of drone live map streaming solutions across diverse sectors.

Regional Outlook

North America currently holds the largest share of the global Drone Live Map Streaming to EOCs market, accounting for approximately 38% of the total market value in 2024. This dominance is primarily attributed to the region’s mature technological ecosystem, early adoption of advanced drone technologies, and robust investments in public safety infrastructure. The United States, in particular, has been a frontrunner due to significant federal and state funding for emergency management, stringent regulatory frameworks that support safe drone operations, and the presence of leading solution providers. Moreover, the collaboration between technology vendors, local governments, and first responder agencies has fostered a conducive environment for innovation and rapid deployment of live map streaming platforms. The established network of Emergency Operations Centers across North America further ensures seamless integration of drone data into command and control workflows, solidifying the region’s leadership position.

The Asia Pacific region is projected to be the fastest-growing market, with a forecasted CAGR of 19.2% from 2025 to 2033. This rapid growth is driven by escalating investments in smart city initiatives, increasing frequency of natural disasters, and rising governmental focus on disaster preparedness and response. Countries such as China, Japan, and India are aggressively deploying drone-based technologies to enhance their emergency response capabilities, leveraging both local innovation and international partnerships. The proliferation of affordable drone hardware, coupled with expanding 5G and cloud infrastructure, is enabling real-time map streaming even in remote or challenging terrains. Additionally, favorable regulatory reforms and public-private collaborations are accelerating the adoption of these solutions across municipal agencies and law enforcement bodies in the region.

Emerging economies in Latin America, the Middle East, and Africa are beginning to recognize the value of drone live map streaming, particularly for disaster management and public safety applications. However, the pace of adoption is tempered by challenges such as limited funding, lack of skilled personnel, and underdeveloped digital infrastructure. In these regions, pilot projects and donor-funded initiatives are laying the groundwork for broader implementation, with localized demand primarily stemming from urban centers prone to flooding, wildfires, or civil unrest. Policy impacts, including evolving drone regulations and spectrum allocation for wireless data transmission, play a pivotal role in shaping market dynamics. As these economies continue to modernize their emergency response frameworks, the demand for scalable and cost-effective live map streaming solutions is expected to rise steadily.

Report Scope

Out of all 50 states, New York had the highest per-capita real gross domestic product (GDP) in 2024, at 92,341 U.S. dollars, followed closely by Massachusetts. Mississippi had the lowest per-capita real GDP, at 41,603 U.S. dollars. While not a state, the District of Columbia had a per capita GDP of more than 210,780 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.

Eggs US fell to 2.25 USD/Dozen on December 1, 2025, down 1.77% from the previous day. Over the past month, Eggs US's price has risen 37.63%, but it is still 42.64% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Eggs US.

Symbols in bright yellow represent areas where more seniors with burdensome housing costs are renters, whereas symbols that are blue represent areas with more owners. Map has national coverage but opens in Milwaukee. Use the map's bookmarks or the search bar to view other cities. Bookmarks include what are generally thought of as "affordable" cities - Fresno, Salt Lake City, New Orleans, Albuquerque, El Paso, Tusla, Raleigh, Milwaukee - but yet there are many seniors whose housing costs are 30 percent or more of their income. "The burden of housing costs combined with climbing health care expenses can significantly reduce financial security at older ages" according to the Urban Institute. The number of senior households is projected to grow in the coming years, making the issue of economic security for seniors even more pressing.Housing costs are defined as burdensome if they exceed 30 percent of monthly income, a widely-used definition by HUD and others in affordable housing discussions. For owners, monthly housing costs include payments for mortgages and all other debts on the property; real estate taxes; fire, hazard, and flood insurance; utilities; fuels; and condominium or mobile home fees.For renters, monthly housing costs include contract rent plus the estimated average monthly cost of utilities (electricity, gas, and water and sewer) and fuels (oil, coal, kerosene, wood, etc.) if these are paid by the renter.Income is defined as the sum of wage/salary income; net self-employment income; interest/dividends/net rental/royalty income/income from estates & trusts; Social Security/Railroad Retirement income; Supplemental Security Income (SSI); public assistance/welfare payments; retirement/survivor/disability pensions; & all other income.Only households with a householder who is 65 and over are included in these maps. The householder is a person in whose name the home is owned, being bought, or rented, and how answers the questionnaire as person 1.This map is multi-scale, with data for states, counties, and tracts. This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

Abstract

Over the past decade, Albania has been seeking to develop the framework for a market economy and more open society. It has faced severe internal and external challenges in the interim – extremely low income levels and a lack of basic infrastructure, the rapid collapse of output and inflation rise after the shift in regime in 1991, the turmoil during the 1997 pyramid crisis, and the social and economic shocks accompanying the 1999 Kosovo crisis. In the face of these challenges, Albania has made notable progress in creating conditions conducive to growth and poverty reduction.

