Reassessing Wealth Data Quality in the Survey of Income and Program 

Participation*

Jonathan S. Eggleston 

Social, Economic, and Housing Statistics Division 

U.S. Census Bureau 

Mark A. Klee 

Social, Economic, and Housing Statistics Division 

U.S. Census Bureau 

Proceedings of the 2015 Federal Committee on Statistical Methodology (FCSM) Research Conference 

 

ABSTRACT: The Survey of Income and Program Participation (SIPP) and the Survey of 

Consumer Finances (SCF) are two principal sources of wealth data for the U.S. population. The 

Social Security Administration sponsored Mathematica Policy Research to write a report that 

identified considerable discrepancies in wealth estimates across these surveys using data from 

1998. While one might expect SIPP and SCF to deliver different estimates for a variety of 

reasons, the magnitude of differences in levels and trends across surveys fostered questions about 

SIPP wealth data quality. To address these concerns, SIPP implemented various strategies that 

the report recommended to close the gaps between wealth estimates. We conduct the first 

analysis of the impact of these changes. We offer potential explanations for why these two 

surveys continue to yield different estimates, and we discuss the broader implications for the 

wording and design of asset questions. 

 

 

*

 We are thankful to Alfred Gottschalck and Marina Vornovitsky for assistance with institutional history and for 

their efforts to improve wealth data quality in the Survey of Income and Program Participation. We also thank 

Michael Gideon and seminar participants at the U.S. Census Bureau for helpful comments. This paper is released to 

inform interested parties of ongoing research and to encourage discussion of work in progress. The views expressed 

in this paper are those of the authors and not necessarily those of the U.S. Census Bureau. Any errors are our own. 

Eggleston: jonathan.s.eggleston@census.gov, (301) 763-2357; Klee: mark.a.klee@census.gov, (301) 763-4730. 

Address: Social, Economic, and Housing Statistics Division; U.S. Census Bureau; 4600 Silver Hill Road; 

Washington, DC 20233. 
1. Introduction 

 The Survey of Income and Program Participation (SIPP) is our nation’s premier source of 

information about program participation. Since eligibility for many social programs depends on 

means and asset tests, SIPP is also a major source of information about the wealth of individuals, 

families, and households. Government agencies and researchers commonly use these data to 

model eligibility for programs such as the Supplemental Nutrition Assistance Program, formerly 

known as food stamps.1 SIPP also offers a rich set of observable characteristics to describe 

wealth holders and to analyze the connection between wealth and outcomes such as employment 

status.2 Additionally, the large sample size enables wealth comparisons across subgroups.3 

 Another major source of information about the wealth of families in the United States is

the Survey of Consumer Finances (SCF), which has been referred to as the “gold standard” for

wealth data.4 Researchers commonly use these data to describe the wealth distribution and to 

highlight changes in the wealth distribution over time.5 This survey offers the most detailed 

micro-level information available about families’ wealth holdings. SCF oversamples highincome households in an effort to improve representativeness in the right tail of the heavily

skewed wealth distribution.6

 SCF also offers a rich set of observable characteristics to describe 

 

1

 Blank and Ruggles (1996) derive measures of eligibility for the Aid to Families with Dependent Children program, 

commonly known as welfare, and food stamps. 

2

 See Chetty (2008), Gruber (2001), and Sullivan (2008) for studies of wealth among unemployed individuals. 

3

 Vornovitsky, Gottschalck, and Smith (2014) compare the distribution of wealth by the age, race and ethnicity, and 

education of the household head. 

4

 National Research Council (2009) is one among many sources that have applied this label in reference to SCF. 

5

 See Wolff (1998, 2014) for examples. 

6

 Bricker et al. (2014) use SCF data to establish that the median family net worth was approximately $81,200 in 

2013 and the mean family net worth was approximately $534,600. Such a large difference between the median and 

mean of a distribution is characteristic of heavy right skewness. They also document that the 95th percentile of the 

wealth distribution – approximately $1,871,800 – exceeds the 82.5th percentile – approximately $505,800 – by more 

than a factor of 3. Even within the top decile of the wealth distribution, the mean net worth of approximately 

$4,024,800 greatly exceeds the median. 

1 
2 

wealth holders and to analyze the connection between wealth and outcomes such as employment 

status.7

 Since both SCF and SIPP are nationally representative surveys, estimated statistics of the 

wealth distribution should compare well across surveys, with sampling error largely explaining 

any disagreement. However, differences in sample design between SCF and SIPP have given 

rise to the concern that estimates from these surveys might not compare well. While SCF 

oversamples high-income households, SIPP oversamples households in low-income areas to 

improve representativeness among program recipients and households in poverty. To address 

this concern, Curtin, Juster, and Morgan (1989) and Wolff (1999) provided early comparisons of 

wealth estimates in SCF and SIPP. They found the level and distribution of wealth to be similar 

in general across these surveys upon excluding the wealthiest individuals. 

 However, the gap between SCF and SIPP wealth estimates grew over the course of the

1990s. In response to this trend, the Social Security Administration sponsored Mathematica 

Policy Research to study wealth estimates from the 1996 panel of SIPP and corresponding 

estimates from SCF. Czajka, Jacobson, and Cody (2003) reported on this analysis, which “seeks 

to attribute the observed disparities to differences in survey design and implementation, explores 

ways to improve the quality of the SIPP estimates..., and presents recommendations regarding 

both the use and production of SIPP wealth data.” They documented that the aggregate net 

worth estimate in SIPP data was just under half of the analog in SCF data, while the median net 

worth estimate in SIPP data was approximately two-thirds of the analog in SCF data. They 

attributed these gaps to a relatively large discrepancy between SCF and SIPP estimates of assets 

and a relatively small discrepancy between SCF and SIPP estimates of liabilities. Czajka et al. 

 

7

 Carroll, Dynan, and Krane (2003) examine whether job-loss risk affects savings. Bricker et al. (2014) describe 

how median wealth varies with demographic characteristics 
(2003) argued that estimated medians in SIPP and SCF compare more favorably than estimated 

aggregates because SIPP underestimated both the number of wealthy families and their average 

wealth. 

 Czajka et al. (2003) offered recommendations aimed at narrowing the gap between SCF 

and SIPP wealth estimates. SIPP subsequently implemented several of these recommendations. 

To our knowledge, this paper represents the first evaluation of whether these changes have made 

SCF and SIPP wealth estimates more comparable. We begin by updating the analysis of Czajka 

et al. (2003) using Wave 7 of the 2008 panel of SIPP and the 2010 SCF. Specifically, we 

compare estimates of net worth both in aggregate and at various percentiles of its distribution. 

We then report how these comparisons vary across asset and liability categories. After studying 

asset and liability values in aggregate, we consider the correlation between households’ assets 

and liabilities. We separate relatively wealthy families from less wealthy families to account for 

the influence of outliers that SCF and SIPP cover differentially. Finally, we examine how 

differences between SCF and SIPP wealth estimates influence conclusions about how wealth 

varies across demographic groups. 

We find that the impact of the changes recommended by Czajka et al. (2003) has been 

mixed.8

 The difference between some key wealth estimates in SCF and SIPP has narrowed; 

SIPP aggregate net worth is approximately three-quarters of the corresponding estimate in SCF, 

while median net worth in SIPP is roughly 84 percent of its SCF analog. 9 Nevertheless, the 

 

8

 Note that we do not have access to standard errors for the estimates presented in Czajka et al. (2003). 

Consequently, whenever relevant we compare point estimates of our results to those from Czajka et al. (2003). 

Apparent differences between our estimates and those from Czajka et al. (2003) may not be statistically significant. 

9

 All comparisons are statistically significant at the 90 percent level unless stated otherwise. The estimates in this 

paper are based on responses from a sample of the population and may differ from actual values because of 

sampling variability or other factors. As a result, apparent differences between the estimates for two or more groups 

may not be statistically significant. For more information on the source of the data and the accuracy of the 

estimates, see http://www.census.gov/programs-surveys/sipp/tech-documentation/source-accuracy-statements.html. 

3 
difference between other key wealth estimates in SCF and SIPP has widened; the 25th percentile 

of the net worth distribution in SIPP is about 28 percent of its SCF analog. Within asset 

categories, estimates are similar across surveys for home values, which are a key portfolio 

component for a majority of households. The SIPP median home value is 100.1 percent of the 

SCF counterpart. On the other hand, SIPP median business equity underestimates its SCF analog 

by almost a factor of 4. Within debt categories, estimates are especially similar across surveys 

for vehicle debt, which is a key liability for many households. SIPP median vehicle debt is 102.7 

percent of its SCF counterpart. SIPP median mortgage debt is less comparable at 109.9 percent 

of its SCF analog. We find that while since Czajka et al. (2003), SIPP continues to measure 

mean debt value well relative to SCF and the mean asset value compares more favorably across 

surveys, the correlation of asset values and debt values for our entire sample has declined.10 In 

general, the gap between SCF and SIPP estimates narrows for aggregate and mean net worth 

when we exclude relatively wealthy families from our sample. Finally, we conclude that the 

differences in wealth estimates that we document across surveys yield no statistically significant 

difference in estimating the Black-White wealth gap. 

Our results have broader implications for the survey methodology literature, especially 

relating to the wording and design of wealth questions. While SIPP and SCF might yield 

different estimates for a variety of reasons, some of these differences are likely to stem from 

differences in question text wording. First, we find evidence consistent with the hypothesis that 

SCF ownership rates for some asset and liability classes exceeds the SIPP analog because the 

SCF questions list more examples of that class. Tourangeau, Conrad, Couper, and Ye (2014) 

argue that providing examples can influence how individuals interpret and answer a question. 

 

10 However, the point estimate of the correlation we calculate is similar to Czajka et al. (2003) once we exclude 

outliers from our sample. 

4 
Second, we find suggestive evidence that small differences in question text wording might 

impact how respondents interpret a question, thereby affecting estimates of asset and debt values. 

Finally, we propose that simpler questions elicit better quality answers. Ideally, one would 

conduct an experiment to determine the effects of variation in question wording on survey 

response. However, relatively few studies offer this kind of insight for wealth questions.11

 

 The remainder of this paper proceeds as follows. In Section 2, we summarize the 

findings of Czajka et al. (2003). Section 3 describes the data that we employ. We highlight our 

methodology and salient issues that complicate a comparison of SCF and SIPP wealth estimates 

in Section 4. We report on statistics of net worth, assets, and liabilities implied by these two 

datasets in Section 5, and we consider what implications the differences in wealth estimates 

between the two datasets have for comparisons of wealth by demographic characteristics such as 

race. Section 6 concludes. 

2. Background 

 The impetus for the report by Czajka et al. (2003) was the finding that SIPP estimates of 

median wealth showed little change over the 1990s, during which time SCF estimates rose

markedly. This gave rise to a question of whether the Social Security Administration should 

continue to employ SIPP wealth data, and if so how SIPP wealth estimates should be adjusted to 

resemble SCF estimates more closely. To that end, Czajka et al. (2003) compared wealth 

estimates between the 1998 SCF and Wave 9 of the 1996 SIPP panel. Both data sources covered 

the period of late 1998 and early 1999. 

