Households' consumption: documentation of statistics
Basic data of the statistics
Unit of measure
The data of the statistics are published on the level of the whole country and by major region (NUTS2).
The representative population sample of the statistics on households' consumption describe the economic well-being of households and persons as a whole more extensively than any other statistical data. In addition to the demographic and regional data on households, the statistics also contain data on households':
– consumption expenditure
– disposable income
– interests on housing loans and other loans – housing conditions
Two time series have been produced based on the data, of which the older one contains comparable data for the years 1966, 1971, 1976, 1981, 1985 and 1990. The newer time series contains data for the years 1985, 1990, 1995, 1998, 2001, 2006, 2012 and 2016.
Frequency of dissemination
- the first adult of the household receives the weight 1
- other over 13-year-olds receive the weight 0.5
- children receive the weight 0.3 (0 to 13-year-olds).
The selected consumption unit scale has a significant effect on income levels and on placement of different population groups in the income distribution.
The formation of disposable income can be described as follows:
+ Wages and salaries
+ Entrepreneurial income
+ Property income (incl. imputed rent from owner-occupied dwellings and sales profits)
= Factor income
+ Current transfers received (incl. imputed rent from a rental dwelling from another household)
= Gross income
– Current transfers paid
= Disposable income
When social current transfers in kind are added to income, adjusted disposable income is obtained. This concept is not formed in the income distribution statistics.
The imputed rent of owner-occupiers was regarded as factor income (property income) and imputed rent for a dwelling rented from another household as current transfers received in the income distribution statistics. Imputed rent is still formed in the income distribution statistics but from the statistical reference year 2011, it is treated as a separate income item (see "Imputed rent"). Similarly, taxable realised capital gains or sales profits are treated as a memorandum item according to international recommendations.
Wages and salaries include income paid for households as pay - either in money or benefit in kind. Income from incentive stock options is included in the income concept in benefits in kind and thus in wages and salaries.
Entrepreneurial income includes income from agriculture and forestry, business activity and business group and copyright fees. Entrepreneurial income in agriculture also contains various subsidies and compensations such as agricultural subsidies, European Union agricultural aid and compensation for harvest losses.
Property income is rental, interest and dividend income received by households, imputed net rent from an owner-occupied dwelling, taxable capital gain and pensions based on private insurance and other income.
Current transfers received comprise earnings-related pensions and national pensions and other social security benefits, social assistance and other current transfers received.
Current transfers paid comprise direct taxes and social security contributions. In addition, current transfers paid comprise compulsory pension and unemployment insurance premiums and in the income distribution statistics also child maintenance support paid.
The key income distribution statistics concept, disposable income, is arrived at when current transfers paid are deducted from gross income. The concept of disposable income in the Household Budget Survey is based on register data, and does not, differing from the income distribution statistics, include wages and salaries subject withholding tax and tax-free interest income and current transfers between several households (e.g. child maintenance support).
The difference between nondurable goods and durable goods is based on whether the goods can be used only once, or repeatedly or continuously during a period of over one year. Durable goods, such as cars, fridges, washing machines and televisions, also have a relatively high purchase price. Semi-durable goods differ from durable goods in that their expected service life, although over one year, is often considerably shorter and their purchase price lower.
Statistics Finland's COICOP handbook can be browsed on the Internet at www.tilastokeskus.fi/tk/tt/luokitukset/popup/coicop.pdf (Handbooks / Statistics Finland, 41).
Households' final consumption expenditure is formed as follows:
+ purchases of consumption goods and services
+ own products (agricultural, gardening and collected products)
+ imputed dwelling income from an owner-occupied dwelling and a dwelling provided as a benefit in kind
+ goods and services received
+ current transfers comparable to consumption (e.g. church tax and labour union membership fees and interest on consumption loans)
= total consumption expenditure
The classification of consumption used in the Household Budget Survey is based on Eurostat's COICOP-HSB consumption classification (Classification of Individual Consumption by Purpose).
The COICOP handbook can be browsed on the Internet at www.tilastokeskus.fi/tk/tt/luokitukset/popup/coicop.pdf (Handbooks / Statistics Finland, 41).
Excluded from the household population are those living permanently abroad and the institutional population (such as long-term residents of old-age homes, care institutions, prisons or hospitals).
The corresponding register-based information is household-dwelling unit. A household-dwelling unit is formed of persons living permanently in the same dwelling or address. More than one household may belong to the same household-dwelling unit. The concept of household-dwelling unit is used in register-based statistics in place of the household concept.
A considerable part of household-specific classifying data is formed on the basis of the reference person's data.
