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.

U.S. Software Developer Salaries

Analyzing Regional Variations

By [source]

About this dataset

This dataset provides an extensive look into the financial health of software developers in major cities and metropolitan areas around the United States. We explore disparities between states and cities in terms of mean software developer salaries, median home prices, cost of living avgs, rent avgs, cost of living plus rent avgs and local purchasing power averages. Through this data set we can gain insights on how to better understand which areas are more financially viable than others when seeking employment within the software development field. Our data allow us to uncover patterns among certain geographic locations in order to identify other compelling financial opportunities that software developers may benefit from

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How to use the dataset

This dataset contains valuable information about software developer salaries across states and cities in the United States. It is important for recruiters and professionals alike to understand what kind of compensation software developers are likely to receive, as it may be beneficial when considering job opportunities or applying for a promotion. This guide will provide an overview of what you can learn from this dataset.

The data is organized by metropolitan areas, which encompass multiple cities within the same geographical region (e.g., “New York-Northern New Jersey” covers both New York City and Newark). From there, each metro can be broken down further into a number of different factors that may affect software developer salaries in the area:

• Mean Software Developer Salary (adjusted): The average salary of software developers in that particular metro area after accounting for cost of living differences within the region.
• Mean Software Developer Salary (unadjusted): The average salary of software developers in that particular metro area before adjusting for cost-of-living discrepancies between locales.
• Number of Software Developer Jobs: This column lists how many total jobs are available to software developers in this particular metropolitan area.
• Median Home Price: A metric which shows median value of all homes currently on the market within this partcular city or state. It helps gauge how expensive housing costs might be to potential residents who already have an idea about their income/salary range expectations when considering a move/relocation into another location or potentially looking at mortgage/rental options etc.. 5) Cost Of Living Avg: A metric designed to measure affordability using local prices paid on common consumer goods like food , transportation , health care , housing & other services etc.. Also prominent here along with rent avg ,cost od living plus rent avg helping compare relative cost structures between different locations while assessing potential remunerations & risk associated with them . 6)Local Purchasing Power Avg : A measure reflecting expected difference in discretionary spending ability among households regardless their income level upon relocation due to price discrepancies across locations allows individual assessment critical during job search particularly regarding relocation as well as comparison based decision making across prospective candidates during any hiring process . 7 ) Rent Avg : Average rental costs for homes / apartments dealbreakers even among prime job prospects particularly medium income earners.(basis family size & other constraints ) 8 ) Cost Of Living Plus Rent Avg : Used here as one sized fits perspective towards measuring overall cost structure including items

Research Ideas

• Comparing salaries of software developers in different cities to determine which city provides the best compensation package.
• Estimating the cost of relocating to a new city by looking at average costs such as rent and cost of living.
• Predicting job growth for software developers by analyzing factors like local purchasing power, median home price and number of jobs available

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking perm...

The Consumer Sentiment Index in the United States stood at 51 in November 2025. This reflected a drop of 2.6 point from the previous survey. Furthermore, this was its lowest level measured since June 2022. The index is normalized to a value of 100 in December 1964 and based on a monthly survey of consumers, conducted in the continental United States. It consists of about 50 core questions which cover consumers' assessments of their personal financial situation, their buying attitudes and overall economic conditions.

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

In 2025, the Consumer Price Index (CPI) for medical professional services in the United States was at 432.46, compared to the period from 1982 to 1984 (=100). The CPI for hospital services was at 1,102.12.

