Distributional wealth accounts: timely data on the distribution of household wealth

A new experimental dataset became available early this year: the distributional wealth accounts for households in Germany. This dataset combines data from the Bundesbank Panel on Household Finances survey of wealth with the quarterly data from the macroeconomic balance sheets in the financial accounts. Compared with the complex and time-consuming household survey, the new dataset has a distinct advantage in terms of lead time for data provision and, unlike the macroeconomic balance sheets, contains valuable information on the distribution of wealth. It thus allows for promising studies at the household level on a quarterly basis, including on monetary policy issues.

With regard to whether and, if so, how the distribution of wealth affects the transmission of monetary policy, for example, the data from the distributional wealth accounts show how differently certain types of household respond to monetary policy with regard to their consumption decisions. This is because households differ, not least, in terms of their saving and financing options. Shifting current consumption into the future following a monetary policy-induced interest rate hike is mainly an option for saver households that hold their wealth in largely liquid assets. For households that tend to have limited options for saving, by contrast, the most relevant factor is how the rise in interest rates would affect their income and employment prospects. The predominant effect for each individual household depends on its individual saving and financing options.

Changes in the distribution and structure of wealth could thus impact the effectiveness of monetary policy. When analysing monetary policy measures, it is therefore generally sensible to take a closer look at individual wealth structures and the associated differences between households and to capture how these change over time. For this reason, the Bundesbank will compile and make use of the quarterly distributional wealth accounts in future. Changes in households’ financial circumstances and their potential effects on the transmission of monetary policy, as well as for aspects of financial stability, will thus be identifiable sooner.

1 Introduction

From the beginning of 2024, the Bundesbank started regularly publishing on its website a new dataset that can be used to examine the wealth situation of individual households on a quarterly basis. This new experimental set of statistics – the distributional wealth accounts DWA – combines data from two perspectives: the findings of the Bundesbank Panel on Household Finances (PHF) survey and the quarterly data from the macroeconomic balance sheets. 1 The new dataset is markedly timelier and available more frequently than the household survey and, unlike the macroeconomic balance sheets, contains valuable information on the distribution of wealth. It is the result of several years of joint work by experts from the European System of Central Banks. Sufficient progress had been made that the dataset could be made available to the public at the beginning of the year and is now regularly published and updated on both the Bundesbank website and the European Central Bank Data Portal. 2

The new dataset enables a wide variety of analyses, including on monetary policy issues. Furthermore, the information on the distribution of wealth provides answers to questions regarding financial stability (see also the supplementary information entitled “Financial stability analysis using the distributional wealth accounts”). For example, this data can be used to obtain valuable insight into the extent to which certain households are particularly affected by economic developments and how this could, in turn, potentially feed back into financial stability or monetary policy. The data from the distributional wealth accounts illustrate how differently certain types of household respond to monetary policy in terms of their consumption decisions. In this context, households’ responses depend heavily on the degree to which they can offset unexpected income losses by using their savings, for example. For this reason, when analysing monetary policy measures, it is generally helpful to take financial differences between households into consideration. Furthermore, the timeliness of the distributional wealth accounts is of particular value from a monetary policy perspective. It enables analyses to be conducted at the household level on a quarterly basis. Changes in wealth distribution and asset structures, as well as their possible consequences for the transmission of monetary policy, are thus revealed more quickly.

The distributional wealth accounts enable the identification of relevant heterogeneous developments that would typically remain hidden in aggregates. This article begins by presenting the distributional wealth accounts of households in Germany and then outlines how the distribution of wealth has developed since 2009. In addition, by way of example, it highlights how this new dataset can be used to illustrate the transmission of monetary policy to the individual household level up to the current end and how the transmission of monetary policy stimuli depends on the financial circumstances of individual households. Here, the more recent data are subject to a somewhat greater degree of uncertainty. This is because they were calculated by extrapolating the instrument-specific distributions from the most recent household survey from 2021 with aggregate data at the current end of the data. 3 With this in mind, it is extremely important to continue conducting regular household surveys in future in order for profound structural changes to be taken into appropriate consideration when compiling the distributional wealth accounts. Overall, the findings are as follows:

The distributional wealth accounts provide higher-frequency and timely data on the distribution of various assets and liabilities among households in Germany.
The distributional wealth accounts allow analyses to be carried out at the level of individual households on a quarterly basis. Differences in the development of wealth between households are thus more quickly apparent.
Theoretical and empirical studies indicate that the distribution and structure of wealth can influence how and to what degree monetary policy measures are transmitted.
Whilst it is conceivable that, due to changed saving and financing options among households, monetary policy tightening could lead to a stronger consumption response than before, the analysis of wealth structures in Germany presented here currently provides no evidence to support this.

2 The distributional wealth accounts

The distributional wealth accounts link micro data with macroeconomic statistics. They are based on two different sets of statistics: they incorporate, first, the findings from the Bundesbank Panel on Household Finances (PHF) survey, which reflects the individual financial circumstances of households in Germany, and, second, the quarterly data from the macroeconomic balance sheets. Although both sets of statistics have a very similar aim – to depict the wealth situation among households – there is a considerable gap in wealth reporting between the aggregate micro data from the PHF study (extrapolated) and the macroeconomic figures. A major factor here is that the PHF study does not adequately cover very wealthy households. While the macroeconomic balance sheets provide aggregate information on the amounts and structures of wealth in the household sector as part of a complete survey, very wealthy households are typically not sufficiently represented in the actual samples of the wealth surveys. In light of this, in order to close these gaps in the data, experts from the European System of Central Banks have been collaborating in various working groups since 2015 to devise an appropriate way of combining data from household surveys with the macroeconomic balance sheets of the household sector within a consistent analytical framework. 4 The resulting dataset provides valuable information from the combination of these two sets of statistics: it takes account of information on wealth distribution from the wealth surveys as well as the quarterly frequency and the data on wealth amounts from the macroeconomic balance sheets for the period since 2009. In this context, the distributional wealth accounts rank households based on their levels of net wealth, which is broken down into the following types of assets and liabilities: deposits, debt securities, listed shares, investment funds, insurance claims (life insurance and private retirement provision), financial and non-financial business wealth, housing wealth, and liabilities in the form of mortgages and other debt. The net wealth of a household is ultimately calculated as the difference between its total assets and its liabilities. It should be noted, in particular, that entitlements to statutory pensions are not taken into consideration here. Thus far, neither the financial accounts nor wealth surveys have comprehensively captured these types of asset.

