Technical Paper Series

Congressional Budget Office

Washington, DC

The Effects of Federal Estate Tax Policy on

Charitable Contributions

Pamela Greene

Robert McClelland

Congressional Budget Office

Washington, DC

March 2001

2001-2

Technical papers in this series are preliminary and are circulated to stimulate

discussion and critical comment. These papers are not subject to CBO’s formal

review and editing processes. The analysis and conclusions expressed in them

are those of the authors and should not be interpreted as those of the

Congressional Budget Office. References in publications should be cleared with

the authors. Papers in this series can be obtained by sending an e-mail to

techpapers@cbo.gov.
The Effects of Federal Estate Tax Policy on Charitable Contributions

Pamela Greene and Robert McClelland*

March 2001

Tax Analysis Division, Room 422

Congressional Budget Office

2nd and D Streets, SW

Washington, DC 20515

pamg@cbo.gov

robm@cbo.gov 

*Comments are welcome and should be directed to the authors. The views expressed

in this paper are those of the authors and should not be interpreted as those of the

Congressional Budget Office. For useful suggestions, we wish to thank Ben Page,

Jonathan Siegel, David Weiner, colleagues in the Tax Analysis Division and

participants in seminars at the Association for Research on Nonprofit Organizations

and Voluntary Action, the Society of Government Economists, and the Congressional

Budget Office.
THE EFFECTS OF FEDERAL ESTATE TAX POLICY

 ON CHARITABLE CONTRIBUTIONS

Abstract

Tax policy encourages charitable contributions during an individual’s life

through both the personal income tax and the estate tax. The effect of the income

tax on contributions is well known. Because annual charitable contributions 

reduce the size of an estate, and hence the likely estate tax burden, the estate tax

should also affect contributions. Recently, Auten and Joulfaian (1996) studied that

issue using the tobit method on 1982 Statistics of Income data and found that the

estate tax significantly increases charitable contributions. In this study, we

estimate a similar model and find similar results when using a tobit. However, our

model is estimated using Health and Retirement Study (HRS) data for 1991. With

those data, the errors obtained from the tobit violate the censored normal

distribution required for consistent estimation, and so we use the more robust

symmetrically trimmed least squares technique.

 The HRS data also allow us to calculate the individual’s subjective life

expectancy. Using several scenarios of asset growth and those life expectancies,

we estimate the estate tax burden that an individual might expect to face. In

addition, our estimation controls for the effect on contributions of behavior such as

volunteering and attendance at religious services—behavior that cannot be

observed through tax data. We find that the existence of the tax has a small but

statistically significant effect on annual charitable contributions regardless of the

assumed rate of growth of assets. 
-1-

1. Introduction

Individuals reported almost $60 billion in charitable contributions on

income tax returns in 1990 and another $6 billion in charitable bequests on estate

tax returns. In any given year, between 5 percent and 10 percent of charitable

donations are bequests, and the remainder are annual charitable contributions.

The estate tax and the income tax can be expected to influence a person’s

charitable contributions during life by reducing disposable income and wealth and

by exerting a price effect. The price effect of the income tax encourages

contributions by those who itemize deductions on their income tax returns. 

Wealthy individuals also face a price effect that induces contributions during their

lifetime because, in addition to the income tax benefit, such contributions reduce

their anticipated estate tax liability. As Boskin (1976) and Auten and Joulfaian

(1996; A&J hereafter) point out, a properly specified demand function for

charitable contributions should include both the income tax price and the bequest

tax price of that contribution.

While Boskin (1976) focuses solely on charitable bequests, A&J examine

both charitable bequests and contributions during life. Using Statistics of Income

(SOI) data, which contain 1982 estate tax returns matched with the 1981 federal

income tax returns of the decedents and their children, they examine how taxes

affect charitable contributions in the year before death. Their results indicate that

both bequest taxes and income taxes cause taxpayers to increase annual charitable

contributions: the estimated elasticity of the bequest tax price varies between 0.6

and 0.75.

In this analysis, we use data from the 1991 Health and Retirement Study

(HRS) to examine the effects of income taxes, estate taxes, and demographic

variables on annual charitable contributions. The HRS data allow us to examine

several important relationships not captured by SOI data, including information on

the characteristics of respondents, such as attendance at religious services, religion,

volunteer work, and “connectivity” to neighbors. Those data also allow us to

examine the effect of the estate tax for a wide range of years before death. We test

the validity of the assumptions underlying different specifications, reestimate when

necessary, and include additional relevant demographic variables.

