Intergenerational transfers: How do they shape the German … · 2017-06-05 · heirs typically di er, next to the monetary transfer heirs by de nition receive. These dimensions encompass

Intergenerational transfers:

How do they shape the German wealth distribution?

Marten von Werder∗†

June 5, 2017

Preliminary Version

(please do not cite)

Abstract

This paper uses SOEP data to study the distributional effect of intergenerational trans-

fers on the wealth distribution of German households. Similar to most other central Euro-

pean countries, Germany faces a period of increasing aggregate bequest flows. At the same

time, there is an ongoing debate on the distributional implications of such wealth shocks.

This study retrievs the causal effect of an annual inheritance flow on the inequality in the

households’ net wealth distribution by estimating the counterfactual distribution of wealth

that would have prevailed if heirs would be non-heirs: By using a combination of an in-

verse probability weighting approach and regression techniques, the study evens out wealth

differences between heirs and non-heirs that are attributable to age, household structure,

parental background and the monetary transfers themselves. The results mostly confirm

previous findings from the literature: Intergenerational transfers tend to reduce relative and

tend to increase absolute wealth inequality. In contrast to previous works, this study how-

ever identifies the heterogeneity in the propensity to save from intergenerational transfers

as the major driver in the equalizing effect of transfers on wealth. Whether transfers are

expected or not plays however no role for inequality considertions.

Keywords: Bequests, saving, wealth distribution, inequality.

JEL Classification Numbers: D63 E21.

∗Economics Department, Free University of Berlin. Boltzmannstraße 14195, Berlin, Germany. E-mail:[email protected]†I am grateful to Timm Bonke, Giacomo Corneo, Frank Fossen, Viktor Steiner, and all participants in

Wirtschaftspolitisches Forschungsseminar at FU Berlin and the members of the PhD program Public Economicsand Inequality for their support and very helpful comments.

1

1 Introduction

Recent research in economics has paid a lot of attention to wealth and its transmission through

inheritances and gifts. While the studies of Piketty [2011] and Piketty and Zucman [2015]

enquire the scale of future aggregate transmissions, other studies have focused on the effects

of intergenerational transfers on the inequality in the distribution of households’ net wealth.1

Particularly the contributions by Boserup et al. [2016], Elinder et al. [2016] and Wolff and

Gittleman [2014] reveal the ambivalent nature of intergenerational transfers: Transfer accrual

and transfer scale typically increase with the net-of-transfer wealth of households (a pattern we

will refer to as the incidence effect of transfer accrual). Richer households are hence more likely to

receive transfers and are more likely to receive sizeable transfers in absolute terms. Inheritances

thus disequalize the absolute wealth distribution. The corresponding effect on relative wealth

inequality however appears to be different: As the relative transfer size tends to decrease with

net-of-transfer wealth, poorer households are more likely to receive higher relative bequests. The

cited papers consistently show that wealth inequality as usually measured by relative means

decreases through bequest receipt.

Despite this compelling evidence, a further look at the matter appears worthwile: The dis-

tributional effect of transfers on wealth depends strongly on the behavioral economic response

of households to the transfer receipt. Brown et al. [2010] for instance establish that individuals

demand more leisure after having received a transfer and thus even anticipate their retirement

entry.2 Hence, one might actually ask what share of a receipt households actually end up saving.

With respect to this question, papers that analyze the impact of transfers on wealth typically

follow one of the following two approaches: A first approach is pioneered by Wolff [2002] and

has found fruitful applications in Wolff and Gittleman [2014], Wolff [2015], Crawford and Hood

[2015] and Bonke et al. [2016]. This approach relates the observed bequest amount to the observed

household net-of-transfer wealth. It thus assumes that the entire transfer amount is saved and

added to the households’ wealth. As it is indistinguishable whether and how regular savings

and inherited wealth are substituted, this approach may be considered to be the theoretically

clearest.3 Following the distinction of Elinder et al. [2016], will denote the inequality effect of

transfer measured by this approach the direct mechanical effect as it reflects the sheer mechanical

impact of transfer receipts on the distribution of household wealth.

The second approach relaxes the assumption of fully saved transfers: As Elinder et al. [2016]

point out, the actual distributional effect of transfers clearly is a function of the households’

economic response to the wealth shock, as determined by its labor supply and consumption

decisions. The authors therefore estimate a behavior adjusted effect of transfers on the inequality

1Net wealth in this study is calculated as the sum of all assets minus the liabilities of a household.2Similar evidence is provided by Bo et al. [2015], Garbinti and Georges-Kot [2016], and for Germany by Doorley

and Pestel [2016]3The assumption that the entire transfer is saved is als underlying the studies of Piketty et al. [2014], Bonke

et al. [2015], Bonke et al. [2016] and Kohli et al. [2006]. One can understand this approach as displaying themaximum amount of wealth that a household possibly could have accumulated when saving the full transferamount and capitalizing the transfer amount with a fixed rate per annum.

2

in household wealth. Interestingly, they however do not explicitly estimate the savings behavior

of households out of transfers. Their empirical set up rather controls implicitly for the savings

behavior of individuals by assuming that it is constant over time. Hence, neither of the two

approaches that are typically used to study the inequality effect of transfers explicitly estimates

the contribution of the savings behavior out of transfers to the total effect. Note further that

studies from both approaches yield comparable results. The major findings of the literature are

thus robust to the savings behavior of individuals.

The general relevance of the savings behavior is however acknowledged in the literature:

Wolff and Gittleman [2014] discuss how heterogeneities in the savings behavior over the wealth

distribution could affect the aggregate inequality effect of transfers on wealth. Using simulations,

the authors show that such heterogeneities could counteract and even turn over the general

finding of an equalizing effect of transfers on wealth. Karagiannaki [2015] eventually estimates

the savings behavior from transfers and in fact finds weak evidence that poorer households tend to

save less from transfers than richer households. She however does not analyze the distributional

repercussions of this heterogeneity.

The propensity to save out of intergenerational transfers thus remains a surprisingly neglected

factor that somehow contributes to the inequality effect of intergenerational transfers. The

current study aims at contributing to the literature by parcelling out this very effect: It provides

a causal estimation of the savings behavior of households out of intergenerational transfers.

In order to check up on potential variations of this effect over the wealth distribution, it will

also apply quantile regression techniques as introduced by Koenker and Basset [1978]. The

paper then incorporates these estimates in a reweithing approach in the fashion of DiNardo

et al. [1996]: This reweighting helps to even out several dimensions by which heirs and non-

heirs typically differ, next to the monetary transfer heirs by definition receive. These dimensions

encompass for example age (as inheritances are typically only received at a later stage in life), the

household structure (as the chance of households to receive a transfer increases with the number

of adults in the household) and the societal origin of heirs. These three factors apparently

contribute to the higher incidence of receipts in the upper wealth quantiles. Keeping these

dimensions constant between heirs and non-heirs withdraws the (explained) incidence effect from

the aggregate inequality effect. Thereby, the inequality effect of transfers on wealth inequality

can be decomposed into incidence and savings effect.

This paper adds to the literature in a threefold way: First, it provides a causal estimation

of the distributional effect of intergenerational transfer on wealth inequality. Such results were

provided for Denmark and Sweden by Elinder et al. [2016] and Boserup et al. [2016], albeit ap-

proaching the matter differently in methodological terms.4 The approach of this paper allows,

and this is the second contribution, to decompose the inequality effect in incidence and savings

effect. The results might be particularly helpful for the design of tax schemes bearing distribu-

tional goals. Thirdly, to my knowledge the current study is also the first to provide a causal

4Fessler and Schurz [2015] actually also apply the decomposition technique by DiNardo et al. [1996] to the topicof inheritances. Their study however focuses on wealth mobility and uses the tool for an introductory illustration.