Average net wealth has risen considerably since 2009. Based on the new statistics, median wealth – i.e. the middle of the wealth distribution – has almost doubled over the observation period, rising from just under €54,000 to around €103,000 (see Chart 2.1). At the same time, net wealth saw especially strong growth in the bottom half of the distribution, albeit from a very low level. 5 As a result, the share of total household wealth in Germany held by the bottom half of the distribution grew more strongly than that held by the top 10% of households (upper decile) (see Chart 2.1). Likewise, the development over time of the Gini coefficient – a measure of net wealth inequality – suggests that there has been a slight decline in wealth inequality, especially since 2014 (see Chart 2.2). 6 Nevertheless, for the time being, this decline does not seem to be continuing at the current end, and the share of total wealth held by less wealthy households has fallen again since 2022 (see Chart 2.1). This development must also be viewed against the backdrop of the recent high rates of inflation, the associated monetary policy tightening, and the subdued economic growth (see also the supplementary information entitled “Current developments in households’ net wealth”).

Overall, however, wealth inequality in Germany remains fairly high – even by international standards. According to the distributional wealth accounts for the euro area, the Gini coefficients among Member States range from 56% to 77%. With a coefficient of just under 77%, Germany is towards the top of the ranking (see Chart 2.2). However, when comparing countries, it should be noted that the underlying definition of wealth used for the distributional wealth accounts does not include entitlements to statutory pensions. If statutory pensions are factored into the calculation, Germany’s net wealth inequality is significantly lower than if they are not taken into consideration. 7

Supplementary information

Current developments in households’ net wealth

Data from the distributional wealth accounts (DWA) allow for a detailed analysis of different liabilities and types of investment at the individual household level, and thus also of different groups of households. This analysis shows that wealth inequality has risen again slightly since the end of 2022. This is because the net wealth of poor households has, in part, developed differently than that of wealthier households as of late. This, in turn, is mainly due to differences in net wealth composition and different developments in the components thereof: financial portfolios, housing wealth, business wealth and liabilities. Chart 2.3 illustrates annual growth along the net wealth distribution since the fourth quarter of 2021 for four groups of households: the top 1% of the wealth distribution, the next 9% of the distribution (90% to 99%), the 40% after that (50% to 90%) and the entire bottom half of the wealth distribution (0% to 50%). Contributions from financial portfolios, housing wealth, business wealth and liabilities are each presented in aggregate form. Net wealth growth represents the difference between the sum of contributions from all forms of investment and liabilities.

Since the end of 2021, growth in net wealth across all wealth groupings has declined sharply. The decline in the growth rate of the bottom half of the distribution was particularly steep, in relative terms. This is primarily due to a substantial drop in insurance claims owing to valuation changes. 1 A simultaneous drop in liabilities in this wealth grouping, on the other hand, has bolstered net wealth since the beginning of 2023, and the robust increase in deposits has also recently had a supportive effect. Overall, the growth rates for the two groups in the upper middle of the distribution (50% to 90% and 90% to 99%) show a consistent pattern. Both groups’ lower net wealth growth rates are largely attributable to a decline in housing wealth due to valuation losses. Their net wealth growth rates were also dampened by a phased decline in holdings of listed shares and investment fund shares.

The growth rate of net wealth in the wealthiest one percent of the distribution was similar to the growth rate in the bottom half. A significant decline in holdings of capital market-based forms of investment and housing wealth initially had a dampening effect here. Since the beginning of 2023, however, holdings of debt securities and investment fund shares have increased markedly, meaning that the development of financial portfolios has recently contributed to a significant increase in net wealth due to valuation changes.

While net wealth inequality declined slightly overall up to 2021, it has been rising again slightly since the end of 2022. This is mainly due to wealthier households, in particular, recently benefiting greatly from positive valuation changes in risky forms of investment, such as listed shares and investment fund shares. In addition, negative valuation changes in housing wealth are also likely to have tended to increase wealth inequality further. 2 The data from the DWA allow us for the first time to take a close look at the role of different liabilities and types of investment in the development of net wealth of different groups of households.

Footnotes

A methodological change was made to the calculation of households’ insurance technical reserves for the 2022 reporting year, leading to a substantial decline in this position. The new method is based on the Solvency II reporting regime, under which the discounted cash flow method is used to calculate/value insurance technical reserves (see Deutsche Bundesbank (2023b)). An increase in the discount factor in the context of rising interest rates, taken in isolation, leads to a reduction in the present value of future cash flows.
According to Adam and Tzamourani (2016), housing price increases, taken in isolation, have a slight balancing effect on the net wealth distribution. The recent declines in housing prices are therefore likely to have contributed to a slight increase in net wealth inequality.

3 A disaggregated analysis of financial circumstances from a monetary policy perspective

The distributional wealth accounts allow a wide variety of issues to be investigated, including aspects relevant to monetary policy. For example, individual household financial circumstances and wealth structures determine the consumption decisions of different types of household in response to monetary policy. According to the theoretical literature, such consumption effects can be categorised into direct and indirect effects. 8 While direct effects encompass the immediate response of consumption and investment to a changing interest rate environment, indirect effects have an impact via the interaction between changes in supply and demand in the different sub-markets of an economy. 9 Direct effects include substitution effects and interest income effects through changed interest income and expenditure. These are associated with an immediate consumption response by households, whereas indirect effects are general equilibrium effects that result in an indirect consumption response. In this case, a rise in interest rates caused by monetary policy initially affects the general equilibrium, which then triggers individual adjustments to consumption expenditure. These include, for example, employment effects, real valuation effects of nominal wealth, and valuation changes in asset prices.

The importance of the various effects for individual households’ consumption decisions depends on individual financial circumstances. In order to better classify the relevance of the individual monetary transmission channels, the theoretical literature uses the following household classification: 10

Poor hand-to-mouth households: These households have no appreciable net wealth. Their income flows almost completely into consumption. Due to low levels of liquid assets, fluctuations in income lead directly to fluctuations in consumption. 11 As a result of the liquidity constraint, they typically cannot optimise their consumption over time. This, in turn, implies that substitution effects are irrelevant in this case: unexpected income losses, for example, require a corresponding reduction in consumption. As these households also lack any significant equity or housing wealth, asset price effects are also ultimately not significant for them in this context.
Wealthy hand-to-mouth households: These households have hardly any liquidity buffers, but do have significant – albeit illiquid – net wealth, typically in the form of housing or business wealth. Due to the constraint on liquidity, substitution effects are also insignificant for these households; asset price effects, however, are of relevance to them.
Non-hand-to-mouth households: These households have both net wealth and liquidity buffers. Therefore, all effects are significant for them in principle, especially substitution effects. According to economic theory, this type of household bases its consumption decisions on permanent income, i.e. it is not very responsive to temporary income changes, instead using savings to smooth consumption.

