The study constructs new series of nominal wages in industry and crafts as well as a new consumer goods price index for the period 1850-1889; the coefficient of the two series gives the real wage. While such information was collected and published by government agencies from the late 1880s onwards, the decades before are part of the pre-statistical age. After all, information is available from municipal authorities, from branches of territorial state authorities and from individual companies. For the construction of a new nominal wage series, the current study refer to Kuczynski´s material (1961/62), supplements it with information from individual studies of the past 50 years, and constructs wage indices for the heavy ironware, machine construction, mining, printing, and municipal construction industries on this basis by means of unbalanced panel regression with fixed effects. Of the 38 individual wage series on which these sector indices are based, 27 come from Kuczynski, the remainder from more recent studies. Wages in the textile sector are represented by those in the cotton industry. The study uses the wage series published by Kirchhain (1977). Weighted according to employment figures, all these sector-specific series (excluding miners´ wages) are aggregated into a Fisher index of nominal wages in industry and crafts. Both this index and the indices at sector level are linked in 1888/89 with the series by Hoffmann (1965); the resulting values denote annual earnings in Marks. The sector indices differ little from those of Kuczynski and Hoffmann despite the expansion of the database and the different methodology of index construction, but the aggregated index shows a stronger growth rate than that of Kuczynski; the latter index is obviously erroneous (Pfister 2018, 576). The consumer goods price index is based on five sub-indices for (1) food, (2) beverages and luxury foods, (3) rent, (4) furniture, household goods and heating, and (5) clothing. The sub-indices for food and rent are new, the other three are from Hoffmann (1965). Weights are determined for 1848/49 and 1889 on the basis of research literature, values in between are interpolated linearly. Both the sub-index of food prices and the overall index are constructed as Fisher indices. Both the rental index and the food prices rise more strongly in the long term than the two corresponding Hoffmann indices (Pfister 2018, 578 and 582). Hoffmann constructs the rental price index only indirectly by multiplying the estimated building capital by an assumed interest rate. The rent index of the current study is based on data from three major cities. Only if it is assumed that large cities are completely unrepresentative for the entire real estate market should Hoffmann´s series still be considered. In the case of food prices, the comparatively stronger long-term increase - compared to previous research - results from the higher weight of prices from the southern parts of the country far from the sea in the new sub-index. Here, the price dampening effect of growing imports of American grain had a weaker effect than in the coastal regions in the north. Thus, one of the main findings of the study is that the assessment of the development of the living standards of urban workers from the 1850s to 1880s strongly depends on how one determines the effect of the first wave of modern globalization on the German price structure. The greater consideration given in this study to food prices in areas distant from the sea results in a more pessimistic view of the development of real wages during this period than has been the case with some previous research. To the data: 1. individual wage series (table set A.01) This set of tables contains wage series from six branches at the level of regions, cities, individual enterprises and in one case (cotton industry) an entire branch. Only series containing data for at least 15 years were taken into account. In detail, the series are the following:Heavy IronwareBochum 1869-1889: Average annual income of the workers of the Bochumer Verein (steelworks) in Mark; Däbritz (1934, Annex Table 4).Essen 1848-1889: Average annual income of the workers of the Krupp works in Mark; Kuczynksi (1961-62, vol. I, 377, vol. II, 227, vol. III, 426).Ruhr 1855-1889: Average annual income of the workers at the blast furnaces in the Ruhr district in Mark; banks (2000, Table A59).Saar 1869-1889: Day wage of workers at the blast furnaces of the Burbach Ironworks in Mark; Kuczynksi (1961-62, vol. III, 426).Silesia 1869-1889: Average annual income of workers at the blast furnaces in Silesia in Mark; banks (2000, Table A59). Machine constructionAugsburg 1851-1889: Average annual income of the workers of the Machine Factory Augsburg in Mark; Vol. II, 227; Kuczynski (1961-62, Vol. III, 426).Chemnitz 1860-1887: Weekly wage of machinists in Mark; Kuczynski (1961-62, vol. II, 227; vol. III, 426).Esslingen 1848-1889: Average annual income of workers at the Ess...

This table contains 1339 series, with data for years 1961 - 1983 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (62 items: Canada; Newfoundland and Labrador; Atlantic provinces ...), Wage earners (2 items: Average weekly hours; Average hourly earnings ...), Standard Industrial Classification, 1960 (SIC) (124 items: Mining; including milling; Metals; Gold; Copper-gold-silver ...).