In addition to gross rent, housing expenditure includes water charges and some other payments such as chimney-sweeping and refuse collection, maintenance repairs made by the tenant and heating costs not included in the rent. Expenditure of free-time residences is also included in housing expenditure.
The socio-economic group of the household is determined by the household's reference person.
The classification is based on the Statistics Finland's classification standard of socio-economic groups from 1989. There account is taken of the person's occupation, status in occupation, nature of work and stage in life (Classification of Socio-economic Group 1989. Helsinki. Statistics Finland, Handbooks, 17).
Accuracy, reliability and timeliness
In the so-called main groups of the 2016 consumption expenditure, the relative standard errors are small apart from education expenditure. The most reliable data come from the biggest consumption expenditure groups (food and non-alcoholic beverage, and housing and energy). The relative standard errors for clothing and footwear and education expenditure are highest.
When using the data of the Household Budget Survey it should be noted that the reliability of consumption data weakens when moving from the main group level to the consumption sub-groups. Standard errors of often purchased commodities are small, but for less often purchased products, standard errors may become quite large. In these cases, the sample may not be sufficient to describe consumption expenditure reliably. Detailed classifications by background variables, such as age, socio-economic group or region, also increase the relative standard errors of consumption items as the observations decrease.
Standard error calculations describe random variation related to the results. In addition, households’ non-response and not remembering cause some consumption items to be only partially collected from sample households, which means that households' average consumption expenditure is underestimated. The error caused by this may in some cases (e.g. alcohol expenditure, games of chance) be considerably bigger than a random error. Underestimation of consumption expenditure items can be studied by comparing the results with corresponding national accounts data, for example. In 2016, the share of total expenditure in the Household Budget Survey of national accounts data was good 80 per cent.
Accuracy and reliability
Errors related to sampling may be random or systematic. Random variation is caused by only part of the population being included in the survey. The size of the random error is influenced by the size of the sample, the method for drawing the sample and estimation of the results. The size of the random error is further affected by variation in the observation values of the variables (dispersion). A random sampling error is assessed with the help of standard errors of estimates.
Systematic errors in the results are caused by coverage, i.e. frame, errors in the sample. The share of coverage errors of total errors related to the Household Budget Survey is estimated to be small.
In the different stages of the Household Budget Survey, attention was paid to finding and minimising the errors. At the start of the study, the focus was on careful planning of the data content and testing of the questionnaire. Efforts have been made to minimise the risk of errors in the data collection stage through interviewer training and standardisation of the interview. Several interviewers had experience of previous Household Budget Surveys. In addition, the computer-assisted (CAPI) interview standardises the interview and enables the use of various limitations to the values entered on the form. Because the data of the Household Budget Survey are mainly collected through interviews and consumption diaries, the work input and motivation of the interviewers are key for the whole survey.
Despite careful planning of the data content and the questionnaire, it is not possible to reliably collect all consumption expenditure of households. This is partly due to households' memory errors or non-response. The Household Budget Survey produces too low consumption, for example, for alcoholic beverages and games of chance and other similar products that are often seen as socially undesirable. The consumption expenditure of the Household Budget Survey is compared with the consumption data of the household sector in the national accounts of the corresponding year. Processing errors here refer to errors during data storage, processing and coding. The aim was to reduce them by means of various checking rules. Systematic errors in the results may also be caused by non-response, that is, a sample household being left outside the survey. Non-response is generally understood as one key indicator of the quality of the data. The increase in non-response has been the most significant factor weakening the quality of survey data in recent years.
Time series corrections caused by methodological changes etc. are made when necessary.
Over-coverage rate / A2
There is quite little item non-response in the interview, because all questions are discussed with the interviewer in a computer-assisted interview. It is difficult to examine item non-response, or individual missing responses, in the Household Budget Survey because it cannot be ascertained from the purchase data of the receipt collection period whether the household has not recorded or not purchased the product in question within two weeks.
Item non-response was imputed for the variables describing consumption. If the interviewee had answered “do not know” or refused to answer a question, the data were imputed using the donor imputation method.
Comparability - geographical
The data of the statistics on households' consumption published nationally in Finland differ from the data supplied to Eurostat. The national classification of consumption expenditure differs partly from the eCOICOP classification used by Eurostat.
The biggest differences compared to the national version relate to the definition of consumption expenditure. Finland's version is considerably more detailed. In addition, in the national survey, consumption expenditure includes the following items of consumption expenditure that are not included in consumption expenditure in Eurostat's classification:
– some tax-like payments (e.g. vehicle tax, dog tax)
– membership fees, fines and other transfer charges
– other items outside consumption expenditure (e.g. payments for estate agents and housing agents, payments for building permits, interests). The reason for these differences is maintaining the comparability of the national time series.