 

This analysis also illuminates the effect of subjective beliefs about the

expected estate tax burden. Unlike the income tax, in which the exact level of the

burden can be readily calculated, taxpayers’ ultimate estate tax liability depends on

their life spans, the growth paths of their assets, and estate tax law at the time of

their death. Those factors result in some uncertainty about the ultimate estate tax

liability of a given taxpayer. As a result, during their lives, individuals will react
1. Smith (1995) finds that the quality of the asset data in the HRS is above that of the Survey of Income

and Program Participation but below that of the SCF. Smith states that “it seems safely within an

acceptable quality range.”

-2-

not to the actual estate tax but to the tax that they expect to pay on the wealth that

they expect to have when they die. We estimate individual life expectancy by using

a respondent’s subjective probability of surviving to age 75 and to age 85; we then

follow different time paths for asset growth for the appropriate number of years. 

That approach allows us to test the sensitivity of our results to different levels of

optimism regarding asset growth rates. Our estimates of the elasticity of the

bequest tax price vary from 0.65 to 0.28.

2. Data Used in the Analysis and Construction of the Variables

Data

The Health and Retirement Study is a biennial panel survey of individuals. 

The first wave of the survey was conducted in 1992 and included 12,654

individuals and their spouses or partners, living in 7,607 households with at least

one household member born between 1931 and 1941. The survey placed

particular emphasis on a complete, accurate inventory of income and assets in

1991. Information on wealth included housing equity, other real estate, vehicles,

business equity, individual retirement accounts (IRAs) or Keogh accounts, 401ktype accounts, stocks and mutual funds, checking and savings accounts, money

market funds, certificates of deposit, government savings bonds and treasury bills,

other bonds, other assets, and other debt. In addition, the HRS surveyed

respondents about their perceptions of their chances of survival and of leaving

bequests. Additional waves of the HRS have been conducted, but only the 1991

data from the 1992 survey are used in this research.

Relative to other datasets, the HRS has advantages and disadvantages. 

One drawback of the data is that there is no oversampling of the wealthy, as in the

Survey of Consumer Finances (SCF). Moreover, data on assets are less detailed

and of lower quality than those in the SCF and in the Statistics of Income dataset.1

The HRS has the advantage, however, of collecting a great deal of additional,

extensive information about health, economic status, and family relationships that

are not available in the other datasets.

In addition, this analysis uses only observations from the primary

respondents who participated in the survey. Eliminating primary respondent

refusals from the sample reduced it to 12,272 individuals in 7,323 households. 

Table 1 presents descriptive statistics for several variables used in this study. 
-3-

About 38 percent of households made a charitable contribution of more than $500. 

For those who made such contributions, mean contributions were $2,020 and

median contributions were $1,000. Respondents were not asked about the size of

contributions under $500, so the data are censored at that point.

 The data show that the surveyed households hold a substantial amount of

wealth, with a mean of $206,918 and a median of $90,000. Households

contributing over $500 to charity had a mean wealth of $339,221 and a median of

$178,190. About 5.8 percent of those with charitable contributions over $500

would face the estate tax, compared with about 3 percent of those contributing

less. Contributors of more than $500 had a substantially higher median income

($54,302), compared with those not contributing ($36,800). The descriptive data

also indicated that those who contributed more than $500 were more likely to be

married (82 percent compared with 67 percent) and to participate in volunteer

activities for more than 100 hours a year (32 percent compared with 18 percent). 

Contributors were also more likely to attend religious services, at least

occasionally (86 percent compared with 74 percent).

Variables

Respondents to the HRS were asked the following question: “In 1991, did

you [or your (husband/wife/partner)] donate money, property or possessions

totaling $500 or more to religious or other charitable organizations?” Their

answers, in log form, are used as the dependent variable in the analysis (the level of

charitable contributions, or Ci

). To assess the accuracy of responses to that

question, we compare the distribution of contributions across gross income with

the distribution from the 1997 Consumer Expenditure Survey and find that the two

distributions are similar for values above $550. For contributions between $500

and $550, the HRS contains fewer records than the Consumer Expenditure

Survey, probably due to the anchoring effect noted in Hurd (1998).