In the process leading to its first Poverty Reduction Strategy (that is the National Strategy for Socioeconomic Development, now renamed the National Strategy for Development and Integration), the Government of Albania reinforced its commitment to strengthening its own capacity to collect and analyze on a regular basis the information it needs to inform policy-making.

Multi-purpose household surveys are one of the main sources of information to determine living conditions and measure the poverty situation of a country. They provide an indispensable tool to assist policy-makers in monitoring and targeting social programs. In its first phase (2001-2006), this monitoring system included the following data collection instruments: (i) Population and Housing Census; (ii) Living Standards Measurement Surveys every 3 years, and (iii) annual panel surveys.

The Population and Housing Census (PHC) conducted in April 2001, provided the country with a much needed updated sampling frame which is one of the building blocks for the household survey structure. The focus during this first phase of the monitoring system is on a periodic LSMS (in 2002 and 2005), followed by panel surveys on a subsample of LSMS households (in 2003, and 2004), drawing heavily on the 2001 census information.

A poverty profile based on 2002 data showed that some 25 percent of the population are poor, with many others vulnerable to poverty due to their incomes being close to the poverty threshold. Income related poverty is compounded by poor access to basic infrastructure (regular supply of electricity, clean water), education and health services, housing, etc.

The 2005 LSMS was in the field between May and early July, with an additional visit to agricultural households in October, 2005. The survey work was undertaken by the Living Standards unit of INSTAT, with the technical assistance of the World Bank.

Geographic coverage

National coverage. Domains: Tirana, other urban, rural; Agro-ecological areas (coastal, central, mountain)

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The Republic of Albania is divided geographically into 12 Prefectures (Prefekturat). The latter are divided into Districts (Rrethet) which are, in turn, divided into Cities (Qyteti) and Communes (Komunat). The Communes contain all the rural villages and the very small cities. For census purposes, the cities and the villages have been divided into enumeration areas (EAs).

1. Sampling frame

The Enumeration Areas (EA) that make up the sampling frame come from the April 2001 General Census of Population and Housing. The EAs in the frame are classified by Prefecture, District, City or Commune. The frame also contains, for every EA, the number of Housing Units (HU), the number of occupied HUs, the number of unoccupied HUs, the number of households, and the population. We are using occupied dwellings and not total number of dwellings since many EAs contain a large number of empty dwellings.

A detailed study of the list of census EAs shows that many have zero population. In order to obtain EAs with the minimum of 50 and the maximum of 120 occupied housing units, the EAs with zero population have been taken off the sampling frame. Since the sizes of the EAs varied from 0 to 395 HUs, the smaller EAs (with less than 50 HU) have been collapsed with geographically adjacent ones and the largest EAs (with more than 120 HU) have been split into two or more EAs. Subsequently, maps identifying the boundaries of every split and collapsed EA were prepared. Given that the 2002 LSMS has been conducted less than a year after the April 2001 census, a listing operation to update the sample EAs was not conducted in the field. However, since the level of construction is very high in the city of Tirana and its suburbs, a quick count of the 75 sample EAs selected in Tirana was carried out followed by a listing operation. The check of the listing based on the Census data revealed two types of discrepancies: - HUs had become invalid, i.e. vacant, nonresidential, demolished, seasonally occupied, etc. - Instead of one small building (with one or two HU), a new one with 15 HUs was identified.

During of the listing update process, HUs identified as invalid were taken off the frame. In the case of a new building, these new HUs were entered with a new sequential code. The listing sheets prepared during the listing operation in Tirana, become the sampling frame for the final stage of selection of 12 HU which has to be interviewed. The unit of analysis and the unit of observation is the household. The universe under study consists of all the households in the Republic of Albania. We have used the Housing Unit (defined as the space occupied by one household) as the sampling unit, instead of the household, because the HU is more permanent and easier to identify in the field.

