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Household Finance and Consumption Survey (HFCS)

About the survey

The HFCS collects household-level data on household finances and consumption. For most countries the fieldwork was carried out in 2010 and 2011 for the first wave (2010), between 2013 and the first half of 2015 for the second wave (2014) and in 2017 for the third wave (2017). While the fourth wave (2021) was initially planned for 2020, the fieldwork was disrupted by the outbreak of the coronavirus (COVID-19) pandemic. It was ultimately carried out between the first half of 2020 and the first half of 2022.

As a consequence, countries conducted the survey during different phases of the pandemic, which needs to be taken into account when comparing results across countries and waves. The pandemic also affected other aspects of the methodology, which are further described in the FAQ, the report on the results and the methodological report.

Anonymised microdata are made available to researchers.

Household Finance and Consumption Network

Results and FAQ

HFCS dashboard (Oesterreichische Nationalbank)

ACCESS TO THE DATA
The HFCS datasets are available to researchers for research purposes.

Please add the following information to your access request:

  • a copy of your government-issued photo identification document (saved as: ID_surname.pdf)
  • a Resume or Curriculum Vitae in English (in PDF or simple text formats saved as: CV_surname.pdf)
  • completed access request form
Request access

Implementation

The HFCS is conducted at national level. In view of the considerable cultural and institutional differences between euro area countries, there needs to be some flexibility in the formulation of the questions for individual countries to obtain comparable data.

Participating institutions produce harmonised output (i.e. survey data) for their respective countries, but do not necessarily use identical questionnaires. However, a common template serves as a benchmark for the country questionnaires, as well as for establishing the desired output.

Common set of output variables

Participating institutions report a set of commonly agreed output variables for their respective countries. Core output variables have to be delivered for all participating countries. A set of non-core variables has also been defined, with participating institutions free to decide which of these they collect and report. Collection of standardised variables ensures cross-country comparability.

Contents of the survey

The HFCS questionnaire consists of two main parts:

  1. Questions relating to the household as a whole, including questions on real assets and their financing, other liabilities/credit constraints, private businesses, financial assets, intergenerational transfers and gifts, and consumption and saving;
  2. Questions relating to individual household members, covering demographics (for all household members), employment, future pension entitlements and income (for household members aged 16 and over).

In addition, there are standardised questions to determine which respondent should answer the questions on the household as a whole (the “financially knowledgeable person”), as well as questions to be answered by the interviewer relating to the appearance and location of the dwelling and the interviewees’ behaviour during the interview. These provided “paradata”, which were particularly useful when editing and imputing data after completion of the fieldwork.

National central banks and national statistical institutes participating in the HFCS
Links to national surveys and other related initiatives
Austria
Oesterreichische Nationalbank
Household Finance and Consumption Survey
Belgium
Nationale Bank van België/Banque Nationale de Belgique
Household Finance and Consumption Survey
Croatia
Hrvatska narodna banka
Household Finance and Consumption Survey
Cyprus
Central Bank of Cyprus
Czech Republic
Česká národní banka
Household Finance and Consumption Survey
Estonia
Eesti Pank
Household Finance and Consumption Survey
Finland
Suomen Pankki - Finlands Bank
Statistics Finland Household Finance and Consumption Survey
France
Banque de France
INSEE Enquête Patrimoine
Germany
Deutsche Bundesbank
Panel on Household Finances
Greece
Bank of Greece
Household Finance and Consumption Network
Hungary
Hungarian National Bank
Hungarian Central Statistical Office
Household Finance and Consumption Survey
Ireland
Central Bank of Ireland
Central Statistics Office; Household Finance Consumption Survey
Italy
Banca d'Italia
Survey on Household Income and Wealth (SHIW)
Latvia
Latvijas Banka
Household Finance and Consumption Survey
Lithuania
Lietuvos bankas
Luxembourg
Banque centrale du Luxembourg
Household Finance and Consumption Survey
Malta
Central Bank of Malta
Household Finance and Consumption Survey
Netherlands
De Nederlandsche Bank
CentERdata DNB Household Survey
Poland
National Bank of Poland
The Household Wealth and Debt Survey
Central Statistical Office of Poland
Portugal
Banco de Portugal
Portuguese Household Finance and Consumption Survey (ISFF)
Instituto Nacional de Estatística (Inquérito à Situação Financeira das Famílias) (in Portuguese)
Slovakia
Národná banka Slovenska
HFCS (in Slovak)
Slovenia
Banka Slovenije
Household finance and consumption (HFCN)
Spain
Banco de España
Encuesta Financiera de las Familias (EFF)