A number of variables are used to estimate the demand for charitable

giving. The bequest price is defined by Boskin (1976) as:

 PB

i

 = (1 + di)[(1 + ri)(1 - ei)]-1 (1)

where d is the discount rate, r is the real growth rate of the asset, and e is the top

marginal estate tax rate faced at death. This analysis also uses that definition for

the bequest price and follows A&J in assuming that the real growth of the asset

represents the opportunity cost of using it, so that di = ri. The bequest price is

therefore measured as 1/(1 - ei).
-4-

Income is defined as total income minus federal taxes calculated with zero

contributions. The income tax price of charitable giving, PI

i

, is defined as one

minus the marginal income tax rate for those who itemize; for those who do not,

the price is one. Because of potential endogeneity of the income tax rate (since

higher contributions are deductible and may change the income tax rate faced by

the household), we calculate a proxy for the income tax price by assuming that

charitable contributions are zero. The HRS does not collect data on federal

income taxes. As a result, we calculate them using the tax calculator of Auerbach

and Siegel (2000). (See Appendix A for details of that calculator.)

Wealth is defined, in a manner consistent with the definition used by A&J, 

as assets minus debts. Total assets include retirement accounts such as 401k plans

and IRAs, the market value of farms and small businesses, and other liquid and

illiquid assets; they exclude the cash value of pensions and the face value of life

insurance policies. Annual charitable contributions are added back to arrive at

total assets. For unmarried couples living together, joint assets are evenly divided,

and individually owned assets are assigned to the appropriate person. To help

ensure that the wealth measure is consistent with the income measure, wealth is

reduced by the calculated estate and gift tax liability.

The estate and gift tax calculator developed for this analysis takes into

account the marital status of the people in the household. It assumes that married

and widowed individuals have engaged in sufficient estate tax planning to take full

advantage of the spousal exemption available in 1991. Thus, the analysis assumes

that married and widowed households exempt $1.2 million of their estate from the

estate and gift tax, while unmarried individuals take advantage of the $600,000

exemption. Inter vivos gifts in the current year are included in the estate and gift

tax calculation if the respondent indicated that those gifts were not intended for

education expenses and if the gift exceeded the tax-exempt amount of $10,000 for

an unmarried household and $20,000 for a married household.

We define the taxable estate variable as assets minus debts, with assets

including those in the wealth measure but with a few exceptions. Specifically, the

face value of life insurance is included in the taxable estate instead of the cash

value, and we use an estimate of the estate-tax valuation of an eligible small

business instead of the market value. In addition, we use information from the

1991 Statistics of Income to determine the average amount of charitable bequests

left by estates in certain size ranges (by gross estate category), and those average

charitable bequests are subtracted from the calculated value of the estate at death

to determine the likely taxable estate. We calculate the marginal estate tax rate

faced by each taxpayer on the basis of our estimated size of the likely taxable

estate and the estate tax rates in 1991. That figure is then used to calculate the

bequest price, PB

i

.
-5-

Additional variables include dummy variables for Married (1 = married),

Race (1 = nonwhite), Sex (1 = male), and several categories for age (d45 = 1 if

age is less than 45; d54 = 1 if age is between 45 and 54; d64 = 1 if age is between

55 and 64; and d74 = 1 if age is between 65 and 74). A separate variable, Age,

represents the age of the primary respondent, and Kids is the number of children

living at home, regardless of the children’s ages. Education measures the years of

schooling.

Attendance measures how often the respondents attend religious services;

higher numbers indicate less frequent attendance (1 = more than once a week, 2 =

once a week, 3 = two or three times a month, 4 = one or more times per year, and

5 = not at all). Similarly, Neighbor measures how many of the 10 to 15 families

living nearby that the respondent would know by name; again, higher numbers

indicate less familiarity with the neighbors (1 = all of them, 2 = most of them, 3 =

some of them, and 5 = none of them). Volunteer measures how many hours over

100 the respondent spent volunteering in 1991

.

