Is the financial market driving income distribution? : an analysis of the linkage between income and wealth in Europe.
Date: 2022-08-23
Institutions: European Central Bank
Subjects: balance sheet, distribution of income, financial market, globalisation, household, wealth

Ilja Kristian Kavonius, Veli-Matti Törmälehto 

Working Paper Series 

Is the financial market driving income 
distribution? – An analysis of 
the linkage between income and 
wealth in Europe 

No 2707 / August 2022 

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. 

Abstract 

Globalisation has a major impact on the levels and distribution of wealth. The financial markets 

are highly integrated, and valuations of financial assets follow international patterns, which has 

contributed to large increases in financial wealth over the past 25 years. Nonetheless, this has 

not  led  to  an  equally  large  increase  in  property  income  because  the  rates  of  return  have 

decreased  during  the  same  era.  Moreover,  changes  in  functional  income  distribution 

(capital/labour shares) have not been fully transmitted to the distribution of primary income 

between households because other institutional sectors – particularly the government sector – 

hold considerable amounts of financial assets. At least in the short term, the decrease in rates 

of  return  seems  to  contradict  claims  that,  due  to  an  increase  in  both  financial  and  inherited 

wealth, we are entering an era of increasing income inequality.  

In this article, the link between financial wealth and pre-tax household income distribution is 

scrutinised  for  three  European  countries  using  a  conceptually  fully  consistent  macro 

framework. First, national balance sheets are combined with the related income flows. After 

this, income flows that are not property income but are considered part of national income (e.g., 

wages and salaries) are added, the national income flows are broken down by institutional sector 

and the household sector income flows separated. Finally, distributional household micro data 

are used to break down the aggregate household sector income flows by income decile. The 

article utilises this framework to analyse the evolution of rates of return and capital and labour 

shares  as  well  as  how  the  property  income  flows  created  by  financial  wealth  have  affected 

household primary income distribution. 

JEL-codes: D10, D31, D32, E21, G51 

Keywords: functional distribution, income distribution, national income, households, wealth 

ECB Working Paper Series No 2707 / August 20221 
 
 
 
 
 
 
Non-technical summary 

This article presents a framework that begins with the balance sheets of the economy, which 

are then connected with the corresponding income flows of national income. Next, the income 

flows that belong to the household sector are separated. This allows examination of rates of 

return from capital for the total economy as well as for the household sector. Finally, household 

sector flows are linked with distributional income data, which allows a scrutiny of capital and 

labour  shares  by  income  decile,  consistent  with  the  concepts  of  national  accounts.  The 

breakdown by income groups is implemented by linking the national income concept with the 

EU-SILC and HFCS micro data, which are the only internationally available data sources which 

can be used for this purpose. The paper includes a comprehensive analysis of the differences 

between the micro and macro data, showing wide variations by income component and country. 

Based on a sensitivity analysis on how adjustments for the observed micro/macro differences 

might impact the results, the article uses the concentration of income components by decile as 

estimated from micro data without adjustments to match national accounts total.  

The analysis is performed for Finland, France and Spain. The analysis in this article is related 

to but different from the analysis performed by Thomas Piketty (2014) and estimates presented 

in  the  World  Inequality  Database  (WID).  Piketty  argues  that  wealth  is  increasingly 

accumulating in wealthy households, and this wealth is playing an increasingly important role 

in  the  generation  of  income,  which  will  lead  to  increased  income  dispersion.  The structural 

changes  in  the  labour  shares  indicate  that,  in  terms  of  income  generation,  the  role  of  both 

property  and  labour  income  is  changing.  In  our  framework,  this  process  should  entail  a 

structural shift in the ratio between capital and labour income in the highest income deciles.  

The stock of wealth has indeed increased much in recent decades. This increase is a result of 

increased investment in financial and non-financial assets, i.e., the investment of savings, as 

well as the price increase of actual assets. Nevertheless, returns were higher some decades ago, 

and  if  they  were  similar  to  levels  20  years  ago,  the  capital/labour  ratio  would  have  moved 

structurally  towards  capital.  This  would  also  have  strongly  increased  inequality,  as  capital 

income is typically concentrated in the highest income deciles. However, as the rates of return 

ECB Working Paper Series No 2707 / August 20222 
 
 
in  these  three  countries  practically  halved  from  1995  to  2019,  the  capital/labour  ratio  has 

actually remained relatively stable.  

Our  (arguably  short-term)  decile-based  analysis  finds  no  evidence  of  a  structural  shift  in 

capital/labour  ratios  in  the  highest  income  deciles.  The  share  of  income  which  households 

receive from wealth is sensitive to economic cycles, but there is no clear structural change. In 

fact, the share of labour income in the highest income deciles appears to have been increasing 

rather than decreasing. Thus, it seems we are moving towards the modern liberal meritocratic 

capitalism  described  by  Milanovic  (2019),  where  people  are  capital-  as  well  as  labour-rich, 

rather  than  towards  a  new  heyday  of  classical  capitalism.  The  timeframe  of  our  analysis  is 

considerably shorter than that used by Piketty, but issues of (micro) data availability prevent a 

longer  timeframe  for  an  analysis  conducted  in  such  detail  and  based  on  actual  (rather  than 

constructed or synthetic) distributional estimates. 

Piketty’s estimation covers the national income, i.e. the assumption is that all the income in the 

economy is ultimately received by the households and consequently, the other economic sectors 

are not having any role in the distribution of the income flows. In our model, we focus on the 

functional distribution of the income which is actually received by households, broken down 

by observed (rather than constructed) household income deciles. We also analyse how these 

differences in the sectoral scope as well as in the definitions of the income impact on rates of 

return and functional income distribution.  

It is also essential to note that if we analyse solely the distribution of gross national income, a 

different picture emerges. In terms of total economic development of all institutional sectors, 

property  income  plays  a  more  pronounced  role  and  has  more  volatility  compared  to  pure 

household  sector  income.  Relatively  volatile  property  income  implies  more  volatility  in  the 

functional  income  distribution  (capital/labour  shares),  but  clear  structural  changes  are  not 

evident. At the national level, the share of capital income is, for obvious reasons, larger than at 

the  household  level,  but  we  argue  that  this  does  not  automatically  entail  increasing  (inter-

personal)  inequality.  In  particular,  the countries  analysed  in  this  article  are characterised  by 

ECB Working Paper Series No 2707 / August 20223 
 
 
considerable  equity  ownership  by  general  government  and  non-profit  institutions  serving 

households, whose returns are used for the public good. 

ECB Working Paper Series No 2707 / August 20224 
 
 
 
The more the capitalist has accumulated, the more is he able to accumulate - Karl Marx, 

Capital: A Critique of Political Economy 

1.  Introduction 

The  last  decade  has  witnessed  much  discussion  on  the  role  of  wealth  in  the  generation  of 

income. Thomas Piketty (2014), example, argues that the growth of wealth plays a central role 

in the distribution of income. After WWII, Europe experienced an era of exceptionally equally 

distributed income. This was the outcome of active income redistribution policies as well as the 

destruction  of  wealth  during  the  two  World  Wars.  The  World  Wars  were  followed  by  an 

exceptionally  long  period  where  government  policy  in  many  countries  aimed  at  equalising 

income distribution. However, Piketty considers this a temporary period, after which we are 

returning  to  the  Gilded  Age,2  where  rich  family  dynasties  play  a  central  role  in  political 

decision-making  and  the  overall  economy.  His  argument  is  that  wealth  is  increasingly 

accumulating in wealthy households and this wealth is playing an increasingly important role 

in the generation of income, which leads to increasing income dispersion. 

Piketty (2014) bases his argument on a formula that relates the rate of return on capital (r) to 

economic growth (g). He argues that when the rate of growth is low, wealth tends to accumulate 

more quickly from capital (r) than from labour. This increasing income from capital tends to 

accumulate unequally, favouring the top 10% and particularly the top 1%, thereby increasing 

inequality. Thus,  the fundamental force  for  divergence and  greater wealth  inequality  can  be 

summed up by the hypothesis r > g. He analyses inheritance from the perspective of that same 

formula. 

The idea behind this can be described as follows: in the case of functional income distribution, 

i.e., the relation between the compensation of employees and profits (operational surplus), if 

2 In United States history, the Gilded Age was an era that occurred during the late 19th century, from the 1870s to 
about 1900. The Gilded Age was an era of rapid economic growth, especially in the Northern and Western United 
States. 

ECB Working Paper Series No 2707 / August 20225 
 
 
 
 
                                                 
employee compensation increases slower than profits, the share of profit in the national income 

increases. As wealth, and thus property income, are typically concentrated on the right tail of 

distribution, such growth leads to increasingly unequal income distribution. Piketty assumes 

that economic growth (g) in the long run defines the increase in employee compensation and 

that property income mostly depends on the rate of return on capital (r). 

Following  his  well-known  book  Capital  in  the  Twenty-First  Century,  Piketty,  together  with 

Emmanuel  Saez  and  Gabriel  Zucman,  has  broken  down  the  national  income  of  different 

countries  by  income  deciles  and  using  this  framework  illustrated  how  the  role  of  property 

income has increased.3 Nonetheless, Branko Milanovic (2017) has observed that the relation 

between  functional income and household  income distribution is more complicated  than  the 

description presented in Piketty’s studies. Piketty assumes that households ultimately receive 

income even though that income is generated and consumed, for instance, in the government 

sector. Here, the assumption is that it is always individuals who ultimately benefit from income. 

However, even though this idea sounds plausible, it is not necessarily correct.4 This does not, 

however,  overturn Piketty’s argument that wealth  and property  income  play  an  increasingly 

important role in economies. 

It is clear that in recent decades the role of wealth has increased. The increasing importance of 

wealth  is  the  outcome  of  increased  stocks  of  wealth,  which  are  largely  an  outcome  of 

globalisation and liberalised financial markets. The liberalisation of financial markets began at 

the beginning of the 1980s, going hand in hand with increasing globalisation. This, together 

with  relatively  favourable  economic  growth,  has  increased  wealth,  in  particular  financial 

wealth. The increase is a consequence of increasing net investment in financial assets as well 

as increased asset prices. This has raised questions related to economic inequality and increased 

interest in income and wealth distributions.5 

3 For instance, for the US these accounts are reported in Piketty et. al. 2016. 
4 For more debate on the assumptions of Piketty, see Krugman (2017), Milanovic (2017) and Solow (2017). 
5 For instance Peter van de Ven (2017) has emphasised that the increasing interest in wealth is a result of three 
factors: (1) the increase of wealth – in particular the increase of financial wealth, (2) an overall increase of 
interest in income and wealth distribution in both society at large and political debate, and (3) the US subprime 
crisis, which was trigged by subprime loans granted to low-income households. 

