In 2023, the U.S. Consumer Price Index was 309.42, and is projected to increase to 352.27 by 2029. The base period was 1982-84. The monthly CPI for all urban consumers in the U.S. can be accessed here. After a time of high inflation, the U.S. inflation rateis projected fall to two percent by 2027. United States Consumer Price Index ForecastIt is projected that the CPI will continue to rise year over year, reaching 325.6 in 2027. The Consumer Price Index of all urban consumers in previous years was lower, and has risen every year since 1992, except in 2009, when the CPI went from 215.30 in 2008 to 214.54 in 2009. The monthly unadjusted Consumer Price Index was 296.17 for the month of August in 2022. The U.S. CPI measures changes in the price of consumer goods and services purchased by households and is thought to reflect inflation in the U.S. as well as the health of the economy. The U.S. Bureau of Labor Statistics calculates the CPI and defines it as, "a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services." The BLS records the price of thousands of goods and services month by month. They consider goods and services within eight main categories: food and beverage, housing, apparel, transportation, medical care, recreation, education, and other goods and services. They aggregate the data collected in order to compare how much it would cost a consumer to buy the same market basket of goods and services within one month or one year compared with the previous month or year. Given that the CPI is used to calculate U.S. inflation, the CPI influences the annual adjustments of many financial institutions in the United States, both private and public. Wages, social security payments, and pensions are all affected by the CPI.

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.

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.

This collection of wage data was published in „Die Geschichte der Lage der Arbeiter in Deutschland von 1789 bis in die Gegenwart“ by Jürgen Kuczynski (volume I and volume II, here quoted after 6th edition, Berlin 1953, 1954). The data contains wage indices of a certain base year and the corresponding wage raw data (hourly wages, weekly wages, annual wages in marks and pfennigs). The wage data is regionally widely spread until the year 1914; it contains single cities as well as bigger regional units. Since 1924 Kuczynski’s surveys rely on the publications of the statistical office. The wage data is ordered by professional groups, industry and agriculture and by certain industrial sectors. Kuczynski’s wage index is mainly based on publications of trade unions and on reports of different chambers of commerce. The weaknesses of the indices are due to the methodological inconsequence and the limited representative status concerning the election of geographical units. Union wages and also actually paid wages are considered in the calculations, like for example daily, weekly and annual wages or layer wages for miners. On the other side important industrial sectors such as the food or the textile sector are not taken into account. Wage data for agriculture relies often on estimations or is calculated with insufficient material. Wages for work at home are not taken into account in the index calculation. There are also problems with the representative status of the index regarding regional units because cities are weighted too important compared with rural regions. Another topic of the survey is the construction of an index of costs of living. For a long time Kuczynski’s index for costs of living was without any concurrence. It was used by different authors without any changes or modifications. The substantial weakness of the index is that for the calculation of the development of the costs of living, it only takes costs of food and rent into account. Prices of food and rent were weighted in the ratio 3 to 1. Kuczynski does not give an explanation for this weighting. Further the certain price indices for food and rent were calculated by the aggregation of incomplete regional price developments.

Data tables in Histat A – Tables for the period from 1800 to1870: A.1 Wage Data (in Mark and Pfennig) A.2 Wage indices, base 1900 = 100 A.3 Costs of living and real wages 1900 = 100

B - Tables for the period from 1870 to 1932: B.1 Wage Data (in Mark and Pfennig) B.2 Wage indices, base 1900 = 100 B.3 Costs of living and real wages 1900 = 100

C - Tables for the period from 1932 to 1945: C.1 Wage Data (in Mark and Pfennig) C.2 Wage indices, base 1900 = 100 C.3 Costs of living and real wages 1932 = 100

Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for Santa Clara County, CA (MWACL06085) from 2009 to 2023 about Santa Clara County, CA; San Jose; adjusted; average; wages; CA; real; and USA.

Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for Davidson County, TN (MWACL47037) from 2009 to 2023 about Davidson County, TN; Nashville; adjusted; TN; average; wages; real; and USA.

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

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.

The present study aims to estimate the development of employment and wages in Germany based on accident insurance statistics. Data on the number of insured persons allow an estimation of employment by economic groups. Thereby it is important to take the increasing share of insured persons in the entire labor force in consideration. Data from the accident insurance is suitable for wage statistics because besides the values of the earned wages it also contains numbers on the yearly average of employees corrected for the number of working days. The investigation period is from 1888 to 1954 with the exception of the years of war and hyperinflation. The first three years after the introduction of the accident insurance are not taken into account as there are no reliable documents for this period. The analysis is restricted to the economic sectors which were subject to compulsory insurance since the beginning of the investigation period: industry, crafts and traffic. In the sector of traffic extra sources for data on railways were used. The increasing significance of the industrial sector regarding the overall economic employment volume as well as the income generation can be seen looking at the development of the number of employees and wages in relation to the per capita income growth. The industrialization process leads to structural changes in the entire economy which results in a steady relative decline in the agrarian sector. Within the industrial sector most chances and developments are in favor of the industries producing mainly investment goods. This process causes that the growth rates of industrial employment, of average wages and of the wage level primarily depend on those industry groups. Due to these different growth processes within the industrial sector a theoretical differentiation of the wage structure of both groups is necessary because the investment goods industries which has a higher need of expansion need to pay higher wages in order to get the necessary workforce for their expansion. At the beginning of the first world war the wage difference between the two industry groups has increased to 36,5% in 1913 ( it was only 26,5% in 1888). But in the following years there is not such a strong tendency. Probably the increasing power of trade unions caused a consolidation of the “traditional” wage structure. This is also supported by the fact that wage differences between all industries are quite small in the period after the First World War. The increases in real wages during the investigation period are smaller than 100%. This results in a yearly average increase of ca 1%. This is a development of real wages on a significantly lower level compared to other countries such as Sweden, France, Great Britain and The US. A reason for this is the missing real wage increase during the years of war and the first years after the war. Register of tables in HISTAT: - Working population in thousands with their main profession in Germany (1882-1950)- Employees in Germany (1882-1954)- Index number for costs of living, nominal wages and real wages in Germany (1888-1954)- Development of average wages in the industry groups in the German (1888-1912)- Shares of different industry groups in the total labor force in Germany (1882-1954)