 

11 Couper et al. (2013) conducts an experiment on a question which requests respondents to consult asset records. 

5 
One important complication in comparing these two surveys is that they each collect data 

from different units of observation. SCF collects data at the level of the “primary economic 

unit” (PEU), which includes a household’s economically dominant individual or couple and their 

financial dependents. By contrast, SIPP collects data at the level of the individual, regardless of 

that individual’s relationship to the household’s economically dominant individual or couple. 

Since SIPP does not collect information about economic dominance within households, Czajka 

et al. (2003) constructed a grouping of SIPP household members to mimic the SCF primary 

economic unit for the sake of comparability. They term this unit an “SCF-like family”. They 

began by including individuals in the primary family.12 Second, they included unmarried 

partners of the household reference person and all of that unmarried partner’s children who are 

younger than age 25. Third, if a subfamily within the primary family had a reference person who 

is age 25 or older, then they excluded that subfamily.13 Finally, they excluded siblings and other 

individuals who are age 25 or older and who are related to the household reference person. 

 After constructing the SCF-like family, they computed the wealth of this unit as the sum 

of two components. First, SIPP collects data about most assets and debts by asking each person 

about the account values of assets and liabilities owned either jointly or in own name only. They 

summed these asset and debt values across members of the SCF-like family to calculate the first 

 

12 The primary family in a household is determined by the householder or household reference person. By

definition, this person owns or leases the housing unit. If more than one person fits this description, then only one 

person is identified as the household reference person, although this distinction bears no economic significance. In 

addition to the household reference person, the primary family includes all individuals living in the household who 

are related to the household reference person by blood, marriage, or adoption. 

13 By definition, subfamilies within the primary family are related to, but do not include, the household reference 

person. For example, if a daughter and her spouse live in a house that her parents own, the daughter and her spouse 

would be categorized as a subfamily within the primary family. If that daughter is 25 years older or more, she and 

her spouse would be excluded from the SCF-like family using the algorithm of Czajka et al. (2003). However, she 

and her spouse would be excluded from the primary economic unit as defined by SCF only if they are economically 

independent of the household reference person. Because of this, Czajka et al.'s (2003) algorithm could potentially 

exclude some individuals from the SIPP sample who would be in the primary economic unit if the household were 

interviewed by SCF. 

6 
component of this unit’s wealth. Second, SIPP collects data about all remaining assets and debts 

at the level of the household. For example, only the household reference person provides the 

value of the home, if owned, and the value of any mortgages on the home.14 SIPP also asks the 

household reference person to identify up to three people in the household who own the home. 

Czajka et al. (2003) summed these household-level asset and debt values, including only portions

held by individuals who are in the SCF-like family, to calculate the second component of this 

unit’s wealth.15

 Czajka et al. (2003) found that the aggregate net worth of SCF-like families in SIPP was 

$14.4 trillion, or 49.5 percent of the SCF estimate of $29.1 trillion for the aggregate net worth of

primary economic units during the late 1990s. The median net worth of SCF-like families in 

SIPP was $48,000, while the median net worth of primary economic units in SCF was $71,800. 

This large gap in net worth estimates across surveys stemmed primarily from a gap in estimates 

of asset values across surveys. SCF-like families in SIPP had aggregate asset values that were 

55 percent of the SCF estimate of $34.1 trillion. By contrast, SCF-like families in SIPP had 

aggregate debt values that were 90 percent of the SCF estimate of $5.0 trillion. 

 Czajka et al. (2003) attempted to understand the drivers of the differences in wealth 

estimates by comparing SCF and SIPP estimates of account values held by owners of each type 

of asset and liability. They found evidence of wide variation across categories. At one extreme, 

the SIPP estimate of aggregate 401(k) and thrift account values was 99 percent of the SCF 

estimate. At the opposite extreme, the SIPP estimate of aggregate business equity was 17 

percent of the SCF estimate. Czajka et al. (2003) found evidence that SCF and SIPP estimates 

 

14 SIPP also collects information about vehicles, other real estate, and recreational vehicles at the level of the 

household. 

15 Czajka et al. (2003) assume that asset values and debt values that SIPP collects at the household level are split 

evenly among all owners. 

7 
generally compared well within debt categories. The SIPP estimate of aggregate mortgage 

values, by far the largest component of a typical household’s liabilities, was 95 percent of the 

SCF estimate. Czajka et al. (2003) also explored the extent to which these differences in 

aggregate values within asset and debt categories stem from differential estimates of ownership 

rates. They document similar ownership rates of some key assets and debts in SCF and SIPP, 

including primary residences, motor vehicles, and mortgages against a primary residence. 

However, estimated SIPP ownership rates of checking and savings accounts, IRA and Keogh 

accounts, and real estate other than the primary home fall short of their SCF analogs. 

 Given SCF’s overrepresentation of high-income households and its heightened focus on 

collecting even less common components of portfolios held by wealthy families, it is unclear that 

one should expect SCF and SIPP to deliver the same estimates. To that end, Czajka et al. (2003) 

examine the extent to which differences in SCF and SIPP wealth estimates can be attributed to 

differences in the number of wealthy families and the value of these families’ portfolios across 

surveys. They report that SIPP underestimates the fraction of all families with wealth exceeding 

$2 million by a factor of 5 relative to SCF. When excluding these families from both surveys, 

the SIPP estimate of aggregate net worth, aggregate assets, and aggregate debts rise to 75 

percent, 80 percent, and 101 percent of their SCF analogs, respectively. The gap between SCF 

and SIPP estimates of aggregate values held within asset and debt categories also generally 

narrows when excluding wealthy primary economic units. 

 Czajka et al. (2003) also examine the implications of differences in wealth estimates 

across SCF and SIPP for estimates of wealth inequality by demographic group. For example, 

they show that differences in median net worth by race tend to be more pronounced in SIPP than 

8 
9 

in SCF.16 The median net worth of SCF-like families headed by a White individual exceeded 

that of SCF-like families headed by a Black individual by a factor of 11.5, while this factor was 

only 6.1 in SCF. They decompose these differentials by race to show that differences in median 

asset values by race are stronger in SIPP than in SCF, while differences in median debt values by 

race are weaker in SIPP than in SCF. 

3. Data 

 We update the Czajka et al. (2003) analysis using the 2008 panel of SIPP. SIPP is a 

large, nationally representative, longitudinal survey, which interviews households every four 

months. During every interview, each person who usually resides in a sampled household 

answers the same core group of questions about the preceding four months. These responses 

provide detailed monthly information about demographics, ownership of interest-earning assets, 

and a variety of other characteristics. Respondents also answer a separate group of topical 

questions that vary from one interview, or wave, to the next. We utilize the topical modules 

accompanying wave 7 of the 2008 panel that collect wealth data.17 These interviews were 

administered between September and December 2010. 

 We also employ the 2010 SCF, which is another nationally representative dataset. 

Interviews were conducted mostly between May and December 2010. SCF is administered 

triennially and is cross-sectional, so households are usually interviewed only once.18

 

 

16 While median net worth differences by race tended to be more pronounced in SIPP than in SCF in percentage 

terms, they were less pronounced in SIPP than in SCF in absolute terms. Median asset value differences exhibited 

the same pattern. 

17 Specifically, these are the Assets and Liabilities Topical Module; the Real Estate, Dependent Care, and Vehicles 

Topical Module; and the Interest Accounts, Stocks, Mortgages, Value of Businesses, Rental Properties, and Other 

Assets Topical Module 

18 2007 SCF respondents were interviewed again in 2009 to obtain information about how the wealth of these

households evolved through the 2008 financial crisis. These SCF panels occur only infrequently. 
Respondents provide detailed information about demographics, wealth, and a variety of other 

characteristics. 

The designs of SCF and SIPP differ in four salient ways for our purpose. First, highincome households are overrepresented in SCF while low-income areas are overrepresented in 

SIPP. The SCF high-income oversample aims to improve coverage of the wealthiest families, 

thereby accommodating the heavy right skewness of the wealth distribution and the thinly held 

assets that are concentrated among the wealthiest families’ portfolios. By contrast, the SIPP lowincome oversample aims to improve coverage of the families who receive social programs, 

thereby accommodating the unique dynamics of their income, wealth, health insurance, and 

household structure.19 To the extent that families in the left tail, the middle, and the right tail of 

the income distribution structure their portfolios differently, we might expect these coverage 

improvement efforts to produce differential wealth estimates across surveys. 

Second, the purpose of SCF is primarily to collect information on wealth in the United 

States, whereas the purpose of SIPP is primarily to measure program participation. 

Consequently, SCF includes questions about the value of some relatively uncommon assets and 

liabilities that SIPP does not mention.20 In principle, SIPP respondents can report the value of 

any remaining financial investments in a catch-all question, but relatively few people exploit this 

opportunity. Even when SCF and SIPP ask about the same type of asset or liability, in some 

 

19 SIPP measures participation in and income from many social programs including SNAP; Old Age, Survivors, and 

Disability Insurance (commonly referred to as social security); Temporary Assistance for Needy Families 

(commonly referred to as welfare); Supplemental Security Income; the Special Supplemental Nutrition Program for 

Women, Infants, and Children; Medicare; and Medicaid. 

20 Examples of these assets and liabilities include: miscellaneous non-financial investments; annuities; trusts; 

mortgage-backed bonds; miscellaneous bonds; call accounts; hedge funds; real estate investment trusts (REITs); 

account-based pension plans other than 401(k), 403(b), thrift savings, and supplemental retirement annuity plans; 

non-actively managed businesses; loans owed for property that has already been sold; loans taken out against life 

insurance; and loans taken out against pension plans. The forthcoming 2014 SIPP panel has introduced questions 

about the value of annuities, trusts, and non-actively managed businesses.

10 
instances SCF collects the value of more accounts than SIPP does. 21 For example, SIPP asks 

respondents to report the value of up to three loans against the primary residence and the values

of and debts against up to three cars and trucks, up to two recreational vehicles, and up to two 

actively managed businesses. By contrast, SCF asks respondents to report the value of all loans 

against the primary residence and the values of and debts against all cars, trucks, recreational 

vehicles, and actively managed businesses.22 For both of these reasons, SCF might yield a 

larger and more accurate estimate of net worth than SIPP does. 

Third, SCF and SIPP impute missing data using different techniques. SCF imputes 

missing asset and debt values using a sequential regression multiple imputation technique.23 By 

contrast, SIPP imputes missing asset and debt values singly using a hot deck procedure.24 

Although the SCF imputation procedure is more demanding computationally than the SIPP 

imputation procedure, it bears at least two potential advantages. First, sequential regression 

techniques allow data producers to condition on a wide array of observable characteristics when 

imputing missing data. The curse of dimensionality makes it infeasible to condition on such a 

large number of observables when imputing data via a hot deck procedure. Second, multiple 

imputation allows users to estimate the uncertainty associated with imputed data. No existing 

study considers how using hot deck imputation versus multiple imputation affects asset data. 