ECB Working Paper Series No 2707 / August 20226 
 
 
                                                 
From the central bank point of view, the role of the increasing wealth and its impact on the 

inequality  is  the  key  issue.  The  ECB’s  asset  purchase  programme  (APP)  has  increased  the 

valuation  of  assets  in  the  past  year  and  thus,  increased  nominal  value  of  wealth.  Dossche, 

Slačálek and Wolswijk (2021) argue that since the distribution of labour and capital income 

differs across countries, the cyclicality of income inequality can also differ. Wealth inequality, 

on the other hand, tends to be mostly pro-cyclical. With only wealthier households being willing 

to shoulder such risk, that pro-cyclicality helps to explain both the limited levels of participation 

in the stock market and the substantial equity premium. The cyclical properties of certain asset 

prices can become a longer-term determinant of wealth inequality. The focus of this paper is 

how  the  changing  wealth  inequality  influences  the  income  equality  and  the  transmission 

mechanisms between these two. 

This  article  analyses  the  relation  between  wealth,  income  and  economic  growth  in  one 

integrated  model  based  on  a framework  of national  accounts.  The  analysis  is  conducted  for 

three  countries:  Finland,  France  and  Spain.  The  motivation  for  selecting  these  countries  is 

threefold. First, they are quite different, both institutionally and in terms of the development of 

wealth, income and growth. Second, there is sufficient available distributional micro data from 

these three countries to allow such an analysis.  The focus of the analysis is  on the past two 

decades.  This  choice  is  mainly  related  to  the  data,  as  such  a  detailed  analysis  cannot  be 

performed for a longer time span with internationally available data sources alone. Moreover, 

the time span is sufficiently long to describe the development between the financial crisis and 

the COVID-19 crisis. Finally, some technical aspects of the micro data (see section 2.2.) have 

motivated us to focus on these countries at this stage, with extension to the whole euro area left 

for possible future work.  

The  model  begins  by  linking  financial  accounts  balance  sheets  (covering  all  financial 

instruments)  with  the  corresponding  income  flows  of  national  accounts.  This  allows  the 

calculation  of  instrument-specific  rates  of  return,  which  corresponds  to  Piketty’s  return  on 

capital (r).  Piketty uses the concept of national accounts income in defining r; i.e., it includes 

profits, dividends, interest, rents and other income from capital. The other income included in 

capital is, in practice, the part of operational surplus that is not distributed as dividends and is 

ECB Working Paper Series No 2707 / August 20227 
 
reinvested  in  production.  This  implies  that  neither  realised  nor  unrealised  holding  gains  are 

considered as income. The same concept is applied in this article. In section 2.1, we discuss the 

impact of and differences between various concepts of income. After this, income flows are 

completed  with  the  missing  flows/components  of  national  income.  In  the  model,  national 

income corresponds to Piketty’s economic growth (g). By comparing these two elements, we 

can also test whether Piketty’s basic condition for increasing inequality is fulfilled. 

After this, the household sector is separated from national income (primary income). Finally, 

the primary income components of the household sector are linked with the income components 

of  micro  data  on  income  distributions.  This  allows  analysis  of  primary  income  distribution 

(functional income distribution) by  income  decile and  the role labour and property incomes 

play in the generation of income. This model covers the basic elements of Piketty’s model in 

one  framework  and  emphasises  Milanovic’s  (2017)  observation  that  functional  income 

(primary income) distribution is not the same as income distribution between households. First, 

the  functional  approach  does  not  cover  the  redistribution  of  income  at  all,  while  income 

distribution between households tends to be analysed accounting for current transfers. Second, 

part of primary income is received by other sectors than the household sector. 

This article is organised as follows: the next section discusses the framework applied in the 

article.  The  first  part  presents  a  detailed  data  framework  and  the  latter  part  discusses  the 

coherence and application of micro data sources (EU-SILC and HFCS). Section 3 introduces 

the results and, finally, Section 4 presents the conclusions and discusses potential avenues for 

future research. 

2.  Framework and data 

2.1. The applied framework 

Table  (1.)  presents  the  overall  framework  applied  in  this  article.  The  first  horizontal  row 

includes  input  data.  The  framework  starts  with  balance  sheets  (A),  which  generate  property 

income. In practice, this is wealth invested in financial instruments and land. In the step (B), 

ECB Working Paper Series No 2707 / August 20228 
 
 
 
 
the  property  income  flows  generated  by  financial  instruments  and  land  are  linked  with  the 

balance sheets. The capital stock of national accounts covers the fixed capital used for actual 

production. In this context, it should be noted that the letting of flats is considered production 

in national accounts.6 

Table  1:  Stepwise  link  between  national  balance  sheets  and  income  flows  and  the  derivation  of  distributional 
national income from income distribution data 

A. Financial balance 
sheets

B. Property income 
flows

C. Corresponding 
national income flows

D. The income flows 
related to households

E. Corresponding flows 
in the household 
surveys

Implicit rates of returns

Households' share of 
the income flows

National income broken 
down by income deciles

After this, the balance sheets are linked at an instrument level with the corresponding property 

income flows (B). These data are available in the non-financial accounts of national accounts.  

The  second  horizontal  row  presents  the  derived  results,  which  are  based  on  the  calculation 

performed  in  this  framework.  In  terms  of  balance  sheets  and  the  corresponding  property 

income, this means instrument-specific rates of return. 

After  this,  national  income  is  completed  by  adding  the  missing  income  flows  to  property 

income.  In  practice,  this  means  employee  compensation  and  production  subsidies  (C).  The 

flows belonging to the household sector are then separated from the flows of the total economy 

(D). Finally, these flows are linked with the corresponding flows of the household surveys (E). 

This framework in detail is presented in Annex (1.). 

2.2 Distributional micro data sources and coherence with national accounts 

Step  E  of  the  framework  presented  in  Table  (1.)  and  Table  (4.)  requires  information  on  the 

distribution of the sub-components of primary income by income decile. In this section, we first 

examine  the  quality  of  the  micro  data  used  for  such  distributions  and,  in  particular,  the 

coherence of income flows in micro and macro aggregates. We then provide an overview of the 

within-component distributions estimated from the survey data.  

6 The same framework but with more detailed national data is also presented in Kavonius (2019, 24–40) and 
Kavonius (2020, 483–494). 

ECB Working Paper Series No 2707 / August 20229 
 
 
 
 
 
                                                 
For the disaggregation of household sector primary income flows by decile, two different micro 

data sets are used. For the most part, we rely on income data from EU Statistics on Income and 

Living Conditions (EU-SILC) provided by Eurostat. For certain income components, we use 

micro data from the Eurosystem Household Finance and Consumption Survey (HFCS), made 

available by the ECB. The EU-SILC micro data cover the income reference years 2007–2018 

(EU-SILC survey years 2008–2019).7 The HFCS micro data are available for three waves in 

roughly three-year intervals (around 2010, 2014 and 2017).8  

Both micro datasets are cross-national sample surveys, which are conducted in the countries in 

a  decentralized  way  based  on  common  ex-ante  agreed  specifications  (so-called  output 

harmonisation). The samples represent resident population in private households and  collect 

information for sampled households and to some extent also for their members aged 16. The 

data  are  collected  primarily  by  household  and  personal  interviews,  however  the  countries 

examined  in  this  paper  are    characterized  by  quite  extensive  use  of  administrative  data  in 

particular in EU-SILC (for income) but also to some extent in the HFCS (for income in France 

and Finland and for wealth in Finland). Di Meglio et al. (2017) provide a detailed overview of 

EU-SILC; for further information on the HFCS, see ECB (2020). 

Although the ratio of a survey estimate of a total amount of income to the corresponding NA 

aggregate does not necessarily imply bias in the relative distribution of an income component, 

such  ratios  –  coverage  rates  –  often  are  used  as  quality  indicators  of  micro  income  data. 

Consequently,  the  differences  between  household  sector  account  aggregates  and  EU-SILC 

estimates  have  been  studied  quite  extensively  (e.g.,  Eurostat  2018;  Fesseau  et.  al,  2013; 

Törmälehto,  2021).  Regarding  the  HFCS,  a  methodological  report  by  the  ECB  contains  a 

chapter on comparability covering coherence with macro data and also with EU-SILC (ECB, 

2020).  

7 We use the EU-SILC UDB version 2021-1 (April 2021). Data from the income reference year 2006 are not 
used because of changes in data collection in France and Spain.  France began to use administrative data 
extensively in EU-SILC in 2007. Similar changes were made in Spain in 2013, but the data were revised 
backwards to the income reference year 2007 (Méndez Martin, 2019, see also Törmälehto et al., 2017).  
8 The HFCS UDB versions we use are 1.5 for wave 1, 2.4 for wave 2 and 3.2 for wave 3. The reference periods 
for wealth were the following: Finland 2009, 2013 and 2016; France 2009, 2014, 2017 ; Spain 2011, 2014, 2017.  

ECB Working Paper Series No 2707 / August 202210 
 
 
                                                 
In order to place the quality of the EU-SILC income data for the three countries examined in 

this  article in context, Graph (1.) compares EU-SILC aggregate disposable income with NA 

GHDI in nearly all EU countries. The graph reproduces the results from Törmälehto (2021) and 

includes  an  adjustment  for  the  main  conceptual  differences  (such  as  operating  surplus  and 

property income  from  insurance policies). After  adjustments, the  coverage rate  of  EU-SILC 

disposable income in France and Finland stands at around 95%, while in Spain it is around 80% 

of  the  adjusted  national  accounts  aggregate.    EU-SILC  income  data  for  Spain,  Finland  and 

France benefit from the use of administrative data, which generally improves coverage rates 

(see, e.g., Trindade and Goedemé 2020; Törmälehto et. al, 2017). Extension of the analysis to 

other countries with reasonably high coverage rates and possibly to the whole euro area is left 

for future work.  