The distributional wealth accounts can be used to determine which role the three different types of households play depending on their individual saving and financing options. At the level of individual households, the individual financial situation can now be viewed at a quarterly frequency, rather than at the three to four-year frequency of the wealth survey, as was previously the case. This allows each household to be roughly assigned to one of the three household types, i.e. poor and wealthy hand-to-mouth households and non-hand-to-mouth households. The quarterly frequency allows for an analysis close to the current end, which allows changes in portfolio structures to be taken into account at an early stage and possible consequences for monetary policy transmission to be detected. The allocation is based primarily on individual holdings of net liquid and illiquid assets. 12 , 13 By definition, poor hand-to-mouth households have neither net liquid assets nor net illiquid assets. 14 Wealthy hand-to-mouth households differ from poor hand-to-mouth households only in that they have, by definition, positive net illiquid wealth. The remaining households not covered by these criteria are ultimately classified as non-hand-to-mouth households.

There are significant differences in households’ net liquid wealth and net illiquid wealth. Compared with hand-to-mouth households, non-hand-to-mouth households each have considerable net liquid and illiquid wealth – the mean values are €127,000 and €354,000, respectively. Wealthy hand-to-mouth households also have comparatively sizeable net illiquid assets on average (€251,000). At the same time, however, like poor hand-to-mouth households, they, by definition, do not have positive net liquid assets. Poor hand-to-mouth households are additionally assumed to have no net illiquid wealth (see also Chart 2.4).

Housing wealth is a key component for wealthy hand-to-mouth and non-hand-to-mouth households (see Chart 2.5). On average, the size of these assets is similar for both household types. In addition, both of these household types hold significant business wealth. This comprises both financial and non-financial business wealth, with the bulk being accounted for by financial business wealth, i.e. equity investments in the form of unlisted shares and other equity. Only non-hand-to-mouth households have significant holdings of listed shares and investment fund shares. Poor hand-to-mouth households do not own any of the three asset types listed here.

There are also significant differences with regard to potential net interest rate exposure, i.e. interest income effects via changes in interest income and expenditure. From an accounting perspective, unhedged interest rate exposure is the difference between maturing assets and liabilities plus the flow of household saving. 15 Accordingly, wealthy hand-to-mouth households, in particular, are exposed to significant negative net interest rate exposure: they have an average unhedged interest rate exposure of around ‑€40,900, meaning that, all other things being equal, their net wealth falls as interest rates rise. By comparison, the net interest rate exposure of poor hand-to-mouth households is also negative, but significantly lower with an unhedged interest rate exposure of around ‑€8,800. The corresponding net interest rate exposure of non-hand-to-mouth households is significantly positive with an unhedged interest rate exposure of around €87,100. Taken in isolation, these differences ultimately imply that, in the event of an interest rate increase, hand-to-mouth households are primarily affected by rising interest expenditure; non-hand-to-mouth households, on the other hand, tend to have rising interest income.

Unexpected increases in the general price level cause real valuation changes in nominal wealth, thereby providing relief to hand-to-mouth households in particular. The net nominal position is significantly negative for wealthy hand-to-mouth households (‑€62,200). 16 Poor hand-to-mouth households, by contrast, show a much lower negative net nominal position on their balance sheet on average (‑€12,300). By contrast, non-hand-to-mouth households have an appreciably positive net nominal position (€48,800). As a result of these divergent net nominal positions, an unexpected rise in the price level is accompanied by real valuation losses for non-hand-to-mouth households. Wealthy hand-to-mouth households, on the other hand, see valuation gains in real terms through the real devaluation of their nominal liabilities.

Supplementary information

Financial stability analysis using the distributional wealth accounts

In its surveillance of risks to financial stability arising from debt in the German household sector, the Bundesbank looks at a variety of indicators. When assessing financial vulnerabilities, the focus has often been on aggregated metrics from the financial accounts. 1 Now, new data from the distributional wealth accounts allow for granular analysis of the financial situation in the household sector, i.e. for individual groups of households and types of borrower. With these statistics, for example, the financial situations of real estate owners can be captured separately from those of other groups. They also allow the distribution of debt within the household sector to be analysed, making it possible to determine the share of households with high debt as well as their levels of debt. This is important when analysing potential financial vulnerabilities in the household sector.

One important metric for households’ financial vulnerability is their ratio of liquid assets to debt. This indicator is used to identify risks arising from liquidity bottlenecks. If borrowers incur unexpected income losses, low liquid assets could make it difficult for them to make principal and interest payments on time without severely restricting their consumption. However, the aggregate ratio of liquid assets to debt derived from the financial accounts provides only an incomplete picture of liquidity buffers, as borrowers’ debt is compared against the liquid assets of all households, including those without any debt.

With the distributional wealth accounts, it is now possible to analyse various borrowers whose asset situations are structurally different. For example, the statistics allow the ratio of liquid assets to debt to be analysed separately for real estate owners with real estate debt ("owners") and non-real estate owners with debt ("tenants"). 2 The median owner has liquid assets amounting to 34% of their debt (see Chart 2.6). Households in the third quintile – which includes the median household – have average debt of around €206,000 and average liquid assets of around €71,000. The median tenant has liquid assets corresponding to 52% of their debt (see Chart 2.6). Their liquid assets are significantly lower at roughly €10,000, but this is offset by much lower debt of around €20,000. In addition to this structural heterogeneity between owners and tenants, the statistics also show that around 30% of indebted households have liquid assets covering less than 15% of their debt (see Chart 2.6). These households are likely to be comparatively vulnerable to income losses. Furthermore, the data reveal that, starting from the 30th percentile onwards, tenants have higher liquid assets relative to their debt than owners. One possible explanation for this is that owners save not only actively by building up liquid assets, but also passively by repaying their loans and thus reducing their debt. Another possible explanation is that the median owner is wealthier than the median tenant and that wealthy households tend to save more in illiquid asset holdings than less wealthy households.