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 dataset contains replication files for "The Fading American Dream: Trends in Absolute Income Mobility Since 1940" by Raj Chetty, David Grusky, Maximilian Hell, Nathaniel Hendren, Robert Manduca, and Jimmy Narang. For more information, see https://opportunityinsights.org/paper/the-fading-american-dream/. A summary of the related publication follows. One of the defining features of the “American Dream” is the ideal that children have a higher standard of living than their parents. We assess whether the U.S. is living up to this ideal by estimating rates of “absolute income mobility” – the fraction of children who earn more than their parents – since 1940. We measure absolute mobility by comparing children’s household incomes at age 30 (adjusted for inflation using the Consumer Price Index) with their parents’ household incomes at age 30. We find that rates of absolute mobility have fallen from approximately 90% for children born in 1940 to 50% for children born in the 1980s. Absolute income mobility has fallen across the entire income distribution, with the largest declines for families in the middle class. These findings are unaffected by using alternative price indices to adjust for inflation, accounting for taxes and transfers, measuring income at later ages, and adjusting for changes in household size. Absolute mobility fell in all 50 states, although the rate of decline varied, with the largest declines concentrated in states in the industrial Midwest, such as Michigan and Illinois. The decline in absolute mobility is especially steep – from 95% for children born in 1940 to 41% for children born in 1984 – when we compare the sons’ earnings to their fathers’ earnings. Why have rates of upward income mobility fallen so sharply over the past half-century? There have been two important trends that have affected the incomes of children born in the 1980s relative to those born in the 1940s and 1950s: lower Gross Domestic Product (GDP) growth rates and greater inequality in the distribution of growth. We find that most of the decline in absolute mobility is driven by the more unequal distribution of economic growth rather than the slowdown in aggregate growth rates. When we simulate an economy that restores GDP growth to the levels experienced in the 1940s and 1950s but distributes that growth across income groups as it is distributed today, absolute mobility only increases to 62%. In contrast, maintaining GDP at its current level but distributing it more broadly across income groups – at it was distributed for children born in the 1940s – would increase absolute mobility to 80%, thereby reversing more than two-thirds of the decline in absolute mobility. These findings show that higher growth rates alone are insufficient to restore absolute mobility to the levels experienced in mid-century America. Under the current distribution of GDP, we would need real GDP growth rates above 6% per year to return to rates of absolute mobility in the 1940s. Intuitively, because a large fraction of GDP goes to a small fraction of high-income households today, higher GDP growth does not substantially increase the number of children who earn more than their parents. Of course, this does not mean that GDP growth does not matter: changing the distribution of growth naturally has smaller effects on absolute mobility when there is very little growth to be distributed. The key point is that increasing absolute mobility substantially would require more broad-based economic growth. We conclude that absolute mobility has declined sharply in America over the past half-century primarily because of the growth in inequality. If one wants to revive the “American Dream” of high rates of absolute mobility, one must have an interest in growth that is shared more broadly across the income distribution.

The 2025 State Employee Pay page provides a comprehensive breakdown of salary structures, cost-of-living adjustments (COLAs), pay raises, and classification changes for state employees across various departments and positions. It includes information on:

Updated salary schedules by classification and grade

Annual cost-of-living adjustments (if approved by legislature)

Bonus or incentive pay (where applicable)

Pay equity adjustments

Job title and classification updates

Agency-specific pay plans

This resource is essential for current state workers, HR professionals, policy analysts, and those considering employment in the public sector.

Whether you're a classified employee, exempt worker, or part of a unionized workforce, this guide outlines how your pay may be affected throughout 2025 based on legislation, union negotiations, and state budget allocations.

Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

Abstract

The Consumer price surveys primarily provide the following: Data on CPI in Palestine covering the West Bank, Gaza Strip and Jerusalem J1 for major and sub groups of expenditure. Statistics needed for decision-makers, planners and those who are interested in the national economy. Contribution to the preparation of quarterly and annual national accounts data.

Consumer Prices and indices are used for a wide range of purposes, the most important of which are as follows: Adjustment of wages, government subsidies and social security benefits to compensate in part or in full for the changes in living costs. To provide an index to measure the price inflation of the entire household sector, which is used to eliminate the inflation impact of the components of the final consumption expenditure of households in national accounts and to dispose of the impact of price changes from income and national groups. Price index numbers are widely used to measure inflation rates and economic recession. Price indices are used by the public as a guide for the family with regard to its budget and its constituent items. Price indices are used to monitor changes in the prices of the goods traded in the market and the consequent position of price trends, market conditions and living costs. However, the price index does not reflect other factors affecting the cost of living, e.g. the quality and quantity of purchased goods. Therefore, it is only one of many indicators used to assess living costs. It is used as a direct method to identify the purchasing power of money, where the purchasing power of money is inversely proportional to the price index.