Graph 1: Coverage rates of EU-SILC disposable income to NA gross disposable income, adjusted for conceptual 
differences, income reference year 2014, EU-SILC survey year 2015 
(sum of EU-SILC variable HY020, % of adjusted/unadjusted NA GHDI) 

Adjusted coverage ratio
Unadjusted coverage ratio
100 %

110

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70

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Source: Törmälehto (2021), based on the author’s computations from EU-SILC UDB 2015-1 (variable HY020) 
and annual sector accounts tables (transaction B6G S14). 

The  focus  of  this  article  is  income  before  taxes  and  transfers  (primary  income)  rather  than 

disposable income, since the aim is to allocate the NA primary income components to income 

deciles using micro data. Indeed, the coverage rates of disposable income mask variation in the 

sub-components. The main components of income before taxes and transfers available from 

EU-SILC are wages and salaries, self-employment income, rental income, interest, dividends 

ECB Working Paper Series No 2707 / August 202211 
 
 
 
 
and  profit  sharing  and  the  imputed  rents  of  owner-occupiers.  As  a  proxy  for  NA  property 

income paid, we use interest repayments on mortgages. EU-SILC also contains a variable on 

employers’  social  contributions,  and  we  also  use  it  in  the  breakdowns,  although  the 

concentration of wages and salaries could have been a sufficient proxy.  

Table  (5.)  shows  the  coverage  rates  of  the  survey  estimates  of  the  total  amounts  of  these 

components  alongside  their  conceptual  counterparts  in  NA.  Such  comparisons  include  a 

number  of  caveats  related  to  conceptual  differences,  data  sources,  and  production  methods, 

including  survey  sampling  and  non-sampling  errors  as  well  as  different  target  populations. 

Nevertheless, the table confirms that wages and salaries have a reasonable coverage rate in all 

countries, while self-employment and property income can display much lower coverage rates, 

which also exhibit significant variation between the countries.  

The level and dispersion of the coverage rates would suggest that the micro estimates should 

be aligned with the NA aggregates. However, for this one would need auxiliary information on 

the  distribution  of  the  gap  within  each  component  by  income  decile.  Such  information  is 

unavailable, and any adjustments would need to rely on strong assumptions. We briefly discuss 

sensitivity of our results to micro data adjustments in the next section.  

For wages and salaries, the conceptual differences are small and, moreover, all three countries 

use administrative data in EU-SILC (Trindade & Goedemé, 2020, annex 2). This may explain 

the  relatively  high  and  stable  coverage  rates.  With  mixed  income,  there  is  more  profound 

variation both across countries and over time, although at least Spain and Finland indicate that 

they  use  administrative  data  (ibid.).    From  EU-SILC,  we  have  added  rental  income  to  self-

employment  income  to  provide  better  conceptual  alignment  with  NA,  which  improves  the 

coverage rates significantly.  

ECB Working Paper Series No 2707 / August 202212 
 
 
 
 
Table 5: Coverage rates (%) of EU-SILC estimated total amounts with respect to national accounts totals in Spain, 
Finland and France 

2007 

2010 

2013 

2016 

2017 

2018 

41 
73 
96 

94 
96 
88 

95 
97 
88 

94 
96 
90 

92 
97 
87 

96 
96 
84 

41 
71 
87 

44 
73 
70 

92 
96 
85 

46 
68 
100 

Wages and salaries 
ES 
FI 
FR 
Self-employment income and rental income / mixed income (gross) 
ES 
FI 
FR 
Imputed rents / operating surplus (gross) 
ES 
FI 
FR 
Interest,  dividends  and  profit  sharing  /  interest  received  and  distributed  income  of 
corporations 
ES 
41 
FI 
60 
347 
FR 
Interest repayments on mortgage / interest paid 
40 
ES 
63 
FI 
43 
FR 

130 
99 
77 

142 
97 
77 

46 
70 
104 

115 
99 
75 

60 
71 
283 

119 
100 
70 

120 
104 
71 

121 
103 
67 

60 
73 
347 

39 
74 
263 

31 
78 
266 

33 
79 
216 

43 
58 
42 

47 
68 
92 

42 
49 
43 

44 
58 
47 

40 
47 
48 

36 
44 
47 

Source: Authors’ calculations from EU-SILC UDB 2021-1 micro data and annual sector account tables. 

The  coverage  rates  of  imputed  rents  and  interest,  dividends  and  profit-sharing  point  to 

comparability issues also within EU-SILC and are hard to explain. The very high coverage rates 

of  property  income  in  France  have  been  noted  before,  but  the  reason  for  this  has  not  been 

adequately  documented.9  It  should  nonetheless  be  noted  that,  while  the  property  income 

coverage rates for Finland and Spain are low, they are still relatively good in comparison to 

many  other  EU-SILC  countries  (Törmälehto,  2019).  Regarding  interest  repayments,  only 

mortgages are covered in EU-SILC, which should explain the level of coverage rates. 

As a proxy for NA household gross operating surplus, we have used imputed rents from EU-

SILC.  Imputed  rents  in  these  countries  are  estimated  with  the  rental  equivalence  method, 

although  the  estimation  method  of  rental  equivalences  differ10.  The  observed  differences  in 

9 Trindade and Goedemé (2020) observed that, in France, a pension or annuity received in the form of interest or 
dividend income from individual private insurance plans is occasionally included under the target variable for 
income from interest, dividends, and profits from capital investment in an unincorporated business (HY090) 
instead of being treated as a pension from an individual private plan (PY080G). 
10 Rental equivalences can be estimated with econometric methods (hedonic regression), stratification or with 
subjective methods (i.e., asking how much the owner would receive if the residence was rented out). For an 
overview of different estimation methods of imputed rents, see for example  Balcazar et al., 2017; Rondinelli and 

ECB Working Paper Series No 2707 / August 202213  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
                                                 
coverage  rates  may  be  due  to  a  number  of  factors,  including  the  concept  of  rents  and  the 

estimation methods in NA and EU-SILC. The methods used in EU-SILC have been examined 

for instance by Törmälehto & Sauli (2013). In general, however, the impact of methodological 

issues on the coverage rates of imputed rents is a complex topic of and detailed exploration is 

beyond the scope of this article.  

EU-SILC lacks a direct counterpart for NA flows of investment income attributable to holders 

of  collective  investment  funds,  life  insurance  policies  and  (voluntary)  pension  entitlements. 

Here, allocation must be based on ownership of the funds because, in micro statistics, income 

flow  is  typically  recorded  after  the  insurance  policy  ends  or  savings  and  accrued  return  are 

withdrawn from the policy or collective fund. In national accounts, the flow is recorded when 

the asset is held in the insurance company or collective investment fund; i.e., there is an annual 

income flow to the household sector.  

For  this  reason,  we  use  the  distributions  of  underlying  assets  (mutual  funds  and  voluntary 

pensions/life  insurance)  by  income  decile  from  the  Eurosystem  Household  Finance  and 

Consumption Survey (ECB, 2020). The HFCS is conducted every three  years, and the latest 

data are from wave 3, with reference year mostly 2017.  

In  the  HFCS,  the  estimated  total  value  of  underlying  assets  (mutual  funds,  life  insurance, 

voluntary  pensions insurance)  are generally  well below the total value  of  the corresponding 

assets in NA. The variation between countries and asset types does not lend itself to an easy 

explanation. For the purpose of this article, the question is whether it is reasonable to use the 

concentration of assets by income decile for the distribution of NA flows. As with EU-SILC, 

we make  no  assumptions  about  the distribution  of  the  observed  gap  by  income  and  use the 

HFCS estimates as derived from the data. Table (6.) shows the coverage rates of mutual funds, 

life insurance and pension entitlements. 

Veronese, 2011;Törmälehto & Sauli, 2013. In EU-SILC, the available documentation suggests that France has 
used the (hedonic) regression method, Finland the stratification method, and Spain a mixture of stratified rents 
and subjective (self-assessed) rents asked in the EU-SILC questionnaire.  

ECB Working Paper Series No 2707 / August 202214 
 
 
 
 
                                                 
Table 6: Coverage rates of mutual funds and life insurance and pension entitlements  
Finland  France 
74,008 
19,832 
311,123 
21,795 
24% 
91% 
612,247 
21,912 

HFCS Mutual funds (€ million) 
NA Investment fund shares (€ million) 
Coverage rate, % 
HFCS voluntary pensions and life insurance  
(€ million) 
NA Life insurance and annuity entitlements, pension 
entitlements (€ million) 
Coverage rate, % 

50,769 

1,905,445* 

172,849 

43% 

32%  

88% 

Spain 
101,225 
313,327 
32% 
151,742 

*NA only life insurance and annuity entitlements 
Source: European Central Bank, Household Finance and Consumption Survey (HFCS), wave 2017 

2.3 Concentration of primary income components in micro sources 

Next, we illustrate the relative concentration of income flows based on EU-SILC and HFCS 

that are later used to disaggregate NA aggregate income flows by decile. Deciles are based on 

equivalised gross income, i.e., income before taxes but after current transfers received.11 The 

concept of income utilised in the HFCS is gross income, which necessitates the use of gross 

income  also  from  EU-SILC  (see:  ECB,  2020,  for  coherence  between  HFCS  and  EU-SILC 

income data). However, income ranks based on gross and disposable income in EU-SILC are 

fairly similar because taxes exert only a modest reranking effect.12 By way of example, Graph 

(2.) shows the within component distributions of interest, dividends and profit-sharing in 2007–

2018 in the top decile based on both gross income and disposable income.  

11 We use the Eurostat equivalence scale (also called modified OECD scale) to equivalise income. In this scale, 
the first adult in a household is given a value of 1, each additional adult aged 14 and over a value of 0.5 and each 
child aged 0-13 years a value of 0.3. In Kavonius (2019) and Kavonius (2020), disposable income is applied in 
this context instead of gross income.  
12 Imputed rents are not included in gross (pre-tax) income although imputed rents tend to re-rank households 
depending on their homeownership status (outright owner, owner with a mortgage, tenant). However, imputed 
rents are not available in the HFCS, and the sensitivity of concentration of income to the inclusion of imputed 
rents is left for future work.  