3

estimation of the inequality effect of transfers for Germany. The only available study for Ger-

many are Kohli et al. [2006] and Bonke et al. [2016] but - similar to the studies by Wolff - these

studies assume fully saved transfers and thus rather appear as descriptives studies. Bonke et al.

[2015] apply the method of Piketty et al. [2014] to German HFCS data and find that the middle

class particularly benefits from inheritances. The study however is not able to infer distribu-

tional repercussions of transfers. A major limitation of my approach is however that the current

paper may only estimate the short term effect of transfers on wealth due to the few available

SOEP wealth containing wealth. The study by Bonke et al. [2015] rather had the goal, to reflect

somewhat like an long term effect of transfers on the wealth accumulation of households.

Combining reweighting and regression techniques has already been done by some publications:

Biewen and Juhasz [2012] for instance analyse the factors contributing to the rising income

inequality in Germany in the mid 2000s. I thus adopt their approach and apply it to the wealth

instead of the income distribution.5 The analysis is based on the socio-economic panel (SOEP)

from Germany, which covers three waves of wealth data (2002, 2007 and 2012) on the household

and the individual level and surveys the receipt of intergenerational transfers on an annual basis.

The results of this study are generally in line with the literature: The cross-sectional effect

of transfers on the wealth distribution appears to slightly equalize the wealth distribution. This

even holds, given that on average only 3/5 of an inherited Euro are saved by households. This

translates into a propensity to consume from the transfer amount by 16% per annum. The

propensity to save from inheritances also varies significantly over the wealth distribution. In

contrast to the findings by Karagiannaki [2015], the effect is however not suggesting a higher

propensity to save for poorer households. The results also show that the absolute wealth in-

equality is consistently disequalized by transfers. In the robustness analysis section, I test on a

restricted sample of observations whether expectations about future transfers are likely to alter

the results. However, while expectations seem to play a role for the savings behavior, these

variations do not translate into changes in the distributional analysis.

The remainder of the paper is organized as follows: Section 2 provides a brief overview of

the literature while section 3 gives more details on the concept behinid the current analysis.

Section 4 introduces the methods, i.e. the reweighting approach, the regression equation and the

quantile regression approach. Section 5 provides information on the SOEP data. Section 6 gives

descriptive statistics of the sample before section 7 presents the results.

2 Literature

The question of how intergenerational transfers affect the inequality in the net wealth distri-

bution is cumbersome. Individuals with perfect foresight will be able to adjust their life time

consumption to inheritances that are only visible to the econometrician long after the adjust-

5Further examples of this approach encompass: Biewen, Herault and Azpitarte (xx); Bourguignon and Ferreira,2004; Bourguignon, Ferreira and Leitz, 2008.

4

ment actually has taken place. A preferable way to study the inequality effect of intergenerational

transers therefore would need to ask how decedents would have saved over the life cycle knowing

that they will eventually not be able to bequeath their wealth. Simultaneously, one would need

to answer, how (potential) heirs would have saved over their life cycle if they had known that

they will not receive parental wealth. While such an approach appears ideal, to my knowledge

there is no such study available. For practial reasons the literature rathers uses ad hoc ap-

proaches, decompositions and utilizes policy evaluation methods in order to identify the effect of

intergenerational transfers on the inequality in wealth.

The works by Ed Wolff (Wolff [2002] Wolff and Gittleman [2014] Wolff [2015]) for instance

base on a decomposition approach of the coefficient of varition. The approach decomposes the

inequality in total wealth into the inequality loadings of transfer wealth, the net of transfer

wealth and a the coefficient of covariance. The results consistently show that the negative

relation between transfer wealth and net of transfer wealth typically causes the inequality in

total wealth to be smaller after transfers.6. The decomposition approach by Wolff furthermore

assumes that the transfer sum has been fully saved by the receiving household. Under this

condition, deducting the sum of the observed transfer from the observed total household wealth

will yield the households’ wealth net of transfers. The only study that evaluates the inequality

effect of transfers on wealth in Germany resorts to the same assumption: Kohli et al. [2006]

subtract the transfer amount from the total household wealth to get the net-of-transfer household

wealth. They then find that the inequality in net-of-transfer wealth is higher than the inequality

in wealth including transfers.

An apparent shortcoming of this approach is that it neglects the savings behavior of transfers:

When households do not save the entire transfer amount from the start, then deducting the

transfer amount will underestimate the net-of-transfer wealth of these households. Hence, the

recipients of transfers might appear poorer than they actually are. The wealth equalizing effect of

transfers might thus well be overstated. This will be particularly true, when poorer households

tend to save less from transfers than richer households. Wolff and Gittleman [2014] simulate

variations in the savings behvior from transfers over the wealth distribution and find that almost

small variations translate in the sizeable inequality effects. The effects of such variations are

discussed in greater length in section 3.

There are however already papers available that circumvent the assumption of fully saved

transfers: Elinder et al. [2016] employ a detailed Swedish register data set on wealth that permits

them to construct what they call heir cohorts. A heir cohort is the totality of individuals that

receive an intergenerational transfer in a certain period. The authors observe several of these

heir cohorts and are thus able to compare the inequality in wealth between a heir cohort that

is about to receive their transfers and a cohort that just had received their transfers. As the

heir cohorts are virtually identical in most relevant characteristics (e.g. age, income, ...), the

difference in inequality is attributable to the inflow of transfers. The results of this neat method

6The approach has also been applied by Bonke et al. [2016] for a number of European countries and yieldssimilar results

5

suggest that transfers equalize the relative wealth distribution within the heir cohort by a small

but significant margin. Absolute inequality in wealth is increasing at the same time.7.

The drawback of the work of Elinder and colleagues is certainyl the sample restriction: While

the authors observe all inheritances in a specified period of time, they do not observe a repre-

sentative sample of the Swedish population. The paper however provides what one might so

far call the gold standard in the literature and as its results are in line with many other stud-

ies. The study basically draws on the randomness of the actual inheritance receipt. Another

study similarly benefits from this identification strategy is the one by Garbinti and Georges-Kot

[2016]: The authors rely on the randomness of the exact timing of transfers. They convincingly

show that even good foresight of future transfer receipts does not lead to a full adjustment of

consumption and savings. The authors infer that individuals usually do not adjust to the ex-

pected inheritance amount but - as riskavers agents - to the certainty equivalent of the expected

transfer. The more riskavers a person is, the less will this person actually adjust to expected

transfers - for the actual amount and the timing are uncertain. Risk aversion then can explain

why some studies find economic responses even to expected receipts (e.g. Brown et al. [2010]).

The savings behavior of individuals that expect to receive a transfer is thus highly endogenous

to the actual transfer receipt, its size and its expected accrual. The identification of the causal

effect of transfers on wealth is thus requires knowledge about the expectations of individuals.

A study by Bo et al. [2015] draws from a very detailed Norwegian wealth register data set. The

authors are able to observe heirs and their economic behavior for at least 3 periods before and

6 periods after transfer receipt. Interestingly, some of the economic responses to transfer receipt

only yield measurable reactions some periods after the transfer. This study marks another threat

to the credibility of a estimating a causal effect: The period in which the transfers is received

may just be a snapshot of the actual response of the household. Dynamic considerations play

an important role in measuring how transfers affect the wealth distribution. Also the study by

Elinder et al. [2016] is only able to follow up the effect for some periods. The effect of transfer

wealth on the life time wealth is however not observed.

The study by Bo and colleagues however also stresses the importance of measuring the eco-

nomic response to transfer receipt at all. As already mentioned in the introduction, most studies

in the field abstract from the economic response. They rather assume that transfer wealth is

fully saved and does not alternate the general behavior of the individual. These studies thus

rather estimate the potential wealth that households might acquire by adding the full transfer to

their wealth and by earning the corresponding interest. Most of these studies use the decompo-

sition of the coefficient of variation as suggested by Wolff [2002]. Bonke et al. [2016] apply this

decomposition to the European HFCS data set and find that transfer wealth has a widely equal-

izing effect of net household wealth in 8 European countries. While this method has certainly

its advantages, it may however conceal how transfers actually impact wealth inequality in the

cross-section.