Geographic coverage

Palestine West Bank Gaza Strip Jerusalem

Analysis unit

The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

Universe

The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

Kind of data

Sample survey data [ssd]

Sampling procedure

A non-probability purposive sample of sources from which the prices of different goods and services are collected was updated based on the establishment census 2017, in a manner that achieves full coverage of all goods and services that fall within the Palestinian consumer system. These sources were selected based on the availability of the goods within them. It is worth mentioning that the sample of sources was selected from the main cities inside Palestine: Jenin, Tulkarm, Nablus, Qalqiliya, Ramallah, Al-Bireh, Jericho, Jerusalem, Bethlehem, Hebron, Gaza, Jabalia, Dier Al-Balah, Nusseirat, Khan Yunis and Rafah. The selection of these sources was considered to be representative of the variation that can occur in the prices collected from the various sources. The number of goods and services included in the CPI is approximately 730 commodities, whose prices were collected from 3,200 sources. (COICOP) classification is used for consumer data as recommended by the United Nations System of National Accounts (SNA-2008).

Sampling deviation

Not apply

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

A tablet-supported electronic form was designed for price surveys to be used by the field teams in collecting data from different governorates, with the exception of Jerusalem J1. The electronic form is supported with GIS, and GPS mapping technique that allow the field workers to locate the outlets exactly on the map and the administrative staff to manage the field remotely. The electronic questionnaire is divided into a number of screens, namely: First screen: shows the metadata for the data source, governorate name, governorate code, source code, source name, full source address, and phone number. Second screen: shows the source interview result, which is either completed, temporarily paused or permanently closed. It also shows the change activity as incomplete or rejected with the explanation for the reason of rejection. Third screen: shows the item code, item name, item unit, item price, product availability, and reason for unavailability. Fourth screen: checks the price data of the related source and verifies their validity through the auditing rules, which was designed specifically for the price programs. Fifth screen: saves and sends data through (VPN-Connection) and (WI-FI technology).

In case of the Jerusalem J1 Governorate, a paper form has been designed to collect the price data so that the form in the top part contains the metadata of the data source and in the lower section contains the price data for the source collected. After that, the data are entered into the price program database.

Cleaning operations

The price survey forms were already encoded by the project management depending on the specific international statistical classification of each survey. After the researcher collected the price data and sent them electronically, the data was reviewed and audited by the project management. Achievement reports were reviewed on a daily and weekly basis. Also, the detailed price reports at data source levels were checked and reviewed on a daily basis by the project management. If there were any notes, the researcher was consulted in order to verify the data and call the owner in order to correct or confirm the information.

At the end of the data collection process in all governorates, the data will be edited using the following process: Logical revision of prices by comparing the prices of goods and services with others from different sources and other governorates. Whenever a mistake is detected, it should be returned to the field for correction. Mathematical revision of the average prices for items in governorates and the general average in all governorates. Field revision of prices through selecting a sample of the prices collected from the items.

Response rate

Not apply

Sampling error estimates

The findings of the survey may be affected by sampling errors due to the use of samples in conducting the survey rather than total enumeration of the units of the target population, which increases the chances of variances between the actual values we expect to obtain from the data if we had conducted the survey using total enumeration. The computation of differences between the most important key goods showed that the variation of these goods differs due to the specialty of each survey. The variance of the key goods in the computed and disseminated CPI survey that was carried out on the Palestine level was for reasons related to sample design and variance calculation of different indicators since there was a difficulty in the dissemination of results by governorates due to lack of weights. Non-sampling errors are probable at all stages of data collection or data entry. Non-sampling errors include: Non-response errors: the selected sources demonstrated a significant cooperation with interviewers; so, there wasn't any case of non-response reported during 2019. Response errors (respondent), interviewing errors (interviewer), and data entry errors: to avoid these types of errors and reduce their effect to a minimum, project managers adopted a number of procedures, including the following: More than one visit was made to every source to explain the objectives of the survey and emphasize the confidentiality of the data. The visits to data sources contributed to empowering relations, cooperation, and the verification of data accuracy. Interviewer errors: a number of procedures were taken to ensure data accuracy throughout the process of field data compilation: Interviewers were selected based on educational qualification, competence, and assessment. Interviewers were trained theoretically and practically on the questionnaire. Meetings were held to remind interviewers of instructions. In addition, explanatory notes were supplied with the surveys. A number of procedures were taken to verify data quality and consistency and ensure data accuracy for the data collected by a questioner throughout processing and data entry (knowing that data collected through paper questionnaires did not exceed 5%): Data entry staff was selected from among specialists in computer programming and were fully trained on the entry programs. Data verification was carried out for 10% of the entered questionnaires to ensure that data entry staff had entered data correctly and in accordance with the provisions of the questionnaire. The result of the verification was consistent with the original data to a degree of 100%. The files of the entered data were received, examined, and reviewed by project managers before findings were extracted. Project managers carried out many checks on data logic and coherence, such as comparing the data of the current month with that of the previous month, and comparing the data of sources and between governorates. Data collected by tablet devices were checked for consistency and accuracy by applying rules at item level to be checked.

Data appraisal

Other technical procedures to improve data quality: Seasonal adjustment processes and estimations of non-available items' prices: Under each category, a number of common items are used in Palestine to calculate the price levels and to represent the commodity within the commodity group. Of course, it is