Comparability - over time
Coherence - cross domain
Coherence - internal
Source data and data collections
– Interviews with the households
– Receipts collected and consumption diaries filled in by the households
– Administrative registers
Efforts have been made to use register data as much as possible to reduce the response burden of households and the costs of data collection. However, actual consumption data cannot be obtained from registers. By contrast, in addition to sample data, the household's income data and data on the members' level of education derive from the register.
The Household Budget Survey is a sample survey. The population of the survey comprises households permanently resident in Finland, i.e. the so-called household population. In 2016, there were 2.677 million households. Excluded from the survey are Finnish citizens resident abroad and the institutional population, which includes long-term residents of hospitals, old people's homes, care institutions, prisons, etc.
In the survey of 2016, the sampling design was a two-phase stratified sampling. In the first phase, a so-called master sample was formed by selecting 100,000 target persons aged 16 or over by means of systematic sampling from Statistics Finland’s population database. In the second phase, the actual sample for the Household Budget Survey was selected by stratum from the master sample. The stratification was made according to the areas of the Regional State Administrative Agencies (AVI) (Greater Helsinki separately) and, in addition, by dividing household-dwelling units into persons aged under 65 living alone and other household-dwelling units. The total number of strata was 16. Within the stratum, the probability of each household entering the sample depends on the number of household members aged 16 or over.
In 2016, the original gross sample consisted of 8,216 persons, some of whom were not included in the population of the survey. This so-called over-coverage includes deceased persons, the institutional population and Finnish citizens resident abroad. The number of over-coverage was 193 persons, so the net sample consisted of 8,023 households.
In the interview, the structure of the household according to register data is checked and data are collected on the members of the household (occupations, activity on the labour market, etc.) and data are inquired on households' expenditure, such as housing, insurance and large purchases, and owning durable consumer goods.
Statistics Finland’s own interviewers carry out the interviews. After the interviews, the households collected receipts or recorded purchases in the consumption diary concerning all their expenses during two weeks. The sample households of the survey were randomly divided into 26 receipt collection periods of 14 days throughout the survey year. Thus, as comprehensive and reliable data as possible are obtained on the consumption in different seasons. The receipts were returned either to the statistical interviewer with whom an agreement had been made about the collection of receipts or directly to Statistics Finland. The receipts were scanned and converted to electronic format at Statistics Finland. Administrative registers were utilised in collecting background data. For example, data on the income and education of household members and on the use of public services were obtained from them.
Frequency of data collection
Cost and burden
We strive to minimise the response burden by e.g. questionnaire designing and developing data collection tools.
Imputation is an integral part of other data editing, that is, the detection and correction of inconsistent or erroneous data. For all corrected variables, information about the correction was entered into the so-called flag variable in the database. The possible values of the flag variables were: 1: Imputed with Banff (donor imputation) 2: Data retrieved from administrative dataset 3: Data distributed to several variables (e.g. insurance packages) 4: Other imputation method (e.g. mean or median) 5: The data have been corrected (e.g. outliers).
The distribution of data to several variables (flag=3) was primarily used for insurance packages, that is, in a situation where the household told what insurances it had and what their total sum was. Generating data with the help of administrative data was used, for example, in connection with variables concerning housing (e.g. year of construction, floor area). Other imputation methods were mainly used for durable consumer goods, of which under two per cent were imputed.
When estimating the total sums and averages of consumption expenditure, the inclusion probabilities and response probabilities related to sampling and additional information available from the population are utilised in weighting the data of each household.
The bias caused by the skewed non-response was corrected with the help of re-weighting. In the re-weighting, the so-called correction for non-response is first made by multiplying the inclusion probabilities with the estimated response probabilities. After this, the weights corrected for non-response are calibrated. The purpose of calibration is to improve the estimates calculated from the sample by means of additional information obtained from the target population. The weighting coefficients of the households having responded to the survey were adjusted so that, with regard to the most important background variables, the marginal distributions of the households having responded corresponded to the marginal distributions of the entire household population. The CALMAR calculation software was used for calibration.
The following marginal distributions for 2016 obtained from registers were used in the weight calibration:
1) distribution of household-dwelling units by region (NUTS3) - Greater Helsinki separately: Helsinki, Espoo (+ Kauniainen) and Vantaa each formed their own “region”
2) distribution of size of household-dwelling units: 1, 2, 3, 4, 5, 6, 7, 8+ persons
3) distribution of the socio-economic group of household-dwelling units
4) distribution of level of education of household-dwelling units
5) population distribution by gender and age
6) total of taxable earned income of household-dwelling units
7) total of taxable capital income of household-dwelling units.
