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

"Cost of living and purchasing power related to average income

We adjusted the average cost of living inside the USA (based on 2021 and 2022) to an index of 100. All other countries are related to this index. Therefore with an index of e.g. 80, the usual expenses in another country are 20% less then in the United States.

The monthly income (please do not confuse this with a wage or salary) is calculated from the gross national income per capita.

The calculated purchasing power index is again based on a value of 100 for the United States. If it is higher, people can afford more based on the cost of living in relation to income. If it is lower, the population is less wealthy.

The example of Switzerland: With a cost of living index of 142 all goods are on average about 42% more expensive than in the USA. But the average income in Switzerland of 7,550 USD is also 28% higher, which means that citizens can also afford more goods. Now you calculate the 42% higher costs against the 28% higher income. In the result, people in Switzerland can afford about 10 percent less than a US citizen."

Source: https://www.worlddata.info/cost-of-living.php

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.

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

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

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

Interesting Task Ideas:

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

If you find this dataset valuable, don't forget to hit the upvote button! 😊💝

Checkout my other datasets

Employment-to-Population Ratio for USA

Productivity and Hourly Compensation

130K Kindle Books

900K TMDb Movies

USA Unemployment Rates by Demographics & Race

Photo by Alev Takil on Unsplash

Average weekly earnings for the whole economy, for total and regular pay, in real terms (adjusted for consumer price inflation), UK, monthly, seasonally adjusted.

Average hourly and weekly wage rate, and median hourly and weekly wage rate by North American Industry Classification System (NAICS), type of work, gender, and age group.

Wages in Russia decreased to 92866 RUB/Month in August from 99305 RUB/Month in July of 2025. This dataset provides the latest reported value for - Russia Average Monthly Wages - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Similar Datasets

• Company Bankruptcy Prediction: LINK
• The Boston House-Price Data: LINK
• California Housing Prices Data (5 new features!): LINK
• Spanish Wine Quality Dataset: LINK

Context

The gender pay gap or gender wage gap is the average difference between the remuneration for men and women who are working. Women are generally considered to be paid less than men. There are two distinct numbers regarding the pay gap: non-adjusted versus adjusted pay gap. The latter typically takes into account differences in hours worked, occupations were chosen, education, and job experience. In the United States, for example, the non-adjusted average female's annual salary is 79% of the average male salary, compared to 95% for the adjusted average salary.

The reasons link to legal, social, and economic factors, and extend beyond "equal pay for equal work".

The gender pay gap can be a problem from a public policy perspective because it reduces economic output and means that women are more likely to be dependent upon welfare payments, especially in old age.

This dataset aims to replicate the data used in the famous paper "The Gender Wage Gap: Extent, Trends, and Explanations", which provides new empirical evidence on the extent of and trends in the gender wage gap, which declined considerably during the 1980–2010 period.

Citation

fedesoriano. (January 2022). Gender Pay Gap Dataset. Retrieved [Date Retrieved] from https://www.kaggle.com/fedesoriano/gender-pay-gap-dataset.

Content

There are 2 files in this dataset: a) the Panel Study of Income Dynamics (PSID) microdata over the 1980-2010 period, and b) the Current Population Survey (CPS) to provide some additional US national data on the gender pay gap.

PSID variables:

