This map shows how expensive an area is based on a score determined by education, healthcare, housing, food, and transportation spending. A higher score means more is spent on living expenses. Areas in orange-red are more expensive while areas in yellow-blue are less expensive. Data is available from state to tract level from Esri's updated demographics.

Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for St. Louis city, MO (MWACL29510) from 2009 to 2023 about St. Louis City, MO; St. Louis; adjusted; MO; average; wages; real; and USA.

This map shows how expensive an area is based on a score determined by education, healthcare, housing, food, and transportation spending. A higher score means more is spent on living expenses. Areas in orange-red are more expensive while areas in yellow-blue are less expensive. Data is available from state to tract level from Esri's updated demographics.

The ACCRA Cost of Living Index (COLI) is a measure of living cost differences among urban areas compiled by the Council for Community and Economic Research. Conducted quarterly, the index compares the price of goods and services among approximately 300 communities in the United States and Canada. This Microsoft Excel file contains the average prices of goods and services published in the ACCRA Cost of Living Index since 1990.

This map shows how expensive an area is based on a score determined by education, healthcare, housing, food, and transportation spending. A higher score means more is spent on living expenses. Areas in orange-red are more expensive while areas in yellow-blue are less expensive. Data is available from state to tract level from Esri's updated demographics.

The U.S. Department of Housing and Urban Development (HUD) maintains that tenants are rent burdened if more than 30 percent of household income is used for rent. Data is collected via the US Census ACS 5-year estimates.

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.

Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for Lake County, IL (MWACL17097) from 2009 to 2023 about Lake County, IL; Chicago; adjusted; IL; average; wages; real; and USA.

We use a dataset with prices and spending on consumer packaged goods matched at the barcode-level across the US and Mexico to measure the price index in Mexico relative to the US. Mexican prices relative to the US are 23% lower compared to the International Comparisons Project's (ICP) price index. We decompose the 23% gap into the biases from imputation, sampling, quality, and variety. Quality bias increases Mexican prices by 48%. Imputation, sampling, and variety bias lowers Mexican prices by 11%, 13%, and 33%, respectively.

Children in Cost-burdened Households reports the share of children living in households that spend at least 30 percent of annual household income on housing costs.

This collection contains data obtained from families of wage earners or salaried workers in industrial locales scattered throughout the United States. The purpose of the survey was to estimate the cost of living of a "typical" American family. The completed questionnaires contain information about income sources and family expenditures including specific quantities and costs of food, housing, clothing, fuel, furniture, and miscellaneous household items for the calendar year. Demographic characteristics recorded for each household member include relationship to head, age, sex, occupation, weeks spent in the household and employed, wage rate, and total earnings.

SPSS data file

SPSS output file

Excel data and sources file

Excel data only file for use with python processing (program on Github and archived on Zenodo)

Climate warming can induce a ‘cost-of-living squeeze’ in ectotherms by increasing energetic expenditures while reducing foraging gains. We used biophysical models (validated by 2,685 field observations) to test this hypothesis for ten ecologically diverse lizards in African and Australian deserts. Historical warming (1950-2020) has been more intense in Africa than in Australia, translating to an energetic ‘squeeze’ for African diurnal species, no net impact on Australian diurnal species, but generating an energetic ‘relief’ (by increasing foraging time) for nocturnal species. Future warming impacts will be more severe in Africa than in Australia, especially in summer, requiring increased rates of food intake (+10% per hour active for diurnal species). The effects of climate warming on energy budgets of desert lizards will be species-specific but predictable.

Abstract

China Living Standards Survey (CLSS) consists of one household survey and one community (village) survey, conducted in Hebei and Liaoning Provinces (northern and northeast China) in July 1995 and July 1997 respectively. Five villages from each three sample counties of each province were selected (six were selected in Liaoyang County of Liaoning Province because of administrative area change). About 880 farm households were selected from total thirty-one sample villages for the household survey. The same thirty-one villages formed the samples of community survey. This document provides information on the content of different questionnaires, the survey design and implementation, data processing activities, and the different available data sets.

Geographic coverage

The China Living Standards Survey (CLSS) was conducted only in Hebei and Liaoning Provinces (northern and northeast China).