The Household Market and Nonmarket Activities (HUS) project started as a joint research project between the Industrial Institute for Economic and Social Research (IUI) and Göteborg University in 1980. The ambition was to build a consistent longitudinal micro data base on the use of time, money and public services of households. The first main survey was carried out in 1984. In addition to a contact interview with the selected individuals, all designated individuals participated in a personal interview and two telephone interviews. All respondents were asked about their family background, education, marital status, labor market experience, and employment. In addition, questions about the household were asked of the head of household, concerning family composition, child care, health status, housing, possession of vacation homes, cars, boats and other consumption durables. At the end of the personal interview the household head had to fill out a questionnaire including questions about financing of current home, construction costs for building a house, house value and loans, imputation of property values and loans, additions/renovations 1983, maintenance and repairs, leasing, sale of previous home, assets and liabilities, and non-taxable benefits. All the respondents had to fill out a questionnaire including questions about tax-return information 1983, employment income, and taxes and support payments. Two telephone interviews were used primarily to collect data on the household´s time use and consumption expenditures. The 1986 HUS-survey included both a follow-up of the 1984 sample (panel study) and a supplementary sample. The 1986 sample included 1) all respondents participating in the 1984 survey, 2) the household heads, partners and third persons who should have participated in 1984 but did not (1984 nonresponse), 3) those individuals who started living together after the 1984 interview with an selected individual who participated or was supposed to participate in 1984, 4) members of the 1984 household born in 1966 or 1967. If entering a new household, for example because of leaving their parental home, the household head and his/her partner were also interviewed. Respondents participating in the 1984 survey were interviewed by telephone in 1986. Questions dealt with changes in family composition, housing, employment, wages and child care, and it was not only recorded whether a change had occurred, and what sort of change, but also when it occurred. The respondents also received a questionnaire by mail with questions mainly concerning income and assets. Respondents not participating in the earlier survey were interviewed in person and were asked approximately the same questions as in the 1984 personal interview. The 1988 HUS-survey was considerably smaller than the previous ones. It was addressed exclusively to participants in the 1986 survey, and consisted of a self-enumerated questionnaire with a nonrespondent follow-up by telephone. The questions dealt with changes in housing conditions, employment and household composition. The questionnaire also contained some questions on household income. In many respect the 1991 HUS-survey replicated the 1988 survey. The questions were basically the same in content and range, and the survey was conducted as a self-enamurated questionnaire sent out by mail. This time, however, in contrast to the 1988 survey, an attempt was made to include in the survey the new household members who had moved into sample households since 1986, as well as young people who turned 18 after the 1986 survey. Earlier respondents received a questionnaire by mail containing questions about their home, their primary occupation and weekly work hours since May 1988 (event-history data), earnings in 1989, 1990 and 1991, household composition and any changes in it that might have occurred since 1988, child care and some questions on income. New respondents were also asked about their education and labor-market experience. With respect to its design and question wording, the 1993 survey is a new version of the 1986 survey. The survey is made up of four parts: 1) the panel survey, which was addressed mainly to respondents in the 1991 survey, with certain additions; 2) the so-called supplementary survey, which focused on a new random sample of individuals; 3) the so-called nonresponse survey, which encompassed respondents who had participated in at least one of the earlier surveys but had since dropped out; 4) the time-use survey, which included the same sample of respondents as those in the panel and supplementary surveys. Individuals in the nonresponse group were not included in the time-use survey. Most of the questions in the first three surveys were the same, but certain questions sequences were targeted to the respondents in a specific survey. Thus certain retrospective questions were asked of the nonresponse group, while specific questions on social background, labor market experience etc. were addressed to new respondents. In the case of respondents who had already participated in the panel, a combined contact and main interview was conducted by telephone, after which a self-enumerated questionnaire was sent out to each respondent by mail. The panel sample also included young people in panel households who were born in 1973 or 1974 as well as certain new household members who had not previously been interviewed. These individuals, like new respondents, were not interviewed by telephone until they had been interviewed personally. Thus technically they were treated in the same manner as individuals in the supplementary sample. The new supplementary sample was first contacted by telephone and then given a fairly lengthy personal interview, at the conclusion of which each respondent was asked to fill out a written questionnaire. In this respect the survey design for the nonresponse sample was the same as for the supplementary sample. The nonresponse sample also included young people born in 1973 or 1974 as well as certain new household members. The time-use interviews were conducted by telephone. For each respondent two days were chosen at random from the period from February 15, 1993 to February 14, 1994 and the respondents were interviewed about their time use during those two days. If possible, the time-use interviews were preceded by the other parts of the survey, but this was not always feasible. In each household the household head and spouse/partner were interviewed, as well as an additional person in certain households. Questions regarding the household as a whole were asked of only one person in the household, preferably the household head. As in earlier surveys, data from the interviews was subsequently supplemented by registry data, but only for those respondents who had given their express consent. There is registry information for 75-80 percent of the sample. The telephone interview is divided into following sections: administrative data; labor market experience; employment; job-seekers; not in labor force; education; family composition; child care; health status; other household members; housing conditions; vacation homes; and cars and boats. The questionnaire was divided into twelve sections: sale of previous home; acquisition of current home; construction costs for building a home; house value and loans; repairs; insurance; home-related expenses; sale of previous home; assets; household income; taxes; and respondent income 1992. The 1996 telephone interview is divided into following sections: administrative data; labor market experience; employment; job-seekers; not in labor force; education; family composition; child care; health status; other household members; housing conditions; vacation homes; cars and boats; and environment. The questionnaire was divided into twelve sections: sale of previous home; acquisition of current home; construction costs for building a home; house value and loans; repairs; insurance; home-related expenses; sale of previous home; assets; household income; taxes; and respondent income 1995. The 1998 telephone interview is divided into following sections: administrative data; labor market experience; employment; job-seekers; not in labor force; education; family composition; child care; health status; other household members; housing conditions; vacation homes; cars and boats; and municipal service. The questionnaire was divided into nine sections: sale of previous home; house value and loans; insurance; home-related expenses; assets; household income; inheritances and gifts; black-market work; and respondent income 1997.

Abstract

The Consumer price surveys primarily provide the following: Data on CPI in Palestine covering the West Bank, Gaza Strip and Jerusalem J1 for major and sub groups of expenditure. Statistics needed for decision-makers, planners and those who are interested in the national economy. Contribution to the preparation of quarterly and annual national accounts data.