This data collection contains the results of the Survey of Low Income Aged and Disabled (SLIAD), conducted in 1973-1974 in order to collect demographic and socioeconomic data necessary for assessing the effect of the Supplemental Security Income (SSI) program on potential recipients. After January 1, 1974, SSI replaced the state-administered welfare programs of Old Age Assistance (OAA), Aid to the Blind (AB), and Aid to the Permanently and Totally Disabled (APTD) and was meant to improve the economic well-being of the adult poor. A national sample of about 18,000 low-income aged, blind, and/or disabled adults was interviewed in 1973, and reinterviewed in 1974, after SSI was implemented. The 1974 re-interviews were conducted only with persons successfully interviewed in 1973. No new cases were added to replace first-year losses, nor were cases dropped because they no longer met SSI eligibility. Part 1 contains data gathered from a sample made up of aged and disabled persons who received OAA, AB, and/or APTD payments in 1973. Part 2 contains data gathered from a sample of low-income aged and disabled people in the general population (generated from Current Population Survey samples). The United States Census Bureau conducted the interviews and collected the data. The 1973 survey placed great emphasis on financial matters. Each respondent was asked to report income received in the preceding month and year by each of three general classes of persons in the household. The questionnaire listed more than 15 income sources including payments and awards from almost every transfer program possible, earnings from jobs and businesses, gifts, and dividends. The financial section of the questionnaire also included items aimed at establishing the value of owned property, savings and investments, the amount of indebtedness, and the amount spent for food, shelter, and other recurring household expenditures. For the most part, the remainder of the questionnaire concerned (1) household composition, (2) personal history, (3) health, health care, and the capacity for self-maintenance, (4) standard of living, as represented by housing, diet, travel, and recreation, (5) factors that might affect the relation between income and standard of living (e.g., personal preference, physical capacity, and access), and (6) attitudinal response to these conditions, circumstances, and types of status. The 1974 survey was similar in that it asked almost all of the earlier income and asset questions, but added a section on SSI payments. It also collected more detail on household living expenses. It did not repeat the biographical section or the inventory of health conditions from the 1973 survey, but did contain new questions on a spouses' funeral expenses as well as the respondent's experience with SSI.

VITAL SIGNS INDICATOR Poverty (EQ5)

FULL MEASURE NAME The share of the population living in households that earn less than 200 percent of the federal poverty limit

LAST UPDATED December 2018

DESCRIPTION Poverty refers to the share of the population living in households that earn less than 200 percent of the federal poverty limit, which varies based on the number of individuals in a given household. It reflects the number of individuals who are economically struggling due to low household income levels.

DATA SOURCE U.S Census Bureau: Decennial Census http://www.nhgis.org (1980-1990) http://factfinder2.census.gov (2000)

U.S. Census Bureau: American Community Survey Form C17002 (2006-2017) http://api.census.gov

METHODOLOGY NOTES (across all datasets for this indicator) The U.S. Census Bureau defines a national poverty level (or household income) that varies by household size, number of children in a household, and age of householder. The national poverty level does not vary geographically even though cost of living is different across the United States. For the Bay Area, where cost of living is high and incomes are correspondingly high, an appropriate poverty level is 200% of poverty or twice the national poverty level, consistent with what was used for past equity work at MTC and ABAG. For comparison, however, both the national and 200% poverty levels are presented.

For Vital Signs, the poverty rate is defined as the number of people (including children) living below twice the poverty level divided by the number of people for whom poverty status is determined. Poverty rates do not include unrelated individuals below 15 years old or people who live in the following: institutionalized group quarters, college dormitories, military barracks, and situations without conventional housing. The household income definitions for poverty change each year to reflect inflation. The official poverty definition uses money income before taxes and does not include capital gains or noncash benefits (such as public housing, Medicaid, and food stamps). For the national poverty level definitions by year, see: https://www.census.gov/hhes/www/poverty/data/threshld/index.html For an explanation on how the Census Bureau measures poverty, see: https://www.census.gov/hhes/www/poverty/about/overview/measure.html

For the American Community Survey datasets, 1-year data was used for region, county, and metro areas whereas 5-year rolling average data was used for city and census tract.

To be consistent across metropolitan areas, the poverty definition for non-Bay Area metros is twice the national poverty level. Data were not adjusted for varying income and cost of living levels across the metropolitan areas.

In 2025, households in California needed an hourly wage of over 50 U.S. dollars to afford the rent of a two-bedroom apartment. Hawaii had the second-least affordable two-bedroom apartments, as a household would have to earn at least around 49 U.S. dollars per hour in order to afford rent payments. These figures are considerably higher than the average minimum wage in place in many states. There was no state in which a minimum wageworker could afford rent for the average two-bedroom apartment, if they worked 40 hours a week. Where are the least affordable counties and metros? The least affordable rents were predominately in Californian counties and metropolitan areas in 2025. District of Columbia has the highest minimum wages in the country, which stood at 17.5 U.S. dollars per hour as of January 2025. Thus, the affordability of two-bedroom apartments highlights how disproportionately high housing costs are in the state.