NOTES: THE VARIABLES WITH fz ADDED TO THEIR NAME REFER TO EXPERIENCE WHERE WE HAVE FILLED IN SOME ZEROS IN THE MISSING PSID YEARS WITH DATA FROM THE RESPONDENTS’ ANSWERS TO QUESTIONS ABOUT JOBS WORKED ON DURING THESE MISSING YEARS. THE fz variables WERE USED IN THE REGRESSION ANALYSES THE VARIABLES WITH A predict PREFIX REFER TO THE COMPUTATION OF ACTUAL EXPERIENCE ACCUMULATED DURING THE YEARS IN WHICH THE PSID DID NOT SURVEY THE RESPONDENTS. THERE ARE MORE PREDICTED EXPERIENCE LEVELS THAT ARE NEEDED TO IMPUTE EXPERIENCE IN THE MISSING YEARS IN SOME CASES. NOTE THAT THE VARIABLES yrsexpf, yrsexpfsz, etc., INCLUDE THESE COMPUTATIONS, SO THAT IF YOU WANT TO USE FULL TIME OR PART TIME EXPERIENCE, YOU DON’T NEED TO ADD THESE PREDICT VARIABLES IN. THEY ARE INCLUDED IN THE DATA SET TO ILLUSTRATE THE RESULTS OF THE COMPUTATION PROCESS. THE VARIABLES WITH AN orig PREFIX ARE THE ORIGINAL PSID VARIABLES. THESE HAVE BEEN PROCESSED AND IN SOME CASES RENAMED FOR CONVENIENCE. THE hd SUFFIX MEANS THAT THE VARIABLE REFERS TO THE HEAD OF THE FAMILY, AND THE wf SUFFIX MEANS THAT IT REFERS TO THE WIFE OR FEMALE COHABITOR IF THERE IS ONE. AS SHOWN IN THE ACCOMPANYING REGRESSION PROGRAM, THESE orig VARIABLES AREN’T USED DIRECTLY IN THE REGRESSIONS. THERE ARE MORE OF THE ORIGINAL PSID VARIABLES, WHICH WERE USED TO CONSTRUCT THE VARIABLES USED IN THE REGRESSIONS. HD MEANS HEAD AND WF MEANS WIFE OR FEMALE COHABITOR.

1. intnum68: 1968 INTERVIEW NUMBER
2. pernum68: PERSON NUMBER 68
3. wave: Current Wave of the PSID
4. sex: gender SEX OF INDIVIDUAL (1=male, 2=female)
5. intnum: Wave-specific Interview Number
6. farminc: Farm Income
7. region: regLab Region of Current Interview
8. famwgt: this is the PSID’s family weight, which is used in all analyses
9. relhead: ER34103L this is the relation to the head of household (10=head; 20=legally married wife; 22=cohabiting partner)
10. age: Age
11. employed: ER34116L Whether or not employed or on temp leave (everyone gets a 1 for this variable, since our wage analyses use only the currently employed)
12. sch: schLbl Highest Year of Schooling
13. annhrs: Annual Hours Worked
14. annlabinc: Annual Labor Income
15. occ: 3 Digit Occupation 2000 codes
16. ind: 3 Digit Industry 2000 codes
17. white: White, nonhispanic dummy variable
18. black: Black, nonhispanic dummy variable
19. hisp: Hispanic dummy variable
20. othrace: Other Race dummy variable
21. degree: degreeLbl Agent's Degree Status (0=no college degree; 1=bachelor’s without advanced degree; 2=advanced degree)
22. degupd: degreeLbl Agent's Degree Status (Updated with 2009 values)
23. schupd: schLbl Schooling (updated years of schooling)
24. annwks: Annual Weeks Worked
25. unjob: unJobLbl Union Coverage dummy variable
26. usualhrwk: Usual Hrs Worked Per Week
27. labincbus: Labor Income from...

Affordability ratios calculated by dividing house prices by gross annual residence-based earnings. Based on the median and lower quartiles of both house prices and earnings in England and Wales.

Dataset Description: Indian Urban Affordability and Economic Productivity (221 Cities) About the Dataset

This dataset represents the comprehensive 221-city version developed and utilized in the research paper “Predicting Urban Affordability and Economic Productivity in India: A Data-Driven KNN and Random Forest Framework with Insights from Selected Major Cities.”

It builds upon the author’s earlier 70-city affordability dataset and significantly expands its scope.

The dataset provides a unified framework to study how urban affordability, digital readiness, and GDP specialization jointly influence economic livability and productivity across different city tiers.

Data Provenance and Construction

Primary Source: Extended web-scraped affordability data originally compiled from LivingCost.org and other verified open-data platforms.

