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.

Household income is a potential predictor for a number of environmental influences, for example, application of urban pesticides. This product is a U.S. conterminous mapping of block group income derived from the 2010-2014 Census American Community Survey (ACS), adjusted by a 2013 county-level Cost-of-Living index obtained from the Council for Community and Economic Research. The resultant raster is provided at 200-m spatial resolution, in units of adjusted household income in thousands of dollars per year.

Wages in Russia increased to 97645 RUB/Month in March from 89646 RUB/Month in February 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.

Context:

The Bureau of Labor Statistics defines the Consumer Price Index (CPI) as “a statistical measure of change, over time, of the prices of goods and services in major expenditure groups--such as food, housing, apparel, transportation, and medical care--typically purchased by urban consumers. Essentially, it compares the cost of a sample of goods and services in a specific month relative to the cost of the same "market basket" in an earlier reference period.

Make sure to read the cu.txt for more descriptive summaries on each data file and how to use the unique identifiers.

Content:

This dataset was collected June 27th, 2017 and may not be up-to-date.

The revised CPI introduced by the BLS in 1998 includes indexes for two populations; urban wage earners and clerical workers (CW), and all urban consumers (CU). This dataset covers all urban consumers (CU).

The Consumer Price Index (CPI) is a statistical measure of change, over time, of the prices of goods and services in major expenditure groups--such as food, housing, apparel, transportation, and medical care--typically purchased by urban consumers. Essentially, it compares the cost of a sample "market basket" of goods and services in a specific month relative to the cost of the same "market basket" in an earlier reference period. This reference period is designated as the base period.

As a result of the 1998 revision, both the CW and the CU utilize updated expenditure weights based upon data tabulated from three years (1982, 1983, and 1984) of the Consumer Expenditure Survey and incorporate a number of technical improvements, including an updated and revised item structure.

To construct the two indexes, prices for about 100,000 items and data on about 8,300 housing units are collected in a sample of 91 urban places. Comparison of indexes for individual CMSA's or cities show only the relative change over time in prices between locations. These indexes cannot be used to measure interarea differences in price levels or living costs.

Summary Data Available: U.S. average indexes for both populations are available for about 305 consumer items and groups of items. In addition, over 100 of the indexes have been adjusted for seasonality. The indexes are monthly with some beginning in 1913. Semi-annual indexes have been calculated for about 100 items for comparison with semi-annual areas mentioned below. Semi-annual indexes are available from 1984 forward.

Area indexes for both populations are available for 26 urban places. For each area, indexes are published for about 42 items and groups. The indexes are published monthly for three areas, bimonthly for eleven areas, and semi-annually for 12 urban areas.

Regional indexes for both populations are available for four regions with about 55 items and groups per region. Beginning with January 1987, indexes are monthly, with some beginning as early as 1966. Semi-annual indexes have been calculated for about 42 items for comparison with semi-annual areas mentioned above. Semi-annual indexes have been calculated for about 42 items in the 27 urban places for comparison with semi-annual areas.

City-size indexes for both populations are available for three size classes with about 55 items and groups per class. Beginning with January 1987, indexes are monthly and most begin in 1977. Semi-annual indexes have been calculated for about 42 items for comparison with semi-annual areas mentioned below.

Region/city-size indexes for both populations are available cross classified by region and city-size class. For each of 13 cross calculations, about 42 items and groups are available. Beginning with January 1987, indexes are monthly and most begin in 1977. Semi-annual indexes have been calculated for about 42 items in the 26 urban places for comparison with semi-annual areas.

Frequency of Observations: U.S. city average indexes, some area indexes, and regional indexes, city-size indexes, and region/city-size indexes for both populations are monthly. Other area indexes for both populations are bimonthly or semi-annual.

Annual Averages: Annual averages are available for all unadjusted series in the CW and CU.

Base Periods: Most indexes have a base period of 1982-1984 = 100. Other indexes, mainly those which have been added to the CPI program with the 1998 revision, are based more recently. The base period value is 100.0, except for the "Purchasing Power" values (AAOR and SAOR) where the base period value is 1.000.