VITAL SIGNS INDICATOR Poverty (EQ5)

FULL MEASURE NAME The share of the population living in households that earn less than 200 percent of the federal poverty limit

LAST UPDATED December 2018

DESCRIPTION Poverty refers to the share of the population living in households that earn less than 200 percent of the federal poverty limit, which varies based on the number of individuals in a given household. It reflects the number of individuals who are economically struggling due to low household income levels.

DATA SOURCE U.S Census Bureau: Decennial Census http://www.nhgis.org (1980-1990) http://factfinder2.census.gov (2000)

U.S. Census Bureau: American Community Survey Form C17002 (2006-2017) http://api.census.gov

METHODOLOGY NOTES (across all datasets for this indicator) The U.S. Census Bureau defines a national poverty level (or household income) that varies by household size, number of children in a household, and age of householder. The national poverty level does not vary geographically even though cost of living is different across the United States. For the Bay Area, where cost of living is high and incomes are correspondingly high, an appropriate poverty level is 200% of poverty or twice the national poverty level, consistent with what was used for past equity work at MTC and ABAG. For comparison, however, both the national and 200% poverty levels are presented.

For Vital Signs, the poverty rate is defined as the number of people (including children) living below twice the poverty level divided by the number of people for whom poverty status is determined. Poverty rates do not include unrelated individuals below 15 years old or people who live in the following: institutionalized group quarters, college dormitories, military barracks, and situations without conventional housing. The household income definitions for poverty change each year to reflect inflation. The official poverty definition uses money income before taxes and does not include capital gains or noncash benefits (such as public housing, Medicaid, and food stamps). For the national poverty level definitions by year, see: https://www.census.gov/hhes/www/poverty/data/threshld/index.html For an explanation on how the Census Bureau measures poverty, see: https://www.census.gov/hhes/www/poverty/about/overview/measure.html

For the American Community Survey datasets, 1-year data was used for region, county, and metro areas whereas 5-year rolling average data was used for city and census tract.

To be consistent across metropolitan areas, the poverty definition for non-Bay Area metros is twice the national poverty level. Data were not adjusted for varying income and cost of living levels across the metropolitan areas.

About the dataset This dataset uses information from the DWP benefit system to provide estimates of children living in poverty for wards in London. In order to be counted in this dataset, a family must have claimed Child Benefit and at least one other household benefit (Universal Credit, tax credits or Housing Benefit) during the year. The numbers are calibrated to the Households Below Average Income (HBAI) dataset used to provide the government's headline poverty statistics. The definition of relative low income is living in a household with equivalised* income before housing costs (BHC) below 60% of contemporary national median income. The income measure includes contributions from earnings, state support and pensions. Further detail on the estimates of dependent children living in relative low income, including alternative geographical breakdowns and additional variables, such as age of children, family type and work status are available from DWP's statistical tabulation tool Stat-Xplore. Minor adjustments to the data have been applied to guard against the identification of individual claimants. This dataset replaced the DWP children in out-of-work benefit households and HMRC children in low income families local measure releases. This dataset includes estimates for all wards in London of numbers of dependent children living in relative low income families for each financial year from 2014/15 to the latest available (2022/23). The figures for the latest year are provisional and are subject to minor revision when the next dataset is released by DWP. Headlines Number of children The number of dependent children living in relative low income across London, rose from below 310,000 in the financial year ending 2015 to over 420,000 in the financial year ending 2020, but has decreased since then to below 350,000, which is well below the number for financial year ending 2018. While many wards in London have followed a similar pattern, the numbers of children in low income families in some wards have fallen more sharply, while the numbers in other wards have continued to grow. Proportion of children in each London ward Ward population sizes vary across London, the age profile of that population also varies and both the size and make-up of the population can change over time, so in order to make more meaningful comparisons between wards or over time, DWP have also published rates, though see note below regarding caution when using these figures. A dependent child is anyone aged under 16; or aged 16 to 19 in full-time non-advanced education or in unwaged government training. Ward level estimates for the total number of dependent children are not available, so percentages cannot be derived. Ward level estimates for the percentage of children under 16 living in low income families are usually published by DWP but, in its latest release, ward-level population estimates were not available at the time, so no rates were published. To derive the rates in this dataset, the GLA has used the ONS's latest ward-level population estimates (official statistics in development). Percentages for 2021/22 are calculated using the 2021 mid year estimates, while percentages for 2022/23 are calculated using the 2022 mid year estimates. As these are official statistics in development, rates therefore need to be treated with some caution. Notes *equivalised income is adjusted for household size and composition in order to compare living standards between households of different types.