1. Sample Size

In the LSMS the sample size is 450 EA and in each EA 8 households were selected. So the total sample size of the LSMS is 3600 households. In addition, since a certain level of nonresponse is expected, 4 reserve units were selected in each sample EA.

1. Stratification

The sampling frame has been divided in three regions (strata) 1. Coastal Area 2. Central Area 3. Mountain Area and Tirana (urban and other urban) is consider as a separate strata.

The first three strata were divided into major cities (the most important cities in the region), other urban (the rest of cities in the region), and rural. In each more importance was given to the major cities and rural areas. We have selected 10 EA for each major city and 65 EAs (75 EAs for Mountain Area) for each region. In the city of Tirana and its suburbs, implicit stratification was used to improve the efficiency of the sample design.

1. Procedure for the Selection of Housing Units

A fixed number of valid dwelling units (12) was selected systematically and with equal probability from the Listing Form pertaining to Tirana and from the Census forms for the other areas. Once the 12 HUs were selected, 4 of them were chosen at random and kept as reserve units. The selected HUs were numbered within the EA and identified with a circle around the number in the listing form, as well as a circle on the maps. The reserve sample (units 9 to 12) were identified from R1 to R4 during data collection to emphasize the fact that they were reserve units.

Two copies of the sample listing sheets and two copies of maps for each EA were printed. The first copy of the listing sheet and the map were given to the supervisor and included the 12 HU, the second copy was given to the enumerator. The enumerator only received the 8 dwelling units, not the reserve ones. Each time the enumerator needed a reserve HU, he/she had to ask the supervisor and explain the reason why a reserve unit was needed. This process helped determine the reason why reserve units were used and provided more control on their use.

In the field the enumerator registered the occupancy status of every unit: - occupied as principal residence - vacant - under construction (not occupied) - demolished or abandoned (not occupied) - seasonally occupied

In the case that one HU was found to be invalid, the enumerator used the first reserve unit (identified with the code R1). In the case that in one EA more than 4 DU selected were invalid, other units from that EA chosen at random by headquarter (in Tirana) were selected as replacement units to keep the enumerator load constant and maintain a uniform sample size in each EA. Before identifying the invalid HUs, the interviewer had to note the interview status of each visit for all the units for which an interview was attempted, whether these are original units or reserve units. This was done to determine the interview status: interview completed, nonresponse, refusal, etc. In other words, this will allow identifying: the completed interviews (responses obtained), the incomplete but usable ones (responses obtained), the incomplete ones but not usable (nonresponse), the refusals (nonresponse) and the "not at home" (nonresponse). Subsequently, the invalid units identified were substituted with the available reserves, always maintaining the sample of 8 HUs.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four survey instruments were used to collect information for the 2005 Albania LSMS: a household questionnaire, a diary for recording household food consumption, a community questionnaire, and a price questionnaire.

The household questionnaire included all the core LSMS modules as defined in Grosh and Glewwe (2000)1, plus additional modules on migration, fertility, subjective poverty, agriculture, non-farm enterprises, and social capital. Geographical referencing data on the longitude and latitude of each household were also recorded using portable GPS devices. Geo-referencing will enable a more efficient spatial link among the different surveys of the system, as well as between the survey households and other geo-referenced information.

The choice of the modules was aimed at matching as much as

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.

Abstract

IHLCA-II is a nationwide quantitative survey of 18660 households with two rounds of data collection (December 2009/January 2010 and May 2010).

IHLCA surveys should support the system of economic statistics that is the basis for modern National Accounts by providing much needed data on value added in household (informal sector) production. IHLCA data will make it possible to estimate the GDP share of private consumption from the use side or alternatively in terms of household production's share of the GDP from the production side.

The main objectives of the survey have been formulated: - To obtain an accurate and holistic assessment of population well-being by measuring a number of indicators related to living conditions from an integrated perspective; - To provide reliable and updated data for identifying different levels of poverty in order to help better focus programmatic interventions and prioritize budget allocations; - To provide quantitative and qualitative data for better understanding the dimensions of wellbeing and poverty in Myanmar and the endogenous and exogenous factors behind the observed patterns and trends in living conditions; - To provide baseline information for monitoring progress towards the achievement of the Millennium Development Goals and other national and international targets; - To develop a rigorous and standardized methodology for establishing a framework for monitoring living conditions and conducting future time-trend analysis.