Data Characteristics: Indexes are stored to one decimal place, except for the "Purchasing Power" values which are stored to three decimal places.

References: BLS Handbook of Methods, Chapter 17, "Consumer Price Index", BLS Bulletin 2285, April 1988.

Acknowledgements:

This dataset was taken directly from the U.S. Bureau of Labor Statistics web...

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.

Prices and wages are key indicators of social and economic processes. Recent research exists for some countries, but little data has been added on present-day Austria since the figures produced in the 1930s by Alfred Francis Pribram and his contributors within the International Scientific Committee on Price History. Our datasets offer a long-term price and wage series for the cities of Salzburg and Vienna (c. 1450–1850), together with regional and time-specific consumer baskets.

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.

United States CPI W: Housing: HFO: FB: Living Room, Kitchen & Dining Furniture data was reported at 86.810 Dec1997=100 in Jun 2018. This records a decrease from the previous number of 87.411 Dec1997=100 for May 2018. United States CPI W: Housing: HFO: FB: Living Room, Kitchen & Dining Furniture data is updated monthly, averaging 90.605 Dec1997=100 from Dec 1997 (Median) to Jun 2018, with 247 observations. The data reached an all-time high of 103.300 Dec1997=100 in Nov 2000 and a record low of 84.547 Dec1997=100 in Aug 2016. United States CPI W: Housing: HFO: FB: Living Room, Kitchen & Dining Furniture data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I012: Consumer Price Index: Urban Wage and Clerical Workers.

SIA197 - Impact of Cost of Living Measures on Income and Poverty Rates. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Impact of Cost of Living Measures on Income and Poverty Rates...

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.

This project was designed to isolate the effects that individual crimes have on wage rates and housing prices, as gauged by individuals' and households' decisionmaking preferences changing over time. Additionally, this project sought to compute a dollar value that individuals would bear in their wages and housing costs to reduce the rates of specific crimes. The study used multiple decades of information obtained from counties across the United States to create a panel dataset. This approach was designed to compensate for the problem of collinearity by tracking how housing and occupation choices within particular locations changed over the decade considering all amenities or disamenities, including specific crime rates. Census data were obtained for this project from the Integrated Public Use Microdata Series (IPUMS) constructed by Ruggles and Sobek (1997). Crime data were obtained from the Federal Bureau of Investigation's Uniform Crime Reports (UCR). Other data were collected from the American Chamber of Commerce Researchers Association, County and City Data Book, National Oceanic and Atmospheric Administration, and Environmental Protection Agency. Independent variables for the Wages Data (Part 1) include years of education, school enrollment, sex, ability to speak English well, race, veteran status, employment status, and occupation and industry. Independent variables for the Housing Data (Part 2) include number of bedrooms, number of other rooms, building age, whether unit was a condominium or detached single-family house, acreage, and whether the unit had a kitchen, plumbing, public sewers, and water service. Both files include the following variables as separating factors: census geographic division, cost-of-living index, percentage unemployed, percentage vacant housing, labor force employed in manufacturing, living near a coastline, living or working in the central city, per capita local taxes, per capita intergovernmental revenue, per capita property taxes, population density, and commute time to work. Lastly, the following variables measured amenities or disamenities: average precipitation, temperature, windspeed, sunshine, humidity, teacher-pupil ratio, number of Superfund sites, total suspended particulate in air, and rates of murder, rape, robbery, aggravated assault, burglary, larceny, auto theft, violent crimes, and property crimes.

SIA202 - Impact of Cost of Living Measures on Income and Poverty Rates. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Impact of Cost of Living Measures on Income and Poverty Rates...

Wages in Peru decreased to 2102 PEN/Month in April from 2114 PEN/Month in March of 2025. This dataset provides - Peru Average Monthly Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset contains the updated 2024 data from the Jobs and Salaries in Data Science dataset. The information is sourced from ai-jobs.net/salaries/2024/.