ECB Working Paper Series No 2707 / August 202215 
 
 
 
 
 
                                                 
Graph 2: Concentration of interest, dividends and profit-sharing to top income decile in Spain, Finland and France 
2007–2018 (y-axis: top 10% share, x-axis income reference year) 

Spain DPI
Finland DPI
France DPI

Spain GI
Finland GI
France GI

80

70

60

50

40

30

20

10

0

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

Note: 

Top 10% is based on equivalised gross/disposable income. DPI=decile based on equivalised disposable income. 
GI=decile based on equivalised gross income.  
Source: Authors’ elaboration from EU-SILC UDB 2021-1.   

The graph shows that the highest concentration for this component is in Finland, followed by 

France and then Spain. The choice of gross versus disposable income causes a slight difference 

in  the levels  in Finland,  but  otherwise the differences  are negligible  and the  time trends  the 

same. As noted earlier, there are extreme differences in the coverage rates of this item, ranging 

from around 30% of the NA total in Spain to 80% in Finland and more than 200% in France. 

However,  we  see  no  firm  grounds  for  adjusting  the  distributions  on  assumptions  which,  by 

necessity, would be of an ad hoc nature, although one may suspect typical under-estimation in 

the top tail of this type of very skewed income component.  

Wages and salaries are the main component of primary income, and their concentration along 

the  gross  income  deciles  is  decisive  for  the  results.  Graph  (3a.)  therefore  shows  the 

concentration shares in the first and the last year of the micro data. Wages and salaries are more 

concentrated  to  the  upper  part  of  income  distribution  in  Spain.  The  data  suggest  a  slightly 

increasing  concentration  in  Spain  and  Finland,  but  overall  the  concentration  shares  are 

relatively stable. By contrast, in France, the top 10% has increased its share of total wages and 

salaries quite markedly from 2007 to 2018.13  

13 Additionally, Annex (4.) shows the decomposition for the four main components of pre-tax income in EU-
SILC. 

ECB Working Paper Series No 2707 / August 202216 
 
 
                                                 
Graph 3a: Concentration of wages and salaries by gross income decile in 2007 and 2018 in Spain, Finland and 
France (y-axis: share of total wages and salaries by decile, x-axis country and income reference year) 

I
II
III
IV
V

VI 
VII 
VIII
IX
X

28.6

29.5

26.0

26.9

17.6

18.0

17.9

17.9

21.6

16.6

25.8

15.7

100.0

90.0

80.0

70.0

60.0

50.0

40.0

30.0

20.0

10.0

0.0

ES 2007

ES 2018

FI 2007

FI 2018

FR 2007

FR 2018

Source: Authors’ elaboration from EU-SILC UDB 2021-1.   

Turning then to the HFCS and the concentration of mutual funds and voluntary pensions/life 

insurance to the top 10%, we find that France has the highest concentration of both asset types, 

followed by Spain and then Finland.14 This is shown in Graph (4.). Given that the shares of 

mutual funds are relatively stable over time, we use the latest observation for the years between 

the waves (i.e., wave 2010 for years 2007 to 2013, wave 2014 for years 2014 to 2016 and wave 

2017  for  2017  and  2018).  Voluntary  pensions/life  insurance  display  more volatility,  but  we 

follow the same strategy with the exception of Finland, where wave 1 data are not comparable 

to waves 2 and 3. For Finland, wave 2 shares are used for the years 2007 to 2016 and wave 

shares from 2017 onwards.  

14 In Finland, HFCS data on mutual funds are based on administrative data, and voluntary pensions are estimated 
from tax data. In France and Finland, HFCS gross income is based on administrative data.  

ECB Working Paper Series No 2707 / August 202217 
 
 
 
                                                 
Graph 4: Concentration of mutual funds and voluntary pensions/life insurance assets to the top income decile in 
Spain, Finland and France 2007–2018 (y-axis: top 10% share, x-axis HFCS wave) 

Spain 
Finland
France

70

60

50

40

30

20

10

0

Wave 2010

Wave 2014

Wave 2017

Wave 2010

Wave 2014

Wave 2017

Mutual Funds

Voluntary pension/whole life insurance

Note: Top 10% is based on equivalised gross income.  
Source: Authors’ elaboration from HFCS UDB (versions 1.5, 2.4 and 3.2).  

2.4 Sensitivity of income concentration to micro/macro discrepancies 

The  ranking  variable  used  in  the  current  article  is  equivalised  gross  income  as  originally 

recorded in the micro data sources. As shown earlier, the coverage of survey estimates with 

respect  to  NA  vary  substantially  by  sub-component  and  by  country,  due  to  conceptual 

differences (such as exclusion of imputed rents), different operationalisations and variations in 

sampling and non-sampling errors. We next briefly examine how sensitive the concentration of 

sub-components with respect to gross income is to such discrepancies from NA totals.  

In  general,  the  estimated  total  amount  of  a  composite  income  variable  𝑌(cid:3552)  in  a  probabilistic 

sample survey (such as EU-SILC or HFCS) can be expressed as a weighted sum of the different 

income components with idiosyncratic error terms, as follows: 

𝑌(cid:3552) = ∑ [∑ ((𝜋(cid:3036)𝑛𝑟(cid:3036)𝑔(cid:3036))
(cid:3041)
(cid:3036)(cid:2880)(cid:2869)

(cid:3038)
(cid:3038)(cid:2880)(cid:2869)

, 
∗ 𝑓(𝑦(cid:3036)(cid:3038), 𝜀(cid:3036)(cid:3038))] 

ECB Working Paper Series No 2707 / August 202218 
 
 
 
 
 
where π is the inverse of the inclusion probability of a sample unit i, nr is the non-response 

adjustment and g is the calibration adjustment to external benchmarks, y is the observed income 

and ε is the measurement error of income component k. 15   

To align a survey estimate to a given national accounts’ total, one may adjust the g weights via 

reweighting and/or make assumptions about the individual error term 𝜀(cid:3036)(cid:3038). A naïve but feasible 

approach is to multiply the observed incomes of component k with a constant equal to an inverse 

of the coverage ratio 𝜆(cid:3038). This is simple proportional scaling which implies no change in scale 

invariant (relative) inequality measures (such as Gini coefficient) of the component k. In turn, 

adding a constant to each income (for instance, the total discrepancy allocated equally to each 

sample unit) would imply no changes in translation invariant (absolute) inequality measures of 

component k. The error terms of the two cases can be expressed then as: 

(a)  

 𝑓(𝑦(cid:3036)(cid:3038), 𝜀(cid:3036)(cid:3038)) = 𝑦(cid:3036)(cid:3038) ∗

(cid:3026)(cid:3286)
(cid:3026)(cid:3552)(cid:3286)

       (b)   𝑓(𝑦(cid:3036)(cid:3038), 𝜀(cid:3036)(cid:3038)) = 𝑦(cid:3036)(cid:3038) +

(cid:3026)(cid:3286)(cid:2879)(cid:3026)(cid:3552)(cid:3286)
(cid:3015)

where  𝑌 𝑎𝑛𝑑 𝑌(cid:3038) are the national account’s total values and N is the total population size.  In 

both cases, measurement errors are assumed to be equal for each household but to vary across 

income components which leads to changes in the composite income measure.  

Other  common  adjustment  tools,  such  as  semi-parametric  modelling  (Pareto-imputations), 

could be used, in particular to adjust the upper tails of the distributions. A natural candidate for 

this could be property income received; however, given the over-coverage in France, this is not 

a uniform approach applicable to all countries. 

For  the  sensitivity  analysis  in  this  article,  we  chose  to  apply  proportional  scaling  to  self-

employment and rental income and interest and dividends. For wages and salaries W, we apply 

reweighting with the adjusted wages and salaries given by the sum of the observe values and 

imputed values, with the latter obtained via calibration of the weights: 

15 To give an example, measurement errors of interest and dividends vary across individuals and are different 
from measurement errors of wages and salaries while for the aggregation usually a single set of calibrated 
sampling weights are used and these vary across households. 

ECB Working Paper Series No 2707 / August 202219 
 
 
 
 
 
                                                 
(c)   𝑊 =   𝑊(cid:3553)  = ∑ (𝜋(cid:3036)𝑛𝑟(cid:3036)𝑔(cid:3036)) ∗ 𝑤(cid:3036)

(cid:3041)
(cid:3036)(cid:2880)(cid:2869)

+   ∑ (𝜋(cid:3036)𝑛𝑟(cid:3036)) ∗ (

(cid:3041)
(cid:3036)(cid:2880)(cid:2869)

(cid:3034)(cid:3564)(cid:3284)
(cid:3034)(cid:3284)

− 1)

∗ 𝑤 (cid:3036)   

In  (c),  W  denotes  total  wages  and  salaries  and   

(cid:3034)(cid:3564)(cid:3284)
(cid:3034)(cid:3284)

    is  the  reweighting  adjustment  based  on 

constrained minimization of changes in the original calibrated weights, with the constraint that 

the reweighted survey estimate of  total wages and salaries must be equal to NA totals.  

The  minimization  is  based  on  logit  distance  function  with  lower  and  upper  bounds  for  the 

adjustment factors set at 0.98 and 1.2, i.e. the change in wages and salaries for each household 

due to reweighting   

(cid:3034)(cid:3564)(cid:3284)
(cid:3034)(cid:3284)

 is constrained to be between -2 % and +20 %. These are the strictest 

settings  that  still  allowed  reweighting  to  converge  in  all  three  countries,  and  in  practise  the 

minimum change was 0 % rather than -2 %. The conditional distribution of wages and salaries 

changes as a result of reweighting, differently from the other adjusted income components.  

Table 7 shows a factor decomposition of Gini coefficient with both the original and adjusted 

EU-SILC equivalized gross income. The first row shows that in Finland and France the changes 

in pre-tax inequality due to the adjustments  are relatively small, but  there is a more marked 

increase in Spain. Moreover, there are only modest changes in the distribution of wages and 

salaries due to the use of the minimum distance calibration approach. The Gini coefficients of 

the other income components are naturally unchanged due to the proportional scaling approach.  

