The HFCS in Austria (fourth wave): key facts
Methodological framework at a glance

Nicolas Albacete, Peter Lindner, Karin Wagner
 

Questionnaire

Reference period

Geographical scope

Sampling

Target population

Sampling frame

Sampling design

• Stratification: NUTS-3 regions divided into 8 classes by municipality size
• Primary sampling unit (PSU): enumeration districts
• Secondary sampling unit (SSU): postal addresses

Survey company

Fieldwork

General information

Interviewer training

Pilot survey

Contact strategy

The interviewers had instructions to make up to five contact attempts per household over a period of at least three weeks. At least two of these attempts were to be made in person, at least one attempt was to be made on a weekend and another outside regular working hours (9:00 a.m. to 5:00 p.m.).

Incentives for participation

Each household that successfully completed an interview received a voucher with a face value of EUR 10.

Documents used during interviews

Interviewer monitoring

The (anonymized) data from the completed household interviews were forwarded to the OeNB in 19 batches during the field phase, to enable prompt assessment of each interview and interviewer.

Follow-up queries by telephone

Editing and consistency checks

Number and type of edits

Consistency checks during the interviews

Postinterview consistency checks

Documentation

Imputations

Sample size and response rate

Weighting

• Model-based adjustment combined with weighting-class adjustment, based on optimal number of classes (nonresponse adjustment)
• Cell adjustment (poststratification adjustment)

Variance estimation

1 Introduction

This publication provides an in-depth view of the data collection process and the methods applied. Based on the methodological documentation from the first three HFCS waves in Austria (Albacete et al., 2012, Albacete et al., 2019), it aims at making the process of data collection as transparent as possible and serves as the basis for correct evaluation of HFCS data. Since the first wave, specific methodological aspects have been discussed in a number of publications. For instance, the information gathered from respondents’ verbatim answers (Lindner and Schürz, 2017) and from the survey of interviewers has been examined in depth and cross-checked with the HFCS data (Albacete and Schürz, 2013b and 2015). Other papers have discussed the relevance of paradata and ways to improve them (Albacete and Schürz, 2014a and 2014b) as well as comparability with other surveys conducted in Austria (Albacete and Schürz, 2013a) and across HFCS countries (Andreasch et al., 2013). Moreover, different approaches to compiling the components of the household balance sheet have been compared (Lindner and Schürz, 2015) and methodological enhancements between the first and the second wave of the HFCS in Austria have been discussed (Lindner et al., 2014).

The chapters are self-contained, each dealing with specific aspects of the HFCS, and can therefore be read independently of each other. Cross-references help the reader recognize links to other chapters or material aspects discussed within them. The sequence of chapters reflects the logical flow of the survey. Closely related topics (e.g. constructing survey weights and estimating the correct variance using HFCS data) are arranged in a way to ensure comprehensibility. To avoid redundancies, only essential details were repeated. The following seven chapters provide a detailed explanation of each step in the survey process, with a further chapter designed as a user guide.

Chapter 2 on the Questionnaire of the HFCS in Austria explains the content of the survey, discussing the individual parts and special features of the questionnaire, the sequence of questions as well as the unit of data collection.

Chapter 3 looks into the role of the interviewers who conducted the face-to-face interviews. Great importance was placed on the qualifications of the interviewers as well as on their professional demeanor and expertise, which all contributes significantly to the quality of data obtained. The chapter also covers details on the contact strategy and incentives for households to participate in the survey. Moreover, it outlines the information material and documents that were made available to the households in the HFCS sample.

All raw data collected by the interviewers were reviewed during the field phase, leading to the collection of further data or data edits where necessary. This process is described in detail in chapter 4 on Consistency checks and editing, which lists all changes to the raw data as well as the flags included in the dataset to document such changes.

Chapter 5 on the Multiple imputations applied in the HFCS deals with item nonresponse. For cases in which respondents were unwilling or unable to answer one or several questions, we performed multiple imputations to obtain the missing information. This approach made it possible to correct distortions due to item nonresponse at least to some extent and also to account for the uncertainty attached to imputations, which, like all edits, have been flagged. Users of the HFCS data may apply our imputations or deal with item nonresponse in a different way.

Chapter 6 on Sampling provides a detailed description of the survey sample design. The complex survey sample design used for the first wave was developed further for the second wave to ensure a sufficiently representative sample of Austrian households that fits the purpose of the Eurosystem and the OeNB. The third and fourth wave retained the sample design methodology of the second wave.

The final household weights were calculated in several steps on the basis of the sampling design. Chapter 7 outlines the procedure for the Construction of survey weights. The sampling design yields design weights for each household already in the sampling process. It takes several steps to process these weights to account for information obtained during the field phase (such as nonparticipation of households and external information regarding the distribution of certain household characteristics).

Another step is required to obtain the correct variance estimation, which is presented in chapter 8 on the Construction of replicate weights for variance estimation.

The User guide in chapter 9 provides basic guidance on the correct use of HFCS data in Stata.

In chapter 10 we describe all the information about COVID-19 pandemic related issues encountered in the fourth wave of the HFCS in AT. The planned start of the field period concidentially subsided with the first lock-down due to the pandemic. This made several changed necessary. All the details are layed out in this chapter. Additionally, boxes in specific chapters clarify special arrangemend in the topic covered.

Finally, an Online appendix (www.hfcs.at/en) serves to provide all the essential documentation and background material used in the HFCS (available in German only). The HFCS website also provides information about the publication of HFCS data from all participating countries by the ECB (expected in summer 2023) and any other HFCS news.

2 Questionnaire

2.1 Introduction

It is the intention to conduct the HFCS every three years. In Austria, the survey has now been conducted four times including the fourth wave documented here (HFCS 2010, HFCS 2014, HFCS 2017 and HFCS 2021). The questionnaire for the fourth HFCS wave was based on the questionnaires used and experiences made in the first three waves. This chapter presents the Austrian questionnaire, which was designed on the basis of the HFCN blueprint questionnaire (drafted in English) ¹ but expanded to include country-specific features (e.g. loan specifics or the national variant of housing association apartments).

This chapter is structured as follows: First we outline the objectives of the HFCS survey in Austria (section 2.2), define the data collection unit (section 2.3), and the reference period (section 2.4). Then we describe the sequence of the questionnaire and highlight some core questions and variables (section 2.5). We subsequently discuss special features of the questionnaire (section 2.6) and list interviewer documents (section 2.7) and participating countries (section 2.8). Section 2.9 refers to further information provided in an online appendix.

2.2 Objectives of the survey

• gain insights into various aspects of the monetary transmission mechanism and of financial stability,
• gain insights into individual household behavior,
• analyze the impact of economic policy measures and macroeconomic shocks, and to
• make cross-country comparisons.

2.3 Unit of collection

2.3.1 Definition of “household”

• people who live in the same household and are related to each other,
• people who share household expenses and live in the same household but are not related,
• people who usually live in the same household (the reference period being the six months before the interview) but are temporarily absent e.g. because of holiday travel, job assignments away from home, hospital stays or boarding school stays, and
• children who are educated away from home but do not constitute a separate household, i.e. remain financially dependent on their family.

Employees of residents, like au-pairs or nursing staff, short-time visitors or subtenants are considered separate households. In shared apartments, all residents are treated as separate households unless they also share household expenses. This means that a particular address may be used by more than one household as defined for HFCS purposes (e.g. people sharing a residence). In such cases, we selected the household whose member received the letter of invitation to participate in the survey.

The definition also includes households with non-German speaking household members, households living at a place registered as their second home in the centralized residence registry and households not officially registered at a particular residence but living there.

2.3.2 Financially knowledgeable person

Since the FKP was typically a member of the household, he or she was also the reference person of the given household. In line with the approach taken in the previous waves, the FKP could also be a third person who was not a household member. This could be a family member (e.g. a son or daughter) who oversaw the household’s finances but was no longer a member of the household or a household’s tax consultant or financial advisor. Only four households in the sample from the fourth wave had an FKP who was not a household member. In this case a member of the household was selected as the reference person for the household.

2.4 Data collection period and reference period

Also for households interviewed in 2022 the reference period for yearly income was kept as 2020.

The field phase included lock-down periods due to the COVID-19 pandemic, in which no interviews could be conducted (see box 1 for more information).

2.5 Interview structure and content

2.5.1 Questionnaire structure

Preinterview

General characteristics

Consumption

Expenses for holiday and travel was asked for the last calendar year. Income and consumption yield to questions on the savings behavior and attitude.

Additionally, this chapter contained a set of questions concerning the impact of the COVID-19 pandemic on the households financial situation (see box 1 and chapter 10 for more details).

Real assets and their financing

Households owning their main residence were asked to indicate when and how they had acquired it as well as the value of the property at the time of the interview and at the time they first acquired ownership. Further, homeowners were asked whether the property was being used as collateral for a loan. In the case of a collateralized loan, the following data were collected separately for a maximum of three loans: purpose of the loan, initial amount, duration of the loan, outstanding principal, interest rate and type of interest, repayment rates and other characteristics. If a household had taken out more than three mortgages, the FKP was asked to provide summary information on the outstanding amounts and repayment rates of these additional (more than three) mortgages.

Households living in a rented home were asked about their rental cost (including and excluding running costs). Tenants of housing association apartments were asked to indicate the deposit made to the housing association. Any debt tenant households had incurred to finance such deposits was recorded under unsecured loans (see “Other liabilities and credit constraints”).

Households that enjoyed free use of their main residence did not have to answer any further questions in the first half of this section.

The next set of questions for all three groups addressed additional real estate holdings, including holdings abroad. Specifically, loop questions, i.e. sets of questions with multiple iterations, ⁶ were used to identify basic information (i.e. type, size, value at the time of ownership transfer and at the time of the interview, and use) for up to three other real estate properties, as well as a summary question serving to establish the total value for any further properties. As was done with mortgages on the main residence, mortgages taken out on these properties were queried using loop questions for a maximum of three loans, and summary information was collected for further mortgages taken out on additional properties. In contrast to relatively recent developments with regard to the ECB blueprint questionnaire the implementation of this part on other real estate remained unchanged in Austria from wave two and three to wave four.

Finally, households were asked to indicate the value of any cars or other vehicles as well as of any valuables (e.g. works of art, antiques) they owned. To conclude this section, respondents were asked whether they had bought a car or any other vehicles in the previous 12 months and if so, at what price.

Other liabilities and credit constraints

For the first three types of liabilities, interviewers asked whether a household held such liabilities and how much was outstanding (for leasing contracts: the amount of lease payments per month). For outstanding balances on credit cards, the question put to households was: after paying the most recent monthly bill, was there any balance outstanding on your credit cards?

Respondents were asked separately whether they had private and noncollateralized loans. Private loans refer to loans from friends or family and noncollateralized loans to any further loans such as for example consumer loans or employer loans. When held by a household, the same information as for collateralized loans was gathered in the form of loop questions for the first three such loans of each of the two types. If a household had more than three of one of these two types of outstanding loans, the outstanding principal and the total monthly repayment rates for these were asked for in a summary question at the end.

This section of the questionnaire also addressed any loan requests that a household had made more recently and potential repayment difficulties of loans and other bills. Finally, respondents were asked about their attitudes to loans, their planning preferences and their level of risk aversion.

Private businesses and financial assets

The next questions focused on assets held in sight accounts, savings accounts, savings plans with building and loan associations, life insurance funds, mutual funds, bonds, listed stock, as well as assets in private foundations and managed accounts. For each of these items, FKPs were asked to indicate whether their household held such assets (yes/no question) and, if the answer was yes, what the total value of these assets was. For life insurance contracts, more detailed questions were asked. These included the date of conclusion, the type and duration of contract (benefits to be provided at the death of the policy holder or at a given date, or a combination thereof) and the frequency and amount of payments into the life insurance plan. This information allows for projections of the amounts held in such funds. In addition, respondents were asked about money owed to the household as well as other financial assets. Interviewers also asked for a household’s estimated total net wealth as well as for the distribution of this wealth across household members. This estimate was used to assess the plausibility ⁷ of the information provided, i.e. the sum of itemized figures was cross-checked with the total.

Inheritances and gifts

Up to this point, almost all questions (of course, excluding those about the sociodemographic makeup of the household) focused on the household as a whole. In contrast, the questions in the three subsequent sections of the HFCS questionnaire, with a few exceptions, related to individual household members aged 16 years and above rather than to the household as a whole.

Employment

Income

The types of income covered were employee and self-employment income, income from the state pension system and from private and company pension plans as well as income from unemployment benefits. For the first four types of income, respondents could provide either gross or net figures. (In the editing stage (see chapter 4) all net figures were converted to gross income figures using the Austrian Finance Ministry’s gross-to-net calculator.)

Pensioners, who did indicate not to have received pension in 2020 were queried about potential income in 2021. This information could be used to estimate 2020 income.

In addition to these individual questions, the household-level part of the interview with FKPs also established whether a household received income from regular (public or private) social transfers, from the rental or leasing of real estate or from financial assets or private businesses. If respondents were unable to provide the gross income from financial investments, the relevant net income was also accepted. Finally, interviewers asked about other sources of income and about expected income growth.

Pensions

Assessments ¹²

Postinterview

2.5.2 Field phase

2.5.2.1 CAPI implementation (questionnaire programming)

2.5.2.2 CAPI test

2.5.2.3 CAPI problems

• Intra-household asset distribution (ahd1940x, ahd1950x): In households with a single adult and children below the age of 16 were not asked these questions at the beginning of the filed period. This filtering was wrong and corrected after very few affected households. Missing observations for households in the beginning were corrected in the imputation model.
• Summary question for uncollateralized loans ((a)hc1200): This question should have been asked for households with more then three uncollateralized loans of this type. After one household this mistake was corrected. This single missing observation was multiple imputed.
• Interval recording of the value of business participation (hd0801i): After this interval recording also the currency was recoded although pre-defined intervals are only collected as euro values. Only one household was affected before correction and the double information was deleted from the dataset.
• Value of bonds (hd1420): If all the recordings of the type of bond (hd1410x) were “Don’t know”, there was wrongfully no value of the bonds collected. This mistake was correct after one household went this way through the questionnaire. The missing information was collected through the after enquiry over the phone.
• Estimates of gains/losses in income/consumption during the time of the COVID-19 pandemic (ahv0210, ahv0610): For these two questions the wrong list of predefined ranges for euro questions was used. For the affected households the broader interval was used in the multiple imputation model.
• Employment status (ape0200x): In the beginning of the field period persons who should reach this question (based on the apa0100b) did not reach it. The filtering error was corrected and only two households affected. The information in these households was collected through after enquiry by phone.
• NACE classification for retired or other inactive persons (pe0470): Due to a filtering error this variable was not collected and is left empty.

2.6 Special features

2.6.1 Loops

Loops were used for the following items:

• mortgage loans using the household’s main residence as collateral (max. number of iterations: 3),
• real estate holdings other than the household’s main residence (max. number of iterations: 3),
• mortgage loans that used these other properties as collateral (max. number of iterations: 5),
• unsecured loans from family and friends (max. number of iterations: 3),
• other unsecured loans (max. number of iterations: 3),
• investments in self-employment businesses (other than holdings of listed shares) (max. number of iterations: 3),
• life insurance contracts (no max. number of iterations),
• ownership transfers through inheritances or gifts (max. number of iterations: 5).

To make the survey as user-friendly as possible, it was made possible for interviewers to exit a loop at any time. In this case, information was collected in summary questions. Interviewers were instructed not to overuse the option of exiting loops; this feature was only meant to prevent respondents from breaking off the interview early if they were unwilling to answer a given question for more than one item.

The loops for real estate holdings other than the household’s main residence and mortgage loans collateralized with these other properties are structured differently in the ECB template questionnaire. Whereas the Austrian questionnaire used two separate loops to establish this information (as in wave one and wave two), the questions on loans collateralized with further property were nested in the loop about further property in the ECB blueprint questionnaire. Additionally, the HFCS in Austria was designed to record in this section which property was being used as collateral for a particular loan. Thus, all the information required by the ECB questionnaire was contained in the Austrian questionnaire and the variables were adapted and renamed as required for the ECB version after the data imputation phase.

2.6.2 Euro loops

In the first step, respondents were asked to provide specific amounts (“How much…?”) in any given currency. After the information had been recorded, respondents were asked to confirm the amount and the currency. (“You said the amount is … [currency]. Is that correct?”)

If no specific amount was provided, respondents were asked to indicate a range (“Could you provide a range for the amount?”). Rather than having an upper and a lower bound, the range could also be limited at one end and open at the other end (e.g. “no more than EUR ...” or “at least ATS …”). If respondents stated a range, the interviewer continued again by asking respondents to first indicate the currency and then confirm the range and the currency.

If respondents were unable (“Don’t know”) or unwilling (“No answer”) to indicate an individual range themselves, they were asked to choose a range from a list. Depending on the question at hand, interviewers used one of three lists of ranges (see table 1).