7A similar approach yielding consistent results is employed by Boserup et al. [2016]

6

3 Conceptual approach

As noted before, the literature has widely aggreed on the finding that intergenerational transfers

tend to equalize the wealth distribution of households.8 The reason for this finding is that

relative transfers, measured as the ratio of the transfer amount to the households’ net wealth

before bequest receipt, tend to increase over the wealth distribution:(BhWneth

)τ=s

<

(BhWneth

)τ<s

(3.1)

Where B is the transfer amount, h is the household, W is the households’ wealth and τ is the

quantile of the household in the wealth distribution. As long as this inequality holds, adding

transfers to the wealth distribution will lead to decreasing relative inequality as measured by

standard measurement tools like the Gini coefficient.9 The intuition here is simply: The share of

wealth of poorer households is increasing due to transfers and thus transfers appear to be welfare

improving. The inequality holds as long as the ratio of bequests between, in a stylized example,

the rich and the poor is bigger than the corresponding ratio in wealth:

BsBτ<s

<Wnets

Wnetτ<s

The literature so far has reliably proven that this inequlity is stable when looking at the nominal

terms. Hence, when looking only at the observed transfer amounts B. When households however

do not add B to their household wealth but only a certain share

Bsaved = β ∗B

then it is not clear whether this inequality still holds: Wolff and Gittleman [2014] provide some

exemplary calculations that show that already small differences in β over the wealth distribution

have a strong impact on the effect that B has on the inequality in the wealth of households.

In fact, there are some theoretical considerations that render variations in β likely: First, β

clearly is part of the behavioral response of the household to the wealth shock. As noted in the

literature section, expectations about future inflows of transfer wealth determine this response.

For example, households that adjust their consumption level to an expected transfer long before

the receipt already consume a share of the actual transfer, which is constant over time. In

contrast to that, households that only learn about the transfer in the period of receipt, will

adjust their consumption level only in the period of receipt. The former household will thus

consume a smaller share from the transfer in the receipt period than the latter household. As

8This holds when looking at the relative inequality as measured e.g. by the standard Gini coefficient. Weneglect the absolute wealth inequality at this stage for which results consistently indicate increasing inequality.

9The Gini is used here as it is probably the most well known measure. Of course, using the Gini is however notstraightforward in this context: Negative net wealth in the sample exists and will render the Gini overestimatinginequality (in the sense of being not bound between 0 and 1 anymore). Hence, I always also refer to the coefficientof variation.

7

expectations of future transfers are highly skewed over the wealth distribution, showing that

richer households much more often expect to receive transfers than poorer households, we would

expect to see a smaller propensity to consume from transfers for richer households.

Secondly, there is some evidence that richer households acquire higher returns to their invest-

ments than poorer households Piketty et al. [2014] The structure of the data that is underlying

this study makes it likely that we do not only observe consumption from the transfer in Bsaved

but also returns to the transfer. Hence, also differences in the returns to investments might

translate into a higher β for richer households. Thirdly, a conceivable reason for a higher β for

poorer households is certainly the marginal utility of savings. For some households, transfer

wealth will be the only opportunity to accumulate a stock of wealth capable to insure the house-

hold against negative income shocks. Hence, poorer households might be more willing to save

the entire transfer e.g. as buffer stock saving or as pension security.

The economic response of households to the receipt of transfers thus will affect by what

margin heirs deviate from non-heirs in wealth. In an extreme scenario, a heir might spend the

entire receipt and by this, enjoy from a much higher consumption than an otherwise comparable

non-heir, but might then end up with a similar wealth record as the non-heir. Note that those

papers that assume that the full transfer is saved, i.e. β = 1, usually form net wealth as

Wneth,t = Wh,t − Bh,t. If heirs actually did not save the entire amount, then their estimate Wh,t

will be smaller than the actual wealth before transfer receipt. Thus, these studies are likely to

overestimate the size of the relative transfer. This bias will be bigger, the less of the transfer the

household actually had saved.

Another reason why the above relation might raise a misleading understanding of the effect

of transfers on wealth inequality is the incidence of transfer accrual. Heirs and non-heirs differ

in several respects that affect household wealth Wh: The above relation suggests that also

households in lower quantiles of the wealth distribution receive transfers. However, households

might end up being in a bottom quantile of the wealth distribution due to their age and not so

much because of their actual potential to acquire wealth. Inheritances typically accrue only at a

later stage in life, i.e. above the age of 50. Hence, heir households will tend to be older and thus

at another stage on the life cycle wealth accumulation path than non-heirs. While this difference

might lead to an overestimation of the disequalization through transfers, gifts (which naturally

accrue earlier in life) might lead to an overestimation of the equalizing effect of transfers on

the inequality in the net wealth distribution. In order to identify the true effect of transfers on

wealth inequality, age has to be hold constant between heirs and non-heirs. Another of such

confounding factors is the number of household members10. The more adults life in a household,

the more wealth this household will typically have at its disposal and the more likely will be

receiving a transfer.

Further factors that one might want to keep constant in the analysis are social origin and the

educational status of heirs and non-heirs. However, with these factors, the matter is not that

10Fessler et al. [2014] use the first wave of HFCS data to show sensitive econometric analysis of wealth data isto the household structure.

8

clear: While social origin is certainly a variable that affects the wealth of an individual and that

is also related to the chance of receiving an inheritance, there will always be an effect of social

origin on individuals wealth as long as children are raised by their parents. Even in the absence

of monetary inheritances, there would be some impact of parents on childrens wealth. One might

nonetheless want to quantify the impact of this factor on the difference in wealth between heirs

and non-heirs and so the results section will provide a corresponding estimate.

Next, the educational level of children is likely to be related to parental wealth as parents

might already invest in the childrens education at an early stage in life. Investments in the

education of children will simply be variation of a monetary bequest; a bequest that is likely to

pay off over the working life span of the children and will thus yield high returns. At the same

time, the educational level of heirs also results from the underlying ability level of the children.

While it is not apparent why the heirs should be more able than non-heirs, there might actually

be a genetic component in wealth accumulation. Black et al. [2015] and Fagereng et al. [2015]

analyse the role of genetics in wealth mobility and albeit finding a minor explanatory power,

there seems to be an impact. By keeping educational levels constant between heirs and non-

heirs, this impact is dismissed. However, the bias in the analysis appears to be more severe when

not controlling for such differences.

4 Methodology

The methodological approach in this paper is a variation of the approach used by Biewen and

Juhasz [2012]. It combines the advantages of a decomposition approach with regression tech-

niques. In essence, reweighting the differences between heirs and non-heirs with the decomposi-

tion approach developed by [DiNardo et al., 1996] permits me to keep the full distribution in my

analysis. The reweighting results are then adjusted with point estimates from regressions that

aim at quantifying the propensity to save out of transfers. In order to allow for variations in

this β coefficient, I will also apply quantile regression techniques as suggested by Koenker and

Basset [1978].

4.1 Regression

The households’ response to the transfer receipt is captured by the share of the transfer sum that

is contributing to the households’ saving effort in a given period. In this study, the share of the

transfer amount that is saved is estimated by regressing the savings flow ∆W = Wh,t−Wh,t−1 on

the transfer amount and a set of control variables. The resulting estimate of interest β thus can

be interpreted as the share of an inherited Euro that is added to households wealth. A similar

specification is estimated by Karagiannaki [2015] and Wolff [2015]:

∆Wh,t = α+ β1Bh,t + β2B2h,t + γBDh,t + δf(Ageh,t) + ηf(Inch,t) + θv,t + εh,t (4.1)

9

Where the age of the household head and the monthly net household income (Inc) enter with

a linear, a squared and a cubic term. θ is a dummy set that controls for the wealth type v of

households interacted with year dummies. This construct is meant to filter out price variations

in wealth over time. ε is the usualy white noise error term.