Growth Scenarios and Calculated Life Expectancies

The actual growth of assets determines their ultimate size and therefore the

estate tax. That growth involves both the assumed growth rate of the asset, 

savings and dissavings behavior consistent with the individual’s life cycle, and his

or her subjective life expectancy. We therefore consider four growth scenarios

when determining the terminal value of wealth and the taxable estate.

 The “zero-growth” case assumes no real growth in the value of the estate

from the time the value was reported for the HRS until the time of death. Further,

savings and dissavings are assumed to be equal. Low-, medium-, and high-growth

scenarios are also used in figuring wealth, taxable estate, and, hence, the calculated

bequest price. Those scenarios are asset growth and accumulation rates calculated

by Havens and Schervish (1999) from Survey of Consumer Finances data. Assets

are assumed to have a real growth rate of 2 percent, 3 percent, or 4 percent,

respectively. Savings and dissavings patterns vary according to the age of the

oldest respondent in the household and the growth scenario (see Table 2 for

details).

To use the growth scenarios to estimate the estate left by each household

at the death of the last surviving member of the household, we first estimate the

life expectancy of each individual. We accomplish that by using two questions in

the HRS that ask each person to assess the probability of surviving to age 75 and
2. Hurd and McGarry (1995) find that those subjective probabilities behave similarly to true

probabilities, including their covariation with other variables.

3. Those probabilities are taken from Medicaid life expectancy tables published by the Health Care

Financing Administration. Life expectancy depends on gender.

4. The preferred method would be to use the first-dollar price as an instrument rather than as a proxy

variable. Future work entails adding that step to an STLS estimator.

-6-

then to age 85.2 Fitting those subjective probabilities to a Weibull distribution

creates a survival function for each individual and therefore an expected life span.

If the probability of living to age 85 equals or exceeds the probability of

living to age 75, the respondent is implicitly stating that he or she has an infinite

life span. In that case, we assume that the question was misunderstood, and we

substitute the probability of living to age 85 conditional on reaching age 75.3

 After

testing the sensitivity of our results to that substitution by deleting all problem

households, we find that only the coefficient dummy variable for marriage is

affected.

3. Model and Estimation

We estimate a model using the following log-linear form:

 lnCi = β0 + β1lnPI

i

 + β2lnYi + β3lnPB

i

 + β4lnWi +Σi

γi

Z+u i (2)

where Ci is the level of charitable contributions, PI

i

 is the tax price of

contributions, Yi is current income, PB

i

 is the bequest tax price, Wi is wealth and Zi

is a set of personal and household characteristics. Because we use the log-linear

model, the estimates of β1 through β4 should be interpreted as elasticities.

As described above, the HRS data censor contributions at $500. With

more than 60 percent of the observations censored at that point, ordinary least

squares estimates are inconsistent. Tobit is the standard tool for that type of

problem, but its consistency relies on assuming a censored-normal likelihood

function. In contrast, the symmetrically trimmed least squares technique (STLS)

can consistently estimate coefficients without the normality requirement, although

at the cost of efficiency if the normality assumption is correct. We use both

methods in the analysis but ultimately rely on STLS. Because the marginal tax rate

may vary with the level of contributions, the tax price PI

i is calculated as the “firstdollar” price of giving: for people who itemize on their federal tax forms, the price

is one minus the marginal tax rate that applies with zero contributions.4

 Similarly,

income is calculated as net of federal taxes due when contributions are zero.
5. Because we are uninterested in the constant, we omit it when applying this test.

-7-

4. Results

Table 3 lists the results from a tobit procedure under the zero-growth

scenario. Most coefficient estimates are significantly different from zero, and all

are of the expected sign. In particular, the log of the bequest price has a significant

impact on charitable contributions, which provides evidence that the estate tax

increases annual charitable contributions. Further, a model using A&J’s wealth

definition and variables produces an elasticity of 0.72, similar to their elasticities of

0.59 to 0.78.

However, the consistency of estimates using the tobit model requires

censored, normally distributed errors. Newey (1987) suggests testing the

assumption of censored normality by comparing tobit estimates with STLS

estimates through a Hausman-type test. Comparing the estimates from the tobit in

Table 3 with those resulting from the use of symmetrically trimmed least squares

for the data in this analysis yields a test statistic of 393, where the 5 percent

rejection point for a χ2 distribution with 14 degrees of freedom is about 23.5 That

decisive rejection of censored normality casts strong doubt on the validity of the

tobit results. Consequently, for the remainder of this analysis, we rely on the

STLS estimator.