Table 1: List of predefined ranges for euro questions  

List A		List B		List C
EUR
A	1 – below 101	A	1 – below 10,001	A	1 – below 1,001
B	101 – below 201	B	10,001 – below 50,001	B	1,001 – below 2,501
C	201 – below 301	C	50,001 – below 75,001	C	2,501 – below 5,001
D	301 – below 401	D	75,001 – below 100,001	D	5,001 – below 7,501
E	401 – below 501	E	100,001 – below 150,001	E	7,501 – below 10,001
F	501 – below 751	F	150,001 – below 200,001	F	10,001 – below 15,001
G	751 – below 1.001	G	200,001 – below 300,001	G	15,001 – below 20,001
H	1,001 – below 1,501	H	300,001 – below 400,001	H	20,001 – below 25,001
I	1,501 – below 2,001	I	400,001 – below 500,001	I	25,001 – below 30,001
J	2,001 – below 3,001	J	500,001 – below 750,001	J	30,001 – below 35,001
K	3,001 – below 5,001	K	750,001 – below 1 mio	K	35,001 – below 40,001
L	5,001 – below 7,501	L	more than 1 million – 3 million	L	40,001 – below 50,001
M	7,501 – below 10,001	M	more than 3 million – 5 million	M	50,001 – below 75,001
N	10,001 – below 25,001	N	more than 5 million – 10 million	N	75,001 – below 100,001
O	25,001 – below 50,001	O	more than 10 million	O	100,001 – below 200,001
P	more than 50,001			P	200,001 – below 300,001
				Q	300,001 – below 500,001
				R	500,001 – 1 million
				S	more than 1 million
Source: HFCS Austria 2021, OeNB.

The three lists of predefined ranges (A to C) are based on the (unweighted) empirical distribution of the answers to numerous questions in the first wave of the HFCS in Austria and were used for survey waves two to four. This evidence showed that, for specific questions, the main part of the distribution called for smaller and hence more specific ranges than the remaining parts of the distribution. List A was used for questions about consumption expenditure and loan repayments. List B was used for questions related to properties and investment in self-employment businesses, and list C was typically used for outstanding loans and incomes. Questions about financial assets were aligned either with list A or list C, depending on the distribution of assets as observed in the first wave of the survey. ¹⁴ The predefined ranges referred to amounts in euro only. Interviewers moved on to the confirmation question as soon as the respondent had chosen a range. The lists of predefined ranges were presented in the form of showcards for all questions involving monetary amounts (see also section 3.5.4).

Only if a respondent also refused to choose from the list of predefined ranges was the status of the question recorded as not answered (“Don’t know” or “No answer”). The information recorded with the ranges was especially important for multiple imputation (see chapter 5).

2.6.3 Recording farming households

• Before the actual interview started, respondents were classified by interviewers as running an “Agricultural business” or as running “No agricultural business.” The classification was straightforward in all but a few cases. But even in the few cases where a respondent was incorrectly classified, the structure of the questionnaire ensured that all essential information was still obtained.
• Specifically, the following extra information was recorded for households classified as farmers:

• Was it possible to separate housing assets (i.e. the household main residence) from business assets? (in the main residence chapter of the questionnaire)
• If not, what percentage of the recorded value did respondents allocate to their main residence? (in the main residence chapter of the questionnaire)
• Does the value recorded for investment in a self-employment business include the main residence recorded? (in the investments into self-employment businesses chapter of the questionnaire)
  • Moreover, for questions relating to the value of their main residence, the yes/no question on properties other than the main residence, as well as for the question about investment in a self-employment business and its value, farming households received detailed guidance as to which components of their household balance sheet were to be recorded under which position.

2.6.4 Other information recorded

2.6.4.1 Contact attempts

The contact attempts were recorded in the dataset, ¹⁶ thus providing additional information. The exact time (year, month, day, hour and minute), mode and outcome of every single contact attempt were documented, as was the total number of contact attempts. Interviewers were instructed to write this information down on paper first and record it in the electronic questionnaire only following the completion of the workload (interview, refusal, etc.) on a particular household.

2.6.4.2 Paradata

The first section covered all information that could be obtained without actually entering a household’s residence or completing an interview: the interviewer’s assessment of the building and construction type, the geographical location (urban or rural area), the condition of the building, the residential area and special security measures.

If an interview took place, interviewers also collected the following additional paradata: the condition of the dwelling’s interior, the interview language (in Austria all interviews were conducted in German), the interviewer’s assessment of the accuracy of the information provided, the village or town in which the interview was conducted, the number of people present during the interview and the interest they showed in the interview, the frequency with which respondents consulted documentation to answer questions, and the type of documentation used. In addition, interviewers had to submit written comments about the interview for every single household. These comments, to be made on five questions covering the interview as a whole, proved very helpful at different stages of the project.

The first section of paradata was recorded in the sample register file, which is not part of the user database due to anonymization requirements. It was used mainly to calculate nonresponse weights. ¹⁷ The second section (excluding interviewer comments) was recorded in variables HR0100 to HR1600 in the household data file, which is part of the HFCS dataset.

2.7 Interviewer documents

• the showcards which were used during interviews to provide respondents with a list of response options for several questions in the questionnaire,
• a glossary, which contained simple definitions of the terminology used in the questionnaire, and
• a copy of the study entitled “Vermögen der privaten Haushalte in Österreich – Gemeinsamkeiten und Unterschiede” (Fessler and Schürz, 2019) to illustrate how the data obtained in the third wave of the HFCS in Austria was used for analysis.

2.8 Participating countries

The survey was prepared by the Household Finance and Consumption Network (HFCN) launched by the ECB. The aim was to achieve ex ante harmonization at as many levels of the survey as possible. In doing so, it was necessary to take features specific to individual countries into account, leading to discrepancies and to additional questions in some cases. In addition to the core output variables, the Austrian survey also collected data that are specific to Austrian households (e.g. information on foreign currency loans). Moreover, the answer options for some questions were categorized in greater detail in the national datasets. A case in point is the question about respondents’ marital status, which came with six answer options in the national dataset but only five answer options in the international dataset. The OeNB is planning to provide the country-specific details as additional information to the datasets that the ECB is expected to publish in summer 2023.

2.9 Online appendix

• the questionnaire,
• the euro loops,
• the paradata questions,
• the variable lists,
• the showcards, and
• the glossary.

3 Interviewers

3.1 The interviewers’ role in the survey process

In the field phase and during the personal interviews, it was possible for interviewers to consult written reference material and, if necessary, receive support from the OeNB.

3.2 General information

In general, the interviewers had specific experience conducting household surveys, having been involved either in past waves of the HFCS in Austria or in surveys of a similar magnitude (e.g. the OeNB Household Survey on Housing Wealth 2008, EU-SILC or SHARE). In fact, almost 90% of the interviewers in the fourth wave of the HFCS in Austria had also conducted interviews at least one of the first three waves. Payment for successfully completed interviews was calculated on the basis of the surveyed household size; a considerably lower remuneration was paid for the collection of paradata when interviews were not completed successfully. Travel expenses were also refunded. To be entitled to a refund of travel expenses for uncompleted interviews, the interviewers were required to have made at least two personal contact attempts and five contact attempts altogether.

3.3 Interviewer training

In total about 15 distinct training session were set. Four of which were repeated short trainings for interviewers who once participated before the COVID-19 delay and once again when the field period started.

3.3.1 Training unit 1

Introduction

Overview of the questionnaire

3.3.2 Training unit 2

Questionnaire – theory

The treatment of other liabilities, private businesses and financial assets, as well as the section on inheritances and gifts were introduced. In particular, participants were walked through the range of financial assets to address possible misunderstandings, and they learned about the fundamentals of the stock and flow data in households’ balance sheets and how to record additional comments.

Lastly, information on household members’ employment status, income and retirement provisions. The training moved towards incomes at the household level and assessment questions. In particular, participants were acquainted with the reference period for income as well as the options for recording income (gross or – if the gross amount was not known – net of tax and social security contributions). The lecturers highlighted the importance of recording comments provided by the respondents.

Questionnaire – practice

3.3.3 Training unit 3

Interviewers‘ tasks, contact specifications and paradata

Guidance on interviewer communication

3.3.4 Training unit 4

Documents and other material

Organizational information

3.4 Contact strategies and specifications

With the advance letters having been sent, interviewers had to make up to five contact attempts with each household. At least two of these contact attempts were to be made personally (by visiting the household’s address in person and trying to establish contact); at least one attempt was to be made at the weekend and another outside normal working hours (9:00 a.m. to 5:00 p.m.). All contact attempts had to be spread out over a period no shorter than three weeks. This approach was necessary in order to rule out distortions as a result of selective participation (e.g. many single-person households cannot be reached during the day and can only be contacted in the evening or at the weekend).

The interviewers were required to document each contact attempt. During at least one of the personal contact attempts, information on the exterior and the location of the property (see also section 2.6.4.2 on paradata) was recorded, even if no successful interview took place with the household in question.

The interviewers were instructed to carry with them all the necessary material (notebook computer, information material, participation incentives, etc.) during each personal contact attempt. This allowed them to react appropriately to different situations, e.g. if a household wanted to participate in the survey immediately, if they requested time to consider or wanted to make an appointment, or if they declined to be interviewed. If requested, interviewers also had to offer interview appointments at the weekend or in the evening as well as the option of meeting respondents outside their main residence (e.g. at the respondent’s office).

3.5 Documents and other supporting material

3.5.1 Letter by the OeNB governor to households

3.5.2 Incentives

3.5.3 Scientific study

3.5.4 Showcards

• Euro amount ranges A
• Euro amount ranges B
• Euro amount ranges C
• Questions for capturing the demographics of household members
• Relation to the reference person
• Housing costs including service charges
• Expenditure for travel and holidays
• Types of income
• Unexpected windfall gain – lottery
• Rent including service charges
• Five value changes
• Reasons for not renting out
• Economic sectors
• Types of life insurance contracts
• Types of mutual funds
• Banks
• Investment behavior
• Type of inheritance/gift
• Percentiles of the distribution
• Employment status I and II
• Employment classifications
• List of possible probabilities
• Health
• Functions
• Mobility I-III
• Taxation I-II

3.5.5 Contact form

Aside from the household’s identification number, the documentation comprised the date, time, type (e.g. personal or by telephone) and outcome (e.g. complete interview or ineligible address) of a contact attempt. Personal identification information (such as name, address or telephone number) was not part of the data and was not forwarded to the OeNB.

3.5.6 Interviewer manual

3.5.7 Glossary

Already at the training stage, the interviewers were instructed to use this glossary to acquire relevant knowledge which they would be able to fall back on during interviews. By virtue of its references to the variables recorded in the survey, the glossary is also of importance when analyzing the collected data, as it explains the technical terms contained in the questionnaire.

3.6 Monitoring

Furthermore, the data from completed household interviews were forwarded to the OeNB promptly, in 19 batches (including answers to queries) during the field phase, to enable OeNB staff experts to monitor interviewer performance in a timely manner (see section 4.4.1). In addition, the following interviewer performance indicators were examined: item nonresponse (both broken down by real assets and financial assets and in aggregate form for the entire interview), the relative duration ³⁰ of an interview, the number of questions asked, the number of households surveyed successfully and unsuccessfully, and the resulting unit nonresponse, as well as the number and quality of interviewers’ comments. The specific comments to be made by the interviewers upon completion of each household interview were also examined.

The OeNB’s goal in this phase was to quickly identify and resolve difficulties with prompt analysis. Monitoring interviewers gave the OeNB a chance to address individual interviewers’ difficulties concerning certain topics or aspects by providing targeted guidance. The OeNB also had the possibility to withdraw with immediate effect interviewers from the survey that did not meet the quality requirements.

Additionally, and due to the COVID-19 related complications a high-level steering committee meeting with participants from the OeNB and the survey agency was held regularly (in general every 2-4 weeks) to address potential problems directly.

3.7 Problems relating to interviewers

One interviewer had to be withdrawn entirely from the survey during the fieldwork due to flaws in conducting the interviews in personal interaction.

3.8 Survey of interviewers

3.9 Online appendix

4 Consistency checks and editing

4.1 Introduction

The raw data collected in surveys do not always contain the information that the questions were intended to elicit. As respondents in the HFCS occasionally either experienced difficulties in understanding the questions asked or had insufficient knowledge on the substance of the survey, they may sometimes have provided inaccurate information. At the same time, data entry errors may have occurred (see also chapter 3), or data may have been processed inaccurately. In the HFCS, great importance was attached to minimizing such errors up front through the structure and wording of the questionnaire; through checks and, if needed, adaptations during the field phase; and ex post through data editing, as will be outlined in this chapter. The COVID-19 pandemic had very little influence on the way editing was conducted in the HFCS.

This chapter provides insights into the consistency analyses and edits performed for the fourth HFCS wave in Austria, starting with information on the number of edits performed (section 4.2) and followed by explanations on the consistency checks conducted during and after the interviews (sections 4.3 and 4.4). Furthermore, we outline the flags used to highlight ex post adjustments of the observations recorded (section 4.5), provide a detailed account of ex post editing (section 4.6) and describe formatting and editing after multiple imputations (section 4.7). The chapter ends with concluding remarks (section 4.8).

4.2 Number and type of edits

The row “Total” indicates the full range of edits that were implemented. Edits that resulted in actual changes to the collected values, i.e. real changes, were limited to some 6,000 observations (see row “Edits based on expert judgment and follow-up phone calls”), which corresponds to a change rate of 0.5% (slight reduction from previous waves). These changes involved primarily inconsistent values that were corrected based on subsequent queries and/or other information or were deleted and replaced through imputation. Just a little under 30% of all amendments (see row “Edits based on other survey information (e.g. verbatim records)”, i.e. a little over 16,000 observations, could be derived from the verbatim records and the use of a flexible questionnaire design for certain questions (e.g. questions about life insurance policies or total annual net income). All in all, around 1.3% of all observations were amended through this type of editing, making them the most prominent form. This indicates how important it is to allow for verbatim records on a large scale. Questionnaires as detailed as the one used for the HFCS in Austria must be user-friendly to ensure the participation of respondents and high-quality standards. Various data – e.g. data on the occupation (ISCO code) of employed individuals – are only collected as verbatim responses to minimize the effort required from respondents. The flexibility for certain questions was achieved by giving respondents more options on how to respond to the question. For some income questions that asked for the gross value, net values could be provided instead if respondents did not know the gross value. The net value was then converted to its gross value during editing.

Table 2: Number and type of edits  

	Total observations2	Number of Edits	Share of edited observations in total observations
			%
Total1	1,215,745	59,177	4.9
Edits based on expert judgement and follow-up phone calls		6,112	0.5
Edits based on other survey information (e.g. verbatim records)		16,155	1.3
Deleted observations		36,910	3.0
Source: HFCS Austria 2021, OeNB
1 The line “Total” contains all edits.
2 Includes only observable information. Filter missings are excluded.

In around 37,000 cases (i.e. around 3% of observations), observations were set to missing (“.”). ³² Edits of this type were made for different reasons, but mostly in the process of data cleanup (see section 4.6.2.1). Further, some items had been entered in a wrong position in the questionnaire. When transferring such information to the right position, the original entry had to be deleted, i.e. set to missing. In some cases, complete sets of entries were set to missing (“.”), among other things because the corresponding head variable had been edited. Below is an example ³³ that demonstrates the editing process resulting from some of the reasons just outlined.

A case in point would be the duplicate recording of income from pensions, first under “Received employee income” and then under “Received income from public pensions.” Here, the head variable “Received employee income” (PG0100) was changed to “No” and the value recorded for this variable was deleted because the respective income figure had been adequately recorded under the pension income variable (PG0300 and PG0310).

4.3 Consistency checks during interviews

However, consistency checks during an interview are subject to limitations in terms of scope. An excessive number of consistency checks during an interview would make it exceedingly long and thus wear out the respondents and in turn decrease the standard of the data collected and/or might even cause respondents to break off an interview.

Moreover, restrictions arise from the fact that all information which should be used for the consistency checks must already be available. These limitations do not apply to simple consistency checks linked to specific predefined benchmarks. Whenever certain limits are exceeded or undercut, pop-up warnings appear that allow the entry to be checked immediately. However, the information necessary for more complex consistency checks often does not become available until answers are received in later stages of the interview.

The digital version of the questionnaire used for the HFCS provided for close to 250 consistency checks, ³⁴ typically in the form of “soft” checks that highlight potential issues but do not prevent the interview from proceeding if the interviewer decides to continue. Whenever a test criterion was violated, a warning message popped up.

For example, if a household with a disposable net monthly income of EUR 1,000 (enough to cover the relevant household’s average consumption) indicated, for instance, that – in addition to consumption expenses totaling EUR 900 – it had typically supported nonhousehold members with EUR 200 per month in the past year, the following message popped up:

“The sum of total consumption expenditure and regular remittances to nonhousehold members exceeds the household’s total net income. Are the figures correct? If yes, please confirm the figure(s), or amend them as necessary.”