Abstract copyright UK Data Service and data collection copyright owner.

The Households Below Average Income (HBAI) data presents information on living standards in the UK based on household income measures for the financial year.

HBAI uses equivalised disposable household income as a proxy for living standards in order to allow comparisons of the living standards of different types of households (that is, income is adjusted to take into account variations in the size and composition of the households in a process known as equivalisation). A key assumption made in HBAI is that all individuals in the household benefit equally from the combined income of the household. This enables the total equivalised income of the household to be used as a proxy for the standard of living of each household member.

In line with international best practice, the income measures used in HBAI are subject to several statistical adjustments and, as such, are not always directly relatable to income amounts as they might be understood by people on a day-to-day basis. These adjustments, however, allow consistent comparison over time and across households of different sizes and compositions. HBAI uses variants of CPI inflation when estimating how incomes are changing in real terms over time.

The main data source used in this study is the Family Resources Survey (FRS), a continuous cross-sectional survey. The FRS normally has a sample of 19,000 - 20,000 UK households. The use of survey data means that HBAI estimates are subject to uncertainty, which can affect how changes should be interpreted, especially in the short term. Analysis of geographies below the regional level is not recommended from this data.

Further information and the latest publication can be found on the gov.uk HBAI webpage. The HBAI team want to provide user-friendly datasets and clearer documentation, so please contact team.hbai@dwp.gov.uk if you have any suggestions or feedback on the new harmonised datasets and documentation.

An earlier HBAI study, Institute for Fiscal Studies Households Below Average Income Dataset, 1961-1991, is held under SN 3300.

Latest Edition Information

For the 19th edition (April 2025), resamples data have been added to the study alongside supporting documentation. Main data back to 1994/95 have been updated to latest-year prices, and the documentation has been updated accordingly.

Using the HBAI files

Users should note that either 7-Zip or a recent version of WinZip is needed to unzip the HBAI download zip files, due to their size. The inbuilt Windows compression software will not handle them correctly.

Labelling of variables
Users should note that many variables across the resamples files do not include full variable or value labels. This information can be found easily in the documentation - see the Harmonised Data Variables Guide.

HBAI versions

The HBAI datasets are available in two versions at the UKDS:

1. End User Licence (EUL) (Anonymised) Datasets:

These datasets contain no names, addresses, telephone numbers, bank account details, NINOs or any personal details that can be considered disclosive under the terms of the ONS Disclosure Control guidance. Changes made to the datasets are as follows:

• All ages above 80 are instead top-coded to 80 years of age.
• The variable for the amount of Council Tax liability for the household and pensioner flags for the head and spouse have been removed.
• All amount variables have been rounded to the nearest £1.
• A very small number of large households (with 10 or more individuals) have been removed from the dataset.

2. Secure Access Datasets:

Secure Access datasets for HBAI are held under SN 7196. The Secure Access data are not subject to the same edits as the EUL version and are, therefore, more disclosive and subject to strict access conditions. They are currently only available to UK HE/FE applicants. Prospective users of the Secure Access version of the HBAI must fulfil additional requirements beyond those associated with the EUL datasets.

The HBAI data provide information on potential living standards in the United Kingdom as determined by net (equivalised) disposable income and allows for the analysis of changes in income patterns over time.

Greece GR: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Outside Region data was reported at 37.630 % in 2016. This records an increase from the previous number of 37.191 % for 2015. Greece GR: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Outside Region data is updated yearly, averaging 20.775 % from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 42.320 % in 2012 and a record low of 6.157 % in 1961. Greece GR: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Outside Region data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Greece – Table GR.World Bank.WDI: Exports. Merchandise exports to low- and middle-income economies outside region are the sum of merchandise exports from the reporting economy to other low- and middle-income economies in other World Bank regions according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

New Zealand NZ: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Europe & Central Asia data was reported at 0.887 % in 2016. This records a decrease from the previous number of 1.552 % for 2015. New Zealand NZ: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Europe & Central Asia data is updated yearly, averaging 0.161 % from Dec 1961 (Median) to 2016, with 49 observations. The data reached an all-time high of 2.892 % in 2011 and a record low of 0.013 % in 1961. New Zealand NZ: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Europe & Central Asia data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s New Zealand – Table NZ.World Bank.WDI: Imports. Merchandise imports from low- and middle-income economies in Europe and Central Asia are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the Europe and Central Asia region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;