Kind of data

Sample survey data [ssd]

Sampling procedure

The CLSS sample is not a rigorous random sample drawn from a well-defined population. Instead it is only a rough approximation of the rural population in Hebei and Liaoning provinces in Northeastern China. The reason for this is that part of the motivation for the survey was to compare the current conditions with conditions that existed in Hebei and Liaoning in the 1930’s. Because of this, three counties in Hebei and three counties in Liaoning were selected as "primary sampling units" because data had been collected from those six counties by the Japanese occupation government in the 1930’s. Within each of these six counties (xian) five villages (cun) were selected, for an overall total of 30 villages (in fact, an administrative change in one village led to 31 villages being selected). In each county a "main village" was selected that was in fact a village that had been surveyed in the 1930s. Because of the interest in these villages 50 households were selected from each of these six villages (one for each of the six counties). In addition, four other villages were selected in each county. These other villages were not drawn randomly but were selected so as to "represent" variation within the county. Within each of these villages 20 households were selected for interviews. Thus the intended sample size was 780 households, 130 from each county.

Unlike county and village selection, the selection of households within each village was done according to standard sample selection procedures. In each village, a list of all households in the village was obtained from village leaders. An "interval" was calculated as the number of the households in the village divided by the number of households desired for the sample (50 for main villages and 20 for other villages). For the list of households, a random number was drawn between 1 and the interval number. This was used as a starting point. The interval was then added to this number to get a second number, then the interval was added to this second number to get a third number, and so on. The set of numbers produced were the numbers used to select the households, in terms of their order on the list.

In fact, the number of households in the sample is 785, as opposed to 780. Most of this difference is due to a village in which 24 households were interviewed, as opposed to the goal of 20 households

Mode of data collection

Face-to-face [f2f]

Research instrument

Household Questionnaire

The household questionnaire contains sections that collect data on household demographic structure, education, housing conditions, land, agricultural management, household non-agricultural business, household expenditures, gifts, remittances and other income sources, and saving and loans. For some sections (general household information, schooling, housing, gift-exchange, remittance, other income, and credit and savings) the individual designated by the household members as the household head provided responses. For some other sections (farm land, agricultural management, family-run non-farm business, and household consumption expenditure) a member identified as the most knowledgeable provided responses. Identification codes for respondents of different sections indicate who provided the information. In sections where the information collected pertains to individuals (employment), whenever possible, each member of the household was asked to respond for himself or herself, except that parents were allowed to respond for younger children. Therefore, in the case of the employment section it is possible that the information was not provided by the relevant person; variables in this section indicate when this is true.

The household questionnaire was completed in a one-time interview in the summer of 1995. The survey was designed so that more sensitive issues such as credit and savings were discussed near the end. The content of each section is briefly described below.

Section 0 SURVEY INFORMATION

This section mainly summarizes the results of the survey visits. The following information was entered into the computer: whether the survey and the data entry were completed, codes of supervisor’s brief comments on interviewer, data entry operator, and related revising suggestion (e.g., 1. good, 2. revise at office, and 3. re-interview needed). Information about the date of interview, the names of interviewer, supervisor, data enterer, and detail notes of interviewer and supervisor were not entered into the computer.

Section 1 GENERAL HOUSEHOLD INFORMATION

1A HOUSEHOLD STRUCTURE 1B INFORMATION ABOUT THE HOUSEHOLD MEMBERS’ PARENTS 1C INFORMATION ABOUT THE CHILDREN WHO ARE NOT LIVING IN HOME

Section 1A lists the personal id code, sex, relationship to the household head, ethnic group, type of resident permit (agricultural [nongye], non-agricultural [fei nongye], or no resident permit), date of birth, marital status of all people who spent the previous night in that household and for household members who are temporarily away from home. The household head is listed first and receives the personal id code 1. Household members were defined to include “all the people who normally live and eat their meals together in this dwelling.” Those who were absent more than nine of the last twelve months were excluded, except for the head of household. For individuals who are married and whose spouse resides in the household, the personal id number of the spouse is noted. By doing so, information on the spouse can be collected by appropriately merging information from the section 1A and other parts of the survey.

Section 1B collects information on the parents of all household members. For individuals whose parents reside in the household, parents’ personal id numbers are noted, and information can be obtained by appropriately merging information from other parts of the survey. For individuals whose parents do not reside in the household, information is recorded on whether each parent is alive, as well as their schooling and occupation.

