Understanding household wealth : linking macro and micro data to produce distributional financial accounts.
Date: 2021-08-12
Institutions: European Central Bank
Subjects: EU Member State, European System of Central Banks, account, debt, distribution of wealth, economic statistics, financial statistics, household income

Expert Group on Linking macro and micro data 
for the household sector 

Statistics Paper Series 

Understanding household wealth: 
linking macro and micro data   
to produce distributional   
financial accounts 

No 37 / July 2020 

Disclaimer: This paper should not be reported as representing the views of the European Central Bank (ECB). 
The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

 
Contents 

Abstract 

Executive summary 

1 

2 

3 

Introduction 

Linking Financial Accounts and HFCS: generic differences 

Linking Financial Accounts and HFCS: instrument-specific 
differences 

3.1  Assessment of instrument-specific comparability 

Box 1   How to improve the coverage and comparability of pension 

wealth 

3.2  Matching the HFCS concept of business wealth with National 

Accounts 

3.3  Matching the HFCS concept of housing wealth with National 

Accounts 

3.4  Breakdowns of liabilities from monetary financial institutions’ 

balance sheet statistics 

3.5  New wealth concept 

4 

Methodology for bridging the gap 

4.1  Methods for integrating macro and micro sources: a review of the 

relevant literature 

4.2  Reweighting methods 

4.3 

4.4 

Imputation methods 

Top tail adjustments of survey-implied wealth distributions: Pareto 
models 

Box 2   Pareto models and the HFCS: an overview 

4.5 

Towards a unified approach 

4.6  Preliminary results 

4.7  Methods for assessing the estimations 

5 

Administrative sources 

5.1  Availability of administrative data 

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5.2  Case studies on the use of administrative data: Finland and 

Ireland 

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7 

5.3  Administrative data: the way forward 

User demands and distributional indicators 

Conclusions and way forward 

7.1  Main conclusions 

7.2  Way forward 

References 

Reports 

Research papers 

Annex 1: Second Mandate of the Expert Group on Linking Macro and 

Micro Data for the household sector 

Annex 2a: Correspondence table between Financial Accounts and HFCS 

for financial instruments 

Annex 2b: Correspondence table between National Accounts and HFCS 

for non-financial assets 

Abbreviations 

Acknowledgements 

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Abstract 

The Expert Group on Linking Macro and Micro Data for the household sector 
(EG-LMM) was established in December 2015 within the European System of Central 
Banks (ESCB) with the aim of comparing and bridging macro data (i.e. National 
Accounts/Financial Accounts) and micro data (i.e. the Household Finance and 
Consumption Survey) on wealth. Furthermore, the Expert Group also focused on 
developing distributional results for household macro balance sheets, starting with 
national data from the euro area Member States. 

The Expert Group assessed the extent to which these two sets of statistics could be 
compared and was able to link most balance sheet items. Since, following 
adjustments, the estimates yielded from the micro data were still lower than the macro 
data, an estimation method was developed to gross up the micro data to be in line with 
the macro data results. The methodology delivers estimates of the distribution of 
household wealth that are closely aligned with Financial Accounts aggregates, 
thereby offering valuable new information for the purposes of macroeconomic 
analyses based on such Financial Accounts. 

Further research is needed to examine the robustness of these results and to improve 
the estimation method taking into account country-specific features and information. 
The Expert Group has therefore recommended further work be undertaken with a view 
to compiling experimental distributional results by end-2022. 

Keywords: Household Debt and Wealth; and their distribution. 

JEL Codes: D31, G51. 

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Executive summary 

The ESCB Expert Group on Linking Macro and Micro Data for the household sector 
(EG-LMM) was established in December 2015 with the aim of comparing and bridging 
macro data (i.e. National Accounts/Financial Accounts1) and micro data (i.e. the 
Household Finance and Consumption Survey 2) on wealth. Over the course of its 
existence, the EG-LMM has produced a detailed assessment of the comparability of 
definitions between the Household Finance and Consumption Survey (HFCS) and 
National Accounts (NA) items and has improved the matching of the two data sources. 
In the comparison of the two data sources carried out in this report, a distinction is 
made between two main sets of explanatory factors. 

• 

• 

The first set of factors concerns generic differences that potentially affect all or 
various components of wealth. These include differences that can be relatively 
easily identified (definition of population and household sector, reference 
periods), along with some issues that are more substantial and difficult to quantify 
and adjust for, such as different valuation concepts (self-assessment versus 
European System of Accounts (ESA) valuation), the effect of non-response or 
wrong responses in the survey, and the accuracy of some Financial Accounts 
(FA) asset categories for which statistical sources are weak. It has also proved 
difficult to determine the extent to which the surveys provide effective coverage of 
the households among which wealth appears to be substantially concentrated. In 
addition, the survey leaves scope for aligning its implementation to national 
circumstances, which results in some heterogeneity across countries. Finally, 
macroeconomic Financial Accounts, while more standardised across countries, 
are only available at a fairly high level of aggregation, which limits the scope for 
improving the quality of the linking by matching the sub-categories that can best 
be compared. 

The second set of factors concerns detailed instrument-specific definitions. The 
EG-LMM has developed alternative concepts of adjusted wealth which cover 
items that have medium or high comparability in the two sources. Items which are 
covered in only one of the two sources (notably currency holdings and non-life 
insurance reserves, which are covered only by the Financial Accounts) or which 
have low comparability (occupational pensions, financial derivatives, other 
accounts receivable/payable) are excluded from adjusted wealth. Overall, for 
more than 90% of the value of households’ financial and non-financial assets 
included in euro area National Accounts statistics, a matching item in, or derived 
from, the HFCS has been defined with high or medium comparability. In 
particular, in the recent work of the EG-LMM, the HFCS concepts of housing 
wealth and (albeit with less accuracy) business wealth were bridged to National 
Accounts instruments, following analytical work on their components and thanks 
to the improved availability of National Accounts data on non-financial assets. In 

1    See van de Ven, Fano (2017), and ECB’s quarterly Household Sector Report. 
2    See Household Finance and Consumption Network, 2016a. 

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a majority of euro area countries, and for the most extended concept of adjusted 
wealth, the ratio of grossed-up HFCS to National Accounts totals is around 70% 
to 80%. However, the coverage is lower when focusing purely on financial assets. 
It also varies considerably across instruments and countries. 

Therefore, a significant gap remains between household sector wealth totals derived 
from the two sources. The second focus of the EG-LMM’s recent work was on 
developing a procedure for closing the gap, i.e. the portions of the National Accounts 
totals that are not captured by the HFCS. The procedure developed makes best use of 
the data sources that were available to the EG-LMM. Following this approach, two 
main sources of differences have been identified, namely incomplete reporting of the 
holdings of the wealthiest households and under-reporting of wealth by certain survey 
respondents. These two components are modelled on the basis of: (i) a hurdle 
method, identifying implausible survey values (mainly zero values) and replacing them 
with replies from similar households; and (ii) an iterative process involving (a) a 
calibration procedure and (b) a Pareto model and drawing on a wide stream of 
economic literature attributing a Pareto distribution to the top part of the wealth 
distribution. The parameters of the Pareto model are determined on the basis of the 
HFCS as well as data from lists of the wealthiest households, where available. This 
model is used to reassess the number of very wealthy households assumed not to be 
(fully) captured in the HFCS weights. 

The estimation method developed by the EG-LMM allows for the compilation of 
baseline estimates for distributional wealth accounts for the euro area countries. 
These estimates can – and should as far as possible – be refined using additional 
information available at national level, such as administrative data sources. 

While initial comparisons have shown that the results are plausible, it is important to 
be aware that these results inevitably carry a degree of uncertainty. The EG-LMM 
therefore considers the estimation methods as a work in progress, and the methods 
will be refined in the light of experience. 

The EG-LMM has identified a number of areas where further improvements should be 
achievable and recommends the following four future workstreams. 

• 

• 

Finalising the fine-tuned conceptual linking of the HFCS and National Accounts, 
e.g. by using more detailed breakdowns taken from monetary financial 
institutions’ (MFI) balance sheet statistics and by including data on non-financial 
assets to achieve greater comparability. 

Fully implementing and further refining estimation methods, including the use of 
administrative sources, wherever possible. The European Central Bank (ECB) 
could run the estimation methods centrally and would submit the estimates to the 
national counterparts, which could validate and, wherever possible, improve the 
estimates on the basis of additional data available from national sources. 
Developing the use of administrative sources is a task mainly performed at the 
national level, although a concerted effort by the ESCB to strengthen the status 
of statistics on Distributional National Accounts allowing national central banks to 
get access to administrative data sources could help drive the process. 

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• 

• 

Compiling annual results, based on interpolation for back data, and developing 
an estimation method for the latest years. 

Conducting research on consistency with estimates on distributional results on 
income and consumption. 

Further steps in areas that are the responsibility of other groups would also be 
beneficial. Such areas include the coverage and valuation of unlisted shares and other 
equity, which is the responsibility of the ESCB Working Group on Financial Accounts 
(WG FA); efforts by the ESCB Household Finance and Consumption Network (HFCN) 
to enhance data collection regarding business wealth or to distinguish incorporated 
from unincorporated sole proprietors; and the work done by Eurostat and the 
European Statistical System to close gaps in the ESA transmission programme and 
improve the availability of data relating to land. 

The work performed by the EG-LMM has also highlighted the increasing relevance of 
distributional financial information for the household sector, as confirmed by a small 
survey of selected users and as reflected by the emergence of websites providing 
estimates compiled by academic researchers. In this context, the EG-LMM highlights 
the importance of making available to the public at large some results on the 
distribution of wealth among European households. This will require adequate 
communication to make clear the current caveats. The results shall be clearly 
distinguished from distributional indicators driven from survey data, the underlying 
uncertainty of indicators resulting from various estimation methodologies must be 
emphasised and the data will need to be labelled ‘‘experimental”. 

Finally, the EG-LMM recommends continuing and stepping up cooperation within the 
scope of the G20 Data Gaps Initiative led by the Organisation for Economic 
Co-operation and Development (OECD) and the related OECD/Eurostat expert group 
(Recommendation 9 of the Data Gaps Initiative). While full – and fully aligned – 
Distributional Financial and Non-Financial Accounts remain a long-term objective, it is 
important that efforts towards achieving this objective are aligned and that experts 
collaborate via continuous interaction between international groups working on 
developing Distributional National Accounts indicators. 

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1 

Introduction 

The financial crisis of 2008 and changes in the economic environment have increased 
demand for more timely, coherent and consistent distributional information for the 
household sector. In particular, from the perspective of central banks, changes in 
interest rates carried out to implement monetary policy may well have different effects 
on different groups of households (as is the case for savers and borrowers, or renters 
and home owners), and these groups may respond to such changes in different ways. 
To that extent, it is important for central banks to have an insight into the distribution of 
wealth and its components in order to assess how their monetary policy is transmitted. 

These new data requirements are reflected in the G20 Data Gaps Initiative, which 
encourages the production and dissemination of distributional information on income, 
consumption, savings and wealth for the household sector. The Organisation for 
Economic Co-operation and Development (OECD) is mandated to coordinate this 
work and cooperate closely with the European Central Bank (ECB) and Eurostat. As 
part of this mandate, the OECD, together with Eurostat, established an expert group to 
investigate how to add distributional information to the National Accounts (NA). In 
addition, Eurostat and the European Statistical System Committee agreed in the 
Vienna Memorandum of 2016 to work towards this objective, in close cooperation with 
the ECB, the European System of Central Banks (ESCB) and the OECD. Both of 
these initiatives currently focus on consumption, income and savings. 

The ESCB Expert Group on Linking Macro and Micro Data for the household sector 
(EG-LMM) was established in December 2015 with the purpose of comparing and 
bridging the Financial Accounts and the Household Finance and Consumption Survey 
(HFCS). The HFCS was launched in 2007, and three waves of the survey have been 
finalised to date 3. In March 2017, the EG-LMM submitted a conclusive report on the 
work conducted during its first mandate. The EG-LMM was subsequently mandated to 
continue its work. 

The EG-LMM’s second mandate was divided into two workstreams. The tasks in the 
first workstream included further assessing the generic and instrument-specific 
differences and linkages, and finding the reasons for the unexplained differences 
between the two sets of statistics. On the basis of this analysis, the EG-LMM was 
mandated to develop recommendations to improve the linking of the HFCS and the 
Financial Accounts and to achieve better coverage ratios. The tasks in the second 
workstream included defining – on the basis of user priorities – distributional indicators 
for the household sector balance sheet and the methodology underpinning annual or 
multi-annual results. As housing wealth and related mortgages are major components 
of household wealth, it was considered essential to extend the analysis from the 
financial to the complete household balance sheet, including non-financial assets. 

3   

In this report results from the two first survey waves are used. The latest HFCS results have been 
published shortly before the publication of this report. 

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The EG-LMM was jointly chaired by the ECB and Banca d’Italia. The members were 
from 15 countries (Denmark, Germany, Greece, Spain, France, Hungary, Ireland, 
Italy, Luxembourg, the Netherlands, Austria, Portugal, Slovakia, Finland and the UK; 
see the composition of the EG-LMM in Annex 2) and contributed expertise both on 
Financial Accounts and on the HFCS. Eurostat and the OECD also participated. 
During the second mandate, the EG-LMM held four meetings. 

This report is organised as follows. Chapters 2 and 3 describe the generic and 
instrument-specific differences between the HFCS and the Financial Accounts, as 
identified in the “linking” exercise in the first workstream of the EG-LMM’s second 
mandate. Chapter 4 proposes a method for estimating the distribution of the remaining 
(and still significant) gap between adjusted HFCS data and National Accounts data. 
Chapter 5 analyses the availability of administrative data sources which could be used 
for improving the results of distributional balance sheets. Chapter 6 reports on the 
results of a user survey carried out to identify users’ priorities in terms of output. 
Chapter 7 presents the main conclusions of the work performed and describes 
avenues envisaged for the way forward. 

The present report summarises the results of the work of the EG-LMM carried out 
between 2017 and early 2019, and summarises possible avenues for further 
improvement. Where useful, additional information is provided on earlier results of 
group. 

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2 

Linking Financial Accounts and HFCS: 
generic differences 

The EG-LMM has identified two sets of factors causing differences between these two 
data sources. The first concerns “generic differences”, i.e. differences affecting all or 
most components of wealth and the macro-micro comparison. The second set of 
factors causing differences relates to instrument-specific issues affecting the various 
asset and debt categories covered by the HFCS and the National Accounts. These 
instrument-specific differences are described in detail in the next chapter. 

The first generic difference relates to the aims and set-up of the Financial Accounts 
and the HFCS. The aim of the HFCS is to gain more insight into the economic 
behaviour of households and into the distribution of wealth and liabilities among 
households and household groups. The main data source is surveys conducted for a 
sample of households via personal interviews. Data collection in the HFCS is based 
on a set of common definitions and descriptive features and follows an output-oriented 
approach. The aim of the Financial Accounts is to provide timely macroeconomic 
information on the balance sheets and on the financing and investment of the entire 
household sector. The Financial Accounts do not exclusively focus on the household 
sector but rather describe relationships between all institutional sectors. For the 
household sector, Financial Accounts data are usually based on counterpart data, 
i.e. data reported by financial corporations, most of them produced under ECB 
statistical regulations. The definitions of instruments, sectors and concepts such as 
valuation are given by the European System of Accounts (ESA) 2010 and are 
mandatory in all EU countries. 

Second, the two data sources have slightly different definitions of a household. In the 
HFCS, a household is defined as “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”. Persons living in institutions, e.g. in prisons or 
retirement homes, as well as homeless people, are excluded from the definition of a 
household in most countries. Non-resident persons can be members of a resident 
household if they are temporarily absent but otherwise fulfil the criteria of a household 
member. In the National Accounts, households have a similar definition, but are 
regarded as an institutional sector, which covers the whole resident population and 
does not exclude any groups per se. Under ESA 2010, the unit is resident in the 
economic territory with which it has the strongest connection, expressed as its centre 
of predominant interest (ESA 2010, 18.08). The distinction between producer 
households (to be classified within the household sector) and quasi-corporations and 
corporations (to be classified within the non-financial corporations sector) in the 
National Accounts is another potential source of discrepancy that will be discussed 
later in this document. Because of the population groups excluded from the HFCS 
target population, the population in the HFCS should be lower than the corresponding 
Financial Accounts figure. Consequently, the data need to be adjusted for differences 

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in the size of the target population before comparing wealth figures between macro 
and micro sources. 

The periodicity and timeliness of the statistics are also dissimilar. The HFCS is 
conducted every three years in most countries, with a relatively long time lag between 
data collection and data availability. In the HFCS, most data refer to the time of the 
interview. The Financial Accounts are published quarterly and are available no later 
than four months after the end of the quarter. The balance sheet is a quarter-end 
balance sheet. Aligning the reference periods is another potential source of concern in 
macro-micro comparisons, particularly for several types of financial assets (e.g. listed 
shares) whose values vary significantly even within individual months. 

The final generic difference refers to the valuation of households’ assets, debt and 
income. In the HFCS, these are based on the households’ self-evaluation. This was a 
conscious choice made when developing the survey, since the self-evaluated value of 
personal wealth is what drives the economic decisions of households. However, 
households’ perception of asset values may not always be aligned with market values, 
particularly during times when asset prices are changing rapidly. In the National 
Accounts, all the financial and non-financial instruments are valued at market prices. 
When a market price is not available, then flows and stocks may be valued at the 
discounted present value of expected future returns. For items with quoted market 
prices (e.g. listed shares and debt securities), the valuation is straightforward. 
However, the valuation for unlisted shares and in particular holdings of other equity is 
less accurate as the valuation of these assets requires assumptions and modelling. As 
regards National Accounts data on non-financial assets, the produced capital stock is 
based on the perpetual inventory method in almost all countries. The estimation of 
National Accounts data on land is more complicated and varies from country to 
country. 