Other consistency checks programmed into the digital version of the HFCS questionnaire in Austria would allow the survey to proceed only once an answer identified as incorrect or inconsistent had been amended. However, these so-called “hard” checks were only used in cases where a particular answer could definitely be ruled out.

If individuals stated, for instance, that they had lived in Austria for 40 years but gave their age as 30, the following error message would appear:

“The respondent has been living in Austria for longer than his/her age allows. This is not possible. Please correct the information as necessary.”

4.4 Postinterview consistency checks

4.4.1 Expert data analysis

The datasets for households actually interviewed and those for households that refused to participate were analyzed on a case-by-case basis. This made it possible to assess and optimize the success of interviewers in convincing households to participate. Hence, it was almost impossible for interviewers to cherry-pick “easy” or more readily accessible households, which would probably have created a bias toward certain households (e.g. those where housewives or pensioners live), thus distorting the data. The interviewers knew that the list of addresses was limited to the 6,300 households of the gross sample (see also chapter 6). This ensured that interviewers would not select the less difficult households and then move on to a new set of addresses. The incentive for interviewers to use the strictly limited address material as efficiently as possible was supported with a performance-related payment system and the relatively high effort that was required from interviewers to participate in the survey in the first place. Furthermore, area managers were advised to avoid allocating new households to interviewers before they had made sufficient effort to survey the households they were assigned at the time. The decision to exclude subsequent draws (substitute households) is among the key criteria for a successful survey, and is moreover essential for ensuring the representativeness of the sample (see e.g. Vehovar, 1999).

Initial analysis of the information on individual households during fieldwork covered the data provided on household structure, financial and real assets, debt and income, whether households had come to ownership of property by inheritance or gift, comments made by households or remarks made by interviewers, as well as the date, time and duration of the interviews. This information enabled a quick initial assessment of the interview’s quality. The microdata on every single household were checked for consistency regarding their content and reviewed by at least two analysts from the OeNB HFCS team. Issues requiring clarification were discussed by the whole team, which then decided on the way forward.

In addition, this stage of the process was also used to assess the interviewers (see also chapter 3) and to address errors or misunderstandings. The shortcomings identified in this process were often minor in their nature, but one interviewer whose results were not up to the required standards (e.g. regarding nonresponse) was excluded.

4.4.2 Follow-up queries

4.4.3 Investigation of outliers

4.4.4 Technical review of filtering and consistency

All hard checks were applied repeatedly to the observations, for instance, in order to assess whether respondents might have given answers that precluded moving on to subsequent questions, thus requiring changes. The technical review also covered the questionnaire’s complete set of filters to prevent programming errors leading to extensive and costly follow-up queries. Comprehensive tests of the questionnaire’s programming prior to the start of fieldwork as well as a pilot survey of 50 households made it possible to largely exclude programming errors from the outset. Minor difficulties did occur, such as incorrect filtering implemented for the summary question for uncollateralized loans ((a)hc1200 (see section 2.5.2.3). This deficiency was identified and corrected in a timely manner. ³⁶ These filter checks also ensured that the coding of variables was consistent throughout the questionnaire. ³⁷

4.5 Flags

Table 3: Flags used in the HFCS Austria  

Group I	0	“Not applicable (i.e. skipped due to routing)”
	1	“Recorded as collected, complete observation”
	2	“Recorded as collected, but moved in iteration”
	12	“Recorded as found in other source, not collected in survey”
Group II	1050	“Not imputed, originally: Don’t know”
	1051	“Not imputed, originally: No answer”
	1052	“Not imputed, originally not collected due to missing answer to a higher order question”
	1053	“Not imputed, originally collected from a range”
	1054	“Not imputed, collected value deleted”
	1055	“Not imputed, value not collected due to a CAPI error”
	1056	“Not imputed, set to missing due to incorrect answer to a higher-order question”
	1057	“Not imputed, collected value deleted but range information available”
	1058	“Not imputed, set to missing due to red button”
	1075	“Not imputed, specific answer code”
Group III	2050	“Missing, set to missing for anonymization purposes”
	2051	“Missing, set to missing because data were not collected”
Group IV	3050	“Edited, set to modified value as considered incorrect or unreliable”
	3051	“Edited, adjusted on the basis of other information obtained in the (national) survey”
	3052	“Edited, adjusted on the basis of verbatim records”
	3053	“Edited, set to missing (.)”
	3075	“Edited, set on the basis of follow-up with household”
	3076	“Edited, set on the basis of follow-up with interviewer”
Group V	4050	“Imputed, originally: Don’t know”
	4051	“Imputed, originally: No answer”
	4052	“Imputed, originally not collected due to missing answer to a higher order question”
	4053	“Imputed, originally collected from a range”
	4054	“Imputed, collected value deleted”
	4055	“Imputed, value not collected due to a CAPI error”
	4056	“Imputed, originally value not recorded due to incorrect answer to a higher-order question”
	4057	“Imputed, collected value deleted but range information available”
	4058	“Imputed, set to missing due to red button”
Source: HFCS Austria 2021, OeNB

Group I

Group II

For example, if gross income was unknown, but information on net income was provided, the variable for gross income was flagged “1075.”

Observations with group II flags were not imputed (see chapter 5).

Group III

Group IV

Group V

Chart 3 indicates how questions were typically structured in the HFCS questionnaire. Let us take employee income to give an example for the structure of question blocks ³⁸ and the use of flags.

For example, the head variable for recording employee income serves to ascertain whether or not a household has an income of this kind. If this yes/no question was answered with “Yes” the amount was recorded and the interview continued with the next head variable in the questionnaire – in this case, the question on self-employment income. If a household had no income of this kind, or if the respondent failed to provide the necessary information (i.e. responded with “Don’t know” or “No answer”), the interview continued with the question on self-employment income (the next head variable). Depending on which answers were given, all the observations recorded were initially flagged “1” or “0.” If the response to a subsequent question (e.g. on employment) revealed that a “No” given the question on employee income was in fact incorrect, the initial response was corrected and flagged “3050” (Edited, set to modified value as considered incorrect or unreliable) and the corresponding data entry field for the value was flagged for imputation. Following imputation, the value was then reflagged “4056” (Imputed, originally value not recorded due to incorrect answer to a higher-order question).

Or, if the question on a household member’s highest education qualification (variable (A)PA0200) was answered by selecting the category “Other qualification” and if that answer was subsequently found to match one of the predefined categories, the observation was flagged “3052” (Edited, adjusted on the basis of the verbatim records) in the flag variable of the individual dataset.

This flag system allows the origin of observations in the HFCS to be tracked. In the Austrian questionnaire the section on pension policies was structured differently to reflect national circumstances and to be consistent with previous waves. This section was adapted ex post to fit the requirements for the international data.

To allow for the merging of datasets, no flags were used to encode the variables for identifying households and individuals, nor were the country codes and the imputation’s iteration number flagged. The flags described here provide for a more detailed breakdown by category than those incorporated into the international HFCS dataset that can be obtained from the ECB. For reasons of international consistency, the flags were aggregated prior to being submitted to the ECB (see section 4.7).

4.6 Ex post editing

4.6.1 Case-by-case review

Both interviewers that produced nonstandard results (see chapter 3) and follow-up queries made by the survey company were reviewed in particular detail.

Expert assessments were generally used to resolve the following issues through ex post edits:

• Double entries: Cases where an inheritance, for instance, was recorded under both “Household main residence inherited” and in the “Inheritances received” chapter, or where the same income was recorded in two different income categories, had to be corrected.
• Missing or additional “zeros”: In a few cases interviewers added or left out a zero by accident when recording amounts; this had to be amended accordingly.
• Implausible values: Values that remained implausible after follow-up queries had to be set to missing and were subsequently imputed.
• Often, information could be gained from the many additional comments made by respondents. If the additional comments made it necessary to change the collected information, changes were made respectively.
• Data entry errors by interviewers: For instance, a wrongly recorded date of a contact attempt by an interviewer was easy to correct because of the potential other contact attempts and the immediate data transmission of completed households.
• Also, all data obtained through follow-up queries were used in this step to correct individual observations in the dataset where necessary.

4.6.2 Structural editing

4.6.2.1 Data cleanup

4.6.2.2 Currency conversion

Typically, amounts were given either in euro or in Austrian schillings. In particular, the value of the main residence (both the purchase price and the current value) was often given in Austrian schillings. All Austrian schilling amounts were subsequently converted into euro at the irrevocably fixed conversion rate of EUR 1 = ATS 13.7603. ³⁹ Some amounts were also given in Deutsche mark (DEM). These amounts were also converted at the irrevocably fixed conversion rate, namely EUR 1 = DEM 1.95583.9

In a few cases amounts were also given in Swiss francs and pound sterling. For the amounts outstanding on a loan and the current value of a property the average exchange rate for the month in which the interview took place were used. ⁴⁰ For income in a foreign currency, the average exchange rate for the year 2020 was applied. ⁴¹

There was one instance in which the given value originated in a time where the euro had not yet been introduced and this value given in foreign currency that were not Austrian schillings. This case concerned the the value of an inheritance in 1992 given in US Dollar. For this, the amount was first converted into Austrian schilling on the basis of the exchange rate applicable at the time and then from Austrian schillings into euro according to the fixed ATS/EUR exchange rate.

4.6.2.3 Verbatim records

4.6.2.4 Navigation of loops

• mortgages on the main residence
• real estate assets apart from the main residence
• mortgages secured against these other properties
• unsecured loans from family and friends
• other unsecured loans
• businesses owned by the household
• life insurance policies
• inheritances and gifts

Recording sequence

Every variable within a loop that was replaced with observations recorded for the same variable in another iteration was flagged “2” (see section 4.5). Wherever a variable set to missing in one iteration was replaced with the same variable set to missing in another iteration, it was flagged “0” (Not applicable (filtered out)).

Skipping questions

In the 6 cases in which a household had taken out only one unsecured loan and had skipped questions within a loop, the type of edit depended on whether the respondent had (1) indicated the outstanding amount only in response to the summary question; or (2) both when going through the first loop of questions and in answering the summary question; or (3) neither during the first loop of questions nor in answer to the summary question. If the respondent had indicated the outstanding amount only in answer to the summary question (variant 1), this amount was entered as the answer to the appropriate question (in the first loop) and the entry under the summary question was set to missing. If the respondent had indicated identical amounts in answering both the loop and the summary question (variant 2), the latter was set to missing since it was a duplicate entry. ⁴² Where no amount was given at all, neither within the loop nor in the summary (variant 3), only the summary question was set to missing.

In cases where a household had taken out two unsecured loans and had skipped questions within a loop, ⁴³ the type of edit depended on whether the respondent had (1) specified the value of the highest outstanding loan and indicated an aggregate amount in response to the summary question; or (2) indicated outstanding amounts in response to both question loops and the summary question; or (3) specified an amount only in the answer to the summary question; or (4) given no amounts at all, neither in the answers to the loop item questions nor in the answer to the summary question.

If variant 1 was the case, the amount outstanding for the lower of the two loans was taken to be the difference between the amount given in the answer to the summary question and that given in the first loop. This, however, was only done if the sum total of the two outstanding loans exceeded the amount outstanding from the first loan. If it was lower, it was assumed that the amount given in the answer to the summary question was not the sum total of the two outstanding loans, but rather the amount outstanding for the second loan. In both instances, the summary question was subsequently set to missing. If variant 2 was the case, the amount given in response to the summary question was set to missing. If only the sum total of the two outstanding loans was given (variant 3), it was used as the upper bound for both the first and the second loan for the imputation model. This was the case for one household that held two other unsecured loans and had skipped some loop questions. If no amounts were given at all, neither in response to the loop questions for each of the two outstanding loans, nor in answer to the summary question (variant 4), the summary question was set to missing.

The editing procedure followed in cases with three loans and skipped loop questions prior to the recording of the individual amounts outstanding, was similar to that used for two loans when loop questions were skipped. ⁴⁴

All edits were again flagged correspondingly. The example layed out above covers all potential variants. Not all these variants have to be seen in every wave of the HFCS in AT.

Summary questions

4.6.2.5 Sight account balances and overdrafts

4.6.2.6 Rent variables

In addition, the item “Rent including utilities” was set as the upper bound for the variable “Rent excluding utilities” and used for imputations whenever the answer to the latter was not an amount (i.e. read “Don’t know,” “No answer” or “Rent excluding utilities unknown”) (see also section 5.4.6 on the use of bounds in the imputations).

4.6.2.7 Agricultural businesses

Very few farmers did not report their agricultural business as an investment in self-employment businesses. For these households, data on investments in a self-employment business had to be imputed. The NACE code for such businesses was set to that for “agricultural businesses,” and at least the individual who stated that he/she worked as a farmer was deemed to be employed in this agricultural business. The legal form of the respective business was edited to read “sole proprietorship.” The additional guidance implemented for the second wave was again used in the fourth and continued to keep the number of such cases low.

For all farmers, additional auxiliary variables were created for the combined value of the main residence and the agricultural business (business assets) as well as for the main residence’s share in this amount. For households that were not able to separate their assets and specify the share themselves, information on the total value and on the main residence’s share was used. For households that had specified both the value of their main residence and that of their private business, as required, the combined value and the share of the main residence was calculated. If information was partially missing, it was flagged for imputation (see section 5.3).

The category of agricultural businesses was subjected to case-by-case reviews. Particularly complex cases were clarified through follow-up queries and corrected where necessary.

4.6.2.8 Individual variables for investments in self-employment businesses

To be able to cover even unusually large households, variables were created for up to 18 individuals per household for the CAPI version of the questionnaire. The largest household successfully interviewed in Austria had only 7 members, however, so all variables in excess of that number were deleted from the dataset. Moreover, the coding was changed from yes/no questions for each household member (the type of coding used in Austria) to the list of individual IDs that were required for the internationally available dataset (which only contains six variables for individuals).

At the same time, all NACE codes for household members employed in the business were checked against the information contained in the P-file and corrected where necessary.

4.6.2.9 Life insurance policies

4.6.2.10 Income variables

• employee income (PG0100 and PG0110)
• income from self-employment (PG0200 and PG0210)
• income from public pensions (PG0300 and PG0310)
• income from private and occupational pension plans (PG0400 and PG0410)
• income from unemployment benefits (PG0500 and PG0510)

• income from public social transfers (HG0100 and HG0110)
• income from private transfers (HG0200 and HG0210)
• income from other private transfers (HG0250 and HG0260)
• income from real estate assets (HG0300 and HG0310)
• income from financial investments (HG0400 and HG0410)
• income from investments in self-employment businesses or partnerships (HG0500 and HG0510)
• income from other sources (HG0600 and HG0610)

Where only a net amount was entered for individual incomes, the gross income was calculated with the aid of the Austrian finance ministry’s gross-to-net calculator, ⁴⁵ based on information on the type of income, the structure of the household (with reference to the tax credits for single parents and single earners), the employment status and age of any children, the province and the respondent’s employment status (employed (holding a “blue collar” manual job; or an office, sales or services job) or retired). ⁴⁶ Wherever both parents were gainfully employed, the single earner’s tax credit was assigned to the main earner, i.e. the parent with the higher income (as long as the legal requirements were fulfilled and the partner did not earn more than EUR 6,000 per annum). COVID-19 related family support was also appropriately taken into account.

Given the far greater scope for tax deductions for self-employed people, the gross-to-net conversion of income from self-employment was not generally based on the precise figures. Precise conversions were made only for gross annual incomes below EUR 11,000, which are tax-free, albeit sometimes subject to social insurance costs. For such cases the gross amount was set equal to the net. For all other gross (or net) incomes from self-employment (22 individuals), ⁴⁷ a range was created for the purpose of imputing specific amounts by adding EUR 10,000 to and by subtracting EUR 10,000 from the gross (or net) amount converted subject to the conditions for employees in office, sales or service jobs. This range reflected the uncertainty that such a conversion entails, without losing the important information of the actual range within which the value is placed. This range then was used as a bound in the imputation of a precise value (see section 5.4.3).

To calculate the gross income from financial investments, 25% withholding tax was added to amounts given for net income.

When individuals reported more than one income from more than one type of employment, a slightly more complex method for calculating the gross and net income values had to be applied to account for the complexities of the Austrian tax system for such situations. For instance, individuals could receive their main income from a job with the status of employee and additionally earn some money from self-employed work. Other common cases included pensioners who topped up their public pension with income emanating from self-employment or also employment. When converting gross income to net, all forms of income of an individual are taken into account. Sometimes respondents reported their net income from one source and their gross income for another. The gross and net values were approximated by converting one of the values to its counterpart gross or net value, applying the conversion to the sum of both net or both gross and using this amount to calculate the still missing net or gross value of the income not used in the initial step.

If the net amount was only recorded as a range, the upper and lower bounds were converted into gross values that were subsequently used in the imputations. All converted values were flagged “3051.”

