Full text: Factors driving wealth inequality in European countries (177)

In recent years the topic of household wealth holdings and their distribution has been discussed 
intensively in the literature. An obvious reason for this is the increase of accumulated private wealth 
in relation to the national income in the affluent industrialised economies, from the late 1970s 
onwards as analysed among many others by e.g. Piketty (2014). In addition to this development, in 
most OECD countries inequality of income rose from the 1980s onwards (see for example OECD 2011). 
Another reason for the increased interest in research on household wealth is that micro data have 
become available in the past two decades for more and more countries that allow us to study wealth 
holdings and inequality, not only at the level of individual countries but also to compare the situation 
across countries, first via the Luxembourg Wealth Study Database and more recently based on data 
from the Eurosystem Household Finance and Consumption Survey (HFCS).  
In a previous paper (Leitner, 2016) I have already applied the Shapley value approach to decomposition 
to wealth inequality based on HFCS 2010 data. The present paper replicates the analysis using data 
from the second wave of the survey (HFCS 2014). Thus the aim is to test the robustness of the results 
obtained previously and analyse potential differences. Similar to the previous research done (Leitner, 
2016), my assumption is that the accumulation of wealth stocks by households is facilitated by the 
receipt of bequests or gifts (mostly of ancestors). Thus the wealth inequality of one generation can be 
passed on to the following, which over longer periods of time may result in an increase in the inequality 
of wealth within a society. In principle, households build up wealth stocks in three ways. Either they 
save out of their income from employment or self-employment or out of financial sources. The second 
way, important for many households, is to receive bequests or gifts and to save them instead of using 
the assets for consumptive purposes. A third form, which however cannot be dealt with in this paper, 
is that the assets owned appreciate in real terms. In my paper I am interested in the process of 
households’ building-up of wealth stocks via the first two processes and the respective inequality in 
asset holdings that results therefrom. In order to detect the sources of wealth inequality across 
countries I apply (as in Leitner, 2016) a decomposition methodology based on the Shapley value 
approach to the inequality measure used most frequently in the literature: the Gini index. This 
decomposition method allows for an assessment of the relative importance of explanatory factors in 
inequality. While some authors (see the literature review below) have already worked for some 
decades on measuring how much of the accumulated stock of household wealth can be attributed to 
inheritance and intergenerational inter vivos transfers (contrary to wealth built up over the life cycle 
via saving and investment), decomposition approaches to the distribution of wealth have been 
performed only recently. However, in the literature one can so far find only decompositions by wealth 
source but not by subgroups. This latter analysis is performed in the following and should highlight the 
relative importance of inheritance, income and household characteristics in shaping wealth inequality 
in a cross-country manner, thus providing a novel contribution to the literature. 
The paper is organised as follows: Since the approach of this paper equals the one of previous research 
performed in Leitner (2016) I will not replicate or present a distinct literature review in this publication. 
Instead I refer the reader to the one presented there, covering the relevant publications on 
developments of household wealth inequality, the effects of inheritance and inter vivos transfers and 
on decomposition methods used to analyse income and wealth inequality. Section 2 discusses the most 
relevant aspects of the data used (sources, measurement issues and definitions) and Section 3

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