 

21 Even when SIPP and SCF both ask about the same asset, in some instances these two surveys define that asset’s 

contribution to net worth differently. For example, SIPP uses the market value of U.S. government securities, 

municipal bonds, and corporate bonds to construct net worth. By contrast, SCF uses the face value of these assets to 

construct net worth. 

22 As another example, SCF respondents provide the value of each individual checking account for up to 6 checking 

accounts, and then provide the combined value of all remaining checking accounts for families with 7 or more 

checking accounts. By contrast, SIPP respondents provide only the combined value of all checking accounts. To 

the degree that respondents incur arithmetic errors when combining values, SCF yields more accurate estimates of 

net worth than SIPP does. The varying degree of mathematical abilities in the population (OECD 2013) suggests 

that this measurement error problem may be large and correlated with observables. 

23 See Kennickell (1998) for an in depth description of the SCF imputation process. 

24 Refer to Chapter 4 of U.S. Census Bureau (2001) for an in depth description of the SIPP imputation process. In 

brief, hot deck imputation assigns nonrespondents data that was reported by individuals with similar observable 

characteristics. 

11 
Andridge and Little (2010) employ health outcome data to show that parametric 

regression and hot deck regression yield quite comparable empirical bias and root mean 

squared error, while multiple imputation yields lower variance estimates than single 

imputation. Tang et al. (2005) concluded that multiple imputation produced different 

estimates for longitudinal clinical trials. 

Finally, SCF has a considerably smaller sample size than SIPP. In the 2010 SCF, 6,942 

families were interviewed, while 33,795 households were interviewed in wave 7 of the 2008 

SIPP panel. SIPP’s larger sample size aids subgroup analysis. Sample size concerns are 

especially relevant to the analysis of wealth data given the generally high rates of non-response 

to wealth questions. Consequently, wealth data are not well suited to the common strategy of 

excluding imputed data from analysis. The need to include imputed data underscores the 

potential for differences in imputation techniques to explain differences in wealth estimates 

across surveys. 

In light of their findings, Czajka et al. (2003) submitted a variety of recommendations for 

improving the quality of SIPP wealth data. The Census Bureau subsequently implemented some 

of these recommendations, so the SIPP wealth data that we employ differs from the wealth data 

in wave 9 of the 1996 SIPP panel in three important ways. First, SIPP incorporated debts into 

the imputation of some asset values. Consequently, we might expect to find a stronger 

correlation between individual families’ assets and debts for wave 7 of the 2008 panel than 

existed for the 1996 SIPP panel.25 Second, imputation hot decks were updated to improve 

consistency. Finally, SIPP made improvements that resulted in a lower imputation rate for 

vehicle values. Both SIPP and SCF construct vehicle values by asking respondents to report the 

 

25 See Thibaudeau, Gottschalck, and Palumbo (2006) for a description of this methodology. 

12 
year, make, and model of their vehicles and using this information to assign a value based on 

assessed values published by the National Automobile Dealers Association (NADA). For wave 

9 of the 1996 panel, SIPP utilized a book of assessed values that extended back only 7 model 

years. Values for all older vehicles were imputed. By wave 7 of the 2008 panel, SIPP utilized a 

book of assessed values that extended back 20 model years.26

 

4. Methodology 

 For the sake of comparability, our methodology strongly resembles the methodology 

applied in Czajka et al. (2003). We employ the same algorithm to construct SCF-like families in 

SIPP data that mimic the primary economic unit in SCF.27 Our method of aggregating SIPP 

wealth data to the level of the SCF-like family differs slightly from that of Czajka et al. (2003). 

They excluded the portion of an asset’s value that was owned by household members outside of 

the SCF-like family by assuming that the asset’s value was split equally among all owners. By 

contrast, we compute the net worth of an SCF-like family by aggregating the full value of all 

assets owned by at least one member of the primary economic unit. One rationale for this 

decision is that we lack the information to know whether an asset’s value would be split evenly 

among owners if sold. Additionally, SIPP does not identify the owners of most jointly held 

assets, including bank accounts, stocks, and mutual funds. Finally, for some assets, such as 

 

26 A third change between the 1996 and 2008 SIPP panels is the introduction of a question about the cash value of 

life insurance policies. Czajka et al. (2003) estimated that asset values such as the cash value of life insurance 

policies that SCF collected and SIPP did not collect accounted for about 10 percent of the SCF estimate of aggregate 

net worth. However, Gottschalck and Moore (2006) provide evidence that the current SIPP cash value of life 

insurance question actually captures a mix of face and cash values. Consequently, the cash value of life insurance is 

excluded from the SIPP total net worth and total asset value recode variables. Similarly, we exclude the cash value 

of life insurance from SIPP estimates in this paper. Thus, the additional question about the cash value of life 

insurance cannot explain any change in comparisons of SCF and SIPP wealth estimates that we document since 

Czajka et al. (2003). 

27 This sample selection criterion excludes from our analysis 9.9 percent of all respondents to the wave 7 topical 

module of the 2008 SIPP panel. 

13 
vehicles, SCF asks respondents to report all assets owned by their family. Including the entire 

value of such assets when computing the net worth of SCF-like families in SIPP might generate 

an estimate that is more comparable to the net worth of primary economic units in SCF. For 

these reasons, we prefer not to exclude the portion of an asset’s value that is held by individuals 

outside of the SCF-like family. Instead, we interpret our estimate of an SCF-like family’s net 

worth as one extreme of a range of estimates. 

We also typically apply the approach of Czajka et al. (2003) in accommodating the 

varying degrees of detail with which SCF and SIPP collect wealth data. To illustrate, SCF asks 

respondents to report the value of all checking accounts, savings accounts, and CDs separately. 

On the other hand, SIPP asks respondents to report the combined value of all interest-earning

checking accounts, savings accounts, and CDs. This distinction disappears after aggregating all 

asset and debt values to compare the total net worth of SCF-like families in SIPP and primary 

economic units in SCF. In order to investigate the sources of differences in net worth across 

surveys, Czajka et al. (2003) defined asset and debt categories that are consistent across SCF and 

SIPP. We only deviate from this categorization when analyzing retirement accounts and residual 

debt. We discuss the motivation for these deviations when discussing our results. 

One key deviation of our methodology from that of Czajka et al. (2003) is that we 

compute SIPP wealth statistics using restricted-use, uncensored value data. In the 2008 panel, 

SIPP censors values that surpass some threshold by replacing the outlying value with the 

threshold itself.28 By contrast, SCF pursues a variety of methods for altering outlying values.29

 

Czajka et al. (2003) list this difference in topcoding procedures as a potentially important source 

 

28 Most censored values lie in the right tail of a variable’s distribution. For a few assets, such as business values, 

censored values also lie in the left tail of a variable’s distribution. 

29 Fries, Johnson, and Woodburn (1997) and Kennickell (1997) describe the methods used to alter dollar values in 

the 2010 SCF. Kennickell and Lane (2007) report that SCF relies on topcoding and bottomcoding only sparingly. 

14 



 

Statistic SIPP 

Estimate

SCF 

Estimate Difference

Difference 

Standard 

Error

SIPP/SCF 

Ratio

Ratio 

Standard 

Error

Czajka et 

al. (2003) 

Ratio

25th Percentile 2,317 8,362 6,045*** 481 27.7 2.5 41.8

Median 64,699 77,006 12,307*** 2,982 84.0 3.4 66.9

75th Percentile 260,373 300,410 40,037*** 13,762 86.7 4.0 74.0

Mean 374,489 498,386 123,897*** 31,105 75.1 6.0 49.5

Aggregate (Sum, 

in trillions) 44.3 58.6 14.3*** 3.7 75.6 6.1 49.5

Table 1: Overview of Net Worth Estimates

Note: Table gives net worth estimates from a sample of all SCF-like families in 2008 SIPP (Wave 7) and all primary economic 

units in 2010 SCF. SCF-like families include the primary family in a household, any unmarried partners of the household 

reference person, and all of that partner's children younger than age 25. SCF-like families exclude subfamilies within the 

primary family that are headed by someone age 25 or older and siblings and other relatives of the household reference person 

who are age 25 or older. The SIPP and SCF estimates are given in 2010 dollars, and the ratio is in percentage terms. The

standard error for the difference was calculated using replicate weights from both surveys and the five imputation implicates 

for SCF. The SIPP standard errors were constructed though balanced repeated replication with Fay’s adjustment factor of 

0.5, and the SCF standard errors were constructed via bootstrapping. The standard error for the ratio was calculated using 

the delta method. Significance asterisks: *** p<.01, ** p<.05, * p<.1.

We next explore SIPP and SCF estimates throughout the wealth distribution. For the 75th

percentile, the SIPP estimate is about 87 percent of the SCF estimate, while for the 25th

percentile the SIPP estimate is about 28 percent of the SCF estimate. In Czajka et al. (2003), the 

SIPP estimate was 75 percent of the SCF estimate for the 75th percentile and 42 percent of the 

SCF estimate for the 25th percentile point estimate. It is unclear why the SIPP and SCF estimates 

are closer together for the upper tail of the wealth distribution but further apart for the lower 

tail.34 The economic environment was much different for the 2008 SIPP than for the 1996 SIPP, 

which could affect the composition of households with net worth around the 25th, 50th, and 75th

percentiles. If the SIPP estimates of net worth are different from the SCF estimates for certain 

 

34 Note that although the SIPP and SCF estimates of the 25th percentile of the net worth distribution are further apart 

than Czajka et al. (2003) documented, this does not necessarily imply that this SIPP estimate has declined in quality. 

Such an inference would require knowledge of the true distribution of net worth. While survey datasets likely 

provide information about this distribution, it is unlikely that analysts can recover the distribution itself from survey 

data. Administrative data on asset and debt values holds the most promise to reveal the true distribution of net 

worth. At present, administrative data in the United States offer information on asset incomes rather than asset and 

debt values. Saez and Zucman (2014) employ capitalization methods to infer the distribution of net worth based on 

these asset incomes. While this might yield more information about the true distribution of net worth than survey 

data can offer, the distribution itself remains unknown. 

18 
types of households, then changes in the composition of households at various points in the 

wealth distribution could affect the estimates of these percentiles. Another possible explanation 

is that the trends at the 25th, 50th, and 75th percentiles of net worth are driven by growing or 

shrinking disparities in certain asset classes across surveys. We return to this possibility later in 

this section.

5.1 Breakdown by Asset Type 

The previous section showed how SIPP and SCF differ in key estimates of net worth. 

This overall discrepancy reflects differences in the data for the underlying asset and debt 

components. In this section, we discuss how the ownership rates and values for each asset and 

debt category vary between the two surveys. We discuss each asset and debt class separately, 

explaining how the question texts differ and why these differences might affect the data. 

19 
Statistic SIPP 

Estimate

SCF 

Estimate Difference

Difference 

Standard 

Error

Czajka et al. 