In addition to the generic differences mentioned above, there are some features both 
in the HFCS and in the Financial Accounts that can explain the differences between 
values on household wealth derived from the two sets of statistics. In household 
surveys, the estimates are affected by reporting and sampling bias. Reporting bias 
occurs when a value that is recorded or a household in a sample departs from the 
actual value. Response bias occurs when the structure of households that participate 
in a survey is different from the structure of the target population. In general, the bias in 
the HFCS caused by unit non-response is reduced by weight adjustments. However, 
sampling bias can have a particularly large impact on wealth data given that the 
distribution of wealth is right-skewed, and consequently it is crucial also to get a 
representative sample from the very top of the distribution. To minimise sampling bias, 
the majority of countries oversample wealthy households in the HFCS. In addition to 
unit non-response, a frequently observed problem in household surveys is item 
non-response, i.e. the inability of participating households to provide answers to 
individual questions. The HFCS provides a full dataset for all balance sheet and 
income variables. Whenever the value of a variable is missing, its value is imputed. 

The Financial Accounts are typically based on other statistical sources and the 
validation of primary statistics. The errors in estimation can be due to one of two 
reasons: either the source statistics do not have the same coverage as the National 

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Accounts concept (and this is not possible to fully correct when they are incorporated 
into the Financial Accounts) or there is a measurement error in the source statistics. In 
practice, the household sector is mostly based on the counterpart reporting data, 
i.e. the Financial Accounts data reported by banks, investment funds, insurance 
corporations and pension funds. It is therefore necessary to review the quality of the 
different statistical sources in order to assess measurement issues. While the 
counterpart data can be considered to be of relatively good overall, there can be 
potential problems in allocating units to the right sectors. Normally, the classification of 
units is done by the reporting institution, i.e. the bank or insurance corporation. Finally, 
the data balancing process affects the Financial Accounts results. The Financial 
Accounts may need to allow some bias in the household sector to satisfy the balancing 
constraints, i.e. the ultimate aim is not to minimise the bias in the household sector, 
but instead there is a dual objective of minimising bias in the estimates for the 
economy as a whole and of minimising statistical discrepancies within the system. 

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3 

Linking Financial Accounts and HFCS: 
instrument-specific differences 

3.1 

Assessment of instrument-specific comparability 

Given the long-term aim of including distributional information within the Financial 
Accounts framework, it is essential to assess the conceptual comparability of items 
included in the comparison. The EG-LMM assessed the similarity of concepts and 
definitions used for asset and liability categories in the Financial Accounts and the 
HFCS. A detailed correspondence table is provided as Annex 2a for financial 
instruments and Annex 2b for non-financial instruments. 

As a result of this work, the following Financial Accounts instruments were identified 
as having high conceptual comparability. 

Deposits are defined in a similar way in the Financial Accounts and the HFCS. 
Furthermore, the split of deposits into “F.22 Transferable deposits” and “F.29 Other 
deposits” in the Financial Accounts mirrors the survey definitions of sight accounts and 
savings deposits. “F.22 Transferable deposits” refers to deposits that can be used 
directly for making payments. The same definition is used for sight accounts in the 
HFCS. Similarly, “F.29 Other deposits” corresponds to the survey definition of savings 
deposits, which covers savings accounts, time deposits, certificates of deposit and 
other such deposits including non-transferable overnight deposits. 

Debt securities as defined in the Financial Accounts include “bonds” as defined in the 
survey. In ESA 2010, debt securities are split into “F.31 Short-term” and “F.32 
Long-term”. This original maturity breakdown is not available in the HFCS. Both the 
Financial Accounts and the HFCS require market valuation for debt securities. Overall, 
the definitions for debt securities are highly comparable. 

The survey asks about any “amount of money owed” to the household, an item which 
is comparable to the Financial Accounts instrument “F.4 Loans” (assets). However, 
the ESA definition may be somewhat narrower, as it refers to features 
(non-negotiable, interest-bearing) that are not explicitly mentioned in the HFCS. In the 
HFCS, loans are defined as any amounts that are expected to be repaid at some point 
in the future. 

The Financial Accounts instrument “F.511 Listed shares” refers to equity securities 
listed on an exchange. This has conceptually high comparability with the HFCS item 
“publicly traded shares”. 

For “Investment fund shares or units”, the definition used in the survey is the same as 
that used in the Financial Accounts. Investment fund shares are subdivided into 
“Money market fund (MMF) shares or units” (F.521) and “Non-MMF investment fund 
shares/units” (F.522) in the Financial Accounts. This classification is an exact match 
for the one in the survey, where there is an even more detailed breakdown into “a – 

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Funds predominantly investing in equity”, “b – Funds predominantly investing in 
bonds”, “c – Funds predominantly investing in money market instruments”, “d – Funds 
predominantly investing in real estate”, “e – Hedge funds” and “f – Other fund types 
(specify)”. However, this split (which is similar to the breakdown in the ECB investment 
fund statistics) is not available in every country, and only the total market value is 
among the standard imputed variables. Market valuation is used both in the HFCS and 
the Financial Accounts. 

On the liabilities side, “F.4 Loans” (liabilities) are split into short-term and long-term 
loans in the Financial Accounts. The concept of maturity is the same as used in the 
survey (original maturity). In the survey, the main categories for loans are mortgage 
debt (which is split into mortgages on the household main residence (HMR) and 
mortgages on other properties), non-mortgage debt, credit line/overdraft balance, 
credit cards balance, private loans and balance of non-collateralised loans. The ECB 
can calculate a breakdown into mortgage and non-mortgage loans based on monetary 
financial institutions’ (MFI) statistics (see Section 3.4). 

The following Financial Accounts instruments have been identified as having medium 
conceptual comparability with the HFCS: "insurance, pension and standardised 
guaranteed schemes”, “unlisted shares”, “other equity” and “non-financial assets”. The 
comparability of “unlisted shares” and “other equity”, as well as that of “non-financial 
assets”, is analysed in detail in Sections 3.2 and 3.3. Regarding pension wealth, life 
insurance and voluntary pension plans have medium comparability. The Financial 
Accounts instrument “life insurance and annuity entitlements” (F.62) matches the 
HFCS category “voluntary pension schemes and whole life insurance contracts”, 
despite different terminology (the Financial Accounts do not refer explicitly to voluntary 
pension schemes). An important difference is, however, caused by the valuation of the 
data. Data on life insurance, annuities and pension funds in the Financial Accounts are 
typically based on actuary information about technical reserves reported by insurance 
corporations or pension funds and reflect the net present value of the contract. In the 
HFCS the value of accounts is measured as the current value, i.e. “how much they are 
worth at the moment”, which could be either an amount similar to the present value or 
a current (and lower) early liquidation value of the insurance contract (deducting a 
surrender charge). A rigorous assessment of this would, however, require further 
information on the national questionnaires and the value that is typically reported by 
households. 

Of the remaining Financial Accounts instruments, data on currency and other 
accounts payable are not collected in the HFCS, while occupational pension wealth, 
financial derivatives and employee stock options and other accounts receivable have 
low conceptual coverage. The HFCS collects data on the item “managed accounts”, 
for which there is no corresponding instrument in the Financial Accounts. The assets 
and liabilities of managed accounts are recorded in the corresponding Financial 
Accounts asset types. 

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Box 1 
How to improve the coverage and comparability of pension wealth 

The lack of comparability and coverage of data on household wealth in pensions has been identified 
as a significant shortcoming in the way that the macro-micro comparison is currently carried out. This 
box discusses how coverage and comparability can be improved. 

Data collection in a household survey is restricted by the feasibility of collecting data on items whose 
values can be reported by respondents. In the case of pension plans without an account balance, 
respondents might know how many years they have contributed for, and what they might expect to 
receive under the current rules of the plans after terminating work, but the present value of this 
entitlement may be determined only through additional estimation methods that are not carried out 
during the production of HFCS statistics. 

Pension plans can be divided into defined contribution schemes, defined benefit schemes (DBSs), 
and hybrid schemes (i.e. those that have characteristics of the two). For DBS’s in particular, it may be 
difficult for households to provide a present value of future pension payments in a survey, since they 
usually do not have an account balance. In the Financial Accounts the value of DBS’s is the actuarial 
value which is based on the insurance companies’ and pension funds’ balance sheet data. These 
data are, in turn, typically based on the undertaking’s data compiled for supervisory reporting 
purposes. Here, the ESA requires the inclusion not only of the net present value of future benefits less 
premiums, but also of the reserve for bonuses and the so-called equalisation reserve to cover 
unexpected losses (see also ESA 16.44). While the internal consistency of the Financial Accounts 
requires that the liabilities/reserves of one sector have to be the assets/claims of another sector, 
these amounts exceed the amounts that the individual household may consider as its personal 
“account”. 

Four countries (Austria, Belgium, France and Slovakia) indicated that they include some DBSs in the 
HFCS data on occupational pension wealth. Other countries either reported that DBSs do not exist or 
that they did not know whether there were DBSs which had an account balance. As regards the 
Financial Accounts, 12 countries indicated that they also include DBSs in National Accounts pension 
entitlements (F.63). Ten of these 12 countries are able to distinguish DBSs from other pension 
entitlements. Moreover, as of the end of 2017, a new ESA transmission table has provided 
comparable data on social insurance pensions (and will continue to do so every three years). 

In addition to the value of pension plans that have an account balance, the HFCS collects information 
that allows the current value of pension funds without an account balance to be estimated. This 
information includes the number of years for which contributions have been made to the pension 
schemes, the expected age at which pensions will be collected and the amount of the pension 
payment flows. For public pension plans, these are part of the core questionnaire data collected in all 
or most countries. For occupational and private pension plans, these details belong to the non-core 
part of the questionnaire and are collected in a few individual countries. In the third wave of the HFCS, 
more information on all types of pension plans (e.g. number of years for which contributions have 
been made) has been collected in all countries as core variables. 

Three countries (France, Italy and the Netherlands) in the first HFCS wave and four countries (the 
same three plus Finland) in the second HFCS wave indicated that they collect data – either as part of 
the HFCS or as part of some other survey – on pension plans for persons that are already receiving 
benefits. Furthermore, because of its high significance at national level, the Netherlands and Finland 

ECB Statistics Paper Series No 37 / July 2020 

14 

 
 
 
provide estimates of the value of occupational pension plans without an account balance for the 
second wave of the HFCS. 

In the Financial Accounts, the main delineation criteria between social security pension plans (which 
are not covered) and other employment related pension schemes (which are covered) is whether the 
government is controlling the pension plan beyond its role as a supervisory body or employer, 
i.e. whether the government can change the conditions of the pension plan such as the retirement 
age or the level of the pension. 

In a survey, the respondent may not be aware of whether the government controls the pension plans 
he/she is participating in. In the national HFCS, the names of pension plans are often collected. In the 
EG-LMM questionnaire, it was asked whether government-controlled pension schemes were 
included in the data collection of the HFCS on occupational pensions. For the second wave, seven 
HFCS countries answered “yes”, 11 countries “no”. Others indicated that the government decisions 
can affect all pension systems, i.e. all the pension systems are subject to pension laws, but this does 
not necessarily mean that these pension schemes are “controlled by the government”. 

To conclude, there are large cross-country differences in the structures of occupational pensions, 
which also complicates the comparable measurement of pensions in surveys and the Financial 
Accounts. The main coverage criteria differ (accounting balance versus non-social security); in 
addition the survey leaves some flexibility as regards the coverage of different pension forms. 
Different terminology complicates the bridging further. Additional work could be carried out in the 
following three areas to establish better bridging and better coverage of pension wealth. 

1.  To correctly understand the collection of occupational pension wealth data in the HFCS, more 
detailed country-level information on different pension schemes is needed, e.g. whether DBSs 
can have an account balance, in other words whether the terms “defined contribution scheme” 
and “with an account balance” are synonyms. 

2. 

In addition, various methods using survey data and/or external data sources could be used to 
estimate the values of occupational pension wealth for plans that do not have an account and for 
individuals that are already receiving pension benefits. Experiences from the Netherlands and 
Finland may be useful in developing such methodologies; 

3.  New and completed data on pension entitlements in the Financial Accounts (ESA Table 29) were 

released at the end of 2017 and may be used to improve the completeness of the bridging for 
pensions. 

Conversely, for the HFCS concepts of business wealth and housing wealth, it was not 
possible to link the two sets of statistics directly, and additional steps were required, as 
described in Sections 3.2 and 3.3. 

ECB Statistics Paper Series No 37 / July 2020 

15 

 
 
 
3.2 

Matching the HFCS concept of business wealth with 
National Accounts 

As mentioned above, the EG-LMM has taken steps to link the HFCS concept of 
business wealth with National Accounts instruments. In addition, this section also 
covers some of the work carried out on non-financial assets, to the extent that the work 
involved the identification, and in some cases collection and/or estimation, of National 
Accounts data on non-financial assets comparable with the HFCS concept of business 
wealth, i.e. non-financial assets excluding housing wealth. 

Splitting the HFCS business wealth concept into National Accounts 
instruments 

In the HFCS, wealth from private businesses is split into self-employment and 
non-self-employment business wealth. Self-employment businesses are defined as 
businesses in which a household member is either self-employed or has an active role 
in running the business. These businesses can be either sole proprietorships, 
independent professionals, partnerships or limited liability companies in which 
self-employed household members are actively participating. Non-self-employment 
business wealth refers to passive investment only. 

In the Financial Accounts, there is no “business wealth” concept. If a household runs a 
business, either the business is recognised (according to certain criteria) as a 
corporation or so-called quasi-corporation, or it is not, and the household is simply 
regarded as a “producer household”. 

• 

• 

In the first case, the assets identified as business wealth in the HFCS would be 
recorded under “F.512 Unlisted shares” or “F.519 Other equity”, which represent 
the market value of unlisted shares and other equity held by households in 
corporations and quasi-corporations. 

In the second case, the assets identified as business wealth in the HFCS would 
be shown in the Financial Accounts as financial and non-financial assets of 
households, not distinguishable from “non-business” assets and liabilities. A 
further difference is that the Financial Accounts record these in gross terms on 
both sides of the balance sheet, while the underlying concept of the HFCS is to 
record the expected value of the business if it is sold (i.e. a net value). 

To maximise the conceptual comparability of business-related assets and liabilities, 
the first step is therefore to separate assets included in self-employed business wealth 
in the HFCS which correspond to the Financial Accounts aggregated item “unlisted 
shares and other equity”. This is conducted in two steps. First, based on information 
available in the HFCS, all legal forms other than sole proprietors and partnerships are 
assumed to issue F.512 and/or F.519. These enterprises are incorporated businesses 
and recognised as such in the Financial Accounts, so that, as explained above, their 
assets are recorded on the Financial Accounts balance sheets of the corporation 
sector, and their equity is recorded as unlisted shares or other equity (as a liability of 

ECB Statistics Paper Series No 37 / July 2020 

16 

 
 
the corporation sector and an asset of the household sector). Second, for 
unincorporated businesses (i.e. referring to the HFCS legal forms other than “sole 
proprietor” and “partnership”), country-specific criteria are applied to make a 
distinction between those regarded in the Financial Accounts as “producer 
households” and those treated as “quasi-corporations”, as explained above. 

In National Accounts practice, and depending on the legal setting and available data 
sources, countries apply different practical borderlines between producer households 
and quasi-corporations. In November 2014, Eurostat’s National Accounts Working 
Group approved several “decision trees” which are meant to assist non-financial 
accounts compilers in approximating this sector delineation. In addition, information 
on the recording of business assets was collected in an EG-LMM questionnaire in 
June 2016. 

These steps allow for a comprehensive reclassification of HFCS self-employment 
business wealth into unlisted shares and other equity and into other Financial 
Accounts instruments. A bridging table assessing the correspondence between HFCS 
business wealth and National Accounts instruments for most EU countries is shown 
below. 

Table 1 
Bridging table for National Accounts instruments and HFCS business wealth 

NA item 

HFCS item 

HFCS details 

Countries   

F.512 Unlisted 
shares + F.519 
Other equity 

DA2104 Non-self-employment private 
businesses 

DA1140 Self-employment business 
wealth 

If legal form not sole proprietor or 
partnership 

All 

All 

Other financial 
instruments and 
non-financial 
assets 

DA1140 Self-employment business 
wealth 

Additionally, if legal form partnership 

Additionally, if legal form partnership, or 
legal form sole proprietor and number of 
employees above threshold (in brackets) 

If legal form sole proprietor or partnership 

If legal form sole proprietor 

BE, EE, ES, LT, LV, HU, 
PT, SI 

AT (50), IT (5), FI (2), IE 
(10), PL (10) 

CY, DE, FR, LU, NL, 
GR, SK 

BE, EE, ES, LT, LV, HU, 
PT, SI 

If legal form sole proprietor and number of 
employees below threshold (in brackets) 

AT (50), IT (5), FI (2), IE 
(10), PL (10) 

Classification criteria for sole proprietors 
and partnerships not available 

HR, MT 

The following National Accounts instruments had previously been assessed as having 
high comparability: deposits, debt securities, loans, listed shares and mutual fund 
shares. Allowing a coherent macro-micro comparison between Financial Accounts 
unlisted shares and other equity on the one hand, and HFCS business assets on the 
other, increases the scope of the distributional analysis significantly. 