Using that estimator produces markedly different results from those

achieved with the tobit. For example, Table 4 shows that the bequest price

elasticity in the zero-growth scenario using the STLS estimator is about half that

found with the tobit. The significance of the STLS estimate still indicates,

however, that the bequest price affects charitable contributions. In contrast, the

elasticity of wealth estimated by STLS is substantially larger than that estimated by

tobit.

Because the estate tax effectively starts at 36 percent, the bequest price

jumps from 1.00 for those not subject to the tax to 1.59 or above for the relatively

few who are, causing the variable used in the equation, ln( PB

i

), to jump from zero

to 0.46 or above. That discrete difference, combined with the small number of

households subject to the tax, suggests that the coefficient on ln(PB

i

) might best be

interpreted as the elasticity for being subject to the estate tax rather than the

elasticity for small changes in the estate tax rate.

We examine that possibility by replacing the log of the bequest price with a 

variable equal to zero for people who are not subject to the estate tax and equal to

the average of the log bequest price for those who are. All of the variation now

comes solely from being subject to the estate tax or not. After transforming the
6. Halvorsen and Palmquist show that if the dependent variable is in a log form, then the coefficient

β of a dummy variable should be transformed by exp(β) - 1. The general transformation when the

dummy variable takes on a value of either zero or ξ is [exp(ξ β)−1]/ξ.

7. We believe that this is due to the small sample size (208). To check the possibility that the

inefficiency of STLS was responsible, we ran the model using tobit and the admittedly biased OLS.

In neither case was PB

i

 significantly different from zero.

-8-

coefficient in a manner similar to that of Halvorsen and Palmquist (1980), we

arrive at an estimate of 0.48, which is extremely close to 0.50.6 Therefore, the

elasticity estimate should be interpreted as the percentage change in contributions

resulting from imposing the estate tax at the existing rates.

We examine that idea further by running the model that produced Table 4

on only those households subject to the estate tax. The coefficient estimate, 0.723,

is insignificantly different from 0.50, but it is also insignificantly different from

zero.7 As a result, we have no strong evidence regarding its interpretation as the

percentage change in contributions from small changes in tax rates for those

subject to the tax.

Using the growth assumptions of Havens and Schervish (1999) also allows

us to test the sensitivity of the elasticity estimates to growth patterns of assets. 

The last three columns in Table 4 list the results from estimating wealth and

bequest taxes using the low-growth, medium-growth, and high-growth scenarios. 

Recall that those growth rates are compounded by the expected number of years

remaining in the individual’s life, calculated from his or her own subjective

probabilities of survival. The resulting coefficients therefore do not reflect our

simply increasing or decreasing wealth by a constant. As shown, estimated

bequest price elasticities vary between 0.649 and 0.283, and all are significantly

different from zero. That range suggests that although individuals adjust their

charitable contributions according to an expected estate tax, the adjustment is

fairly small.

Several of the remaining coefficient estimates show the expected effect. 

The coefficient on the variable for religious service attendance is negative and

significant, indicating that those who attend services make significantly larger

contributions. The large t-statistic indicates that few households violate that rule. 

Education is significant and positive, consistent with the results of other studies of

giving (see, for example, Bradley et al., 2000). Volunteering appears to be a

complement to gifts of money, as found in previous work such as Andreoni et al.

(1996).

Age and age squared, while traditionally statistically significant, suffer from

limited variation within a sample frame in which most heads of household are
8. The negative coefficient on race appears because of higher variation in giving among nonwhites. We

examine this by replacing the variable measuring attendance at religious services with a dummy

variable equal to one for any level of attendance and zero otherwise and an interaction term equal to

the product of the race dummy variable and the attendance dummy. The results indicate that although

nonwhite service attenders contribute more than either whites who attend services or whites who do

not, nonwhite nonattenders contribute far less than either group. Those nonwhite nonattenders, then,

are responsible for the overall coefficient on race being negative. To check that finding, we ran the

model on the subset of households that had primary respondents who attended religious services. In

that case, the coefficient on race was significant and positive. Because of that variation, it is possible

that a small difference in the sample surveyed would have led to a significant and positive coefficient

on race.