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

In 2025, just over 45 percent of American households had an annual income that was less than 75,000 U.S. dollars. On the other hand, some 16 percent had an annual income of 200,000 U.S. dollars or more. The median household income in the country reached almost 84,000 U.S. dollars in 2024. Income and wealth in the United States After the economic recession in 2009, income inequality in the U.S. is more prominent across many metropolitan areas. The Northeast region is regarded as one of the wealthiest in the country. Massachusetts, New Hampshire, and Maryland were among the states with the highest median household income in 2024. In terms of income by race and ethnicity, the average income of Asian households was highest, at over 120,000 U.S. dollars, while the median income among Black households was around half of that figure. What is the U.S. poverty threshold? The U.S. Census Bureau annually updates the poverty threshold based on the income of various household types. As of 2023, the threshold for a single-person household was 15,480 U.S. dollars. For a family of four, the poverty line increased to 31,200 U.S. dollars. There were an estimated 38.9 million people living in poverty across the United States in 2024, which reflects a poverty rate of 10.6 percent.

Residents in private households between 16 and 75 years of age

Management summary

Decent Wage Bangladesh phase 1

The aims of the project Decent Wage Bangladesh phase 1 aimed to gain insight in actual wages, the cost of living and the collective labour agreements in four low-paid sectors in three regions of Bangladesh, in order to strengthen the power of trade unions. The project received funding from Mondiaal FNV in the Netherlands and seeks to contribute to the to the knowledge and research pathway of Mondiaal’s theory of change related to social dialogue. Between August and November 2020 five studies have been undertaken. In a face-to-face survey on wages and work 1,894 workers have been interviewed. In a survey on the cost-of-living 19,252 prices have been observed. The content of 27 collective agreements have been analysed. Fifth, desk research regarding the four sectors was undertaken. The project was coordinated by WageIndicator Foundation, an NGO operating websites with information about work and wages in 140 countries, a wide network of correspondents and a track record in collecting and analysing data regarding wage patters, cost of living, minimum wages and collective agreements. For this project WageIndicator collaborated with its partner Bangladesh Institute of Development Studies (BIDS) in Dhaka, with a track record in conducting surveys in the country and with whom a long-lasting relationship exists. Relevant information was posted on the WageIndicator Bangladesh website and visual graphics and photos on the project webpage. The results of the Cost-of-Living survey can be seen here.

Ready Made Garment (RMG), Leather and footwear, Construction and Tea gardens and estates are the key sectors in the report. In the Wages and Work Survey interviews have been held with 724 RMG workers in 65 factories, 337 leather and footwear workers in 34 factories, 432 construction workers in several construction sites and 401 workers in 5 tea gardens and 15 tea estates. The Wages and Work Survey 2020 was conducted in the Chattagram, Dhaka and Sylhet Divisions.

Earnings have been measured in great detail. Monthly median wages for a standard working week are BDT 3,092 in tea gardens and estates, BDT 9,857 in Ready made garment, Bangladeshi Taka (BDT) 10,800 in leather and footwear and BDT 11,547 in construction. The females’ median wage is 77% lower than that of the males, reflecting the gender pay gap noticed around the world. The main reason is not that women and men are paid differently for the same work, but that men and women work in gender-segregated parts of the labour market. Women are dominating the low-paid work in the tea gardens and estates. Workers aged 40 and over are substantially lower paid than younger workers, and this can partly be ascribed to the presence of older women in the tea gardens and estates. Workers hired via an intermediary have higher median wages than workers with a permanent contract or without a contract. Seven in ten workers report that they receive an annual bonus. Almost three in ten workers report that they participate in a pension fund and this is remarkably high in the tea estates, thereby partly compensating the low wages in the sector. Participation in an unemployment fund, a disability fund or medical insurance is hardly observed, but entitlement to paid sick leave and access to medical facilites is frequently mentioned. Female workers participate more than males in all funds and facilities. Compared to workers in the other three sectors, workers in tea gardens and estates participate more in all funds apart from paid sick leave. Social security is almost absent in the construction sector. Does the employer provide non-monetary provisions such as food, housing, clothing, or transport? Food is reported by almost two in ten workers, housing is also reported by more than three in ten workers, clothing by hardly any worker and transport by just over one in ten workers. Food and housing are substantially more often reported in the tea gardens and estates than in the other sectors. A third of the workers reports that overtime hours are paid as normal hours plus a premium, a third reports that overtime hours are paid as normal hours and another third reports that these extra hours are not paid. The latter is particularly the case in construction, although construction workers work long contractual hours they hardly have “overtime hours”, making not paying overtime hours not a major problem.