(2003) 

Difference

Checking Accounts 60.4 85.1 24.7*** 0.6 —

Non-Checking Bank Accounts 57.0 61.6 4.6*** 0.8 —

Bank Accounts (Any) 72.7 91.6 18.8*** 0.5 13.4

Bonds 2.3 1.5 -0.8*** 0.2 —

Savings Bonds 10.1 12.0 1.9*** 0.4 4.2

Stocks and Mutual Funds 17.3 19.5 2.2*** 0.5 5.3

Business Equity (Positive) 9.2 12.2 3.0*** 0.4 3.8

Other Assets 2.0 8.5 6.5*** 0.4 7.6

IRA/Keogh 28.0 28.0 0.0 0.6 6.7

401(k)/Thrift 38.7 34.9 -3.8*** 0.6 1.3

Retirement Account (Any) 49.5 50.4 0.9 0.7 —

Primary Residence 65.5 67.2 1.7*** 0.2 -0.3

Mortgages 41.5 47.0 5.5*** 0.6 1.4

Rental Properties 9.8 18.3 8.5*** 0.4 6.4

Rental Property Debt 2.4 5.4 3.0*** 0.3 0.9

Vehicles 83.0 86.7 3.7*** 0.4 -1.5

Vehicle Debt 30.4 33.2 2.8*** 0.6 -0.3

Credit Cards 38.8 39.4 0.6 0.7 -6.5

Residual Debt 21.8 27.7 5.9*** 0.6 —

Table 2: Ownership Rates

Note: Table gives net worth estimates from a sample of all SCF-like families in 2008 SIPP (Wave 7) and all 

primary economic units in 2010 SCF. SCF-like families include the primary family in a household, any

unmarried partners of the household reference person, and all of that partner's children younger than age 

25. SCF-like families exclude subfamilies within the primary family that are headed by someone age 25 or 

older and siblings and other relatives of the household reference person who are age 25 or older. The

SIPP and SCF estimates are given in percentage terms. The standard error for the difference was 

calculated using replicate weights from both surveys and the five imputation implicates for SCF. The

SIPP standard errors were constructed through balanced repeated replication with Fay’s adjustment 

factor of 0.5, and the SCF standard errors were constructed via bootstrapping. Significance asterisks: *** 

p<.01, ** p<.05, * p<.1.

20 
Statistic SIPP 

Estimate

SCF 

Estimate Difference

Difference 

Standard 

Error

Czajka et al. 

(2003) 

Difference

Bank Accounts 2,600 4,000 1,400*** 164 600

Bonds 35,000 130,680 101,800*** 29,130 —

Savings Bonds 800 1,000 200 163 0

Stocks and Mutual Funds 30,000 37,400 7,400** 3,393 4,000

Business Equity (Positive) 25,000 99,800 74,800*** 4,045 60,000

Other Assets 41,000 5,000 -36,000*** 3,685 -21,000

IRA/Keogh 30,578 40,120 9,542*** 2,853 0

401(k)/Thrift 30,000 31,200 1,200 2,311 -4,558

Retirement Account (Any) 44,567 44,000 -567 2,804 —

Primary Residence 170,000 169,800 -200 4,281 0

Mortgages 120,000 109,200 -10,800*** 2,208 -3,000

Rental Properties 125,000 98,600 -26,400*** 8,874 500

Rental Property Debt 125,000 97,200 -27,800*** 9,358 -500

Vehicles 9,563 15,000 5,437*** 293 1,725

Vehicle Debt 10,000 9,740 -260 225 -800

Credit Cards 4,000 2,612 -1,388*** 172 -700

Residual Debt 10,000 10,040 40 519 —

Table 3: Median Values Conditional on Ownership

Note: Table gives net worth estimates from a sample of all SCF-like families in 2008 SIPP (Wave 7) and all primary 

economic units in 2010 SCF. SCF-like families include the primary family in a household, any unmarried partners 

of the household reference person, and all of that partner's children younger than age 25. SCF-like families 

exclude subfamilies within the primary family that are headed by someone age 25 or older and siblings and other 

relatives of the household reference person who are age 25 or older. The SIPP and SCF estimates are given in 

2010 dollars. The standard error for the difference was calculated using replicate weights from both surveys and 

the five imputation implicates for SCF. The SIPP standard errors were constructed through balanced repeated 

replication with Fay’s adjustment factor of 0.5, and the SCF standard errors were constructed via bootstrapping. 

Significance asterisks: *** p<.01, ** p<.05, * p<.1.

Table 2 presents household-level ownership rates for each asset and debt variable, and 

Table 3 presents median values conditional on ownership. Overall, SCF tends to have higher 

ownership rates, although generally the ownership rates are quite comparable across surveys. 

SCF also tends to have higher values conditional on ownership. Various features of these two 

surveys could lead to this result. For one, SCF has many more asset and debt questions. This

could generate higher ownership rates, as probing respondents with more questions about wealth 

might help them remember owning less salient components of their portfolio. For example, 

SIPP employs a residual question (“Do you own any other assets you have not yet told me 

about?”) in the hope of capturing less common assets, such as annuities and trusts. If the 

21 
respondent’s only opportunity to mention owning an obscure asset is in a residual 

question, then the respondent might forget to mention that asset. Given that recognition 

is easier than recall in general (Tourangeau 1984), requiring respondents to recall all the 

other assets they own rather than recognize them in a list of asset categories could 

produce lower ownership rates. The differential imputation techniques across surveys 

and the wider use of an account-by-account approach to reporting asset values might also 

explain the SCF-SIPP gap in median values. However, it is unclear why these 

differences would cause SIPP to underestimate SCF. 

Bank Accounts 

Next, we discuss how SIPP and SCF compare for particular asset and debt groups. 

For bank accounts, the largest source of discrepancy relates to the ownership rates for 

checking accounts.35 The SIPP ownership rate for checking accounts is 60.4 percent, 

while the SCF ownership rate for checking accounts is 85.1 percent. The ownership rate 

for non-checking bank accounts is also lower in SIPP, but the difference is not as large 

compared with checking accounts. For ownership rates of any type of bank account, the 

SCF estimate is 18.8 percentage points higher. This is a greater point estimate of the 

SCF-SIPP gap than in Czajka et al. (2003), which calculated a 13.4 percentage point 

higher rate for SCF. For median values, the point estimate of the difference between 

SIPP and SCF is larger now than it was in Czajka et al. (2003). They estimate that the 

 

35 For our analysis, bank accounts include interest earning and regular checking accounts, savings accounts, 

certificates of deposit, and money market deposit accounts. 

22 
median value was $600 higher in SCF, while we estimate that the median value is $1,400 higher 

in SCF.36

 

Checking account ownership rates could vary across data sets for a number of possible 

reasons. In SIPP, there are separate questions for interest-earning checking account and noninterest earning checking accounts. One possible explanation for the discrepancy results from 

the wording of question about non-interest earning checking account ownership: “Did you own 

any checking accounts in your OWN name which did NOT earn interest? (Do not include any 

interest-earning checking accounts reported earlier.)” Respondents might have been confused by 

the qualifier “which did NOT earn interest”. In this case, they might not respond affirmatively, 

even if they do have a checking account that pays no interest. The SCF question asks if the 

respondent owns a checking account without any additional qualifiers. 

In addition, SIPP asks about non-interest earning checking account ownership only in 

occasional topical modules, while it asks about interest-earning checking account ownership at 

every interview. Additionally, questions about non-interest earning checking accounts occur 

much later in the interview than questions about ownership of interest earning checking account. 

When they receive the question about interest-earning checking account ownership, respondents 

do not know that they will also receive questions about non-interest earning checking account 

ownership. This gap between the two checking account questions could cause some respondents 

 

36 As discussed in Section 1, we do not have access to standard errors for the estimates presented in Czajka et al. 

(2003). Consequently, whenever relevant we compare point estimates of our results to those from Czajka et al. 

(2003). We make no claim about the statistical significance of these comparisons. As a result, apparent differences 

between our estimates and those from Czajka et al. (2003) may not be statistically significant. 

23 
to forget that they have not said “yes” to any of the checking account ownership 

questions, resulting in lower ownership rates in SIPP than in SCF.37

Retirement Accounts 

The difference between SIPP and SCF ownership rates for IRAs is statistically 

insignificant at 27.99 percent and 28.02 percent, respectively. The point estimate of this 

difference is an improvement over Czajka et al. (2003), in which SCF had a 6.7 

percentage point higher ownership rate. SIPP’s median value for IRAs is about $10,000 

less than the SCF’s value ($30,600 SIPP vs. $40,120 SCF). SCF asks about the value of 

different types of IRAs separately (e.g. Roth, Roll-Over), so this difference could 

somehow lead to differences in median values. 

As for 401(k) and other employer-sponsored retirement plans, SIPP and SCF have 

fairly different questions. SIPP asks respondents if they have a “401k, 403b, or thrift 

plan.” On the other hand, SCF takes a three-pronged approach to collecting data on these 

plans. First, respondents answer whether they are “included in any pension, retirement, 

or tax-deferred savings plans connected with the job [the respondent] just told [the 

interviewer] about”. Second, respondents answer whether they have “earned rights to 

any other pensions or retirement accounts from a previous employer.” Third, respondents 

answer whether they are “currently receiving any (other) type of retirement, pension, or 

disability payments” or if they are “making withdrawals from a pension or retirement 

account”. 

 

37 In the 2014 SIPP Panel, the question text for non-interest earning checking account ownership is simplified and 

placed immediately after the question about interest earning checking account ownership. These changes might 

reduce the discrepancy between SIPP and SCF for checking accounts. 

24 
When comparing SIPP and SCF data, Czajka et al. (2003) only consider SCF data on 

retirement accounts from the current job. By contrast, we include data on retirement accounts

from all employers in SCF data. We argue that this yields a more even comparison because the 

SIPP 401(k) question elicits data on all employer-sponsored plans with a personal account 

balance. 

 Given this construction of the 401(k) variable, SIPP has a higher ownership rate (38.7 

percent vs. 34.9 percent), but the difference in median values is statistically insignificant. As the 

structure of the 401(k) questions is very different between the two surveys, there are a number of 

possible reasons why SIPP has a higher ownership rate. One consistent explanation is that SCF 

does not use the word 401(k) in the ownership questions for employer-sponsored plans. 

Respondents might identify with the word “401k” but perhaps not recall that a 401(k) is 

classified as a retirement account. Another possibility is that respondents latch on to the word 

“pension” and interpret the question as asking about defined benefit plan ownership only.38

 

 Finally, since respondents might be unable to distinguish types of retirement accounts, we 

aggregate these plans and compare the resulting estimates for SIPP and SCF. This exercise 

causes the disparities between the surveys to disappear. The difference in ownership rates (49.5 

percent SIPP vs 50.4 percent SCF) and the difference in median values ($44,567 SIPP vs. 

$44,000 SCF) are both statistically insignificant. Therefore, while the surveys do have some 

differences for particular types of retirement accounts, it appears that both surveys do collect 

comparable data about retirement accounts as a whole. 