When extrapolating individual household data for the distributional wealth accounts, it is assumed that households’ saving behaviour and investment preferences do not change significantly over time. 3 In an environment with significant crisis periods – such as the unexpectedly sharp rise in energy prices in 2022 and the high inflation in the wake of the coronavirus pandemic – this approximation is likely to be subject to greater uncertainty. In such crisis periods, different types of borrower could be affected differently and therefore react differently, too, for example with regard to their saving behaviour. Observing these responses in a timely manner can provide important indications of potential risks to financial stability. For example, a lower saving rate could indicate a decrease in household resilience. Timely monthly data from the Bundesbank Online Panel – Households (BOP-HH) survey can supplement data from the distributional wealth accounts when identifying potential adjustments in saving behaviour, but these are less granular.

Data from the BOP-HH show that saving ratios, as an indicator of households’ current saving behaviour, fell from 2021 to the end of 2022 for both groups (see Chart 2.7). 4 The general decline is likely to have initially been due to catch-up effects following the reopening of consumption opportunities after the peak of the coronavirus pandemic. The decline in saving ratios amongst indebted owners of residential real estate is greater, not least because their consumption opportunities were restricted by the coronavirus pandemic to a greater extent. In addition, owners have higher incomes and can thus build up savings more easily. 5 Thereafter, the rising costs of living are likely to have dampened households’ ability to save. However, the decline here is greater for tenants who spend a larger proportion of their income on living costs. 6 Only from the beginning of 2023 did saving rates rise again. Despite the aforementioned differences, saving rates are seeing fairly parallel development in both groups of households and therefore do not indicate any fundamental changes in saving behaviour in either group.

Footnotes

For more information, see Deutsche Bundesbank (2023c).
Real estate owners with real estate debt are all households that have loans secured by real estate, including households with and without other loans. Non-real estate owners with debt are all households that do not own real estate and have at least one loan. Total household debt is taken into account for both groups. The data is from the third quarter of 2023.
See Deutsche Bundesbank (2022a), p. 21.
The saving ratio is approximated using data provided by households on their absolute monthly savings and monthly net income. Net income is only available in the form of grouped data.
High-wealth households, in particular, have built up large amounts of pandemic-related liquid savings. See Deutsche Bundesbank (2022a), pp. 34 f.
See Deutsche Bundesbank (2022b), p. 36.

4 The importance of individual financial circumstances from the perspective of monetary policy transmission

Differences in portfolio structures are associated with different consumption effects as a result of a monetary policy impulse. In the context of a simple practical exercise, individual consumption adjustments in response to a monetary policy interest rate hike can be detected using the data from the distributional wealth accounts. Against the backdrop of the portfolio structures outlined above, further assumptions – for example, on individual and macroeconomic employment responses and macroeconomic price responses – can be used to approximate how strongly a household reacts to a monetary policy interest rate hike in connection with the direct and indirect effects explained above. In addition, it is possible to deduce which of the aforementioned effects, i.e. substitution effect, interest income effect, employment effect, real valuation effect or asset price effect, are of particular quantitative significance at the aggregate level (see also the supplementary information entitled Quantifying the effects of a monetary policy interest rate hike on household consumption). The consumption effects depend largely on the individual financial circumstances reported in the distributional wealth accounts (see Chart 2.8).

Direct consumption effects as a result of a hypothetical monetary policy interest rate increase are primarily observed for non-hand-to-mouth and wealthy hand-to-mouth households. With regard to substitution effects, it should be noted that these largely dominate the consumption responses of non-hand-to-mouth households. This means that these households respond to a restrictive monetary policy stimulus in large part by shifting current consumption into the future; the other transmission channels are, in quantitative terms, of relatively minor significance. Consumption-dampening income effects via direct changes in interest income and expenditure can be observed for wealthy hand-to-mouth households in particular. This is because, on average, these households report a relatively large total volume of either short-term or variable rate liabilities on their balance sheets compared with the poor hand-to-mouth and non-hand-to-mouth households. 17

Indirect labour income effects are particularly evident for poor hand-to-mouth households. These effects are also the most significant for poor hand-to-mouth households. In the event of a hypothetical monetary policy interest rate hike, consumption expenditure by these households is driven largely by the impact of monetary policy on employment.

Overall, real valuation effects of nominal wealth appear to be of relatively minor importance. As the restrictive monetary policy stimulus dampens macroeconomic price developments, the real value of both nominal assets and nominal liabilities increases. This results in a consumption-dampening effect primarily for wealthy hand-to-mouth households, as these households have a markedly negative net nominal position. In this case, the real increase in the value of nominal liabilities outweighs the real increase in the value of assets.

Asset price effects arising from share ownership are particularly significant among wealthy hand-to-mouth households and non-hand-to-mouth households. Looking at the consumption responses shown in Chart 2.8, it is also striking that the valuation effects of housing wealth have only a very minor impact on consumption. In this context, empirical evidence suggests that the valuation effects of housing wealth are of rather limited importance with regard to monetary policy transmission. Rather, the extent to which a monetary policy impulse changes interest expenditure linked to the financing of real estate is the crucial factor for monetary policy transmission. 18 The higher the share of homeowners with mortgages and the more prevalent variable-rate loans taken out for this purpose are, the greater the consumption effects. In the current analysis, these effects are reflected by the direct response of interest income.

Valuation effects on business wealth could, in principle, be particularly relevant for wealthy hand-to-mouth households. In addition to the asset price responses mentioned above, significant valuation changes are likely to occur for business wealth. 19 Business wealth (mainly consisting of equity investments in the form of unlisted shares and other equity) is a key indicator, especially for very wealthy households. The amount of such wealth belonging to the top 1% of all households is approximately €2,250 billion. The high volume of this wealth component at the upper tail of the distribution also reflects the increased importance of corporate savings in the wealth development of very wealthy households in recent decades. Although these are typically retained corporate profits, they ultimately belong to shareholders. 20 The response of this wealth component to a monetary policy stimulus is therefore generally likely to be similar to the response observed for listed shares. 21 Potential consumption effects due to valuation changes will probably occur for wealthy hand-to-mouth households first and foremost, and, to a slightly lesser extent, for non-hand-to-mouth households, too. As the components of financial business wealth, in particular, are not traded in organised markets, these are subject to considerably heightened liquidity risk, on the one hand; on the other hand, the effort involved in actually introducing this form of investment as loan collateral is probably far greater relative to using marketable securities. 22 Seen from this perspective, it is likely to be significantly more difficult overall to convert valuation gains on business wealth into additional private consumption. For this reason, the potential consumption responses of business wealth to monetary policy-induced valuation changes are largely disregarded in this article. 23

An increasing share of hand-to-mouth households amplifies the impact of monetary policy. The results of the use case presented here show that the consumption responses of the three types of households to a monetary policy-induced interest rate hike differ slightly in their overall magnitude while varying considerably in their composition. In a comparison across all three groups, an unexpected one percentage point interest rate hike causes a reduction in consumption expenditure of around 0.4% to 0.7%. The effect tends to be slightly greater for hand-to-mouth households. As a result, the aggregate consumption effect is always slightly greater if the share of (wealthy) liquidity-constrained households is particularly high.