-9-

between 51 and 61. The marginal significance of the neighborhood variable is

somewhat surprising, but the feeling of inclusion in an area may already be greatly

accounted for in the religious service attendance and volunteering variables.

The marriage dummy variable may also reflect the same phenomena as the

neighborhood variable. However, as mentioned earlier, deleting all households in

which the probability of reaching age 85 equaled or exceeded the probability of

reaching age 75 noticeably changes the coefficient estimate on the marriage

variable. In those circumstances, the estimate was insignificantly different from

zero. We therefore conclude that its significance is more an artifact of our

treatment of these problem households than a reflection of real differences in

behavior. The race coefficient was negative and significant.8

Our results should be interpreted cautiously for several reasons. First,

Pagan and Vella (1989) warn that using STLS residuals for diagnostic testing, as

in the specification test discussed in Appendix B, may lack power if the

misspecification occurs in observations that are trimmed or deleted by the

procedure. However, that is unlikely to affect our results because a high

proportion of people who are subject to the estate tax make charitable

contributions and therefore are unlikely to be deleted. Second, although the results

from a properly specified model can be extended to those outside the sample (such

as the extremely wealthy), the results from badly specified models cannot be

extended. Our specification tests fail to detect a problem, but it is still possible

that households outside the sample differ dramatically from those in the sample. 

Third, because we are unaware of any theory regarding instrumental variables and

STLS, we use the traditional first-dollar tax prices rather than instruments. 

Finally, we are using only the first wave of the HRS, making our results sensitive

to all of the problems inherent in studies of cross-sectional data. Using the

longitudinal aspects of the HRS data eliminates that problem but greatly

complicates the use of the STLS estimator.
-10-

5. Conclusion

This analysis uses data from the Health and Retirement Survey to estimate

how bequest taxes and income taxes affect annual charitable contributions. Those

data allow us to examine the effect of the estate tax on the contributions of

individuals who do not die within one year of making contributions, and to

examine the effect of several important demographic characteristics. The

estimates we obtained using the tobit method are similar to the estimates that

Auten and Joulfaian (1996) achieved using a similarly specified tobit model with

tax return data and indicate that the estate tax and the income tax increase

charitable contributions during life.

We reestimated the model using a more general specification containing

additional demographic characteristics of survey respondents. We tested the

assumption, required for the consistency of tobit estimates, that the likelihood

function is a censored normal. Decisively rejecting that assumption for our data,

we reestimated the model using symmetrically trimmed least squares. By using

information on the individual's subjective probability of survival, we tested the

sensitivity of our estimates to four growth paths that an individual's assets might

take over his or her remaining life span. The bequest price elasticities ranged from

0.303 to 0.685, indicating that while the effect of the estate tax on annual

charitable contributions is clearly discernible, contributions are inelastic with

respect to the bequest price.
-11-

Table 1

Descriptive Statistics

Item All

Respondents with

charitable

contributions > $500

Number of Observations 7323 2808

Wealth (zero growth)

 Mean

 25th percentile

 Median

 75th percentile

$206,918

$23,100

$90,000

$224,000

$339,221

$86,050

$178,190

$372,350

Household Income

 Mean

 25th percentile

 median

 75th percentile

$46,423

$19,062

$36,800

$60,144

$66,385

$35,200

$54,302

$81,200

Percentage subject to estate tax (zero

growth)

 No estate tax liability

 Top marginal bracket

 37

 39

 41

 43

 45

 49

 53

 55

97.0

0.7

0.7

0.3

0.3

0.3

0.2

0.1

0.4

94.2

1.1

1.3

0.7

0.6

0.7

0.2

0.2

1.0

Demographic variables

 Average age of primary respondent

 Percentage in each age group

 age<45

 45<=age<54

 54<=age<64

 64<=age<74

 74<=age

56.1

 1.5

29.6

63.2

 5.3

 0.4

56.3

 1.1

28.6

63.3

 6.8

 0.2

 Spouse’s age 54.9 54.7

Percentage married 67.3 81.6
Item All

Respondents with

charitable

contributions > $500

-12-

 Percentage with highest education

level 

 Less than HS

 HS diploma

 Some college

 Bachelor’s degree

 Graduate education

 

28.1

33.5

19.0

 8.5

10.9

13.3

30.9

22.8

13.11

19.94

Average number of children 0.66 0.70

Percentage with children living at

home

42.1 44.7

Percentage non-white 29.8 22.0

Volunteer >100 hours/year

 Percentage

 Mean hours per year

 Median hours per year

18.0

326

200

32.4

322

200

How many neighbors do you know by

name?