Chart 1 distributes the instruments included in Financial Accounts (gross) financial 
wealth by their conceptual comparability with HFCS assets. The blue bar indicates the 
share of Financial Accounts instruments with high comparability to related HFCS 
components. The red and green bars show 2 items with medium comparability, i.e. the 

ECB Statistics Paper Series No 37 / July 2020 

17 

 
 
 
 
Financial Accounts instrument “F.62 Life insurance and annuity entitlements” 
corresponding to the HFCS item “voluntary pensions” (red bar) and the Financial 
Accounts item “F.512+F.519 Unlisted shares and other equity” related to the HFCS 
item “business wealth” (green bar). After adding up these items with high and medium 
conceptual comparability, the macro-micro comparison covers more than 
three-quarters of the Financial Accounts balance sheets in most euro area countries. 

Chart 1 
Proportion of Financial Accounts financial wealth which can be compared with HFCS 
results 

High comparability
Life insurance and annuity entitlements

Unlisted shares
Other

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

AT

BE CY DE

EE

ES

FI

FR GR HU

IE

IT

LU

LV MT NL

PL

PT

SI

SK

Having achieved a relatively coherent comparison between HFCS assets 
corresponding to the Financial Accounts items unlisted shares and other equity, the 
next step should be to match the HFCS self-employment business wealth with the 
financial assets and non-financial assets in the balance sheets of sole proprietors and 
partnerships in the Financial Accounts. However, the breakdown into non-financial 
and financial assets is not available from the HFCS. 

The EG-LMM also concluded that the comparability between the HFCS and the 
National Accounts could be further improved in the future with respect to the following 
two aspects. 

First, as described above, the HFCS data do not include information on how to classify 
the remaining part of self-employment business wealth into various Financial 
Accounts instruments and non-financial assets. The EG-LMM has therefore included 
all self-employment business assets of producer households recorded in the HFCS in 
the non-financial wealth of households until better information becomes available. 
This assumption may be justified by the fact that small businesses typically do not 
have high levels of financial assets, but mostly own machines, transport vehicles or 
buildings used for their business. 

Second, the EG-LMM has proposed splitting the HFCS category “sole proprietor” into 
“incorporated” and “unincorporated” sole proprietors. This change will be implemented 
in the 2020 wave of the HFCS and will improve the sector delineation. In the 

ECB Statistics Paper Series No 37 / July 2020 

18 

 
 
 
 
macro-micro comparison, the net assets of incorporated sole proprietors would be 
recorded as unlisted shares and other equity held by households. 

In addition to these two areas of potential improvement, the EG-LMM concluded that a 
remaining general limitation on comparability is the fact that the HFCS collects the net 
value of self-employment businesses, while the Financial Accounts record assets and 
liabilities separately on a gross basis. There is, however, a certain heterogeneity as to 
how exactly the net recording is implemented in the national versions of the HFCS, 
and the actual impact of this factor at country level could not be fully clarified. 

Identification in National Accounts of non-financial assets 
comparable with HFCS business wealth 

As explained above, the household sector in the National Accounts includes 
households running businesses not recognised as corporations or quasi-corporations. 
In order to improve the link with the HFCS, a distinction needs to be made between 
housing wealth (collected as a separate asset in the HFCS) and other non-financial 
assets. The relevant instrument in the National Accounts is “produced non-financial 
assets”, which is made up of three components, namely: (i) dwellings; (ii) inventories; 
and (iii) land. To be able to link HFCS with these National Accounts data, efforts are 
needed to solve two practical difficulties, namely incomplete National Accounts data 
on non-financial assets (covered by the ESA transmission programme) in many 
countries and limited availability of data on “land underlying dwellings” (not covered by 
the ESA transmission programme). It is useful to be able to distinguish between land 
that is part of housing wealth and land that is mostly used for business purposes 
(e.g. agricultural land). 

The EG-LMM identified available data sources on the various components of 
non-financial assets and produced estimates of the split between “housing wealth” 
and “other non-financial assets”, the latter being part of the HFCS concept of 
non-financial business wealth. The main challenge with these tasks has been 
estimating housing wealth; this is explained in detail in Section 3.3. 

Table 2 summarises the current data availability of the National Accounts components 
of “non-financial assets”. Three approaches can be used to distinguish non-financial 
business wealth (while countries may have more detailed data available to improve 
the linking). 

•  Method N1: According to this method, “non-financial business wealth” is equal to 
the sum of fixed assets excluding dwellings, plus land not underlying dwellings, 
plus inventories. This is the most correct estimation, although it includes assets 
of non-profit institutions serving households (NPISHs). 

•  Method N2: If the country provides all the required ESA transmission programme 
data but no separate data for land underlying dwellings, “non-financial business 
wealth” is estimated as the sum of fixed assets minus dwellings and inventories. 
This estimate does not include land used for production purposes (e.g. that of 
sole proprietor farmers). NPISHs are included as in method N1. 

ECB Statistics Paper Series No 37 / July 2020 

19 

 
 
•  Method N3: If the country provides only fixed assets and dwellings, “non-financial 
business wealth” is the difference between these two items. In contrast to method 
N2, inventories are not included, though this is generally a very small item in the 
National Accounts (less than 2% of produced non-financial assets in many 
countries). 

Table 2 
Methods of estimating “non-financial business wealth” from National Accounts 
sources 

Method 

Land not underlying dwellings 

Inventories 

Fixed assets – dwellings 

Belgium 

Germany 

Estonia 

Ireland 

Greece 

Spain   

France 

Italy 

Cyprus 

Latvia 

Luxembourg 

Hungary 

Malta 

Netherlands 

Austria 

Poland 

Portugal 

Slovenia 

Slovakia 

Finland 

N1 

N1 

N2 

None 

N2 

None 

N1 

N1 

N3 

N2 

N3 

N2 

None 

N1 

N3 

N2 

N1 

N2 

N2 

N1 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

x 

X 

X 

x 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

Note: “Land not underlying dwellings” in the table refers to the difference between total land and land underlying dwellings. The “X” 
indicates that these data are available. 

3.3 

Matching the HFCS concept of housing wealth with 
National Accounts 

The ESA transmission programme does not contain a variable on housing wealth. 
However, such data can be derived by adding up dwellings and land underlying 
dwellings. There are three issues that make the comparison difficult: (i) in the ESA 
transmission programme, NPISHs are included; (ii) data underlying dwellings are 
often not separated from other land; and (iii) National Accounts data on non-financial 
assets do not include housing wealth abroad, i.e. mainly holiday homes which are 
abroad, but instead these are recorded under “other equity” held by households. 
Conversely, in the HFCS, NPISHs are excluded, land underlying dwellings and other 
land are separated, and holiday homes abroad are included in the concept of housing 
wealth. 

ECB Statistics Paper Series No 37 / July 2020 

20 

 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
In addition, even though the National Accounts data became obligatory as a part of the 
transmission programme at the end of 2017, there are still some country-specific 
derogations in place (ending at the latest in 2020). 

To derive best estimates of housing wealth, land data reported in the ESA 
transmission programme had to be complemented either by national data sources on 
housing wealth or by available national data on land underlying dwellings. Once 
housing wealth is estimated, the rest of the non-financial assets are assumed to be 
self-employed non-financial assets, as described in the second part of Section 3.2. 

Table 3 shows the components that are available for deriving National 
Accounts-based estimates of housing wealth. For seven countries, information on 
dwellings and land underlying dwellings can be combined to derive a good estimate of 
housing wealth (although it covers NPISHs): these are the countries for which “method 
N1” was used to assess non-financial business wealth. In Table 3 this approach is 
described as “method H1”. For a further six countries, described as using “method H2” 
in Table 3, all land (along with dwellings) needs to be included to derive housing 
wealth. Finally, for the last seven countries, no estimate for National Accounts housing 
wealth can be derived, as data on either dwellings or land, or both, are missing: this is 
described as “method H3” (where dwellings are available) or “none” (where dwellings 
are not available). As mentioned above, by 2020 the situation is expected to improve, 
as data on “total land” and on “dwellings” are to be transmitted to Eurostat. 

Table 3 
Estimates of housing wealth from National Accounts sources 

Method 

Land underlying dwellings 

Total land 

Dwellings 

Belgium 

Germany 

Estonia 

Ireland 

Greece 

Spain   

France 

Italy 

Cyprus 

Latvia 

Luxembourg 

Hungary 

Malta 

Netherlands 

Austria 

Poland 

Portugal 

Slovenia 

Slovakia 

Finland 

H1 

H1 

H2 

None 

H3 

None 

H1 

H1 

H3 

H3 

H2 

H3 

H3 

H1 

H2 

H2 

H1 

H2 

H2 

H1 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

Note: all the data for Ireland are confidential except for those relating to land. Therefore, no housing estimate is calculated for Ireland. 
The “X” indicates that these data are available. 

ECB Statistics Paper Series No 37 / July 2020 

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3.4 

Breakdowns of liabilities from monetary financial 
institutions’ balance sheet statistics 

Although conceptually comparable, households’ liabilities in the Financial Accounts 
and the HFCS can only be compared at a very aggregated level, i.e. for total liabilities. 
However, statistics on monetary financial institutions’ (MFI) balance sheet items (MFI 
BSI statistics) provide more detailed breakdowns of loans granted to households at 
the macro level. 4  In these statistics, households’ liabilities are reported separately for 
housing loans, consumption loans and other loans. In addition, other loans granted to 
sole proprietors and partnerships without legal status can be identified separately for 
most euro area countries. The separation of housing loans from non-housing loans in 
particular is very useful for the macro-micro comparison, since housing loans are 
generally more accurately reported in survey data than non-housing loans, and the 
distribution of housing loans across different household groups is very different from 
the distribution of non-housing loans. The aggregate levels of liabilities in MFI BSI 
statistics are coherent with the Financial Accounts data, making it a useful data source 
to further improve the macro-micro linkage. 

In practice, the Financial Accounts figure on total liabilities can be multiplied by the 
share of housing loans of all households’ liabilities in a country, as reported in the MFI 
BSI statistics. This aggregate can be considered conceptually comparable to the 
HFCS concept of housing loans. Similarly, Financial Accounts total liabilities multiplied 
by the share of non-housing loans, as reported in the MFI BSI statistics, is comparable 
with the HFCS concept non-housing loans. The HFCS variables on mortgages and 
non-mortgage loans cannot be applied as such, since mortgages are defined as loans 
collateralised by real estate, while housing loans are defined as loans taken out to 
purchase real estate. However, the HFCS collects information on the purpose of loans 
that can be used to identify housing and non-housing loans. This new split was agreed 
by the EG-LMM and should be applied to the results in future work on Distributional 
Financial Accounts. 

The EG-LMM also discussed the possibility of using information on sole proprietor 
loans to further improve the macro-micro linkage. However, since the assets of sole 
proprietors are only collected as net of liabilities in the HFCS, improving the linkage is 
not straightforward. Consequently, using information on sole proprietor loans has 
been left for future consideration (see Chapter 7). 

3.5 

New wealth concept 

The EG-LMM initially concentrated on items that are conceptually comparable 
between macro and micro data sources. However, the wealth concepts have been 
gradually expanded to cover as much of the total household balance sheets as 
feasible. For this report, three different wealth concepts have been developed, which 
are summarised in Table 4 below. 

4    Regulation (EU) No 1071/2013 of the ECB of 24 September 2013 concerning the balance sheet of the 

monetary financial institutions sector (recast) (ECB/2013/33), OJ L 297, 7.11.2013, p. 1. 

ECB Statistics Paper Series No 37 / July 2020 

22 

 
 
                                                                    
The narrow adjusted financial wealth concept A includes only items that have high 
conceptual comparability. These include deposits, debt securities, loans, listed shares 
and investment fund shares on the assets side, and all liabilities of households. The 
adjusted financial wealth concept B includes, in addition to the items included in the 
narrow concept, financial wealth items that have medium conceptual comparability. 
Additional items in this concept include life insurance and annuity entitlements, as well 
as unlisted shares and other equity. Concept B includes most of total financial wealth 
as defined by the Financial Accounts, excluding currency, financial derivatives, other 
accounts receivable and all instruments in the “insurance, pension and standardised 
guarantee schemes” category except life insurance and annuity entitlements. These 
instruments are not collected comprehensively or do not have comparable definitions 
in the HFCS. 

The most comprehensive wealth concept is the adjusted broad wealth concept C that 
also includes non-financial assets, as defined in Section 3.3. While there are some 
conceptual differences between macro and micro statistics as regards the collection of 
data on non-financial wealth, the inclusion of non-financial wealth significantly 
increases the scope of this exercise. In survey data, real assets account for more than 
80% of household wealth. 

ECB Statistics Paper Series No 37 / July 2020 

23 

 
 
Table 4 
Wealth concepts 

ESA 2010 
code 

NA instrument   

HFCS 
variable(s) 

HFCS Wave 2 variable and details 

Conceptual 
comparability 

Narrow financial wealth concept A 

Assets 

F.22 

F.29 

F.3   

F.4 

F.511 

F.52 

Liabilities 

Transferable deposits 

DA21011 

Sight accounts 

Other deposits 

DA21012 

Savings accounts 

Debt securities 

DA2103 

Bonds 

Loans 

DA2107 

Amount owed to household 

Listed shares 

DA2105 

Publicly traded shares 

Investment fund shares 

DA2102 

Mutual fund shares 

HIGH 

HIGH 

HIGH 

HIGH 

HIGH 

HIGH 

F.4 

Loans 

Part of DL1000  HFCS loans taken out for the purpose of 

HIGH 

Housing loans: F.4 multiplied 
by the share of housing loans 
in MFI BSI statistics 

purchasing the household main 
residence or to refurbish and renovate 
the residence. 

F.4 

Loans 

Non-housing loans: F.4 
multiplied by the share of 
non-housing loans in MFI 
BSI statistics 

Part of DL1000  HFCS loans taken out for any purpose 
other than to purchase the household 
main residence or to refurbish and 
renovate the residence. 

HIGH 

Adjusted financial wealth concept B (in addition to concept A) 

Assets 

F.512 + F.519  Unlisted shares +   

Other equity 

DA2104 +   
Part of DA1200   

MEDIUM 

Non-self-employment not publicly 
traded businesses + value of 
self-employment businesses, 
depending on legal form of business and 
number of employees. Delineation rules 
vary across countries; see Section 
3.2.1. 

F.62 

Life insurance and annuity 
entitlements 

DA2109   

Voluntary pension/whole life insurance 
schemes   

MEDIUM 

Adjusted broad wealth concept C (in addition to concept B) 

Assets 

AN.111 + 
AN.2111 

(AN.11 – 
AN.111) + 
AN.12 + 
(AN.2 – 
AN.2111) 

Dwellings +   
Land underlying buildings 
and structures 

(Fixed assets – Dwellings) + 
Inventories + (Non-produced 
non-financial assets – Land 
underlying buildings and 
structures) 

DA1110 + 
DA1120 

Household main residence +   
Other real estate properties 

MEDIUM 

Part of DA1200  Value of self-employment businesses, 

MEDIUM 

depending on legal form of business and 
number of employees. Delineation rules 
vary across countries; see Section 
3.2.1. 

The HFCS/NA coverage ratios of each adjusted wealth concept are shown in Chart 2. 
In the calculation of all concepts, both the denominator and the numerator include the 
items shown in Table 4 for the HFCS and National Accounts respectively. 

The resulting HFCS/NA coverage ratios may be summarised as follows: by expanding 
the instrument coverage, the coverage ratios increase for most countries. The biggest 
effect is observed when including housing wealth. This reflects the fact that the 
HFCS/NA coverage ratio for housing is typically higher than for financial instruments. 
In some cases, the concept including housing wealth shows coverage higher than 
100% (albeit based on broad estimates on the National Accounts side for certain 
countries). 

ECB Statistics Paper Series No 37 / July 2020 

24 

 
 
 
The coverage of concepts A and B, which cover only financial instruments, is below 
75% for the largest countries (and below 30% for Italy), and lower than 50% for a 
number of smaller-sized countries. In most cases, the HFCS/NA ratios are smaller for 
the narrow concept A than for the broader concept B, though this does not necessarily 
indicate better data quality. In the case of business wealth particularly, a larger 
coverage ratio may reflect uncertainties in the instrument allocation when matching 
HFCS to National Accounts. For some countries, the HFCS/NA coverage ratio of 
business wealth versus unlisted shares and other equity is larger than 100%, possibly 
implying that the HFCS value includes assets not recorded under unlisted shares and 
other equity in National Accounts. However, the impact of this outcome on the results 
is generally small (1% to 4% of financial assets for the large countries). 

Chart 2 
Coverage of National Accounts by HFCS 

(HFCS as % of NA)5 

Assets – concept A
Assets – concept B

Assets – concept C
Liabilities

120%

100%

80%

60%

40%

20%

0%

AT

BE

CY

DE

EE

ES

FI

FR GR HU

IT

LU

LV MT

NL

PL

PT

SI

SK

5    Concept C could not be computed for Cyprus, Spain, Hungary, Malta and Slovenia, as data on land were 

not published. For technical reasons concept C could not be computed for Poland. 

ECB Statistics Paper Series No 37 / July 2020 

25 

 
 
 
 
                                                                    
4 

4.1 

Methodology for bridging the gap 

Methods for integrating macro and micro sources: a 
review of the relevant literature 

This section provides an overview of the methods that could be used to adjust survey 
data for differential non-response at the top of wealth distribution and for 
under-reporting behaviour. The final adjusted dataset produces estimates that are in 
line with the National Accounts totals. In all cases it is assumed that the methods are 
employed after all possible statistical adjustments have been applied – i.e. after the 
best possible alignment of definitions and categories in the HFCS and Financial 
Accounts has been achieved. 