The parameter of interest is β which indicates by what margin household savings increase given

a one unit increase in the transfer amount. The estimation approach differs from Karagiannaki

[2015] and Wolff [2015] in that it does not restrict the effect of inheritances to be linear on

savings. Hence, equation 4.1 allows the transfer amount to show e.g. a positive albeit decreasing

effect on savings. For the sake of readability, transfer amounts are expressed in 10.000 Euros.11

BDh,t is a dummy equalling one when a household receives a transfer in period t. Note that

this is an implicit interaction with the transfer amount as both variables are zero at the same

time. The corresponding parameter γ introduces another form of non-linearity in the estimation

of the savings effect and distinguishes heir households from non-heirs in the period of receipt.

The interpretation of the parameter is the marginal change in the savings of a household given

a transfer receipt of zero. As only a share of the households in the sample receive a transfer,

using this dummy is an important mean to differentiate between receivers and non-receivers.

The nature of implicit interactions requires however that β1,2 and γ are interpreted jointly.

The estimation strategy differs in another respect from the estimations in Karagiannaki [2015]

and Wolff [2015]: In order to control for unobserved time invariant heterogeneity, equation 4.1

is also estimated in a first difference fashion. This procedure appears particularly reasonabel as

heirs and non-heirs are naturally likely to differ by background characteristics difficult to control

for.

As argued in section 3, expectations about future receipts are likely to affect β. There are no such

control variables in the baseline model. Section ?? however investigates whether expectations

are a threat to the identification strategy.

4.2 Quantile regression

In order to identify variations in the households’ savings response to transfer receipt, this study

resorts to the quantile regression approach as initially introduced by Koenker and Basset [1978].

By doing so, equation 4.1 yields estimates for each quantile of the conditional savings distribution

τ .

Qτ (y|x) = x′βτ

Note that the quantiles of the conditional savings distribution are not expected to correspond

to the quantiles of the wealth distribution. The quantiles τ result from weighting household

observations according to the absolute deviation between households’ savings and the estimated

11The parameter estimates of the squared transfer amount term are typically numerically very small parameter.

10

conditional quantile of the savings distribution.

βτ = arg minβ

∑i

ρτ (yi − xiβ)

Where ρ is the weighting scheme. That is, the estimates βτ do not directly lead to the B refer-

ring to the specific Wh,t of equation 3.1. In this study, we however relate the estimated βτ to

the conditional savings distribution and then to the wealth distribution Wt−1 (see 4.4). Hence,

even when the βτ as estimated from the conditional savings distribution is not informative about

the savings behavior of households in the corresponding wealth distribution, the current method

reflects a suitable change in the households wealth distribution Wh,t. Hence, while the pattern of

βτ might be misleading in describing the variation in β over the wealth distribution, the results of

the simulation study are not biased and well reflect how variations in treating transfers translate

into certain levels of inequality.

Note that naheliegende (XX) alternatives are not necessarily an improvement to this procedure:

Estimating the set of explanatory varibles in 4.1 on the stock of wealth dismisses that explaining

the stock of wealth with the flow of inheritances might contradict economic intuition. Also,

in absence of a rank preservation assumption, a households quantile in the conditional wealth

distribution might well deviate from its quantile in the unconditional distribution.

This controversy might be the reason why Karagiannaki [2015] tries to infer on variations in

the savings behavior of households from different quantiles from the unconditional wealth distri-

bution by adding indicator variables as explanatory variables to the estimation that reflect the

households quantile in the unconditional wealth distribution. This proceddure is tested in this

paper in ?? as a robustness check to the main estimation. The reader however should notice

that the estimation of the paramters of this dummy set is highly problematic: Even though

Karagiannaki [2015] uses only the households’ wealth quantile of the lagged wealth distribution,

the indicators are likely to contain information on the dependent variable savings (which are

constructed from wealth differences) and are thus likely to be endogenous.

In order to retrieve a more precise estimate of the actual differences in saving over the savings

distribution, the quantile regression is also conducted in a first-differenced way.

4.3 Reweighting

The reweighting approach bases on DiNardo et al. [1996] and is often used to retrieve counter-

factual distributions. For instance, comparing the wealth distribution in Germany in period t

and in t+ 1, I could use this approach in order to show how the wealth distribution in t would

have looked like, when inheritances would have accrued as they did in t + 1. I however use the

approach to reweight the differences in characteristics between individuals of two groups. Hence,

I want to get the wealth distribution of heirs that would prevail if the group of heirs would have

the same age structure as non-heirs. This is done by reweighting certain observations of the

heirs according to whether they are under- or overrepresented in the non-heir distribution. We

11

can express the underlying idea using integral notation in the following way: Let fh(w) be the

wealth distribution of heirs, denoted by h:

fh(w) =

∫fh(w|x)dFh(x)

Then the counterfactual distribution is

fhnh(w) =

∫fh(w|x)dFnh(x)

∫fh(w|x)

[dFnh(x)

dFh

]dFh(x)∫

fh(w|x)ΨxdFh(x)

in which nh denotes non-heirs and where Ψ is defined as

Ψx =P (Dnh = 1|x)

P (Dnh = 0|x)· P (Dnh = 0)

P (Dnh = 1)

Note here that P (Dnh = 1|x) can be estimated by a logit and P (Dnh = 0|x) simply reflects

the sample share of the heirs and non-heirs. Multiplied with the sample weights, the resulting

vector Ψx reweights the wealth distribution of heirs so that it represents the wealth distribution

of heirs that would have prevailed if heirs resembled the non-heirs in the x dimension. Specif-

ically, I calculate reweighting schemes for reweighting the age structure of heirs, the number of

household members and the educational attainment of heirs and denote the corresponding Ψ

correspondingly.

4.4 Combinig reweighting and regression

Combining the two approaches allows me to estimate the counterfactual distribution of the wealth

of heirs that would have realized if heirs had the same age structure, household structure and

education as non heirs and had never received the inheritance. I follow here the approach by

Biewen and Juhasz [2012].

f(w|Dk) = θΨageΨHHΨeduK

(w − (wh,t−1 + (∆wh,t − ˆbh,τ

sav))

bw

)1

bw(4.2)

where θ are the sample weights, ΨD denotes the estimated reweighting schemes, K denotes the

kernel density estimator and bw the corresponding bandwidth. Note further that we adjust the

observed household wealth by subtracting the share of wealth that is estimated to stem from the

transfer amount:ˆbh,τ

sav= βτ ∗Bh

12

This estimate gives us the share of the savings ∆wh,t attributable to the receipt of the intergen-

erational transfer. By subtracting this share, we get a counterfactual estimate ˆwh,t of the net

wealth of household h from the conditional quantile τ in period t without transfers. Hence, using

equation 4.2 we are estimating the density at point w given the reweighting structure as the

reweighted Kernel density estimate and the bequest correction. The difference between w and

data point wh then determines how much that particular data point contributes to the density

at w.

5 Data

The analysis is based on data from the Socio-Economic Panel (SOEP) which is a representative

longitudinal panel study in Germany covering 11.000 households each year. The data contains

information on wealth stocks on the individual and household level for the survey waves of 2002,

2007 and 2012. Wealth covers real estate holdings, financial wealth (savings, stocks and shares,

any type of private insurance based wealth12), company assets, tangbile assets and any kind of

debts. This study resorts to the net wealth (assets minus debts) of households only, which is

expressed in Euro prices of 2010.

The data is edited and imputed (5 implicates) and thoroughly treated according to Rubin [1987].