Given the breadth of information that was to be generated by the survey and the range of stakeholders involved in the project, there were also a number of secondary objectives including: - The compilation of updated statistics for a series of indicators that were also addressed in previous surveys in Myanmar for comparative time-trend analyses on specific aspects of living conditions where appropriate; - The compilation of precise statistics on the spatial distribution of poor and non-poor households for poverty mapping; - For economic and social analysis, improved data for monitoring differentials in living conditions by urban-rural residence, gender and other population sub-groups; - For policy and programmatic formulation, comprehensive data on the population’s perceptions of living conditions, in particular prioritization in terms of their preferences to improve wellbeing and reduce poverty across regions of the country.

The IHLCA-II results have been used to prepare three separate reports: - Poverty Profile - MDG Data Report - Poverty Dynamics Report

In addition two supplementing reports have been prepared: - Technical Report (Survey Design and Implementation) - Quality Report

Sampling procedure

Sampling design

The main focus of the IHLCA-II was to assess the changes in the living conditions of people in Myanmar since IHLCA-I. The national research team considered that the survey design, sampling units and other survey instruments therefore should be as similar as possible to those used in the IHLCA-I.

A stratified multi-stage sample design was used for the IHLCA-I survey with 62 districts as the strata.

Given their special importance, Yangon City and Mandalay City were treated as separate strata. The selection plan in each stratum was as follows. Townships across all districts were used as first stage sampling units (FSU). The sampling frame for the first stage was an official list of townships with their estimated number of households in each district.

The estimated number of households in the excluded 45 townships and from other wards/village tracts represented 5% of the total population.

The second stage sampling unit (SSU) was the ward (urban) or village tract (rural) within the selected townships. The sampling frame for the second stage was the list of wards and villages in the selected townships along with their estimated numbers of households. All wards and village tracts in each selected township within a particular district were grouped into urban/rural substrata. A predetermined number of wards/villages tracts were then drawn with PPES systematic random selection from those township frames.

Listings of Street segments in selected wards (urban) and villages in selected village tracts (rural) with the number of households were made prior to the household survey. Moreover, the survey teams of supervisors drew sketch maps of the street segment inwards and villages prior to the data collection activities and selected the sample households in each community. With the predetermined path in the community on the sketch map and the sampling interval calculated using the total number of household and the fixed sample size, a unique systematic sample could then be drawn conforming to the random selection with a known selection probability.

The IHLCA-II sample design is a modified IHLCA-I sample design which takes into account of changes in the sample frame since 2004 and retains a panel of 50% from IHLCA-I sample of households.

The same sample of areas (street segments and villages) as the IHLCA-I survey areas were kept. There are altogether 1555 areas. Within each area a sample of 12 households was selected. Six households from the 12 IHLCA-I household sample were selected randomly. An additional six households were selected from the “non-IHLCA-I households in the village or street segment. In some (fairly few) cases there were less than six old IHLCA-I households remaining in the village or street segment due to migration and other causes. In that case all remaining IHLCA-I households were included in the sample. If that was the case then the sample of non-IHLCA-I households were increased so the total sample from the village or street segment added up to 12.

The 50 % panel would allow for studies of gross changes (household dynamics) on a sufficiently large sample while at the same time we also make sure that changes in the population are taken into account.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey questionnaires were used for the IHLCA survey11:

The Household questionnaire, administered at household level, included 9 modules covering different aspects of household living conditions: - Module 1: Household Basic Characteristics; - Module 2: Housing; - Module 3: Education; - Module 4: Health; - Module 5: Consumption Expenditures; - Module 6: Household Assets; - Module 7: Labour and Employment; - Module 8: Business - Module 9: Finance and Savings.

The Community questionnaire, administered to local key informants included 4 modules that aimed at providing general information on the village/wards where the survey was being undertaken and at reducing the length of the household interview. The questionnaire was only administered in the first round. Modules included in the Community questionnaire were: - Module 1.1: Village/Ward Infrastructure; - Module 1.2: Population; - Module 1.3: Housing; - Module 1.4: Labour and Employment - Module 1.5: Business Activities; - Module 1.6: Agricultural Activities; - Module 1.7: Finance and Savings; - Module 2: Schools - Module 3: Health facilities - Module 4: Pharmacies and Drug Stores

The Community price questionnaire which aimed at providing information on the prices of specific items in each village/ward surveyed. These prices were collected in case the quality of implicit prices calculated from the household survey was not satisfactory. Since there were no problems with implicit prices, community level prices were not used. The Community price questionnaire comprised of only one module.