Primary Residence 

 

38 Tourangeau et al. (2014) discuss how changing the examples provided in survey questions can have a large effect 

on how respondents interpret a question. 

25 
The SIPP estimates for primary residences are very close to their SCF analogs. 

The SIPP ownership rate is 1.7 percentage points lower (65.5 percent SIPP vs. 67.2 

percent SCF), but there is no statistically significant difference in median values 

conditional on ownership ($170,000 SIPP vs $169,800 SCF). The point estimates of 

these differences in ownership rates are comparable to Czajka et al. (2003), in which 

SIPP had a 0.3 percentage point higher ownership rate. The median values in SIPP and 

SCF estimates were identical in Czajka et al. (2003). The SIPP and SCF question texts 

are fairly similar to each other, which could explain the very similar values across 

surveys. SCF asks “What is the current value of this property? I mean, without taking 

any outstanding loans into account, about what would it bring if it were sold today?” and 

SIPP asks “What is the current value of this property; that is, how much do you think it 

would sell for on today's market if it were for sale?” Both questions ask about value and 

then define value as the amount that the respondent would receive if the residence were 

sold today. 

For home-related debt, SCF has higher ownership rates (41.5 percent SIPP vs. 

47.0 percent SCF) but lower median values ($120,000 SIPP vs. $109,200 SCF).39 One 

consistent explanation for this finding is that SCF has separate questions on home-equity

loans, while the SIPP question text primarily mentions mortgages. The SIPP question 

asks if the respondent has any “mortgages or loans” for the property without mentioning 

any examples. Respondents might not consider a home-equity loan to be a “mortgage or 

loan.” However, many people who have home-equity loans also have mortgages, so it is 

 

39 The estimated ownership rate was calculated for our entire sample, including SCF-like families that were not 

homeowners. The estimated median value was calculated for the sample of SCF-like families who had home-related 

debt. 

26 
unclear how the separate question on home-equity loans would explain the observed differences 

in ownership rate and value. 

Vehicle Values 

The SIPP estimate of median vehicle value is about $5,400 less than the SCF estimate 

($9,600 SIPP vs. $15,000 SCF). The point estimate of this disparity is larger than the $1,725 

SCF-SIPP gap documented by Czajka et al. (2003). Recall that both surveys follow similar 

procedures to assign values to cars, vans, and trucks. For recreational vehicles, such as boats and 

motorcycles, both surveys ask respondents to estimate the vehicle values. Even with these

similarities, there are a variety of methodological differences between the surveys which could 

result in SIPP having a lower estimate. For one, SIPP uses average trade-in values, while SCF 

uses the average retail values. Trade-in values are lower than retail values typically. A second 

difference is how SIPP incorporates depreciation. In the 2008 Panel, SIPP assigned vehicle 

values based on NADA data at the start of the panel. Rather than repeating this process for later 

waves of the panel, a constant and uniform rate of depreciation was applied for all vehicles to 

construct vehicle values for those waves.40 SCF, on the other hand, directly referenced NADA 

assessments for each survey. These differences would lead to the observed differences in vehicle 

value if the constant depreciation rate assumed by SIPP overestimates the true average 

depreciation rate. Third, the incorporation of assessments for vehicles between 7 and 20 years 

old in the 2008 SIPP panel might explain the wider SCF-SIPP gap in our results relative to 

Czajka et al. (2003). If the average values previously imputed based on model year tended to 

overestimate the value of older vehicles, the less frequent imputation in the 2008 SIPP panel

 

40 The 2014 SIPP Panel will directly reference NADA assessments for each wave. 

27 
could yield a lower median vehicle value relative to SCF than was present in the 1996 

SIPP panel. 

Rental Properties 

SIPP has lower ownership rates for rental properties (9.8 percent SIPP vs 18.3 

percent SCF) but higher value conditional on ownership ($125,000 SIPP vs. $98,600 

SCF). The differences potentially are due to the examples of rental properties that SCF 

gives. For instance, the SCF question text lists timeshares as an example of a rental 

property, while the SIPP question does not list any examples. If survey respondents do 

not associate timeshares with rental property unless prompted, and if a timeshare is less 

valuable than other types of rental property on average, this could lead to lower 

ownership rates but higher value conditional on ownership.41

Bonds 

SIPP and SCF have comparable ownership rates and similar median values for 

U.S. Savings Bonds. The SIPP ownership rate is 10.1 percent and the SCF rate is 12.0 

percent. The difference in median value between these datasets is statistically 

insignificant ($800 SIPP vs $1,000 SCF). This comparability might be due to the high 

similarity in question text. For municipal and corporate bonds, SIPP has higher 

ownership rates (2.3 percent SIPP vs 1.5 percent SCF) but substantially lower value 

conditional on ownership. The SIPP median is $35,000, while the SCF median is 

$130,680. The reasons for the large discrepancy are unclear. SCF asks whether the 

respondent owns any bonds and then asks about ownership of particular types of bonds, 

including municipal and corporate bonds as well as mortgage-backed bonds and foreign 

 

41 In the 2014 SIPP Panel, examples of rental properties were added to the question text. 

28 
bonds. The SIPP question asks whether the respondent owns any municipal or corporate bonds, 

and does not ask about other types of bonds collected by SCF. The additional questions in SCF 

might explain part of the difference in values. Because such a small proportion of the population 

owns bonds, the differential coverage of high-income households could also explain part of the 

SCF-SIPP gap. In SCF, 55.5 percent of bond owners have net worth over $2 million. Because 

of this, sampling error in the proportion of high wealth households who own bonds could have 

especially large effects on estimated median values conditional on ownership. 

Stocks and Mutual Funds 

SCF has higher ownership rates for directly-owned stocks and mutual funds, and higher 

medians as well.42 The SIPP ownership rate is 17.3 percent and the SCF rate is 19.5 percent. 

The SIPP median is $30,000 and the SCF median is $37,400. The reasons for this are unclear. 

SIPP asks whether the respondent owns any “stocks” and whether they own any “mutual funds”. 

SCF asks whether the respondent has any “mutual funds or hedge funds?” or owns any “stock 

which is publicly traded?” This variation in wording could change responses, but it is unclear 

why this would lead to a higher ownership rate and value for SCF. 

Business Equity 

For business equity, SIPP has a lower ownership rate (9.2 percent SIPP vs. 12.2 percent 

SCF) and a lower median value ($25,000 SIPP vs. $99,800 SCF).43 In Czajka et al. (2003), SCF 

had a median value point estimate that was $60,000 higher. To collect business data, both 

surveys use questions which vary in structure and placement in the survey. SIPP asks about 

business ownership first during the questions on employment status. SCF, on the other hand, 

 

42 These are stocks and shares in mutual funds a respondent owns outside of any retirement account. 

43 Czajka et al. (2003) discuss that SCF asks respondents how much they would receive if they sold their share of a 

business. By construction, this variable cannot be negative. Because of this, they transform the SIPP variable as the 

business value minus the business debt, and they bound this number from below by zero. 

29 
asks the business questions in a similar manner to other asset questions. Another notable

difference is that 2008 SIPP does not ask about businesses owned as an investment but 

actively managed.44

 Since respondents who own such businesses are most likely high 

individuals, this could explain the higher estimated value in SCF. 

Other Assets 

For the other asset category, SIPP has a lower ownership rate (2.0 percent SIPP vs 

8.5 percent SCF) but a higher value ($41,000 SIPP vs. $5,000 SCF). The SIPP question 

asks if the respondent owns “any other financial investments”, while SCF asks if the 

respondent owns “any other substantial assets that I haven't already recorded for example, 

artwork, precious metals, antiques...” The SIPP help screen for the other assets question 

does provide examples, but help screens in SIPP are used infrequently.45 Moreover, 

respondents are unlikely to recognize their assets in this help screen as the list consists of 

relatively uncommon assets, such as “a non-corporate business venture managed by 

others (e.g. a limited partnership), investments in a corporation, and ... part-ownership of 

a race horse.” Respondents might not consider assets that do not generate income to be 

financial assets unless primed to think of these objects as assets. Such assets might have 

lower values than financial assets that do generate income. This would be consistent with 

a lower ownership rates yet a higher value conditional on ownership in SIPP relative to 

SCF. 

Credit Cards 

 

44 The 2014 SIPP Panel introduced questions about non-actively managed businesses 

45 To our knowledge, no evidence exists regarding the frequency with which interviewers access help screens in the 

2008 panel of SIPP. Fee, Campanello, and Marlay (2014) document that interviewers access help screens 

infrequently in wave 1 of the 2014 SIPP panel. 

30 
SIPP and SCF have similar ownership rates for credit card debt (38.8 percent SIPP vs. 

39.4 percent SCF), with a difference that is not statistically significant. This point estimate is 

different from Czajka et al. (2003), in which SIPP had a 6.5 percentage point higher ownership 

rate. SIPP has a higher median, with a value of $4,000 compared to SCF’s $2,612. The higher 

median value could be due to the timeframe referenced in the question. SCF asks “after the last 

payment was made, what was the total balance still owed?” The SIPP analog asks what the 

amount was “as of the last day of the reference period.”46 Respondents might interpret the SIPP 

question as asking for the credit card balance at the end of the reference period, regardless of 

whether that balance will be erased with the next payment. This is consistent with a larger 

median credit card debt in SIPP than in SCF to the extent that the time of the last payment does 

not align with the end of the reference period. However, this difference in timing should also 

increase ownership rates in the SIPP. Respondents who have a credit card balance at the end of 

the reference period but who pay the balance off completely would be classified as credit card 

debt owners by SIPP but not by SCF. Our finding that ownership rates are not statistically 

different between the surveys does not support that hypothesis. 

Residual Debt 

Both SIPP and SCF have an “Other Loan” and “Other Debt” question, which asks 

respondents to provide the value of any other loans or debt that the respondent has not already 

reported. When comparing the SIPP “Other Loan” and “Other Debt” data to their SCF analogs, 

we combine these two SIPP variables. We also combine SCF data on other debt, other loans, 

and forms of debt about which SIPP does not ask respondents to report separate values. Student 

loans, home-improvement loans, lines of credit besides credit cards, and other debt from a 

 

46The ownership question for credit card debt also mentions the last day of the reference period, asking “As of the 

last day of the reference period, did ... owe any money for store bills or credit card bills?” 

31 
property purchase all fit into this category. Some of these types of debt, such as student 

loans, are given as examples in SIPP’s “other debt” question. 

Our combination of the “Other Loan” and “Other Debt” is in contrast with Czajka 

et al. (2003), who consider these variables separately. We combine these two variables 

because the “Other Loan” question differs between the surveys. 47 In addition, it is 

unclear whether some of the additional SCF debt variables should be placed in the “Other 

Loan” or “Other Debt” category.48 For this residual debt variable, SCF has higher 

ownership rates (21.8 percent SIPP vs. 27.7 percent SCF), but the median values are not 

statistically different from each other ($10,000 SIPP vs. $10,040 SCF). The difference in 

ownership rates might be due to the additional questions in SCF which allow respondents 

to recognize rather than recall which types of debt they hold. 