Consumer Prices and indices are used for a wide range of purposes, the most important of which are as follows: Adjustment of wages, government subsidies and social security benefits to compensate in part or in full for the changes in living costs. To provide an index to measure the price inflation of the entire household sector, which is used to eliminate the inflation impact of the components of the final consumption expenditure of households in national accounts and to dispose of the impact of price changes from income and national groups. Price index numbers are widely used to measure inflation rates and economic recession. Price indices are used by the public as a guide for the family with regard to its budget and its constituent items. Price indices are used to monitor changes in the prices of the goods traded in the market and the consequent position of price trends, market conditions and living costs. However, the price index does not reflect other factors affecting the cost of living, e.g. the quality and quantity of purchased goods. Therefore, it is only one of many indicators used to assess living costs. It is used as a direct method to identify the purchasing power of money, where the purchasing power of money is inversely proportional to the price index.

Geographic coverage

Palestine West Bank Gaza Strip Jerusalem

Analysis unit

The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

Universe

The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

Kind of data

Sample survey data [ssd]

Sampling procedure

A non-probability purposive sample of sources from which the prices of different goods and services are collected was updated based on the establishment census 2017, in a manner that achieves full coverage of all goods and services that fall within the Palestinian consumer system. These sources were selected based on the availability of the goods within them. It is worth mentioning that the sample of sources was selected from the main cities inside Palestine: Jenin, Tulkarm, Nablus, Qalqiliya, Ramallah, Al-Bireh, Jericho, Jerusalem, Bethlehem, Hebron, Gaza, Jabalia, Dier Al-Balah, Nusseirat, Khan Yunis and Rafah. The selection of these sources was considered to be representative of the variation that can occur in the prices collected from the various sources. The number of goods and services included in the CPI is approximately 730 commodities, whose prices were collected from 3,200 sources. (COICOP) classification is used for consumer data as recommended by the United Nations System of National Accounts (SNA-2008).

Sampling deviation

Not apply

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

A tablet-supported electronic form was designed for price surveys to be used by the field teams in collecting data from different governorates, with the exception of Jerusalem J1. The electronic form is supported with GIS, and GPS mapping technique that allow the field workers to locate the outlets exactly on the map and the administrative staff to manage the field remotely. The electronic questionnaire is divided into a number of screens, namely: First screen: shows the metadata for the data source, governorate name, governorate code, source code, source name, full source address, and phone number. Second screen: shows the source interview result, which is either completed, temporarily paused or permanently closed. It also shows the change activity as incomplete or rejected with the explanation for the reason of rejection. Third screen: shows the item code, item name, item unit, item price, product availability, and reason for unavailability. Fourth screen: checks the price data of the related source and verifies their validity through the auditing rules, which was designed specifically for the price programs. Fifth screen: saves and sends data through (VPN-Connection) and (WI-FI technology).

In case of the Jerusalem J1 Governorate, a paper form has been designed to collect the price data so that the form in the top part contains the metadata of the data source and in the lower section contains the price data for the source collected. After that, the data are entered into the price program database.

Cleaning operations

The price survey forms were already encoded by the project management depending on the specific international statistical classification of each survey. After the researcher collected the price data and sent them electronically, the data was reviewed and audited by the project management. Achievement reports were reviewed on a daily and weekly basis. Also, the detailed price reports at data source levels were checked and reviewed on a daily basis by the project management. If there were any notes, the researcher was consulted in order to verify the data and call the owner in order to correct or confirm the information.

At the end of the data collection process in all governorates, the data will be edited using the following process: Logical revision of prices by comparing the prices of goods and services with others from different sources and other governorates. Whenever a mistake is detected, it should be returned to the field for correction. Mathematical revision of the average prices for items in governorates and the general average in all governorates. Field revision of prices through selecting a sample of the prices collected from the items.

Response rate

Not apply

Sampling error estimates

The findings of the survey may be affected by sampling errors due to the use of samples in conducting the survey rather than total enumeration of the units of the target population, which increases the chances of variances between the actual values we expect to obtain from the data if we had conducted the survey using total enumeration. The computation of differences between the most important key goods showed that the variation of these goods differs due to the specialty of each survey. For example, for the CPI, the variation between its goods was very low, except in some cases such as banana, tomato, and cucumber goods that had a high coefficient of variation during 2019 due to the high oscillation in their prices. The variance of the key goods in the computed and disseminated CPI survey that was carried out on the Palestine level was for reasons related to sample design and variance calculation of different indicators since there was a difficulty in the dissemination of results by governorates due to lack of weights. Non-sampling errors are probable at all stages of data collection or data entry. Non-sampling errors include: Non-response errors: the selected sources demonstrated a significant cooperation with interviewers; so, there wasn't any case of non-response reported during 2019. Response errors (respondent), interviewing errors (interviewer), and data entry errors: to avoid these types of errors and reduce their effect to a minimum, project managers adopted a number of procedures, including the following: More than one visit was made to every source to explain the objectives of the survey and emphasize the confidentiality of the data. The visits to data sources contributed to empowering relations, cooperation, and the verification of data accuracy. Interviewer errors: a number of procedures were taken to ensure data accuracy throughout the process of field data compilation: Interviewers were selected based on educational qualification, competence, and assessment. Interviewers were trained theoretically and practically on the questionnaire. Meetings were held to remind interviewers of instructions. In addition, explanatory notes were supplied with the surveys. A number of procedures were taken to verify data quality and consistency and ensure data accuracy for the data collected by a questioner throughout processing and data entry (knowing that data collected through paper questionnaires did not exceed 5%): Data entry staff was selected from among specialists in computer programming and were fully trained on the entry programs. Data verification was carried out for 10% of the entered questionnaires to ensure that data entry staff had entered data correctly and in accordance with the provisions of the questionnaire. The result of the verification was consistent with the original data to a degree of 100%. The files of the entered data were received, examined, and reviewed by project managers before findings were extracted. Project managers carried out many checks on data logic and coherence, such as comparing the data of the current month with that of the previous month, and comparing the data of sources and between governorates. Data collected by tablet devices were checked for consistency and accuracy by applying rules at item level to be checked.