Let 

  be the population total for variable of interest 

  to be estimated 

  participants. A classical estimator is the Horvitz-Thompson 

𝑦𝑦 = (𝑦𝑦1, … , 𝑦𝑦𝑁𝑁)

using survey data of 
𝑡𝑡𝑦𝑦
one: 

𝑁𝑁

𝑁𝑁

𝐻𝐻𝐻𝐻
𝑡𝑡𝑦𝑦
where 

= � 𝑤𝑤𝑖𝑖𝑦𝑦𝑖𝑖
𝑖𝑖=1

  is the sampling weight for the i–th household. 

If both micro and macro data were perfect, 

𝑤𝑤𝑖𝑖

  should be equal to 

, the 

corresponding macro aggregate. In practice, however, this is seldom the case. In fact, 
because of non-response and under-reporting, the value of the aggregated micro data 
is generally below the macro aggregates. The methods that can be used to adjust 
survey data to fill such a gap can be grouped into two broad categories. 

𝑡𝑡𝑦𝑦

𝐻𝐻𝐻𝐻
𝑡𝑡𝑦𝑦

𝑦𝑦𝑖𝑖

𝑤𝑤𝑖𝑖

The first one is the design-based approach, which is aimed at correcting survey data 
  through reweighting methods while keeping the 
by modifying the sampling weights 
individual responses 
  unchanged (Deville and Särndal, 1992; Särndal, 2007). In the 
literature, this approach is mainly used: (i) to force consistency of certain survey 
estimates to known population quantities, (ii) to reduce non-sampling errors such as 
non-response errors and coverage errors; and (iii) to improve the precision of 
estimates (Haziza and Beaumont, 2017). To address the problem of non-response, 
the sample selection is implemented as a two-phase process. In the first phase, a 
“theoretical” sample is selected. In the subsequent phase, the sample of respondents 
actually interviewed is treated as the result of a second stage of sampling. Each unit in 
the population has a certain probability of participating in this second phase, which can 
be estimated in various ways and then used to adjust the sampling weights and 
construct estimators with better asymptotic properties. 

The second, model-based approach is aimed at adjusting the individual responses 
collected through the survey 
requires a model for the distribution of the measurement error and auxiliary 
information to estimate the parameters of the model. Among the several models 
available in the literature, those most suitable for our purposes are imputation 

  while sampling weights 

are left unchanged. It 

𝑤𝑤𝑖𝑖 

𝑦𝑦𝑖𝑖

ECB Statistics Paper Series No 37 / July 2020 

26 

 
 
 
methods. For a general description, see the seminal work of Rubin (1978, 1987). 
These methods can be used both for the problem of under-reporting and for the 
problem of non-response (a relevant example is the use of the Pareto distribution; see 
Vermeulen, 2018). 

The two approaches have some shared traits, so that the distinction is not always 
clear-cut. For example, the weighting adjustment can also be seen as a method of 
imputation consisting in compensating for the missing responses by using the 
respondents with the most similar characteristics; in the same way, the imputation of 
plausible estimates in lieu of respondents’ claimed values can be thought of as a 
reweighting method. 

However, it should be borne in mind that the choice of the method of adjustment is 
basically driven by three factors. First, it depends on the estimator of interest. For 
example, if the interest is to estimate the share of total wealth held by rich households, 
the use of the Pareto method could be sufficient. Second, the choice depends on the 
magnitude of the gap to fill. If the gap is considerable, it may be desirable to combine 
different methods. Finally, the choice depends on the information that is available. If, 
for example, the only available auxiliary information is in the form of population totals, 
the design-based approach might be the only one feasible; but if auxiliary data are 
available at the individual level, then the model-based methods may represent the 
most effective solution. 

4.2 

Reweighting methods 

In the design-based approach, the calibration method for estimating the population 
total 

  is computed as follows: 

𝑅𝑅𝑅𝑅𝑅𝑅
𝑡𝑡𝑦𝑦

𝑁𝑁

∗
            𝑤𝑤𝑖𝑖

𝑦𝑦𝑖𝑖

∗
=  � 𝑤𝑤𝑖𝑖
𝑖𝑖=1

𝑅𝑅𝑅𝑅𝑅𝑅
𝑡𝑡𝑦𝑦
The adjustment factor 
constraints 
constraints, 

𝒛𝒛𝒊𝒊 = (𝑧𝑧𝑖𝑖1, … , 𝑧𝑧𝑖𝑖𝑖𝑖)

= 𝑤𝑤𝑖𝑖𝑎𝑎𝑖𝑖
  is a function of the variables used in calibration of the 

  and it is computed so that final weights meet benchmark 

𝑎𝑎𝑖𝑖

𝑁𝑁

𝑧𝑧𝑖𝑖

= 𝒕𝒕𝑧𝑧

∗
� 𝑤𝑤𝑖𝑖
𝑖𝑖=1
while, at the same time, being kept as close as possible to the initial ones. Benchmark 
constraints are defined with respect to 
population totals or counts of the calibration variables. The final output is a single new 
set of weights to be used for all variables. The magnitude of the adjustment factors 
and, therefore, the variability of the final set of weights is a function of the number of 
constraints (dimension of the vector 
Horvitz-Thompson estimate and the population total). A high variability of weights 
hinder the quality of final estimates for sub-populations and for variables not involved 
in the calibration procedure. For these reasons, weights are usually required to meet 
range restrictions such as to be positive and/or within a chosen range. 

) and the imbalance (difference between the 

, that is the known vector of 

𝒕𝒕𝑧𝑧 = (𝑡𝑡𝑧𝑧1, … , 𝑡𝑡𝑧𝑧𝑖𝑖)

𝒛𝒛𝒊𝒊

ECB Statistics Paper Series No 37 / July 2020 

27 

 
 
 
 
The method was originally developed to improve the efficiency of the estimators and to 
ensure coherence with population information, but then it was also largely applied to 
adjust for non-response (Särndal and Lundström, 2005). For example, Little and 
Vartivarian (2005) show that if the variables used to construct the weights are 
associated both with non-participation and with the variable of interest, the bias and 
the variance of the estimator are reduced. 

𝑧𝑧

𝑦𝑦

  and 

. The latter can be interpreted as an estimate of the inverse of the 

Calibration is a model-assisted procedure, and the model implicitly assumed by the 
procedure is essentially a linear regression between 
. The procedure does 
not require an explicit modelling of non-response or measurement error: this is implied 
by the choice of the calibration variables and the functional form chosen for the 
adjustment factor 
response probability of unit 
usually refers to socio-demographic characteristics but could also include variables 
relating to households’ income or wealth such as the amount of deposits or housing 
wealth. When using auxiliary information on household wealth, the implicit adjustment 
model tends to increase the weight of richer households since this is the most effective 
way to meet benchmark constraints without modifying the initial weights too much. So, 
in a sense, the gap, when positive, is allocated more than proportionally to rich 
households. A negative gap would be allocated more to poor households. 

. The vector of auxiliary variables used in calibration 

𝑎𝑎𝑖𝑖

𝑖𝑖

The main problem with the use of household balance sheet data in reweighting 
methods is that wealth is generally very skewed and concentrated in the hands of a 
small group of the population that has both low propensity to participate in the survey 
and different socio-demographic characteristics from the average population. As a 
consequence, calibration may produce a very variable final set of weights or, in some 
instances, may fail to find a set of weights that meets all the constraints (benchmarks 
and range restrictions). One interesting generalisation of the method that addresses 
this issue is ridge calibration (Rao and Singh, 1997; Beaumont and Bocci, 2008; 
Montanari and Ranalli, 2009). 

In classical calibration, benchmark constraints are met exactly, that is 

𝑁𝑁

𝑧𝑧𝑖𝑖

� − 𝒕𝒕𝑧𝑧 = 0

∗
�� 𝑤𝑤𝑖𝑖
𝑖𝑖=1
In ridge calibration, by contrast, constraints are relaxed in order to reduce weight 
variability and/or meet range restrictions. In particular, 

𝑁𝑁

𝑧𝑧𝑖𝑖

� − 𝒕𝒕𝑧𝑧� ≤ diag(𝝉𝝉)𝒕𝒕𝑧𝑧

∗
��� 𝑤𝑤𝑖𝑖
𝑖𝑖=1
  is a vector of tolerances6, one for each benchmark constraint. 
where 
Tolerances can take positive or negative values. Clearly, the benchmark is met exactly 
if the corresponding value of the tolerance is equal to 0. This method could be used to 

𝝉𝝉 = (𝜏𝜏𝟏𝟏, … , 𝜏𝜏𝒌𝒌)

𝐻𝐻

6    The tolerance is the remaining gap between micro and macro estimates resulting after the adjustment 

process is carried out. A value of the tolerance equal to 0 means that the adjusted survey-based estimate 
should be exactly equal to the macro estimate. The higher the tolerance, the higher the discrepancy that 
one is willing to accept. The use of a positive tolerance could be useful when there are significant 
conceptual differences between the two sources and/or in case of a low reliability of the benchmark. 

ECB Statistics Paper Series No 37 / July 2020 

28 

 
 
 
 
                                                                    
accept a discrepancy between micro and macro estimates that is due to conceptual 
differences and/or to a lower reliability of the benchmark. 

In the case of weights adjustments, the internal consistencies between the variables 
are preserved by definition. This represents a definite advantage, especially for micro 
analysis. At the same time, a modification of the weights results in a modification of the 
final estimates for all surveyed variables and should therefore be carefully monitored. 

4.3 

Imputation methods 

Imputation models have been widely used in the literature to adjust for non-response 
and measurement errors. 

These methods, working at the level of the single observation, generally yield 
estimates with smaller variance than would be obtained by modifying the weights. 
Consider, for example, financial assets, which are heavily concentrated in the hands 
of a limited number of households and subject to significant under-reporting. This 
means that a part of the sample could be subject to very substantial weight 
adjustment, which could increase overall variability. Furthermore, in these cases it is 
not uncommon for weight calibration not to converge, as it fails to align the sample with 
both financial and socio-demographic external information. In addition, imputation 
models are more flexible than reweighting since they can be tailored on the basis of 
the variable to correct (and of the information available). 

In this section, we describe three possible methods that could be used to deal with 
under-reporting: (i) the proportional adjustment method, (ii) the difference-type 
adjustment methods and (iii) the hurdle models. 

The proportional adjustment procedure modifies sample values 

  by multiplying 

them by the inverse of the coverage ratio 

, that is the ratio of the known 
𝑦𝑦𝑖𝑖

𝐻𝐻𝐻𝐻

𝑡𝑡𝑦𝑦

macro aggregate to the total sample estimate. 

𝐶𝐶𝐶𝐶 =  

𝑡𝑡𝑦𝑦

𝑁𝑁

∗
𝑡𝑡𝑦𝑦 = � 𝑤𝑤𝑖𝑖𝑦𝑦𝑖𝑖
𝑖𝑖=1
The adjustment procedure can be summarised in the following steps. 

∗
𝑦𝑦𝑖𝑖

𝑡𝑡𝑦𝑦
𝐻𝐻𝐻𝐻 𝑦𝑦𝑖𝑖
𝑡𝑡𝑦𝑦

=  

First, select the figures from National Accounts to be used as benchmark. One option 
is to limit the choice to the most comparable items (deposits, bonds, shares, mutual 
funds, financial debts and housing wealth). Second, compute coverage ratios for each 
of the chosen items. Third, compute an average coverage ratio (weighted average 
across instruments using the portfolio composition as weights). Fourth, use the 
coverage ratios for adjusting the amounts collected in the survey (
income flows accordingly, in order to allocate households over the adjusted income 
classes. 

). Fifth, adjust 

𝑦𝑦𝑖𝑖

This method is based on the assumption that under-reporting only depends on the 
type of instrument (

) but not on respondents’ characteristics. Indeed, for all 

𝑦𝑦𝑖𝑖

ECB Statistics Paper Series No 37 / July 2020 

29 

 
 
            
 
households the under-reporting is assumed to be equal to a constant fraction of the 
true amount. In addition, the method does not allow adjustments to be made for 
non-reporting, i.e. failure to report the ownership of an asset held by the household, as 
only the positive amounts are inflated. 

This method has two main advantages: (i) it is easy to implement and (ii) it minimises 
the variance of estimators since it preserves the univariate distribution of the adjusted 
variables. However, when multiple wealth components are involved in the adjustment, 
the distribution of total wealth changes in a way that is difficult to predict ex ante. 

The difference-type adjustment method modifies the amounts collected in the survey 
using the following model: 

𝐻𝐻𝐻𝐻
=   𝑦𝑦𝑖𝑖 + 𝑏𝑏𝑖𝑖�𝑡𝑡𝑦𝑦 − 𝑡𝑡𝑦𝑦

∗
𝑦𝑦𝑖𝑖
The difference with respect to proportional allocation is that the method allocates the 
difference between macro and micro estimate instead of the ratio 

. The 

  are 

�

households’ specific parameters that may, for instance, reflect their propensity to 
provide reliable answers. 

𝑡𝑡𝑦𝑦

𝑏𝑏𝑖𝑖

𝑡𝑡𝑦𝑦
𝐻𝐻𝐻𝐻

This method has two main advantages over proportional allocation. First, it allows 
differentiated allocation of under-reporting to households, whereas in the proportional 
adjustment the under-reporting is assumed to be equal across household groups. 
Second, this method allows non-reporting to be addressed. Even if the household 
), as long as the coefficient 
reports a zero value (
household will be assigned part of the “missing wealth”. 
𝑏𝑏𝑖𝑖

  is different from zero, the 

𝑦𝑦𝑖𝑖 = 0

A third imputation approach that could be used is based on hurdle models. These 
models are founded on the idea that, for given instruments, some “zeros” are 
unreliable (non-reporting). The models therefore impute the assumed “true values” 
using the information provided by other similar respondents. The adjustment is based 
on the following steps. 

• 

• 

• 

• 

First, for each instrument, estimate the probability the 
the basis of some observable characteristics. Binomial models can be used to 
estimate this probability, and the covariate set can vary across countries, 
depending on the determinants of non-reporting behaviour. 

–th household owns it on 

𝑖𝑖

Second, for some “zeros”, impute the ownership by a random experiment, 
i.e. where the reported zero is detected as unreliable, given the predicted 
probability of reporting for the individual household. If some external information 
such as the number of households holding a given financial instrument is 
available, this can be used in the imputation process. 

Third, once new ownerships are created, impute the value of the amount held. 

Finally, compute the new sample estimate of the total and redistribute the 
(eventual) remaining gap using, for instance, the proportional adjustment method 
or calibration. 

ECB Statistics Paper Series No 37 / July 2020 

30 

 
 
 
Imputation methods may be very useful when auxiliary data at the individual level are 
available. They allow for explicit modelling, for both non-reporting and under-reporting 
behaviours. 

4.4 

Top tail adjustments of survey-implied wealth distributions: 
Pareto models 

In this section, we provide an overview of adjustments to compensate for the fact that 
surveys may under-represent very rich households (those at the top tail of the wealth 
distribution) and discuss ways these methods can be used to improve survey data 
from the HFCS. 

A relatively small number of missing billionaires can seriously affect distributional 
aspects of a wealth survey. Wealth distributions have always been characterised by 
their skewness, with few individuals at the top of the distribution holding most of the 
fortunes of an economy: most often, the sampling process is unable to capture those 
individuals whose wealth exceeds a certain threshold. 

This could happen for a variety of reasons, including non-reporting, under-reporting or 
simply the low number of these households. What matters is that, when a survey is 
unable to record these observations in the tail, the entire distribution is affected. The 
HFCS, like many other wealth surveys, is not immune to this. 

(the summation of the product of weight 

It is well known that these issues emerge when computing the Horvitz-Thompson 
estimator 
  for each 
individual household i) from the HFCS: indeed, the estimator will only cover a fraction 
of the total reflected in the Financial Accounts. Any attempt, however, to correct for 
these mismatches between the Financial Accounts and survey data without 
accounting for the missing top wealth is bound to generate biased figures. 

𝑁𝑁
∑ 𝑝𝑝𝑖𝑖𝑤𝑤𝑖𝑖
𝑖𝑖=1

  and wealth 

𝑤𝑤𝑖𝑖

𝑝𝑝𝑖𝑖

Many researchers have focused on finding the “missing wealthy” 7, and most have 
relied on the Pareto distribution to do so. The Pareto distribution is a probability 
distribution based on power law. It was first created by the Italian economist Vilfredo 
Pareto in his studies on wealth distributions. 

An overview of the literature making use of the Pareto is provided in Box 1. 
), the 
Essentially, these approaches assume that, over a certain wealth threshold (
empirical cumulative distribution of wealth is approximated by a power law, which can 
be expressed as: 

𝑤𝑤0

, for 

𝛼𝛼

𝑤𝑤0
𝑃𝑃(𝑊𝑊 ≤ 𝑤𝑤𝑖𝑖 ) = 1 − �
𝑤𝑤𝑖𝑖�
where the parameter 
approximation by a power law is that wealth accumulation is thought to follow a 
random multiplicative process, leading to a “fat-tailed” distribution of wealth. 

𝑤𝑤𝑖𝑖 ≥ 𝑤𝑤0
  indicates the shape of the tail. The rationale for this 

𝛼𝛼 ∈ ℝ≥0

7    See e.g. Vermeulen (2016), and Chakraborty and Waltl (2018). 

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31 

 
 
 
                                                                    
With regard to the HFCS survey, the Pareto distribution has notably been used to 
produce figures for total wealth above the threshold (
Vermeulen (2016) tested a number of methods for estimating the parameter 
empirically, finding his “regression approach” to yield the most consistent results. He 
also found, along with many others, that the estimation process was more precise if 
some sort of upper bound was defined: the solution was to integrate information from 
the Forbes – or similar – “rich list” to anchor the very top of the distribution. 

), replacing the empirical tail. 