In terms of wealth structures, households’ aggregate consumption response is unlikely to have changed fundamentally over time. The depiction over time of the macroeconomic consumption effect resulting from a hypothetical monetary policy-induced interest rate hike at time t makes it possible to take adequate account of changing wealth structures as well as increasing or decreasing liquidity constraints in the context of a monetary policy assessment. To give a concrete example, Chart 2.9 illustrates how households would have reacted overall in a given quarter if there had been a monetary policy-induced interest rate hike of 100 basis points in that quarter. Over the period considered here, slight changes can be seen in the relevance of the individual effects, which have, however, more or less cancelled each other out over time. The decline in net interest income contributions is particularly striking. This is likely due to the observed decline in the leverage ratio, coupled with a decrease in net interest-bearing liabilities. Overall, there are no indications that monetary policy transmission in the household sector in Germany has intensified with regard to consumption responses via changes in wealth structures at the current end. This is also supported by the fact that the shares of poor and wealthy hand-to-mouth households are currently at a relatively low level, measured by their development over the observation period (see also Chart 2.10). Nevertheless, at least with regard to business wealth, asset price effects, seen in isolation, theoretically have the potential to amplify the overall impact of monetary policy stimuli.

5 Conclusion

The distributional wealth accounts for households in Germany represent a new dataset that allows for an examination of the wealth situation of individual households on a quarterly basis. These new experimental statistics link the micro data from the Bundesbank’s household survey with the national accounts statistics. Compared with the complex and time-consuming wealth survey, the distributional wealth accounts have a distinct advantage in terms of timely availability, allowing analyses to be carried out at the individual household level on a quarterly basis. Here, however, it should also be noted that the distributional wealth accounts data at the current end are subject to some degree of uncertainty owing to the extrapolated instrument-specific distribution in the latest available household survey from 2021. From this perspective, it is important to continue conducting regular household surveys in future in order to be able to take appropriate account of profound structural changes.

The use case outlined above illustrates the heterogeneity of households’ consumption responses to a monetary policy-induced interest rate hike on the basis of the distributional wealth accounts. Distributional wealth accounts data demonstrate how significantly the composition of households’ wealth differs: the assets of poor households consist, to a large extent, of low-risk forms of investment such as deposits and insurance claims. The assets of wealthy households, by contrast, include a much greater volume of capital market instruments and, above all, housing and business wealth. 24 In turn, these differences in wealth structure determine how individual households’ individual consumption decisions change as a result of liquidity constraints – in response to a monetary policy-induced interest rate hike, for instance. While the classic substitution effect – in other words, current consumption being shifted into the future due to a monetary policy-induced interest rate hike – plays an important role, particularly for non-hand-to-mouth households, interest income and asset price effects dominate for wealthy hand-to-mouth households. In the case of poor hand-to-mouth households, by contrast, a monetary policy-induced interest rate increase is primarily transmitted through indirect labour income and employment effects.

The relative importance of direct and indirect consumption responses resulting from a monetary policy stimulus depends on individual financial circumstances, amongst other things. Wealth structure and wealth distribution as well as changes in these metrics can thus influence the efficacy of monetary policy. For this reason, it is generally helpful to focus more on households’ individual financial circumstances and potential liquidity constraints when analysing monetary policy measures. Against this backdrop, it may in future be particularly relevant for central banks to produce and use the quarterly distributional wealth accounts for monetary policy analysis purposes. Changes in households’ financial circumstances and potential effects for the transmission of monetary policy can thus be identified sooner. In this connection, the present study shows that monetary policy transmission in Germany’s household sector is not currently expected to have changed significantly.

Supplementary information

Quantifying the effects of a monetary policy-induced interest rate hike on household consumption

The effects of a monetary policy-induced interest rate hike on household consumption can be calculated using model-theoretical relationships. To this end, a hypothetical scenario of a one-off, unexpected monetary policy-induced interest rate increase of 100 basis points is analysed. In a heterogeneous agent model, households maximise their utility subject to budget constraints. Households respond to the monetary policy impulse by adjusting their consumption expenditure. The aggregate effect can be divided into direct and indirect effects (see also Chart 2.11).

One such direct effect is the substitution effect: households respond to a monetary policy-induced interest rate increase by shifting consumption into the future and saving more in the present. The idea behind this is that changes in interest rates influence the relative attractiveness of current consumption. The degree of substitution depends on the extent to which a household is willing and able to change its consumption behaviour in this way.
Another direct effect is the interest income effect through changed interest income and expenditure. For instance, households that hold large amounts of interest-bearing, short-term assets are likely to benefit initially from an increase in interest rates: their net interest income rises. By contrast, households whose balance sheet is dominated by a large, floating-rate loan would suffer losses, all other things being equal: their net interest income would fall. In both cases, this ultimately affects the household income available for consumption purposes. 1
While direct effects elicit an immediate consumption response on the part of households, indirect effects represent general equilibrium effects. Here, the monetary policy impulse triggers a consumption response only indirectly. This is the case, for example, where wage income falls as a result of a slowdown in macroeconomic activity following a monetary policy decision, thus dampening income and, ultimately, consumption expenditure.
A further indirect effect results from changes in the real value of nominal assets. Households with a positive net nominal position, i.e. where nominal assets exceed nominal liabilities, suffer losses in real terms in the event of unexpected increases in the price level; by contrast, households with negative net nominal assets, i.e. classic borrower households, benefit from a real devaluation of their nominal debt. In the first case, less consumption is possible in real terms, whereas the second category of households is ultimately able to consume more.
In addition to the indirect transmission channels cited above, valuation effects in relation to the prices of assets such as equities and housing should also be mentioned. Thus, rising asset prices equate to more credit collateral, for example, which can be used to finance additional consumption expenditure.