The imputation approach helps to address non-random item non-response issues which commonly

occur with survey data on wealth [Vermeulen, 2014]. The SOEP imputation strategy is docu-

mented in Frick et al. [2010].

Inheritances and gifts are systematically surveyed in the SOEP on the household level on a yearly

basis since 200113. Until 2004 the SOEP only covered transfers above 2500 Euro. Since 2005 all

transfers above 500 Euros are reported. The results of this paper are however robust to these

small variations14. In addition to that, the data also contains retrospectively collected responses

on transfers received prior to 2001.

Despite the commonly acknowledged high quality of SOEP data, some concerns are remaining:

Vermeulen [2014] shows that even imputations and weighting strategies might fail in representing

the top 1% of the wealth distribution.15 In the same way, non-random item non-response issues

may also occur in the inheritance data where the remedy of imputations is lacking. The SOEP

is nonetheless the data source that meets the requirements of this analysis best. Tax data for

wealth and inheritances are either absent at all or only apply to the very top of the distribution.16

The data availability requires some aggregation of the data: Transfer data are aggregated over

12The data does not cover claims against public insurances as e.g. public pensions.13The question about the reception of transfers always refer to the previous year, i.e. in 2001 transfers of 2000

are recorded. I took this aspect into account when merging so that 2001 data also now refers to transfers receivedin 2001.

14I do not address this minor truncation issue in the model.15Further work on the full depiction of household wealth dealing with non-observation bias and differential

non-response bias is provided by e.g. ?, or with special focus to Germany by ?16Generally, the transfer values in the SOEP are assumed to be net of taxes, as these are the values that heirs

finally have received.

13

the 5 years prior to the observations of wealth in 2002, 2007 and 2012 respectively. As transfers

are only observed on the household level, the analysis resorts to the household level wealth data

in the SOEP. This yields a data set with 3 time periods containing the household wealth and

the aggregated intergenerational transfers accruing in the 5 years prior to a wealth observation.

The time span covered reaches from 1997 (xx or 1998?) to 2011 for transfers and 2002 to 2012

for household worth.

Where the analysis resorts to individual characteristics as e.g. age, marital status and educa-

tional attainment, the characteristics from the household head are used.

As argued below, controlling for expectations about future transfer receipts plays a role in the

identification of the response of households to transfer receipt. The SOEP in fact has surveyed

in 2001 whether households expect to receive a transfer and if so, whether they consider the

receipt likely. Note however that using this variable means to exclude receipts before 2001 from

the analysis. Also, the variable has only been surveyed in 2001. Expectations might change

quickly and it is thus unclear how informative the snapshot information from 2001 is for trans-

fers that accrue several years after. This is why expectations are neglected in the main analysis,

the robustness section ?? however will resume the topic.

6 Descriptive Statistics

This section is supposed to give the reader a brief overview of the key variables in the sample

underlying the analysis. Note that the current analysis does not restrict the sample by any

means: Households from east and west Germany are used and furthermore, there are no restric-

tions on the wealth or inheritance data that is used. In order to show that the results do not

depend on outliers, there is section ?? that replicates the main results with restricted sets of data.

Table 1 summarizes the accrual pattern of intergenerational transfers over the wealth dis-

tribution. Panel a of Table 1 shows that roughly 10% of the households report to receive a

transfer within a perdiod of 5 years. The share of these households seems to be increasing over

time, which might already reflect the increasing relevane of inheritances that some scholars await

[Piketty and Zucman, 2015]. A more pronounced finding is that the share of heir households is

consistently increasing over the wealth distribution. This pattern has also been found by Wolff

and Gittleman [2014] for the US and by Karagiannaki [2015] for the UK. The more frequent

accrual of inheritances in higher wealth quantiles is typically judged to be a major cause for the

finding that intergenerational transfers tend to enhance absolute wealth inequality.

Panel b reports the absolute numbers of inheritances over years and wealth quantiles that

are reported in the SOEP. Hence, the analysis conducted in this paper is build on the absolute

number of 2142 observed transfers.17

17These transfers can either be inheritances or gifts. As differentiating between the two has not changed the

14

Table 1: Descriptive Statistics - Transfer accrual

2002 2007 2012 Total

a.Share of receiving households [%]Transfers 5.85 9.81 11.90 9.40

1st wealth quintile 2.23 2.56 6.14 3.86

2nd wealth quintile 3.23 6.12 9.38 6.33

3rd wealth quintile 6.90 10.13 13.52 10.77

4th wealth quintile 6.97 13.97 14.95 12.1


b. Number of receiptsAbsolute count 587 874 681 2142

1st wealth quintile 43 53 72 168

2nd wealth quintile 61 101 91 262

3rd wealth quintile 133 180 159 451

4th wealth quintile 146 243 186 577

5th wealth quintile 204 297 173 684

Results base on SOEP v30, own calcualtions.

Table 2: Descriptive Statistics - Transfer amount

2002 2007 2012 Total

a. Mean acqusition (Euro), conditional on receiptAmount 81956.82 61113.54 83204.97 75490.85

1st wealth quintile 34617.72 18375.65 11580.29 16993.73





b. Relative mean acquisitionAmount 1.0924 0.6511 .6015 0.7077

1st wealth quantile -3.5607 -4.665431 -4.7275 -4.6107





c. Transfer size (Euro)

10th percentile 5643.34 3121.75 1969.88 2699.10Median 28569.95 15993.88 16842.71 16930.02

90th percentile 203160.30 175842.60 233194.6 192123Mean 81956.82 61113.54 83204.97 75490.85Std. deviation 153464.8 130945.6 195459.8 153573.8


results of this study, only their aggregate number is provided here.

15

Table 2 further describes the key explanatory variable. Panel a reports the mean transfer

amounts in Euro for all the receiving households. The transfer sum tends to increase over the

wealth quantiles which adds to the effect that transfers increase the absolute wealth inequality.

Panel b reports the relative mean acquisition sizes, i.e. the ratio of the transfer amount to the

stock of wealth housholds had before the receipt of the transfer. It provides the key intuition why

transfers tend to decrease the relative wealth inequality: Relative transfers tend to be bigger for

poorer households and thus increase the share of total wealth that belongs to the poorer part of

the wealth distribution. The marginal utility from these transfers, so to say, is higher for poorer

households.

Panel c then provides some summary statistics for intergenerational transfers.

Table 3 provides summary statistics for the dependent variable of the analysis: The values

reflect net wealth as the sum of assets and liabilities and include also already the transfer sum (if

transfers accrued). The households median wealth revolves around 48.000 Euro over the sample

period. The mean in wealth is slightly decreasing from 2002 to 2012 and on average equals

150.000 Euro. The apparent gap between mean and median wealth reflects the strong positive

skew of the wealth distribution.18

Panel b of the table contrasts the mean and median wealth of heirs and non-heirs. As expected,

heirs consistently show a higher mean wealth than non-heirs. While this pattern might vary

when looking at different age groups, it broadly reflects what the intuition suggests: Heirs tend

to be richer than non-heirs as they have already received a monetary transfer. They also tend to

be richer than heirs as they furthermore differ in some more respects from non-heirs that tend

to influence wealth strongly: Heirs are on average older than non-heirs and life in significantly

bigger households. They are on average also better educated and earn higher wages.19

7 Results

7.1 Regression results

First of all, I present the evidence of estimating equation 4.1, i.e. the flow model of savings

regressed on the linear transfer amount and control variables. Note that the estimates stem from

a model that is similar to those estimated by Karagiannaki [2015] and Wolff [2015]. The non-

linear specification however entails that only the estimated average marginal effects are directly

comparable to the estimates.