The Township Profile questionnaire aimed at collecting administrative information about the Townships included in the survey. It was not used in the data analysis.

All final questionnaires were translated from English to Myanmar.

Depending on the nature of the information to be collected, different types of questions (current status and retrospective) were included in the survey instruments. For instance, current status questions were used to assess Housing condition and level of education and literacy. On the other hand, retrospective questions were also used to collect information on other items including household consumption expenditures. Thus one important issue was the reference period for specific consumption items. In order to minimize recall errors, different reference periods were used for different types of items. In particular, shorter periods were used for smaller items (such as 7days for frequently bought food items and 30 days for less frequently bought food items and non-food items), and longer periods for larger items (such as six months for bulky non-food items and equipment). All above was in line with IHLCA-I.

Cleaning operations

Data editing and coding

Overall editing and coding of the questionnaires received from the field was under the responsibility of the State and Region Level Data Entry Management Committee. The operations involved mainly: - Checking and correcting for inconsistencies in the data; - Identifying and correcting for outliers; - Recoding of variables when necessary.

Data appraisal

First assessment

With regard to potential non-sampling errors, when collecting information from the respondent it was important to plan for several controls: (i) immediately during the interview by the enumerator; (ii) after the interview during the review of the completed questionnaire by the field supervisor and before data entry; and (iii) during data entry. For instance, ranges for data on the monetary value of household expenditures were set, such as minimum and maximum acceptable prices for a given quantity of each major food and non-food item (based on independently obtained data of market prices). The appropriate ranges

Amsterdam is set to maintain its position as Europe's most expensive city for apartment rentals in 2025, with median costs reaching 2,500 euros per month for a furnished unit. This figure is double the rent in Prague and significantly higher than other major European capitals like Paris, Berlin, and Madrid. The stark difference in rental costs across European cities reflects broader economic trends, housing policies, and the complex interplay between supply and demand in urban centers. Factors driving rental costs across Europe The disparity in rental prices across European cities can be attributed to various factors. In countries like Switzerland, Germany, and Austria, a higher proportion of the population lives in rental housing. This trend contributes to increased demand and potentially higher living costs in these nations. Conversely, many Eastern and Southern European countries have homeownership rates exceeding 90 percent, which may help keep rental prices lower in those regions. Housing affordability and market dynamics The relationship between housing prices and rental rates varies significantly across Europe. As of 2024, countries like Turkey, Iceland, Portugal, and Hungary had the highest house price to rent ratio indices. This indicates a widening gap between property values and rental costs since 2015. The affordability of homeownership versus renting differs greatly among European nations, with some countries experiencing rapid increases in property values that outpace rental growth. These market dynamics influence rental costs and contribute to the diverse rental landscape observed across European cities.

The County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. This feature layer contains 2022 County Health Rankings data for nation, state, and county levels. The Rankings are compiled using county-level measures from a variety of national and state data sources. Some example measures are:adult smokingphysical inactivityflu vaccinationschild povertydriving alone to workTo see a full list of variables, as well as their definitions and descriptions, explore the Fields information by clicking the Data tab here in the Item Details. These measures are standardized and combined using scientifically-informed weights."By ranking the health of nearly every county in the nation, County Health Rankings & Roadmaps (CHR&R) illustrates how where we live affects how well and how long we live. CHR&R also shows what each of us can do to create healthier places to live, learn, work, and play – for everyone."Counties are ranked within their state on both health outcomes and health factors. Counties with a lower (better) health outcomes ranking than health factors ranking may see the health of their county decline in the future, as factors today can result in outcomes later. Conversely, counties with a lower (better) factors ranking than outcomes ranking may see the health of their county improve in the future.Some new variables in the 2022 Rankings data compared to previous versions:COVID-19 age-adjusted mortalitySchool segregationSchool funding adequacyGender pay gapChildcare cost burdenChildcare centersLiving wage (while the Living wage measure was introduced to the CHRR dataset in 2022 from the Living Wage Calculator, it is not available in the Living Atlas dataset and user’s interested in the most up to date living wage data can look that up on the Living Wage Calculator website).Data Processing Notes:Data downloaded April 2022Slight modifications made to the source data are as follows:The string " raw value" was removed from field labels/aliases so that auto-generated legends and pop-ups would only have the measure's name, not "(measure's name) raw value" and strings such as "(%)", "rate", or "per 100,000" were added depending on the type of measure.Percentage and Prevalence fields were multiplied by 100 to make them easier to work with in the map.Ratios were set to null if negative to make them easier to work with in the map.For demographic variables, the word "numerator" was removed and the word "population" was added where appropriate.Fields dropped from analytic data file: yearall fields ending in "_cihigh" and "_cilow"and any variables that are not listed in the sources and years documentation.Analytic data file was then merged with state-specific ranking files so that all county rankings and subrankings are included in this layer.2010 US boundaries were used as the data contain 2010 US census geographies, for a total of 3,142 counties.

