Statistical processing

Source data

The Household Budget Survey is calculated at household level, and is based on a combination of interviews and accounting of the participating households. All households are simply randomly selected. In areas where data are already known through registers, data are taken from those registers. The survey used records from: - Income Register - CPR register - BBR register - Training Register and The Employment Classification Module
- Hospital Utililisation statistics

Frequency of data collection

Data are collected annually. Households participate continuously throughout the year in the survey. In this way we ensure that seasonal consumption are represented in the survey.

Data collection

An external service provider takes care of the data collection for the Household Budget Survey. Households that are randomly selected are sent a letter about participation in the survey via E-box and are subsequently contacted by telephone. If the household agrees to participate, the household must keep a 14-day account of their consumption and subsequently be visited by an interviewer who asks questions about the household's fixed expenses and major expenditure items a year back in time. The 14-day account is digital, but can also be completed on paper. The interview is conducted in CAPI (a computer-based personal in

Data 2021 has been collected via Computer Assisted Personal Inteviews (CAPI), Computer Assisted Telephone Interviews (CATI) and Computer Assisted Web Interviews (CAWI), while data regarding 2022 is exclusively collected via CATI and CAWI.

Data from administrative registers is retrieved per 31 December in the reference year, or the latest year available. If data is obtained from an earlier year than the reference year, price and quantity data are converted to the price level for the reference year.

Data validation

Interview data is validated both during and immediately after the visit interview. The validation during the interview consists partly of logical and partly of probable checks, while the validation after the interview is done manually. A logical check could be, for example, whether the household has a TV, but has not reported expenses for a license or antenna association, or that the household has a car, but does not report expenses for weight tax, car insurance, etc. A likely check could be, for example, that very high or low amounts are investigated directly in the program used for the interview and that the household is confronted with this and must deal with whether it is correct.

When data is received in Statistics Denmark, it goes through a validation which, for example, involves assessing the household's consumption in relation to its size. If, for example, there is only one person and a very high water consumption, or there are, for example, two adults with children, where it has not been reported how many months have been used for daycare and school, the household will be contacted to clarify the accuracy of the information. Some corrections are made without contacting the household, where the description of the purchase and the amount seem contradictory. It could be, for example, that a liter of milk is registered with an amount of DKK 1,000. This will be corrected to DKK 10.00.

The 14-day accounts are validated continuously when they are received, and collectively when the collection of accounts for a year has been completed. In the overall validation, it will be checked, for example, whether all purchases are coded correctly according to the classification, ECOICOP.

Data compilation

When we have finished the validation of the interviews and accounts booklets the registry variable are linked in the data set. Sometimes it's difficult to find the household in the sample in the register data, this kind of difficulties can often be attributed to differences in the calculation date. When this happens we make manual imputation of for example, an individual's level of education.

After finishing the processing of Micro-data the enumeration process of making the data representative for the entire country begins. The figures in all tables are weighted this is done in order to partially resolve the gaps, as different dropout and pure random coincidences leads. Those types of Household where the risk for not participating in the survey is relatively large, which therefore results in too few households in the survey are assigned a relatively large weight, while household types, as there are too many of, is assigned a relatively small weight.

Information about both the enumerated number of households in Denmark after the weighting and on the actual number of households in the survey can be found in most tables. This last statement is relevant to assessing the sampling uncertainty, since a small number of households results in a relatively large uncertainties.

The weights are calculated using a regression estimate. The focus is on each characteristics of the relationship between sample and population. The advantage of this method is that many more features are considered than in the former method were post-stratification was used. Following characteristics are involved in the estimation:

• Household size and composition
• Income
• Main Income Recipient's socio-economic status
• The household owns or rents the dwelling
• What type of urban household lives in
• Education
• Gender
• Geography

Adjustment

We do not make other corrections of data besides those corrections described during data validation and data processing.