About Dataset

work_year: The year in which the data was recorded. This field indicates the temporal context of the data, important for understanding salary trends over time.

job_title: The specific title of the job role, like 'Data Scientist', 'Data Engineer', or 'Data Analyst'. This column is crucial for understanding the salary distribution across various specialized roles within the data field.

job_category: A classification of the job role into broader categories for easier analysis. This might include areas like 'Data Analysis', 'Machine Learning', 'Data Engineering', etc.

salary_currency: The currency in which the salary is paid, such as USD, EUR, etc. This is important for currency conversion and understanding the actual value of the salary in a global context.

salary: The annual gross salary of the role in the local currency. This raw salary figure is key for direct regional salary comparisons.

salary_in_usd: The annual gross salary converted to United States Dollars (USD). This uniform currency conversion aids in global salary comparisons and analyses.

employee_residence: The country of residence of the employee. This data point can be used to explore geographical salary differences and cost-of-living variations.

experience_level: Classifies the professional experience level of the employee. Common categories might include 'Entry-level', 'Mid-level', 'Senior', and 'Executive', providing insight into how experience influences salary in data-related roles.

employment_type: Specifies the type of employment, such as 'Full-time', 'Part-time', 'Contract', etc. This helps in analyzing how different employment arrangements affect salary structures.

work_setting: The work setting or environment, like 'Remote', 'In-person', or 'Hybrid'. This column reflects the impact of work settings on salary levels in the data industry.

company_location: The country where the company is located. It helps in analyzing how the location of the company affects salary structures.

company_size: The size of the employer company, often categorized into small (S), medium (M), and large (L) sizes. This allows for analysis of how company size influences salary.

🏡 Cost of Living in Nairobi, Kenya

📌 Overview

This dataset provides a detailed time-series estimate of the monthly cost of living across 20 different areas in Nairobi, Kenya from 2019 to 2024. It covers essential expenses such as rent, food, transport, utilities, and miscellaneous costs, allowing for comprehensive cost-of-living analysis.

This dataset is useful for:
✅ Individuals planning to move to Nairobi
✅ Researchers analyzing long-term cost trends
✅ Businesses assessing salary benchmarks based on inflation
✅ Data scientists developing predictive models for cost forecasting

📊 Data Summary

• Total Records: 60,000 (5 years of monthly data)
• Columns:
  • 🏠 Area: The residential area in Nairobi
  • 💰 Rent: Estimated monthly rent (KES)
  • 🍽️ Food: Grocery and dining expenses (KES)
  • 🚕 Transport: Public and private transport costs (KES)
  • ⚡ Utilities: Water, electricity, and internet bills (KES)
  • 🎭 Misc: Entertainment, personal care, and leisure expenses (KES)
  • 🏷️ Total: Sum of all expenses
  • 📆 Date: Monthly timestamp from January 2019 to December 2024

📍 Areas Covered

This dataset provides cost estimates for 20+ residential areas, including:
- High-End Areas 🏡: Kileleshwa, Westlands, Karen
- Mid-Range Areas 🏙️: South B, Langata, Ruaka
- Affordable Areas 🏠: Embakasi, Kasarani, Githurai, Ruiru, Umoja
- Satellite Towns 🌿: Ngong, Rongai, Thika, Kitengela, Kikuyu

🛠️ How the Data Was Generated

This dataset was synthetically generated using Python, incorporating realistic market variations. The process includes:

✔ Inflation Modeling 📈 – A 2% annual increase in costs over time.
✔ Seasonal Effects 📅 – Higher food and transport costs in December & January (holiday season), rent spikes in June & July.
✔ Economic Shocks ⚠️ – A 5% chance per record of external economic effects (e.g., fuel price hikes, supply chain issues).
✔ Random Fluctuations 🔄 – Expenses vary slightly month-to-month to simulate real-world spending behavior.

🔍 Potential Use Cases

• 📊 Cost of Living Analysis – Compare affordability across different Nairobi areas.
• 💵 Salary & Real Estate Benchmarking – Businesses can analyze salary expectations by location.
• 📉 Time-Series Forecasting – Train predictive models (ARIMA, Prophet, LSTM) to estimate future living costs.
• 📈 Inflation Impact Studies – Measure how economic conditions influence cost variations over time.