Table 4: Number and share of edits of gross employee income based on flags  

	Number of persons	Share
		%
Number of persons receving employee income	1.782	100
Answer recorded, complete observation (flag 1)	989	55,5
Not imputed, originally: „Don’t know“ (flag 1050)	30	1,7
Not imputed, originally: „No answer“ (flag 1051)	23	1,3
Not imputed, originally: originally not collected due to higher order missing (flag 1052)	3	0,2
Not imputed, originally collected from a range (flag 1053)	52	2,9
Not imputed, collected value deleted (flag 1054)	5	0,3
Not imputed, set to missing due to incorrect answer to higher order question ( flag1056)	10	0,6
Not imputed, collected value deleted, but range information available (flag 1057)	24	1,3
Edited, set to modified value as considered incorrect or unrealiable (flag 3050)	3	0,2
Edited, adjusted on the basis of other information obtained in the (national) survey (flag 3051)	626	35,1
Edited, adjusted on the basis of verbatim records (flag 3052)	1	0,1
Edited, set on the basisi of follow-up with the household (flag 3075)	16	0,9
Source: HFCS Austria 2021, OeNB

Using flags as a basis, table 4 gives an indication of the number of edits relating to employee income. The table also illustrates the use of flag variables (see also section 4.5).

The question on the amount of employee income received (variable PG0110) was put to a total of 1,782 individuals. 989 respondents (55.5%) expressed their annual income in gross terms. A further 30 respondents (1.7%) answered “Don’t know” and 66 individuals (1.3%) opted for “No answer.” 52 respondents (2.9%) specified their income amount using a range. For 24 individuals, a range could be calculated from other information given, i.e. the range was given for net income and converted. The responses of 10 individuals (about 1%) were edited and mostly set to missing and flagged for imputation. 626 of the respondents (35.1%) provided their net income, which was then converted as described earlier. The responses of the remaining 16 individuals (around 0.9%) were corrected on the basis of follow-up queries.

4.6.2.11 ISCO and NACE classification

Also, the main activity of the company (PE0400) where the respondent worked was first recorded verbatim and then assigned a single-digit NACE rev. 2 code. ⁴⁹

4.6.2.12 Highest education qualification

4.6.2.13 Exclusion of successful interviews

4.6.2.14 CAPI errors encountered with the questionnaires

• Few young persons below the age of 16 did not reach the questions on intra-household asset distribution (ahd1940x, ahd1950x), although they should have been asked this information. This CAPI error was corrected by setting the observation missing to be imputed.
• One household, who did not reach the summary question for uncollateralized loans ((a)hc1200), was also corrected in the imputation model.
• The CAPI mistake for fixed intervals on business participation (hd0801i) yielded more information then necessary and thus this information was deleted.
• The wrong fixed interval list for questions of gains / losses in income / consumption during the beginning of the COVID-19 pandemic (ahv0210, ahv0610) was appropriately taken care of in the editing process.
• Finally, the missing information of pe0470 due to a CAPI error was left missing.

4.7 Formatting and editing after multiple imputations

• Marital status: The categories “Married and living together with spouse” and “Married, but separated” were aggregated as “Married.”
• Education: Categories specific to Austria were paired with ISCED ⁵⁰ 2011 codes ⁵¹ and classified as ISCED level 0 (“Early childhood education or no education”) ⁵² ; ISCED level 1 (“Primary education”); ISCED level 2 (“Lower secondary education”); ISCED level 3 (“Upper secondary education”); ISCED level 4 (“Post-secondary nontertiary education”); ISCED level 5 (“Short-cycle tertiary education”); ISCED level 6 (“Bachelor’s or equivalent level”), ISCED level 7 (“Master’s or equivalent level”) and finally ISCED level 8 (“Doctoral or equivalent level”).
• Employment status/relationship: More detailed categories were aggregated.
• Main residence – tenure status: More detailed categories were aggregated.
• Reasons for refinancing: With regard to collateralized loans, “For the conversion of a foreign currency loan” was available in Austria as an additional category for this variable. This category was added to “Other” in the international dataset.
• Loan repayments: The installments for repaying (secured and unsecured) bullet loans were set to “0” as such loans are repaid with a single lump sum upon maturity. Assets accumulated for repayment can be analyzed on the basis of variables that are specific to Austria.
• Use of additional real estate property: In this variable, “Buy-to-let apartment” was available as an additional category in Austria; in the international core dataset, this category was added to “Other.”
• For the international version the question on loans secured with further property was structured differently than in the previous waves in that loans secured by real estate were asked together with different characteristics of the property instead of in a separate loop as before. The Austrian questionnaire kept the previous structure of asking about further properties and any loans secured with these in two separate loops. To bring the data into the structure required by the international dataset, a question on which property was used to secure a particular loan was used to link the two loops.
• Due to the high complexity of bringing the flags of the national data into the structure required by the international dataset, the flags in the international dataset corresponding to the variables concerning loans secured by other real estate next to the household’s main residence were simplified by using the flag value “0” if the national value was missing and “13” otherwise.
• Purpose of private and noncollateralized loans: The categories “To finance a deposit for the housing association” and “To support friends and family” were allocated to “Other.”
• Rejection of a loan application: Information of this type was recorded in three variables with multiple responses; it was then aggregated into two variables.
• Business’ legal form: More detailed categories were aggregated.
• Money in savings plans with building and loan associations and life insurance policies: Data recorded on these two investment methods are aggregated into savings (HD1200 and HD1210).
• Type of assets received (survey questions on inheritances and gifts, HH030$a-i): The sequence based on values was abandoned.
• Provider of assets (survey questions on inheritances and gifts): More detailed categories were aggregated.
• Relative change of income due to the COVID-19 pandemic: This question was not asked but calculated based on net income and the absolute change of income at the household level due to the COVID-19 pandemic.
• Purpose of saving: The sequence ordered by relevance was abandoned.
• Relative change of consumption due to the COVID-19 pandemic: This question was not asked but calculated based on the absolute change of consumption at the household level due to the COVID-19 pandemic.
• Type of dwelling (paradata): More detailed categories were aggregated.

• flag “1057” was recoded as “0”
• flag “1058” was recoded as “1050”
• flags “3075”, “3076”, “1075” and 2 were recoded as “1”
• flags “1055” and “1056” were recoded to “1050”
• flags “4055” and “4056” were recoded to “4052”
• flag “3051” was recoded as “1”
• flag “3052” was recoded as “1”
• flag “3053” was recoded as “0”
• flag “4057” was recoded as “4053”
• flag “4058” was recoded as “4050”

• The variable featuring rent excluding utilities (HB2300) was flagged “1075” to identify the special cases when only the rent including utilities was known to respondents. These costs were subsequently imputed, with the rent including utilities serving as bounds, and reflagged “4053.”

4.8 Concluding remarks and online appendix

Knowledge of the steps undertaken to check the consistency of the data is essential both for analyzing the data and for understanding how the observations originated. In addition, the use of flags makes it possible for users to develop an imputation model of their own, to dispense with imputations, or to resolve the problem of item nonresponse in another way.

The online appendix which contains the information provided here on the edits and consistency checks applied in the HFCS in Austria includes a list of the consistency checks programmed into the digital version of the questionnaire. ⁵³

5 Multiple imputations

5.1 Introduction

Disregarding the problem of missing information due to item nonresponse would lead to biased estimates. For the HFCS data, we therefore used multiple imputation with chained equations.

The idea behind this approach is to substitute missing values in the dataset with several values that have been estimated based on an iterative Bayesian model. The main aim of this procedure is to impute in such a way that the associations between all variables are preserved in terms of maintaining the correlation structure of the dataset. Under this approach, the missing values of each variable are estimated by taking into account a maximum number of available variables. To account for the uncertainty of the missing values, not just one value per missing value is imputed, but several (in the case of the HFCS, five).

This data imputation approach is also used by similar surveys, such as the U.S. Survey of Consumer Finances (SCF – see Kennickell, 1998) and the Spanish Survey of Household Finances (EFF – see Barceló, 2006).

As multiple imputation is a very time-consuming process, most institutions that carry out surveys, including the HFCS, provide users with datasets, which are already imputed. This ensures that all users can work with the same imputed datasets. In the case of the HFCS, users can identify every imputed value of any variable by looking at the corresponding flag variable (section 4.5). Thus, they have the possibility to carry out nonresponse analyses or imputations on their own, or to use other methods for dealing with item nonresponse in their analyses.

This chapter is structured as follows: In section 5.2, we present data on item nonresponse in the HFCS. Section 5.3 describes the imputation procedure used, and in section 5.4 we explain the imputation model specification and how the imputations were executed. Finally, some imputation results are presented in section 5.5.

5.2 Item nonresponse

There are different ways of analyzing datasets that include variables with missing values. ⁵⁵ In most statistical packages, the default method is the complete-case analysis method. This method entails deleting all households that have missing values in any of the variables of interest and basing the analyses solely on complete observations. However, the loss of information resulting from this method leads to two problems: First, it biases estimates if complete observations differ systematically from incomplete ones; second, even if an estimate is unbiased, the estimation would be less precise due to the observations lost. To illustrate how significant the loss of information would be in the case of the HFCS, table 6 shows item nonresponse rates across some selected variables.

Table 5: Item nonresponse per household in wave 4 (unweighted)  

	Mean	Median	Minimum	Maximum
Number of variables asked
all variables	1,917.8	1,947.0	1,382	2,241
euro variables	117.6	122.0	42	166
Number of variables with missing values
all variables	17.3	8.0	0	370
euro variables	4.6	2.0	0	57
Share of variables with missing values in %
all variables	0.9	0.4	0	19
euro variables	3.9	1.6	0	47.6
Source: HFCS Austria 2021, OeNB.
Note: Interval responses are considered as missing values with regard to the corresponding euro variable and are not included as a separate variable. A question addressed to several household members is entered as several variables, one for each household member.

For example, when asked about the value of their main residence, 84.3% of households provided a specific amount (column 3 of table 6). The other 15.7% of households are item nonrespondents: Either they provided a (prespecified or individual) range (11.4%, column 4), responded with “Don’t know” or “No answer” (4%, column 5) or their response was set to missing ⁵⁶ (0.4%, column 6) in the editing process. Nonresponse rates ⁵⁷ vary substantially across items. Variables with high nonresponse rates include e.g. questions related to the monthly amount paid as rent excluding utilities (100% – 65.3 % = 34.7%) and the household’s gross income from financial investments (100% – 57.2% = 42.8%). However, 34.5% and 32.1% of households provided at least a range for these two questions, which confirms the importance of asking range questions when a euro question remained unanswered. Range questions provide valuable and often very precise information (see the online appendix and section 2.6.2 for the questionnaire and information on the design of euro loops). Variables with low nonresponse rates include variables, such as the amount spent on food consumed at home (100% – 91.8% = 8.2%).

Table 6: Item nonresponse for selected variables (unweighted)  

	Household has item		Responses by households that have the item
	Yes(1)	Unknown(2)	Amount(3)	Range(4)	“Don’t know”/ “No answer” (5)	Other missing values1 (6)
	%
Value of main residence2	43.0	0.0	84.3	11.4	4.0	0.4
HMR mortgage 1: amount still owed	11.1	0.2	85.5	5.5	9.0	0.0
Monthly amount paid as rent excluding utilities	49.5	0.0	65.3	34.5	0.2	0.0
Other property 1: current value	9.8	0.2	78.2	15.6	4.9	1.3
Other property mortgage 1: amount still owed	1.2	0.0	88.9	3.7	7.4	0.0
Value of sight accounts	99.7	0.0	83.2	8.6	8.0	0.1
Value of saving accounts	98.7	1.3	81.8	6.0	7.5	4.7
Value of publicly traded shares	4.9	0.3	68.8	14.3	15.2	1.8
Amount owed to household	6.0	0.1	87.0	3.6	9.4	0.0
Employment status (main activity) (person 1)	100.0	0.0	100.0	0.0	0.0	0.0
Gross employee income (person 1)	41.9	0.0	93.5	2.2	3.4	0.8
Gross income from unemployment benefits (person 1)	4.8	0.0	85.3	8.3	6.4	0.0
Gross income from financial investments	61.4	5.2	57.2	32.1	10.2	0.6
Gift/inheritance 1: value	37.1	1.2	81.5	8.4	10.0	0.1
Amount spent on food at home	100.0	0.0	91.8	8.1	0.1	0.0
Source: HFCS Austria 2021, OeNB.
1 Missing values due to editing measures and exits from loops.
2 Based on the HB0900 variable.
Note: HMR = household main residence.

Table 6 (column 2) also shows another aspect of item nonresponse in the HFCS: There are variables known as branch variables (see chart 3 in chapter 4) that may also have missing values due to nonresponses to a higher-order question (head variable) and that are thus set to missing. For example, before the euro question on gross income from financial investments is asked, households are asked a yes/no question determining whether they have this type of income or not. Only those that answer affirmatively (61.4%) are then asked the question on the amount of income; the other households, including the 5.2% of households that did not answer the yes/no question, automatically skip the euro question. As it is unknown, however, whether the 5.2% of households that did not answer the yes/no question have a positive gross income from financial investments or not, their nonresponses must also be considered as second-order (or higher-order) missing values when analyzing nonresponse to a euro question.

Table 7: Logit regression of nonresponse in the euro question on value of sight accounts (unweighted)  

Covariates	Coefficient
Female (person 1)	0.0298
	(0.116)
Age (person 1)	0.00832*
	(0.00504)
Tertiary education level (person 1)	–0.0465
	(0.144)
Employed/self-employed (person 1)	–0.0997
	(0.167)
Residence is in Vienna	0.389***
	(0.136)
Size of main residence	0.00423***
	(0.00107)
Household size	0.0841
	(0.0619)
Constant	–2.780***
	(0.399)
Observations1	2,261
Source: HFCS Austria 2021, OeNB.
1 The remaining 32 observations of the dataset show missing values in one of the covariates and/or filter missing remarks in the dependent variable and are thus not included in the regression.
Note: Standard erros in parentheses; *** p<0.01, ** p<0.05, * p<0.1.

Thus, if a complete-case analysis were to be carried out with the HFCS data, the loss of information and the resulting loss in precision of unbiased estimates would be considerable owing to the large amount of variables with higher-order missing values. Furthermore, as complete observations usually differ systematically from incomplete ones, complete-case analysis would bias the estimates.

For illustrative purposes, table 7 shows a regression of nonresponse to the question regarding the balance of sight accounts (“1” if the value is missing, “0” otherwise) for several explanatory variables. We can see that item respondents differ significantly from item nonrespondents, because respondents tend to be younger, tend to live in smaller main residences and smaller households and tend not to live in Vienna. Thus, a complete-case analysis of the value of sight accounts would bias the estimates toward a population with these household characteristics.

5.3 HFCS imputation procedure

• Step 1: Select the P variables Y1 ,Y2 ,…,YP to be imputed.
• Step 2: Fill the missing values of Y1 ,Y2 ,…,YP with random selected values which were actually observed.
• Step 3: For each Y1 ,Y2 ,…,YP

• run a Bayesian regression of the variable to be imputed on a broad set of independent variables, which is chosen from among the HFCS variables without missing values and the variables selected in step 1 (except the one being regressed); the regression sample is restricted to those observations that are not missing in the dependent variable;
• randomly draw a vector of regression parameters from their posterior distribution;
• calculate the corresponding predicted values and use them as the imputed values;
• replace the missing values of the imputed variable with its imputed values.
  • Step 4: Repeat step 3 t times. Each time, replace previous imputed values with updated ones obtained from the latest regression. This creates the first imputation sample (or implicate).
  • Step 5: Repeat steps 3 and 4 M times independently to obtain M imputation samples.

Furthermore, the procedure is multivariate in the sense that the estimation of the missing values is repeated (t times); variables that are being conditioned in each regression are replaced by the observed values or those currently being imputed (step 4). It is important that each regression model also contains a broad set of independent variables with missing values in order to preserve the joint distribution of variables with missing values. If t tends to infinity, the imputations of missing values of Y1 ,Y2 , …, YP in each cycle are expected to converge to an approximation of a draw from their joint posterior predictive distribution.

In the final step (step 5), the procedure provides multiple imputations of each missing value by repeating steps 3 and 4 M times independently. This is done to take into account the uncertainty of the imputed values when estimating any variances with imputed variables with missing values. The M imputations of the missing values of Y1 ,Y2 , …, YP are expected to converge to an approximation of M draws from the joint posterior predictive distribution of the missing values.

Although it is theoretically possible that the sequence of draws based on the regressions above might not converge to a stationary predictive distribution, simulation studies provide evidence that the approach yields estimates that are unbiased (Van Buuren et al., 2006). Furthermore, separate regressions for each variable reflect the data better, given that the HFCS data contain a large number of variables, many of which have bounds, skip patterns, bracketed (i.e. range) responses, interactions or constraints in relation to other variables. This approach thus makes more sense than specifying a joint distribution for all variables together, as is the case for example in the joint modeling approach. ⁶⁰

It should be noted that the HFCS imputation procedure is based on the assumption that the nonresponse probabilities of variables with missing values are only dependent on observed information – never on unobserved information such as the variables with missing values themselves. In the literature this assumption is referred to as ignorability assumption.