Cleaning & Standardization: City names normalized (e.g., “Bengaluru” → “Bangalore”), and all numeric fields standardized to INR using a consistent USD→INR conversion rate for comparability.

Features Included

Each record (row) corresponds to one city and contains the following metrics:

Cost of Living (INR)

Monthly Rent (INR)

Monthly After-Tax Salary (INR)

Income After Rent (INR)

Affordability Ratio (“Months Covered”)

Intended Applications

This dataset can be used for:

🧮 Cross-city affordability and livability analysis

🤖 Machine Learning model development (affordability or salary prediction)

🌆 Urban economics and policy simulation studies

📈 Correlation and regression-based research in ICT and GDP domains

📊 Dashboard and visualization projects (Power BI, Tableau, SAP SAC, etc.)

It is designed for use by researchers, policymakers, educators, and data analysts seeking a reliable, structured, and multi-domain dataset on Indian urban dynamics.

Data Quality and Transparency

✅ Uniform currency and value scaling

✅ Reproducible preprocessing (Python-based pipelines with Scikit-Learn)

✅ Missing values imputed using KNN-based methodology

✅ Verified against baseline datasets used in prior research

✅ Released under Creative Commons Attribution 4.0 International (CC BY 4.0) license

Significance

This dataset forms the empirical backbone of the author’s second research paper, providing the quantitative base for the KNN baseline model and the Random Forest multi-output regressor used to predict salary and affordability across Indian cities.

It enables city-level insight generation for policymakers and supports reproducible, data-driven research in urban economics, digital inclusion, and sustainable development.

Future Extensions

An upcoming enhancement will include:

Complete AQI integration for all 221 cities to examine the affordability–environment linkage.

Time-series extension for multi-year trend analysis.

Inclusion of healthcare, safety, and green infrastructure indicators for a broader livability framework.

A additional file used in my paper on T30 cities of India with justification is also attached.

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.

We adjust SNAP maximum allotments, deductions, and income eligibility standards at the beginning of each Federal fiscal year. The changes are based on changes in the cost of living. COLAs take effect on October 1 each year. Maximum allotments are calculated from the cost of a market basket based on the Thrifty Food Plan for a family of four, priced in June that year. The maximum allotments for households larger and smaller than four persons are determined using formulas that account for economies of scale. Smaller households get slightly more per person than the four-person household. Larger households get slightly less. Income eligibility standards are set by law. Gross monthly income limits are set at 130 percent of the poverty level for the household size. Net monthly income limits are set at 100 percent of poverty.

Rental Affordability Analysis Based on Median Income

Trends in Tier-Based Affordability Across the U.S

By Zillow Data [source]

About this dataset

This dataset contains rental affordability data for different regions in the US, giving valuable insights into regional rental markets. Renters can use this information to identify where their budget will go the farthest. The cities are organized by rent tier in order to analyze affordability trends within and between different housing stock types. Within each region, the data includes median household income, Zillow Rent Index (ZRI), and percent of income spent on rent.

The Zillow Home Value Forecast (ZHVF) is used to calculate future combined mortgage pay/rent payments in each region using current median home prices, actual outstanding debt amounts and 30-year fixed mortgage interest rates reported through partnership with TransUnion credit bureau. Zillow also provides a breakdown of cash vs financing purchases for buyers looking for an investment or cash option solution.

This dataset provides an effective tool for consumers who want to better understand how their budget fits into diverse rental markets across the US; from condominiums and co-ops, multifamily residences with five or more units, duplexes and triplexes - every renter can determine how their housing budget should be adjusted as they consider multiple living possibilities throughout the country based on real-time price data!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Introduction

Getting Started

• First, you'll need to download the TieredAffordability_Rental.csv dataset from this Kaggle page onto your computer or device.

• After downloading the data set onto your device, open it with any CSV viewing software of your choice (ex: Excel). It will include columns for RegionName**RegionName** , homes type/housing stock (All Homes or Condo/Co-op) SizeRank , Rent tier tier , Date date , median household income income , Zillow Rent Index zri and PercentIncomeSpentOnRent percentage (what portion of monthly median house-hold goes toward monthly mortgage payment) .