𝑤𝑤0

𝛼𝛼

After a reliable approximation of the shape parameter 
  has been obtained, total 
wealth in the top tail of the survey can be re-estimated. This adjustment is usually 
performed by multiplying the sum of survey weights after the threshold 
  by the 
mean of the Pareto distribution: the resulting figure gives the total amount of wealth for 
all households whose wealth exceeds 
summing this value with the wealth at the bottom of the survey. 

, and the new survey total is obtained by 

𝑤𝑤0

𝛼𝛼

𝑤𝑤0

While yielding promising increases in the coverage between the HFCS survey and the 
Financial Accounts, these methods alone are unable to fill the remaining gap. These 
contributions rely on the implicit assumption that the HFCS adequately samples 
household wealth for the range of wealth before the distribution starts according to the 
Pareto power law. It may be recalled that, if observations are ranked in order of wealth, 
an unbiased estimate of the wealth of the lower part of the distribution will be provided 
by the Horvitz-Thompson estimator: 

𝑛𝑛
= � 𝑝𝑝𝑖𝑖 𝑤𝑤𝑖𝑖 
𝑖𝑖=1

   ∀  𝑖𝑖 = (1, … , 𝑛𝑛, 𝑤𝑤0, … , 𝑁𝑁)

𝐻𝐻𝐻𝐻
𝑡𝑡𝑅𝑅𝑏𝑏
However, these approaches also assume that the sum of the weights of the top tail 
adequately estimates the number of households in the top tail. Should this be the 
case, top tail adjustments should only involve re-estimating the total amount of wealth 
at the top of the Pareto distribution, as e.g. Vermeulen (2018) and Chakraborty and 
Waltl (2018) have done. There are, however, reasons to move beyond this 
assumption, as survey weights are rarely calibrated on the basis of household wealth, 
and these approaches still leave a sizeable gap in the coverage between the HFCS 
and the Financial Accounts. For these reasons, top tail adjustments should focus not 
only at estimating top tail wealth, but also the number of households at the very top. 

Furthermore, the integration of rich lists in household surveys has generated a number 
of concerns regarding the reliability of such a source of information. The methodology 
behind the compilation of said lists is often obscure, and often only net worth is 
provided, with no instrument breakdown being given. In contrast, generalised Pareto 
adjustments (recommended by Blanchet et al., 2017 and implemented in Chakraborty 
and Waltl, 2018; see explanations in Box 2) provide the advantage of relying on 
percentile totals obtained from external sources (administrative records such as tax 
data, as in the case of France), rather than relying on rich lists. 

However, this is only possible when such information exists and is accessible for 
national central banks (NCBs) and national statistical institutes (NSIs), which is often 
not the case at present (see Chapter 5, Administrative sources). 

ECB Statistics Paper Series No 37 / July 2020 

32 

 
 
 
 
Box 2 
Pareto models and the HFCS: an overview 

Measuring wealth distributions from survey data has two major advantages compared with the use of 
tax and register data. First, wealth surveys are specifically designed to capture all relevant 
components of wealth, including those not producing income flows or for which current market values 
are not readily available. In addition, the sampling strategy is designed to generate a weighting 
scheme that yields results representative of the (target) population. 

Despite these advantages, the inability of voluntary wealth surveys to accurately capture the top of 
the distribution challenges the exclusive use of survey data (Vermeulen, 2016). There are two main 
reasons for this: first, the large concentration of wealth among just a few households and, second, the 
decreasing likelihood of rich households to participate in surveys. 

The first of these two reasons is statistical: a strong concentration within a small sub-population 
requires a large number of observations from this part of the distribution to achieve a satisfactory 
degree of precision. Strategically oversampling rich households in a survey is well suited to 
addressing this issue. Indeed, most HFCS countries apply oversampling strategies, although these 
differ strongly in their implementation and effectiveness (HFCN, 2016a). As certain asset classes – in 
particular risky financial assets and non-owner-occupied properties – are heavily concentrated at the 
very top, reliable measurement of these components requires strategic sampling approaches. For 
instance, Kennickell (2008) reports that in the US Survey of Consumer Finances, roughly 90% of the 
households were captured by means of oversampling. 

The second reason is related to differential response rates: even when a rich household is sampled, it 
is still less likely to be interviewed and hence to be included in the survey results (see Kennickell and 
Woodburn, 1999). This differential non-response introduces bias in both aggregate and distributional 
results. Increasing the sample size – the overall sample size or the specific sample size among the 
rich – does not correct this bias. Reweighting survey responses can adjust for the bias only if detailed 
information on the characteristics of non-responding households (and particularly some information 
on their wealth) is available. 

Both issues, precision and bias, can be addressed by applying ex post adjustments to the survey, 
making use of additional auxiliary data describing the wealth or the share of total wealth held by the 
very rich. 

Recently, several authors addressed this topic. All of them use Pareto adjustments to approximate 
the top tail of the wealth distribution, following Davies and Shorrocks (1999), who describe “two 
enduring features of the shape of the distribution of wealth. First, it is positively skewed […]. Second, 
the top tail is well approximated by a Pareto distribution”. 

Vermeulen (2018) uses observations from the Forbes World’s Billionaires list, which he combines 
with top survey observations for nine HFCS countries (first wave), along with the United States and 
the United Kingdom, to estimate a Pareto model. The top of the empirical distribution implied by the 
survey is subsequently replaced by the estimated Pareto model. He points out that estimating a 
Pareto model from survey data alone is not suited to adjusting for the missing wealthy, but when 
including additional observations from rich lists, such an approach is able to address both issues, 
namely low precision and non-response bias. 

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33 

 
 
 
Bach et al. (2018) follow Vermeulen’s Pareto approach but use more comprehensive national rich 
lists for France, Germany and Spain to analyse the importance of the missing rich in the first and 
second wave of the HFCS. They find large changes in aggregates and the distribution, measured by 
wealth shares and standard inequality indicators. 

While Bach et al. (2018) focus on micro data only, Vermeulen (2016), Chakraborty et al. (2018), 
Chakraborty and Waltl (2018) and Waltl (2019) benchmark survey results to the National Accounts. 
For most asset classes, the amounts reported in the National Accounts are (substantially) larger than 
the amounts reported in the survey. More specifically, Chakraborty et al. (2018) discuss conceptual 
and generic differences between survey and Financial Accounts data, based on the work of the 
EG-LMM. Additionally, they empirically compare Pareto-adjusted (making use of the Forbes list) and 
unadjusted survey-implied totals to macro aggregates. They do not consider a comprehensive 
measure of wealth but rely on several truncated definitions of financial wealth. They use the first wave 
of the HFCS and study Austria, Germany, France, Spain and Finland. 

Chakraborty and Waltl (2018) use national rich lists together with the Pareto approach to adjust the 
second wave of the HFCS for Austria and Germany. Taking into account typical portfolio structures for 
wealthy households (which differ strongly from portfolio structures of households belonging to other 
parts of the distribution), they quantify the importance of the missing wealthy households for several 
components of wealth. Their study shows that the largest adjustments are for equity and mutual 
funds, while changes to deposits, liabilities and bonds are quite low. They demonstrate how such 
adjustments can be used to compile Distributional National Accounts. 

Waltl (2019) uses data from the second wave of the HFCS for Austria, Finland, France, Germany and 
Spain, together with national rich lists. She analyses the impact of different oversampling techniques 
on the magnitude of changes in total wealth when applying top tail adjustments. She finds a large 
negative correlation between the quality of the oversampling strategy applied and the impact of top 
tail adjustment, and hence confirms concerns regarding cross-country comparability of HFCS data. 
Furthermore, she demonstrates how to extend the Pareto approach to include a generalised Pareto 
adjustment. Finally, she compares adjustments based on rich lists with those based on top wealth 
shares estimated from tax/register data, as well as leaked data from wealth stored in offshore tax 
havens as reported by Alstadsæter et al. (2018). Not surprisingly, she finds that using tax/register 
data yields lower adjustments than when additionally taking into account hidden wealth stored in 
offshore tax havens. Rich list adjustments lead to strikingly similar results as the top share 
adjustments including hidden wealth for Spain and France, the two countries for which such data are 
available. Finally, she compiles distributional accounts from top tail-adjusted HFCS data: adjusted 
wealth at the top is broken down to identify contributions from each wealth component; these 
contributions are then redistributed to the different “vertical groups”, including wealth groups, income 
groups and different functions of wealth. 

There are two important conclusions to be drawn from this stream of literature: first, the amount of 
missing wealth in surveys attributable to the top is large. Second, the distributional impact of the 
missing wealthy differs strongly across HFCS countries. The first observation implies that the degree 
of measured wealth inequality from the HFCS only constitutes a lower bound and that true wealth 
inequality is very probably higher in all countries. The second conclusion is that cross-country 
comparisons relying on unadjusted statistics are very likely to be hampered by differences in the 
survey implementation and, in particular, differences in applied oversampling strategies. A top tail 
adjustment is hence not only important to approximate the true distribution of wealth, but also for the 
sake of internationally comparable results. 

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34 

 
 
Notwithstanding technical difficulties (broadly discussed in Bach et al., 2018, and Chakraborty and 
Waltl, 2018) and issues regarding the reliability of auxiliary data (particularly in the case of rich lists), 
the importance of the wealthiest of the wealthy for any overall measure of wealth, together with the 
substantial differences in their coverage across countries, undoubtedly calls for a top tail adjustment 
of survey-implied wealth distributions. Assessing and collecting auxiliary data that can be used to 
perform such an adjustment and developing objective decision criteria for the methodological 
implementation must therefore be prioritised in future work towards compiling distributional 
accounts. 8 

4.5 

Towards a unified approach 

Undersampling of the rich is generally the most tangible issue that wealth surveys 
suffer from. However, all surveys are also affected (albeit to varying degrees) by 
under-reporting and differential non-response at the tail of the wealth distribution. 
These considerations suggest that the use of a single adjustment may not be enough, 
and that, instead, several adjustments should be performed iteratively. 

For the Pareto tail estimation, the threshold where the tail starts needs to be chosen, 
and the share of the population above that threshold needs to be estimated. If there 
were no under-reporting, this information could be based on the unadjusted survey 
data. Since this is not always the case, a preliminary adjustment for under-reporting 
may be necessary before applying the Pareto method. At the same time, in order to 
correct for the under-reporting behaviour in the rest of the wealth distribution, it is 
necessary to adjust the estimate of the share of total wealth held by the rich 
households. 

To address these issues, it is also necessary to supply the Pareto model with external 
information in order to adjust the estimate for the share of total wealth held by the rich 
households. The estimated shape parameter of a Pareto curve is unaffected by the 
underlying number of observations constituting its empirical counterpart: if information 
on the number of households after the threshold is available, then the whole 
distribution can be corrected accordingly; otherwise, it can be estimated using survey 
weights and the Pareto shape parameter. 

This leads to a methodology which iteratively estimates the Pareto while adjusting for 
measurement error across the wealth distribution. In its current form, this method 
relies on Financial Accounts totals and rich list imputation to iteratively reconstruct the 
missing wealth from the top. To achieve the best conceptual comparability, following 
on from the previous work of the EG-LMM, we adopt the wealth concept C, although 
the procedure can account for different instrument compositions. 

The envisaged procedure, involving a simultaneous Pareto-calibration allocation, 
consists of the following steps. 

8    For example, some EG-LMM members have suggested using income as an auxiliary variable. 

ECB Statistics Paper Series No 37 / July 2020 

35 

 
 
 
                                                                    
Step (1): Select instrument benchmarks from the National Accounts. Generally, the 
wealth concept C has been used, but other approaches may be tested. 

Step (2): Taking the size of the gap into account, as well as any other available 
information on the quality of the HFCS and National Accounts data, perform an 
adjustment for under-reporting using hurdle methods. Conversely, if no 
under-reporting is assumed, skip to step (3). 

Step (3): Choose the threshold where the tail starts (
), i.e. the segment of the 
distribution of (unknown) population whose wealth distribution is assumed to be 
Pareto distributed. This choice is discretionary. In their study, Vermeulen (2018) and 
Waltl (2018) set this threshold at €1 million, but a number of alternative approaches 
can be considered. Automatic threshold selection can be achieved by finding the 
lowest threshold 
  above which the Mean Excess Function 
increases linearly with 

𝑤𝑤0

. 

𝐸𝐸[𝑤𝑤𝑖𝑖 − 𝑤𝑤0 |𝑤𝑤𝑖𝑖 > 𝑤𝑤0]

∗
𝑤𝑤0

𝑤𝑤0

Step (4): Estimate the shape parameter of Pareto distribution for the richest 
households. To obtain a more precise estimate of the shape of the tail, extreme wealth 
observations from an external source (such as the Forbes World’s Billionaires list), 
where available, are pooled with the survey data. 

Step (5): Estimate the density of households with wealth above the threshold and 
rescale survey weights based on this information. This information could be available 
from an external source or could be estimated from the survey and rich lists. In the 
, compute the ratio between the Pareto density 
latter case, for any given value of 
functions estimated with and without the rich list, and multiply survey weights by the 
resulting parameter; this process will achieve reweighting and ensure the conformity 
of the survey wealth distribution with the true wealth distribution. 

𝑤𝑤𝑖𝑖

Step (6): To obtain the true total number of rich households, divide the sum of survey 
weights at the top by the value of the Pareto cumulative distribution relative to the 
maximum recorded value of wealth in the survey 
the implied number of households at the top (which we will call HHT), given the 
Financial Accounts total, to which top survey weights can again be rescaled. The 
weights of the bottom part of the distribution should be adjusted accordingly. 

. The resulting parameter gives 

𝑤𝑤𝑚𝑚𝑚𝑚𝑚𝑚

Step (7): To ensure consistency with demographic parameters, use calibration to 
readjust survey weights. Repeat steps 5 to 7 until the sum of the survey weights at the 
top equals the implied number of households estimated through the Pareto CDF 
(HHST) or until the differences between Pareto density functions estimated with and 
without the rich are minimised. Convergence is usually achieved in few iterations. 

Step (8): Estimate for each financial asset and for liabilities the total amount held by 
rich households. This can be done using the ratio of real assets to gross wealth, the 
ratio of financial asset to wealth, and the ratio of liabilities to gross wealth computed in 
the adjusted survey data. 

Step (9): For each asset and for liabilities, subtract the total amount held by rich 
households from the benchmark. This total is obtained by multiplying the Pareto mean 
by HHT and by the resulting Pareto-adjusted coverage ratio. This new benchmark 

ECB Statistics Paper Series No 37 / July 2020 

36 

 
 
 
should then be used to allocate reported values relating to households below and 
above the threshold separately. This adjustment, which essentially aims to reflect 
(further) under-reporting by households while maintaining the distributional 
information from the Pareto, should be performed via one (or more) of the imputation 
or reweighting methods described in the previous sections (according to the 
information available); the standard approach involves the calibration method. If the 
calibration approach is selected, instruments with similar comparability should be 
calibrated together. 

As a result of this process, the estimated total assets and liabilities will closely 
approximate the totals from the National Accounts. A measure of the adequacy of the 
adjustment will then be given by other indicators, such as the share of wealth of 
households in the top percentiles, or by instrument-specific coverage ratios (which can 
be estimated through bootstrapping). At the same time, the sum of weights is not 
increased, and all amendments are made on the original micro data observations, so 
that micro-level analysis can still be accomplished. 

4.6 

Preliminary results 

Initial results have been produced for the first three methods, reflecting three 
scenarios: 

• 

• 

• 

pure HFCS data; 

proportional allocation, which assumes under-reporting by all surveyed 
households to the HFCS and does not make any correction for very rich 
households; 

simultaneous Pareto-calibration allocation. 

The last two methods lead to full allocation of National Accounts wealth to the 
households surveyed. In other words, they allow the gap between HFCS and National 
Accounts to be fully bridged, so that the resulting distributional data cover exactly the 
National Accounts (adjusted) wealth concepts chosen. 

The estimates have been implemented on an experimental basis for four countries, 
namely France, Italy, Germany and Finland, as summarised in Table 5. Given that, 
owing to both reweighting and imputation, both methods affect the number of wealthy 
households, Table 5 also presents the number of households whose wealth exceeds 
the threshold of €1 million. In addition, estimates have been produced for the wealth of 
the top decile, which can be compared with World Wealth and Income Database 
(WID) estimates or administrative sources for some countries. To further assess the 
differences between the results produced by these methods, a comparison has been 
performed on: (i) key indicators such as the debt-to-asset ratio for the highest quintile 
and the Gini coefficient; and (ii) the overall wealth distribution (the latter is shown in 
Charts 3 and 4). 

ECB Statistics Paper Series No 37 / July 2020 

37 

 
 
The table shows that, while the magnitude of the adjustment varies between countries, 
the proportional allocation method and the simultaneous Pareto-calibration allocation 
tend, as expected, to increase the number of wealthy households by comparable 
margins. The proportional allocation, however, tends to underestimate the top tail 
distributional implications of wealth owing to its lack of information on the top tail of the 
distribution. This implies that the more information on the top tail of the distribution is 
missing from the base survey, the more Gini coefficients and top 10% shares will 
increase after performing the simultaneous Pareto-calibration allocation. 

In the case of France, findings from the World Inequality Database show results 
similar to our preliminary estimates: indeed, Garbinti, Goupille-Lebret and Piketty 
(2016) estimated that, in 2014, the top 10% of French households held 54.9% of the 
country’s wealth, a figure not too distant from the estimates that we obtain after our 
process has reached convergence (58.0%). Finland’s case is also particularly 
interesting: since, here, most information on wealth is sourced from tax and other 
income registers (which are also used for stratification during survey sampling), the 
base HFCS distribution should not suffer from under-reporting and non-reporting in 
the same way as other surveys: indeed, after reweighting the distribution with 
information on the missing wealth from the Pareto, the coverage ratio for Finland’s 
assets comes close to 100%, so that no further calibration needs to be implemented 
on the asset side. 