For this article the interest of the exercise lies in the changes in the concentration coefficients, 

and larger changes are therefore highlighted in grey. The concentration coefficient of all income 

components  increase  in  France,  including  wages  and  salaries.  In  Spain,  we  observe  sizable 

increases  in  the  concentrations  of  self-employment  and  rental  income  and  interest  and 

dividends. In Finland, only the concentration of self-employment and rental income increases 

significantly.  

ECB Working Paper Series No 2707 / August 202220 
 
 
 
 
 
 
Table 7. Factor decomposition of EU-SILC 2018 Gini coefficient of gross income Gini after adjusting to match 
NA totals 

Gini, gross income 
Wages and salaries 

Gini   
Concentration    
Income share 

Self-employment and rental income 

Gini   
Concentration    
Income share 

Interest and dividends 

Gini   
Concentration    
Income share 

Other income 

Gini   
Concentration    
Income share 

Spain 

Finland 
Original   Adjusted  Original   Adjusted  Original   Adjusted 
0.37 

France 

0.33 

0.33 

0.31 

0.41 

0.33 

0.56 

0.43 
0.64 

0.90 
0.43 
0.10 

0.96 
0.68 
0.01 

0.69 
0.18 
0.25 

0.57 
0.41 
0.57 

0.90 
0.66 
0.18 

0.96 
0.78 
0.03 

0.69 
0.13 
0.22 

0.53 
0.42 
0.63 

0.91 
0.46 
0.06 

0.95 
0.78 
0.04 

0.54 
0.42 
0.61 

0.91 
0.57 
0.08 

0.95 
0.79 
0.05 

0.57 
-0.04 
0.28 

0.57 
-0.05 
0.26 

0.53 
0.37 
0.58 

0.92 
0.64 
0.09 

0.90 
0.71 
0.06 

0.62 
0.06 
0.27 

0.54 

0.42 
0.63 

0.92 
0.60 
0.08 

0.90 
0.61 
0.02 

0.62 
0.01 

0.26 

Note: Adjusted refers to EU-SILC gross income adjusted to match the total amount of wages and salaries, self-
employment  and  rental  income  and  interest  and  dividends  in  National  Accounts.  Income  share  is  the  sub-
component’s share of gross income. Concentration reflects how sub-component is concentrated on ranks based on 
gross income, with higher values implying more concentration to those with high gross income. 

The table above aims to illustrate that there are distributional consequences of relatively simple 

adjustments  to  NA  totals,  and  that  there  is  also  variation  across  countries  due  to  different 

coverage rates. To avoid arbitrary assumptions, our choice for this article has been to use the 

concentration shares of the components as they come from the data, without any adjustments 

to match the NA totals.  

3  Results 

Using the framework and methods described in section 2, we next examine the evolution of 

rates of return on capital and labour/capital shares, the latter also by income deciles.  

3.1 Rates of return 

Graph  (5.)  depicts  rates  of return  using  different  rate  of  return  concepts.  The  same  data  are 

shown  in  a  form  table  in  Annex  (2.).  In  practice,  these  are  the  outcome  of  Step  3  of  the 

ECB Working Paper Series No 2707 / August 202221  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
framework (Table 1). The concept and application of rates of return is far from straightforward. 

Returns  are  typically  a  real  economy  concept  and  refer  to  returns  from  corporations’  fixed 

investment rather than to financial assets. In his concept, Piketty (2014, p. 25) includes profits, 

dividends,  interest,  rents  and  other  income  from  (physical)  capital.  This  is  in  line  with  the 

generic booking-keeping based definition that the rate of return is equal to net operating income 

after tax and, in practice, covers distributed property income and retained earnings. 

From the perspective of corporations, and particularly production, this makes much sense. The 

rate  of  return  is  the  part  of  income  accumulated  by  the  capital  invested  in  the  production 

process. In turn, paid wages and social contributions are income accumulated by labour.  

From the household investment perspective, this concept nonetheless raises several questions. 

First, this national accounts concept of income refers to the income generation of corporations, 

and, as the majority of corporation owners are not domestic households, most of the distributed 

profits are received by other actors and entities, such as foreign households or corporations. In 

the  case  of  a  corporation,  one might  assume  that,  ultimately,  the  money  always  benefits  its 

owners. However, the owner can be a national or local government or a non-profit institution 

serving households, and these institutions change the logic of distribution completely.16  

Second,  typically,  retained  earnings  are  never  received  by  households  or  equity  holders.17 

Theoretically, retained earnings increase the value of equity, depending on how they are further 

invested. Therefore, when returns are discussed in the context of households and, in particular, 

in the context of investment in financial assets, the concept refers to received property income 

such  as  dividends,  interest  and  often  also  realised  holding  gains.  These  are  income  truly 

received by households. 

Furthermore,  there  is  an  additional  complication  to  this  approach.  When  operating  in  the 

national  account  framework,  as  Piketty  (2014)  does,  holding  gains  are  not  recognised  as 

16 Cf. Milanovic 2017. 
17 Reinvested earnings from direct foreign investment are similarly imputed as distributed income and 
subsequent reinvestment. However, this does not impact the calculation presented in this article, as at the total 
economy level these are netted out and, additionally, these transactions do not concern the household sector. 

ECB Working Paper Series No 2707 / August 202222 
 
 
 
                                                 
income, and unrealised gains are accounted for as value changes in the financial accounts; i.e., 

Piketty’s framework does not cover holding gains.18 This is not exactly in line with the Hicksian 

concept of income, which is the concept usually used in economic theory. Hicks (1939, p. 172) 

defines “a man’s income as the maximum value which he can consume during a week, and still 

expect to be as well off at the end of the week as he was at the beginning”. This implies that all 

holding gains (realised and unrealised) should be included in income, and thus the price changes 

of assets would affect the income of households. 

Ranaldi and Milanovic (2021) in their article defines the concept of labour and property income 

slightly differently. The central difference is related to the pension income. Otherwise it is line 

with the national accounts’ concept. In the national accounts’ private voluntary pensions are 

property  income  but  the  income  from  the  legally  obligatory  employment  related  pension 

systems are social transfers. This means that the latter ones do not have an impact on analysis 

based on functional income distribution. Ranaldi and Milanovic (2021) takes two approaches 

concerning these employment related pensions: in the first approach they define all pensions 

(private  and  public)  as  labour  income  as  in  the  second  approach  they  define  income  from 

pensions as property income. 

Graph (5.) shows four different measures of returns for Finland, France and Spain. The main 

purpose of this graph is to illustrate that however we view this conceptually, the development 

of income is similar. The denominator here is the market value of underlying stock, for instance, 

in the case of dividends, the stock of the underlying equity. It is impossible to include average 

investment in capital in the national accounts, which would be an appropriate denominator, and 

thus the value of equity is normally used instead. Theoretically, this should not differ markedly 

from the value of the capital stock of the company. In balance-sheet terms, equity additionally 

includes goodwill. The measures presented in the graph are the following: 

  The measure “total” covers all the received property income and retained earnings. This 

concept is line with that used by Piketty (2014). 

18 Kavonius 2006. 

ECB Working Paper Series No 2707 / August 202223 
 
 
                                                 
  The  measure  “hh”  covers  all  the  property  income  actually  received  by  households. 

Compared  to  concept  “total”,  this  excludes  both  property  income  received  by  other 

sectors and also retained earnings, as the latter are actually not distributed to households 

either. 

  The measure “total wo. retained earnings” is the same as the concept “total”, but it does 

not include retained earnings. In principle, this covers all the property income actually 

paid. 

  The measure “returns (r)/compensation (c)” covers, as a numerator, the same measure 

as  “total”  and,  as  a  denominator,  wages  and  salaries  and  social  contributions.  As 

Piketty’s g is equal to r+c, when the ratio decreases, r grows more slowly than g and, 

respectively, when the ratio increases, r grows faster than g. This measure is added to 

illustrate the extent to which actual income distribution is dependent on rates of return. 

Graph  5:  The  development  of  rates  of  return,  using  different  rates  of  return  concepts,  and  the  share  of  labour 
(returns/compensation) for Spain (ES), France (FR) and Finland (FI). The lines show the development of rates of 
return by percentage (left-hand y-axis) and the bars indicate the share of labour by percentage (right-hand y-axis). 

Source: European Central Bank and authors’ calculations. 

In terms of the general trend, Graph (5.) shows that it makes little difference which concept of 

returns is used: returns decreased in the period 1995 to 2019. This can be seen more in detailed 

ECB Working Paper Series No 2707 / August 202224 
 
 
in Annex (2.) where the growth rate is in white cell if the value is decreasing and highlighted if 

the value is increasing. Indeed, by 2019, rates of return had decreased to below half the levels 

of the 1990s. Nevertheless, level differences obviously exist depending on the income concept; 

for  instance,  retained  earnings  clearly  increase  the  level  of  returns.  The  trend  of  decreasing 

returns is clearer in France and Spain than in Finland, but, in Finland too, returns are lower than 

for most of the time since 1995. 

Moreover,  the  effect  of  economic  cycles  can  be  clearly  seen.  In  the  European  context,  the 

development is logical. When the euro was first introduced, i.e., from 1999 onwards, the interest 

rates of euro area countries converged. In these countries, this signified a decrease in interest 

rates.  There  are  two  peaks  in  returns:  one  in  2000/01  and  another  in  2008.  The  first  is  the 

outcome of the economic boom of 1995–2000, which was also reflected strongly, particularly 

at the  end of the boom, in the financial markets. The second one is the peak before the last 

financial crisis.  

Thus, as mentioned earlier, the overall development is logical. The second half of the 1990s 

witnessed an economic boom and consequent increases in profits, which were also increasingly 

distributed. From the start of 1999, the  euro became a  real  currency,  and  a  single  monetary 

policy  was  introduced  under  the authority  of the  ECB.  A  three-year  transition  period began 

before  the  introduction  of  actual  euro  notes  and  coins,  but,  legally,  national  currencies  had 

already ceased to exist. Monetary integration led also to the convergence of interest rates and, 

in these three countries, interest levels converged with German interest rates, i.e., decreased. At 

the beginning of the 2000s, the economy began to boom again, leading to increasing distributed 

profits, while, at the same time, the ECB kept interest rates relatively high. This can be seen in 

the graph as increased returns. In 2008, this turned to bust and decreasing distributed profits 

and interest rates. 