Living Wage calculations aim to indicate a wage level that allows families to lead decent lives. It represents an estimate of the monthly expenses necessary to cover the cost of food, housing, transportation, health, education, water, phone and clothing. The prices of 61 food items, housing and transportation have been collected by means of a Cost-of-Living Survey, resulting in 19,252 prices. In Chattagram the living wage for a typical family is BDT 13,000 for a full-time working adult. In Dhaka the living wage for a typical family is BDT 14,400 for a full-time working adult. In both regions the wages of the lowest paid quarter of the semi-skilled workers are only sufficient for the living wage level of a single adult, the wages of the middle paid quarter are sufficient for a single adult and a standard 2+2 family, and the wages in the highest paid quarter are sufficient for a single adult, a standard 2+2 family, and a typical family. In Sylhet the living wage for a typical family is BDT 16,800 for a full-time working adult. In Sylhet the wages of the semi-skilled workers are not sufficient for the living wage level of a single adult, let alone for a standard 2+2 family or a typical family. However, the reader should take into account that these earnings are primarily based on the wages in the tea gardens and estates, where employers provide non-monetary provisions such as housing and food. Nevertheless, the wages in Sylhet are not sufficient for a living wage.

Employment contracts. Whereas almost all workers in construction have no contract, in the leather industry workers have predominantly a permanent contract, specifically in Chattagram. In RMG the workers in Chattagram mostly have a permanent contract, whereas in Dhaka this is only the case for four in ten workers. RMG workers in Dhaka are in majority hired through a labour intermediary. Workers in the tea gardens and estates in Chattagram in majority have no contract, whereas in Sylhet they have in majority a permanent contract. On average the workers have eleven years of work experience. Almost half of the employees say they have been promoted in their current workplace.

COVID-19 Absenteeism from work was very high in the first months of the pandemic, when the government ordered a general lock down (closure) for all industries. Almost all workers in construction, RMG and leather reported that they were absent from work from late March to late May 2020. Female workers were far less absent than male workers, and this is primarily due to the fact that the tea gardens and estates with their highly female workforce did not close. From 77% in March-May absenteeism tremendously dropped till 5% in June-September. By September the number of absent days had dropped to almost zero in all sectors. Absenteeism was predominantly due to workplace closures, but in some cases due to the unavailability of transport. More than eight all absent workers faced a wage reduction. Wage reduction has been applied equally across the various groups of workers. The workers who faced reduced earnings reported borrowing from family or friends (66% of those who faced wage reduction), receiving food distribution of the government (23%), borrowing from a micro lenders (MFI) (20%), borrowing from other small lenders (14%), receiving rations from the employer (9%) or receiving cash assistance from the government or from non-governmental institutions (both 4%). Male workers have borrowed from family or friends more often than female workers, and so did workers aged 40-49 and couples with more than two children.

COVID-19 Hygiene at the workplace After return to work workers have assessed hygiene at the workplace and the supply of hygiene facilities. Workers are most positive about the safe distance or space in dining seating areas (56% assesses this as a low risk), followed by the independent use of all work equipment, as opposed to shared (46%). They were least positive about a safe distance between work stations and number of washrooms/toilets, and more than two in ten workers assess the number of washrooms/toilets even as a high risk. Handwashing facilities are by a large majority of the workers assessed as adequate with a low risk. In contrast, gloves were certainly not adequately supplied, as more than seven in ten workers state that these are not adequately supplied. This may be due to the fact that use of gloves could affect workers’ productivity, depending on the occupations.