Table 3 reports four estimations: The first reports the results from a simple OLS estimation of

the savings flow on a linear and a squared term of transfer amounts. The average marginal effect

of column 1 suggests that an inheritance of 10.000 Euro on average yields an increase of savings

by roughly 8700 Euro. The estimate of parameter γ hints that this effect is only to expect above

18Note that the wealth data is imputed. The displayed numbers reflect the mean over the 5 implicates. xx19The reader is very right in expecting another table here that puts these differences in perspective. The table

will certainly be included in time for the conference. (xx)

16

Table 3: Descriptive Statistics - Wealth

2002 2007 2012 Total

a. Wealth descriptive statisticsMedian 45146.73 51612.91 47790.59 47968.40Mean 165505.9 153668.5 132362.2 149260Min -4897798 -1510926 -982593.6 -4897798Max 12799097 7592340 9855908 12799097Std. deviation 420414.1 331331.8 252548 336040.4

b. Heirs vs. non-heirsMean (heirs) 223766.30 224748.00 194792.70 211052.00Mean (non-heirs) 155310.10 137936.00 114191.90 134951.60Median (heirs) 116252.80 136316.30 110470.70 119692.60Median (non-heirs) 34988.71 38868.89 29586.94 33860.05

c. Wealth quintiles

20th wealth quantile 0 0 0 0

40th wealth quantile 17311.51 22892.82 18059.56 19212.29




transfer sizes of 30.000 Euros. However, as we will see, particularly γ tends to vary strongly

over the wealth distribution. The parameters are jointly significant on the 1% level. As wealth

is only observed every 5 years, these estimates represent a weighted average of the (unobserved)

annual β estimates. Thus, calculating the annual propensity to consume out of transfers yields

for large receipts (1−β)/2.5 = 5.2, i.e. 5.2% of the transfer are on average consumed per annum.

The estimates take values well comparable to those in the literature: Karagiannaki [2015] gets

β ≈ 0.67 for a sample in which at maximum 9 years are between inheritance receipt and wealth

observation. Wolff [2015] estimates β ≈ 0.8 with annual data.

All these estimates describe an immediate effect of transfers on savings in that they do not allow

for any dynamics in the households’ responses. The immediate respose of households however

is well described, suggesting that a non-negibible share of the transfers is actually consumed

instantaneously. These results confirm previous doubts on the accuracy of the plain deduction

approach by e.g. Wolff [2002] or Kohli et al. [2006]. A bias in the effect of transfers on the

inequality in household wealth would only be likely, if the propensity to consume actually differs

significantly over the wealth distribution as hypothesized by Wolff and Gittleman [2014].

The corresponding results from a quantile regression of equation 4.1 reported in column 2 of table

6. The pattern of estimates resembles that of the quantile regression results of Karagiannaki

[2015] as they suggest an increasing propensity to save out of transfers over the conditional

savings distribution. While her estimates are consistently higher, the pattern here is consistent

with her findings.

The data structure of the SOEP permits to estimate this model also in a fixed effects framework,

17

thus controlling for time invariant unobserved heterogeneity. The results of the mean estimation

are reported in column 4 and only deviate slightly from the cross-sectional estimate. Most

interestingly, the corresponding quantile regression20 then yields a very different pattern of saving

behavior over the conditional savings distribution thatn the cross-sectional estimation: In fact,

the savings pattern reverses fully, suggesting that households at the bottom end of the conditonal

savings distribution show the highest propensity to save out of transfers. The higher the quantile

in the conditional distribution, the lower the estimated propensity to save out of the transfer. It

is furthermore noteworthy that there is a significant difference in the quantile estimates.

Table 4 then displays the estimates of the same model simply estimated on the stock of

wealth. The first two columns show the stock model estimated without a further lag of the

transfer variables and only serves the purpose of being comparable to the results in table 3. It

is apparent that the estimates are much smaller, indicating that the stock of wealth reacts less

to the receipt of transfers. Note that we can interpret these estimates as long-term estimates

suggesting that 1/3 of the stock of wealth is related to transfer wealth. These estimates are very

close to the results by Bonke et al. [2015] who calculate the share of transfer-based wealth in

total household wealth following a method by Piketty et al. [2014]. Column 2 of table 4 also

suggests that there is virtually no variation in the share of wealth coming from transfers over

the quantiles of the conditional wealth distribution.21 The last two columns of table 4 then

present estimates from equation 4.2, i.e. a dynamic specification of the effect of transfers on

the stock of household wealth. Column 4 then reads as follows: Looking at the short term, the

share of household wealth that is attributable to inheritances is only varying marginally over the

conditional wealth distribution. Households in the bottom decile tend to hold wealth of which

2/5 are tracable to inheritance receipts. This share slightly decreases over the conditional wealth

distribution eventually equalling slightly less than a third for the top decile.

7.2 Counterfactual distributions

In order to estimate the counterfactual distribution, I now use the βτ estimates from the fourth

column of table 4. The FD estimation of the quantile regression should yield the most reliable

estimates of the saving behavior from transfers. I subtract transfers from the current period

with the individually predicted share of the savings that households had saved from the transfer.

This share is described by the β estimates.

Figure ?? displays the wealth distribution of heirs net of the monetary transfers that they

received during the sample period (1998-2012). Recall that I subtract only the share bsav from

20As it is not possible to combine FE estimations with quantile regression techniques, I estimated a quantileregression on first differenced variables.

21The reader has to keep in mind that quantile regressions use the conditional quantils instead of the quantilesof the unconditional distribution. Hence, as we do not use any rank preservation assumption, households that areat the bottom decile in the unconditional distribution might not need to be at the bottom decile of the conditionaldistribution. See Koenker and Basset [1978] for further information on quantile regressions.

18

Table 4: Flow regression - OLS and Quantile regression results

OLS QReg FE QReg+FD

Dependent Variable: Savings flow (∆W )

Selected Explanatory Variables (Amount in 10.000 Euro):Transfer Amount 8762.57*** 6263.48***

(2534.49) (2240.87)Transfer Amount squared -65.13*** 5.57

((18.20)) (23.67)Transfer Dummy -32385.17** -8703.98

(12667.87) (20662.90)10th QuantileTransfer Amount -2608.72 10431.76***

(1609.84) (1755.22)Transfer Amount squared -30.05*** -74.28*

(9.05) (38.61)Transfer Dummy 3776.83 -67260.41**

(11920.95) (26525.04)30th QuantileTransfer Amount 4590.97*** 6264.35***

(645.23) (472.51)Transfer Amount squared -61.30*** -15.37**

(3.17) (5.84)Transfer Dummy -4785.34 -20609.99***


(748.63) (448.55)Transfer Amount squared -52.04** -23.08***

(18.49) (5.18)Transfer Dummy -6518.21** -5314.82


(500.11) (632.20)Transfer Amount squared -23.30*** 10.15

(3.42) (14.15)Transfer Dummy -7788.88** 6619.37


(1206.04) (1608.07)Transfer Amount squared 8.54 242.61***

(10.50) (34.51)Transfer Dummy -9501.92 8779.08

(7891.98) (22733.13)Number of Observations 11732 11732 4889 48891 Control variables: All models are estimated conditional on age, household income and price effects.2 Estimated with robust standard errors.3 Estimations are based on a multiple imputed dataset (5 imputations).4 Estimations are based on SOEP v30.5 Coefficients marked with *,**,*** are statistically significant on the 10, 5, 1 percent significance level.

the observed wealth as this is the share of wealth that is estimated to stem from the observed

inheritances. The result in Figure 1 does not include the reweighting.

Figure 1 shows that subracting the share of transfers that is attributable to observed inheri-

tances only has a small effect. The dashed line of corrected wealth is only slightly below the solid

line of the factual wealth of heirs. The most density mass has been shifted to the area close to

zero. The simple reason for this effect is probably that many households only have accumulated

wealth in the size of their transfers. Subtracting transfers thus pushes these households back

19

Figure 1: Factual wealth of heirs vs wealth net of (full) transfers

01.