The results for Germany are strongly influenced by the fact that the housing wealth 
data collected in the HFCS covers 95% of the corresponding aggregates in the 
National Accounts, thus leading to a much higher coverage ratio for concept C than for 
A and B, as shown in Section 3.5. In this context, the Gini coefficient and – to a lesser 
extent – the number of rich households in Germany are only marginally affected by the 
adjustment, though the share of the top 10% of households increases from 57.5% to 
61.4%. 

In other cases, where under-reporting is assessed to play a significant role – as might 
be argued to be the case for Italy – proportional methods are bound to overestimate 
the number of wealthy households in the economy, overcompensating for the lower 
wealth indicated by respondents, indicating that adjustments such as simultaneous 
Pareto-calibration allocation are more appropriate. In these cases, marked increases 
in Gini coefficients (rising to 0.74 in Italy) and top 10% shares (now 62.6%) can be 
expected. The Italian survey is also characterised by its lack of oversampling for rich 
households. In such a situation, it could be advisable to use a fixed parameter for the 
share of rich households for more consistency in the results. 

In any case, it is worth highlighting that the proposed methodology can be amended at 
each step: better precision can be obtained e.g. by refining the reweighting from 
Step 5 in cases where further information is available on that parameter, or by 
imposing more restrictive assumptions on the under-reporting behaviour of 
households. Automated selection of the threshold can also improve the estimates, 
even though our results are relatively robust to different threshold choices (within the 
€1 million to €2 million interval). Most importantly, access to administrative data can 
help bring about significant improvements: such data may assist in improving 

ECB Statistics Paper Series No 37 / July 2020 

38 

 
 
estimates by providing a benchmark for specific parts of the distribution, or by 
providing a fixed parameter for the share of top households. 

Table 5 
Comparison of the results of alternative estimation methods 

HFCS 

Households above 

1,001,778 

681,305 

1,350,286 

60,794 

France 

Italy 

Germany 

Finland 

  (1) 

𝑤𝑤0

Top 10% (2) 

Debt to fin. assets 
(top quintile) 

49.4% 

0.99 

42.3% 

0.29 

57.5% 

0.62 

41.6% 

0.80 

Proportional method 

Households above 

2,179,070 

1,961,780 

1,888,289 

86,400 

Gini index 

0.66 

0.60 

0.74 

0.60 

Simultaneous 
Pareto-calibration 
allocation 

  (1) 

𝑤𝑤0

Top 10% (2) 

Debt to fin. assets 
(top quintile) 

50.9% 

0.49 

49.6% 

0.19 

55.9% 

0.44 

52.2% 

0.58 

Gini index 

0.67 

0.64 

0.73 

0.67 

Households above 

2,186,614 

1,437,417 

1,995,667 

59,024 

  (1) 

𝑤𝑤0

Top 10% (2) 

Debt to fin. assets 
(top quintile) 

58.0% 

0.50 

62.6% 

0.21 

61.4% 

0.42 

46.0% 

0.54 

Gini index 

0.71 

0.74 

0.78 

0.63 

Notes: (1) Estimated number of households above the wealth threshold, based on gross wealth. Pareto wealth threshold 
1 million.   
(2) Wealth share of the top 10% of households. Top % shares are computed with the HFCS total as the reference (which, in the 
Pareto-adjusted survey, is very close to the Financial Accounts total). Calculated only for the assets comparable with Financial Accounts. 

  set at EUR 

𝑤𝑤0

ECB Statistics Paper Series No 37 / July 2020 

39 

 
 
 
 
 
 
Chart 3 
Assets distribution by quintile and method 

(EUR billions) 

HFCS
Pareto-calibration
Proportional

France

Italy

8,000

7,000

6,000

5,000

4,000

3,000

2,000

1,000

0

9,000

8,000

7,000

6,000

5,000

4,000

3,000

2,000

1,000

0

9,000

8,000

7,000

6,000

5,000

4,000

3,000

2,000

1,000

0

Q1

Q2

Q3

Q4

Q5

Q1

Q2

Q3

Q4

Q5

Germany

Finland

500

400

300

200

100

0

DE

Q1

Q2

Q3

Q4

Q5

Q1

Q2

Q3

Q4

Q5

ECB Statistics Paper Series No 37 / July 2020 

40 

 
 
 
 
Chart 4 
Liabilities distribution by quintile and method 

(EUR billions) 

HFCS
Pareto-calibration
Proportional

France

700

600

500

400

300

200

100

0

Italy

400

350

300

250

200

150

100

50

0

Q1

Q2

Q3

Q4

Q5

Q1

Q2

Q3

Q4

Q5

Germany

1,000

900

800

700

600

500

400

300

200

100

0

Finland

60

50

40

30

20

10

0

Q1

Q2

Q3

Q4

Q5

Q1

Q2

Q3

Q4

Q5

4.7 

Methods for assessing the estimations 

Obviously, as for any macroeconomic aggregates, no full certainty can be reached on 
the accuracy of the results compiled. However, various methods can be followed to 
assess the likelihood of the estimates produced. At this stage, four main approaches 
can be used: (i) robustness; (ii) conformity with economic developments; 
(iii) comparison with other available data; and (iv) use of National Accounts identities. 
However, further work needs to be carried out to further expand the scope of the 
assessment of the estimates. 

1.  Robustness 

It is useful to assess the sensitivity of estimates with regard to changes in relevant 
parameters. However, where there is a large range of possible values to be estimated, 

ECB Statistics Paper Series No 37 / July 2020 

41 

 
 
 
 
it is likely that the estimates will vary significantly depending on the assumptions 
performed. 

2.  Conformity with economic developments 

In some cases, the use of additional information on economic developments can help. 
For example, in countries where economic policies have aimed to limit inequality in the 
last few decades and high taxes on the richest households have been imposed, or 
where the population generally thinks that inequality is low, one might not be easily 
convinced by estimates showing very high inequality. 

3.  Comparison with other available data 

In a number of cases, administrative data allow at least some parts of the wealth 
distribution to be assessed, as described in Chapter 5. Even where this does not allow 
for an assessment of the full distributional data, it may allow some parts of the data to 
be checked, e.g. the most indebted quintile. 

Similarly, new sources have emerged, such as the World Inequality Database, where 
academics have managed to design useful estimates of income and wealth for many 
countries. The data produced often reflect extensive efforts using public sources 
(e.g. in some cases inheritance tax data are used, while in income data are capitalised 
over a long period). Estimates of Distributional Financial Accounts (DFA) can be 
compared with such results in some cases. 

Furthermore, as with other euro area statistics, the comparison of results, or of the 
differences between the HFCS and the DFA estimates, across countries can provide 
meaningful information for assessing the results for individual countries. 

4.  Use of National Accounts identities 

A further proposal is making use of National Accounts identities to assess the 
estimated data. In particular, the sum of financial transactions (namely purchases and 
sales of financial instruments, such as quoted shares, or increases/decreases in 
instruments such as deposits) carried out by a given sub-group of households has to 
be equal to the balance of their non-financial transactions, i.e. their income, savings 
and other similar transactions (forming the “net lending/net borrowing” aggregate in 
the National Accounts). On the financial side, few data are available that enable 
estimates of financial transactions to be made, but as transactions are equal to 
differences in outstanding amounts, adjusted for price, foreign exchange and “other” 
changes (e.g. changes in entities’ activities, resulting in the need to allocate their 
activities to a different economic sector), it may be feasible to compile reasonable 
estimates of these transactions based on Distributional National Accounts data and 
estimates of price changes for each asset type. Taking into account the work 
performed by the Expert Group on Disparities in a National Accounts framework (EG 
DNA) on non-financial transactions, it may be possible to put together distributional 
non-financial accounts data with financial transactions, and thus to assess the 
consistency of both sources. 

ECB Statistics Paper Series No 37 / July 2020 

42 

 
 
5 

Administrative sources 

Administrative data can be used to improve macro-micro comparisons in various 
ways, depending on the kind of data available. Even if register data are available only 
at the aggregate level, the aggregates can be compared with both Financial Accounts 
and HFCS data to assess the overall quality and coverage of both data sets. In cases 
where register data can be disaggregated into different household groups with 
classifications available in the HFCS data, the degree of reporting bias across various 
household groups can be assessed. Both aggregate and sub-group-level data can 
also be helpful in the estimation of the wealth shares of the top tail. In the most 
favourable scenario, granular administrative data can be linked with HFCS individuals. 
This allows the distributional results of the survey, as well as the sampling bias, to be 
assessed in great detail. 

Section 5.1 gives an overview of the available data sources, Section 5.2 provides two 
examples of the potential benefits of administrative data for DFA purposes. 

5.1 

Availability of administrative data 

The EG-LMM has collected information with the purpose of assessing the availability 
of administrative data on household income, wealth and liabilities. A summary of this 
assessment is presented in Table 6. for 18 EU countries that responded to the 
questionnaire. The table shows large cross-country differences in the availability of 
administrative data, particularly concerning data on individuals that can be linked to 
the HFCS data with personal identifiers. In Estonia, Finland and Denmark 
administrative data on income, most financial wealth items and debt can be used as 
such to produce HFCS variables. Administrative data that relate to income and are 
linkable at the individual level are also available in Ireland, France and Lithuania. 
Income data are in most cases derived directly from tax registers. Data on financial 
assets are taken either from tax registers (Finland, Denmark) or are reported by banks 
(Estonia). In most other countries, relevant administrative data are available only at 
the aggregated level, if at all. 

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43 

 
 
Table 6 
Availability9  of administrative sources on income, wealth and liabilities 

Income 

Deposits 

Listed shares 

Investment fund 
shares 

Land and 
dwellings 

Loan liabilities 

CZ 

DE 

DK 

EE 

IE 

ES 

GR 

FR 

IT 

LV 

LT 

LU 

HU 

NL 

AT 

PT 

SK 

FI 

B 

B 

A 

A 

A 

B 

- 

A 

B 

B 

- 

B 

B 

B 

C 

C 

B 

A 

- 

- 

A 

A 

C 

B 

- 

C 

C 

- 

- 

- 

C 

B 

C 

- 

C 

- 

- 

C 

A 

A 

- 

C 

- 

B 

A 

- 

C 

C 

C 

C 

- 

C 

C 

A 

- 

C 

A 

A 

- 

C 

- 

B 

- 

- 

C 

C 

C 

C 

- 

C 

C 

A 

B 

- 

A 

C 

- 

B 

A 

- 

C 

B 

- 

C 

- 

B 

B 

- 

- 

A 

- 

- 

A 

A 

C 

A 

- 

C 

A 

B 

- 

- 

B 

B 

C 

B 

C 

A 

Source: EG-LMM. 
Notes: A = Available at granular level, can be linked to HFCS individuals 
B = Available at granular level, cannot be linked to HFCS individuals 
C = Available at aggregated level 
- = Not available 

Several countries reported that granular-level administrative data are in principle 
available but said that the data cannot be accessed by the NCBs due to legal 
constraints (limiting the access to such sources, with the aim to protect their 
confidentiality). For example, several NCBs reported that individual level credit 
registers existed, but that only a few countries were able to use it in their data 
production or analysis of macro statistics. 

The experiences of NCBs with access to personal level data have revealed several 
ways to overcome legal constraints. Cooperation among central banks and statistical 
institutes has proven to be useful, since national statistical institutes (NSIs) more 
frequently have access to administrative registers. Some NCBs and NSIs mentioned 
in response to the questionnaire that conferring the status of “official statistics” on the 
wealth statistics produced by the HFCS has enabled access to register data. The 
criteria for statistics to be classified as “official” vary from one country to the next. 
Some countries have indicated that the existence of EU legislation is a prerequisite. 
NCBs that have access to administrative data emphasised the importance of 
negotiations with register authorities and legal assistance in the interpretation of 
national legislation. Sharing best practices among countries could facilitate such 
negotiations. 

9    Availability means that either the national central bank or the national statistical institute has access to 

the data source mentioned. 

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44 

 
 
 
                                                                    
Even if access to micro-level administrative data is not possible, information at the 
meso level (i.e. at a low level of aggregation) can be useful for the macro-micro 
comparison. Examples of such information might be the share of assets owned by 
household groups (e.g. by age, wealth deciles or of the richest 1%) derived from 
administrative data, or the number of households or individuals above a predefined 
wealth threshold. The use of such information was described in Chapter 4. Information 
of this kind is available in most of the responding countries, at least for some 
instruments. A detailed assessment of its usefulness for DFA purposes will require an 
in-depth, country-by-country investigation to be carried out in the future. 

5.2 

Case studies on the use of administrative data: Finland 
and Ireland 

Two countries presented their experiences with the use of administrative data in the 
EG-LMM. 

Finland presented a comparison between HFCS data and register data on listed 
shares and investment funds. Register data can be linked to the HFCS data via 
personal identifiers, and observations from administrative sources are used instead of 
interview data as HFCS output variables. This makes it possible to carry out an 
assessment of the reasons behind the differences between Financial Accounts and 
HFCS data, namely population coverage and sampling bias. Since no interview data 
were collected, the assessment does not cover reporting bias. 

The HFCS/FA coverage of both listed shares and investment funds per capita is 
almost 90% in Finland, which is exceptionally high compared with other euro area 
countries. By comparison, in more than half of the HFCS countries, the survey per 
capita totals are less than 50% of corresponding Financial Accounts totals. 

A decomposition of register data on these two items shows that the difference 
between Financial Accounts and HFCS totals is mainly down to different household 
definitions. The impact of the sampling bias is much lower than for the aggregate 
difference between Financial Accounts and HFCS. The Financial Accounts total for 
listed shares is very close to the register total (including non-residents and institutional 
households). On the other hand, the register total for mutual funds is 6% lower than 
the Financial Accounts total. After adjusting the register data for the differences in the 
Financial Accounts and survey household definitions, the remaining bias of the HFCS 
data is minor. A comparison between the share of assets held by the top 10% and that 
held by the top 1% implies that the small difference between register and survey totals 
is mainly caused by the survey not covering households at the very top of the 
distribution. 

ECB Statistics Paper Series No 37 / July 2020 

45 

 
 
Table 7 
Comparison between register, Financial Accounts and HFCS data in Finland 

Listed shares 

Investment funds 

Total value 

Financial Accounts 

Register data 

  - Total 

  - Resident population 

  - Private households 

  - HFCS sample (weighted) 

EUR millions 

29,389 

28,979 

26,965 

26,805 

26,903 

% 

100 

99 

92 

91 

89 

EUR millions 

16,424 

15,446 

14,901 

14,789 

14,691 

% 

100 

94 

91 

90 

89 

Top 10% share (of households owning the asset) 

Register data 

HFCS sample (weighted) 

Top 1% share (of households owning the asset) 

Register data 

HFCS sample (weighted) 

Source: Statistics Finland. 

80.2% 

79.8% 

45.3% 

43.5% 

70.4% 

68.7% 

33.6% 

32.0% 

In Ireland, the HFCS/FA coverage ratio for deposits was only 29%. Consequently, the 
Central Bank of Ireland compared the HFCS data with the granular deposit-level 
dataset to assess distributional information and to identify potential areas of 
under-reporting by different parts of the population. These data were collected 
temporarily in the year 2014 for analytical purposes. The dataset could not be 
matched with HFCS identifiers, as the basic unit in the dataset is an individual bank 
customer and their deposit with one bank, instead of a household and the total 
deposits held by the household across all banks. However, the exercise provided 
some important insight into the under-reporting patterns in Ireland. 

The Irish case study concluded that while the distributional information from the HFCS 
cannot be invalidated by the comparison with administrative data, some possible 
caveats were recognised. Under-reporting of larger deposits was observed to be 
higher than under-reporting of lower value deposits. The coverage rate for deposits is 
lowest for the top decile, but only a few percentage points lower than the total 
coverage rate. Higher under-reporting among older age groups was observed, but the 
results are not significant enough to be a key explanatory factor. Regional differences 
in under-reporting behaviour were detected. 

5.3 

Administrative data: the way forward 

While the above examples are country-specific, they demonstrate the potential 
usefulness of administrative or other granular data sources. Even if they are not 
available on a regular basis, they make it possible to assess potential sources of gaps 
in the micro-macro comparisons. They can therefore give useful guidance on possible 
additional adjustments to be made so that distributional results can be compiled which 
are closer to the National Accounts totals. In an ideal case, administrative data make it 

ECB Statistics Paper Series No 37 / July 2020 

46 

 
 
 
 
 
 
 
 
possible to assess the distributional information from the survey data, under-reporting 
patterns by household groups and sampling bias. 

Furthermore, even if such data are available for single instruments only or, conversely, 
if they are not available at micro level but at a more aggregated level, the data can be 
used to improve the micro-macro linkage or provide input into adjustment methods 
such as the Pareto estimation described in Chapter 4. Examples of more aggregated 
indicators derived from administrative data sources to improve the accuracy of 
estimation models include the share of wealth held by the top percentiles and the 
share of individuals/households with wealth holdings above a certain threshold. Such 
indicators could be accessible from granular level data sources which, as such, cannot 
be accessed by NCBs or NSIs. 

Finally, the EG-LMM also discussed a presentation by experts from the World 
Inequality Database (WID) project10. This project generally places high importance on 
the use of administrative sources, in particular tax data sources, which are considered 
to be more reliable than survey data and the Pareto adjustments using rich list 
information. However, the discussions also made it clear that the use of administrative 
data, which is generally uneasy due to confidentiality constraints, is even more 
demanding for wealth studies than it is for income studies (given the lack of wealth tax 
data in many countries) and requires various assumptions and estimations (e.g. when 
applying the so-called income capitalisation and estate multiplier methods). 
Nevertheless, the EG-LMM concluded that the work of the ESCB may certainly benefit 
from the work and experiences of the WID team. The EG-LMM therefore considers 
this to be an area of future collaboration. 