⚠️ Limitations

• Synthetic Data – The dataset is not based on real survey data but follows market trends.
• No Lifestyle Adjustments – Differences in household size or spending habits are not factored in.
• Inflation Approximation – While inflation is simulated at 2% annually, actual inflation rates may differ.

📁 File Format & Access

• nairobi_cost_of_living_time_series.csv – 60,000 records in CSV format (time-series structured).

📢 Acknowledgments

This dataset was generated for research and educational purposes. If you find it useful, consider citing it in your work. 🚀

📥 Download and Explore the Data Now!

This updated version makes your documentation more detailed and actionable for users interested in forecasting and economic analysis. Would you like help building a cost prediction model? 🚀

Population with Income per Capita below Living Cost: % of Total: NC: Chechen Republic data was reported at 15.200 % in 2024. This records a decrease from the previous number of 17.400 % for 2023. Population with Income per Capita below Living Cost: % of Total: NC: Chechen Republic data is updated yearly, averaging 19.700 % from Dec 2012 (Median) to 2024, with 13 observations. The data reached an all-time high of 21.700 % in 2012 and a record low of 14.200 % in 2014. Population with Income per Capita below Living Cost: % of Total: NC: Chechen Republic data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA015: Population with Income per Capita below Living Cost.

The Consumer Price Index (CPI) measures over time the prices of goods and services in major expenditure categories typically purchased by urban consumers. The expenditure categories include food, housing, apparel, transportation, and medical care. Essentially, the Index measures consumer purchasing power by comparing the cost of a fixed set of goods and services (called a market basket) in a specific month relative to the cost of the same market basket in an earlier reference period, designated as the base period. The CPI is calculated for two population groups: urban wage earners and clerical workers (CPI-W) and all urban consumers (CPI-U). The CPI-W population includes those urban families with clerical workers, sales workers, craft workers, operatives, service workers, or laborers in the family unit and is representative of the prices paid by about 40 percent of the United States population. The CPI-U population consists of all urban households (including professional and salaried workers, part-time workers, the self-employed, the unemployed, and retired persons) and is representative of the prices paid by about 80 percent of the United States population. Both populations specifically exclude persons in the military, in institutions, and all persons living outside of urban areas (such as farm families). National indexes for both populations are available for about 350 consumer items and groups of items. In addition, over 100 of the indexes have been adjusted for seasonality. The indexes are monthly with some beginning in 1913. Area indexes are available for 27 urban places. For each area, indexes are presented for about 65 items and groups. The area indexes are produced monthly for 5 areas, bimonthly for 10 areas, and semiannually for 12 urban areas. Regional indexes are available for four regions with about 95 items and groups per region. Beginning with January 1987, regional indexes are monthly, with some beginning as early as 1966. City-size indexes are available for four size classes with about 95 items and groups per class. Beginning with January 1987, these indexes are monthly and most begin in 1977. Regional and city-size indexes are available cross-classified by region and city-size class. For each of the 13 cross-classifications, about 60 items and groups are available. Beginning with January 1987, these indexes are monthly and most begin in 1977. Each index record includes a series identification code that specifies the sample (either all urban consumers or urban wage earners and clerical workers), seasonality (either seasonally adjusted or unadjusted), periodicity (either semiannual or regular), geographic area, index base period, and item number of the index. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08166.v3. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future and includes additional years of data.

Abstract copyright UK Data Service and data collection copyright owner.

The Integrated Household Survey (IHS), which ran from 2009-2014, was a composite survey combining questions asked on a number of social surveys conducted by the Office for National Statistics (ONS) to produce a dataset of 'core' variables. The ONS stopped producing IHS datasets from 2015 onwards; variables covering health, smoking prevalence, forces veterans, sexual identity and well-being will be incorporated into the Annual Population Survey - see the Which surveys (or modules) are included in the IHS? and What is the IHS? FAQ pages for further details.