 

47 The SCF “Other Loan” question asks respondents whether they “owe any money or have any other loans for any 

reason,” while the SIPP question asks if respondents whether they “owe any money for loans obtained through a 

bank or credit union,” which has an additional qualifier for the source of the other loan that SCF does not have. This 

additional qualifier might yield uneven comparisons. 

48 For example, SIPP gives student loans as an example in its “Other Debt” question. However, SIPP’s “Other 

Loan” question asks about loans from banks, which some respondents might interpret as asking about their student 

loans. 

32 
Table 4: Ownership Rates By Net Worth Percentiles

Asset

Rate S.E. Rate S.E. Rate S.E.

Bank Accounts (Any) 61.0 0.8 75.8 0.7 85.0 0.6

Bonds 0.1 0.0 0.7 0.1 2.4 0.2

Savings Bonds 3.0 0.2 8.0 0.5 16.0 0.6

Stocks and Mutual Funds 2.7 0.2 11.6 0.5 27.4 0.7

Business Equity (Positive) 3.3 0.3 8.2 0.5 12.2 0.5

Other Assets 0.1 0.1 1.5 0.2 3.2 0.3

Retirement Account (Any) 21.6 0.7 51.2 0.9 71.3 0.7

Primary Residence 18.8 0.7 84.7 0.6 94.5 0.4

Mortgages 13.9 0.6 55.9 0.8 52.3 0.7

Rental Properties 0.9 0.1 5.2 0.4 16.0 0.6

Rental Property Debt 0.2 0.1 1.3 0.2 3.5 0.3

Vehicles 85.6 0.6 87.8 0.6 91.6 0.4

Vehicle Debt 24.0 0.7 35.0 0.9 30.9 0.7

Credit Cards 25.1 0.7 41.7 0.9 40.7 0.8

Residual Debt 13.8 0.5 23.1 0.7 15.8 0.6

25th Percentile 50th Percentile 75th Percentile

Note: Table gives ownership rates of assets for households around the 25th, 50th, and 75th 

percentiles of the net worth distribution from a sample of all SCF-like families in 2008 SIPP 

(Wave 7). SCF-like families include the primary family in a household, any unmarried 

partners of the household reference person, and all of that partner's children younger than 

age 25. SCF-like families exclude subfamilies within the primary family that are headed by 

someone age 25 or older and siblings and other relatives of the household reference person 

who are age 25 or older. We construct the ownership rates in this table using only 

households within ±5 percentiles of a given net worth percentile. We weighted each 

household equally within each 10 percentile band. The standard errors were constructed 

through balanced repeated replication with Fay’s adjustment factor of 0.5. 

Ownership Rates by Percentile in Net Worth Distribution 

The trends in how SIPP and SCF estimates compare by asset class established above 

could in part account for the trends in how SIPP and SCF estimates of total net worth compare 

at the 25th, 50th, and 75th percentiles of the net worth distribution. For example, the point 

estimate of the SCF-SIPP gap between median estimates of both bank account values and vehicle 

values conditional on ownership has increased since Czajka et al. (2003). If these assets 

comprise a large proportion of net worth for households around the 25th percentile of the net 

worth distribution, then the trends for these underlying assets could explain the increased SCFSIPP gap at this area of the net worth distribution. To examine this hypothesis, Table 4 shows 

how ownership rates by asset class vary across the net worth distribution in SIPP. Table 4 shows 

that bank accounts and vehicles are commonly owned by households around the 25th percentile 

33 
of the net worth distribution. This suggests that the larger point estimate of the SCF-SIPP 

gap in median bank account and vehicle values since Czajka et al. (2003) helps to explain 

the larger point estimate of the SCF-SIPP gap in the 25th percentile of the net worth 

distribution since Czajka et al. (2003). 

Table 4 might also explain in part why in the cross-section we find a closer 

correspondence between SIPP and SCF at the 50th and 75th percentiles than we do at the 

25th percentile. While ownership rates for all assets increase between the 25th and 50th

percentiles of the wealth distribution, some ownership rates increase more than others do. 

For example, ownership of vehicles only increases from 85.6 to 87.8 percent between the 

25th and 50th net worth percentiles. By contrast, ownership of primary residences 

increases from 18.8 to 84.7 percent and ownership of retirement accounts increases from 

21.6 to 51.2 percent. Table 3 demonstrates that there is no statistical difference in median 

primary residence values and median retirement account values between SCF and SIPP. 

This difference in the average portfolio composition at the 25th and 50th percentiles of the 

net worth distribution suggests that variation in ownership rates potentially explains part 

of the closer correspondence between SIPP and SCF for higher net worth percentiles. 

Summary of Asset and Debt Analysis 

In Section 5.1, we discussed how ownership rates and median values of asset and 

debt categories compare between the 2008 SIPP and 2010 SCF. When possible, we 

argue how these disparities for given asset and debt variables could result from question 

text or other survey design differences between the SIPP and SCF. We believe these

analyses help further explain why SIPP and SCF have different estimates for net worth 

percentiles. To the extent that these discrepancies stem from question text or other 

34 
survey design differences, our investigation gives guidance to survey methodologists about how 

to design wealth questions. One as yet unresolved question is whether these question text 

differences contributed to the changes in net worth estimates over time that we have 

documented. Between the 1996 and 2008 SIPP Panels, there were some changes in how asset 

income was collected and dependent interviewing was introduced to collect asset ownership data 

in subsequent interviews, but there were very few question text changes to the asset ownership 

and value questions.49 Consequently, while these question text differences may help explain the 

current cross-sectional difference between the SIPP and SCF net worth estimates, they do not 

give guidance for why these net worth estimates have changed over time. More promising 

potential sources of these time trends include the changes implemented on the basis of Czajka et 

al. (2003) and our use of uncensored wealth amounts to derive estimates. 

5.2 Asset and Debt Comparisons 

Up to now, we have shown how SIPP and SCF wealth estimates compare in aggregate 

and within asset and debt categories. Next, we explore how well correlated households’ assets 

and debt are to each other. Table 5 presents summary statistics of total asset values and debt 

values as well as correlations between certain variables. We exclude households with net worth 

over $2 million to mitigate the effect of outliers in our analyses. Czajka et al. (2003) offered 

comparisons between total assets and total debt in order to investigate further the differences in 

net worth estimates. They find that SIPP matches SCF estimates of mean assets and debt well, 

with the SIPP estimate of mean assets being 79.2 percent of the SCF estimate in 1998 and the 

SIPP estimate of mean debt being 99.5 percent of the SCF estimate in 1998 for households with 

 

49 See Moore and Griffiths (2003) for a detailed discussion on the changes made to the SIPP Asset Income and 

Ownership questions between the 2001 and 2004 Panels.

35 
net worth under $2 million. In the 2008 Panel, we find that the ratio of point estimates 

for mean assets is higher, with the SIPP estimate being 89.4 percent of the SCF estimate. 

The SIPP estimate of mean debt in the 2008 Panel is 100 percent of the SCF estimate, 

which is similar to the point estimates from Czajka et al. (2003). This shows that SIPP 

and SCF continue to compare well with respect to mean debt, and the point estimate for 

assets has improved over time.50

 

Statistic SIPP 

Estimate

SCF 

Estimate Difference

Difference 

Standard 

Error

Ratio 

SIPP/SCF

Ratio 

Standard 

Error

Czajka et al. 

(2003) 

Difference

Asset Mean 267,813 299,681 31,868*** 5,971 89.4 1.8 44,369

Debt Mean 85,400 85,404 4.0 2,991 100.0 3.5 216.0

Asset & Debt 

Correlation 23.5 53.7 30.3*** 7.9 — — —

Primary Residence 

Value & Debt 

Correlation

50.6 72.6 22.0*** 6.5 — — —

Vehicle Value & Debt 

Correlation 55.5 50.3 -5.2 5.4 — — —

Rental Property Value 

& Debt Correlation 77.1 39.3 -34.8** 15.9 — — —

Table 5: Asset and Debt Comparisons (Excluding High-Wealth Households)

Note: Table gives net worth estimates from a sample of all SCF-like families in 2008 SIPP (Wave 7) and all primary economic 

units in 2010 SCF. SCF-like families include the primary family in a household, any unmarried partners of the household 

reference person, and all of that partner's children younger than age 25. SCF-like families exclude subfamilies within the 

primary family that are headed by someone age 25 or older and siblings and other relatives of the household reference 

person who are age 25 or older. The SIPP and SCF estimates for Asset & Debt Mean are expressed in 2010 dollars, and the 

correlations are expressed in percentages. All statistics exclude observations with net worth over $2 million. For the 

Primary Residence, Vehicle, and Rental Property Correlations, we condition on both the value and debt variables being 

greater than zero. The standard error for the difference was calculated using replicate weights from both surveys and the 

five imputation implicates for SCF. The SIPP standard errors were constructed through balanced repeated replication with 

Fay’s adjustment factor of 0.5, and the SCF standard errors were constructed via bootstrapping. The standard error for the 

ratio was calculated using the delta method. Significance asterisks: *** p<.01, ** p<.05, * p<.1.

 

50 Czajka et al. (2003) also created estimates of mean asset and debt values including families with more than $2 

million in net worth. SIPP and SCF estimates compared less well for this sample, especially for asset values. We 

also computed mean asset and debt values including families with more than $2 million in net worth. We find that 

the point estimate of the SCF-SIPP gap has narrowed since Czajka et al. (2003). Estimates are available upon 

request. 

36 
However, Czajka et al. (2003) found that the correlation between asset values and debt 

values in SIPP was very low compared to SCF, which could account for some of the SIPP-SCF 

gap in net worth estimates. For example, if SIPP imputes mortgage values without taking into 

account the value of the house, then SIPP could have a sizable difference in median net worth 

relative to SCF even if the median asset values and debt values are similar across the two 

surveys. Using the entire sample from both surveys, the 1998 SCF had a correlation between 

total assets and total debt of 0.401, while SIPP’s correlation for waves around the 1998 calendar 

year were 0.066 in wave 6 of the 1996 Panel and 0.118 in wave 9 of the 1996 Panel. The Census 

Bureau has since modified its imputation method for some asset and debt variables to improve

their correlation. We find that excluding high wealth households, the correlation for SIPP is 

0.234 and the SCF estimate is 0.537. We exclude high wealth households because it appears that 

this correlation is particularly sensitive to outliers. Therefore, the low point estimate of 

correlations found in Czajka et al. (2003) for SIPP might have been driven by outliers rather than 

a low quality of SIPP wealth data. For example, SIPP’s correlation for our entire sample is only 

0.020. Recall that the Census Bureau implemented the change recommended by Czajka et al. 

(2003) of imputing home values and home debts jointly in the hope of improving the correlation 

between asset values and debt values. Our finding that the point estimate of this correlation for 

the entire sample has declined suggests that this change was insufficient to increase the 

correlation for the entire sample. 