Before running through the five steps above, we need to prepare the data and specify all the parameters of our imputation model: e.g. the selection of variables to be imputed, the imputation order, the regression model for each variable, the number of cycles t, the number of imputation samples M, etc. The next section describes how this was done.

5.4 Creating the imputations

5.4.1 Choosing the variables to be imputed

The imputation of as many variables as possible is intended to minimize the number of cases in which users are forced to conduct a complete-case analysis with HFCS data because the variables they are interested in have not been imputed. Another important reason for adopting this strategy is that we do not want to bias the correlation structure of the data with our imputations. If we were to reject many variables for imputation, we could not use them in the regression models as independent variables with missing values either, and we would thus bias the associations between the unimputed variables with missing values and the imputed ones.

5.4.2 Imputation order

5.4.3 Types of regression models

5.4.4 Use of weights in regressions

5.4.5 Variable transformations

One important transformation of continuous variables involves using the natural logarithm. These types of variables usually have a highly skewed distribution; using the logarithm helps to bring the distribution closer to assumption of normality that is necessary for the forecast. Another very helpful transformation for year variables is to impute time periods rather than years. For example, instead of imputing the purchase year of a house, we impute the time elapsed since the house was purchased. In such cases, the logarithmic transformation mentioned above is carried out on the durations and not on the years.

Another transformation used for some variables with values between “0” and “1” is the log-odds transformation (log(y/(1–y))), for example for the amount of an outstanding consumer loan (HC0801 to HC0803). Instead of imputing these variables individually, the original amount of the consumer loan (HC0601 to HC0603) is imputed as a first step. Additionally, an indicator showing whether the amount outstanding is smaller than the original amount of the loan, is imputed, and if so, the outstanding amount is imputed as a percentage of the original amount. This share is imputed as a log-odds transformation, considerably improving the quality of the imputed values. Subsequently, the individual variables (HC0801 to HC0803) are calculated from the original loan amounts and shares.

For categorical variables, two types of transformations may be used. First, some of the nominal variables can be transformed into ordinal variables by reordering categories. This improves the stability of the imputation model, as fewer parameters need to be estimated for ordinal regression models than for multinomial regression models. Second, multiple response variables are transformed into several binary variables by generating one binary variable for each response category (“1” if the category applies, “0” otherwise). This makes it possible to impute more than one response category for the same question per imputation sample.

A transformation that is done for both continuous variables with missing values and categorical variables with missing values involves splitting the original variable into head and branch variables; this is done when there is a certain heterogeneity in the original variable. For example, some loan-length variables have the value “–4,” indicating that “The loan has no set length.” When imputing such a loan-length variable, it would not make sense to run the regression over these observations together with those variables that do provide a loan-length value. In such cases, the variables are split into two: (1) a binary head variable indicating whether the loan has a set term or not (imputed with a logit regression model), and (2) a continuous branch variable indicating the loan length if the loan has a set term (imputed with range regression).

A further transformation, which is carried out both for continuous and categorical variables with missing values, is that of individual IDs. ⁶⁴ Individual variables are modeled and imputed separately for each ID in order to avoid biased imputations (section 5.4.8); this should ensure that household members with the same IDs display relatively homogenous characteristics if they are modeled together. For this reason, respondents are grouped into new individual ID categories created specifically for the imputations prior to imputation. The criteria for this categorization are as follows: All male financially knowledgeable persons (FKPs), all male partners of FKPs that were individual 2 and all other FKPs are classified as individual 1 (ID = 1). All female partners of FKPs that were individual 2 and all women that were individual 1 before their male partners became individual 1 are classified as individual 2 (ID = 2). All other people are ordered and numbered by age in descending order and are numbered starting with ID = 3.

In the case of households with members that engage in farming, we use a special transformation of the variables for the value of the household’s business(es) (HD0801 to HD0803) and the variable for the value of the household’s main residence (HB0900). Instead of imputing these variables individually, we first impute the sum of these variables and, additionally, the percentage of this sum that is attributable to the farm. Then we calculate the individual variables (HD0801 to HD0803 and HB0900) based on the sum and percentages imputed. The reason for using this transformation is that it considerably improves the imputed values, as some households with members that engage in farming did not state separate values for their main residence and their agricultural business but indicated only the combined value (see section 4.6.2.7 for further details).

5.4.6 Bounds

General bounds, which are the same for all households and individuals, are used to avoid imputing values that are not defined or are very unrealistic. Examples of this type of bound include nonnegativity constraints on continuous or count variables (e.g. income or age). For all households the lower bound for these variables is zero. For some continuous variables, we assume that a value above or below a particular general bound cannot occur in practice. As a case in point, the lower bound for the year a loan was taken out (HB1301 to HB1303) is 1945. We assume that no loan that is still outstanding in Austria was taken out, renegotiated or refinanced more than 77 years ago. The use of such empirical bounds helps avoid imputing extreme outliers of these variables without providing biased results. More examples of general bounds include percentage variables (e.g. share of homeownership), where we set the lower bound to zero and the upper bound to 100, or some year variables (e.g. the purchase or inheritance year of the household’s main residence), where the upper bound is 2022, i.e. the year in which the last survey interviews were carried out.

Unlike general bounds, individual bounds take different values depending on each household or individual; they usually ensure consistency with other variables from the same household. Most of the HFCS bounds fall into this category. For example, when imputing the amount spent on food eaten at home, we set the total consumption expenditure estimated by the household as the upper bound. Inversely, when imputing the total estimated consumption expenditure, we set the sum of the amounts spent on food and drink consumed at home and outside of the home as the lower bound. Individual bounds are also used when a household provides a range (either prespecified or individual) in a euro question instead of a specific value. Such ranges are requested if respondents do not provide specific amounts in response to euro questions; they prove very useful for imputation purposes, as they yield valuable and precise information on the missing value from a euro question (see also section 5.2 in connection with table 6).

Individual bounds in the HFCS are, for example, also used when imputing rents (e.g. rent including utilities is used as an upper bound for rent excluding utilities and vice versa), or when imputing several count variables (e.g. the birth year of the oldest household member is used as a lower bound for the year of acquisition of the main residence). If an observation has more than one lower and/or upper bound (e.g. general and individual bounds), we take the lower and/or upper bound that is the most restrictive.

5.4.7 Selecting predictors

Thus, we choose as many predictors as possible (broad conditioning approach). In a large dataset, such as that of the HFCS containing several hundred variables, it is, however, not feasible to include all variables, as this may lead to both multicollinearity problems and computational problems. In line with Van Buuren et al. (1999) and Barceló (2006), we have therefore adopted the following strategy for selecting predictor variables:

1. Include the variables that are determinants of nonresponse. These are necessary to satisfy the ignorability assumption on which our imputation model relies (see section 5.3). Variables included as typical determinants of nonresponse in the HFCS imputation model are, for example, variables that describe the household (e.g. estimated household income, household size, number of children) and household members (e.g. age, education, sex and employment status of the household’s first individual and his/her partner) as well as stratification variables (e.g. province, municipality size) and information provided by the interviewers (e.g. standard of living, type of neighborhood, building condition, interview atmosphere, etc.). The latter pieces of information (paradata) were extremely important for the imputations, since they provided plausible explanations for item nonresponse for many variables.
2. In addition, include variables that are well suited to predicting and explaining the relevant variable to be imputed. This is the classic criterion for using predictors, and it helps to reduce the statistical uncertainty surrounding the imputations. These predictors are identified by their correlation with the variable to be imputed. For example, when imputing loan variables, we typically use the original loan amount (as mentioned above), the repaid loan amount or principal outstanding as predictors because, in most regressions, these variables can explain a considerable amount of variance. When imputing the market value of various types of real estate property, we usually include the purchase value and the length of time (in years) for which the household has already owned the respective property. When imputing loan variables, we typically (as described above) use the original amount, the loan repayment amount or the loan amount outstanding. Usually, these variables are connected logically (e.g. the outstanding principal is the original loan amount minus the sum of all loan repayments). However, in the course of imputation, it is not possible to preserve all of these logical connections, in particular if several of these variables are being imputed simultaneously.
3. Remove the aforementioned predictor variables that have too many missing values in the subsample of missing observations of the variable to be imputed and substitute them with more complete predictors of these predictors. As a rule of thumb, predictors where the percentage of observed cases within this subsample is below 50% are removed and replaced by more complete predictors. This criterion helps to make the imputations more robust. Typical predictors of predictors include essential household characteristics, such as household size, the number of children, region, age, as well as the employment and marital status of the first individual.
4. Include all variables that appear in the models that will be applied to the data after imputation. In other words, consider which economic theories might be tested based on the data and include those variables as predictors that are expected, according to these theories, to influence or explain the variable to be imputed. Failure to do so will tend to bias the results of potential data users when testing the hypothesis of one particular model. For example, the HFCS data provide detailed information on different components of households’ wealth, e.g. real assets or financial assets. This information is used for the analysis of wealth effects on consumption. Therefore, we use these variables both for the imputation of consumption expenditure and for the imputation of wealth variables.

We also include the final survey weights in all regression models (see the discussion in section 5.4.4) and an interaction term, as well as a main effect dummy for each of the abovementioned predictor variables that were only asked from a subsample of the households asked about the variable to be imputed. For example, suppose that we want to impute a household’s consumption expenditure using the mortgage amount as one of our predictors. While every household in the sample was asked about consumption expenditure, not all of them were asked about mortgage amounts. If, for those households that do not have a mortgage, we just set the mortgage amount to zero (corresponds to an interaction term), the estimates would be biased, because the information on whether a household has a mortgage or not would be omitted. This information should thus be additionally included as a main effect dummy in the regression model. But again, not all households were asked whether they have a mortgage, just homeowners. Thus, we should also include a homeowner dummy in the regression.

Finally, the number of predictors is restricted by the size of the subsample for which the regression is estimated. In cases where the subsample size is smaller than the number of predictors selected according to the above strategy, we use the Akaike information criterion to choose the subset of predictors which best fits the data, ensuring that, if possible, each of the above four predictor categories is represented in each regression equation. Typically, the number of predictors used for each regression model is around 20% of the number of observations for the variable to be imputed. More details on the specification of subsamples can be found in the next section.

5.4.8 Specification of subsamples

A further example is the imputation of individual variables. These are also only regressed over the subsample of people that share the same ID. To ensure the homogeneity of people with the same IDs, respondents are grouped into new ID categories created specifically for the imputation (see section 5.4.5), and which then form the mentioned subsamples. When imputing question by question, as we do, the bias will be very small, though at the cost of precision because, consequently, the subsample sizes are often small.

5.4.9 Number of cycles

Typically, we check convergence graphically by plotting the mean of the imputed values against the iteration number t. Convergence is judged to have occurred as soon as the pattern of the imputed means becomes random and a definite trend can no longer be observed.

In the fourth wave of the HFCS, we additionally examined the convergence of selected variables using the Gelman-Rubin convergence diagnostic, which is used very frequently in literature (for more details, see e.g. Cowles and Carlin, 1996). According to this diagnostic, convergence of a variable is reached when the variance of an estimate of this variable (e.g. the mean, median or other percentiles) is relatively small within chains of multiple imputation samples compared to the variance of the same estimate between cycles. ⁶⁶ All variables examined in the fourth wave of the HFCS meet this criterion. ⁶⁷

Of course, such tests (just like any other diagnostic test to assess chain convergence) can never confirm the existence of convergence (see section 5.3). But they are useful for pointing out weaknesses of the imputation model or other unusual results that could indicate nonconvergence.

5.4.10 Number of imputation samples

5.5 Selected results

For several variables the means are, on average, higher after imputation than before imputation. If imputations are close to the true values, the result suggests that households that do not respond to the relevant variables tend to be households with higher (unobserved) amounts in these variables. For example, the mean value of the first gift/inheritance (without main residence) is EUR 97,930 before imputation. After the respective imputations, it increases to EUR 120,590 in m = 1, EUR 109,209 in m = 2, EUR 118,374 in m = 3, EUR 117,520 in m = 4, and EUR 120,175 in m = 5. Beware the ECB changed the recording of inherited HMR into the loop. Thus, on average the imputations increase the mean value of the first gift/inheritance from EUR 97,930 to EUR 117,174, i.e. by 20%. Additionally, about 40% of the values imputed in this context are based on range responses by households, which suggests that households with more valuable inheritances tend to respond to this question less often than households with less valuable inheritances. Further large increases in comparison to the unimputed sample occur when imputing mortgage loans. Households’ range responses again play an important part here, as they provide valuable and often very precise information for the imputations (see also table 6).

However, for other variables, the mean does not change significantly, or even decreases in some cases. For example, the mean amount spent on food eaten at home does not change significantly after imputation, due to the low item nonresponse rate of this variable (see table 6). The mean gross income from financial investments is even lower after imputation than before imputation, which suggests households receiving a substantial amount of income in this category are more likely to know it.

Finally, table 8 also shows that the uncertainty of imputations can vary a lot depending on the variables. For some variables (e.g. other property mortgage 1), the means show a relatively high variance among the five multiple imputation samples, signaling the uncertainty of the imputed values due to the lower number of observations for these variables. For other variables (e.g. gross income from unemployment benefits or the monthly amount paid as rent) the mean values show a relatively low variance among the five multiple imputation samples, which in turn signals a higher precision of the imputed values. Had we conducted a single imputation of the variables – with only one imputation sample – instead of multiple imputations, the variance of the estimates would be too low, since the uncertainty behind the imputed values would be disregarded, and they would thus be treated like true values. The variance among the five multiple imputation samples is within the range of the previous waves.

Table 8: Means for selected variables before and after multiple imputation (weighted)  

	Mean beforeimputation	Multiple imputation sample means
	m=0	m=1	m=2	m=3	m=4	m=5
	EUR
Value of main residence1	380,145	384,192	382,733	383,990	383,539	386,126
HMR mortgage 1: amount still owed	88,621	85,730	87,748	89,044	88,749	88,290
Monthly amount paid as rent	439	415	411	408	413	412
Other property 1: current value	317,025	308,453	317,038	335,956	355,280	363,385
Other property mortgage 1: amount still owed	113,494	110,837	114,354	118,980	106,954	104,792
Value of sight accounts	4,967	5,366	5,114	5,055	5,162	5,050
Value of saving accounts	27,320	32,145	31,133	31,834	30,514	31,928
Value of publicly traded shares	31,485	77,291	28,197	30,489	33,184	32,386
Gross employee income (person 1)	35,033	35,208	35,214	35,088	35,040	35,226
Gross income from unemployment benefits (person 1)	8,410	8,411	8,381	8,076	8,287	8,326
Gross income from financial investments	443	324	321	324	338	328
Gift/inheritance 1: value	97,930	120,590	109,209	118,374	117,520	120,175
Amount spent on food at home	413	416	417	416	416	417
Source: HFCS Austria 2021, OeNB.
1 Based on the HB0900 variable.
Note: All means are estimated over the observations “Household has item = yes.” The number of these observations may vary across the different imputation samples m if we imputewhether households have the relevant item or not. HMR = household main residence.

5.6 Concluding remarks

6 Sampling

6.1 Introduction

The survey sample was initially drawn before the outbreak of COVID-19 in Austria. After the delay of the field period, we kept the initial sample. As the time between sample design including the data in the background and contacting the household was extraordinary long, we expected some more neutral dropouts as people move away or die. Furthermore, the gross sample included 11 addresses that declined to be contacted during the delay of the field period. They were classified as “inaccessible” and treated as addresses with an unknown eligibility status.

This chapter describes the sampling procedure for the HFCS in Austria and is structured as follows: First, we define the target population (section 6.2) and provide a short overview of the sampling design in box 4. This part is followed by a description of the required external data on geography and population (section 6.3). Next, we detail the stratification process (section 6.4) and the two stages of drawing the survey’s sample population (section 6.5), which form the main part of the sampling procedure. Section 6.6 completes the chapter with some concluding remarks.

6.2 Target population and sampling frame

“a person living alone or a group of people who live together in the same private dwelling and share expenditures, including the joint provision of the essentials of living. Employees of other residents (i.e. live-in domestic servants, au-pairs, etc.) and roommates without other family or partnership attachments to household members (e.g. resident boarders, lodgers, tenants, visitors, etc.) are considered separate households.” ⁶⁸

More specifically, according to the ECB’s definition the following persons are to be regarded as household members if they share household expenses (which includes both benefiting from and contributing to their coverage):

1. persons usually resident, related to other members
2. persons usually resident, not related to other members
3. persons usually resident, but temporarily absent from dwelling (for reasons of holiday travel, work, education or similar)
4. children of household being educated away from home
5. persons absent for long periods, but having household ties: persons working away from home
6. persons temporarily absent but having household ties: persons in hospital, nursing home, boarding school or other institution.

• homes for elderly people,
• military compounds,
• monasteries,
• prisons, and
• boarding schools.