000e

-062.

000e

-06 3.

000e

-064.

000e

-06 5.

000e

-06

dens

ity: O

rigin

al d

ata,

ie. m

=0

-200000 0 200000 400000 600000 800000Original data, ie. m=0

Factual heirs Counterfactual heirs without reweighting

Figure 2: Factual wealth of heirs vs estimated counterfactual

01.

000e

-062

.000

e-06

3.00

0e-0

64.0

00e-

065.

000e

-06

-200000 0 200000 400000 600000 800000

Factual heirs Counterfactual heirs without reweighting

01.

000e

-062

.000

e-06

3.00

0e-0

64.0

00e-

065.

000e

-06

-200000 0 200000 400000 600000 800000

Factual heirs Counterfactual heirs (transfer and age)

01.

000e

-062

.000

e-06

3.00

0e-0

6 4.0

00e-

065.

000e

-06

-200000 0 200000 400000 600000 800000

Factual heirs Counterfactual heirs (transf., age, HH)

01.

000e

-062

.000

e-06

3.00

0e-0

64.0

00e-

065.

000e

-06

-200000 0 200000 400000 600000 800000

Factual heirs Counterfactual heirs (transf, age, HH, edu)

close to having no wealth at all.

Figure 2 then adds to the subtraction of monetary transfers the effect of the reweighting factors.

It thus shows the wealth distribution of heirs that would prevail if heirs had the same age struc-

ture, the same household structure and the same educational attainment as non-heirs and had

not received the observed monetary transfer.

20

Figure 3: Factual wealth of non-heirs vs counterfactual heirs (≈ non-heirs)

05.

000e

-06

.000

01.0

0001

5

-200000 0 200000 400000 600000 800000

Factual non-heirsCounterfactual heirs

05.

000e

-06

.000

01

-200000 0 200000 400000 600000 800000

Counterfactual: Wealth distribution without transfersFactual wealth distribution (incl. all transfers)

The differences between factual wealth distribution of heirs and the estimated counterfactual

are now much clearer. The differences between heirs and non-heirs in wealth are thus attributable

to a large share to age, household structure and educational attainment.

Finally, Figure 3 presents the full wealth distributions of heirs and non-heirs together. The

actually observed distribution is depicted by the solid line while the dashed line shows the dis-

tribution under the condition that heirs’ wealth is as if heirs had never received any kind of

transfers. Figure 3 rather serves the purpose to illustrate the total effect. The effect size is how-

ever clearer when expressing the effects in inequality indices. Table 5 summarizes the results:

The first panel compares the actually observed inequality in household wealth as measured by

the Gini and the coefficient of varition (CV) to the inequality that we measure after having only

subtracted the full transfer amount from the observed net wealth distribution. Thus, the effect

of 1.5 Gini points are what we have denoted the mechanical effect of transfers on wealth. The

following panel then compares again the observed inequality with the inequality that results from

subtracting transfers in the size as suggested by the FE estimates in table 4, column 2. This

panel displays an effect that is theoretically comparable to the analysis that e.g. Elinder et al.

[2016] implement as it recovers the actual share of transfer-based wealth. The size of the effect

is then also quite in line with their results and suggests that the immediate effect of transfers on

the inequality in wealth is equalizing.

The estimates in Panel b are also very similar over the wealth distribution and are thus particu-

larly useful for showing the distributional impact of the reweighting scheme. For the equalizing

effect of transfers that we observe in panel b is due to the monetary transfers only; panel c how-

ever then additionally uses the reweighting scheme. Hence, subtracting transfers and reweight

observations suggests that intergenerational transfers actually disequalize the wealth distribution.

Panel d and e of Table 5 then show the final stage of the analysis: Here, I also reweight all

heirs that have reported to have received transfers in the periods before 1998 (i.e. the time for

which we do not observe wealth). These heirs also report the time and the transfer size of those

past transfers. I deduct transfer amounts in the scope suggested by the estimation results in

21

Table 5: Actual wealth inequality and inequality in counterfactuals:

Gini CV p90/p25 p75/p25 p75-p25

a.BasisIndex .7339 2.2522 7.9103 95.9596 195650Standerd error .002 .0273 .1133 6.92 1323.825Difference to basis 0 0 0 0 0P-value of difference 1 1 1 1 1

a.Mechanical effectIndex .7643 2.3677 8.9914 964.0896 185032Standerd error .0022 .024 .111 6.0374 1256.965Difference to basis -.0304 -.1155 -1.0811 -868.13 10618P-value of difference .6419 .6101 .0225 0 0

b.Adjusted transfersIndex .8309 2.6312 7.4 158.5883 189227Standerd error .0141 .0652 .1477 32.3487 1406.046Difference to basis -.097 -.379 .5103 -62.6287 6423P-value of difference .4453 .2127 .3179 0 0

c.Transfers and ageIndex .8437 2.9251 7.3892 102.7962 193520Standerd error .0158 .1469 .1995 21.5036 1683.351Difference to basis -.1098 -.6729 .5211 -6.8366 2130P-value of difference .411 .1069 .3515 .1998 0

d.Transfers, age and HHIndex 1.1124 4.3177 4.0435 20.7728 251452Standerd error .0896 .5229 .1851 1.6201 3475.411Difference to basis -.3785 -2.0655 3.8668 75.1868 -55802P-value of difference .2111 .0054 0 0 0

e.Transfers, age, HH and educationIndex .8738 3.0055 7.2216 101.8611 193777Standerd error .0248 .1601 .1891 11.165 1589.715Difference to basis -.1399 -.7533 .6887 -5.9015 1873P-value of difference .3931 .0818 .2105 .1653 0

Note: Difference is calculated as index basis - index counterfactual, hence: positive

differences reflect that inequality is smaller in the counterfactual; negative differences

indicate that inequality is bigger in the counterfactual.

22

table 4 column 4. The effects as displayed in Panel d are sizeable: The gini coefficient increases

by more than 10 gini points. Hence, relative wealth inequality witnesses a sizeable increase when

deducting transfers. At the same time, the absolute wealth inequality as described by the most

right column shows a significant reduction in inequality.

8 Robustness

8.1 Expecting bequests

Theoretical approaches that base on the lifecycle hypothesis will consistently predict that ex-

pectations about future transfers play a key role in explaining the economic response to bequest

receipt. The literature has however not yet been able to draw a consistent picture of the role of

expectations: Looking at the effect of transfer receipt on retirement behavior Brown et al. [2010]

find that unexpected transfers lead to a stronger anticipation of retirement entry than expected

transfers. While this finding would well fit to theoretical predictions, the estimates fail to be

statistically different to each other. Elinder et al. [2016] compare the effect of receipts resulting

from a typically sudden death cause (e.g. an accident) to causes that only lead to death after

some time (e.g. specific illnesses). The authors also do not succeed in establishing a difference in

the effect between expected and unexpected transfers. Doorley and Pestel [2016] use the same

data as the current study and assess labor supply patterns after transfer receipt. They do not

find evidence that expectations alter the immediate response to receipts. They however find that

expectations matter for when (i.e. in later periods) heirs adjust their behavior.22

8.2 Results from the unconditional wealth distribution

As discussed in 4.2, the main results of this study base on the quantile regression estimates

drawing on the conditional savings distribution. Similar to Karagiannaki [2015], one might

however also resort to a set of dummy variables indicating the quantile of a household in the

unconditional wealth distribution. This subsection will present β estimates of the savings reaction

of households to transfer receipt basing on the unconditional wealth distribution. It will also

show whether these estimates entail other effects of transfer on wealth inequality than those

presented and discussed in the main analysis.