To decompose this correlation further, we examine the correlation between primary 

residence values and mortgage debt, vehicle values and vehicle debt, and rental property values

and debt. We find that the SIPP correlation for primary residence values and debt is lower than 

the SCF correlation, while the SIPP correlation for vehicles and rental properties is higher than 

37 
the SCF estimate. Home equity is often a large portion of a household’s net worth. 

Consequently, the discrepancy between the correlation of primary residence values and 

mortgage values in SIPP and SCF is likely one of the driving factors for the discrepancy 

between the correlation of overall asset values and overall debt values in SIPP and SCF. 

5.3 High Wealth Analysis 

Sample: Statistic SIPP 

Estimate

SCF 

Estimate Difference

Difference 

Standard 

Error

Czajka et al. 

(2003) 

Difference

High-Wealth HH: Net Worth Mean (in millions) 11.7 6.6 -5.1*** 1.7 2.1

Non-High-Wealth HH: Net Worth Mean 182,413 217,901 35,489*** 5,550 44,154

High-Wealth HH: Net Worth Sum (in trillions) 23.1 34.1 11.0*** 3.7 10.4

Non-High-Wealth HH: Net Worth Sum (in trillions) 21.2 24.5 3.3*** 0.6 4.3

Proportion of High-Wealth HH in Sample 1.7 4.4 2.8*** 0.3 1.4

Unweighted Sample Size of High-Wealth HH 550 1,082 — — —

Table 6: Net Worth Estimates for High Wealth Analysis

Note: Table gives net worth estimates from a sample of all SCF-like families in 2008 SIPP (Wave 7) and all primary economic units in 

2010 SCF. SCF-like families include the primary family in a household, any unmarried partners of the household reference person, 

and all of that partner's children younger than age 25. SCF-like families exclude subfamilies within the primary family that are headed 

by someone age 25 or older and siblings and other relatives of the household reference person who are age 25 or older. The 

statistics on net worth are expressed in 2010 dollars, and the proportion is expressed in percentages. In this table, a high-wealth 

household is a household with net worth over $2 million. For the proportion of high-wealth households, the ratio of the SCF 

estimate to the SIPP estimate is 2.66, with a standard error of 0.19 as constructed by the delta method. The standard error for the 

difference was calculated using replicate weights from both surveys and the five imputation implicates for SCF. The SIPP standard 

errors were constructed through balanced repeated replication with Fay’s adjustment factor of 0.5, and the SCF standard errors were 

constructed via bootstrapping. Significance asterisks: *** p<.01, ** p<.05, * p<.1. 

 

One use of wealth data is measuring wealth inequality. To understand how we 

might expect SIPP and SCF estimates of inequality to compare, we analyze aggregate and 

mean net worth of high wealth households. Given the highly skewed nature of the wealth 

distribution, we expect differences in SCF and SIPP estimates for the upper tail of the 

wealth distribution to drive differences in wealth inequality estimates. SCF oversamples 

high-income households while SIPP oversamples low-income areas, so SCF is inherently 

designed to obtain more representative wealth estimates for the upper tail than SIPP. 

38 
Nevertheless, many researchers are interested in wealth inequality and aggregate wealth, so it is 

important to investigate how well the SIPP estimates of the net worth of high-wealth households 

correspond to their SCF analogs. 

Table 6 presents mean and aggregate wealth, both for higher wealth households and 

lower wealth households. As in Czajka et al. (2003), we classify a household as high wealth if 

that household’s net worth is over $2 million dollars. There are 550 unweighted high wealth 

households in the SIPP sample and 1,082 in the SCF sample.51 Therefore, even though SCF 

oversamples high-income households, the large sample size of SIPP somewhat mitigates this 

coverage difference. The estimated proportion of high wealth households in the U.S. population 

is 1.7 percent for SIPP and 4.4 percent for SCF. In Czajka et al. (2003), the estimated proportion 

of high wealth households was 1.8 percent in SCF vs. 0.3 percent in SIPP. While the point 

estimate of the gap between the SIPP and SCF estimates has increased in percentage points (1.4 

for Czajka et al. (2003) vs. 2.8 for our estimates), the point estimate of the ratio of the SCF 

estimate to the SIPP estimate has gone down (5.3 for Czajka et al. (2003) vs. 2.7 for our 

estimate). Therefore, in certain respects, the SIPP estimate of the proportion of high wealth 

households has improved. However, any improvement is likely due to our use of uncensored 

values, at least in part. 

The SIPP estimates of mean and aggregate net worth are lower than their SCF analogs for 

most estimates. However, SIPP has a higher estimate of mean net worth for high wealth 

households ($11.7 million for SIPP vs. $6.6 million for SCF). The reasons for this are unclear, 

 

51 Note that we classify households as high wealth using uncensored SIPP data but censored SCF data. These counts 

of high wealth sample households might vary when we compute net worth using censored data for both surveys. 

However, Board of Governors of the Federal Reserve System (2011) note that the SCF censoring procedure is 

designed “to ensure that any distortions induced in key population statistics would be minimal,” so our SCF 

estimates might be similar to those obtained with uncensored data. 

39 
but could be due to sampling error. With so few wealthy households, each response has 

an especially large impact on estimates of aggregate and mean wealth for this 

subpopulation. 

5.4 Implication for Racial Wealth Inequality 

Many analysts are interested in how wealth varies across racial groups (e.g. National Urban 

League 2015, Altonji and Doraszelski 2005). In Table 7, we analyze how wealth varies between 

White and Black households.52 Overall, the wealth gap is similar for SIPP and SCF. The SIPP 

estimate of the median net worth of White households is about $101,600, while SCF estimates it

at about $104,600, a difference that is statistically insignificant. For Blacks, the SIPP estimate of 

median net worth is about $5,000, while the SCF estimate is about $15,600. Thus, the difference 

in wealth between Black and White households is $96,600 in SIPP and $89,100 in SCF. The 

difference in these differences across surveys of -$7,531 is not statistically significant. In other 

words, there is no statistically significant difference in the wealth gap as measured by the two 

surveys. This suggests that even though the two surveys that we examine yield different 

estimates of key statistics, these differences have no implications for estimates of median wealth 

differences for Black and White households. 

 

52 We define the race of the household by the race of the household reference person. 

40 
41 

Statistic SIPP 

Estimate

SIPP 

Standard 

Error

SCF 

Estimate

SCF 

Standard 

Error

Difference

Difference 

Standard 

Error

Czajka et al. 

(2003) 

Difference

White Median 101,618 1,711 104,638 5,735 3,020 5,985 24,500

Black Median 5,003 486 15,554 1,606 10,551*** 1,678 9,400

White - Black Difference 96,615*** 1,730 89,084*** 5,706 -7,531 5,962 15,100

Table 7: Wealth by Race

Note: Table gives net worth estimates from a sample of all SCF-like families in 2008 SIPP (Wave 7) and all primary economic units 

in 2010 SCF. SCF-like families include the primary family in a household, any unmarried partners of the household reference 

person, and all of that partner's children younger than age 25. SCF-like families exclude subfamilies within the primary family that 

are headed by someone age 25 or older and siblings and other relatives of the household reference person who are age 25 or 

older. The SIPP and SCF estimates are given in 2010 dollars. The standard error for the difference was calculated using replicate 

weights from both surveys and the five imputation implicates for SCF. The SIPP standard errors were constructed through 

balanced repeated replication with Fay’s adjustment factor of 0.5, and the SCF standard errors were constructed via 

bootstrapping. The standard errors for the difference between black and white households for each survey do not incorporate 

the covariance between the estimates within each survey. The standard error for the ratio was calculated using the delta method. 

Significance asterisks: *** p<.01, ** p<.05, * p<.1. 

The point estimate of this difference-in-differences is an improvement over the results 

presented in in Czajka et al. (2003). They estimate a wealth gap in SIPP of $64,300 but a wealth 

gap of $79,400 for SCF, with a difference-in-differences of $15,100. These improvements 

appear to be driven by the improved correspondence between net worth estimates for White 

households across surveys. In Czajka et al. (2003), the gap in net worth estimates between the 

two surveys for White households was $24,500, while we find a current gap of only $3,020. For 

Black households, Czajka et al. (2003) estimated that the gap was $9,400, which is not 

statistically different from our estimate of $10,551.53

The improvement for White households is potentially related to the evidence in Table 1 

that the gap between SIPP and SCF net worth estimates has narrowed for the 75th percentile. 

 

53 Note that even though we do not have access to standard errors for the estimates in Czajka et al. (2003), we can 

infer that our estimates of the SCF-SIPP gap in median wealth of Black households are not statistically different. 

Specifically, we test for the equality of these two estimates by treating the estimate in Czajka et al. (2003) as a 

constant. This test assumes incorrectly that the estimate in Czajka et al. (2003) has a standard error of zero. We 

should instead construct the difference of the two estimates and the standard error of that difference, testing the null 

hypothesis that the difference is equal to zero. The standard error of our estimate alone necessarily understates the 

standard error of the difference between our estimate and that of Czajka et al. (2003). Therefore, if we fail to reject 

the null hypothesis when treating the Czajka et al. (2003) estimate as a constant, then we must also fail to reject the 

null hypothesis of an equivalent test that treats the Czajka et al. (2003) estimate as uncertain. 
Vornovitsky, Gottschalck, and Smith (2014) document that White individuals tend to 

have higher wealth than Black individuals do across the wealth distribution. This 

suggests that White households are more likely to fall at the 75th percentile of the wealth 

distribution than Black households. However, Table 1 also shows that the gap between 

SIPP and SCF net worth estimates of the 25th percentile has widened. If Black 

households are more likely to fall at the 25th percentile of the wealth distribution than 

White households, we would expect to see the SCF-SIPP gap for Black households widen 

as well, yet we observe no difference. 

6. Conclusion 

In this paper, we compare various wealth estimates between the 2010 Survey of 

Consumer Finances and wave 7 of the 2008 panel of the Survey of Income and Program 

Participation. Czajka, Jacobson, and Cody (2003) issued recommendations for improving SIPP 

wealth data after documenting considerable gaps between wealth estimates from the 1998 SCF 

and wave 9 of the 1996 SIPP panel. We provide the first evaluation of the extent to which SIPP 

wealth estimates have improved relative to SCF since implementing some of these

recommendations. Our methodology strongly resembles that of Czajka et al. (2003) for the sake 

of comparability. 

We find mixed evidence regarding the comparability of SIPP and SCF estimates. The 

difference between some key point estimates in SCF and SIPP has narrowed compared to Czajka 

et al. (2003). Specifically, SIPP mean net worth increased from approximately half to 

approximately three-quarters of the corresponding estimate in SCF, while median net worth in 

SIPP increased from roughly two-thirds to 84 percent of its SCF analog. Nevertheless, the 

42 
difference between other key wealth estimates in SCF and SIPP has widened. For example, the 

25th percentile of the net worth distribution in SIPP declined from about 42 percent to about 28 

percent of its SCF analog. Within asset and debt categories, SIPP and SCF continue to compare 

well with respect to home values, savings bond values, 401(k) account values, and vehicle debt

relatively well. However, the SCF-SIPP gap has grown for bank account values, vehicle values, 

and mortgage values. We find that while SIPP continues to measure the mean debt value

relatively well, and the SIPP estimate of the mean asset value compares more favorably to SCF, 

the correlation of asset values and debt values for the full sample has declined. We also show 

that the gap between SCF and SIPP estimates narrows for mean and aggregate net worth and the 

correlation of asset values and debt values when we exclude wealthy families from our sample. 