In order to draw a sample from this target population, we would need a complete list of households in Austria. As such a list does not exist, we use a complete list of postal addresses in Austria as our sampling frame. These external data, explained in more detail below, provide the best possible sampling frame in the sense that (almost) all households in Austria appear in the data (and appear only once) and that the data are highly up to date.

6.3 Background – the (external) datasets used

For the HFCS in Austria, we relied on two different sources: We used data from Statistics Austria for the purpose of stratification and for selecting a random sample of PSUs (primary sampling units; in Austria those are the enumeration districts) and we used post office data to draw the households, the actual SSUs (secondary sampling units), at random. The advantage of the post office data is that they are up to date and that the data fit the HFCS definition of households.

6.3.1 Statistics Austria

In addition, we relied on the municipality directory of 2019 (to categorize by municipality size). Unlike in the early waves, we did not use population data from Statistics Austria’s register-based census from 2011 to determine the number of sampling units to be drawn within each stratum. Instead, we relied on post office data, which also contained the population (households) of each stratum (see section 6.3.2 below).

6.3.2 Austrian Post Office

Thus, our starting point was some 4.39 million private mail addresses. Very few remaining commercial addresses and ineligible addresses had to be removed after the first contact by the interviewer (e.g. if the interviewer arriving at a given address reported the address as incorrect or found it to house a commercial building) and were given weights of zero, since they do not belong to the target population (see chapters 4 and 7). Moreover, households at secondary residences whose main residence address was identifiable as such were also excluded from the dataset of the sampling frame or given a weight of zero to ensure that every household was included only once in the list of post office-certified address codes. After removing these commercial addresses and ineligible addresses, the total of all weights comes to roughly 4.07 million, which means that Austria has an estimated 4.1 million households.

The post office data we used do not reflect whether a given address is a household’s main residence or not or whether the residence at that address is registered in the Austrian residence registry (Zentrales Melderegister). Yet they provide a realistic picture of households and thus meet the HFCS requirement of reflecting actual living situations. Unlike other data sources (e.g. EU SILC), the post office data cover households at addresses that are registered as a secondary home or that are not registered at all, but fulfill the HFCS definition of a household. They have thus been included in the sampling frame because they have a post-certified address code. ⁷⁴

6.3.3 Profile.Address and IFES

This information was needed in the contact phase when households received individualized letters of invitation to participate in the survey. ⁷⁵

6.4 Stratification and sample size

6.4.1 Stratification

With the exception of the capital city Vienna, each NUTS-3 region was divided further into the following eight municipality population size categories. The data from Data.Door from the Austrian Post contain information that serves to map each address to a province, municipality and enumeration unit as they are defined by Statistics Austria.

• Up to 2,000 inhabitants
• 2,001 – 3,000 inhabitants
• 3,001 – 5,000 inhabitants
• 5,001 – 10,000 inhabitants
• 10,001 – 20,000 inhabitants
• 20,001 – 50,000 inhabitants
• 50,001 – 1 million inhabitants
• Over 1 million inhabitants

This very fine stratification yielded 198 strata. Where the number or proportional share of households per stratum was too small to allow the selection of enumeration districts, individual strata were merged with neighboring strata to increase the share of households and thus insure the selection of at least one PSU from each stratum. This exercise left the HFCS with 188 strata for sampling covering all households in Austria. The distribution of strata across provinces and municipality size categories can be seen in table 9.

Table 9: Allocation of strata within the sample  

	Municipality size (number of inhabitants)1
	up to 2,000	2,001– 3,000	3,001– 5,000	5,001– 10,000	10,001– 20,000	20,001– 50,000	50,001– 1 million	over 1 million	Total
Vienna	0	0	0	0	0	0	0	23	23
Lower Austria	7	7	7	7	6	5	1	0	40
Burgenland	3	2	1	2	1	0	0	0	9
Styria	6	5	6	6	5	1	1	0	30
Carinthia	3	3	3	3	2	1	1	0	16
Upper Austria	5	5	5	5	3	2	1	0	26
Salzburg	3	2	2	2	2	1	1	0	13
Tyrol	5	5	3	3	3	0	1	0	20
Vorarlberg	2	2	2	2	2	1	0	0	11
Total	34	31	29	30	24	11	6	23	188
Source: Statistics Austria (municipality directory 2019).
1 Municipality size accounts for municipality mergers up to and including 2019.

Each stratum contained about 86 PSUs on average, which in turn contained around 482 households ⁷⁶ on average.

6.4.2 Sample size

Table 10: Identification of the number of primary sampling units (PSUs) to be drawn  

	% of households	Target sample	Gross sample	Number of households per PSU (enumeration district)	Number of PSUs to be drawn
Vienna	25.1	754	1,936	8	242
Lower Austria	18.6	558	1,020	8 / 12	87
Burgenland	3.2	97	180	12	15
Styria	13.7	412	824	8 / 12	81
Carinthia	6.1	184	376	8 / 12	38
Upper Austria	15.0	451	892	8 / 12	85
Salzburg	6.0	180	368	8 / 12	37
Tyrol	8.0	240	476	8 / 12	45
Vorarlberg	4.1	123	228	12	19
Total	100	3,000	6,300		649
Source: Austrian Post, data.door October 2019, HFCS Austria 2021, OeNB

The targeted net sample of n = 3,000 was divided between the nine provinces, based on their share of private addresses as collected by the Austrian Post Office (table 10, column 1). These figures, which corresponded to the targeted number of secondary sampling units (SSUs, column 2), were subsequently translated into gross samples of SSUs based on the estimated participation rates (column 3). Due to the shorter distances between buildings, 8 households were selected in Vienna and in strata with more than 50,000 inhabitants, whilst this number was 12 in the rest of Austria (column 4). The number of PSUs to be drawn in each province was calculated on this basis (column 5).

In total the Austrian HFCS sample design produced 649 (598 unique) PSUs across all strata and a gross sample size of 6,300 households that were invited to participate in the HFCS (see box 5 in chapter 7 for information on the number of households interviewed successfully). Drawing the PSUs was done with replacement sampling, which entails some PSUs being drawn multiple times (see section 6.5.1). Drawing possible substitute addresses was excluded from the HFCS to begin with to ensure that all households from the gross sample would be interviewed with the same commitment so as to prevent data distortions (see also section 4.4.1).

6.5 The two stages of the random draw

• stage one: random draw of PSUs (enumeration districts) from each stratum
• stage two: random draw of a predefined number of households (postal addresses) from each PSU

6.5.1 First stage

Table 11: Matching of Statistics Austria data with post office and commercial data(fictitious example)  

First stage		Second stage
Statistics Austria		Austrian Post Office				Profile.Address/IFES
Municipality code (1)	Enumeration district (2)	Postal code (3)	Street (4)	House number (5)	Mail delivery point (PAC) (6)	Name of household (7)
90101	90101001	XXXX	Sample street	6	101255765	John Doe
90101	90101001	XXXX	Sample street	6	101255766	Jane Doe
90101	90101002	XXXX	Sample street	9	101255767	John Doe
90101	90101001	XXXX	Sample street	10	101255768	Jane Doe
Source: Statistics Austria, Austrian Post Office, Profile.Address/IFES.

After determining how many PSUs were to be drawn per stratum, the PSUs – unlike during the first wave of the HFCS in Austria ⁷⁸ , but as in the second and third wave – were drawn proportionally to their size (measured in terms of the number of households in a PSU). ⁷⁹ The purpose of this approach is to reduce the standard errors of estimates by reducing sample design weight variance (see also section 7.2.2). Likewise, it ensures that every household within a stratum has the same probability of being drawn in the gross sample of the HFCS. PSUs were drawn with replacement, meaning that a PSU can be drawn multiple times. This meant that a total of 649 PSUs were drawn in the third wave of the HFCS in Austria, only 598 of which were unique.

6.5.2 Second stage

Mail delivery points were randomly selected from each PSU drawn, with 8 being chosen in Vienna or in strata with more than 50,000 inhabitants and 12 being chosen in all other strata. In this process, every household in a given PSU has an equal probability of being selected in the sample, which is measured as a ratio of 1 to the number of households in that PSU. This procedure resulted in a gross sample of 6,300 households in Austria.

6.5.3 Practical implementation

Since the first contact with a household is very important for a successful interview, every household selected for the HFCS survey received an individualized letter signed by the governor of the OeNB. This letter contained information on the survey and an invitation to take part (see section 3.5.1). ⁸⁰

6.6 Concluding remarks

The sampling method used for the HFCS has a number of advantages, with the following aspects being particularly important:

• The fact that the probability of drawing a PSU was proportional to its size in terms of the number of households ensured a better efficiency of sampling design as compared to a design in which all PSUs could be drawn with the same probability by reducing the variance of the design weights.
• As sampling does not differentiate between main residences and second homes (as recorded in the residence registry), all households that correspond to the HFCS household definition have a positive probability of being selected.
• The very fine stratification structure ensures that all segments of the Austrian population are represented in the survey.

7 Construction of survey weights

7.1 Introduction

The target population of the HFCS consists of all households in Austria, with a household being defined as an individual or a group of people who live together in the same private dwelling and share expenses. ⁸¹ However, the sample may contain several types of biases that may cause a misrepresentation of this target population: unequal probability sampling bias, frame bias and nonresponse bias (see chart 5).

As mentioned above, the unequal probability sampling bias is due to the fact that not every household has the same probability of being selected into the sample, reflecting the fact of oversampling of households in urban areas (like Vienna) in the HFCS sample, which is used to address the known problem of the relatively low survey participation propensity of urban households. To correct these misrepresentations, we constructed design weights, which will be explained in section 7.2.2. Further details about the HFCS sampling design can be found in chapter 6.

Imperfections in the survey frame from which the sample is drawn can lead to frame bias. In the HFCS, the sampling frame is a list of all personal postal addresses in Austria (see chapter 6). Erroneous exclusion of households could imply an imperfection with respect to the target population. In other words, there is the possibility that households without a postal address, for example, one-person households living together in residential communities and sharing an address that contains only one of these households, were excluded. These types of households would then be underrepresented. Another imperfection of the frame could be caused by erroneous inclusion, that is, the inclusion of addresses not belonging to households, for example, those of companies or households in care residences. ⁸² Finally, there is a third type of imperfection called frame multiplicity, which means that households may be duplicated because they have two (or more) addresses, for example multiple domiciles of commuters. Depending on its type, the frame bias can be reduced by using design weights ⁸³ (to address erroneous inclusion and frame multiplicity) or poststratification weights (to address erroneous exclusion). We explain the construction of these weights in more detail in sections 7.2.2 and 7.2.4.

The nonresponse bias is caused by the fact that only a subset of the households included in the gross sample is willing to participate in the survey. Certain groups of households have a lower probability of participating in the HFCS than other groups – a phenomenon widely corroborated in literature (see e.g. Kennickell and McManus, 1993). Thus, estimates for the sampling frame would be biased with respect to these group characteristics, even though they are unbiased for the participating population. Using nonresponse weights can correct this bias (section 7.2.3).

Furthermore, as mentioned above, survey weights can help to reduce sampling variance, and, hence, to increase the precision of the estimators. Ideally, the precision of the estimators should be improved by stratification prior to sampling. However, some variables (e.g. household size) that would have been very good for stratification and, thus, for improving the precision of the estimators, were not available until after the sample had been drawn and the sample households had been contacted. Some of the gain in precision that would have been possible by using these variables for stratification can be achieved by using these variables for poststratification. These poststratification weights were also utilized for correcting erroneous exclusion (see chapter 7.2.4). ⁸⁴

The construction of survey weights is very important for the HFCS. The following sections will explain how design, nonresponse and poststratification weights were constructed and how the final set of survey weights was derived from these weights. Finally, we will present some descriptive results that take these weights into account.

7.2 Construction of survey weights

7.2.1 Weight components

wi = wDi · wNRi · wPSi

7.2.2 Design weights

• Step 1: Calculate the probability that a certain PSU is selected. As described in section 6, this sampling probability is defined depending on the relative number of households in a PSU. The probability that PSU j will be selected in stratum h is

• Step 2: Calculate the probability that a SSU is selected. Under the condition that a PSU is chosen, each household in this enumeration district has the same probability of being chosen. Thus, the probability of being selected is given by

This procedure ensures that every household that has the same probability of selection within a stratum on account of the sample design also has the same design weight. The design weights vary across the strata, due to the differing assumptions on the willingness of a household to participate, which determine the SSUs to be drawn, and the different size of the strata as a result of the number of households.

Our sample included 352 Viennese addresses that could randomly not be contacted because the time for the field phase had already expired due to the low number of interviewers resulting from the extraordinary circumstances under which they had to work during the COVID-19 pandemic (see chapter 4 and chapter 10 for more details). For the the survey weights construction these 352 addresses were left out of the sample and, as a consequence, the design weights of their strata were adjusted, too, in order to still represent the frame population (see Box 5 for more details).

Table 12: HFCS design weights by federal province  

	Mean	Median	Minimum	Maximum
Vienna	690	585	0	1,102
Lower Austria	790	776	0	1,197
Burgenland	784	796	0	1,002
Styria	720	691	0	1,186
Carinthia	715	568	0	1,202
Upper Austria	735	769	0	1,224
Salzburg	702	715	0	1,095
Tyrol	730	733	0	1,109
Vorarlberg	792	801	421	1,044
Total	730	735	0	1,224
Source: HFCS Austria 2021, OeNB

Table 12 shows some statistics of the obtained HFCS design weights across Austria’s provinces. Carinthia and Vienna is the province with the lowest median weight, which is plausible, as households living in urban areas were oversampled during the fourth HFCS wave in Austria because of their low willingness to participate, which would have created a bias had they not been reweighted downward using the design weights. Very similar results are found in wave 3.

7.2.3 Nonresponse weights

This bias can be corrected by using nonresponse weights, i.e. by attaching a higher nonresponse weight to households with a low probability of responding than to households with a high probability of responding. To calculate the response probabilities and the corresponding nonresponse weights, the weighting class adjustment method is combined with the model-based adjustment method (see Biemer and Christ, 2008). The weighting classes are chosen optimally using the method described by Haziza and Beaumont (2007). The algorithm can be summarized in the following three steps:

• Step 1: The logit regression model shown in table 13 was used to estimate the probability of response for each household (assuming that the household was selected into the sample).
• Step 2: These households’ response probabilities were grouped into seven classes. The number of classes and their resultant sizes are chosen optimally in line with Haziza and Beaumont (2007). To this end, a k-means algorithm is used to cluster households into a prespecified number of response classes with low variance and similar size. Next, class indicators are used as explanatory variables for the response propensity based on an ordinary least squares (OLS) regression from the logit regression model estimated in step 1. Beginning with one class, the number of classes is increased in an iterative process until the corrected R2 of this OLS regression exceeds 95%. This is the case for seven classes in the fourth wave of the HFCS in Austria. ⁸⁵ Finally, the average response propensity for each class was calculated (unweighted total number of respondent households/unweighted total number of households). ⁸⁶
• Step 3: The nonresponse weight of a class is obtained by inverting the average response propensity of the respective class.

  Table 13: Response propensity estimates based on a logit regression model  

  Covariates	Coefficients
  Paradata on the interview, place of residence and neighborhood
  Household interview order	0.000219
  	(0.000260)
  Building characteristics (reference group: detached single-family house)
  Semi-detached single-family house	–0.140
  	(0.154)
  Single-family townhouse	0.115
  	(0.152)
  Residential farm building	0.0824
  	(0.178)
  Apartment in a (high-rise) apartment building	0.848***
  	(0.0812)
  Student dormitory/rented room	1.465*
  	(0.764)
  Other type of building	–1.012**
  	(0.411)
  Building design characteristics (reference group: premium)
  Very good	–0.0620
  	(0.176)
  Medium	–0.402**
  	(0.180)
  Basic	–0.0690
  	(0.207)
  Very basic	–0.226
  	(0.291)
  Location characteristics (reference group: city center)
  Between the city center and suburbs	0.0736
  	(0.0882)
  Suburbs and city outskirts	0.0264
  	(0.0984)
  Countryside	0.114
  	(0.114)
  Graffiti in the neighborhood (reference group: many)
  Location – graffiti = 2, some	0.410
  	(0.642)
  Location – graffiti = 3, few	0.607
  	(0.636)
  Location – graffiti = 4, none at all	0.375
  	(0.631)
  Paradata not available	–19.976
  	.
  Interviewer characteristics
  Female interviewer	0.0219
  	(0.0630)
  Interviewer’s age	0.00173
  	(0.00275)
  Interviewer’s second-stage tertiary education	–0.0895
  	(0.0722)
  Interviewer’s working experience in months	0.000301
  	(0.000343)
  Variables at the municipality level
  Average per capita income per municipality in 2020	–1.70e–05*
  	(9.63e–06)
  Share of employees in the primary sector per municipality in 2019	0.00256
  	(0.00434)
  Share of university-trained population per municipality in 2019	0.00153
  	(0.00558)
  Unemployment rate per municipality in 2019	–0.0640***
  	(0.0113)
  Average age of population per municipality in 2021	0.0313**
  	(0.0158)
  Variables at the district level
  Average crime rate per district in 2021	–0.00480***
  	(0.00175)
  Constant	–1.306
  	(0.975)
  Observations1	5,881
  Source: HFCS Austria 2021, OeNB
  1 The remaining 67 observations in the dataset are ineligible including 3 observations with unknown eligibility and are therefore not included in the regression.
  Note: Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.