The relevance of the individual effects depends on the individual portfolio structure, such as the volume of interest-bearing assets and liabilities, nominally denominated assets or non-financial assets held. These financial circumstances are associated with different consumption responses. In this context, the empirical literature shows that hand-to-mouth households typically have a comparatively high sensitivity of consumption. 2 This is because these households have relatively small holdings of liquid assets. This makes it more difficult for them to cushion a decline in income triggered by monetary policy, say, by running down liquidity buffers. 3 In addition to data on individuals’ assets, information on the sensitivity of consumption, individual and aggregate employment responses/labour income responses and aggregate price responses is likewise needed. The information specific to households in Germany is summarised in Table 2.1. 4

Differences in portfolio structures are associated with differences in sensitivity of consumption. Hand-to-mouth household types are assumed to have a consumption sensitivity of 0.5. 5 By contrast, non-hand-to-mouth households are attributed a significantly lower value of 0.05 in line with the theory and empirical evidence. 6 With hand-to-mouth households making up around 27% of total households, on average, this implies an average sensitivity of consumption across all households of roughly 17%. This figure is consistent with data from an online survey conducted by the Bundesbank, which likewise points to an average sensitivity of consumption of 17%. 7 Looking at wealth-based marginal propensities to consume, empirical evidence for the euro area suggests values between 0.01 and 0.03 for households in aggregate. 8 If the minimum of 0.01 is set for non-hand-to-mouth households, a wealth-based marginal propensity to consume for hand-to-mouth households of roughly 0.07 can be derived as the residual variable. 9 Looking at individual income elasticities, which indicate the extent to which individual labour incomes respond to fluctuations in aggregate gross wages and salaries, clear differences also emerge for the three household types. The estimated country-specific responses are based on data provided by the European Union Labour Force Survey (EU LFS) and the Household Finance and Consumption Survey (HFCS) and were taken from Slacalek et al (2020). The results for households in Germany suggest that poor hand-to-mouth households, in particular, have a high degree of elasticity. By contrast, the income elasticity of non-hand-to-mouth households is somewhat lower. Meanwhile, the income elasticity of wealthy hand-to-mouth households is significantly lower than the value for the other two household types.

Table 2.1: Assumptions for consumption responses¹
Household characteristics
	Sensitivity of consumption²						Individual income elasticity
Household type	Income		Housing wealth		Equity
Poor hand-to-mouth	0.50		–		–		1.7
Wealthy hand-to-mouth	0.50		0.07		0.07		0.3
Non-hand-to-mouth	0.05		0.01		0.01		1.1
Aggregate response to a monetary policy impulse³
	Interest rate⁴	Income⁵		Real estate prices^5,6		Equity prices⁵		Inflation⁷
Change	+0.8	-0.5		-0.4		-27.0		-0.1
1 Based on Slacalek et al. (2020). 2 Consumption sensitivity is measured by the marginal propensity to consume. This indicates the percentage of a transitory income or wealth change (housing wealth or equity) that is used for additional consumption (of non-durable consumer goods). 3 Monetary policy-induced interest rate increase of 100 basis points. 4 Average response over four quarters in percentage points. 5 Response after four quarters in percent. 6 Based on the results of Nocera and Roma (2017) and Bundesbank estimates, we have deviated here from the zero response assumed by Slacalek et al. (2020) and admitted a slight response of real estate prices to a monetary policy-induced interest rate change. 7 Percentage change in price level after four quarters.

In addition to household-specific responses, an analysis of indirect effects also requires information on aggregate responses to a monetary policy impulse. This concerns, for example, aggregate price and employment effects, but also valuation effects on housing and equities. 10 The corresponding results for Germany are shown in the lower part of Table 2.1. According to this, aggregate gross wages and salaries fall by 0.5% over the course of a year in the event of a restrictive monetary policy impulse (interest rate increase by 100 basis points). 11 By contrast, inflation declines significantly less over the course of the year, by 0.1 percentage point. Equity prices, meanwhile, show a clear decline of 27%. Housing prices, by contrast, show a slight response (-0.4%). 12 All this additional information can finally be used to approximate the direct and indirect consumption effects for individual households in response to a hypothetical monetary policy-induced interest rate hike, taking into account individual financial circumstances and portfolio structures. 13

Footnotes

For more on this topic, see Auclert (2019).
See Kaplan et al. (2014) and Kaplan and Violante (2018).
See, inter alia, Bayer et al. (2019), Kaplan and Violante (2022) and Slacalek et al. (2020).
In their analysis, Slacalek et al. (2020) look at the four major euro area countries and use the country-specific results to determine corresponding outcomes for the euro area as a whole. The behavioural equations for the different household types are parameterised individually for each country.
Consumption sensitivity is measured by marginal propensity to consume. This indicates the percentage of a transitory income or wealth change (housing wealth or equity) that is used for additional consumption (of non-durable consumer goods).
See Slacalek et al. (2020) and the sources cited therein.
See Bernard et al. (2020).
See Guerrieri and Mendicino (2018) and de Bondt et al. (2020).
See Slacalek et al. (2020).
The aggregate responses are based on a vector autoregressive (VAR) model estimated by Slacalek et al. (2020). The dataset covers the period from the first quarter of 2000 to the fourth quarter of 2014. It includes the countries Germany, Spain, France and Italy. Monetary policy impulses are identified based on high-frequency financial market data with the focus on the short end of the interest rate structure. The information required to identify the monetary policy impulse is taken from the Euro Area Monetary Policy Event Database. For more information, see Altavilla et al. (2019). For further details on shock identification and specification of the estimation equation, see Slacalek et al. (2020). Taking into account estimation uncertainty, the estimation results that Slacalek et al.(2020) arrive at appear plausible overall (see, for example, Alessi and Kerssenfischer (2019), Corsetti (2021) or Mandler et al. (2022)).
At this point, it should be noted that Slacalek et al. (2020) present the responses to an expansionary monetary policy impulse in their analysis. However, the authors use a linear model, meaning that the results also apply to a restrictive monetary policy impulse (with reversed signs).
According to the results of Slacalek et al. (2020), housing prices in Germany do not react to a monetary policy impulse. If, however, the results of Nocera and Roma (2017), for example, are applied to a quantitatively equivalent monetary policy impulse, the results point, approximately, to a decline in housing prices in Germany of between 0.5% and 0.8% over the course of the year. At -0.4%, back-of-the-envelope calculations by the Bundesbank suggest a similar magnitude. Against the backdrop of the monetary policy tightening that is currently under way and the significant declines in housing prices observed at the same time, we deviate explicitly from Slacalek et al. (2020) at this point and assume a slight decline in housing prices of -0.4% in response to a monetary policy-induced interest rate increase of 100 basis points.
In addition, it should be noted that the elasticity of substitution is set to 0.5; see Slacalek et al (2020).