In addition, in the calibration, the sums of the weight coefficients of the receipt collection periods are forced to be the same in order for the data to represent all periods equally well.
– review of the comments written by the interviewer in connection with the interview
– checking the minimum and maximum of interview data
– checking the 15 biggest and smallest values of each code in the receipt and diary data – logical checks (e.g. that each household has certain mandatory data).
Documentation on methodology
Principles and outlines
Contact organisation unit
Legal acts and other agreements
The compilation of statistics is guided by the Statistics Act. The Statistics Act contains provisions on collection of data, processing of data and the obligation to provide data. Besides the Statistics Act, the Data Protection Act and the Act on the Openness of Government Activities are applied to processing of data when producing statistics.
Statistics Finland compiles statistics in line with the EU’s regulations applicable to statistics, which steer the statistical agencies of all EU Member States.
Further information: Statistical legislation
Confidentiality - policy
Further information: Data protection | Statistics Finland (stat.fi)
Confidentiality - data treatment
An individual household cannot under any circumstances be identified from the published data.
Release of data for research use is done centrally by Statistics Finland's research services. The survey data of the Household Budget Survey are mainly household-specific and households are identified with consecutive numbers. Some of the classification data of the household are person-based. The data do not contain identification data. In addition, before granting a licence to use the data and releasing the data for research use, the data protection of the data is assessed in accordance with the normal practice of the research services and the data are made less detailed if necessary.
Unit-level data are released to Eurostat. The released data do not contain identification data.
Further information: Publication principles for statistics at Statistics Finland
Eurostat, the Statistical Office of the European Union, is responsible for compiling statistics on the HBS and for the release of its statistical data for research use. Research use requires an application for licence to use statistical data.
Accessibility and clarity
Statistical data are published as database tables in the StatFin database. The database is the primary publishing site of data, and new data are updated first there. When releasing statistical data,
existing database tables can be updated with new data or completely new database tables can be published.
In addition to statistical data published in the StatFin database, a release on the key data is usually published in the web service. If the release contains data concerning several reference periods (e.g. monthly and annual data), a review bringing together these data is published in the web service. Database tables updated at the time of publication are listed both in the release and in the review. In some cases, statistical data can also be published as mere database releases in the StatFin database. No release or review is published in connection with these database releases.
Releases and database tables are published in three languages, in Finnish, Swedish and English. The language versions of releases may have more limited content than in Finnish.
Information about changes in the publication schedules of releases and database tables and about corrections are given as change releases in the web service.
The survey data of the Household Budget Survey are mainly household-specific and households are identified with consecutive numbers. Some of the classification data of the household are person-based. The data do not contain identification data.
Unit-level data are released to Eurostat. The released data do not contain identification data.
Data revision - policy
Revisions – i.e. improvements in the accuracy of statistical data already published – are a normal feature of statistical production and result in improved quality of statistics. The principle is that statistical data are based on the best available data and information concerning the statistical phenomenon. On the other hand, the revisions are communicated as transparently as possible in advance. Advance communication ensures that the users can prepare for the data revisions.
The reason why data in statistical releases become revised is often caused by the data becoming supplemented. Then the new, revised statistical figure is based on a wider information basis and describes the phenomenon more accurately than before.
Revisions of statistical data may also be caused by the calculation method used, such as annual benchmarking or updating of weight structures. Changes of base years and used classifications may also cause revisions to data.
(Consumer Price Index, level and structure of household sector consumption in national accounts), for decision-making within the European Union to describe differences in welfare by population group and region in the Member States (especially poverty and social exclusion), and nationally to describe the preconditions and distribution of economic well-being. The data are also widely used for planning and monitoring social policy measures. The Household Budget Survey also produces data for statistics describing the use of energy, use of information technology, transport, mass media and culture as well as for market analyses.
The Household Budget Survey describes households' resources of economic well-being, which is why it is close in terms of content to the income distribution and wealth surveys. Households' consumption also includes activities which link the Household Budget Survey to time use, cultural and leisure surveys, as well as surveys and statistics describing the environment and the mass media.
Universities and research institutions use the data of the Household Budget Survey when studying, for example, the distribution of well-being by population group or region, poverty and social exclusion or social problems. The data are also used in the so-called lifestyle survey and econometric surveys. Consumption data are also linked to the microsimulation model (SISU). In addition, Statistics Finland makes data analyses and printouts as information service, based on the needs of the users.
Further information: Quality management | Statistics Finland (stat.fi)
Data are released to all users at the same time. Statistical data may only be handled at Statistics Finland and information on them may be given before release only by persons involved in the production of the statistics concerned or who need the data of the statistics concerned in their own work before the data are published. Further information: Publication principles for statistics
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