Background and history of the IHS
The aim of the IHS was to produce high-level estimates for particular themes to a higher precision and lower geographic level than current ONS social surveys. The 'core' set contained around 100 questions, but a respondent was only asked a proportion of those depending on routing from answers to questions. The core questions were asked, where possible, at the beginning of the component surveys.
In January 2008, a set of core questions was introduced within three ONS surveys in the General Lifestyle Survey, Living Costs and Food Survey, and the Opinions and Lifestyle Survey. In April 2008 the IHS core questions were also introduced on the English Housing Survey, bringing the family of modules on the IHS up to four. The IHS dataset for 2008-2009 was used as a pilot for the concept, developing the systems and designing the weighting methodology. The IHS data for that period have not been published as they do not provide better quality information than that within existing surveys. Hence, the earliest IHS data currently available cover 2009-2010. In April 2009 the IHS core questions were introduced on the Labour Force Survey (LFS) and Annual Population Survey (APS) questionnaires and from June 2009 the Life Opportunities Survey (LOS, which also ran from 2009-2014) was included in the IHS family of modules. With the inclusion of these new surveys the IHS became complete, with an achieved annual sample size of approximately 450,000 individuals from interviews undertaken in Great Britain and Northern Ireland. Therefore, the first IHS dataset released covers the period April 2009-March 2010, starting the IHS data series from the point that all surveys were included. The large sample size and UK-wide coverage meant that various geographical breakdowns were possible in the IHS, and it is possible to use a geographical hierarchy to drill down to lower level detail within an area. The IHS also contained data collected from the following surveys: General Lifestyle Survey; Living Costs and Food Survey; Opinions and Lifestyle Survey; English Housing Survey; Labour Force Survey; Annual Population Survey; and Life Opportunities Survey. All questions had been removed from the component surveys by 2014 and the IHS closed that year. Further information is available from the ONS Integrated Household Survey (Experimental statistics): January to December 2014 webpage.

Available IHS data: End User Licence and Secure Access
Users should note that there are two versions of the IHS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version (SN 8075). The Secure Access version contains more detailed variables relating to age, age of youngest dependent child, country of birth, family unit type, household and household reference person, industry class, sub-class and division, month left last job, cohabitation, country of residence history, multiple households at address, nationality, New Deal training types, National Statistics Socio-Economic Classification (NS-SEC) long version, qualifications, household relationships, minor Standard Occupational Classification (SOC) groups, sexual identity, training and working age. The more detailed geographic variables present include county, unitary/local authority, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions and Training and Enterprise Councils (TECs). Users should note that the user guide also mentions variables that are not included in either the EUL or Secure Access datasets held at the Archive.

The EUL version contains less detailed variables. For example, the lowest geography available is Government Office Region, only major (3-digit) SOC groups are included for main, second and last job, and only industry sector for main, second and last job. Users are advised to first obtain the standard EUL version of the data before making an application for the Secure Access version to see if they are sufficient for their research requirements.


The SL version of the IHS January - December, 2010 is available under SN 6808.

Main Topics:
The IHS core questions cover several themes. These include:

• economic activity
• education
• health and...

Supplementary files for article "Spatial inequality in prices and wages within a late-developing economy: Serbia, 1863–1910"Serbia emerged as a small independent nation-state in the economic periphery of nineteenth-century Europe. This article leverages uniquely abundant town-level data to examine spatial inequality in prices and wages within this late-developing economy. I first build a new dataset on prices of traded and household goods, and wages of skilled and unskilled workers for a panel of 42 urban settlements in Serbia in the period from 1863 to 1910. I apply the welfare ratio approach to calculate real wages of day labourers and masons. Second, I find strong spatial convergence in grain prices and costs of living, but divergence in wages, both nominal and real. Lastly, I investigate the determinants of price convergence and wage divergence with panel-data models. The results suggest that falling transport costs decreased price gaps between locations, whereas rising population differences increased inter-urban wage gaps.© The Authors, CC BY 4.0

This dataset is from the Iowa Wage survey which is based on the Occupation Employment Statistics (OES) program from the Bureau of Labor Statistics (BLS). This data is updated to reflect more current statistics using cost of living indicators.