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.

Overburdened Communities Census Tracts web layer configured in Map Viewer Classic for the EV Publicly Available application. (Current Version)This layer represents the overall ranking for Environmental Exposures, Environmental Effects, Socioeconomic Factors and Sensitive Populations. More information is available here https://fortress.wa.gov/doh/wtnibl/WTNIBL/.This layer uses 2010 Census Tracts based on the current version of the Washington Tracking Network's Environmental Health Disparities data.The source data is derived from the Environmental Health Disparities map displayed on WTN's Information by Location (IBL) tool. The data on the map include 19 indicators and are divided into four themes:Environmental Exposures (PM2.5-diesel emissions; ozone concentration; PM2.5 Concentration; proximity to heavy traffic roadways; toxic release from facilities (RSEI model))Environmental Effects (lead risk from housing; proximity to hazardous waste treatment, storage, and disposal facilities (TSDFs); proximity to National Priorities List sites (Superfund Sites); proximity to Risk Management Plan (RMP) facilities; wastewater discharge)Sensitive Populations (death from cardiovascular disease; low birth weight)Socioeconomic Factors (limited English; no high school diploma; poverty; race - people of color; transportation expense; unaffordable housing; unemployed)Learn about how the Environmental Health Disparities map is being used to support Washington's clean energy transformation.Washington State Department of Health provides an index of Environmental Health Disparities for all the census tracts in Washington. Both deciles (ranks 1 - 10) and quintiles (ranks 1 - 5) have been calculated for cardiovascular disease, low birth weight, people without a high school diploma, people who speak English less than "very well" or "not at all", people living at or below 185% of federal poverty level, people of color, transportation costs, unemployment, housing costs, people exposed to air pollution near busy road ways, exposure to diesel emissions, exposure to average ozone, exposure to particulate matter, exposure to toxic releases, proximity to hazardous waste generators, exposure to lead, proximity to Superfund sites, proximity to facilities with highly toxic substances, proximity to wastewater discharge, general environmental exposure, general environmental effects, general sensitive populations, general socioeconomic factors, and overall environmental health disparities. Please see the Washington environmental health disparities page for more information: https://www.doh.wa.gov/DataandStatisticalReports/WashingtonTrackingNetworkWTN/InformationbyLocation/WashingtonEnvironmentalHealthDisparitiesMap The map was a collaborative project that took several years to develop. It went live to the public in January of 2019. Those involved in the initial collaboration include: University of Washington's Department of Environmental and Occupational Health Sciences, Front and Centered, Washington State Department of Health, Washington State Department of Ecology, and Puget Sound Clean Air Agency. The effort included listening sessions with communities in Washington State. The communities gave input that informed development of the map. Since the map was published, several laws and rules highlight it as a resource. Healthy Environment for All (HEAL) Act, passed in 2021, led to the first dedicated, ongoing state funding to maintain and update the map. The HEAL Act (RCW 43.70.815) requires DOH to: Further develop the EHD map, engaging with communities, tribes, researcher, and EJ CouncilTrack changes in disparities over time Perform a comprehensive evaluation every three yearsExpand online video trainings and guidance on how to use the EHD mapProvide support and consultation to state agencies on how to use the EHD map\DOH continues to add data to the EHD map to reflect additional health risks. DOH is currently working with partners to develop indicators for: wildfire smoke, asthma, tree canopy and greenspace, water quality, pesticide exposure, redlining index, and a group of climate change indicators.The EHD map is a living tool. Developing and improving it is an ongoing process, incorporating feedback and new data. The map will never fully reflect communities’ experiences and should not be used to replace community engagement or tribal consultation. If you have feedback about how we could improve the map, please contact us at EHDmap@doh.wa.gov.