Overall, the quality of DFA estimates can be validated and improved by making use of 
administrative data, even if it is not at granular level. The data availability in countries 
is broader than expected, and some countries have made considerable progress in 
this field in the last few years. The options available and actions required are 
country-specific, and national experts are best placed to carry out this work. The work 
at country level should be supported by overall coordination of the efforts at ESCB 
level. 

10    World Inequality Database. 

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6 

User demands and distributional 
indicators 

On the basis of a literature and publication review looking at uses of distributional 
results, a consultation was conducted by the EG-LMM in April 2018. It was carried out 
among 12 selected users in the economics and financial stability departments of the 
ECB (five users), the European Commission (four), the OECD (one) and Banco de 
Portugal (two). Although a consultation among only 12 users does not provide a 
representative picture of all potential future user areas, the selected expert users were 
able to share their specific experience in using distributional and macro-financial 
economic statistics in their work. Some users stressed that the distributional indicators 
would make it considerably easier to estimate the impact of monetary policy on 
households’ investment behaviour and wealth across income and wealth groups. The 
indicators would also provide a clearer and possibly more timely picture of the 
distribution of debt across households – and thus of risks to debt sustainability and 
financial stability. The results, underlying framework and data could potentially also be 
used to conduct different types of macro-financial scenarios for monetary policy and 
financial stability purposes. 

The main results of the consultation can be summarised as follows. 

• 

Almost all respondents consider DFA potentially highly useful in their own 
business area. Most of these users have experience in using HFCS results, and 
about half of them also use results from the EU Statistics on Income and Living 
Conditions (EU-SILC) survey. Interest in using DFA on the monetary policy and 
financial stability side was slightly higher than interest in using it for other 
economic analysis. 

•  Most of the users interviewed would welcome DFA figures on an annual 

frequency. Three-yearly DFA results (i.e. results at the same intervals as the 
HFCS) would not provide significant added value. At the same time, only one of 
the users said they would need quarterly results. 

•  Most users would require DFA country data in combination with euro area 
aggregates. Euro area aggregates alone would not meet the needs. 

• 

All users would require DFA stock data. The demand for flow data varies, with 
about half of the users considering flow data as essential. While financial stability 
users give priority to stock data, the monetary and economic policy users who 
responded to the consultation would generally prefer to obtain flow data in 
addition. 

•  Groupings of households by income quintiles and wealth quintiles are considered 
as especially relevant. This was followed by breakdowns by work status, housing 
status and age. 

ECB Statistics Paper Series No 37 / July 2020 

48 

 
 
• 

• 

Users were asked to list and rank the five most important possible DFA indicators 
(out of a list of 15). The results showed a clear preference for well-known 
concepts such as indebtedness ratios, the debt-to-asset ratio and net financial 
wealth measures. On the other hand, concepts that are often used in 
distributional analysis, such as the Lorenz, Gini and medians concepts, were not 
ranked highly. 

Users taking part in the consultation gave priority to broad coverage of wealth 
components and results for wealth and debt aggregates over results for single 
financial instruments. This feedback highlighted that there is a trade-off here. On 
the one hand, there is evidently demand among users for wealth concepts that 
match the broad NA concepts of wealth (total financial wealth and net worth). On 
the other hand, purely from a data availability and quality point of view, the DFA 
results focusing on a narrower concept excluding components such as non-life 
insurance, pensions and other accounts receivable might be preferable, as it is 
hardly possible to link HFCS and Financial Accounts for these components. 

The following table summarises the conclusions to be taken on board in the further 
work on DFA. 

Table 8 
Summary of the results of the DFA user consultation 

Topic 

Highest demand/use   

Main users 

Frequency 

Financial stability and monetary policy analysis, followed by other economic analysis   

Annual 

Geographic coverage 

Countries and euro area aggregate 

Stock/flow   

Stocks essential, flows desirable 

Household groupings 

Income and wealth quintiles, followed by work and housing status, age 

DFA indicators 

Indebtedness, gross and net wealth   

Coverage of instruments 

Broad, as close as possible to Financial Accounts total assets and debt 

ECB Statistics Paper Series No 37 / July 2020 

49 

 
 
 
7 

Conclusions and way forward 

7.1 

Main conclusions 

In view of the potential role that distributional data can play in explaining 
macroeconomic developments, the Expert Group on Linking Macro and Micro Data for 
the household sector (EG-LMM), consisting of Household Finance and Consumption 
Survey (HFCS) and Financial Accounts experts from European Union countries along 
with experts from Eurostat and the OECD, was set up within the Eurosystem in 
December 2015. 

The EG-LMM’s mandate was twofold. As a first step, its purpose was to understand, 
quantify and explain the main differences between the HFCS and the Financial 
Accounts. A second step involved developing distributional information within the 
household Financial Accounts. 

In the systematic comparison of the two sources carried out in this report, a distinction 
has been made between two main sets of factors explaining differences. 

• 

• 

The first set of factors concerns generic differences that potentially affect all or 
various components of wealth. These include differences that can be relatively 
easily identified (definition of population and household sector, reference 
periods), along with some issues that are more substantial and difficult to quantify 
and adjust for, such as different valuation concepts (self-assessment versus 
valuation defined by statistical standards), the effect of non-response or wrong 
responses in the survey, and the accuracy of some Financial Accounts asset 
categories for which statistical sources are weak. It has also proved difficult to 
determine the extent to which the surveys provide effective coverage of the 
households among which wealth appears to be substantially concentrated. In 
addition, the survey leaves scope for aligning its implementation to national 
circumstances, which results in some heterogeneity across countries. Finally, 
macroeconomic Financial Accounts, while more standardised across countries, 
are only available at a fairly high level of aggregation, which limits the scope for 
improving the quality of the linking by matching the sub-categories that can best 
be compared. 

The second set of factors concerns detailed instrument-specific definitions. The 
EG-LMM has developed alternative concepts of adjusted wealth (as opposed to 
concepts covering all instruments) which cover items that have medium or high 
comparability in the two sources. Items which are covered in only one of the two 
sources (notably currency holdings and non-life insurance reserves, which are 
covered only by the Financial Accounts) or which have low comparability 
(occupational pensions, financial derivatives, other accounts receivable/payable) 
are excluded from adjusted wealth. The EG-LMM produced an overall 
comparison covering items with high or medium comparability. This showed that, 
at euro area level, these items account for more than 90% of households’ 
financial and non-financial assets together with liabilities (see also Table 9). 

ECB Statistics Paper Series No 37 / July 2020 

50 

 
 
Table 9 
Proportion of National Accounts items with high or medium comparability to HFCS 

ESA 2010 code 

National Accounts instrument 

Relative size 

Assets 

Items with high or medium comparability 

% of total 
financial assets 

% of total financial and 
non-financial assets 

F.22 and F.29 

Deposits 

F.3   

F.4 

F.511 

F.52 

Debt securities 

Loans 

Listed shares 

Investment fund shares 

F.512 + F.519 

Unlisted shares + Other equity 

F.62 

Life insurance and annuity entitlements 

AN.211+AN.11+AN.12  Land + Fixed assets (including dwellings) + Inventories 

Total financial assets 

Total financial and non-financial assets 

Items excluded from the comparison 

Currency 

Non-life insurance technical reserves + Provisions for calls 
under standardised guarantees 

F.21 

F.61+F.66 

F.63+F.64+F.65 

Pension entitlements + Claims of pension funds on pension 
managers + Entitlements to non-pension benefits 

F.7 

F.8   

Liabilities 

Financial derivatives 

Other accounts receivable 

F.4 

Loans 

30% 

4% 

0% 

4% 

8% 

15% 

18% 

79% 

2% 

3% 

13% 

0% 

3% 

% of liabilities 

91% 

14% 

2% 

0% 

2% 

4% 

7% 

8% 

55% 

91% 

1% 

1% 

6% 

0% 

1% 

In addition, estimation methods have been developed to close the remaining gaps 
between HFCS and National Accounts. These methods address the main potential 
reasons for differences between National Accounts and household survey data and 
experimental calculations for four countries have shown their impact on the 
distributional accounts. These methods would benefit from refinement at national 
level. 

The EG-LMM has also recognised the need to collect administrative data to 
complement survey data in the production of Distributional National Accounts 
indicators. 

• 

Administrative data should ideally be available at granular level, or at least at 
household group level. However, for the methodologies presented in Section 4.3, 
less detailed administrative information can also be useful, e.g. information on 
the number of households with wealth holdings over specific thresholds. 

•  Given the evident legal constraints in getting access to several administrative 

data sources, related with the high confidentiality of this information, the EG-LMM 
encourages assessments of the availability of such data at the national level. In 
addition, it believes that cooperation between National Central Banks (NCBs) 
and National Statistical Institutes (NSIs) would be useful, given that NSIs 
frequently have access to administrative data sources not accessible by NCBs. 

ECB Statistics Paper Series No 37 / July 2020 

51 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
As described in Chapter 6, a user consultation was conducted to find out which 
breakdowns and indicators users would like to have, and to establish when and how 
often they would like this information. A demand for annual time series data and for a 
broad coverage of household wealth components was commonly put forward. 

7.2 

Way forward 

This Section outlines a possible plan for implementing estimates of Distributional 
Financial Accounts (DFA) in euro area countries, including non-financial assets, on an 
experimental basis over the next three years. To provide a better overview, the plan is 
divided into four workstreams. 

Workstream A: Finalise the fine-tuned linking of the HFCS 

The work performed has shed light on certain weaknesses in both datasets. Further 
efforts will be needed to overcome these weaknesses. 

As regards the Financial Accounts, the following limitations should be addressed 
further. 

• 

• 

Estimates of unlisted shares and other equity in the Financial Accounts are lower 
than the HFCS results for some countries. It is questionable whether the current 
Financial Accounts totals can be considered a reliable benchmark for the 
micro-macro linking for all countries. Investigations into ways of improving data 
sources and harmonising valuations have already been launched by the ESCB11 
Working Group on Financial Accounts (WG FA). The EG-LMM also regards 
these investigations as an important part of the efforts to improve comparability 
between HFCS and Financial Accounts statistics. Further relevant aspects are 
the coverage, the valuation and the recording of dwellings held abroad as “other 
equity” in the Financial Accounts. 

Similarly, improving coverage of assets held abroad and estimates of the 
“missing” assets abroad is important for distributional accounts, as these are 
highly concentrated in the higher quintiles. 

As regards National Accounts/non-financial assets, further work is needed in the 
following areas – which are the responsibility of Eurostat and the European Statistical 
System – to help ensure progress in compiling distributional accounts of households’ 
wealth. 

• 

National data on non-financial assets, including dwellings and land, are 
incomplete in many countries. Derogations expire by 2020 at the latest, so it is 
hoped that by then the mandatory requirements of the ESA12  transmission 
programme will have been met by all countries. 

11  European System of Central Banks 
12  European System of Accounts 

ECB Statistics Paper Series No 37 / July 2020 

52 

 
 
                                                                    
• 

The data in the ESA transmission programme have two main weaknesses 
regarding their use for DFA purposes: (i) the category “land” covers “all land”, 
while separate data on land underlying dwellings are not available, implying that 
housing wealth and a separation of this wealth from business wealth can only be 
roughly estimated; and (ii) data for non-profit institutions serving households 
(NPISHs) are not separated from those for households. The review of the ESA 
transmission programme envisaged in the coming years could be an opportunity 
to consider such changes. 

On the HFCS side, potential improvements have been identified in the following areas. 

•  Work is under way within the ESCB Household Finance and Consumption 

Network (HFCN) to obtain a clearer picture of the collection of data on assets 
related to business activities, the aim being to make sure all of these data are 
recorded once and are identified as business wealth. 

• 

• 

• 

In addition, collecting data on the business wealth of sole proprietors and 
partnerships in gross terms (i.e. recording assets separately from liabilities), 
identifying the main assets held and classifying these assets under the heading 
of “business wealth”, would significantly improve the bridge between HFCS and 
National Accounts. 

It is understood that in the 2020 wave, the HFCS will modify the definition of sole 
proprietorship in such a way that incorporated businesses can be separated from 
unincorporated businesses. This change is very welcome from the perspective of 
the link between HFCS and National Accounts. 

Enhancing oversampling or developing/identifying specific sources from which to 
collect more information on the wealthiest households would further improve the 
consistency between HFCS and National Accounts results. 

In many cases, the link between HFCS and National Accounts is now as best as can 
be, taking into account the general limitations described above – essentially that the 
survey reflects households’ understanding and valuation of wealth rather than 
statistical definitions and valuation methods. 

Nevertheless, potential for further improvements has been identified in a few areas. 
These are as follows. 

• 

• 

Data collected as part of Monetary Financial Institutions balance sheet statistics 
can be used to distinguish mortgage loans from other loans in Financial Accounts 
data (see Section 3.4) and on this basis improve the comparison with the HFCS, 
which makes a distinction between these components. The EG-LMM agreed to 
implement this in compiling the DFA. 

For sole proprietors and partnerships in the HFCS, a distinction should be made 
between producer households and quasi-corporations until improved information 
becomes available from the HFCS. The plan is to make this information available 
as of 2020. The underlying assumption is that sole proprietorships and 
partnerships which are unincorporated belong to the household sector and the 

ECB Statistics Paper Series No 37 / July 2020 

53 

 
 
rest to the corporation sector. It was agreed that this approach would be 
implemented in compiling the DFA. 

•  Within non-financial assets, dwellings and land underlying dwellings should be 
distinguished as much as possible from: (i) other non-financial assets; and 
(ii) land not underlying dwellings. In addition, the focus should be only on those 
assets held by households, while those of NPISHs should be excluded. These 
breakdowns are not part of the ESA transmission programme (see Section 3.3). 
However, including these breakdowns in the compilation of the DFA at national 
level would be very useful as a means of improving DFA estimates. 

• 

• 

• 

• 

Leasing is generally recorded as loans in the Financial Accounts, while it is 
measured as separate flows in the HFCS. Investigations should be carried out to 
ascertain whether HFCS flow data on leasing payments can be used to derive a 
proxy for the outstanding loans in order to make totals more comparable. 

The distribution of holdings of currency, i.e. banknotes and coins, currently not 
covered in the HFCS, could be estimated in Distributional Financial Accounts for 
instance as being proportional to consumption or to income. 

The possibility of estimating not only the distribution of Financial Accounts 
positions, but also that of flows, namely transactions, other changes and 
revaluations, should be further considered. 

Finally, there should be further investigations into the possibility of using 
information available in MFI balance sheet statistics regarding loans to (and in 
some countries deposits by) sole proprietors. This would help to better 
distinguish corresponding amounts in the Financial Accounts, thus improving the 
link with the HFCS. 

Workstream B: Fully implement, apply and improve estimation 
methods 

The remaining gap between HFCS and the National Accounts is thought to be mainly 
attributable to: (i) under-reporting by the households participating in the survey; and 
(ii) insufficient coverage of the wealthiest households. Beyond the above-mentioned 
recommendations to the HFCN for improving these elements where possible, the 
EG-LMM recommends estimating this remaining gap in order to produce DFA 
consistent with the (adjusted) National Accounts wealth concept. An R-package has 
been developed to run these estimates at country level. The package has been 
designed to allow for the flexible incorporation of additional information at national 
level (including in particular administrative sources), where feasible. The application of 
the package is expected to deliver useful information where further development of the 
method is needed. In particular, a process table should be produced, showing the 
effect of each step of the estimation process on the final result, as suggested in 
Roymans (2016). 

ECB Statistics Paper Series No 37 / July 2020 

54 

 
 
Workstream C: Towards experimental annual results 

Within the scope of the work presented in this report, the EG-LMM was not able to 
devote time to developing and establishing a method for interpolating and, if possible, 
extrapolating DFA results outside those years which are also HFCS survey years. 
Obviously, this issue needs to be addressed as soon as possible, and the further work 
will benefit from the ongoing releases of the third wave of the HFCS. 

A consultation among users has highlighted the need for DFA, focusing in particular 
on a list of specific indicators. In addition, the widespread interest in this information 
among research groups and the public has led to a situation whereby the public only 
has access to data estimated by academics, such as the data from the World 
Inequality Database. Although it is recognised that the publication by the Eurosystem 
of DFA results requires adequate communication to make clear the current caveats 
regarding data quality, the EG-LMM recommends considering at some stage 
publishing the results for the euro area and individual countries, with these results 
labelled as experimental statistics. The data would first be disseminated only within 
the ESCB in order to collect user feedback on the results. The feasibility of publishing 
these experimental statistics should be assessed on the basis of that feedback. 

The ultimate aim of this workstream would be to obtain experimental annual statistics 
for the years from about 2010 onwards. 

Workstream D: Research into consistency with estimates of 
distributional income accounts 

As both the ESCB’s work on distributional wealth accounts and the OECD’s/Eurostat’s 
work on distributional income accounts are progressing, active steps should be made 
to coordinate the compilation approaches and to benefit from closer cooperation. In 
particular, one possible area of improvement for these estimates would be to explore 
the feasibility of comparing results obtained by the Expert Group on Disparities in a 
National Accounts framework (EG DNA) on gross savings and net lending/borrowing 
by quintile with estimates of financial transactions by quintile. This workstream will 
deliver results only in the longer term, but it is important to coordinate the development 
of methods to minimise “reconciliation work” further down the line. In addition, some 
EG-LMM members should cooperate with the EG DNA in their efforts to make 
progress on distributional wealth. 

ECB Statistics Paper Series No 37 / July 2020 

55 

 
 
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Annex 1: Second Mandate of the Expert 
Group on Linking Macro and Micro Data 
for the household sector 

1. 