Data appraisal

Other technical procedures to improve data quality: Seasonal adjustment processes

The present study aims to estimate the development of employment and wages in Germany based on accident insurance statistics. Data on the number of insured persons allow an estimation of employment by economic groups. Thereby it is important to take the increasing share of insured persons in the entire labor force in consideration. Data from the accident insurance is suitable for wage statistics because besides the values of the earned wages it also contains numbers on the yearly average of employees corrected for the number of working days. The investigation period is from 1888 to 1954 with the exception of the years of war and hyperinflation. The first three years after the introduction of the accident insurance are not taken into account as there are no reliable documents for this period. The analysis is restricted to the economic sectors which were subject to compulsory insurance since the beginning of the investigation period: industry, crafts and traffic. In the sector of traffic extra sources for data on railways were used. The increasing significance of the industrial sector regarding the overall economic employment volume as well as the income generation can be seen looking at the development of the number of employees and wages in relation to the per capita income growth. The industrialization process leads to structural changes in the entire economy which results in a steady relative decline in the agrarian sector. Within the industrial sector most chances and developments are in favor of the industries producing mainly investment goods. This process causes that the growth rates of industrial employment, of average wages and of the wage level primarily depend on those industry groups. Due to these different growth processes within the industrial sector a theoretical differentiation of the wage structure of both groups is necessary because the investment goods industries which has a higher need of expansion need to pay higher wages in order to get the necessary workforce for their expansion. At the beginning of the first world war the wage difference between the two industry groups has increased to 36,5% in 1913 ( it was only 26,5% in 1888). But in the following years there is not such a strong tendency. Probably the increasing power of trade unions caused a consolidation of the “traditional” wage structure. This is also supported by the fact that wage differences between all industries are quite small in the period after the First World War. The increases in real wages during the investigation period are smaller than 100%. This results in a yearly average increase of ca 1%. This is a development of real wages on a significantly lower level compared to other countries such as Sweden, France, Great Britain and The US. A reason for this is the missing real wage increase during the years of war and the first years after the war.

Register of tables in HISTAT: - Working population in thousands with their main profession in Germany (1882-1950) - Employees in Germany (1882-1954) - Index number for costs of living, nominal wages and real wages in Germany (1888-1954) - Development of average wages in the industry groups in the German (1888-1912) - Shares of different industry groups in the total labor force in Germany (1882-1954)

The Household Market and Nonmarket Activities (HUS) project started as a joint research project between the Industrial Institute for Economic and Social Research (IUI) and Göteborg University in 1980. The ambition was to build a consistent longitudinal micro data base on the use of time, money and public services of households. The first main survey was carried out in 1984. In addition to a contact interview with the selected individuals, all designated individuals participated in a personal interview and two telephone interviews. All respondents were asked about their family background, education, marital status, labor market experience, and employment. In addition, questions about the household were asked of the head of household, concerning family composition, child care, health status, housing, possession of vacation homes, cars, boats and other consumption durables. At the end of the personal interview the household head had to fill out a questionnaire including questions about financing of current home, construction costs for building a house, house value and loans, imputation of property values and loans, additions/renovations 1983, maintenance and repairs, leasing, sale of previous home, assets and liabilities, and non-taxable benefits. All the respondents had to fill out a questionnaire including questions about tax-return information 1983, employment income, and taxes and support payments. Two telephone interviews were used primarily to collect data on the household´s time use and consumption expenditures. The 1986 HUS-survey included both a follow-up of the 1984 sample (panel study) and a supplementary sample. The 1986 sample included 1) all respondents participating in the 1984 survey, 2) the household heads, partners and third persons who should have participated in 1984 but did not (1984 nonresponse), 3) those individuals who started living together after the 1984 interview with an selected individual who participated or was supposed to participate in 1984, 4) members of the 1984 household born in 1966 or 1967. If entering a new household, for example because of leaving their parental home, the household head and his/her partner were also interviewed. Respondents participating in the 1984 survey were interviewed by telephone in 1986. Questions dealt with changes in family composition, housing, employment, wages and child care, and it was not only recorded whether a change had occurred, and what sort of change, but also when it occurred. The respondents also received a questionnaire by mail with questions mainly concerning income and assets. Respondents not participating in the earlier survey were interviewed in person and were asked approximately the same questions as in the 1984 personal interview. The 1988 HUS-survey was considerably smaller than the previous ones. It was addressed exclusively to participants in the 1986 survey, and consisted of a self-enumerated questionnaire with a nonrespondent follow-up by telephone. The questions dealt with changes in housing conditions, employment and household composition. The questionnaire also contained some questions on household income. In many respect the 1991 HUS-survey replicated the 1988 survey. The questions were basically the same in content and range, and the survey was conducted as a self-enamurated questionnaire sent out by mail. This time, however, in contrast to the 1988 survey, an attempt was made to include in the survey the new household members who had moved into sample households since 1986, as well as young people who turned 18 after the 1986 survey. Earlier respondents received a questionnaire by mail containing questions about their home, their primary occupation and weekly work hours since May 1988 (event-history data), earnings in 1989, 1990 and 1991, household composition and any changes in it that might have occurred since 1988, child care and some questions on income. New respondents were also asked about their education and labor-market experience. With respect to its design and question wording, the 1993 survey is a new version of the 1986 survey. The survey is made up of four parts: 1) the panel survey, which was addressed mainly to respondents in the 1991 survey, with certain additions; 2) the so-called supplementary survey, which focused on a new random sample of individuals; 3) the so-called nonresponse survey, which encompassed respondents who had participated in at least one of the earlier surveys but had since dropped out; 4) the time-use survey, which included the same sample of respondents as those in the panel and supplementary surveys. Individuals in the nonresponse group were not included in the time-use survey. Most of the questions in the first three surveys were the same, but certain questions sequences were targeted to the respondents in a specific survey. Thus certain retrospective questions were asked of the nonresponse group, while specific questions on social background, labor market experience etc. were addressed to new respondents. In the case of respondents who had already participated in the panel, a combined contact and main interview was conducted by telephone, after which a self-enumerated questionnaire was sent out to each respondent by mail. The panel sample also included young people in panel households who were born in 1973 or 1974 as well as certain new household members who had not previously been interviewed. These individuals, like new respondents, were not interviewed by telephone until they had been interviewed personally. Thus technically they were treated in the same manner as individuals in the supplementary sample. The new supplementary sample was first contacted by telephone and then given a fairly lengthy personal interview, at the conclusion of which each respondent was asked to fill out a written questionnaire. In this respect the survey design for the nonresponse sample was the same as for the supplementary sample. The nonresponse sample also included young people born in 1973 or 1974 as well as certain new household members. The time-use interviews were conducted by telephone. For each respondent two days were chosen at random from the period from February 15, 1993 to February 14, 1994 and the respondents were interviewed about their time use during those two days. If possible, the time-use interviews were preceded by the other parts of the survey, but this was not always feasible. In each household the household head and spouse/partner were interviewed, as well as an additional person in certain households. Questions regarding the household as a whole were asked of only one person in the household, preferably the household head. As in earlier surveys, data from the interviews was subsequently supplemented by registry data, but only for those respondents who had given their express consent. There is registry information for 75-80 percent of the sample. The telephone interview is divided into following sections: administrative data; labor market experience; employment; job-seekers; not in labor force; education; family composition; child care; health status; other household members; housing conditions; vacation homes; and cars and boats. The questionnaire was divided into twelve sections: sale of previous home; acquisition of current home; construction costs for building a home; house value and loans; repairs; insurance; home-related expenses; sale of previous home; assets; household income; taxes; and respondent income 1992. The 1996 telephone interview is divided into following sections: administrative data; labor market experience; employment; job-seekers; not in labor force; education; family composition; child care; health status; other household members; housing conditions; vacation homes; cars and boats; and environment. The questionnaire was divided into twelve sections: sale of previous home; acquisition of current home; construction costs for building a home; house value and loans; repairs; insurance; home-related expenses; sale of previous home; assets; household income; taxes; and respondent income 1995. The 1998 telephone interview is divided into following sections: administrative data; labor market experience; employment; job-seekers; not in labor force; education; family composition; child care; health status; other household members; housing conditions; vacation homes; cars and boats; and municipal service. The questionnaire was divided into nine sections: sale of previous home; house value and loans; insurance; home-related expenses; assets; household income; inheritances and gifts; black-market work; and respondent income 1997.

