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Income and wealth of euro area households in times of ultra-loose monetary policy: stylised facts from new national and financial accounts data

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Abstract

It is often purported that the ECB’s ultra-loose monetary policy significantly affects household income and financial wealth. Considering selected euro area countries, this paper addresses two questions: first, how did households net interest income develop in recent years? Second, did portfolio structures change? Based on recently extended national accounts data, I derive stylised facts suggesting that net interest income indeed changed with monetary policy. However, patterns differ across countries. Whereas households in some countries suffered from declining incomes, in other countries they achieved incomes which, considered in real terms, were substantially higher than before. Tentative links to household balance sheets and their interest rate fixation suggest that these factors outweigh the significance of monetary policy. Regarding portfolios, however, less divergence is observed. Risk-taking did not increase in any country, despite the low yields of safe assets. General statements claiming that the ECB’s monetary policy has solely negative effects for household finances therefore seem to be inadequate.

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Fig. 1

Source: Rupprecht (2015). NFC stands for non-financial corporations, MFI is the abbreviation for monetary financial institutes and ICPF stands for insurance corporations and pension funds. The arrows point to the relevant sectors to which a given sector has a claim

Fig. 2

Source: Eurostat and own calculations. Proportions in %

Fig. 3

Source: Eurostat and the ECB. Proportions in %. Interest income and disposable income compiled by using moving four-quarter sums

Fig. 4

Source: Eurostat, the ECB and own compilations. Real net interest income is standardised: 1999 = 100. Net interest income compiled by using four-quarter moving sums, deflated by country-specific HICPs (base year: 2015)

Fig. 5

Source: Eurostat, the ECB and own calculations. Interest-bearing assets include deposits, debt securities, loans and other claims receivable, including financial derivatives. Debt refers to total debt

Fig. 6

Source: Eurostat, the ECB and own calculations. Interest-bearing assets include deposits, debt securities, loans and other claims receivable, including financial derivatives. Debt refers to total debt. Flows are compiled by using four-quarter moving sums

Fig. 7

Source: The ECB, Banca d’Italia and own calculations. Proportions were compiled by adding financial transactions after 2009 (left part) and 2000 (right part) to the stock of the relevant financial asset as of 2009/2000 (“notional stocks”). Risky assets are defined as the sum of debt securities, loans, equity and investment fund shares. The long-term average comprises the years 1999–2016

Fig. 8

Source: The ECB, Banca d’Italia and own calculations. Although detailed debtor–creditor information is available for listed shares only, in line with Deutsche Bundesbank (2015) it is assumed that the same structure holds for unlisted shares as well

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Notes

  1. Most studies in this field are based on macro data, while approaches based on micro data are generally scarce. Regarding the latter, Attanasio and Weber (2010) provided an overview of the existing literature.

  2. Annuß and Rupprecht (2017) provided a more detailed critique of these approaches.

  3. Deutsche Bundesbank (2014a) and Rupprecht (2017) provided a comprehensive overview of the implications of ESA 2010 for financial accounts. For instance, according to ESA 2010 principles, changes in stocks can also occur due to valuation changes and other changes in volume (reclassifications etc.).

  4. More precisely, ESA 2010 classifies property income as distributive transactions. In line with the sequence of the accounts, property income is abbreviated to D.4 and its components are subordinated accordingly (D.41–D.45).

  5. Although the MRO rate is not ideal for reflecting the monetary policy stance given the variety of unconventional measures in place, its use is adequate here. After all, my purpose is not to produce an exact measurement of the monetary policy stance but to give an idea of the direction in which monetary policy changed. For a more elaborate discussion of measures of the monetary policy stance at the zero lower bound, see Deutsche Bundesbank (2017a).

  6. This aspect addresses the distributional effects of monetary policy, which is briefly discussed below. A more detailed discussion is given in Deutsche Bundesbank (2016b).

  7. According to the EIOPA (2017), insurance corporations in the euro area have been able so far to maintain previous levels of profitability. However, if low interest rates persist and regulation remains unchanged, it is very likely that profits will decrease in the medium term. However, the extent to which these developments will be passed on to households remains unclear.

  8. Deutsche Bundesbank (2014b, 2017b) provided a comprehensive discussion of the debt dynamics in the euro area, including its causes and consequences. McCarthy and McQuinn (2017) have complemented this discussion by disentangling the role of household characteristics for deleveraging in the euro area.

  9. Rupprecht (2017) has provided a more comprehensive discussion of the compilation and effects of such valuation changes with respect to German financial accounts.

  10. Note that this definition of risky assets is a subjective one. It solely refers to the perspective of households. For the purpose of this paper, however, this perspective is the relevant one. From an objective point of view, bank deposits and claims against insurances and pension funds are to some extend also risky, since those financial institutions invest households’ savings in assets like shares, debt securities and of course loans. Furthermore, those investment patters might differ across countries. For instance, whereas insurances in Germany might invest in German government bonds only, insurances in Spain could invest in shares or other equity, depending on the respective regulation. As a result, the effective risk profile of those institutions typically widely differs. Whereas these characteristics could in principle be captured by making use of a so called “look-through-approach”, thereby trying to identify the exact allocation of households’ funds via bank deposits as well as insurances and pension funds, this is not done here. The main reason is that, for the purpose of this paper, it’s the households’ perspective which is key. Surveys regularly show that households perceive bank deposits and claims against insurances typically as safe assets. Reasons include existing protection schemes like deposit insurances or similar schemes for insurances, guaranteed returns and the fact that valuation changes are largely absent. Considering the shortfalls in financial literacy documented by Cooper and Zhu (2016), it is also likely that many households are not aware of the particular investments of their banks and insurances. Moreover, such a “look-through-approach” makes high demands on the underlying financial accounts data, which are—to the best of my knowledge—not fulfilled in all countries under review. Against this background, risky assets solely comprise debt securities, equity and loans.

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Rupprecht, M. Income and wealth of euro area households in times of ultra-loose monetary policy: stylised facts from new national and financial accounts data. Empirica 47, 281–302 (2020). https://doi.org/10.1007/s10663-018-9416-8

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