This is the proportion of children aged under 16 (0-15) living in families in absolute low income during the year. The figures are based on the count of children aged under 16 (0-15) living in the area derived from ONS mid-year population estimates. The count of children refers to the age of the child at 30 June of each year.

Low income is a family whose equivalised income is below 60 per cent of median household incomes. Gross income measure is Before Housing Costs (BHC) and includes contributions from earnings, state support, and pensions. Equivalisation adjusts incomes for household size and composition, taking an adult couple with no children as the reference point. For example, the process of equivalisation would adjust the income of a single person upwards, so their income can be compared directly to the standard of living for a couple.

Absolute low income is income Before Housing Costs (BHC) in the reference year in comparison with incomes in 2010/11 adjusted for inflation. A family must have claimed one or more of Universal Credit, Tax Credits, or Housing Benefit at any point in the year to be classed as low income in these statistics. Children are dependent individuals aged under 16; or aged 16 to 19 in full-time non-advanced education. The count of children refers to the age of the child at 31 March of each year.

Data are calibrated to the Households Below Average Income (HBAI) survey regional estimates of children in low income but provide more granular local area information not available from the HBAI. For further information and methodology on the construction of these statistics, visit this link. Totals may not sum due to rounding.

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

Persons between 16 and 75 years, living in private households

Attitudes towards one´s own region.

Topics: Satisfaction with life; confidence about the future; importance of selected areas of life (children, good relationship with parents, secure job, interesting job, leisure time, friends, partner, earning a lot of money, getting to know the world, good education, good housing situation, voluntary work, career advancement, participation in public life in the place of residence, protection against crime); satisfaction with selected areas of life (housing situation, professional situation, financial situation, personal standard of living, contacts with neighbours, friends, acquaintances and work colleagues, family or partnership, medical care, opportunities for political participation, social safety net in Germany, politics of the Federal Government and the State Government, local politics, democracy in Germany, protection against crime).

Attitudes towards one´s own region: Advantages and disadvantages of one´s own place of residence (open); voluntary commitment; area of voluntary commitment (open); intention to move; general considerations or concrete plans to move; duration of residence at one´s current place of residence; development of one´s own region; closeness to a place (feeling of home); economic future prospects of one´s own region; provision of the region in various areas (childcare and schools, police stations, shopping facilities, offers for young people and the elderly, medical care, pharmacies, public transport, leisure facilities, road conditions, cultural offerings, job opportunities, fast Internet connections, service offerings, affordable housing, transport connections, banks, ATMs, post offices and parcel counters, care and nursing offers for the elderly); future prospects for the supply of the district in the aforementioned areas; assessment of living conditions in one´s own region in comparison with other counties in the state and federal government; personal worries (possible unemployment, not being able to maintain or achieve a standard of living, not being able to make professional progress, loneliness in old age, health, not being able to withstand the strains of everyday life); concerns with regard to the development in Germany (rising national debt, politics cannot solve Germany´s problems, rising unemployment, fixed-term employment relationships are becoming the rule, increasing crime and violence, more and more refugees in Germany, inadequate protection against illness, unemployment and old age, rising energy costs, living at the expense of future generations); personal expectations of the state (comprehensive protection of citizens against risks vs. creating framework conditions for protection by the citizens themselves); the state should continue to ensure equal living conditions in all regions of Germany in the future; opinion on compensation payments for weaker regions; areas in which the state would have to do more for the region (public transport, road construction and repairs, settlement of rural doctors, fast Internet, etc.); sufficient commitment of the state with regard to future challenges in the region.

Demography: sex; age; employment; occupational status; household size; number of persons in the household aged 18 and over; number of school-age children in the household; education; self-rating of class; party sympathy; household net income.

Additionally coded: Respondent ID; region; survey region; federal state; city size; weighting factor.