6 List of references

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Adam, K. and J. Zhu (2016), Price level changes and the redistribution of nominal wealth across the euro area, Journal of the European Economic Association, Vol. 14(4), pp. 871-906.

Alessi, L. and M. Kerssenfischer (2019), The response of asset prices to monetary policy shocks: Stronger than thought, Journal of Applied Econometrics, Vol. 34(5), pp. 661-672.

Altavilla, C., L. Brugnolini, R. Gürkaynak, R. Motto and G. Ragusa (2019), Measuring euro area monetary policy, Journal of Monetary Economics, Vol. 108, pp. 162-179.

Auclert, A. (2019), Monetary Policy and the Redistribution Channel, American Economic Review, Vol. 109(6), pp. 2333-2367.

Bartels, C., T. Bönke, R. Glaubitz, M. Grabka, and C. Schröder (2023), Accounting for pension wealth, the missing rich and under-coverage: a comprehensive wealth distribution for Germany, Economics Letters, Vol. 231.

Bauluz, L., F. Novokmet and M. Schularick (2022), The Anatomy of the Global Saving Glut, ECONtribute Discussion Paper, No 161.

Bayer, C., R. Luetticke, L. Pham-Dao and V. Tjaden (2019), Precautionary Savings, Illiquid Assets, and the Aggregate Consequences of Shocks to Household Income Risk, Econometrica Vol. 87(1), pp. 255-290.

Bernard, R., P. Tzamourani and M. Weber (2020), How are households’ consumption plans affected by the COVID-19 pandemic?, Bundesbank Research Brief, 35th edition, November 2020.

Corsetti, G., J. B. Duarte and S. Mann (2022), One Money, Many Markets, Journal of the European Economic Association, Vol. 20(1), pp. 513-548.

de Bondt, G. J., A. Gieseck and Z. Zekaite (2020), Thick modelling income and wealth effects: a forecast application to euro area private consumption, Empirical Economics, Vol. 58(1), pp. 257-286.

Deutsche Bundesbank (2024), Bundesbank releases Distributional Wealth Accounts for households in Germany, press release, 8 January 2024.

Deutsche Bundesbank (2023a), Household wealth and finances in Germany: results of the 2021 survey, Monthly Report, April 2023, pp. 25-58.

Deutsche Bundesbank (2023b), Acquisition of financial assets and external financing in Germany in the fourth quarter of 2022, press release, 21 April 2023.

Deutsche Bundesbank (2023c), Financial Stability Review 2023.

Deutsche Bundesbank (2023d), Model-based recommendations for monetary policy decision-making, Monthly Report, December 2023, pp. 37-54.

Deutsche Bundesbank (2022a), Distributional Wealth Accounts for households in Germany – results and use cases, Monthly Report, July 2022, pp. 15-38.

Deutsche Bundesbank (2022b), Financial Stability Review 2022.

Deutsche Bundesbank (2020), Sectoral portfolio adjustments in the euro area during the low interest rate period, Monthly Report, April 2020, pp. 19-44.

Dobrew, M., R. Gerke, S. Giesen and J. Röttger (2023), Make-up strategies with incomplete markets and bounded rationality, Deutsche Bundesbank Discussion Paper, No 01/2023.

Dobrew, M., R. Gerke, S. Giesen and J. Röttger (2021), A comparison of monetary policy rules in a HANK model, Deutsche Bundesbank Technical Paper, 02/2021.

Doepke, M. and M. Schneider (2006), Inflation and the Redistribution of Nominal Wealth, Journal of Political Economy, Vol. 114(6), pp. 1069-1097.

Ederer, S., P. Ćetković, S. Humer, S. Jestl and E. List (2022), Distributional National Accounts (DINA) with Household Survey Data: Methodology and Results for European Countries, Review of Income and Wealth, Vol. 68(3), pp. 667-688.

Engel, J., P. G. Riera, J. Grilli, and P. Sola (2022), Developing reconciled quarterly distributional national wealth – insight into inequality and wealth structures, ECB Working Paper Series, No 2687.

European Central Bank (2024), ECB publishes new statistics on the distribution of household wealth, press release, 8 January 2024.

European Central Bank (2023), The role of housing wealth in the transmission of monetary policy, Economic Bulletin, Issue 5/2023, pp. 69-76.

European Central Bank (2020), Understanding household wealth: linking macro and micro data to produce distributional financial accounts (Expert Group on Linking Macro and Micro Data for the household sector), ECB Statistics Paper Series, No 37.

Gerke, R., S. Giesen, M. Lozej and J. Röttger (2024), On household labour supply in sticky-wage HANK models, Deutsche Bundesbank Discussion Paper, No 01/2024.

Guerrieri, C. and C. Mendicino (2018), Wealth effects in the euro area, ECB Working Paper Series, No 2157.

Kaplan, G. and G. Violante (2022), The Marginal Propensity to Consume in Heterogeneous Agent Models, NBER Working Paper Series, No 30013.

Kaplan, G. and G. Violante (2018), Microeconomic Heterogeneity and Macroeconomic Shocks, Journal of Economic Perspectives, Vol. 32(2), pp. 167-194.

Kaplan, G., G. Violante and J. Weidner (2014), The Wealthy Hand-to-Mouth, Brookings Papers on Economic Activity, Vol. 45(1), pp. 77-153.

Mandler, M., M. Scharnagl and U. Volz (2022), Heterogeneity in euro area monetary policy transmission: Results from a large multicountry BVAR model, Journal of Money, Credit and Banking, Vol. 54(2-3), pp. 627-649.

Mian, A. R., L. Straub and A. Sufi (2020), The Saving Glut of the Rich, NBER Working Paper Series, No 26941 (revised February 2021).

Nocera, A. and M. Roma (2017), House prices and monetary policy in the euro area: evidence from structural VARs, ECB Working Paper Series, No 2073.

Slacalek, J., O. Tristani and G. Violante (2020), Household Balance Sheet Channels of Monetary Policy: A Back of the Envelope Calculation for the Euro Area, Journal of Economic Dynamics and Control, Vol. 115, No 103879.