Finally, we conclude that despite the differences in wealth estimates across surveys, there is no 

statistical difference across surveys in an estimate of the Black-White wealth gap. 

We offer several explanations for why SIPP and SCF data might deliver different 

estimates. First, respondents might report better quality data about asset ownership by 

recognizing rather than recalling the assets that they own. Second, we argue that small variations 

in question text wording condition how respondents interpret questions, and these varying 

interpretations yield differences in wealth estimates. Finally, we propose that simpler questions 

elicit better quality answers. These conclusions support hypotheses that have been offered by the 

survey methodology literature, and they are especially informative for producers and users of 

wealth data. 

The scope for future work is tremendous given the forthcoming 2014 SIPP panel. As 

with much of the survey, the wealth data have been redesigned with an eye towards improving 

quality. The 2014 panel continues to incorporate the suggestions of Czajka et al. (2003) by 

43 
editing home values and mortgage debt jointly, utilizing assessed vehicle values to assign 

average trade-in values for vehicles up to 20 years old, and comprehensively reevaluating hot 

decks. 

Moreover, the 2014 panel has implemented various additional initiatives which were 

originally recommended by Czajka et al. (2003). First, SIPP now collects the value of annuities 

and trusts. Second, SIPP incorporates debts into the imputation of assets and vice versa for more 

asset categories, including other real estate and rental properties. Third, the 2014 panel has 

overhauled its approach to assigning vehicle values by updating value assessments rather than 

assuming a fixed depreciation rate, applying hot deck imputation more broadly to yield a 

distribution of values, and using more comparable vehicles to impute average trade-in values for 

newer vehicles when no assessment is available. Fourth, the 2014 panel publishes means of 

asset and debt values above the topcode threshold, allowing public data users to estimate 

aggregate and mean net worth more precisely. Finally, non-response range follow-up questions 

have been introduced for various asset and debt categories, and the range options have been 

evaluated for consistency and revised where necessary. 

The 2014 SIPP panel will also introduce changes which Czajka et al. (2003) did not 

discuss. For example, SIPP incorporates incomes into the imputation of asset values and vice 

versa for more asset categories. Second, the 2014 panel collects the market value of educational 

savings accounts and non-actively managed businesses for the first time. Third, the 2014 panel 

collects both the face value and cash value of life insurance policies in order to improve the data 

on cash values for life insurance. The 2008 panel asked respondents about cash values only, but 

Gottschalck and Moore (2007) argue that the reported cash values actually reflect a mix of cash 

and face values. Finally, the text for questions relating to other financial investments has been 

44 
revised to prime respondents better with examples of these assets in the hope of closing the gap 

between SIPP and SCF in the ownership rate and aggregate value of these assets. 

The substantial changes to SIPP wealth data for the 2014 panel underscore the 

importance of future evaluations of the extent to which these initiatives have closed the persistent 

gap with SCF wealth data. These evaluations will also have broader implications, informing data 

producers and users about how changes in survey question text and imputation procedures affect 

wealth data quality. 

 

45 
46 

References 

Altonji, Joseph G. and Ulrich Doraszelski. 2005. "The role of permanent income and 

demographics in black/white differences in wealth." Journal of Human Resources 40(1): 

1-30. 

Andridge, Rebecca R. and Roderick J. A. Little. 2010. “A Review of Hot Deck Imputation for 

Survey Non-response”. International Statistical Review 78(1): 40–64. 

Blank, Rebecca M. and Patricia Ruggles. 1996. “When Do Women Use Aid to Families with 

Dependent Children and Food Stamps? The Dynamics of Eligibility Versus 

Participation”. Journal of Human Resources 31(1): 57–89. 

Board of Governors of the Federal Reserve System. 2011. “Survey of Consumer Finances 

Codebook”. Washington, D.C., Board of Governors of the Federal Reserve System. 

Bricker, Jesse, Lisa J. Dettling, Alice Henriques, Joanne W. Hsu, Kevin B. Moore, John 

Sabelhaus, Jeffrey Thompson, and Richard A. Windle. 2014. “Changes in U.S. Family 

Finances from 2010 to 2013: Evidence from the Survey of Consumer Finances”. 

Federal Reserve Bulletin 100(4): 1–41. 

Carroll, Christopher D., Karen E. Dynan, and Spencer D. Krane. 2003. “Unemployment Risk 

and Precautionary Wealth: Evidence from Households’ Balance Sheets”. The Review of 

Economics and Statistics 85(3): 586–604. 

Chetty, Raj. 2008. “Moral Hazard versus Liquidity and Optimal Unemployment Insurance”. 

Journal of Political Economy 116(2): 173–234. 

Couper, Mick P., Mary Beth Ofstedal, and Sunghee Lee. 2013. "Encouraging Record Use for 

Financial Asset Questions in a Web Survey." Journal of survey statistics and 

methodology 1, no. 2: 171-182. 

Curtin, Richard T., Thomas Juster, and James L. Morgan. 1989. “Survey Estimates of Wealth: 

An Assessment of Quality”. In Robert E. Lipsey and Helen Stone Tice, eds., The 

Measurement of Saving, Investment, and Wealth. Chicago, IL: University of Chicago 

Press. 

Czajka, John L., Jonathan E. Jacobson, and Scott Cody. “Survey Estimates of Wealth: A 

Comparative Analysis and Review of the Survey of Income and Program Participation”. 

Mathematica Policy Research Report PR03-45, Washington, DC. 

Fay, Robert E. and George F. Train. 1995. “Aspects of Survey and Model-Based Postcensal 

Estimation of Income and Poverty Characteristics for States and Counties”. Joint 

Statistical Meetings, Proceedings of the Section on Government Statistics: 154–159. 
Fee, Holly, Patrick Campanello, and Matthew Marlay. 2014. “Using Audit Trail Files in the 

Reengineered SIPP.” 2015 Federal CASIC (Computer-Assisted Survey Information 

Collection) Workshop, Suitland, MD. 

Fries, Gerhard, Barry Johnson, and R. Louise Woodburn. 1997. “Analyzing the Disclosure 

Review Procedures for the 1995 Survey of Consumer Finances”. Unpublished 

Manuscript, Board of Governors of the Federal Reserve System. 

Gottschalck, Alfred O. and Jeffrey C. Moore. 2007. “Evaluation of Questionnaire Design 

Changes on Life Insurance Policy Data”. Research Report Series, Survey Methodology 

#2007–14, U.S. Census Bureau. 

Gruber, Jonathan. 2001. “The Wealth of the Unemployed”. Industrial and Labor Relations 

Review 55(1): 79–94. 

Kennickell, Arthur B. 1997. “Multiple Imputation and Disclosure Protection: The Case of the

1995 Survey of Consumer Finances”. Unpublished Manuscript, Board of Governors of 

the Federal Reserve System. 

Kennickell, Arthur B. 1998. “Multiple Imputation in the Survey of Consumer Finances”. 

Unpublished Manuscript, Board of Governors of the Federal Reserve System. 

Kennickell, Arthur B. and Julia Lane. 2007. “Measuring the Impact of Data Protection 

Techniques on Data Utility: Evidence from the Survey of Consumer Finances”. 

Unpublished Manuscript, Board of Governors of the Federal Reserve System. 

Moore, Jeffry C. and Julia Klein Griffiths. 2003. "Asset Ownership, Program Participation, and 

Asset and Program Income: Improving Reporting in the Survey of Income and Program 

Participation" 2003 Joint Statistical Meetings - Section on Government Statistics. San 

Francisco, California. 

OECD. OECD Skills Outlook 2013: First Results from the Survey of Adult Skills. OECD 

Publishing, 2013. 

Rubin, Donald B. 1987. Multiple Imputation for Nonresponse in Surveys. New York: John 

Wiley & Sons. 

National Research Council. 2009. Reengineering the Survey of Income and Program 

Participation. Constance F. Citro and John Karl Scholz, eds. Committee on National 

Statistics, Division of Behavioral and Social Sciences Education. Washington, DC: The 

National Academies Press. 

Saez, Emmanuel and Gabriel Zucman. 2014. “Wealth Inequality in the United States since 

1913: Evidence from Capitalized Income Tax Data”. NBER Working Paper No. 20625, 

National Bureau of Economic Research 

47 
State of Black America – “Save Our Cities: Education, Jobs + Justice." National Urban League, 

2015. 

Sullivan, James X. 2008. “Borrowing During Unemployment: Unsecured Debt as a Safety 

Net”. The Journal of Human Resources 43(2): 383–412. 

Survey of Consumer Finances. 2010. “Codebook for 2010 Survey of Consumer Finances.” 

Washington, D.C., Board of Governors of the Federal Reserve System. 

Tang, Lingqi, Juwon Song, Thomas R. Belin, and Jürgen Unützer. 2005. "A comparison of 

imputation methods in a longitudinal randomized clinical trial." Statistics in medicine

24(14): 2111-2128. 

Thibaudeau, Yves, Alfred Gottschalck, and Thomas Palumbo. 2006. “The Predictive-Mean 

Method of Imputation for Preserving Coupling Between Assets and Liabilities”. 

Research Report Series, Computing #2006–1, U.S. Census Bureau. 

Tourangeau, Roger. 1984. "Cognitive sciences and survey methods." Cognitive aspects of 

survey methodology: Building a bridge between disciplines National Academy Press: 73-

100. Washington, D.C. 

Tourangeau, Roger, Frederick G. Conrad, Mick P. Couper, and Cong Ye. 2014. "The effects of 

providing examples in survey questions." Public opinion quarterly 78(1): 100-125. 

U.S. Census Bureau. 2001. “Survey of Income and Program Participation User’s Guide”. Third 

Edition. Washington, D.C., U.S. Department of Commerce. Bureau of the Census. 

Vornovitsky, Marina, Alfred Gottschalck, and Adam Smith. 2014. “Distribution of Household 

Wealth in the U.S.: 2000 to 2011”. Unpublished Manuscript, U.S. Census Bureau. 

Wolff, Edward N. 1998. “Recent Trends in the Size Distribution of Household Wealth”. 

Journal of Economic Perspectives 12(3): 131–150. 

Wolff, Edward N. 1999. “The Size Distribution of Wealth in the United States: A Comparison 

among Recent Household Surveys”. In James P. Smith and Robert J. Willis, eds., 

Wealth, Work, and Health: Innovations in Measurement in the Social Sciences. Essays 

in Honor of F. Thomas Juster. Ann Arbor, MI: The University of Michigan Press. 

Wolff, Edward N. 2014. “Household Wealth Trends in the United States, 1962–2013: What 

Happened over the Great Recession?”. NBER Working Paper No. 20733, National 

Bureau of Economic Research 

48