In December 2015, the ESCB Statistics Committee (STC) mandated the Working 
Group on Financial Accounts (WG FA) to establish, in cooperation with the 
Household Finance and Consumption Network (HFCN), a temporary Expert 
Group on Linking Macro and Micro Data for the household sector (EG-LMM). The 
EG-LMM’s first mandate focused on comparing and bridging macro and micro 
data. In March 2017, the EG-LMM submitted its first report to the STC. As 
envisaged at the outset, the STC then gave the EG-LMM a second mandate, this 
time with the focus on developing distributional results for household macro 
balance sheets. 

2.  The task of the EG-LMM under this second mandate is to contribute to linking 

macro and micro statistics for the household sector, i.e. to link the Financial 
Accounts (FA) and Household Finance and Consumption Survey (HFCS), 
building on the results of its first mandate. Two workstreams have been defined 
for that purpose. 

The tasks in the first workstream are as follows: 

• 

• 

• 

further assessing the impact of generic and instrument-specific differences 
on HFCS-FA coverage ratios and their varying impact across instruments 
and across countries; 

developing recommendations for improving the link between the HFCS and 
the Financial Accounts and for achieving better coverage ratios for future 
HFCS waves; 

assessing the availability of administrative sources for improving HFCS-FA 
linking. 

The tasks in the second workstream are as follows: 

• 

• 

• 

• 

defining a set of distributional indicators for the household sector balance 
sheet, with a focus on items with “medium” and “high” comparability; 

calculating for these indicators experimental results for 2010 and 2014 (or 
the two periods closest to the HFCS fieldwork) and assessing the feasibility 
of deriving estimates at annual frequency; 

extending the comparison to non-financial assets, subject to the availability 
of new National Accounts data on land due at end-2017 under the ESA 2010 
transmission programme; 

seeking the views of potential ECB/ESCB users to identify their priorities. 

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3.  The EG-LMM should consider the ongoing work of Eurostat and the OECD, and 
its relevant expert groups, with the objective of contributing to a joint framework 
for linking National Accounts and household survey data on wealth, income and 
consumption. 

ECB Statistics Paper Series No 37 / July 2020 

61 

 
 
Annex 2a: Correspondence table 
between Financial Accounts and HFCS 
for financial instruments 

ESA 2010 
code 

NA instrument 
name 

HFCS 
variable(s) 

HFCS variable label 
and/or codes 

Conceptual 
comparability 

Remarks 

Assets 

F.2 

Currency and 
deposits 

F.21 

Currency 

N/A 

N/A 

N/A 

F.22 

Transferable 
deposits 

HD1110 

Sight accounts 

HIGH 

F.29 

Other deposits 

HD1210 

Savings accounts 

HIGH 

F.3 

Debt securities 

HD1420 

Bonds 

HIGH 

NA: holdings of households 
included but estimated due to the 
lack of direct sources. 

HFCS: not collected. 

Specific conceptual differences 
exist but are unlikely to be 
significant or can be adjusted 
(deposit-like instruments with 
non-deposit-taking corporations 
are included in the HFCS and can 
be reclassified from short-term 
loans in FA). 

Conceptual differences are not 
known. 

F.31/ F.32  Short-term/long-term 

debt securities 

a – State or other general 
government 

N/A 

Maturity breakdown not covered in 
the HFCS 

b – Banks / Other financial 
intermediaries 

c – Non-financial 
corporation 

d – Other (specify) 

F.4 

Loans 

HD1710 

Amount owed to household  HIGH 

F.41/.42 

Short-term/long-term 

F.5   

Equity and 
investment fund 
shares 

F.511 

Listed shares 

HD1510 

Publicly traded shares 

HIGH 

F.512 

Unlisted shares 

HD1010 

F.519 

Other equity 

HD0200 

MEDIUM 

(LOW with 
regard to the 
split between 
“unlisted 
shares” and 
“other equity”) 

Investment in 
non-self-employment not 
publicly traded businesses 
(ownership only as an 
investor or silent partner) 

Investments in 
self-employment 
businesses 

1 – Sole proprietorship / 
independent professional 

2 – Partnership 

3 – Limited liability 
companies 

4 – Co-operative societies 

5 – Non-profit making 
bodies 

6 – All other forms (Spain) 

Only total market value of all 
bonds given in HFCS/amount not 
split into types except in the case 
of Italy. 

HFCS has a somewhat broader 
definition. Loans between 
households missing from NA in 
practice for most countries. For 
reclassification of some loans see 
F.2. 

- HFCS classification is based on 
the household’s activity in the 
enterprise. 

- NA value includes assets that are 
classified as real wealth in the 
HFCS (value of self-employment 
businesses) 

- The split between “unlisted 
shares” and “other equity” is not 
available in the survey. 
Investments in self-employed 
businesses could be included in 
“unlisted shares” or “other equity”. 

- The value of a sole proprietorship 
or partnership is spread over the 
different instruments in the NA if it 
is not considered as a separate 
legal entity (quasi-corporation). 

- HFCS provides the value for the 
different legal status (but legal 
status is not imputed in all 

ECB Statistics Paper Series No 37 / July 2020 

62 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7 – Unknown (not imputed) 

F.521 

Money market fund 
(MMF) shares or 
units 

1320c 

Investments in mutual 
funds 

HIGH 

c – Funds predominantly 
investing in money market 
instruments 

F.522 

Non-MMF 
investment fund 
shares/units 

HD1320x 

a – Funds predominantly 
investing in equity 

HIGH 

countries, so there is an 
“unknown” category). In addition, 
in the HFCS the number of 
employees in self-employed 
businesses is collected. 

Value dependent on fund type not 
imputed in every country. The 
breakdown by type of fund may 
not be available and only the total 
HD1330 is imputed in all 
countries. Hence the distinction 
between MMF and non-MMF 
funds may not be made in these 
countries. 

b – Funds predominantly 
investing in bonds 

d – Funds predominantly 
investing in real estate 

e – Hedge funds 

f – Other fund types 
(specify) 

F.6 

F.61 

F.62 

Insurance, pension 
and standardised 
guaranteed schemes 

Non-life insurance 
technical reserves 

Life insurance and 
annuity entitlements 

N/A 

N/A 

N/A 

Voluntary pension/whole 
life insurance 

MEDIUM 

DA2109 = 
sum of 
(PF0920) 
over 
household 
members 

F.63 

Pension entitlements  Sum of 

(PF0700) 
over 
household 
members 

Current value of all 
occupational pension plans 
that have an account 

LOW 

Non-life assets in FA (e.g. health 
insurance, term insurance) can be 
significant. 

Value reported in HFCS not clearly 
defined (net present value, 
liquidation value?). FA value likely 
to be higher than HFCS value. 

Clarification required as to what 
extent defined benefit schemes 
should be and are in fact included 
in this variable in the HFCS. 
Valuation issue as for F.62. 

Claims of pension 
funds on pension 
managers 

N/A 

N/A 

N/A 

F.64 not applicable to households. 

Entitlements to 
non-pension benefits 

N/A 

N/A 

Provision for calls 
under standardised 
guarantees 

N/A 

N/A 

N/A 

N/A 

F.65 likely to be insignificant. 

F.66 not applicable to households. 

F.64 

F.65 

F.66 

F.7 

F.8 

Financial derivatives  HD1920 

Other financial assets 

LOW 

Other accounts 
receivable/payable 

HD1620 

Managed accounts 

LOW 

F.L 

F.4 

Liabilities 

Loans 

DL1000 

Short-term/long-term  DL1100 

Total outstanding balance 
of household’s liabilities 

Outstanding balance of 
mortgage debt 

HIGH 

HIGH 

DL1110 

Outstanding balance of 
household main residence 
(HMR) mortgages 

DL1120 

Outstanding balance of 

ECB Statistics Paper Series No 37 / July 2020 

63 

Financial derivatives are not a 
separate item in the HFCS and are 
included in “Other financial 
assets”. Definition of “Other 
accounts receivable/payable” not 
comparable with “Other financial 
assets” owing to different 
definitions. 

May be spread over the FA 
balance sheet of the household 
depending on set-up of the 
management and depending on 
the assets invested. Does not, 
however, affect comparability of 
total financial assets. 

High conceptual comparability 
refers here to the maturity. The 
maturity is covered in the HFCS 
for the different loans with a similar 
concept to that in the NA: length of 
the loan at the time of borrowing or 
refinancing. Therefore, it is also 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
mortgages on other 
properties 

Outstanding balance of 
non-mortgage debt 

Amount of outstanding 
credit line/overdraft balance 

Amount of outstanding 
credit cards balance 

Outstanding balance of 
non-collateralised loans 

DL1200 

HC0220 

HC0320 

Sum of 
HC080$x 
and HC1100 

F.8 

Other accounts 

LOW 

possible to make the distinction 
between short-term and long-term 
in the HFCS. 

Maturity not imputed in every 
country in the survey. 

Matching data on mortgage loans 
can be derived in the FA as 
estimates are based on MFI 
balance sheet data. 

May be partly included in the 
outstanding balance of 
non-collateralised loans or in the 
outstanding balance of credit 
cards in the HFCS. 

Notes: Interpretation of the conceptual comparability of instruments between the HFCS and NA. 
The table only indicates the conceptual comparability of the specific instrument. Common or generic differences due e.g. to sole 
proprietorship or the coverage of the wealthy households are evaluated in Chapter 2. 
N/A = Not comparable: item not included in either NA or the HFCS. 
LOW = Low comparability: item included in both NA and the HFCS, but in different categories or the concepts differ substantially. 
MEDIUM = Medium comparability: similar items included, but they differ conceptually between the HFCS and NA in regards to the items 
included in the asset or liability category or in the definition used. 
HIGH = High comparability: comparable items included in regards to the definition used. The definition might still differ marginally 
between the survey and NA. 

ECB Statistics Paper Series No 37 / July 2020 

64 

 
 
 
 
Annex 2b: Correspondence table 
between National Accounts and HFCS 
for non-financial assets 

NA code 

NA description 

HFCS code 

HFCS description 

N 

Non-financial assets 

AN.1 

Produced non-financial assets   

AN.11 

Fixed assets 

AN.111 

Dwellings 

HB0900 

Household main residence 

This covers buildings primarily used as 
residences and excludes land. 

What is the value of this property, i.e. if you could sell it now 
how much do you think would be the price of it? Value 
includes land. 

AN.112 

Buildings other than dwellings and other 
structures 

In the household sector, these are 
buildings for production purposes by sole 
proprietorships and partnerships (and 
NPISHs if they are not separated from 
households). 

HB280$x 

Properties other than the household main residence 

Property types “house or flat”, “apartment building”, “hotel”, 
“building plot/estate”. Value includes land. 

HB280$x 

Properties other than the household main residence 

Property types “industrial building/warehouse”, “garage”, 
“shop”, “office”, “farm” and “other”. Value includes land. 

HD080$x 

Self-employment business wealth 

This can be a part of business wealth if respondents are 
asked to exclude properties belonging to a business in the 
section on other properties. Buildings are not separated from 
the rest of business wealth. Value of business provided in 
net terms. 

AN.113 

Machinery and equipment 

HD080$x 

Business wealth 

Machines and equipment used by sole 
proprietorships and partnerships fall into 
this category. Cars and other durable 
goods in the household sector are not 
included in capital stock. 

Machinery and equipment are part of business wealth. 
Machines and equipment are not separated from the rest of 
business wealth. Value of business provided in net terms. 
Cars and other durable goods may be included in business 
wealth. 

AN.115 

Cultivated biological resources 

HD080$x 

Business wealth 

Agricultural plantation without land which 
belongs to sole proprietorships and 
partnerships falls into this category. 

These assets are part of business wealth. They are not 
separated from the rest of business wealth. Value of 
business provided in net terms. 

AN.117 

Intellectual property products 

HD080$x 

Business wealth 

Intellectual property products which belong 
to sole proprietorships and partnerships fall 
into this category. 

These assets are part of business wealth. They are not 
separated from the rest of business wealth. Value of 
business provided in net terms. 

AN.12 

Inventories 

HD080$x 

Business wealth 

(in the case of households, those 
belonging to sole proprietorships and 
partnerships) 

These assets are part of business wealth. They are not 
separated from the rest of business wealth. Value of 
business provided in net terms. 

AN.13 

Valuables 

HB4710 

Valuables 

(Do you/Does your household) own any valuables such as 
jewellery, works of art, antiques, etc.? 

Valuables are produced assets that are not 
used primarily for production or 
consumption, that are expected to 
appreciate or at least not to decline in real 
value, that do not deteriorate over time 
under normal conditions and that are 
acquired and held primarily as stores of 
value. 

AN.2 

Non-produced non-financial assets 

AN.21 

Natural resources 

AN.211 

Land 

HB0900 

Household main residence 

This covers all the land, i.e. land underlying 
dwellings and buildings as well as land 
used for agriculture and forestry. 

Land included in the value of the property. 

HB280$x 

Properties other than the household main residence 

Property types “building plot/estate”, “farm”. 

ECB Statistics Paper Series No 37 / July 2020 

65 

 
 
 
 
 
 
 
 
 
 
 
 
Land included in the values of other property types. 

HD080$x 

This can be a part of business wealth If respondents are 
asked to exclude properties belonging to a business in the 
section on other properties. Land is not separated from the 
rest of the business wealth. Value of business provided in 
net terms. 

AN.22 

Contracts, leases and licences 

HD080$x 

Business wealth 

This covers patents, leases, etc. held by 
sole proprietorships and partnerships (in 
the case of the household sector). 

These assets are part of business wealth. They are not 
separated from the rest of business wealth. 

ECB Statistics Paper Series No 37 / July 2020 

66 

 
 
 
 
Abbreviations 

Countries 

BE 
BG 
CZ 
DK 
DE 
EE 
IE 
GR 
ES 
FR 

Belgium 
Bulgaria 
Czech Republic 
Denmark 
Germany 
Estonia 
Ireland 
Greece 
Spain 
France 

HR 
IT 
CY 
LV 
LT 
LU 
HU 
MT 
NL 
AT 

Croatia 
Italy 
Cyprus 
Latvia 
Lithuania 
Luxembourg 
Hungary 
Malta 
Netherlands 
Austria 

PL 
PT 
RO 
SI 
SK 
FI 
SE 
UK 

Poland 
Portugal 
Romania 
Slovenia 
Slovakia 
Finland 
Sweden 
United Kingdom 

 
 
 
 
 
Acknowledgements 

This paper was prepared based on a report by the ESCB Expert Group on Linking macro and micro data for the household sector, and 
edited by Henning Ahnert, Ilja Kristian Kavonius, Juha Honkkila and Pierre Sola (ECB). 
This paper has also benefited from feedback provided by the Editorial Board of the ECB Statistics Paper Series. 

List of participants of the Expert Group on Linking macro and micro data for the household sector 

Henning Ahnert (Co-chairperson) 
European Central Bank 

Andrea Neri (Co-chairperson) 
Banca d'Italia 

Ilja Kristian Kavonius (Co-secretary) 
European Central Bank 

Juha Honkkila (Co-secretary) 
European Central Bank 

Kristina Altmann 
Deutsche Bundesbank 

Alexander Erler 
Deutsche Bundesbank 

Stine Ludvig Bech 
Danmarks Nationalbank 

Cormac O'Sullivan 
Central Bank of Ireland 

Stelios Corres 
Bank of Greece 

Javier Arribas Vela 
Banco de España 

Michel Mouliom 
Banque de France 

Clémence Charavel 
Banque de France 

Stefano Piermattei 
Banca d'Italia 

Ingber Roymans 
Banque centrale du Luxembourg 

Gábor Huszár 
Magyar Nemzeti Bank 

Béla Simon 
Magyar Nemzeti Bank 

Federica Teppa 
De Nederlandsche Bank 

Michael Andreasch 
Oesterreichische Nationalbank 

Peter Lindner 
Oesterreichische Nationalbank 

Rita Poiares Mourinho Felix 
Banco de Portugal 

Jarmila Hrbačkova 
Národná banka Slovenska 

Francesca Tartamella 
Eurostat 

Sigita Grundiza 
Eurostat 

Jorrit Zwijnenburg 
OECD 

Freddy Farias Arias 
Office for National Statistics (UK) 

David Matthewson 
Office for National Statistics (UK) 

Sofiya Stoyanova 
Office for National Statistics (UK) 

Joshua Parry 
Office for National Statistics (UK) 

Riikka Tupala 
Statistics Finland 

Veli-Matti Törmälehto 
Statistics Finland 

Arjan Bruil 
Statistics Netherlands 

Maria Giovanna Ranalli 
University of Perugia 

Michele Cantarella 
European Central Bank 

Guilherme Ferreira 
European Central Bank 

Lise Reynaert Lefebvre 
European Central Bank 

Pierre Sola 
European Central Bank 

Monika Stuhldreher 
European Central Bank 

Mika Tujula 
European Central Bank 

Sofie Waltl 
European Central Bank 

Henning Ahnert 
European Central Bank, Frankfurt am Main, Germany; email: henning.ahnert@ecb.europa.eu   

Ilja Kristian Kavonius 
European Central Bank, Frankfurt am Main, Germany; email: Ilja_kristian.kavonius@ecb.europa.eu 

Juha Honkkila 
European Central Bank, Frankfurt am Main, Germany; email: juha.honkkila@ecb.europa.eu 

Pierre Sola 
European Central Bank, Frankfurt am Main, Germany; email: pierre.sola@ecb.europa.eu 

 
 
 
 
 
 
 
 
 
© European Central Bank, 2020 

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This paper can be downloaded without charge from the ECB website or from RePEc: Research Papers in Economics. Information on all of 
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ISBN 978-92-899-4366-6, ISSN 2314-9248, doi:10.2866/236032, QB-BF-20-005-EN-N