 All

 Most

 Some

 None

20.5%

29.4%

42.9%

7.1%

20.2%

33.3%

42.5%

3.9%

Attendance of religious services

 More than once a week

 Once a week

 2-3 times per month

 One or more times per year

 Doesn’t attend services

13.9%

22.6%

15.6%

22.9%

26.0%

23.7%

28.7%

15.9%

17.6%

14.1%

Charitable Giving > $500

 Percentage

 Mean

 Median

38.3

$2,020

$1,000

100

$2,020

$1,000
-13-

Table 2

Asset growth rates

Age of HH

Gen.

growth rate

Saving as %

of wealth

Dissaving as

% of wealth

Total

Low Growth

 30 Years or

Less

 31-40 Years

 41-50 Years

 51-60 Years

 61-70 Years

 71-80 Years

 81+ Years 

2%

2%

2%

2%

2%

2%

2%

9.1%

3.2%

4.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.1%

2.5%

3.7%

7.6%

11.1%

 5.2%

 6.0%

 1.9%

-0.5%

-1.7%

-5.6%

Medium Growth

 30 Years or

Less

 31-40 Years

 41-50 Years

 51-60 Years

 61-70 Years

 71-80 Years

 81+ Years 

3%

3%

3%

3%

3%

3%

3%

10.1%

4.2%

5.0%

0.9%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

1.7%

2.5%

5.1%

13.1%

 7.2%

 8.0%

 3.9%

 1.3%

 0.5%

 -2.1%

High Growth

 30 Years or

Less

 31-40 Years

 41-50 Years

 51-60 Years

 61-70 Years

 71-80 Years

 81+ Years 

4%

4%

4%

4%

4%

4%

4%

11.1%

5.2%

6.0%

1.9%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.8%

1.2%

2.5%

15.1%

 9.2%

10.0%

 5.9%

 3.2%

 2.8%

 1.5%

Source: Havens and Schervish (1999)
-14-

Table 3

No asset growth, Tobit model

Variable Coefficient

Asymptotic tStatistic

Constant -1.710 -3.174

ln PB

i 0.827 10.701

ln Wealth 0.020 3.349

ln PI

i -0.923 -14.383

ln Income 0.073 5.348

Attendance -0.175 -29.855

Education 0.025 8.162

10-2 *Volunteer 0.024 7.378

Married 0.079 3.845

Neighbor -0.005 -0.634

Kids -0.012 -1.308

Age 0.048 2.607

10-2 *Age 2 -0.042 -2.570

Sex -0.114 -6.614

Race -0.082 -4.151
-15-

Table 4

Symmetrically trimmed least squares results

Variable

Zero Asset

Growth

Low Asset

Growth

Medium Asset

Growth High AssetGrowth

Constant -5.257 

-3.707

-5.937

-4.519

-5.783

-4.246

-6.095

-4.331

ln PB

i 0.536

3.301

0.685 

4.232

0.421

4.044

0.303

4.557

ln Wealth 0.163 

7.153

0.115

6.180

0.134

6.137

0.115

5.034

ln PI

i -1.621

-10.465

-1.734

-11.342

-1.679

-11.071

-1.575

-10.284

ln Income 0.317 

7.228

0.352

7.675

0.327

7.556

0.334

7.719

Attendance -0.519 

-26.411

-0.527

-25.822

-0.523

-26.461

-0.521

-25.879

Education 0.063 

7.237

0.072

8.023

0.067

7.632

0.061

7.026

10-2

*Volunteer

0.027

4.721

0.026

4.848

0.027

4.896

0.028

5.112

Neighbor -0.037 

-1.859

-0.044 

-2.155

-0.038

-1.927

-0.042

-2.027

Married 0.281 

4.961

0.364

6.041

0.310

5.385

0.248

4.423

Kids -0.087

-3.728

-0.091

-3.814

-0.088

-3.748

-0.096

-4.077

Age 0.028

0.607

0.026

0.615

0.030

0.669

0.050

1.093

10-2 *Age2 -0.031 

-0.756

-0.025

-0.659

-0.027

-0.685

-0.043

-1.075

Sex -0.224 

-5.512

-0.219

-5.255

-0.205

-5.017

-0.194

-4.719

Race -0.350 

-6.414

-0.381

-6.958

-0.362

-6.627

-0.344

-6.326

Asymptotic t-statistics are shown in italics
-16-

Appendix A. Income Tax Calculations

The tax calculator used in this paper is based on that of Auerbach and

Siegel (2000). Any income information available only at the household level was

evenly divided between both members of a couple if they were unmarried. With

the exception of capital gains realizations, information from all major sources of

income was used from the survey. Although sales of assets were available, the

basis was not, and so we assumed that the basis equaled 50 percent of the value of

the asset at the time of sale. Extensive information on contributions to and

withdrawals from pension and retirement accounts was incorporated, as was

income from salaries, interest, alimony, business and farm income, unemployment

and social security income, rents, royalties, self-employment income, and so forth.

In the survey, individuals are asked about their itemization status as well as