Section 1C collects information for children of household members who are not living in home. Children who have died are not included. The information on the name, sex, types of resident permit, age, education level, education cost, reasons not living in home, current living place, and type of job of each such child is recorded.

Section 2 SCHOOLING

In Section 2, information about literacy and numeracy, school attendance, completion, and current enrollment for all household members of preschool age and older. The interpretation of pre-school age appears to have varied, with the result that while education information is available for some children of pre-school age, not all pre-school children were included in this section. But for ages 6 and above information is available for nearly all individuals, so in essence the data on schooling can be said to apply all persons 6 age and above. For those who were enrolled in school at the time of the survey, information was also collected on school attendance, expenses, and scholarships. If applicable, information on serving as an apprentice, technical or professional training was also collected.

Section 3 EMPLOYMENT

3A GENERAL INFORMATION 3B MAJOR NON-FARM JOB IN 1994 3C THE SECOND NON-FARM JOB IN 1994 3D OTHER EMPLOYMENT ACTIVITIES IN 1994 3E SEARCHING FOR NON-FARM JOB 3F PROCESS FOR GETTING MAJOR NON-FARM JOB 3G CORVEE LABOR

All individuals age thirteen and above were asked to respond to the employment activity questions in Section 3. Section 3A collects general information on farm and non-farm employment, such as whether or not the household member worked on household own farm in 1994, when was the last year the member worked on own farm if he/she did not work in 1994, work days and hours during busy season, occupation and sector codes of the major, second, and third non-farm jobs, work days and total income of these non-farm jobs. There is a variable which indicates whether or not the individual responded for himself or herself.

Sections 3B and 3C collect detailed information on the major and the second non-farm job. Information includes number of months worked and which month in 1994 the member worked on these jobs, average works days (or hours) per month (per day), total number of years worked for these jobs by the end of 1994, different components of income, type of employment contracts. Information on employer’s ownership type and location was also collected.

Section 3D collects information on average hours spent doing chores and housework at home every day during non-busy and busy season. The chores refer to cooking, laundry, cleaning, shopping, cutting woods, as well as small-scale farm yard animals raising, for example, pigs or chickens. Large-scale animal

The burden to the patient with Chronic obstructive pulmonary disease (COPD) is high, both in terms of health-related quality of life and health status. Exacerbations are associated with significant mortality, lead to frequent hospitalisation, and are a major determinant of COPD costs. Due to the natural history of COPD and its relentlessly progressive nature it needs long-term care, which means the range of medical and/or social services designed to help people with disabilities or chronic care needs. Hence designing the method to measure direct medical cost, direct non-medical cost, indirect cost and loss of productivity collectively, with reference to COPD, would be value-added information.

In this study, family, health system and societal perspective will be adopted which will be useful for informing policy decisions to local stakeholders and this data will be used to generate evidence to write further large studies. Interviews would be conducted for key stakeholders in the community to understand the burden of COPD to the society. Mixed method approach will give a comprehensive data to design the measure for estimating cost of illness for COPD for future cost of illness studies.

For more information, please see : https://www.ed.ac.uk/usher/respire/chronic-respiratory-disorders/estimating-the-costs-of-care-quality-of-life-and-w

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.

Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for El Paso County, CO (MWACL08041) from 2009 to 2023 about El Paso County, CO; Colorado Springs; adjusted; CO; average; wages; real; and USA.

Origin Energy reported AUD3.44B in Cost of Sales for its fiscal semester ending in December of 2024. Data for Origin Energy | ORG - Cost Of Sales including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for Marion County, IN (MWACL18097) from 2009 to 2023 about Marion County, IN; Indianapolis; adjusted; IN; average; wages; real; and USA.

Explore the Consumer Price Index dataset for United Arab Emirates, covering various categories such as Food and Beverages, Transportation, Housing, and more. Stay informed about the cost of living trends with this valuable resource.

Consumer price index, Recreational and culture, Food and Beverages, Restaurants and Hotels, Education, Transportation, Communications, Medical care, Miscellaneous goods and services, Textiles, clothing and footwear, Furniture, household goods, Tobacco, Housing, Water, Electricity, Gas, CPI, Cost of living, Household, Food, Transportation, Price

United Arab EmiratesFollow data.kapsarc.org for timely data to advance energy economics research..(2014=100)