As indicated before, the level of rates of return depend on the income concept applied. The key 

is whether holding gains are included in the concept. Graph (6.) shows rates of return based on 

the national accounts income concept, which does not include holding gains, and the Hicksian 

income concept, which includes unrealised holding gains. There are two main messages in this 

ECB Working Paper Series No 2707 / August 202225 
 
 
graph. First, in this time period, Hicksian rates of return are no higher than those of national 

accounts. Holding gains increase the volatility of the rates of return, but overall  they do not 

exceed the level of rates of return without holding gains. Second, no matter which returns we 

use, rates of return have decreased roughly to half the level they were at in the 1990s. 

Graph 6: Rates of return based on the national accounts and Hicksian concept of income for Finland, France and 
Spain 

Source: European Central Bank and authors’ calculations. Note: the Finnish Hicksian income concept is available 
only for 2012 onwards, as that is when the other changes to Finland’s financial balance sheets are available. 

3.2  The development of labour and property income 

Table (8.) shows the labour share of national income, the labour share of household primary 

income and households’ share of primary income in Spain, France and Finland. In practice, this 

is the outcome of Step C of the framework. The first observation is that the ranges within the 

shares vary are relatively narrow: In Spain and Finland the ranges up to six-seven percentage 

points and in France up to four percentage points. This means that the changes have not been 

particularly  large  during  this  period.    The  highlighted  cells  indicate  the  value  is  above  the 

average of the corresponding time series.   

The labour share of national income shows the level of employee compensation paid in relation 

to primary income in the whole economy. This does not only cover the property income paid 

ECB Working Paper Series No 2707 / August 202226 
 
 
 
 
to households but also property income paid to other sectors and abroad. In Finland and Spain, 

the levels in 2018 are below the average and also below the level of 1995 as in France the labour 

share in 2018 is above the average and also level of 2018. However, as can be seen in the table, 

in the past 25 years, no large-scale changes in levels have occurred. This contradicts Heather 

Boushey’s (2019, pp. 126–129) argument that the past 40 years have witnessed a global shift 

from labour to capital.19 At least these European countries seem to differ from the development 

in  the  US.  Similarly,  Goodhart  and  Pradhan  (2020,  p.  94) argue that  the  corporate sector  is 

likely to respond to demographic changes in Western economies by raising the capital/labour 

ratio, i.e., by adding capital to compensate for labour, which is a factor of production that is 

becoming scarcer and more expensive. The table rather illustrates the cyclical development of 

the labour share and in all these three countries the shares of labour have been above the average 

since the financial crisis 2008 and have returned around the average only in last few years.  

As Table (8.) demonstrates, developments have been quite different in the three countries. In 

France the labour share was around 60% from 1995 to 2008, after which it has increased to 

62.0%. The table particularly highlights this development as it shows cells which are above the 

average of the whole time series. This emphasises the cyclicality of the labour share. The shares 

are typically high in the economic downturns. This can be clearly seen in all the countries after 

2008.  The 2008  financial crisis is  the typical turning point in  economic trends, as  the crisis 

caused  the  profitability  of  corporations  to  decrease  considerably.  Overall,  profits  are  more 

volatile  than  employee  compensation,  as  employment  does  not  react  particularly  rapidly  to 

business cycles. Typically, changes in the labour share of income also reflect changes in the 

profitability  of  companies,  with  rapid  decreases  in  the  labour  share  being  an  indication  of 

improved profitability. In Spain, the labour share increased from around 55 per cent in 1995 to 

60%  in  2009.  After  this,  the  share  decreased  to  around  53%  in  2018.  In  Finland  the  share 

decreased from 61% in 1995 to 55% in 2007. Then, the share increased rapidly, reaching 61% 

in 2012. It then began to decrease, reaching 57% in 2018. This development is line with overall 

19 Concerning the income/capital share, it should be noted the income of sole proprietorships and partnerships is 
often defined as mixed income, which is according to the system of national accounts defined to be capital. 
However, mixed income can be considered to a borderline case as it includes both elements: compensation of 
capital and labour. The practical reason for this treatment is that the share of capital and labour cannot often be 
separated. 

ECB Working Paper Series No 2707 / August 202227 
                                                 
economic development: in years of faster growth the labour share tends to decrease, and, during 

slower growth or recessions, the share tends to decrease. 

Table 8: Labour share of national income (net), labour share of household primary income and households’ share 
of  primary  income  in  Spain,  France  and  Finland  from  1995  to  2018.  The  last  four  columns  show  minimum, 
maximum, range and average of the corresponding series. The highlighted cells are above the average of the time 
series and the white cells below the average 

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 min max range ave

79.6 79.9 81.0 81.0 80.6 81.1 82.0 80.9 80.4 80.1 80.0 79.6 79.6 80.1 79.1 78.7 80.4 77.5 77.5 77.0 76.2 75.5 75.5 75.2 75.2 82.0
HH share of primary income ES
79.2 79.1 78.1 77.3 76.9 77.9 78.6 79.5 79.2 78.7 78.4 78.7 78.5 79.3 80.8 80.3 79.7 80.7 79.7 79.8 78.8 78.9 78.7 79.0 76.9 80.8
HH share of primary income FR
75.1 74.3 71.6 70.5 70.8 71.3 70.1 69.9 71.1 70.1 70.4 69.8 69.3 70.9 74.9 73.6 73.8 75.0 75.0 74.1 73.4 73.2 71.3 71.3 69.3 75.1
HH share of primary income FI
54.5 54.5 55.8 56.0 56.3 56.3 55.9 55.6 54.9 55.0 55.1 55.5 56.3 58.8 59.4 58.8 58.3 56.1 54.9 54.8 54.2 53.4 53.2 53.3 53.2 59.4
labour share of NI ES
60.2 59.8 59.1 58.4 58.6 58.9 59.2 60.3 60.4 59.8 59.8 59.6 59.1 59.6 62.0 61.6 61.2 62.1 62.2 62.3 61.6 61.8 62.0 62.2 58.4 62.3
labour share of NI FR
labour share of NI FI
60.8 60.6 57.8 57.3 57.0 56.3 55.8 55.9 56.7 55.6 56.8 56.5 55.4 57.3 60.9 59.3 59.5 61.1 60.7 59.6 58.9 58.4 56.6 56.9 55.4 61.1
labour share of HH primary income ES 68.5 68.2 68.8 69.1 69.8 69.5 68.1 68.7 68.3 68.6 68.9 69.7 70.7 73.4 75.1 74.7 72.5 72.4 70.9 71.1 71.2 70.7 70.5 70.9 68.1 75.1
labour share of HH primary income FR 76.0 75.6 75.7 75.6 76.1 75.6 75.3 75.9 76.3 76.0 76.3 75.7 75.3 75.2 76.7 76.7 76.8 77.0 78.0 78.1 78.1 78.4 78.8 78.7 75.2 78.8
80.9 81.5 80.7 81.2 80.5 79.0 79.6 79.9 79.7 79.3 80.6 80.8 79.9 80.8 81.4 80.6 80.6 81.4 80.9 80.5 80.2 79.8 79.4 79.8 79.0 81.5
labour share of HH primary income FI
Source: European Central Bank and authors’ calculations. 

6.9 79.1
3.8 79.0
5.9 72.1
6.2 55.7
3.9 60.5
5.7 58.0
7.0 70.4
3.6 76.6
2.5 80.4

Households’ share of primary income indicates the amount of generated income in the economy 

that is received by the household sector. Primary income consists of employee compensation, 

which  households  receive  by  definition,  and  operating  surplus,  which  can  be  distributed  to 

whichever  holding  sector.  Income  that  is  not  distributed  to  households  decreases  the  labour 

share. Often a decreasing labour share of income is interpreted as an indication of increasing 

inequality. However, this relatively large share of income is received by other sectors. In the 

case  of  corporations,  it  can  be  used,  for  instance,  to  pay  dividends  or  further  invest.  If  the 

receiving sector is either general government or a non-profit institution serving households, this 

money benefits households.  It should be noted that the French and, particularly, the Finnish 

general government sectors are large owners of equity.20 In the case of Finland, pension funds, 

which are considered part of the general government, are large owners of equity because they 

are partly funded, i.e. comparing to the other European pension system which have not saved 

any money for the future pensions, the Finnish pension funds have some assets. Moreover, the 

government holds equity in some strategically important corporations. Dividends from these 

corporations are typically  accounted  for in the state budget. Foreign owners,  in turn,  can  be 

anything from a private person to a pension fund or central bank. State ownership of equity is 

one of the factors cited by Anthony B. Atkinson (2015, pp. 68–74) as reducing inequality after 

WWII due to the more equal distribution of capital. 

20 Lillqvist, Kavonius and Pantzar 2020. 

ECB Working Paper Series No 2707 / August 202228 
 
 
 
                                                 
In terms of households’ share of primary income, the three countries have experienced quite 

different development. In France the share varied from 77 to 80% between 1995 and 2008. In 

turn, from 1995 to 2011 the share in Spain was around 80%, after which it steadily decreased 

to 75% in 2018. In Finland the share was 75% in 1995, falling steadily to below 70% by 2007 

and then increasing back to 75% in 2009. After this, it slowly decreased to 71% by 2018. 

The labour share of household primary income describes the actual distribution of property and 

labour income in the household sector; i.e., this indicates the extent to which households live 

off either their work or capital. As can be seen in the graph, these shares have either remained 

relatively stable or, alternatively, the labour share has increased. This means that the role of 

property  income  at  the  aggregate  level  has  not  increased.  In  Finland,  the  labour  share  of 

household primary income remained at around 80% for the whole period from 1995 to 2018. 

The labour share of household primary income varies slightly and follows the same trend as the 

labour  share  and  household  share  of  primary  income.  The  reason  for  its  more  muted 

development  is  twofold.  First,  the  share,  and  thus  the  role,  of  property  income  is  smaller. 

Second, the portfolio of households differs from the portfolio of other sectors. The main obvious 

difference is the stock of owner-occupied housing. Returns on housing are typically less volatile 

than returns  on  financial  assets.  In  Spain,  the labour  share of  primary income  has  remained 

mainly around 70%. During the 2008 financial crisis, the share peaked at 75 per cent, mostly 

due to the reduced returns in the housing markets. In France, the share steadily increased from 

76 per cent in 1995 to 79 per cent in 2018.  