The Household Market and Nonmarket Activities (HUS) project started as a joint research project between the Industrial Institute for Economic and Social Research (IUI) and Göteborg University in 1980. The ambition was to build a consistent longitudinal micro data base on the use of time, money and public services of households. The first main survey was carried out in 1984. In addition to a contact interview with the selected individuals, all designated individuals participated in a personal interview and two telephone interviews. All respondents were asked about their family background, education, marital status, labor market experience, and employment. In addition, questions about the household were asked of the head of household, concerning family composition, child care, health status, housing, possession of vacation homes, cars, boats and other consumption durables. At the end of the personal interview the household head had to fill out a questionnaire including questions about financing of current home, construction costs for building a house, house value and loans, imputation of property values and loans, additions/renovations 1983, maintenance and repairs, leasing, sale of previous home, assets and liabilities, and non-taxable benefits. All the respondents had to fill out a questionnaire including questions about tax-return information 1983, employment income, and taxes and support payments. Two telephone interviews were used primarily to collect data on the household´s time use and consumption expenditures. The 1986 HUS-survey included both a follow-up of the 1984 sample (panel study) and a supplementary sample. The 1986 sample included 1) all respondents participating in the 1984 survey, 2) the household heads, partners and third persons who should have participated in 1984 but did not (1984 nonresponse), 3) those individuals who started living together after the 1984 interview with an selected individual who participated or was supposed to participate in 1984, 4) members of the 1984 household born in 1966 or 1967. If entering a new household, for example because of leaving their parental home, the household head and his/her partner were also interviewed. Respondents participating in the 1984 survey were interviewed by telephone in 1986. Questions dealt with changes in family composition, housing, employment, wages and child care, and it was not only recorded whether a change had occurred, and what sort of change, but also when it occurred. The respondents also received a questionnaire by mail with questions mainly concerning income and assets. Respondents not participating in the earlier survey were interviewed in person and were asked approximately the same questions as in the 1984 personal interview. The 1988 HUS-survey was considerably smaller than the previous ones. It was addressed exclusively to participants in the 1986 survey, and consisted of a self-enumerated questionnaire with a nonrespondent follow-up by telephone. The questions dealt with changes in housing conditions, employment and household composition. The questionnaire also contained some questions on household income. In many respect the 1991 HUS-survey replicated the 1988 survey. The questions were basically the same in content and range, and the survey was conducted as a self-enamurated questionnaire sent out by mail. This time, however, in contrast to the 1988 survey, an attempt was made to include in the survey the new household members who had moved into sample households since 1986, as well as young people who turned 18 after the 1986 survey. Earlier respondents received a questionnaire by mail containing questions about their home, their primary occupation and weekly work hours since May 1988 (event-history data), earnings in 1989, 1990 and 1991, household composition and any changes in it that might have occurred since 1988, child care and some questions on income. New respondents were also asked about their education and labor-market experience. With respect to its design and question wording, the 1993 survey is a new version of the 1986 survey. The survey is made up of four parts: 1) the panel survey, which was addressed mainly to respondents in the 1991 survey, with certain additions; 2) the so-called supplementary survey, which focused on a new random sample of individuals; 3) the so-called nonresponse survey, which encompassed respondents who had participated in at least one of the earlier surveys but had since dropped out; 4) the time-use survey, which included the same sample of respondents as those in the panel and supplementary surveys. Individuals in the nonresponse group were not included in the time-use survey. Most of the questions in the first three surveys were the same, but certain questions sequences were targeted to the respondents in a specific survey. Thus certain retrospective questions were asked of the nonresponse group, while specific questions on social background, labor market experience etc. were addressed to new respondents. In the case of respondents who had already participated in the panel, a combined contact and main interview was conducted by telephone, after which a self-enumerated questionnaire was sent out to each respondent by mail. The panel sample also included young people in panel households who were born in 1973 or 1974 as well as certain new household members who had not previously been interviewed. These individuals, like new respondents, were not interviewed by telephone until they had been interviewed personally. Thus technically they were treated in the same manner as individuals in the supplementary sample. The new supplementary sample was first contacted by telephone and then given a fairly lengthy personal interview, at the conclusion of which each respondent was asked to fill out a written questionnaire. In this respect the survey design for the nonresponse sample was the same as for the supplementary sample. The nonresponse sample also included young people born in 1973 or 1974 as well as certain new household members. The time-use interviews were conducted by telephone. For each respondent two days were chosen at random from the period from February 15, 1993 to February 14, 1994 and the respondents were interviewed about their time use during those two days. If possible, the time-use interviews were preceded by the other parts of the survey, but this was not always feasible. In each household the household head and spouse/partner were interviewed, as well as an additional person in certain households. Questions regarding the household as a whole were asked of only one person in the household, preferably the household head. As in earlier surveys, data from the interviews was subsequently supplemented by registry data, but only for those respondents who had given their express consent. There is registry information for 75-80 percent of the sample. The telephone interview is divided into following sections: administrative data; labor market experience; employment; job-seekers; not in labor force; education; family composition; child care; health status; other household members; housing conditions; vacation homes; and cars and boats. The questionnaire was divided into twelve sections: sale of previous home; acquisition of current home; construction costs for building a home; house value and loans; repairs; insurance; home-related expenses; sale of previous home; assets; household income; taxes; and respondent income 1992. The 1996 telephone interview is divided into following sections: administrative data; labor market experience; employment; job-seekers; not in labor force; education; family composition; child care; health status; other household members; housing conditions; vacation homes; cars and boats; and environment. The questionnaire was divided into twelve sections: sale of previous home; acquisition of current home; construction costs for building a home; house value and loans; repairs; insurance; home-related expenses; sale of previous home; assets; household income; taxes; and respondent income 1995. The 1998 telephone interview is divided into following sections: administrative data; labor market experience; employment; job-seekers; not in labor force; education; family composition; child care; health status; other household members; housing conditions; vacation homes; cars and boats; and municipal service. The questionnaire was divided into nine sections: sale of previous home; house value and loans; insurance; home-related expenses; assets; household income; inheritances and gifts; black-market work; and respondent income 1997.