their medical expenses. To avoid a possible endogeneity problem, taxes were

calculated with zero charitable contributions. The mortgage interest deduction

was calculated from the years lived in the house, the remaining balance owed on

the mortgage, and the lowest mortgage rate from the time the house was

purchased. State taxes were estimated from the average taxes in the household’s

region.
9. This point is discussed in Feenberg (1989) and Steinberg (1990).

10. See, for example, Bierens (1990).

11. The residuals were also regressed against those higher-order terms of ln wealth after the other

explanatory variables had been “partially led out” with similar results.

-17-

Appendix B. Specification Testing

The model in this paper was tested for possible specification problems. 

Problems examined include the possible sensitivity of the bequest price to

measures of wealth and the correct definition of wealth.

 

Because the bequest price is a function of deductions and wealth (a variable

appearing in log form in the regression equation), the separate identification of the

effect of the bequest price and wealth relies strongly on the nonlinearity of that

function and independent variation between wealth and the bequest price.9

 This

implies that the estimated bequest price elasticity could be relatively sensitive to

misspecification of the relationship between wealth and contributions.

 

That sensitivity was tested by adding the square and cube of ln(wealth) to

the model. In that case, the precision and size of the estimated price elasticity

dramatically fell as the standard error of the estimate increased by 50 percent and

the coefficient estimate was cut in half.

While exposing a potential fragility to the proper specification of wealth,

that result does not imply that wealth is improperly defined. To test the definition,

we first considered the most logical alternative to the natural log of wealth,

namely, the level of wealth itself. One reasonable approach would be to use a Jtest by including the predicted contributions when using the natural log of wealth

in a regression employing the level of wealth. However, STLS endogenously

truncates the data so that each regression has a potentially different number of

observations that depends in part on the regressors. As an alternative, we used a

non-nested F test in which both forms of wealth were included in the same

regression. If only one coefficient is significant, that is more likely to be the proper

definition. As is often the case with non-nested tests, the results are ambiguous:

the coefficient on the log of wealth is quite significant while the coefficient on the

level of wealth is marginally significant.

We did a further test of the definition of wealth. In a correctly specified

model, the expected correlation of the regression errors with any function of the

right-hand side variables is almost surely zero.10 A regression of the symmetrically

trimmed least squares residuals (as defined in Pagan and Vella, 1989) against the

log of wealth raised to the second, third, and fourth powers should have an

insignificant R2.

11 Or, put in the form of a RESET-type test, N*R2

 from this

regression should have a χ2

(3) distribution under the null hypothesis of proper
-18-

specification but a “high” value for any of a large number of misspecifications. 

The result fails to reject the null hypothesis of proper specification, and so the log

of wealth appears to be the most appropriate measure of wealth.

Finally, we visually inspected the smoothed residuals from the STLS

procedure. Although they appeared to be symmetric, the tails of the distribution

were fatter than those of a normal distribution. That may be caused by kurtosis or

heteroskedasticity. Since both cause inconsistent tobit estimates but do not affect

the consistency of STLS, we did not investigate further.
-19-

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