• To begin analyzing rental prices across different regions using this dataset, look first at column four: SizeRank; which ranks each region based on size - smallest regions listed first and largest at last - so that you can compare a similar range of Regions when looking at affordability by home sizes larger than one unit multiplex dwellings.*Duples/Triplex*. Once there is an understanding of how all homes compare overall now it is time to consider home types Multifamily 5+ units according to rent tiers tier .

• Next, choose one or more region(s) for comparison based on their rank in SizeRank column –so that all information gathered about them reflects what portionof households fall into certain categories ; eg; All Homes / Small Home /Large Home / MultiPlex Dwelling and what tier does each size rank falls into eg.: Affordable/Slightly Expensive/ Moderately Expensive etc.. This will enable further abstraction from other elements like date vs inflation rate per month or periodical intervals set herein by Rate segmentation i e dates givenin ‘Date’Columns – making the task easier and more direct while analyzing renatalAffordibility Analysis Based On Median Income zri 00 zwi & PCISOR 00 PCIRO

Research Ideas

• Use the PercentIncomeSpentOnRent column to compare rental affordability between regions within a particular tier and determine optimal rent tiers for relocating families.
• Analyze how market conditions are affecting rental affordability over time by using the income, zri, and PercentageIncomeSpentOnRent columns.
• Identify trends in housing prices for different tiers over the years by comparing SizeRank data with Zillow Home Value Forecast (ZHVF) numbers across different regions in order to identify locations that may be headed up or down in terms of home values (and therefore rent levels)

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: TieredAffordability_Rental.csv | Column name | Description | |:-----------------------------|:-------------------------------------------------------------| | RegionName | The name of the region. (String) ...

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.

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

Affordability ratios calculated by dividing house prices by gross annual workplace-based earnings. Based on the median and lower quartiles of both house prices and earnings in England and Wales.

This table contains data on the percent of households paying more than 30% (or 50%) of monthly household income towards housing costs for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Department of Housing and Urban Development (HUD), Consolidated Planning Comprehensive Housing Affordability Strategy (CHAS) and the U.S. Census Bureau, American Community Survey (ACS). The table is part of a series of indicators in the [Healthy Communities Data and Indicators Project of the Office of Health Equity] Affordable, quality housing is central to health, conferring protection from the environment and supporting family life. Housing costs—typically the largest, single expense in a family's budget—also impact decisions that affect health. As housing consumes larger proportions of household income, families have less income for nutrition, health care, transportation, education, etc. Severe cost burdens may induce poverty—which is associated with developmental and behavioral problems in children and accelerated cognitive and physical decline in adults. Low-income families and minority communities are disproportionately affected by the lack of affordable, quality housing. More information about the data table and a data dictionary can be found in the Attachments.

URA28 - Gross Median Household Income Compared to Median Property Prices - Dataset - data.gov.ie

This dataset provides insights into the cost of living and average monthly income across various countries and regions worldwide from 2000 to 2023. It includes critical economic indicators such as housing costs, taxes, healthcare, education, transportation expenses, and savings rates. The data is ideal for analyzing economic trends, regional comparisons, and financial planning.

Column Descriptions: Country: The name of the country where the data was recorded. Region: The geographical region to which the country belongs (e.g., Asia, Europe). Year: The year when the data was recorded. Average_Monthly_Income: The average monthly income of individuals in USD. Cost_of_Living: The average monthly cost of living in USD, including essentials like housing, food, and utilities. Housing_Cost_Percentage: The percentage of income spent on housing expenses. Tax_Rate: The average tax rate applied to individuals' income, expressed as a percentage. Savings_Percentage: The portion of income saved monthly, expressed as a percentage. Healthcare_Cost_Percentage: The percentage of income spent on healthcare services. Education_Cost_Percentage: The percentage of income allocated to educational expenses. Transportation_Cost_Percentage: The percentage of income spent on transportation costs.