Abstract copyright UK Data Service and data collection copyright owner.

The Opinions and Lifestyle Survey (formerly known as the ONS Opinions Survey or Omnibus) is an omnibus survey that began in 1990, collecting data on a range of subjects commissioned by both the ONS internally and external clients (limited to other government departments, charities, non-profit organisations and academia).

Data are collected from one individual aged 16 or over, selected from each sampled private household. Personal data include data on the individual, their family, address, household, income and education, plus responses and opinions on a variety of subjects within commissioned modules.

The questionnaire collects timely data for research and policy analysis evaluation on the social impacts of recent topics of national importance, such as the coronavirus (COVID-19) pandemic and the cost of living, on individuals and households in Great Britain.

From April 2018 to November 2019, the design of the OPN changed from face-to-face to a mixed-mode design (online first with telephone interviewing where necessary). Mixed-mode collection allows respondents to complete the survey more flexibly and provides a more cost-effective service for customers.

In March 2020, the OPN was adapted to become a weekly survey used to collect data on the social impacts of the coronavirus (COVID-19) pandemic on the lives of people of Great Britain. These data are held in the Secure Access study, SN 8635, ONS Opinions and Lifestyle Survey, Covid-19 Module, 2020-2022: Secure Access.

From August 2021, as coronavirus (COVID-19) restrictions were lifting across Great Britain, the OPN moved to fortnightly data collection, sampling around 5,000 households in each survey wave to ensure the survey remains sustainable.

The OPN has since expanded to include questions on other topics of national importance, such as health and the cost of living. For more information about the survey and its methodology, see the ONS OPN Quality and Methodology Information webpage.

Secure Access Opinions and Lifestyle Survey data

Other Secure Access OPN data cover modules run at various points from 1997-2019, on Census religion (SN 8078), cervical cancer screening (SN 8080), contact after separation (SN 8089), contraception (SN 8095), disability (SNs 8680 and 8096), general lifestyle (SN 8092), illness and activity (SN 8094), and non-resident parental contact (SN 8093). See Opinions and Lifestyle Survey: Secure Access for details.

Main Topics:
Each month's questionnaire consists of two elements: core questions, covering demographic information, are asked each month together with non-core questions that vary from month to month.
The non-core questions for this month were:

Changes in Family Circumstances (Module 277): This module was asked on behalf of the Inland Revenue although this was not indicated to the respondents. The Inland Revenue rely on the Omnibus to provide them with information about family events which cause changes in family income.