8.3 Without capitalization of bequests

results

22XX Some more papers have looked at expectations, the results remain however inconclusive:

23

Table 6: Flow regression - OLS and Quantile regression results

Expect Expect FD Anticipate Anticipate FD

Dependent Variable: Savings flow (∆W )

Selected Explanatory Variables (Amount in 10.000 Euro):Transfer Amount 5916.71** 5970.06** 8669.19*** 6360.48***

(2734.83) (2311.53) (2568.92) (2197.60)Transfer Amount squared -62.93*** 3.89 -64.69*** 6.19

(11.03) (24.44) (18.37) (23.76)Expect 127.73 73678.63**

(11095.04) (32495.53)Expect * Amount -1304.97 53311.14

(5397.21) (51934.99)Expect * Amount squared 87.26* 1537.33

(44.83) (1649.38)Transfer Dummy -27309.92* -5884.75 -32082.70** -2752.15

(14947.18) (20175.97) (13140.85) (17198.69)Expect * Transfer 13704.26 -355373.88

(26010.56) (248368.95)1.Expect * Anticipate -6998.68 12342.52

(42157.53) (18750.31)2.Expect * Anticipate -18801.24

(22241.65)3.Expect * Anticipate 10487.10



(120350.31)Number of obs 11644 4853 11644 48531 Control variables: All models are estimated conditional on age, household income and price effects.2 Estimated with robust standard errors.3 Estimations are based on a multiple imputed dataset (5 imputations).4 Estimations are based on SOEP v30.5 Coefficients marked with *,**,*** are statistically significant on the 10, 5, 1 percent significance level.

9 Conclusion

The paper utilizes data from the SOEP from Germany in order to estimate the causal effect

of intergenerational transfers on the inequality in households’ net wealth. The effect is identi-

fied by comparing the factual wealth distribution with the wealth distribution that would have

prevailed if heirs would resemble non-heirs in the age structure, the household structure and

their educational attainment and would never have received the monetary transfers. I retrieve

this counterfacutal distribution by combining a reweighting approach [DiNardo et al., 1996] with

quantile regression techniques [Koenker and Basset, 1978].

Specifically, I deduct the share of savings from the observed savings of heirs that is attributable

to the receipt of inheritances. This particular share is identified quantile-wise using the quantile

regression approach. The causal effect of the monetary transfers on wealth inequality is then

identified by first evening out other differences between heirs and non-heirs using the reweighting

approach.

The results of my analysis suggest that there is indeed an equalizing effect of intergenerational

transfers on the inequality in household wealth in the short term. That is, an annual bequest

inflow will tend to equalize the relative wealth distribution. This effect is mainly attributable to

24

the heterogeneity in the savings behavior of households: In fact, the lowest 10% of the conditional

savings distribution tend to save about the entire wealth transfer. Whereas at higher quantiles

of the conditional wealth distribution, these saving shares are much smaller.

Despite the significant progress that the literature has recently taken on the study of inter-

generational transfers, there are some open questions left: In particular the study of the dynamic

effects of transfers on heirs’ wealth appear worthwhile. Current studies focus mostly on short

term effects and might thus actually miss to identify the true effect of transfers on the wealth

distribution.

References

Martin Biewen and Andos Juhasz. Understanding rising income inequality in germany, 1999/2000

- 2005/2006. The Review of Income and Wealth, 58(4), 2012.

Sandra E. Black, Paul J. Devreux, Petter Lundborg, and Kaveh Majlesi. Poor little rich kids?

the determinants of the intergenerational transmission of wealth. IZA Discussion Paper Series,

9227, 2015.

Timm Bonke, Giacomo Corneo, and Christian Westermeier. Erbschaft und eigenleistung im

vermogen der deutschen: Eine verteilungsanalyse. Diskussionsbeitrage Economics Freie Uni-

versitat Berlin, (10), 2015.

Timm Bonke, Marten von Werder, and Christian Westermeier. How wealth transfers shape the

wealth distribution: An international comparison. Discussion Paper Series School of Business

and Economics, (17), 2016.

Erlend E. Bo, Elin Halvorsen, and Thor O. Thoresen. Heterogeneity of the carnegie effect. CESifo

Working Paper, (5339), May 2015.

Simon Boserup, Wojciech Kopczuk, and Claus T. Kreiner. The role of bequests in shaping wealth

inequality: Evidence from danish wealth records. American Economic Reivew: Papers and

Proceedings, 106(5):656–661, 2016.

Jeffrey R. Brown, Courtney C. Coile, and Scott J. Weisbenner. The effect of inheritance receipt

on retirement. The Review of Economics and Statistics, 92(2):425–434, 2010.

Rowena Crawford and Andrew Hood. A tale of three distributions: inheritances, wealth and

lifetime income. IFS Working Paper, 2015.

John DiNardo, Nicole Fortin, and Thomas Lemieux. Labor market institutions and the distri-

bution of wages, 1973-1992. Econometrica, 64(5), 1996.

Karina Doorley and Nico Pestel. Labour supply after inheritances and the role of expectations.

IZA Discussion Paper, 9822, 2016.

25

Mikael Elinder, Oscar Erixson, and Daniel Waldenstrom. Inheritance and wealth inequality:

Evidence from population registers. CEPR Discussion Paper Series, 11191, 2016.

Andreas Fagereng, Magne Mogstad, and Marte Ronning. Why do wealthy parents have wealth

children? Statistics Norway Discussion Paper Series, 813, 2015.

Pirmin Fessler and Martin Schurz. Private wealth across european countries: the role of income,

inheritance and the welfare state. European Central Bank Working Paper Series, 2015.

Pirmin Fessler, Peter Lindner, and Esther Segalla. Net wealth across the euro area - why

household structure matters and how to control for it. 2014.

Joachim R. Frick, Markus M. Grabka, and Jan Marcus. Editing und multiple imputation der

vermogensinformation 2002 und 2007 im soep. Technical report, DIW Berlin, 2010.

Bertrand Garbinti and Simon Georges-Kot. Time to smell the roses? risk aversion, the timing

of inheritance receipt, and retirement. 2016.

Eleni Karagiannaki. The impact of inheritance on the distribution of wealth: Evidence from

great britain. Review of income and wealth, 2015.

R. Koenker and G. Basset. Regression quantiles. Econometrica, 46(1), 1978.

Martin Kohli, Harald Kunemund, Andrea Schafer, Jurgen Schupp, and Claudia Vogel. Erb-

schaften und ihr einfluss auf die vermogensverteilung. Vierteljahreshafte zur Wirtschafts-

forschung, 75(1), 2006.

T. Piketty, G. Postel-Vinay, and J-L. Rosenthal. Inherited vs. self-made wealth: Theory and

evidence from a rentier society. In Explorations in Economic History, volume 51. North-

Holland, 2014.

Thomas Piketty. On the long-run evolution of inheritance: France 1820 - 2050. Quarterly Journal

of Economics, 126(3):1071–1131, 2011.

Thomas Piketty and Gabriel Zucman. Handbook of Income Distribution, volume 2B, chapter

Wealth and Inheritance in the Long Run. Elsevier, 2015.

D. B. Rubin. Multiple Imputation for for Nonresponse in Surveys. J. Wiley and Sons, New York,

1987.

Philip Vermeulen. How fat is the top tail of the wealth distribution? European Central Bank

Working Paper Series, 2014.

Edward N. Wolff. Inheritances and wealth inequality, 1989-1998. American Economic Review,

92(2):260–264, 2002.

26

Edward N. Wolff. Inheriting Wealth in America: Future Boom or Bust? Oxford University

Press, 2015. New York.

Edward N. Wolff and Maury Gittleman. Inheritances and the distribution of wealth or whatever

happened to the great inheritance boom? Journal of Economic Inequality, 2014.

27

Documents

Intergenerational transfers: How do they shape the German … · 2017-06-05 · heirs typically di er, next to the monetary transfer heirs by de nition receive. These dimensions encompass