3.3 The development of labour and property income shares by income decile 

Next, we focus on the importance of labour and property income in the generation of the income 

of different income deciles. The assumption is that if the capital/labour ratio increases then the 

capital income is the leading factor income in the development of distribution and respectively, 

if the ratio decreases labour income is the leading factor. As noted earlier, the income deciles 

are based on gross income. In practice, this is the outcome of step (E) of the framework; i.e., 

the  household  labour  shares  of  primary  income  are  broken  down  by  income  decile  using 

estimates derived from EU-SILC and HFCS micro data.  

ECB Working Paper Series No 2707 / August 202229 
 
 
Graph (7.) illustrates the Finnish distribution of property income by income decile. In Annex 

(3.) a table with corresponding numbers is attached. In Annex (3.) the cells which above the 

average of the all values in the time series are highlighted. In the graph the thick line shows the 

total labour share of household primary income; i.e., this corresponds to the results presented 

in Table (7.) on the last three rows. The remaining deciles, i.e., those that are above the total 

but which do not have the highest labour share, are indicated as “normal lines”. Graphs (7.), 

(8.) and (9.) are constructed in the same way. 

Graph 7: Labour share of household primary income by income decile in Finland from 2007 to 2018 

Source: European Central Bank and authors’ calculations. 

Between 2007 and 2018, the labour share of households remained relatively stable at around 

80%. Several observations can be made concerning the distribution of labour share between the 

different income deciles. Both the top income decile and the three lowest income deciles (first, 

second and third deciles) are below the total or average labour share. The reasons for this are 

of course completely different. The lower deciles consist largely of people living primarily off 

transfers, and the level of primary income is relatively low. Therefore, relatively low wages or 

levels  of  property  and  entrepreneurial  income  strongly  impact  the  labour  share.  Moreover, 

ECB Working Paper Series No 2707 / August 202230 
 
 
 
property  income  is  probably  derived  mainly  from  owner-occupied  housing.  It  is  also  worth 

noting that the labour share has slightly decreased in these groups from 2007, which indicates 

that for these groups participation in the labour market has also decreased.  

By contrast, the tenth decile contains households with relatively high salaries. Moreover, not 

only are they high earners, but their property income is also relatively high. Branko Milanovic 

(2019, pp. 14–21) writes that this is typical for modern liberal meritocratic capitalism, where 

people are capital- as well as labour-rich. This is a difference from classical capitalism or social 

democratic  capitalism,  where  the  rich  are  typically  only  capital-rich,  commonly  being 

landlords, financiers and owners of large industrial holdings. In meritocratic capitalism, the rich 

are highly paid managers or experts. These people are wage workers who must work in order 

to draw their large salaries. These same people, whether through inheritance or because they 

have saved enough money through their working lives, also possess large financial assets and 

draw a significant amount of income from them. It is also interesting to note in Graph (7.) that 

the labour share of such people has increased and for instance the labour share of tenth decile 

in the last years was clearly above its average. 

This  is  particularly  interesting  observation  also  against  the  background  that  Ranaldi  and 

Milonovic (2021) concludes that Finland (and the other Nordic countries) has typically high 

concentration  of  capital  income  and  low  concentration  of  labour  income.  At  same  time  the 

Finnish labour share of the highest deciles are considerable higher than France and Spain and 

additionally, the shares also in past years rather increased than decreased. 

The highest labour shares are found in the eighth and ninth income deciles. Their labour shares 

were around 88% in 2017 and slightly decreased to 85% in 2018. These people are relatively 

high earners – roughly speaking, for single person families, those with gross monthly salaries 

of up to 5 000 EUR. Families in these deciles often live in owner-occupied housing, but due to 

their  relatively  high  salary  level,  the  labour  share  also  remains  comparatively  high.  In  the 

remaining income deciles, the labour shares are slightly lower, and the same slightly decreasing 

trend is evident.  

ECB Working Paper Series No 2707 / August 202231 
 
 
 
Graph (8.) shows the labour share of primary income by decile in France. In Annex (3.) can be 

found the corresponding figures. In France, the total (average) labour share has increased from 

76%  to  80%.  The  French  data  contain  a  certain  particularity  which  may  result  from  the 

increased concentration of wages and salaries in the right tail of income distribution (see Graph 

3 earlier): the top income decile dominates the whole development of labour share. This appears 

in two aspects. First, it is the only income decile in which the labour share is clearly increasing. 

This is  also reflected in the development of total labour share, which is actually an income-

weighted average of the labour shares of different deciles. Consequently, if one or more deciles 

receive a large share of income, they exert more influence on the total; i.e., this is typically an 

indirect indication of large income distribution between households.  Second, only the first and 

tenth income deciles are below the total income labour share. This also clearly indicates the 

relatively large dominance of the tenth decile. The labour share of the remaining deciles stands 

at roughly 80–85%, and this share has also remained relatively stable during the period studied. 

Graph 8: Labour share of household primary income by income decile in France from 2007 to 2018 

Source: European Central Bank and authors’ calculations 

Graph (9.) (and Annex (3.)) shows the labour share of primary income by decile in Spain. This 

graph is constructed in the same way as the Finnish and French graphs. However, it reveals a 

considerable structural difference compared to the Finnish and French economies: the labour 

share of all deciles from the first to the fifth is below the total share for households. This was 

ECB Working Paper Series No 2707 / August 202232 
 
 
already the case in 2007, but, typically, the lower the income decile is, more its labour share 

has fallen after the financial crisis. In the case of Spain, this reflects the relatively poor labour-

market situation. Spain has one of the highest shares of the population partially or fully outside 

the labour market.  

In Spain, the tenth decile follows the total (average) share of households relatively closely, and 

the ninth and tenth deciles possess the highest labour shares. It is worth noting, however, that 

the  labour share of  primary  income for  these  deciles  is  lower than  in  the  Finnish  or  French 

economies. Moreover, the total (average) development of labour share has remained relatively 

stable.  

Graph 9: Labour share of household primary income by income decile in Spain from 2007 to 2018 

Source: European Central Bank and authors’ calculations 

ECB Working Paper Series No 2707 / August 202233 
 
 
 
 
4  Conclusions 

This article presents a framework that begins with the balance sheets of the economy, which 

are  then  connected  with  the  corresponding  income  flows  of  national  income.  Next,  income 

flows that belong to households are separated. Finally, those flows are linked with the income 

distribution  data,  which  allows  scrutiny  of  capital  and  labour  income  by  income  decile 

consistent with national account  concepts. This  is performed for Finland, France and  Spain, 

with the first part of the exercise focused on the years 1995 to 2018. The calculations presented 

in  this  article  are based  on internationally  available data  sources, i.e.,  national accounts  and 

micro data from EU-SILC and HFCS. Due to the availability of micro data, the last part of this 

exercise, i.e., the labour income shares of household primary income, could only be estimated 

for the years 2007–2018. Moreover, as international data sources contain less detail than their 

country-level  counterparts,  this  analysis  is  necessarily  less  granular  than  that  offered  by 

Kavonius (2019, 2020) for Finland.  

The article aimed to analyse whether income generation is driven by income from wealth or 

labour compensation. The structural changes in labour shares indicate that, in terms of income 

generation, the role of both property and labour income is changing. Piketty argues that wealth 

is increasingly accumulating in wealthy households, and this wealth is playing an increasingly 

important role in the generation of income, which will lead to increased income dispersion. In 

our  framework,  this  process  should  entail  a  structural  shift  in  the  ratio  between  capital  and 

labour income in highest income deciles. 

Nevertheless, our (arguably short-term) decile-based analysis finds no evidence of such a shift. 

The share of income which households receive from wealth is sensitive to economic cycles, but 

there is no clear structural change. In fact, the share of labour income in the highest income 

deciles is actually increasing rather than decreasing. Thus, it seems we are moving towards the 

modern  liberal  meritocratic capitalism described  by Milanovic,  where people  are capital-  as 

well as labour-rich, rather than towards a new heyday of classical capitalism. The timeframe of 

our  analysis is considerably  shorter  than that used  by Piketty, but  issues of data availability 

prevent a longer timeframe for an analysis conducted in such detail. Moreover, Piketty’s focus 

ECB Working Paper Series No 2707 / August 202234 
 
 
is  the  richest  1%  or  less,  who  are  seldom  captured  by  macroeconomic  frameworks  or  even 

official (cross-national) statistics based on sample surveys. 

It is also essential to note that if we analyse solely the distribution of gross national income, a 

different picture emerges. In terms of total economic development, property income plays a 

more pronounced role than in pure household sector income, and therefore overall development 

is more volatile. The picture changes little when viewing national income distribution for the 

total economy or household functional income distribution. Relatively volatile property income 

changes the ratio, but clear structural changes are not evident. At the national level, the share 

of capital income is, for obvious reasons, larger than at the household level, but this does not 

automatically entail increasing (inter-personal) inequality. In particular, the countries analysed 

in this article are characterised by considerable equity ownership by general government and 

non-profit institutions serving households, whose returns are used for the public good. 

What then is actually occurring in these economies? The stock of wealth has indeed increased 

much in recent decades. This increase is a result of increased investment in financial and non-

financial assets, i.e., the investment of savings, as well as the price increase of actual assets. 

Nevertheless, returns were higher some decades ago, and if they were similar to levels 20 years 

ago, the capital/labour ratio would have moved structurally towards capital. This would also 

have strongly increased inequality, as capital income is typically concentrated in the highest 

income deciles. However, as the rates of return in these three countries practically halved from 

1995 to 2019, the capital/labour ratio has actually remained relatively stable. Not yet, at least, 

has  Goodhart’s  and  Pradhan’s  predicted  rise  in  the ratio  of  capital  to  labour  occurred,  even 

though demographic change, i.e. a decreasing share of economically active people, is already 

the reality in these countries. 

ECB Working Paper Series No 2707 / August 202235 
 
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Linking Micro and Macro, Review of Income and Wealth 63: S266–S286. 

ECB Working Paper Series No 2707 / August 202238 
 
 
 
 
 
 
 
 
 
Trindade,  L.  Z.  and  Goedemé,  T.  (2020).  The  comparability  of  EU-SILC  income  variables: 

review and recommendations, Eurostat Statistical Working Papers, Publications Office of the 

European Union, Luxembourg. 