Table 14: HFCS nonresponse weights by response propensity  

Response Classes	Predicted response propensity	Weight
	%
I	0 to 18	6.465
II	18 to 27	3.729
III	27 to 33	3.754
IV	33 to 39	2.766
V	39 to 45	2.385
VI	45 to 54	2.048
VII	54 to 100	1.652
Source: HFCS Austria 2021, OeNB.

The HFCS nonresponse weights are shown in table 14. A value was calculated for each of the seven response groups and, by design, households with a high response propensity were assigned a lower weight than those with a low response propensity. Nonrespondent households were assigned a nonresponse weight equal to zero.

7.2.4 Poststratification weights

Unfortunately, such a dataset does not exist in Austria. Similar surveys, like the EU SILC (EU Statistics on Income, and Living Conditions) or the Austrian microcensus, target a different population of households due to their specific household definition. While the target population of the HFCS includes all households (according to the above definition), the EU SILC and the Austrian microcensus only include households living in a dwelling officially registered in the central residence registry as their main residence. This definition excludes a subset of households included in the HFCS household definition, namely all households living in dwellings that are not registered as a main residence or not registered at all. There are various reasons as to why in some cases households’ actual main residences are not registered as such. For instance, students studying away from home may keep their main residence at their parents’ address even though they are already a household of their own according to the HFCS definition; others may have just forgotten to register the address where they actually live as their main residence. Statistics Austria also acknowledges these problems and others when using main residence addresses for sampling households via the Austrian residence registry. ⁸⁸

Given that these datasets also suffer from erroneous exclusion, it does not make sense to reweight the entire sample according to the target population size of these datasets. However, following an adjustment made in the second wave of the HFCS in Austria, the survey establishes whether a household’s residence is registered as a main residence or not, so it would appear to make sense to reweight this group of households to the microcensus. In particular, this may deliver a better picture with regard to the proportions of households in the Austrian provinces, as the Austrian microcensus uses a much larger sample than the HFCS. For the small group of remaining households in the HFCS sample that are not registered at their main residence, reweighting the microcensus does not seem sensible. Yet the erroneous exclusion bias in the HFCS sample is likely to be very small in this case, as the vast majority of households do have postal addresses. We constructed poststratification weights that put more weight on households with a lower probability of being included in the frame and less weight on households with a higher probability. We adjusted the HFCS sampling frame size only for households registered at their main residence according to the microcensus. Households not registered at their main residence are then added. ⁸⁹ This increases comparability between the HFCS and the microcensus since the second wave and at the same time reduces the erroneous exclusion bias. Furthermore, poststratification weights can also reduce the sampling variance and, hence, increase the precision of the estimators; moreover, they can eliminate sample-specific random misrepresentations of the target population (see section 7.1).

The HFCS poststratification weights are constructed following the poststratification cell adjustment method (Biemer and Christ, 2008) and using the Austrian microcensus data (2021 Q4) available during the field phase of the HFCS in Austria. The procedure was as follows:

• Step 1: Choose suitable predictors for including a household in the HFCS frame and cross-tabulating these variables to compute the poststratification cells. Different poststratification cells were defined depending on the registration status. For households registered at their main residence, the province, the tenure status of the main residence, household size and the age of the reference person serve as poststratification variables. No poststratification is performed for all other households, because, as described above, they are not included in the external dataset.
• Step 2: Calculate the average propensity to be included in the sampling frame for each cell.

• Step 3: In each cell, the propensity was adjusted by a constant factor, thus adjusting the external dataset total.
• Step 4: Obtain the poststratification weight by inverting the average inclusion in-frame propensity for each cell.

Table 15 shows the HFCS poststratification weights – 67 values, i.e. one value per combination of registration status, province, tenure status of the main residence, household size and age of the reference person. The table shows, for example, that single tenant households are underrepresented in the HFCS sampling frame, as they exhibit above-average poststratification weights.

7.2.5 Final weights

wi = wDi · wNRi · wPSi

.

Table 15: HFCS poststratification weights for registration status of main residence, province, tenure status, household size and age by response propensity  

	Official main residence								Other households
	Household size (number of persons)								Household size (number of persons)
	1		2 to 4	5 or more	1		2 to 4	5 or more	1		2 to 4	5 or more	1		2 to 4	5 or more
	Age of household’s reference person				Age of household’s reference person				Age of household’s reference person				Age of household’s reference person
	0-64	65+			0-64	65+			0-64	65+			0-64	65+
	Homeowners				Tenants				Homeowners				Tenants
Vienna	1.405	0.458	0.794	3.316	1.589	0.391	0.929	1.950
Lower Austria	1.852	0.975	1.039	2.820	1.065	0.557	0.837
Burgenland	1.433	0.589	0.523	0.854	3.192	0.626	1.887
Styria	0.525	0.616	0.710	1.494	0.850	0.766	1.494	2.691
Carinthia	1.674	0.782	0.953		1.063	0.816	1.403	4.307				1
Upper Austria	2.533	0.528	0.888	1.184	1.757	0.513	1.143	4.371
Salzburg	1.372	0.662	0.994	1.872	1.559	0.336	0.577	1.495
Tyrol	2.862	1.179	1.387	1.855	1.006	0.511	1.030
Vorarlberg	0.791	0.663	2.129		1.534	0.472	2.683
Source: HFCS Austria 2021, OeNB.

The combination of nonresponse weights and poststratification weights results in 469 different weight adjustment cells based on registration status, province, tenure status of the main residence, household size, age of the reference person and the response propensity classes described above. Each household is represented in precisely one of these cells.

Finally, once we have taken the design weights into account, we obtain the HFCS final weights, whose distribution is shown in chart 6. The HFCS final weights range from 318 to 10,475, with the mean being 1,773 and the median 1,558. Their distribution is slightly skewed to the right, which is not atypical for unequal probability sample designs. After all, households with a higher probability of selection (below average design weights) dominate the sample. This effect is reinforced by the further weight adjustments.

7.3 Selected results

The use of the final HFCS weights is sufficient when calculating the weighted statistics shown in table 16. To calculate the appropriate correct variances or standard errors of these estimators, however, replicate weights, which are described in chapter 8, are necessary.

7.4 Concluding remarks

Table 16: Comparison of weighted and unweighted means of selected HFCS variables (imputed)  

	Mean
	Unweighted	Weighted
Household size (number of persons)	1.95	2.09
	% of households
Vienna	22.2	23.0
Lower Austria	15.8	18.5
Burgenland	3.9	3.2
Styria	17.1	13.9
Carinthia	6.5	6.4
Upper Austria	15.5	16.1
Salzburg	8.0	6.2
Tyrol	8.4	8.4
Vorarlberg	2.6	4.3
	EUR
Estimated household monthly net income	2,721	2,848
Household net wealth	249,780	293,000
Source: HFCS Austria 2021, OeNB.

While the weighted HFCS sample enables unbiased population estimates, it also increases the variance of the population estimates, which makes them less precise. ⁹² According to the unequal weighting effect (UWE) statistic developed by Kish (1995), the variance of HFCS population estimates may be increased by a maximum of 32.9% (UWE = 1 + coefficient of variation2 = 1.329) as a result of weighting. The increased unit-nonresponse caused to worsen this value compared to the previous waves (see also Box 5 and chapter 10.7). However, it is still not necessary to apply weight trimming methods. Furthermore, a small increase in variance is acceptable in return for a significant reduction in the bias if it helps to avoid distorted results being classified as significant too often.

An explanation of how to correctly use the weights in Stata is provided in chapter 9, User guide.

8 Construction of replicate weights for variance estimation

8.1 Introduction

A problem that occurs frequently when statistical analysis takes into account a complex survey design with all its features is that the mathematical functions of the variance estimators are unknown. Therefore, performing a statistical analysis requires methods developed especially for the purpose of variance estimation. There are two general categories of variance estimation methods: replicate weight methods (also called replication or resampling methods) and linearization. ⁹³

Until recently, linearization was preferred to replication in the literature, as linearization requires less computational power. However, linearization comes with the major disadvantage that data protection regulations prevent some information necessary for linearization from being shared. When replicate weights are used, data availability and privacy are not an issue. After all, replicate weights consist of numerous variables which are not available to data users (e.g. stratum and primary sampling unit (PSU) variables). Leaving out this information makes it virtually impossible for individual respondents to be identified from the data by the data user (see Heeringa et al., 2017).

Moreover, the linearization method is unsuitable for estimating the variance of nonlinear statistics (medians, quartiles, etc.), as it requires computing derivatives of continuous functions; however, quantile functions, for instance, are discontinuous. Replicate weights, by contrast, are well suited for estimating the variance of such statistics (see Heeringa et al., 2017).

Given the data protection requirements mentioned above and because the HFCS data facilitate in particular the analysis of distributional parameters such as medians and quantiles, we use – in accordance with ECB requirements – replicate weights for variance estimation in the HFCS. ⁹⁴ In the following section, we describe how replicate weights were constructed for the HFCS in Austria.

8.2 Construction of replicate weights

8.2.1 The replication method

Instead of saving a whole sample for each replicate, it is more practical to vary the final survey weights. For example, instead of removing a sample observation to construct a certain replicate, it can be given a weight of zero in the replicate. Then the weights of the other observations in the same stratum need to be increased to ensure that the totals are unbiased for each replicate r (see Kolenikov, 2010). The replicate weights wi(r) for r = 1,…, R will be published together with the HFCS dataset.

There are different methods to form such replicates. The three major replication methods used in survey literature are balanced repeated replication, jackknife repeated replication and bootstrap replication. Although in most simulations in the literature, the estimators of all three replication methods converge towards one another as the sample size increases, simulation studies have shown that bootstrap and balanced repeated replication are better suited to quantile estimation than jackknife (see Kovar et al., 1988). Finally, as balanced repeated replication works only in designs with exactly two PSUs per stratum, which is not the case in the HFCS in Austria, we decided to use the (rescaling) bootstrap procedure proposed by Rao and Wu (1988) and enhanced by Rao et al. (1992). This procedure is also in line with the provisions of the ECB’s Household Finance and Consumption Network.

The bootstrap procedure forms replicates based on repeated with-replacement sampling of the PSUs within a stratum. The idea is to mimic the original sampling procedure in order to obtain approximations for the sampling distributions of the relevant statistics.

8.2.2 Sampling error calculation model

In the HFCS in Austria, one necessary simplification of the sampling error calculation model compared with the original sampling procedure is to collapse, i.e. merge, strata with one single PSU because the bootstrap procedure requires at least two PSUs per stratum. Due to the specific stratification of the HFCS sample design, single-PSU strata are quite common in the sample: Only one PSU was drawn from 90 out of 188 strata. For the sampling error calculation model, every single-PSU stratum is paired with the geographically nearest stratum to form a single pseudo stratum, taking into account how many PSUs are in this stratum. Aggregation is carried out with the nearest stratum containing a smaller number of PSUs, reducing the frequency of necessary aggregations. Although collapsing the strata produces an upward bias in the estimated variance, this bias is kept as small as possible by collapsing geographically close strata, which keeps the PSUs within one pseudo-stratum very homogeneous. In this context it must be pointed out that upward biases of standard errors lead to a loss in statistical power. In general, however, this is more acceptable than negative biases of standard errors, which lead to results that are too often considered statistically significant.

Table 17 shows how stratum size (number of PSUs drawn per stratum) changes when the HFCS sampling error calculation model is used instead of the original HFCS sample design: When collapsing strata in the sampling error calculation model, their number decreases from 188 to 134, which means stratification is still very high. Moreover, the mean stratum size increases from 3.2 PSUs to 4.2 PSUs per stratum.

Table 17: Comparison of HFCS design strata and HFCS pseudo strata  

	Design Strata	Pseudo Strata
Number of strata	188	134
Mean size	3.2	4.2
Median size	2.0	2.0
Minimum size	1	2
Maximum size	37	37
Source: HFCS Austria 2021, OeNB.
Note: Stratum size as measured by PSUs drawn per stratum.

Another simplification performed in the HFCS sampling error calculation model in contrast to the original sample design is to assume that sampling variance stems mostly from the first stage of sampling (i.e. the selection of PSUs, and not that of households within each PSU). Therefore, two-stage sampling is reduced to single-stage sampling where all gross sample households within drawn PSUs are selected in the replicate sample.

In addition, all PSUs have the same probability of being selected into the replicate sample. Thus, the sampling error calculation model simplifies sampling by making a PSU’s probability of being drawn independent of its size as measured by the number of households.

No further simplifications are required by the sampling error calculation model. The nonresponse and poststratification weight adjustments are implemented in the same way as in the original weighting procedures (see chapter 7), and a finite population correction ⁹⁵ is performed.

8.2.3 Construction of replicate weights

• Step 1: Draw mh PSUs with replacement within each pseudo stratum h.
• Step 2: Adjust the final survey weights of the drawn observations to create a new set of replicate weights. In particular, apply the same nonresponse and poststratification weight adjustments (sections 7.2.3 and 7.2.4) as for the final design weights and perform a finite population correction.
• Step 3: Repeat steps 1 and 2 R times to obtain r = 1,…, R sets of replicate weights.

In step 2, the final survey weights must be adjusted because some PSUs may be duplicates and some may not have been drawn at all. As a consequence, each replicate will be biased with respect to the target population and therefore, to obtain the replicate weights, the design weights must be adjusted in the same way they were adjusted when constructing the final survey weights (see chapter 7). In addition, a finite population correction is required, as PSUs are sampled without replacement in the original HFCS sample design (see footnote 3). ⁹⁶

Finally, in step 3, the higher the number of replicates R is, the more precise the standard error estimates are. We choose R = 1,000, which lies in the upper bound of the usual recommendations found in literature (see Kolenikov, 2010).

Table 18 shows some descriptive statistics of a selection of HFCS replicate weights. We can see that owing to the homogeneous weighting adjustments, the mean and the total sum of replicate weights remain unchanged. Moreover, compared with the final survey weights in the HFCS, the replicate weights have smaller minimum values, however none are equal to zero. These values correspond to the nonselected PSUs, which, instead of being assigned a weight equal to zero, are assigned a small positive weight in the finite population correction. The fact that the replicate weights also have larger maximum values than the final survey weights can be explained by the weight adjustments that were carried out: As some PSUs are not drawn in the replicates, and in order to obtain the same estimated population sizes as in the original sample, the weights of the observations in the drawn PSUs must be increased.

Table 18: Selected HFCS replicate weights  

	Mean	Median	Minimum	Maximum	Total
Final Survey weights	1,773	1,558	318	10,476	4,066,627
1st set of replicate weights	1,773	1,184	6	16,422	4,066,627
2nd set of replicate weights	1,773	1,166	4	20,439	4,066,627
3rd set of replicate weights	1,773	1,178	7	35,642	4,066,627
998th set of replicate weights	1,773	1,192	5	19,600	4,066,627
999th set of replicate weights	1,773	1,079	5	16,068	4,066,627
1,000th set of replicate weights	1,773	1,197	6	20,083	4,066,627
Source: HFCS Austria 2021, OeNB
Note: Statistics refer to successfully interviewed households only.

8.3 Concluding remarks

While it is true that correctly calculating the standard errors by using replicate weights requires more computational power than analyzing the data without using replicate weights, in practice it is not necessary to use all 1,000 sets of replicate weights for variance estimation. Thus, for example, it is possible to perform variance estimations using fewer replicates more quickly but less precisely. The number of replicates used depends on the type of estimator and the size of the population surveyed (see e.g. Pattengale et al., 2010). For instance, estimating the means for the total population will, as a rule, require fewer replicates than estimating the medians for specific population subgroups.

See the HFCS User guide (chapter 9) for an explanation of how to use the replicate weights correctly in Stata.