The U.S. Investing in Opportunities Act, passed last year as part of the new federal tax bill, created tax incentives for investment in designated census tracts called Opportunity Zones. The statute, introduced with bipartisan sponsors led by Senators Cory Booker (D-NJ) and Tim Scott (R-SC), was designed to spur growth in low-income communities by encouraging reinvestment of capital gains into certified Opportunity Funds.Opportunity Zones (OZs) are probably best understood not as a new grant program but as a new investment tool – similar to the home mortgage interest deduction that creates tax preferences, which then drive individual and market behavior.With minor exceptions, the federal statute is not prescriptive in terms of the types of qualified investments, from affordable housing to clean energy to infrastructure to small business to workforce. This provides flexibility – as well as the need – to craft local and state strategies that will focus these investments to ensure they deliver living wage jobs, increase affordable housing, prevent unwanted gentrification and build resilient communities. This work is just beginning and there is time for communities to consider the benefits of the OZ tool, as the U.S. Treasury Department has yet to issue the full set of investment rules. Investors are expected to begin forming Opportunity Funds in the later part of 2018, after the Treasury issues final rules. More info is here. Source: https://opzones.ca.gov

The rental housing developments listed below are among the thousands of affordable units that are supported by City of Chicago programs to maintain affordability in local neighborhoods. The list is updated periodically when construction is completed for new projects or when the compliance period for older projects expire, typically after 30 years. The list is provided as a courtesy to the public. It does not include every City-assisted affordable housing unit that may be available for rent, nor does it include the hundreds of thousands of naturally occurring affordable housing units located throughout Chicago without City subsidies. For information on rents, income requirements and availability for the projects listed, contact each property directly. For information on other affordable rental properties in Chicago and Illinois, call (877) 428-8844, or visit www.ILHousingSearch.org.

Digital Nomad Co-Living Memberships Market Outlook

According to our latest research, the Global Digital Nomad Co-Living Memberships market size was valued at $1.2 billion in 2024 and is projected to reach $4.6 billion by 2033, expanding at a CAGR of 16.2% during 2024–2033. This robust growth trajectory is primarily fueled by the increasing prevalence of remote work and the growing appeal of location-independent lifestyles among professionals worldwide. As organizations continue to embrace flexible work policies, and as digital infrastructure improves globally, the demand for co-living spaces tailored to digital nomads is rising rapidly. The market is also benefiting from the surge in entrepreneurship, gig economy participation, and the desire for community-centric living experiences, all of which are driving the adoption of digital nomad co-living memberships across various regions and demographics.

Regional Outlook

North America currently holds the largest share of the Global Digital Nomad Co-Living Memberships market, accounting for approximately 38% of total revenue in 2024. This dominance can be attributed to the region's mature digital economy, widespread adoption of remote work, and a high concentration of tech-savvy professionals seeking flexible living arrangements. The United States, in particular, has seen significant investments in co-living infrastructure, driven by major cities such as New York, San Francisco, and Austin, where demand for short- and long-term memberships is robust. In addition, favorable visa policies and the proliferation of digital nomad hubs across North America have further strengthened the region’s leadership position, offering a wide variety of accommodation types and membership options tailored to diverse end-user needs.

The Asia Pacific region is witnessing the fastest growth in the Digital Nomad Co-Living Memberships market, with a projected CAGR of 19.8% from 2024 to 2033. This rapid expansion is propelled by the region’s burgeoning startup ecosystems, affordable cost of living, and increasing popularity of remote work among younger populations. Countries such as Thailand, Indonesia, and Vietnam have become global hotspots for digital nomads, offering attractive co-living solutions in cities like Bali, Chiang Mai, and Ho Chi Minh City. Government initiatives to attract remote workers, coupled with investments in digital infrastructure, are accelerating market penetration. The rise of online booking platforms and flexible membership models is also making it easier for digital nomads to access and customize their living experiences in Asia Pacific.

Emerging economies in Latin America and the Middle East & Africa are gradually integrating into the global digital nomad ecosystem, although they face unique challenges related to infrastructure, regulatory environments, and localized demand. In Latin America, cities like Medellín and Mexico City are gaining popularity due to their vibrant cultures and affordable living costs, but inconsistent internet connectivity and safety concerns remain barriers to widespread adoption. Meanwhile, the Middle East & Africa region is beginning to explore policy reforms and investment in co-living spaces, particularly in urban centers such as Dubai and Cape Town. However, market growth in these regions is tempered by varying levels of digital readiness and the need for further regulatory support to attract and retain digital nomad populations.

Report Scope