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Footnotes

In total, the results include four household surveys covering the years 2011, 2014, 2017 and 2021. For more information, see Deutsche Bundesbank (2023a).
See also Deutsche Bundesbank (2024). Comparable statistics are also available for other euro area countries. See European Central Bank (2024).
Here, “instrument” refers to the various forms of assets and liabilities, for example deposits or mortgages.
For further details on the concept and methodology, see Deutsche Bundesbank (2022a), Engel et al. (2022), and European Central Bank (2020).
The heterogeneous developments in wealth along the net wealth distribution are a result of unevenly distributed forms of wealth. While wealth growth among the bottom half of the distribution was dominated by financial portfolios, the upper middle of the distribution benefited to a comparatively large degree from an increase in the value of housing wealth. Alongside housing wealth, the growth in wealth among the top 10% of the distribution was ultimately and mainly attributable to gains in business wealth. For more information, see, inter alia, Adam and Tzamourani (2016) and Deutsche Bundesbank (2022a, 2023a).
The Gini coefficient measures the extent to which the distribution of wealth in a country deviates from equal wealth distribution. Specifically, a coefficient of 0% represents perfect equality of net wealth. In this case, all households have the same amount of net wealth. By contrast, a coefficient of 100% represents maximum inequality. In this case, all wealth is held by a single household.
See also Bartels et al. (2023).
This is a class of model that has become established in the literature, which, amongst other purposes, is used regularly for monetary policy analysis; see also Deutsche Bundesbank (2023d), Dobrew et al. (2021, 2023) and Gerke et al. (2024).
The description of the corresponding consumption effects and their classification as direct or indirect effects follows Slacalek et al. (2020) and the sources provided therein.
See, inter alia, Kaplan et al. (2014) and Slacalek et al. (2020).
See Bayer et al. (2019), Kaplan and Violante (2022) and Slacalek et al. (2020).
Following Slacalek et al. (2020), liquid assets consist of deposits, debt securities, listed shares and investment fund shares. Illiquid assets include housing and business wealth. Unlike Slacalek et al. (2020), insurance claims are also classified as illiquid assets. Although insurance claims are generally easy to liquidate, policyholders generally have to accept very high markdowns. This means that liquidity risk is de facto significantly higher than for the aforementioned liquid assets (see Deutsche Bundesbank (2020)). Illiquid liabilities consist of mortgages; liquid liabilities consist of other loans. The item other loans in the distributional wealth accounts includes all loans other than mortgages, such as consumer loans.
The definition of the three household types follows Kaplan et al. (2014) and Slacalek et al. (2020). In addition to the stock data from the distributional wealth accounts, this also requires information on household income. However, primary income information is not available in the distributional wealth accounts. In order to still be able to make use of income information in approximate terms, the corresponding income information from the wealth survey is initially used for households that originally stem from the wealth survey dataset. For the synthetically generated very wealthy households in the distributional wealth accounts (see Deutsche Bundesbank 2022), corresponding household income is estimated using the income-to-wealth ratio derived from the information provided by wealthy households in the wealth survey. Household income can be roughly broken down into gross wages and salaries, income generated from the ownership of various assets (housing wealth, financial portfolio, equity investments) and monetary social benefits. Further sub-items such as interest income, capital income and rental income are calculated on the basis of individual stock data in conjunction with corresponding interest and yield information (see Deutsche Bundesbank 2022). Finally, following Ederer et al. (2022), the micro data for households are compared with the corresponding national accounts values, such that the aggregate and extrapolated individual household income is consistent with the levels and developments in the national accounts statistics. Assuming additionally that the income of hand-to-mouth households flows fully into consumption and that non-hand-to-mouth households save a percentage of household income such that the extrapolated savings volume of individual households corresponds to the aggregate saving ratio, corresponding consumption expenditure can be determined for each household (see Slacalek et al. (2020)).
Following Slacalek et al. (2020), this means that, in the case of positive net liquid assets, net liquid assets amount to less than half of monthly disposable household income. In the case of a financially indebted household (negative net liquid assets), the household’s net liquid assets are close to the individual credit limit. The credit limit corresponds to a single monthly income. For more information on the derivation of these thresholds, see Kaplan et al. (2014).
Based on Auclert (2019) and Tzamourani (2021), the interest rate risk of a household is determined in analogy to Slacalek et al. (2020). The volume of maturing assets is defined as deposits plus 25% of the holdings of listed shares, investment fund shares and debt securities. The flow of household saving is the difference between household income and consumption expenditure. Maturing liabilities comprise other liabilities and variable rate mortgages. Since this is an annualised analysis, an implicit maturity of one year is assumed. For households that originally stem from the wealth survey dataset, the volume of variable rate mortgages is determined by the individual variable rate mortgages observed in the wealth survey as a share of the household’s total volume of mortgages. For the synthetically generated very wealthy households in the distributional wealth accounts, this share is set at the average value of wealthy households in the top percentile of the wealth survey.
Following Slacalek et al. (2020), the net nominal positions are calculated according to Doepke and Schneider (2006) and Adam and Zhu (2016). Specifically, the net nominal position of a household is obtained by first deducting nominal liabilities from directly held nominal financial assets. Subsequently, the indirect nominal position, which arises from holdings of listed shares, investment funds and financial business wealth, are added. For further details on the determination of indirect net nominal positions, see, in particular, Adam and Zhu (2016).
According to these figures, wealthy hand-to-mouth households currently have an average of €52,100 in short-term and variable rate loans. The corresponding figure for poor hand-to-mouth households currently stands at €10,400, and for non-hand-to-mouth households at €14,400. Overall, however, variable-rate loans are used much less frequently by households in Germany compared to the rest of the euro area (see Tzamourani (2021)).
See also European Central Bank (2023), inter alia.
For more information on returns on these types of assets, see Deutsche Bundesbank (2022a).
See also Bauluz et al. (2022) and Mian et al. (2020).
However, since the returns on business wealth display considerably lower volatility than returns on shares, viewed as a whole (see also Deutsche Bundesbank (2022a)), the response of business wealth to a monetary policy-induced interest rate hike relative to that of shares is slightly scaled down. The scaling factor is based on the ratio of standard deviations to each other (business wealth to shares). The specific factor used is 0.4.
See also Deutsche Bundesbank (2020).
In this regard, the consumption effects of monetary policy-induced valuation changes in business wealth presented in the charts are only intended to reflect theoretical possibilities in the interests of a providing a complete assessment of all wealth components recorded in the distributional wealth accounts.
See also Deutsche Bundesbank (2022a).

Distributional wealth accounts: timely data on the distribution of household wealth Monthly Report – April 2024