Purpose of the data

Overall aim of the data

Survey data are key to:

  • understanding both individual behaviour and developments in aggregate variables
  • evaluating the impact of shocks, policies and institutional changes, both for households and for different institutional structures
  • better understanding the implications of shocks for macroeconomic variables
  • building and calibrating realistic economic models incorporating heterogeneous agents
  • gaining important insights into issues such as monetary policy transmission and financial stability.

Importance of household sub-populations

Gathering information on the behaviour of sub-populations of households is essential. For instance, the financial crisis has demonstrated that a relatively small percentage of households – those who are highly indebted – can have a major impact on market outcomes. Another example of an influential sub-group is the top wealthiest households. Though small in number, these have a highly disproportionate effect on aggregate statistics.

Areas of research

The following questions in the field of household finance may be of particular interest to researchers and policymakers.

Debt and financial pressures
  • What is the cross-sectional distribution of debt (e.g. for different levels of income and net worth), and how does this change over time?
  • Are there mismatches between the assets and liabilities of individual households in terms of their size, volatility, interest-rate sensitivity or liquidity?
  • Which types of debt are taken on by households (home equity withdrawal, credit card debt, interest rate modalities, etc.), and what, for instance, is the relationship between debt and collateral?
  • What drives households' indebtedness?
  • How do macroeconomic and financial shocks affect indebtedness at the household level?
  • How do highly indebted households cope with risk and mitigate shocks? Is their consumption more volatile? How does their labour supply respond?
  • Counterfactual simulations of the effects that adverse shocks have on individual households
Portfolio choice and demand for assets
  • How unevenly is wealth distributed?
  • Measuring portfolio diversification and home bias
  • What determines demand for assets (e.g. stock market participation)?
  • What determines the share of riskier assets?
  • What drives entrepreneurship?
  • What determines home ownership/demand for housing? To what extent are houses/flats purchased for investment purposes (e.g. second homes)?
  • Do households that are subject to larger economic risks accumulate more assets?
  • What effect did the economic and financial crisis have on households' balance sheets?
Saving, liquidity constraints and the smoothing of consumption
  • What is the extent (and cross-sectional distribution) of liquidity constraints?
  • What is the extent of precautionary saving? What determines the extent of saving for retirement?
  • Do households have enough savings to maintain their consumption after retirement?
  • How well do different households mitigate adverse income and wealth shocks?

Computational finance

  • How well do standard models of consumption choices and labour supply in the presence of uncertainty match wealth distribution/inequality, the structure of balance sheets, home ownership, etc.?
  • Using structural models to estimate relevant "deep" parameters
  • Using structural models to carry out counterfactual simulations of policy-relevant experiments

HFCS supporting microsimulation modelling via EUROMOD

The European Central Bank and the Joint Research Centre (JRC) of the European Commission have signed an agreement to link HFCS data to EUROMOD, the EU tax-benefit microsimulation model maintained by the JRC. EUROMOD is the JRC’s flagship model for calculating the effects of taxes and social benefits on households and work incentives for each EU country and the EU as a whole. In recent years the JRC has, in close collaboration with the ECB, been using the HFCS to develop a new extension of EUROMOD. This extension allows economists to generate household-level data on net income after taxes and social benefits using the HFCS survey, and will help us better understand how monetary policy affects household incomes and work incentives.

Documents and links

Access to the HFCS data
Implementation documents
Background documents
Other international initiatives

Feedback on the HFCS

Users of the HFCS data are invited to provide feedback about the survey and the survey data to hfcs@ecb.europa.eu. This feedback may include, for example:

  • Views on the usefulness of the contents and the user-friendliness of the structure of the micro data
  • Comments and suggestions on the documentation of the HFCS
  • Suggestions on how to improve the contents of the survey
  • Specific questions on the data, such as clarification needs for individual observations or variables
  • Information about possible inconsistencies or pitfalls detected in the micro data
SEE ALSO

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