9 User guide

9.1 Introduction

9.2 Merging the data files

********************************************************************

***Merging the files of the HFCS data

********************************************************************

*Set macro for the path to the data (must be specified by the user)

global hfcsdata=“path to the appropriate folder where the data are stored“

*Set macro for the path to the do-files (must be specified by the user)

global hfcsdofile=“path to the appropriate folder where the do-files are stored“

*Set working directory

cd „$hfcsdata“

*Reshaping and merging the p and h files together (wide format)

forvalues i=1(1)5 {

use „$hfcsdata\P`i‘.dta“, clear

drop id hid survey

foreach var of varlist sa0010- fra0500 {

local `var‘lab: variable label `var‘

}

gen idpers_temp1=“_“

egen idpers_temp2=concat(idpers_temp1 ra0010)

drop ra0010

reshape wide ra0?0* fra0?0* ra0020 fra0020 ra0030 fra0030 ra0040 fra0040 p* fp* /// , i(sa0010 sa0100) j(idpers_temp2) string

drop idpers_temp*

foreach j of varlist ra* fra* p* fp* {

local last2car=substr(„`j‘“, `=length(„`j‘“)-1‘, 1)

local last1car=substr(„`j‘“, length(„`j‘“), 1)

if „`last2car‘“==“1“ {

local firstcar=substr(„`j‘“,1, `=length(„`j‘“)-3‘)

label variable `firstcar‘_`last2car‘`last1car‘ ///„``firstcar‘lab‘ - `last2car‘`last1car‘“

}

else {

local firstcar=substr(„`j‘“,1, `=length(„`j‘“)-2‘)

label variable `firstcar‘_`last1car‘ „``firstcar‘lab‘ - `last1car‘“

}

}

save „$hfcsdata\P`i‘_temp.dta“, replace

clear

use „$hfcsdata\H`i‘.dta“, clear

merge 1:1 sa0010 sa0100 im0100 using „$hfcsdata\P`i‘_temp.dta“, nogen

save „$hfcsdata\M`i‘.dta“, replace

erase „$hfcsdata\P`i‘_temp.dta“

}

*Merging the core with the derived variables

forvalues i=1(1)5 {

use „$hfcsdata\M`i‘.dta“, clear

merge 1:1 sa0010 im0100 sa0100 using „$hfcsdata\D`i‘.dta“

save „$hfcsdata\temp`i‘.dta“, replace

}

*Merging the implicates together1

use „$hfcsdata\temp1.dta“, clear

forvalues j=2(1)5 {

append using „$hfcsdata\temp`j‘.dta“

}

*Drop unnecessary variables and labels

drop _merge

label drop _merge

*Save the HFCS data

save „$hfcsdata\hfcs.dta“, replace

9.3 Multiple imputations

********************************************************************

***Preparing the data for mi import

********************************************************************

*Create the zero implicate to simulate the original data

*Use one implicate of the data

use „$hfcsdata\temp1.dta“, clear

*Replace the implicate number by „0” to simulate the original data

replace im0100=0

*Append all other implicates

append using „$hfcsdata\hfcs.dta“

*For some reason string variables do not play well with mi commands and need to be encoded into numeric variables

foreach var of varlist hb* hc* hd* hg* hh* hi* hr* pa* pe* pf* pg* ra* sa0100 ///sb1000 {

capture confirm numeric variable `var‘

if _rc {

rename `var‘ `var‘_string

encode `var‘_string, gen(`var‘)

drop `var‘_string

}

}

*Set as soft missing („.“) in im0100==0 all values varying, and also those whose flags set them as imputed

global IMPUTEDVARS=““

foreach var of varlist hb* hc* hd* hg* hh* hi* hr* pa* pe* pf* pg* ra* {

capture confirm numeric variable `var‘

if !_rc {

tempvar sd count

quietly bysort sa0100 sa0010 : egen `sd‘=sd(`var‘)

quietly bysort sa0100 sa0010 : egen `count‘=count(`var‘)

quietly count if ( (`sd‘>0 & `sd‘ <. ) | `count‘<6 | /// (f`var‘>4000 & f`var‘<5000) ) & im0100==0

if r(N)>0 global IMPUTEDVARS „$IMPUTEDVARS `var‘“

quietly replace `var‘=. if ( (`sd‘>0 & `sd‘ <. ) | `count‘<6 | ///(f`var‘>4000 & f`var‘<5000) ) & im0100==0

drop `sd‘ `count‘

disp „.“, _continue

}

}

*Here we need to set all derived variables for im0100==0 missing because it is passively imputed

foreach var of varlist d* hb3001-hb40033 hb4099 hb4105 hb4205 {

local type1: type `var‘

local type2=substr(„`type1‘“,1,3)

if „`type2‘“!=“str“ {

replace `var‘=. if im0100==0

}

}

*Drop unnecessary variables

drop id _merge

*Save the HFCS data

save „$hfcsdata\hfcs.dta“, replace

*Erase temporary files that will not be needed anymore

forvalues i=1(1)5 {

erase „$hfcsdata\temp`i‘.dta“

}

********************************************************************

*****Import as multiply imputed data

********************************************************************

*Import the imputation structure of the data into Stata

mi import flong, m(im0100) id(sa0100 sa0010) clear

*Register the variables that are imputed

mi register imputed $IMPUTEDVARS

*Register derived variables as passively imputed

mi register passive d*

*Check whether all imputed variables are registered

mi varying

*Save the HFCS-data with mi structure

save „$hfcsdata\hfcs.dta“, replace

9.4 Survey variables

********************************************************************

***Setting up Complex Survey Design

********************************************************************

*Encode country indicator

use „$hfcsdata\W.dta“, clear

rename sa0100 sa0100_string

encode sa0100_string, gen(sa0100)

drop sa0100_string

save „$hfcsdata\Wtemp.dta“, replace

*Using the HFCS data with mi structure

use „$hfcsdata\hfcs.dta“, clear

*Merging the data with replicate weights

merge m:1 sa0100 sa0010 using „$hfcsdata\Wtemp.dta“

*Drop unnecessary variable and files

drop _merge

erase „$hfcsdata\Wtemp.dta“

*Setting the appropriate survey structure using replicate weights

mi svyset [pw=hw0010], bsrweight(wr0001-wr1000) vce(bootstrap)

*Save the HFCS-data with mi svyset structure

save „$hfcsdata\hfcs.dta“, replace

9.5 Standard estimation procedures

********************************************************************

***Using Standard Estimation Procedures

********************************************************************

*Using the HFCS-data with mi svyset structure

use „$hfcsdata\hfcs.dta“, clear

*Mean of current value of primary housing unit

mi estimate, esampvaryok vceok: svy: mean hb0900

*Mean of current value of primary housing unit for part owner of the primary housing unit

gen partowner=(hb0300==2)

mi estimate, esampvaryok vceok: svy, subpop(partowner): mean hb0900

*Proportions of owner/renter of primary housing unit

mi estimate, esampvaryok vceok: svy: proportion hb0300

*Ratio of current to acquisition value of primary housing unit

mi estimate, esampvaryok vceok: svy: ratio hb0900 hb0800

*Regression of current value of primary housing on acquisition value and year of acquisition

mi estimate, esampvaryok vceok: svy: regress hb0900 hb0800 hb0700

*Average level deposits according to gender of the first person

mi estimate, esampvaryok vceok: svy: mean da2101, over(ra0200_1)

9.6 Additional estimation procedures

********************************************************************

***Including Additional Estimation Procedures

********************************************************************

*ECB-written command to calculate medians (and some other quantile statistics), which should be run before the estimation command

capture program drop medianize

do „$hfcsdofile\medianize.do“

*Median of amount still owned in the first loan collateralized with primary housing unit

mi estimate, esampvaryok vceok: svy: medianize hb1701

*Median of amount still owned in the first loan collateralized with primary housing unit over gender of first person

mi estimate, esampvaryok vceok: svy: medianize hb1701, over(ra0200_1)

*Median of amount still owned in the first loan collateralized with primary housing unit over gender of first person

mi estimate, esampvaryok vceok: svy: medianize hb1701, over(ra0200_1) stat(p10)

9.7 Online appendix

10 Changes from the third to the fourth wave of the HFCS due to COVID-19

10.1 Introduction

This chapter offers a short but comprehensive insight into what has changed from the third to the fourth HFCS wave due to COVID-19 for readers who already have experience in evaluating data from previous waves in Austria. Furthermore, this chapter provides the foundation for evaluations based on at least the last two waves of the HFCS in Austria, which require an understanding of how the survey waves differ from each other.

The structure of this chapter mirrors the structure of the documentation as a whole: Following an overview of the key COVID-19-related changes to the questionnaire (section 10.2) and to interviewer training and selection (section 10.3), we discuss editing measures (section 10.4), the multiple imputation process (section 10.5) and the sampling design (section 10.6). The final two sections deal with the construction of survey weights (section 10.7) and replicate weights (section 10.8). The user guide (chapter 9) is not discussed here, since it was left broadly unchanged (except for an extension to include R code). The chapter finishes with concluding remarks.

10.2 Questionnaire

In contrast to some other countries taking part in the HFCS, we kept the computer assisted personal interview (CAPI) technique for this survey wave, because it is the best available method given the complexity of the survey.

The field period was initially planned to start in March 2020, but the pandemic made it necessary to postpone the start by more than one year. The Austrian field period of wave four eventually started in May 2021 and lasted until February 2022. Despite this extended period, about 350 randomly selected Viennese addresses of the initial gross sample were not contacted – due to the low number of interviewers – and eventually left out of the sample (see chapter 10.7). Furthermore, because of (regional) restrictions and warnings, the field period had to be interrupted (from November 22, 2021, to December 12, 2021) and restarted. No interviews were conducted during lockdowns.

Health risks made interviewing particularly difficult during this survey wave. However, once a household could be convinced to voluntarily participate, and as safety measures (such as wearing FFP2 masks) were complied with, interviewing was comparable to previous waves.

10.3 Interviewers

After the delayed start of the field period, interviewer training was redesigned to take place online via Zoom meetings. This increased flexibility in training and reduced the health risks for everybody involved. While the training content remained unchanged, the schedule was split into two meetings on two days to help trainees keep a high level of concentration. Also, trainees were able to use the break between the two sessions to work on their additional take-home exercise interviews. Particular effort was put into keeping the training interactive in the online setting. Interviewers asked questions for clarification – also those that came up during homework – throughout the training. Interviewers that had been trained already in March 2020 had a reduced training schedule of just one day to refresh their knowledge of the survey questionnaire. In total, four one-day refresher training sessions and seven complete two-day training sessions took place for wave four of the HFCS in Austria.

The extraordinary circumstances reduced the readiness of interviewers to work in the HFCS. Especially experienced and older interviewers were more reluctant to work for the survey on account of increased health risks. As a result, the number of interviewers decreased from about 70 in wave three to 47 in wave four, which, in turn, led to a longer field period. The reduction in the number of interviewers may had other effects, such as interviewer effects. Further methodological analyses concentrating on the boundaries of the distribution as well as the variance estimation based on the survey data could shed some light on these issues and their connection to specific results.

During the field period, maximum security measures were implemented to reduce the risk of infection for both respondents and interviewers. Interviewers were required to show proof of vaccination or recovery or a negative test (PCR test or antigen test performed by a pharmacist). Additionally, strict hygiene standards (washing hands, not touching one’s own face, etc.) were implemented in the same manner as wearing an FFP2 mask. No work-related health difficulties were reported in the HFCS.

10.4 Consistency checks and editing

Once data were collected, the procedure to assess quality and correct potential mistakes stayed the same. Also, follow-up enquiries by phone were conducted as usual in the HFCS in Austria.

10.5 Multiple imputations

Table 19: Item nonresponse per household in the third and fourth waves (unweighted)  

	Third wave (2017)				Fourth wave (2021)
	Mean	Median	Minimum	Maximum	Mean	Median	Minimum	Maximum
Number of variables asked
All variables	1,994.1	2,005.0	1,440	2,506	1,917.8	1,947.0	1,382	2,241
Euro variables	116.7	118.0	62	167	117.6	122.0	42	166
Number of variables with missing values
All variables	20.7	10.0	0	467	17.3	8.0	0	370
Euro variables	5.8	3.0	0	78	4.6	2.0	0	57
Share of variables with missing values in %
All variables	1.0	0.5	0	19	0.9	0.4	0	19
Euro variables	4.9	2.6	0	54.5	3.9	1.6	0	47.6
Source: HFCS Austria 2017 and 2021, OeNB.
Note: Interval responses are considered as missing values with regard to the corresponding euro variable and are not included as a separate variable. A question addressed to several household members is entered as several variables, one for each household member.

Consequently, the total number of variables to be imputed over the whole survey decreased by 134 from 907 in wave 3 to 773 in wave 4 (see table 20). Most of this reduction is attributable to the reduction in item nonresponse rather than to changes in the set of variables (variables that are either new or no longer included in wave 4) (see table 20).

Table 20: Number of variables to be imputed across waves  

	Third wave (2017)		Fourth wave (2021)
	Number of variables	%	Number of variables	%
All variables to be imputed	907	100.0	773	100.0
Distinct from other wave	274	30.2	140	18.1
New/old variable	110	12.1	118	15.3
New/old missing	164	18.1	22	2.8
Common in both waves	633	69.8	633	81.9
Chained equations	602	66.4	593	76.7
Ad hoc methods	31	3.4	40	5.2
Source: HFCS Austria 2017 and 2021, OeNB.

However, at the same time, the number of variables that cannot be imputed using the regular HFCS imputation procedure via chained regression equations increased. Variables with missing values are not imputed with the HFCS imputation procedure when they have insufficient variance or when they have insufficient observations for running a regression. A very small fraction of these variables are imputed with ad hoc methods such as hotdeck imputation after the HFCS procedure has been completed. Due to the smaller net sample size in wave 4 resulting from the increase in unit nonresponse (see section 10.6 on the changes in sampling due to COVID-19), ad hoc imputation methods had to be used more often than in the previous wave. Table 20 shows that among those variables to be imputed in both surveys, 5.2% are imputed using ad hoc methods in wave 4 compared to only 3.4% in wave 3. ¹⁰⁹

Apart from this, the HFCS imputation procedure described in chapter 5 remained the same.

10.6 Sampling

10.7 Construction of survey weights

Table 21: Response behavior indicators across waves  

Response behavior indicator	Third wave (2017)	Fourth wave (2021)
Gross sample size	6,280	6,300
Net sample size	3,072	2,293
Response rate	49.8	39.0
Refusal rate	45.3	56.8
Cooperation rate	50.6	39.5
Contact rate	98.5	98.7
Eligibility rate	98.2	98.9
Source: HFCS Austria 2017 and 2021, OeNB.
Note: Response rate = achieved interviews / eligible sample units; refusal rate = sample units refusing to participate / eligible sample units; cooperation rate = achieved interviews / contacted sample units; contact rate = contacted sample units / eligible sample units; eligibility rate = eligible units / gross sample size.

A further challenge possibly related to the COVID-19 impact on households’ nonresponse behavior is the fact that the unweighted HFCS sample is biased toward households with older household members. Actually, the estimation of the logit regression used for constructing the nonresponse weights shows a significant positive effect of average age in the municipality on the probability of household participation in the survey (see chapter 7). However, despite including this variable when constructing the nonresponse weights, the age bias still remained. Table 22 shows the age distribution of all household members – after weighting with nonresponse weights, but before weighting with post-stratification weights – in comparison with the previous wave. It can be seen that the proportion of household members aged 65 years or above is 11 percentage points higher than in wave 3 (31% instead of 20%). For this reason, we decided to include the age of the household’s reference person as an additional post-stratification variable when constructing the post-stratification weights (see chapter 7). With this step, we reduced the proportion of individuals aged 65 or over further, to 25%.

Table 22: Distribution of household members’ age across waves (unweighted)  

Age	Third wave (2017)	Fourth wave (2021)
1–19 years	19.0	15.7
20–64 years	60.4	52.9
65 years and over	20.5	31.4
Source: HFCS Austria 2017 and 2021, OeNB.
Note: Unweighted for post-stratification but weighted for nonresponse.

Finally, there was one more COVID-19-related issue when constructing the survey weights of wave 4: The HFCS sample included 352 Viennese addresses distributed across 15 strata that could not be processed in time before the end of the field phase was declared. Which addresses were left open within each strata is purely random. The reason why they could not be processed in time is the low number of interviewers available during the pandemic (see chapter 3). As these addresses were never contacted, it is unknown whether they are eligible for the HFCS or not, and their paradata are also not available. For the construction of the survey weights, we decided to treat them as if they had never been part of the gross sample and dropped them from the sample. Therefore, we just had to adjust the design weights in the corresponding strata so that they again sum up to the population total. The construction of the survey weights then followed the same procedure as in previous waves and as described in chapter 7. We also tried using an alternative approach of how to treat these addresses, but the nonresponse weights remained quite robust against the alternative approach (see table 23). ¹¹⁰

Table 23: HFCS nonresponse weights compared with alternative approach  

Responseclasses	Final nonresponse weights	Alternative nonresponse weights (bootstrap sample)
		Mean	Standard deviation	Minimum	Maximum
I	6,465	6,356	0.602	5.301	6,911
II	3,729	3,902	0.035	3.811	4,008
III	3,754	3,602	0.039	3.522	3,740
IV	2,766	2,721	0.047	2.591	2,903
V	2,385	2,289	0.015	2.237	2,322
VI	2,048	1,874	0.014	1.854	1,961
VII	1,652	1,670	0.004	1.660	1,683
Source: HFCS Austria 2021, OeNB.
Note: The alternative nonresponse weights included additional 352 Viennese addresses for which the paradata variables had to be multiply imputed.

10.8 Construction of replicate weights for variance estimation

10.9 Concluding remarks

References

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