Törmälehto, V-M (2021). Reconciliation of EU-SILC data with National Accounts. Chapter 19 

in Guio, A-C, Marlier, E. and Nolan, B. (edited): Improving the understanding of poverty and 

social  exclusion  in  Europe.  Eurostat  Statistical  Working  Papers.  Luxembourg,  Publishing 

Office of the European Union.  

Törmälehto, V-M, Jäntti, M. and Marlier, E. (2017). The Use of Registers in the Context of 

EU-SILC. Chapter 28 in Atkinson, A., Guio, A-C and Marlier, E. (edited): Monitoring Social 

Inclusion  in  Europe.  Eurostat  Statistical  Books.  Luxembourg,  Publications  Office  of  the 

European Union.  

Törmälehto, V-M and Sauli, H. (2013). The distributional impact of imputed rent in EU-SILC 

2007-2010. Eurostat methodologies and working papers, Publications Office of the European 

Union, Luxembourg.   

ECB Working Paper Series No 2707 / August 202239 
 
 
 
 
Annex 1: Detailed framework 

In the following section, the framework will be explained in detail. The letters/steps presented 

in  the  tables  refer  to  the  letters/steps  in  Table  (1.).  Table  (2.)  presents  steps  A  and  B, 

demonstrating how national balance sheets are linked with the corresponding property income 

flows. On the left-hand side of the table, balance sheets and their asset types are presented and, 

on the right-hand side, the corresponding income flows are depicted. 

 Table 2: National balance sheets and related income flows 
A: Financial balance sheets:

B: Property income flows (corresponding):

Deposits (F.2)
Debt seucrities (F.3)
Loans (F.4)
Other accounts payable/receiveable (F.8)

Listed shares (F.511)
Unlisted shares (F.512)

Other equity (F.519)

Interest payable/receivable (D.41)

Dividends (D.421)

Withdrawals from income of quasi-corporations (D.422)

Investment fund shares/units (F.52)

Investment income attributable to collective investment fund shareholders (D.443)

Reinvested earning on foreign direct investment (D.43)

Non-life insurance technical reserves (F.61)
Life insurance and annuity entitlements (F.62)

Pension entitlements (F.63)
Claims of pension fund on pension managers (F64)

Investment income attributable to insurance policy holders (D.441)

Investment income payable on pension entitlements (D.442)

Natural resources (N.21)

Rent (D.45)

Financial derivates and ESOs (F.7)

By nature do not accumulate any income

In Table (3.) the national income flows, which are missing in Step B, are added. These flows 

are, in practice, operating surplus, i.e., profits before the distribution of profits and taxes (which 

covers also rental income from dwellings and imputed rents),21 employee compensation, and 

taxes  and subsidies  on  production. On  the right  hand-side, entrepreneurial  income,  which  is 

operating  surplus  plus  net  property  income  related  to  entrepreneurial  activities,  is  separated 

from the rest of the income flows.22 It is important to note that imputed rents are based on a 

21 This corresponds in bookkeeping to the concept of EBIT (earnings before interest and taxes).  
22 This corresponds in bookkeeping to the concept of EBT (earnings before taxes). 

ECB Working Paper Series No 2707 / August 202240 
 
 
 
 
                                                 
similar  calculation  to  entrepreneurial  income;  i.e.,  by  definition,  imputed  rents  are 

entrepreneurial  income  generated  by  owner-occupied  housing.23  Unfortunately,  very  few 

countries separate entrepreneurial income, and therefore we cannot use this distinction in this 

article.  Kavonius  (2019,  2020)  nevertheless  used  this  breakdown  when  analysing  income 

development in Finland. Due to data availability, the detail level of linking income and balance 

items also features some other differences. In the analysis presented in this article, all income 

flows are included in the breakdown presented in the table on the left-hand side. 

Table 3: National income flows  

C: Gross National Income (primary income):

1. Property and entrepreunerial income (income flow)

2. Entrepreneurial income

Operating surplus, gross (B.2G) / mixed income (B.3G)

Operating surplus, gross (B.2G) / mixed income (B.3G)

minus
plus 
minus
plus 

Interest, payable (D.411)
Interest, receivable (D.411)
FISIM correction, payable (D.412)
FISIM correction, receivable (D.412)

minus
plus

Dividends, payable (D.421)
Dividends, receivable (D.421)

of which minus
of which

plus 
minus
plus 

Interest, payable (D.411)
Interest, receivable (D.411)
FISIM correction, payable (D.412)
FISIM correction, receivable (D.412)

of which

plus 

Dividends, receivable (D.421)

Withdrawals from income of quasi-corporations (D.422) = net zero

of which

plus

Withdrawals from income of quasi-corporations, receivable (D.422)

minus
plus

Investment income attributable to collective investment, payable (D.443)
Investment income attributable to collective investment, receivable (D.443)

of which

plus
minus

Investment income attributable to collective investment, payable (D.443)
Investment income attributable to collective investment, receivable (D.443)

minus
plus

Reinvested earning on foreign direct investment, payable (D.43)
Reinvested earning on foreign direct investment, receivable (D.43)

of which

plus

Reinvested earning on foreign direct investment, receivable (D.43)

minus
plus

Investment income attributable to insurance policy holders, payable (D.441)
Investment income attributable to insurance policy holders, receivable (D.441)

of which minus

plus

Investment income attributable to insurance policy holders, payable (D.441)
Investment income attributable to insurance policy holders, receivable (D.441)

minus
plus

Investment income payable on pension enetitlements, payable (D.442)
Investment income payable on pension enetitlements, receivable (D.442)

of which minus

plus

Investment income payable on pension enetitlements, payable (D.442)
Investment income payable on pension enetitlements, receivable (D.442)

minus
plus

Rent, payable (D.45)
Rent, receivable (D.45)

of which minus

plus

Rent, payable (D.45)
Rent, receivable (D.45)

plus

Compensation of employees, receivable (D.1)
     Wages and salaries (D.11)
     Employers' social contributions  (D.12)

plus

Taxes on products (D.2)

minus

Subsidies (D.3)

Table (4.) shows how the transactions of the household sector are linked with the corresponding 

household  survey income flows. This  linkage  is  further  discussed  in the  next section on  the 

micro sources (EU-SILC and HFCS) used in this article.  

23 In practice, this is operating surplus generated by owner-occupied housing and from which the corresponding 
(mortgage) interest flows are deducted. 

ECB Working Paper Series No 2707 / August 202241 
 
 
 
 
                                                 
Table  4:  Household  share  of  national  income  (total  primary  income)  and  the  corresponding  flows  of  income 
distribution statistics 
D: National income: of which: household sector

E: Corresponding flows in household surveys

Operating surplus, gross (B.2G)

Imputed rents (EU-SILC)

Mixed income, gross (B.3G)

plus 
minus

Interest, receivable (D.411)
FISIM correction, receivable (D.412)

plus 
minus

Interest, payable (D.412)
FISIM correction, payable (D.412)

Dividends, receivable (D.421)

Self-employment income + rental income from 
property and land (EU-SILC)

Interest, dividends and profit sharing (EU-SILC)

Interest repayments on mortgage (EU-SILC)

Interest, dividends and profit sharing (EU-SILC)

plus

plus

plus

plus

plus

Withdrawals from income of quasi-corporations (D.422)

Interest, dividends and profit sharing (EU-SILC)

Investment income attributable to collective investment, receivable (D.443)

Mutual funds (HFCS)

Reinvested earning on foreign direct investment, payable (D.43)
Reinvested earning on foreign direct investment, receivable (D.43)

Interest, dividends and profit sharing (EU-SILC)

Investment income attributable to insurance policy holders, receivable (D.441)

Life insurance (HFCS)

Investment income payable on pension enetitlements, receivable (D.442)

Voluntary pension insurance (HFCS)

minus
plus

Rent, payable (D.45)
Rent, receivable (D.45)

2. Compensation of employees
plus

Wages and salaries (D.11)

Self-employment income + rental income from 
property and land (EU-SILC) entrepreneurial 
income entrepreneurial income

Wages and salaries (EU-SILC)

Employers' social contributions  (D.12)

plus
The transactions on the left-hand side (D) are from the national accounts and on the right-hand side (E) from the 
income distribution statistics. Transactions on the same line indicate which transactions have been used in breaking 
down national income transactions by income decile. The grey areas in the table emphasise the differences between 
the national income and income distribution statistics. 

Employers' social contributions (EU-SILC)

The linkage used above is, in principle, consistent with that applied in Kavonius and Törmälehto 

(2003), but due to updated reporting details and the insurance and pension related items in the 

HFCS  data  used  in  this  article,  the  level  of  data  aggregation  slightly  differs.  Table  (4.) 

corresponds to steps D and E in Table (1.). The grey areas in the table emphasise the differences 

between national accounts and household surveys. In practice, micro income components tend 

to differ in important ways from their NA counterparts even when the conceptual link is strong 

(e.g.,  imputed  rents  and  household  sector  operating  surplus  as  briefly  discussed  in  the  next 

section; see, e.g., Törmälehto [2019] for a more detailed discussion).   

ECB Working Paper Series No 2707 / August 202242 
 
 
 
 
 
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ECB Working Paper Series No 2707 / August 202248 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Acknowledgements 

We would like to thank anonymous referees and the Editor for their valuable comments. 
The views expressed are those of the authors and do not necessarily reflect those of the European Central Bank or Statistics Finland. 
Any remaining errors are solely ours. 

Ilja Kristian Kavonius 
European Central Bank, Frankfurt am Main, Germany; University of Helsinki, Helsinki, Finland; 
email: Ilja_Kristian.Kavonius@ecb.europa.eu 

Veli-Matti Törmälehto 
Statistics Finland, Helsinki, Finland; email: Veli-Matti.Tormalehto@stat.fi 

© European Central Bank, 2022 

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All rights reserved. Any reproduction, publication and reprint in the form of a different publication, whether printed or produced 
electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors.   

This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library or 
from RePEc: Research Papers in Economics. Information on all of the papers published in the ECB Working Paper Series can be found 
on the ECB’s website. 

PDF 

ISBN 978-92-899-5292-7 

ISSN 1725-2806 

doi:10.2866/934742 

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